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sophiahhuang/Data8_Project2_EstimationInference_SophiaHuang_Final.ipynb

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Data8_Project2_EstimationInference_SophiaHuang_Final.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 132,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"import requests\n",
"import string\n",
"import io\n",
"from pandas.io.json import json_normalize\n",
"from datascience import *\n",
"\n",
"\n",
"# These lines do some fancy plotting magic.\n",
"import matplotlib\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"plt.style.use('fivethirtyeight')\n",
"from matplotlib.ticker import AutoMinorLocator, MultipleLocator, FuncFormatter\n",
"import warnings\n",
"warnings.simplefilter('ignore', FutureWarning)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Load URL, read CSV, and display as panda frame"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"moma_artworks_url = \"https://media.githubusercontent.com/media/MuseumofModernArt/collection/master/Artworks.csv\"\n",
"\n",
"artworks = pd.read_csv(moma_artworks_url)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"moma_artists_url = \"https://media.githubusercontent.com/media/MuseumofModernArt/collection/master/Artists.csv\"\n",
"\n",
"artists = pd.read_csv(moma_artists_url)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Explore data"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(15668, 9)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#get column, row to understand size of data\n",
"artists.shape "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['ConstituentID', 'DisplayName', 'ArtistBio', 'Nationality', 'Gender',\n",
" 'BeginDate', 'EndDate', 'Wiki QID', 'ULAN'],\n",
" dtype='object')"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"artists.columns"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 15668 entries, 0 to 15667\n",
"Data columns (total 9 columns):\n",
"ConstituentID 15668 non-null int64\n",
"DisplayName 15668 non-null object\n",
"ArtistBio 13402 non-null object\n",
"Nationality 13146 non-null object\n",
"Gender 12476 non-null object\n",
"BeginDate 15668 non-null int64\n",
"EndDate 15668 non-null int64\n",
"Wiki QID 3272 non-null object\n",
"ULAN 2939 non-null float64\n",
"dtypes: float64(1), int64(3), object(5)\n",
"memory usage: 1.1+ MB\n"
]
}
],
"source": [
"artists.info()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(139003, 29)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#get column, row to understand size of data\n",
"artworks.shape "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Title', 'Artist', 'ConstituentID', 'ArtistBio', 'Nationality',\n",
" 'BeginDate', 'EndDate', 'Gender', 'Date', 'Medium', 'Dimensions',\n",
" 'CreditLine', 'AccessionNumber', 'Classification', 'Department',\n",
" 'DateAcquired', 'Cataloged', 'ObjectID', 'URL', 'ThumbnailURL',\n",
" 'Circumference (cm)', 'Depth (cm)', 'Diameter (cm)', 'Height (cm)',\n",
" 'Length (cm)', 'Weight (kg)', 'Width (cm)', 'Seat Height (cm)',\n",
" 'Duration (sec.)'],\n",
" dtype='object')"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"artworks.columns"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 139003 entries, 0 to 139002\n",
"Data columns (total 29 columns):\n",
"Title 138964 non-null object\n",
"Artist 137690 non-null object\n",
"ConstituentID 137690 non-null object\n",
"ArtistBio 132878 non-null object\n",
"Nationality 137690 non-null object\n",
"BeginDate 137690 non-null object\n",
"EndDate 137690 non-null object\n",
"Gender 137690 non-null object\n",
"Date 136773 non-null object\n",
"Medium 128408 non-null object\n",
"Dimensions 128768 non-null object\n",
"CreditLine 136486 non-null object\n",
"AccessionNumber 139003 non-null object\n",
"Classification 139003 non-null object\n",
"Department 139003 non-null object\n",
"DateAcquired 131850 non-null object\n",
"Cataloged 139003 non-null object\n",
"ObjectID 139003 non-null int64\n",
"URL 83113 non-null object\n",
"ThumbnailURL 72891 non-null object\n",
"Circumference (cm) 10 non-null float64\n",
"Depth (cm) 14075 non-null float64\n",
"Diameter (cm) 1468 non-null float64\n",
"Height (cm) 120202 non-null float64\n",
"Length (cm) 741 non-null float64\n",
"Weight (kg) 289 non-null float64\n",
"Width (cm) 119280 non-null float64\n",
"Seat Height (cm) 0 non-null float64\n",
"Duration (sec.) 2661 non-null float64\n",
"dtypes: float64(9), int64(1), object(19)\n",
"memory usage: 30.8+ MB\n"
]
}
],
"source": [
"artworks.info()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Create a master dataset (artists and artworks)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ConstituentID</th>\n",
" <th>Title</th>\n",
" <th>Artist</th>\n",
" <th>ArtistBio</th>\n",
" <th>Nationality</th>\n",
" <th>BeginDate</th>\n",
" <th>EndDate</th>\n",
" <th>Gender</th>\n",
" <th>Date</th>\n",
" <th>Medium</th>\n",
" <th>...</th>\n",
" <th>ThumbnailURL</th>\n",
" <th>Circumference (cm)</th>\n",
" <th>Depth (cm)</th>\n",
" <th>Diameter (cm)</th>\n",
" <th>Height (cm)</th>\n",
" <th>Length (cm)</th>\n",
" <th>Weight (kg)</th>\n",
" <th>Width (cm)</th>\n",
" <th>Seat Height (cm)</th>\n",
" <th>Duration (sec.)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>6210</td>\n",
" <td>Ferdinandsbrücke Project, Vienna, Austria (Ele...</td>\n",
" <td>Otto Wagner</td>\n",
" <td>(Austrian, 1841–1918)</td>\n",
" <td>(Austrian)</td>\n",
" <td>(1841)</td>\n",
" <td>(1918)</td>\n",
" <td>(Male)</td>\n",
" <td>1896</td>\n",
" <td>Ink and cut-and-pasted painted pages on paper</td>\n",
" <td>...</td>\n",
" <td>http://www.moma.org/media/W1siZiIsIjU5NDA1Il0s...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>48.6000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>168.9000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>7470</td>\n",
" <td>City of Music, National Superior Conservatory ...</td>\n",
" <td>Christian de Portzamparc</td>\n",
" <td>(French, born 1944)</td>\n",
" <td>(French)</td>\n",
" <td>(1944)</td>\n",
" <td>(0)</td>\n",
" <td>(Male)</td>\n",
" <td>1987</td>\n",
" <td>Paint and colored pencil on print</td>\n",
" <td>...</td>\n",
" <td>http://www.moma.org/media/W1siZiIsIjk3Il0sWyJw...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.6401</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>29.8451</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>7605</td>\n",
" <td>Villa near Vienna Project, Outside Vienna, Aus...</td>\n",
" <td>Emil Hoppe</td>\n",
" <td>(Austrian, 1876–1957)</td>\n",
" <td>(Austrian)</td>\n",
" <td>(1876)</td>\n",
" <td>(1957)</td>\n",
" <td>(Male)</td>\n",
" <td>1903</td>\n",
" <td>Graphite, pen, color pencil, ink, and gouache ...</td>\n",
" <td>...</td>\n",
" <td>http://www.moma.org/media/W1siZiIsIjk4Il0sWyJw...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>34.3000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>31.8000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>7056</td>\n",
" <td>The Manhattan Transcripts Project, New York, N...</td>\n",
" <td>Bernard Tschumi</td>\n",
" <td>(French and Swiss, born Switzerland 1944)</td>\n",
" <td>()</td>\n",
" <td>(1944)</td>\n",
" <td>(0)</td>\n",
" <td>(Male)</td>\n",
" <td>1980</td>\n",
" <td>Photographic reproduction with colored synthet...</td>\n",
" <td>...</td>\n",
" <td>http://www.moma.org/media/W1siZiIsIjEyNCJdLFsi...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>50.8000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>50.8000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>7605</td>\n",
" <td>Villa, project, outside Vienna, Austria, Exter...</td>\n",
" <td>Emil Hoppe</td>\n",
" <td>(Austrian, 1876–1957)</td>\n",
" <td>(Austrian)</td>\n",
" <td>(1876)</td>\n",
" <td>(1957)</td>\n",
" <td>(Male)</td>\n",
" <td>1903</td>\n",
" <td>Graphite, color pencil, ink, and gouache on tr...</td>\n",
" <td>...</td>\n",
" <td>http://www.moma.org/media/W1siZiIsIjEyNiJdLFsi...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>38.4000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>19.1000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 29 columns</p>\n",
"</div>"
],
"text/plain": [
" ConstituentID Title \\\n",
"0 6210 Ferdinandsbrücke Project, Vienna, Austria (Ele... \n",
"1 7470 City of Music, National Superior Conservatory ... \n",
"2 7605 Villa near Vienna Project, Outside Vienna, Aus... \n",
"3 7056 The Manhattan Transcripts Project, New York, N... \n",
"4 7605 Villa, project, outside Vienna, Austria, Exter... \n",
"\n",
" Artist ArtistBio \\\n",
"0 Otto Wagner (Austrian, 1841–1918) \n",
"1 Christian de Portzamparc (French, born 1944) \n",
"2 Emil Hoppe (Austrian, 1876–1957) \n",
"3 Bernard Tschumi (French and Swiss, born Switzerland 1944) \n",
"4 Emil Hoppe (Austrian, 1876–1957) \n",
"\n",
" Nationality BeginDate EndDate Gender Date \\\n",
"0 (Austrian) (1841) (1918) (Male) 1896 \n",
"1 (French) (1944) (0) (Male) 1987 \n",
"2 (Austrian) (1876) (1957) (Male) 1903 \n",
"3 () (1944) (0) (Male) 1980 \n",
"4 (Austrian) (1876) (1957) (Male) 1903 \n",
"\n",
" Medium ... \\\n",
"0 Ink and cut-and-pasted painted pages on paper ... \n",
"1 Paint and colored pencil on print ... \n",
"2 Graphite, pen, color pencil, ink, and gouache ... ... \n",
"3 Photographic reproduction with colored synthet... ... \n",
"4 Graphite, color pencil, ink, and gouache on tr... ... \n",
"\n",
" ThumbnailURL Circumference (cm) \\\n",
"0 http://www.moma.org/media/W1siZiIsIjU5NDA1Il0s... NaN \n",
"1 http://www.moma.org/media/W1siZiIsIjk3Il0sWyJw... NaN \n",
"2 http://www.moma.org/media/W1siZiIsIjk4Il0sWyJw... NaN \n",
"3 http://www.moma.org/media/W1siZiIsIjEyNCJdLFsi... NaN \n",
"4 http://www.moma.org/media/W1siZiIsIjEyNiJdLFsi... NaN \n",
"\n",
" Depth (cm) Diameter (cm) Height (cm) Length (cm) Weight (kg) Width (cm) \\\n",
"0 NaN NaN 48.6000 NaN NaN 168.9000 \n",
"1 NaN NaN 40.6401 NaN NaN 29.8451 \n",
"2 NaN NaN 34.3000 NaN NaN 31.8000 \n",
"3 NaN NaN 50.8000 NaN NaN 50.8000 \n",
"4 NaN NaN 38.4000 NaN NaN 19.1000 \n",
"\n",
" Seat Height (cm) Duration (sec.) \n",
"0 NaN NaN \n",
"1 NaN NaN \n",
"2 NaN NaN \n",
"3 NaN NaN \n",
"4 NaN NaN \n",
"\n",
"[5 rows x 29 columns]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# rearrange artworks with Constituent ID at first\n",
"# use Consitituent ID as unique identifier to join two datasets\n",
"\n",
"artworks = artworks[['ConstituentID', 'Title', 'Artist', 'ArtistBio', 'Nationality',\n",
" 'BeginDate', 'EndDate', 'Gender', 'Date', 'Medium', 'Dimensions',\n",
" 'CreditLine', 'AccessionNumber', 'Classification', 'Department',\n",
" 'DateAcquired', 'Cataloged', 'ObjectID', 'URL', 'ThumbnailURL',\n",
" 'Circumference (cm)', 'Depth (cm)', 'Diameter (cm)', 'Height (cm)',\n",
" 'Length (cm)', 'Weight (kg)', 'Width (cm)', 'Seat Height (cm)',\n",
" 'Duration (sec.)']]\n",
"\n",
"artworks.head()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"ConstituentID object\n",
"DisplayName object\n",
"ArtistBio object\n",
"Nationality object\n",
"Gender object\n",
"BeginDate int64\n",
"EndDate int64\n",
"Wiki QID object\n",
"ULAN float64\n",
"dtype: object"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# change ConstituentID to string\n",
"\n",
"artists['ConstituentID'] = artists['ConstituentID'].astype(str)\n",
"\n",
"artists.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"ConstituentID object\n",
"Title object\n",
"Artist object\n",
"ArtistBio object\n",
"Nationality object\n",
"BeginDate object\n",
"EndDate object\n",
"Gender object\n",
"Date object\n",
"Medium object\n",
"Dimensions object\n",
"CreditLine object\n",
"AccessionNumber object\n",
"Classification object\n",
"Department object\n",
"DateAcquired object\n",
"Cataloged object\n",
"ObjectID int64\n",
"URL object\n",
"ThumbnailURL object\n",
"Circumference (cm) float64\n",
"Depth (cm) float64\n",
"Diameter (cm) float64\n",
"Height (cm) float64\n",
"Length (cm) float64\n",
"Weight (kg) float64\n",
"Width (cm) float64\n",
"Seat Height (cm) float64\n",
"Duration (sec.) float64\n",
"dtype: object"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"artworks.dtypes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Join two datasets"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"# change both column names to be the same\n",
"artworks.rename(columns={'ConstituentID':'UniqueID'}, inplace = True)\n",
"artists.rename(columns={'ConstituentID':'UniqueID'}, inplace = True)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" <th></th>\n",
" <th>UniqueID</th>\n",
" <th>Title</th>\n",
" <th>Artist</th>\n",
" <th>ArtistBio</th>\n",
" <th>Nationality</th>\n",
" <th>BeginDate</th>\n",
" <th>EndDate</th>\n",
" <th>Gender</th>\n",
" <th>Date</th>\n",
" <th>Medium</th>\n",
" <th>...</th>\n",
" <th>ThumbnailURL</th>\n",
" <th>Circumference (cm)</th>\n",
" <th>Depth (cm)</th>\n",
" <th>Diameter (cm)</th>\n",
" <th>Height (cm)</th>\n",
" <th>Length (cm)</th>\n",
" <th>Weight (kg)</th>\n",
" <th>Width (cm)</th>\n",
" <th>Seat Height (cm)</th>\n",
" <th>Duration (sec.)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>6210</td>\n",
" <td>Ferdinandsbrücke Project, Vienna, Austria (Ele...</td>\n",
" <td>Otto Wagner</td>\n",
" <td>(Austrian, 1841–1918)</td>\n",
" <td>(Austrian)</td>\n",
" <td>(1841)</td>\n",
" <td>(1918)</td>\n",
" <td>(Male)</td>\n",
" <td>1896</td>\n",
" <td>Ink and cut-and-pasted painted pages on paper</td>\n",
" <td>...</td>\n",
" <td>http://www.moma.org/media/W1siZiIsIjU5NDA1Il0s...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>48.6000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>168.9000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>7470</td>\n",
" <td>City of Music, National Superior Conservatory ...</td>\n",
" <td>Christian de Portzamparc</td>\n",
" <td>(French, born 1944)</td>\n",
" <td>(French)</td>\n",
" <td>(1944)</td>\n",
" <td>(0)</td>\n",
" <td>(Male)</td>\n",
" <td>1987</td>\n",
" <td>Paint and colored pencil on print</td>\n",
" <td>...</td>\n",
" <td>http://www.moma.org/media/W1siZiIsIjk3Il0sWyJw...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.6401</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>29.8451</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>7605</td>\n",
" <td>Villa near Vienna Project, Outside Vienna, Aus...</td>\n",
" <td>Emil Hoppe</td>\n",
" <td>(Austrian, 1876–1957)</td>\n",
" <td>(Austrian)</td>\n",
" <td>(1876)</td>\n",
" <td>(1957)</td>\n",
" <td>(Male)</td>\n",
" <td>1903</td>\n",
" <td>Graphite, pen, color pencil, ink, and gouache ...</td>\n",
" <td>...</td>\n",
" <td>http://www.moma.org/media/W1siZiIsIjk4Il0sWyJw...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>34.3000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>31.8000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>7056</td>\n",
" <td>The Manhattan Transcripts Project, New York, N...</td>\n",
" <td>Bernard Tschumi</td>\n",
" <td>(French and Swiss, born Switzerland 1944)</td>\n",
" <td>()</td>\n",
" <td>(1944)</td>\n",
" <td>(0)</td>\n",
" <td>(Male)</td>\n",
" <td>1980</td>\n",
" <td>Photographic reproduction with colored synthet...</td>\n",
" <td>...</td>\n",
" <td>http://www.moma.org/media/W1siZiIsIjEyNCJdLFsi...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>50.8000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>50.8000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>7605</td>\n",
" <td>Villa, project, outside Vienna, Austria, Exter...</td>\n",
" <td>Emil Hoppe</td>\n",
" <td>(Austrian, 1876–1957)</td>\n",
" <td>(Austrian)</td>\n",
" <td>(1876)</td>\n",
" <td>(1957)</td>\n",
" <td>(Male)</td>\n",
" <td>1903</td>\n",
" <td>Graphite, color pencil, ink, and gouache on tr...</td>\n",
" <td>...</td>\n",
" <td>http://www.moma.org/media/W1siZiIsIjEyNiJdLFsi...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>38.4000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>19.1000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 29 columns</p>\n",
"</div>"
],
"text/plain": [
" UniqueID Title \\\n",
"0 6210 Ferdinandsbrücke Project, Vienna, Austria (Ele... \n",
"1 7470 City of Music, National Superior Conservatory ... \n",
"2 7605 Villa near Vienna Project, Outside Vienna, Aus... \n",
"3 7056 The Manhattan Transcripts Project, New York, N... \n",
"4 7605 Villa, project, outside Vienna, Austria, Exter... \n",
"\n",
" Artist ArtistBio \\\n",
"0 Otto Wagner (Austrian, 1841–1918) \n",
"1 Christian de Portzamparc (French, born 1944) \n",
"2 Emil Hoppe (Austrian, 1876–1957) \n",
"3 Bernard Tschumi (French and Swiss, born Switzerland 1944) \n",
"4 Emil Hoppe (Austrian, 1876–1957) \n",
"\n",
" Nationality BeginDate EndDate Gender Date \\\n",
"0 (Austrian) (1841) (1918) (Male) 1896 \n",
"1 (French) (1944) (0) (Male) 1987 \n",
"2 (Austrian) (1876) (1957) (Male) 1903 \n",
"3 () (1944) (0) (Male) 1980 \n",
"4 (Austrian) (1876) (1957) (Male) 1903 \n",
"\n",
" Medium ... \\\n",
"0 Ink and cut-and-pasted painted pages on paper ... \n",
"1 Paint and colored pencil on print ... \n",
"2 Graphite, pen, color pencil, ink, and gouache ... ... \n",
"3 Photographic reproduction with colored synthet... ... \n",
"4 Graphite, color pencil, ink, and gouache on tr... ... \n",
"\n",
" ThumbnailURL Circumference (cm) \\\n",
"0 http://www.moma.org/media/W1siZiIsIjU5NDA1Il0s... NaN \n",
"1 http://www.moma.org/media/W1siZiIsIjk3Il0sWyJw... NaN \n",
"2 http://www.moma.org/media/W1siZiIsIjk4Il0sWyJw... NaN \n",
"3 http://www.moma.org/media/W1siZiIsIjEyNCJdLFsi... NaN \n",
"4 http://www.moma.org/media/W1siZiIsIjEyNiJdLFsi... NaN \n",
"\n",
" Depth (cm) Diameter (cm) Height (cm) Length (cm) Weight (kg) Width (cm) \\\n",
"0 NaN NaN 48.6000 NaN NaN 168.9000 \n",
"1 NaN NaN 40.6401 NaN NaN 29.8451 \n",
"2 NaN NaN 34.3000 NaN NaN 31.8000 \n",
"3 NaN NaN 50.8000 NaN NaN 50.8000 \n",
"4 NaN NaN 38.4000 NaN NaN 19.1000 \n",
"\n",
" Seat Height (cm) Duration (sec.) \n",
"0 NaN NaN \n",
"1 NaN NaN \n",
"2 NaN NaN \n",
"3 NaN NaN \n",
"4 NaN NaN \n",
"\n",
"[5 rows x 29 columns]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"artworks.head(5)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" <th></th>\n",
" <th>UniqueID</th>\n",
" <th>DisplayName</th>\n",
" <th>ArtistBio</th>\n",
" <th>Nationality</th>\n",
" <th>Gender</th>\n",
" <th>BeginDate</th>\n",
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" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>Robert Arneson</td>\n",
" <td>American, 1930–1992</td>\n",
" <td>American</td>\n",
" <td>Male</td>\n",
" <td>1930</td>\n",
" <td>1992</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>Doroteo Arnaiz</td>\n",
" <td>Spanish, born 1936</td>\n",
" <td>Spanish</td>\n",
" <td>Male</td>\n",
" <td>1936</td>\n",
" <td>0</td>\n",
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" <td>Bill Arnold</td>\n",
" <td>American, born 1941</td>\n",
" <td>American</td>\n",
" <td>Male</td>\n",
" <td>1941</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>Charles Arnoldi</td>\n",
" <td>American, born 1946</td>\n",
" <td>American</td>\n",
" <td>Male</td>\n",
" <td>1946</td>\n",
" <td>0</td>\n",
" <td>Q1063584</td>\n",
" <td>500027998.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>Per Arnoldi</td>\n",
" <td>Danish, born 1941</td>\n",
" <td>Danish</td>\n",
" <td>Male</td>\n",
" <td>1941</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" UniqueID DisplayName ArtistBio Nationality Gender \\\n",
"0 1 Robert Arneson American, 1930–1992 American Male \n",
"1 2 Doroteo Arnaiz Spanish, born 1936 Spanish Male \n",
"2 3 Bill Arnold American, born 1941 American Male \n",
"3 4 Charles Arnoldi American, born 1946 American Male \n",
"4 5 Per Arnoldi Danish, born 1941 Danish Male \n",
"\n",
" BeginDate EndDate Wiki QID ULAN \n",
"0 1930 1992 NaN NaN \n",
"1 1936 0 NaN NaN \n",
"2 1941 0 NaN NaN \n",
"3 1946 0 Q1063584 500027998.0 \n",
"4 1941 0 NaN NaN "
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"artists.head(5)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(143085, 37)"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# join by Unique ID\n",
"artists_columns = ['UniqueID', 'DisplayName', 'ArtistBio', 'Nationality', 'Gender', 'BeginDate','EndDate']\n",
"master = pd.merge(artworks, artists, on='UniqueID', how='outer')\n",
"master.shape"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(143085, 38)"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# sanity check on the problem of duplicates by creating another master dataframe \n",
"# whereby total count of each UniqueID in artists vs artworks is checked first\n",
"# Dedup column is the total count of \n",
"# GroupBy.cumcount(self, ascending: bool = True)\n",
"# Number each item in each group from 0 to the length of that group - 1.\n",
"artworks['Dedup'] = artworks.groupby('UniqueID').cumcount(ascending=False)\n",
"artists['Dedup'] = artists.groupby('UniqueID').cumcount(ascending=False)\n",
"draft = pd.merge(artworks, artists, on=['UniqueID', 'Dedup'], how='outer')\n",
"draft.shape"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
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" <th></th>\n",
" <th>UniqueID</th>\n",
" <th>Title</th>\n",
" <th>Artist</th>\n",
" <th>ArtistBio_x</th>\n",
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" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>6210</td>\n",
" <td>Ferdinandsbrücke Project, Vienna, Austria (Ele...</td>\n",
" <td>Otto Wagner</td>\n",
" <td>(Austrian, 1841–1918)</td>\n",
" <td>(Austrian)</td>\n",
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" <td>1896</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>7470</td>\n",
" <td>City of Music, National Superior Conservatory ...</td>\n",
" <td>Christian de Portzamparc</td>\n",
" <td>(French, born 1944)</td>\n",
" <td>(French)</td>\n",
" <td>(1944)</td>\n",
" <td>(0)</td>\n",
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" <td>20</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>7605</td>\n",
" <td>Villa near Vienna Project, Outside Vienna, Aus...</td>\n",
" <td>Emil Hoppe</td>\n",
" <td>(Austrian, 1876–1957)</td>\n",
" <td>(Austrian)</td>\n",
" <td>(1876)</td>\n",
" <td>(1957)</td>\n",
" <td>(Male)</td>\n",
" <td>1903</td>\n",
" <td>Graphite, pen, color pencil, ink, and gouache ...</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>7056</td>\n",
" <td>The Manhattan Transcripts Project, New York, N...</td>\n",
" <td>Bernard Tschumi</td>\n",
" <td>(French and Swiss, born Switzerland 1944)</td>\n",
" <td>()</td>\n",
" <td>(1944)</td>\n",
" <td>(0)</td>\n",
" <td>(Male)</td>\n",
" <td>1980</td>\n",
" <td>Photographic reproduction with colored synthet...</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>60</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>7605</td>\n",
" <td>Villa, project, outside Vienna, Austria, Exter...</td>\n",
" <td>Emil Hoppe</td>\n",
" <td>(Austrian, 1876–1957)</td>\n",
" <td>(Austrian)</td>\n",
" <td>(1876)</td>\n",
" <td>(1957)</td>\n",
" <td>(Male)</td>\n",
" <td>1903</td>\n",
" <td>Graphite, color pencil, ink, and gouache on tr...</td>\n",
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" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>Emil Hoppe</td>\n",
" <td>Austrian, 1876–1957</td>\n",
" <td>Austrian</td>\n",
" <td>Male</td>\n",
" <td>1876.0</td>\n",
" <td>1957.0</td>\n",
" <td>Q1336246</td>\n",
" <td>500232997.0</td>\n",
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" <tr>\n",
" <th>5</th>\n",
" <td>7056</td>\n",
" <td>The Manhattan Transcripts Project, New York, N...</td>\n",
" <td>Bernard Tschumi</td>\n",
" <td>(French and Swiss, born Switzerland 1944)</td>\n",
" <td>()</td>\n",
" <td>(1944)</td>\n",
" <td>(0)</td>\n",
" <td>(Male)</td>\n",
" <td>1976-77</td>\n",
" <td>Gelatin silver photograph</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>59</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>6</th>\n",
" <td>7056</td>\n",
" <td>The Manhattan Transcripts Project, New York, N...</td>\n",
" <td>Bernard Tschumi</td>\n",
" <td>(French and Swiss, born Switzerland 1944)</td>\n",
" <td>()</td>\n",
" <td>(1944)</td>\n",
" <td>(0)</td>\n",
" <td>(Male)</td>\n",
" <td>1976-77</td>\n",
" <td>Gelatin silver photographs</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>58</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>7</th>\n",
" <td>7056</td>\n",
" <td>The Manhattan Transcripts Project, New York, N...</td>\n",
" <td>Bernard Tschumi</td>\n",
" <td>(French and Swiss, born Switzerland 1944)</td>\n",
" <td>()</td>\n",
" <td>(1944)</td>\n",
" <td>(0)</td>\n",
" <td>(Male)</td>\n",
" <td>1976-77</td>\n",
" <td>Gelatin silver photograph</td>\n",
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" <td>NaN</td>\n",
" <td>57</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>8</th>\n",
" <td>7056</td>\n",
" <td>The Manhattan Transcripts Project, New York, N...</td>\n",
" <td>Bernard Tschumi</td>\n",
" <td>(French and Swiss, born Switzerland 1944)</td>\n",
" <td>()</td>\n",
" <td>(1944)</td>\n",
" <td>(0)</td>\n",
" <td>(Male)</td>\n",
" <td>1976-77</td>\n",
" <td>Gelatin silver photograph</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>56</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>7056</td>\n",
" <td>The Manhattan Transcripts Project, New York, N...</td>\n",
" <td>Bernard Tschumi</td>\n",
" <td>(French and Swiss, born Switzerland 1944)</td>\n",
" <td>()</td>\n",
" <td>(1944)</td>\n",
" <td>(0)</td>\n",
" <td>(Male)</td>\n",
" <td>1976-77</td>\n",
" <td>Gelatin silver photograph</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>55</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>10 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" UniqueID Title \\\n",
"0 6210 Ferdinandsbrücke Project, Vienna, Austria (Ele... \n",
"1 7470 City of Music, National Superior Conservatory ... \n",
"2 7605 Villa near Vienna Project, Outside Vienna, Aus... \n",
"3 7056 The Manhattan Transcripts Project, New York, N... \n",
"4 7605 Villa, project, outside Vienna, Austria, Exter... \n",
"5 7056 The Manhattan Transcripts Project, New York, N... \n",
"6 7056 The Manhattan Transcripts Project, New York, N... \n",
"7 7056 The Manhattan Transcripts Project, New York, N... \n",
"8 7056 The Manhattan Transcripts Project, New York, N... \n",
"9 7056 The Manhattan Transcripts Project, New York, N... \n",
"\n",
" Artist ArtistBio_x \\\n",
"0 Otto Wagner (Austrian, 1841–1918) \n",
"1 Christian de Portzamparc (French, born 1944) \n",
"2 Emil Hoppe (Austrian, 1876–1957) \n",
"3 Bernard Tschumi (French and Swiss, born Switzerland 1944) \n",
"4 Emil Hoppe (Austrian, 1876–1957) \n",
"5 Bernard Tschumi (French and Swiss, born Switzerland 1944) \n",
"6 Bernard Tschumi (French and Swiss, born Switzerland 1944) \n",
"7 Bernard Tschumi (French and Swiss, born Switzerland 1944) \n",
"8 Bernard Tschumi (French and Swiss, born Switzerland 1944) \n",
"9 Bernard Tschumi (French and Swiss, born Switzerland 1944) \n",
"\n",
" Nationality_x BeginDate_x EndDate_x Gender_x Date \\\n",
"0 (Austrian) (1841) (1918) (Male) 1896 \n",
"1 (French) (1944) (0) (Male) 1987 \n",
"2 (Austrian) (1876) (1957) (Male) 1903 \n",
"3 () (1944) (0) (Male) 1980 \n",
"4 (Austrian) (1876) (1957) (Male) 1903 \n",
"5 () (1944) (0) (Male) 1976-77 \n",
"6 () (1944) (0) (Male) 1976-77 \n",
"7 () (1944) (0) (Male) 1976-77 \n",
"8 () (1944) (0) (Male) 1976-77 \n",
"9 () (1944) (0) (Male) 1976-77 \n",
"\n",
" Medium ... \\\n",
"0 Ink and cut-and-pasted painted pages on paper ... \n",
"1 Paint and colored pencil on print ... \n",
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" UniqueID Title \\\n",
"0 6210 Ferdinandsbrücke Project, Vienna, Austria (Ele... \n",
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" <td>NaN</td>\n",
" <td>100.0000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>19.0000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Q312838</td>\n",
" <td>500024982.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>7470</td>\n",
" <td>LVMH Tower, New York, NY (Study model)</td>\n",
" <td>Christian de Portzamparc</td>\n",
" <td>(French, born 1944)</td>\n",
" <td>French, born 1944</td>\n",
" <td>(Male)</td>\n",
" <td>Male</td>\n",
" <td>(French)</td>\n",
" <td>French</td>\n",
" <td>(1944)</td>\n",
" <td>...</td>\n",
" <td>30.0000</td>\n",
" <td>NaN</td>\n",
" <td>100.0000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>19.0000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Q312838</td>\n",
" <td>500024982.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>7470</td>\n",
" <td>LVMH Tower, New York, NY (Study model)</td>\n",
" <td>Christian de Portzamparc</td>\n",
" <td>(French, born 1944)</td>\n",
" <td>French, born 1944</td>\n",
" <td>(Male)</td>\n",
" <td>Male</td>\n",
" <td>(French)</td>\n",
" <td>French</td>\n",
" <td>(1944)</td>\n",
" <td>...</td>\n",
" <td>30.0000</td>\n",
" <td>NaN</td>\n",
" <td>100.0000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>19.0000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Q312838</td>\n",
" <td>500024982.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>7470</td>\n",
" <td>LVMH Tower, New York, NY, Study model</td>\n",
" <td>Christian de Portzamparc</td>\n",
" <td>(French, born 1944)</td>\n",
" <td>French, born 1944</td>\n",
" <td>(Male)</td>\n",
" <td>Male</td>\n",
" <td>(French)</td>\n",
" <td>French</td>\n",
" <td>(1944)</td>\n",
" <td>...</td>\n",
" <td>30.0000</td>\n",
" <td>NaN</td>\n",
" <td>100.0000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>19.0000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Q312838</td>\n",
" <td>500024982.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>7470</td>\n",
" <td>LVMH Tower, New York, NY (Study model)</td>\n",
" <td>Christian de Portzamparc</td>\n",
" <td>(French, born 1944)</td>\n",
" <td>French, born 1944</td>\n",
" <td>(Male)</td>\n",
" <td>Male</td>\n",
" <td>(French)</td>\n",
" <td>French</td>\n",
" <td>(1944)</td>\n",
" <td>...</td>\n",
" <td>30.0000</td>\n",
" <td>NaN</td>\n",
" <td>100.0000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>19.0000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Q312838</td>\n",
" <td>500024982.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>10 rows × 37 columns</p>\n",
"</div>"
],
"text/plain": [
" UniqueID Title \\\n",
"0 6210 Ferdinandsbrücke Project, Vienna, Austria (Ele... \n",
"1 6210 Armchair \n",
"2 6210 Stool \n",
"3 6210 Railing \n",
"4 7470 City of Music, National Superior Conservatory ... \n",
"5 7470 LVMH Tower, New York, NY (Study model) \n",
"6 7470 LVMH Tower, New York, NY (Study model) \n",
"7 7470 LVMH Tower, New York, NY (Study model) \n",
"8 7470 LVMH Tower, New York, NY, Study model \n",
"9 7470 LVMH Tower, New York, NY (Study model) \n",
"\n",
" Artist ArtistBio_x ArtistBio_y \\\n",
"0 Otto Wagner (Austrian, 1841–1918) Austrian, 1841–1918 \n",
"1 Otto Wagner (Austrian, 1841–1918) Austrian, 1841–1918 \n",
"2 Otto Wagner (Austrian, 1841–1918) Austrian, 1841–1918 \n",
"3 Otto Wagner (Austrian, 1841–1918) Austrian, 1841–1918 \n",
"4 Christian de Portzamparc (French, born 1944) French, born 1944 \n",
"5 Christian de Portzamparc (French, born 1944) French, born 1944 \n",
"6 Christian de Portzamparc (French, born 1944) French, born 1944 \n",
"7 Christian de Portzamparc (French, born 1944) French, born 1944 \n",
"8 Christian de Portzamparc (French, born 1944) French, born 1944 \n",
"9 Christian de Portzamparc (French, born 1944) French, born 1944 \n",
"\n",
" Gender_x Gender_y Nationality_x Nationality_y BeginDate_x ... \\\n",
"0 (Male) Male (Austrian) Austrian (1841) ... \n",
"1 (Male) Male (Austrian) Austrian (1841) ... \n",
"2 (Male) Male (Austrian) Austrian (1841) ... \n",
"3 (Male) Male (Austrian) Austrian (1841) ... \n",
"4 (Male) Male (French) French (1944) ... \n",
"5 (Male) Male (French) French (1944) ... \n",
"6 (Male) Male (French) French (1944) ... \n",
"7 (Male) Male (French) French (1944) ... \n",
"8 (Male) Male (French) French (1944) ... \n",
"9 (Male) Male (French) French (1944) ... \n",
"\n",
" Depth (cm) Diameter (cm) Height (cm) Length (cm) Weight (kg) Width (cm) \\\n",
"0 NaN NaN 48.6000 NaN NaN 168.9000 \n",
"1 51.5000 NaN 78.5000 NaN NaN 56.5000 \n",
"2 40.6401 NaN 46.9901 NaN NaN 40.6401 \n",
"3 7.6000 NaN 72.4000 NaN NaN 117.5000 \n",
"4 NaN NaN 40.6401 NaN NaN 29.8451 \n",
"5 30.0000 NaN 100.0000 NaN NaN 19.0000 \n",
"6 30.0000 NaN 100.0000 NaN NaN 19.0000 \n",
"7 30.0000 NaN 100.0000 NaN NaN 19.0000 \n",
"8 30.0000 NaN 100.0000 NaN NaN 19.0000 \n",
"9 30.0000 NaN 100.0000 NaN NaN 19.0000 \n",
"\n",
" Seat Height (cm) Duration (sec.) Wiki QID ULAN \n",
"0 NaN NaN Q84287 500016971.0 \n",
"1 NaN NaN Q84287 500016971.0 \n",
"2 NaN NaN Q84287 500016971.0 \n",
"3 NaN NaN Q84287 500016971.0 \n",
"4 NaN NaN Q312838 500024982.0 \n",
"5 NaN NaN Q312838 500024982.0 \n",
"6 NaN NaN Q312838 500024982.0 \n",
"7 NaN NaN Q312838 500024982.0 \n",
"8 NaN NaN Q312838 500024982.0 \n",
"9 NaN NaN Q312838 500024982.0 \n",
"\n",
"[10 rows x 37 columns]"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# rearrange columns of master\n",
"\n",
"master.columns\n",
"\n",
"master = master[['UniqueID', 'Title', 'Artist', 'ArtistBio_x', 'ArtistBio_y', 'Gender_x', 'Gender_y', 'Nationality_x', 'Nationality_y', 'BeginDate_x',\n",
" 'EndDate_x', 'BeginDate_y', 'EndDate_y', 'DisplayName', 'Date', 'Medium', 'Dimensions', 'CreditLine',\n",
" 'AccessionNumber', 'Classification', 'Department', 'DateAcquired',\n",
" 'Cataloged', 'ObjectID', 'URL', 'ThumbnailURL', 'Circumference (cm)',\n",
" 'Depth (cm)', 'Diameter (cm)', 'Height (cm)', 'Length (cm)',\n",
" 'Weight (kg)', 'Width (cm)', 'Seat Height (cm)', 'Duration (sec.)',\n",
" 'Wiki QID', 'ULAN']]\n",
"\n",
"master.head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data Cleaning: drop nulls, deduplication, data types, unique counts, and classification"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Make a copy of master and remove duplicated columns "
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>UniqueID</th>\n",
" <th>Title</th>\n",
" <th>Artist</th>\n",
" <th>ArtistBio_x</th>\n",
" <th>ArtistBio_y</th>\n",
" <th>Gender_x</th>\n",
" <th>Gender_y</th>\n",
" <th>Nationality_x</th>\n",
" <th>Nationality_y</th>\n",
" <th>BeginDate_x</th>\n",
" <th>...</th>\n",
" <th>Depth (cm)</th>\n",
" <th>Diameter (cm)</th>\n",
" <th>Height (cm)</th>\n",
" <th>Length (cm)</th>\n",
" <th>Weight (kg)</th>\n",
" <th>Width (cm)</th>\n",
" <th>Seat Height (cm)</th>\n",
" <th>Duration (sec.)</th>\n",
" <th>Wiki QID</th>\n",
" <th>ULAN</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>6210</td>\n",
" <td>Ferdinandsbrücke Project, Vienna, Austria (Ele...</td>\n",
" <td>Otto Wagner</td>\n",
" <td>(Austrian, 1841–1918)</td>\n",
" <td>Austrian, 1841–1918</td>\n",
" <td>(Male)</td>\n",
" <td>Male</td>\n",
" <td>(Austrian)</td>\n",
" <td>Austrian</td>\n",
" <td>(1841)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>48.6000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>168.9000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Q84287</td>\n",
" <td>500016971.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>6210</td>\n",
" <td>Armchair</td>\n",
" <td>Otto Wagner</td>\n",
" <td>(Austrian, 1841–1918)</td>\n",
" <td>Austrian, 1841–1918</td>\n",
" <td>(Male)</td>\n",
" <td>Male</td>\n",
" <td>(Austrian)</td>\n",
" <td>Austrian</td>\n",
" <td>(1841)</td>\n",
" <td>...</td>\n",
" <td>51.5000</td>\n",
" <td>NaN</td>\n",
" <td>78.5000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>56.5000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Q84287</td>\n",
" <td>500016971.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>6210</td>\n",
" <td>Stool</td>\n",
" <td>Otto Wagner</td>\n",
" <td>(Austrian, 1841–1918)</td>\n",
" <td>Austrian, 1841–1918</td>\n",
" <td>(Male)</td>\n",
" <td>Male</td>\n",
" <td>(Austrian)</td>\n",
" <td>Austrian</td>\n",
" <td>(1841)</td>\n",
" <td>...</td>\n",
" <td>40.6401</td>\n",
" <td>NaN</td>\n",
" <td>46.9901</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.6401</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Q84287</td>\n",
" <td>500016971.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>6210</td>\n",
" <td>Railing</td>\n",
" <td>Otto Wagner</td>\n",
" <td>(Austrian, 1841–1918)</td>\n",
" <td>Austrian, 1841–1918</td>\n",
" <td>(Male)</td>\n",
" <td>Male</td>\n",
" <td>(Austrian)</td>\n",
" <td>Austrian</td>\n",
" <td>(1841)</td>\n",
" <td>...</td>\n",
" <td>7.6000</td>\n",
" <td>NaN</td>\n",
" <td>72.4000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>117.5000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Q84287</td>\n",
" <td>500016971.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>7470</td>\n",
" <td>City of Music, National Superior Conservatory ...</td>\n",
" <td>Christian de Portzamparc</td>\n",
" <td>(French, born 1944)</td>\n",
" <td>French, born 1944</td>\n",
" <td>(Male)</td>\n",
" <td>Male</td>\n",
" <td>(French)</td>\n",
" <td>French</td>\n",
" <td>(1944)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.6401</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>29.8451</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Q312838</td>\n",
" <td>500024982.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 37 columns</p>\n",
"</div>"
],
"text/plain": [
" UniqueID Title \\\n",
"0 6210 Ferdinandsbrücke Project, Vienna, Austria (Ele... \n",
"1 6210 Armchair \n",
"2 6210 Stool \n",
"3 6210 Railing \n",
"4 7470 City of Music, National Superior Conservatory ... \n",
"\n",
" Artist ArtistBio_x ArtistBio_y \\\n",
"0 Otto Wagner (Austrian, 1841–1918) Austrian, 1841–1918 \n",
"1 Otto Wagner (Austrian, 1841–1918) Austrian, 1841–1918 \n",
"2 Otto Wagner (Austrian, 1841–1918) Austrian, 1841–1918 \n",
"3 Otto Wagner (Austrian, 1841–1918) Austrian, 1841–1918 \n",
"4 Christian de Portzamparc (French, born 1944) French, born 1944 \n",
"\n",
" Gender_x Gender_y Nationality_x Nationality_y BeginDate_x ... \\\n",
"0 (Male) Male (Austrian) Austrian (1841) ... \n",
"1 (Male) Male (Austrian) Austrian (1841) ... \n",
"2 (Male) Male (Austrian) Austrian (1841) ... \n",
"3 (Male) Male (Austrian) Austrian (1841) ... \n",
"4 (Male) Male (French) French (1944) ... \n",
"\n",
" Depth (cm) Diameter (cm) Height (cm) Length (cm) Weight (kg) Width (cm) \\\n",
"0 NaN NaN 48.6000 NaN NaN 168.9000 \n",
"1 51.5000 NaN 78.5000 NaN NaN 56.5000 \n",
"2 40.6401 NaN 46.9901 NaN NaN 40.6401 \n",
"3 7.6000 NaN 72.4000 NaN NaN 117.5000 \n",
"4 NaN NaN 40.6401 NaN NaN 29.8451 \n",
"\n",
" Seat Height (cm) Duration (sec.) Wiki QID ULAN \n",
"0 NaN NaN Q84287 500016971.0 \n",
"1 NaN NaN Q84287 500016971.0 \n",
"2 NaN NaN Q84287 500016971.0 \n",
"3 NaN NaN Q84287 500016971.0 \n",
"4 NaN NaN Q312838 500024982.0 \n",
"\n",
"[5 rows x 37 columns]"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# make copy\n",
"\n",
"master_copy = master\n",
"master_copy.head(5)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"# remove duplicated columns \"_x\" and keep \"_y\"\n",
"master_copy = master_copy.drop(['ArtistBio_x', 'ArtistBio_y', 'Gender_x', 'Nationality_x', 'BeginDate_x', 'EndDate_x', 'DisplayName'], axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"# rename columns for readability\n",
"\n",
"master_copy.rename(columns={'Nationality_y':'Nationality', 'Gender_y':'Gender',\n",
" 'BeginDate_y': 'Birth', 'EndDate_y': 'Death'}, inplace = True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Time"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"NaN 6312\n",
"1967 1855\n",
"1971 1830\n",
"1966 1661\n",
"1968 1615\n",
"1965 1587\n",
"1969 1503\n",
"1973 1480\n",
"1964 1407\n",
"1970 1403\n",
"1962 1349\n",
"2003 1265\n",
"1963 1260\n",
"1991 1225\n",
"1972 1154\n",
"1930 1130\n",
"1928 1118\n",
"1980 1098\n",
"1976 1079\n",
"1931 1067\n",
"2001 1060\n",
"1961 992\n",
"1990 976\n",
"1974 959\n",
"1977 954\n",
"1984 954\n",
"2002 952\n",
"1983 901\n",
"1960 883\n",
"1994 875\n",
" ... \n",
"(c. 1913) 10\n",
"1996-2004 10\n",
"1927-28 10\n",
"1968-69 10\n",
"Avril 1926 10\n",
"1914-20 10\n",
"1972-1974 10\n",
"1913-14 10\n",
"1917–18 10\n",
"1959, published 1960 10\n",
"1973, published 1974 10\n",
"1985-90 10\n",
"c. 1909 10\n",
"2005–2007 10\n",
"1925–1929 10\n",
"1977-78 10\n",
"1966-1968 10\n",
"1950-1953 10\n",
"(1981-83) 10\n",
"1890, published 1923 10\n",
"c.1952 10\n",
"(1979-80) 10\n",
"1964-1966 10\n",
"1995-1999 10\n",
"2001–02 10\n",
"1958-59 10\n",
"(1966-70) 10\n",
"(1987-1992) 10\n",
"(1934) 10\n",
"c. 1917–19 10\n",
"Name: Date, Length: 1000, dtype: int64"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# time function - to standardize date, begin/end date, acquisition date\n",
"\n",
"#https://www.dataquest.io/blog/data-cleaning-with-python/\n",
" \n",
"# Pattern 1: “1976-77” (year ranges)\n",
"# Pattern 2: “c. 1917”\n",
"# Pattern 3: “Unknown”\n",
"# Pattern 4: “n.d.”\n",
"\n",
"master_copy['Date'].value_counts(dropna=False).head(1000)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"nan 6312\n",
"1967 1950\n",
"1966 1883\n",
"1971 1861\n",
"1968 1735\n",
"1965 1675\n",
"1969 1580\n",
"1973 1563\n",
"1964 1457\n",
"1970 1442\n",
"2003 1431\n",
"1962 1392\n",
"1963 1308\n",
"1991 1252\n",
"1972 1228\n",
"1930 1136\n",
"1928 1135\n",
"1980 1128\n",
"2001 1114\n",
"1976 1111\n",
"2002 1083\n",
"1931 1076\n",
"1961 1056\n",
"1974 1042\n",
"1990 1003\n",
"1977 989\n",
"1984 972\n",
"2004 955\n",
"1960 934\n",
"1983 922\n",
" ... \n",
"IssylesMoulineauxsummer1913 1\n",
"February24271967 1\n",
"February16201962 1\n",
"Montroiglatesummerfall1925 1\n",
"September11966 1\n",
"c1982–83 1\n",
"May111995 1\n",
"196869published1969 1\n",
"19141917 1\n",
"May2271969 1\n",
"August1935 1\n",
"1949–1951 1\n",
"1959–1967 1\n",
"August241893 1\n",
"1979–1981 1\n",
"July151894 1\n",
"2004–07 1\n",
"Printexecuted1905laterprinting1955 1\n",
"January221981 1\n",
"June21July31965 1\n",
"newspaperpublishedDecember42000 1\n",
"1952printsexecuted195052 1\n",
"April8171963 1\n",
"July161936 1\n",
"August16171966 1\n",
"IssylesMoulineauxfall1911 1\n",
"1942reprinted1990 1\n",
"c1980signed2007 1\n",
"April171912 1\n",
"2011originallypublished1932 1\n",
"Name: Date, Length: 8604, dtype: int64"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def strip_punctuation(row):\n",
" # turn each start date into string \n",
" start_date = str(row['Date'])\n",
" # remove any whitespace outside of the start up\n",
" start_date = start_date.strip()\n",
" # replace inside whitespace with punctuation\n",
" start_date = start_date.replace(\" \", \"?\")\n",
" # separate start date into 2 elemtns if \"-\" is found\n",
" final_date = start_date.translate(str.maketrans({a: None for a in string.punctuation}))\n",
" return final_date\n",
"\n",
"# assign the results of \" strip_punctuation\" to the 'Date' column\n",
"# for Pandas to go row-wise so we set \"axis = 1\"\n",
"# for Pandas to go column-wise so we set \"axis = 0\"\n",
"\n",
"master_copy['Date'] = master_copy.apply(lambda row: strip_punctuation(row), axis=1)\n",
"master_copy['Date'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"nan 6312\n",
"1967 1950\n",
"1966 1883\n",
"1971 1861\n",
"1968 1735\n",
"1965 1675\n",
"1969 1580\n",
"1973 1563\n",
"1964 1457\n",
"1970 1442\n",
"2003 1431\n",
"1962 1392\n",
"1963 1308\n",
"1991 1252\n",
"1972 1228\n",
"1930 1136\n",
"1928 1135\n",
"1980 1128\n",
"2001 1114\n",
"1976 1111\n",
"2002 1083\n",
"1931 1076\n",
"1961 1056\n",
"1974 1042\n",
"1990 1003\n",
"1977 989\n",
"1984 972\n",
"2004 955\n",
"1960 934\n",
"1983 922\n",
" ... \n",
"IssylesMoulineauxsummer1913 1\n",
"February24271967 1\n",
"February16201962 1\n",
"Montroiglatesummerfall1925 1\n",
"September11966 1\n",
"c1982–83 1\n",
"May111995 1\n",
"196869published1969 1\n",
"19141917 1\n",
"May2271969 1\n",
"August1935 1\n",
"1949–1951 1\n",
"1959–1967 1\n",
"August241893 1\n",
"1979–1981 1\n",
"July151894 1\n",
"2004–07 1\n",
"Printexecuted1905laterprinting1955 1\n",
"January221981 1\n",
"June21July31965 1\n",
"newspaperpublishedDecember42000 1\n",
"1952printsexecuted195052 1\n",
"April8171963 1\n",
"July161936 1\n",
"August16171966 1\n",
"IssylesMoulineauxfall1911 1\n",
"1942reprinted1990 1\n",
"c1980signed2007 1\n",
"April171912 1\n",
"2011originallypublished1932 1\n",
"Name: Date, Length: 8604, dtype: int64"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# for some reason the above function does not remove '-'\n",
"\n",
"def split_dates(row):\n",
" # start date contains the current value for the Date column\n",
" start_date = str(row['Date'])\n",
" # split start_date into two elements if \"-\" is found \n",
" # remove - again\n",
" split_date = start_date.split('-')\n",
" # if a \"-\" is found, split_date will contain a list with at least two items\n",
" # else not found, split_date will have 1 item, the initial_date\n",
" # use final_date from previous function's list\n",
" if len(split_date) >1:\n",
" final_date = split_date[0]\n",
" else:\n",
" final_date = start_date\n",
" return final_date\n",
"\n",
"master_copy['Date'] = master_copy.apply(lambda row: split_dates(row), axis=1)\n",
"master_copy['Date'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"# delete rows with letters\n",
"\n",
"master_copy = master_copy[~master_copy['Date'].str.contains(\"[a-zA-Z]\").fillna(False)]"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1967 1950\n",
"1966 1883\n",
"1971 1861\n",
"1968 1735\n",
"1965 1675\n",
"1969 1580\n",
"1973 1563\n",
"1964 1457\n",
"1970 1442\n",
"2003 1431\n",
"1962 1392\n",
"1963 1308\n",
"1991 1252\n",
"1972 1228\n",
"1930 1136\n",
"1928 1135\n",
"1980 1128\n",
"2001 1114\n",
"1976 1111\n",
"2002 1083\n",
"1931 1076\n",
"1961 1056\n",
"1974 1042\n",
"1990 1003\n",
"1977 989\n",
"1984 972\n",
"2004 955\n",
"1960 934\n",
"1983 922\n",
"1978 901\n",
" ... \n",
"184952 1\n",
"19682004 1\n",
"19861987 1\n",
"18881964 1\n",
"1982–84 1\n",
"1961–65 1\n",
"193349 1\n",
"20052007 1\n",
"1947–49 1\n",
"1954–1956 1\n",
"19831984 1\n",
"1945–51 1\n",
"194547 1\n",
"19772001 1\n",
"19751979 1\n",
"1961–2010 1\n",
"1988–1995 1\n",
"20072015 1\n",
"19501965 1\n",
"192730 1\n",
"19661978 1\n",
"1932–34 1\n",
"187980 1\n",
"1811 1\n",
"1920–29 1\n",
"19201937 1\n",
"193655 1\n",
"19191953 1\n",
"1962–1990 1\n",
"19451958 1\n",
"Name: Date, Length: 2145, dtype: int64"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"master_copy['Date'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dtype('O')"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"master_copy['Date'].dtypes"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"# drop dates with len() < 4 or > 4\n",
"# year is 4 digit\n",
"\n",
"def drop_dates(row):\n",
" # start date contains the current value for the Date column\n",
" start_date = row['Date']\n",
" \n",
" # if larger than 4 digits, then drop above four digits\n",
" # if less than 4 digits, then keep the 4 digit format\n",
" # anything else, keep the same 4 digit format\n",
" \n",
" if len(start_date) > 4:\n",
" final_date = np.NaN\n",
" elif len(start_date) < 4:\n",
" final_date = np.NaN\n",
" else:\n",
" final_date = start_date\n",
" \n",
" return final_date"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1967 1950\n",
"1966 1883\n",
"1971 1861\n",
"1968 1735\n",
"1965 1675\n",
"1969 1580\n",
"1973 1563\n",
"1964 1457\n",
"1970 1442\n",
"2003 1431\n",
"1962 1392\n",
"1963 1308\n",
"1991 1252\n",
"1972 1228\n",
"1930 1136\n",
"1928 1135\n",
"1980 1128\n",
"2001 1114\n",
"1976 1111\n",
"2002 1083\n",
"1931 1076\n",
"1961 1056\n",
"1974 1042\n",
"1990 1003\n",
"1977 989\n",
"1984 972\n",
"2004 955\n",
"1960 934\n",
"1983 922\n",
"1978 901\n",
" ... \n",
"1879 14\n",
"1885 14\n",
"1855 12\n",
"1864 11\n",
"1851 11\n",
"1880 11\n",
"1861 11\n",
"1883 9\n",
"1840 9\n",
"1837 7\n",
"1863 7\n",
"1844 6\n",
"1870 6\n",
"1882 6\n",
"1768 5\n",
"1862 5\n",
"1850 5\n",
"1878 5\n",
"1884 5\n",
"1849 4\n",
"1843 3\n",
"1847 2\n",
"1832 2\n",
"1841 2\n",
"1845 2\n",
"1811 1\n",
"1809 1\n",
"1842 1\n",
"1848 1\n",
"1805 1\n",
"Name: Date, Length: 186, dtype: int64"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# assign the results of \" drop_dates\" to the 'Date' column\n",
"# for Pandas to go row-wise so we set \"axis = 1\"\n",
"# for Pandas to go column-wise so we set \"axis = 0\"\n",
"master_copy['Date'] = master_copy.apply(lambda row: drop_dates(row), axis=1)\n",
"master_copy['Date'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"# delete those two rows where the 'Date' is an error = 4271\n",
"master_copy = master_copy[master_copy.Date != '4271']"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"9707 1768\n",
"9710 1768\n",
"9711 1768\n",
"9709 1768\n",
"9708 1768\n",
"101410 1805\n",
"101924 1809\n",
"69875 1811\n",
"64060 1818\n",
"63955 1818\n",
"63954 1818\n",
"63953 1818\n",
"63952 1818\n",
"63951 1818\n",
"63950 1818\n",
"63948 1818\n",
"63956 1818\n",
"63947 1818\n",
"63946 1818\n",
"63945 1818\n",
"63944 1818\n",
"64067 1818\n",
"64068 1818\n",
"63949 1818\n",
"63943 1818\n",
"63957 1818\n",
"63959 1818\n",
"63976 1818\n",
"63975 1818\n",
"63974 1818\n",
" ... \n",
"138633 NaN\n",
"138634 NaN\n",
"138635 NaN\n",
"138636 NaN\n",
"138650 NaN\n",
"138651 NaN\n",
"138683 NaN\n",
"138713 NaN\n",
"138739 NaN\n",
"138740 NaN\n",
"138741 NaN\n",
"138742 NaN\n",
"138743 NaN\n",
"138744 NaN\n",
"138745 NaN\n",
"138746 NaN\n",
"138747 NaN\n",
"138748 NaN\n",
"138749 NaN\n",
"138750 NaN\n",
"138751 NaN\n",
"138752 NaN\n",
"138761 NaN\n",
"138911 NaN\n",
"138914 NaN\n",
"138965 NaN\n",
"138966 NaN\n",
"138967 NaN\n",
"138984 NaN\n",
"138989 NaN\n",
"Name: Date, Length: 112980, dtype: object"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"master_copy['Date'].sort_values()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 1896\n",
"1 1902\n",
"2 1904\n",
"3 1899\n",
"4 1987\n",
"25 1903\n",
"26 1903\n",
"27 1980\n",
"53 1980\n",
"54 1978\n",
"55 1980\n",
"56 1979\n",
"57 1979\n",
"58 1979\n",
"59 1980\n",
"60 1979\n",
"61 1979\n",
"62 1979\n",
"63 1979\n",
"64 1979\n",
"65 1979\n",
"66 1980\n",
"82 1984\n",
"83 1986\n",
"84 1983\n",
"85 1992\n",
"86 1986\n",
"88 1968\n",
"89 1957\n",
"91 1962\n",
" ... \n",
"138962 2018\n",
"138963 2014\n",
"138964 2011\n",
"138968 2018\n",
"138969 2018\n",
"138970 2014\n",
"138974 2019\n",
"138976 2007\n",
"138977 1968\n",
"138978 1946\n",
"138979 1958\n",
"138980 2015\n",
"138981 1900\n",
"138982 1995\n",
"138985 2019\n",
"138986 2019\n",
"138987 1957\n",
"138988 2018\n",
"138990 1924\n",
"138991 1968\n",
"138992 1937\n",
"138993 1937\n",
"138994 1938\n",
"138995 1939\n",
"138996 1939\n",
"138997 2019\n",
"138998 2018\n",
"138999 2019\n",
"139000 2019\n",
"139001 2018\n",
"Name: Date, Length: 93503, dtype: object"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"master_copy['Date'].dropna()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dtype('float64')"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# convert back to float\n",
"master_copy['Date'] = master_copy['Date'].astype(float)\n",
"master_copy['Date'].dtype"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1964-10-06 9717\n",
"NaN 5203\n",
"1968-03-06 4534\n",
"2008-10-08 3115\n",
"2005-05-10 2546\n",
"2001-01-24 1822\n",
"1940-04-05 1140\n",
"1949-09-29 905\n",
"1965-11-09 847\n",
"2000-10-12 847\n",
"2013-10-24 826\n",
"1974-01-10 826\n",
"2018-11-05 787\n",
"2015-10-27 688\n",
"2012-10-03 685\n",
"2019-04-04 636\n",
"2008-06-18 635\n",
"2015-05-28 608\n",
"2014-10-06 604\n",
"1967-10-18 560\n",
"Name: DateAcquired, dtype: int64"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"master_copy['DateAcquired'].value_counts(dropna=False).head(20)\n",
" \n",
"# convert to pandas"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"master_copy['DateAcquired'] = master_copy['DateAcquired'].astype(str)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"master_copy['DateAcquired'] = pd.to_datetime(master_copy['DateAcquired'], infer_datetime_format=True, errors = 'coerce')"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"master_copy['DateAcquired_Year'] = master_copy['DateAcquired'].dt.year"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"master_copy['DateAcquired_Month'] = master_copy['DateAcquired'].dt.month"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"UniqueID object\n",
"Title object\n",
"Artist object\n",
"Gender object\n",
"Nationality object\n",
"Birth float64\n",
"Death float64\n",
"Date float64\n",
"Medium object\n",
"Dimensions object\n",
"CreditLine object\n",
"AccessionNumber object\n",
"Classification object\n",
"Department object\n",
"DateAcquired datetime64[ns]\n",
"Cataloged object\n",
"ObjectID float64\n",
"URL object\n",
"ThumbnailURL object\n",
"Circumference (cm) float64\n",
"Depth (cm) float64\n",
"Diameter (cm) float64\n",
"Height (cm) float64\n",
"Length (cm) float64\n",
"Weight (kg) float64\n",
"Width (cm) float64\n",
"Seat Height (cm) float64\n",
"Duration (sec.) float64\n",
"Wiki QID object\n",
"ULAN float64\n",
"DateAcquired_Year float64\n",
"DateAcquired_Month float64\n",
"dtype: object"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"master_copy.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"33878 1929.0\n",
"28558 1929.0\n",
"17197 1929.0\n",
"17196 1929.0\n",
"31349 1929.0\n",
"33900 1929.0\n",
"105361 1929.0\n",
"34594 1929.0\n",
"62583 1930.0\n",
"73796 1930.0\n",
"109027 1930.0\n",
"70944 1930.0\n",
"62582 1930.0\n",
"63174 1931.0\n",
"72606 1931.0\n",
"105203 1931.0\n",
"31354 1932.0\n",
"31355 1932.0\n",
"5222 1932.0\n",
"31352 1932.0\n",
"31356 1932.0\n",
"31350 1932.0\n",
"70878 1932.0\n",
"431 1932.0\n",
"70879 1932.0\n",
"69316 1932.0\n",
"106739 1932.0\n",
"31304 1932.0\n",
"31303 1932.0\n",
"31302 1932.0\n",
" ... \n",
"137116 NaN\n",
"137117 NaN\n",
"137118 NaN\n",
"137119 NaN\n",
"137120 NaN\n",
"137121 NaN\n",
"137122 NaN\n",
"137123 NaN\n",
"137124 NaN\n",
"137125 NaN\n",
"137126 NaN\n",
"137127 NaN\n",
"137128 NaN\n",
"137178 NaN\n",
"137197 NaN\n",
"137785 NaN\n",
"137845 NaN\n",
"137846 NaN\n",
"137946 NaN\n",
"137954 NaN\n",
"137955 NaN\n",
"137956 NaN\n",
"137957 NaN\n",
"137958 NaN\n",
"137960 NaN\n",
"137962 NaN\n",
"137963 NaN\n",
"138718 NaN\n",
"138760 NaN\n",
"138894 NaN\n",
"Name: DateAcquired_Year, Length: 112980, dtype: float64"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"master_copy['DateAcquired_Year'].sort_values()"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"master_copy['Date'] = master_copy['Date'].astype(float)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Birth"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.0 4673\n",
"1857.0 4573\n",
"1911.0 3678\n",
"1886.0 2730\n",
"1934.0 2131\n",
"1928.0 2022\n",
"1901.0 1912\n",
"1941.0 1887\n",
"1923.0 1841\n",
"1887.0 1791\n",
"1867.0 1775\n",
"1938.0 1612\n",
"1930.0 1460\n",
"1943.0 1459\n",
"1935.0 1436\n",
"1885.0 1390\n",
"1940.0 1365\n",
"1937.0 1298\n",
"1927.0 1249\n",
"1933.0 1246\n",
"1881.0 1163\n",
"1947.0 1148\n",
"1924.0 1141\n",
"1955.0 1130\n",
"1931.0 1107\n",
"1939.0 1098\n",
"1898.0 1077\n",
"1936.0 1073\n",
"1944.0 1057\n",
"1926.0 1050\n",
" ... \n",
"1847.0 4\n",
"1812.0 4\n",
"1842.0 4\n",
"1804.0 4\n",
"1799.0 3\n",
"2003.0 3\n",
"2004.0 3\n",
"1800.0 3\n",
"1846.0 3\n",
"2010.0 3\n",
"1808.0 3\n",
"1998.0 2\n",
"1817.0 2\n",
"1809.0 2\n",
"1795.0 1\n",
"1810.0 1\n",
"1731.0 1\n",
"1835.0 1\n",
"1838.0 1\n",
"1850.0 1\n",
"1787.0 1\n",
"1782.0 1\n",
"1765.0 1\n",
"2012.0 1\n",
"2007.0 1\n",
"2000.0 1\n",
"1996.0 1\n",
"1994.0 1\n",
"1993.0 1\n",
"1789.0 1\n",
"Name: Birth, Length: 215, dtype: int64"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"master_copy['Birth'].value_counts(ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"NaN 6918\n",
" 0.0 4673\n",
" 1857.0 4573\n",
" 1911.0 3678\n",
" 1886.0 2730\n",
" 1934.0 2131\n",
" 1928.0 2022\n",
" 1901.0 1912\n",
" 1941.0 1887\n",
" 1923.0 1841\n",
" 1887.0 1791\n",
" 1867.0 1775\n",
" 1938.0 1612\n",
" 1930.0 1460\n",
" 1943.0 1459\n",
" 1935.0 1436\n",
" 1885.0 1390\n",
" 1940.0 1365\n",
" 1937.0 1298\n",
" 1927.0 1249\n",
" 1933.0 1246\n",
" 1881.0 1163\n",
" 1947.0 1148\n",
" 1924.0 1141\n",
" 1955.0 1130\n",
" 1931.0 1107\n",
" 1939.0 1098\n",
" 1898.0 1077\n",
" 1936.0 1073\n",
" 1944.0 1057\n",
" ... \n",
" 1804.0 4\n",
" 1842.0 4\n",
" 1812.0 4\n",
" 1847.0 4\n",
" 1799.0 3\n",
" 2010.0 3\n",
" 1808.0 3\n",
" 2004.0 3\n",
" 1800.0 3\n",
" 1846.0 3\n",
" 2003.0 3\n",
" 1809.0 2\n",
" 1817.0 2\n",
" 1998.0 2\n",
" 1795.0 1\n",
" 1782.0 1\n",
" 1810.0 1\n",
" 1838.0 1\n",
" 1731.0 1\n",
" 1787.0 1\n",
" 1835.0 1\n",
" 1850.0 1\n",
" 1765.0 1\n",
" 1993.0 1\n",
" 1994.0 1\n",
" 1996.0 1\n",
" 2012.0 1\n",
" 2007.0 1\n",
" 2000.0 1\n",
" 1789.0 1\n",
"Name: Birth, Length: 216, dtype: int64"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"master_copy['Birth'].value_counts(dropna=False).head(1000)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"master_copy['Birth'] = master_copy['Birth'].astype(str)\n",
"master_copy = master_copy[~master_copy['Birth'].str.contains(\"[a-zA-Z]\").fillna(False)]"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.0 4673\n",
"1857.0 4573\n",
"1911.0 3678\n",
"1886.0 2730\n",
"1934.0 2131\n",
"1928.0 2022\n",
"1901.0 1912\n",
"1941.0 1887\n",
"1923.0 1841\n",
"1887.0 1791\n",
"1867.0 1775\n",
"1938.0 1612\n",
"1930.0 1460\n",
"1943.0 1459\n",
"1935.0 1436\n",
"1885.0 1390\n",
"1940.0 1365\n",
"1937.0 1298\n",
"1927.0 1249\n",
"1933.0 1246\n",
"1881.0 1163\n",
"1947.0 1148\n",
"1924.0 1141\n",
"1955.0 1130\n",
"1931.0 1107\n",
"1939.0 1098\n",
"1898.0 1077\n",
"1936.0 1073\n",
"1944.0 1057\n",
"1926.0 1050\n",
" ... \n",
"1842.0 4\n",
"1812.0 4\n",
"1804.0 4\n",
"1847.0 4\n",
"2003.0 3\n",
"1800.0 3\n",
"1846.0 3\n",
"2004.0 3\n",
"1808.0 3\n",
"1799.0 3\n",
"2010.0 3\n",
"1809.0 2\n",
"1998.0 2\n",
"1817.0 2\n",
"1835.0 1\n",
"1996.0 1\n",
"1765.0 1\n",
"2000.0 1\n",
"1838.0 1\n",
"2012.0 1\n",
"1787.0 1\n",
"1795.0 1\n",
"2007.0 1\n",
"1850.0 1\n",
"1782.0 1\n",
"1789.0 1\n",
"1994.0 1\n",
"1810.0 1\n",
"1993.0 1\n",
"1731.0 1\n",
"Name: Birth, Length: 215, dtype: int64"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"master_copy['Birth'].value_counts(ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 1841.0\n",
"1 1841.0\n",
"2 1841.0\n",
"3 1841.0\n",
"4 1944.0\n",
"5 1944.0\n",
"6 1944.0\n",
"7 1944.0\n",
"8 1944.0\n",
"9 1944.0\n",
"10 1944.0\n",
"11 1944.0\n",
"12 1944.0\n",
"13 1944.0\n",
"14 1944.0\n",
"15 1944.0\n",
"16 1944.0\n",
"17 1944.0\n",
"18 1944.0\n",
"19 1944.0\n",
"20 1944.0\n",
"21 1944.0\n",
"22 1944.0\n",
"23 1944.0\n",
"24 1944.0\n",
"25 1876.0\n",
"26 1876.0\n",
"27 1944.0\n",
"28 1944.0\n",
"29 1944.0\n",
" ... \n",
"138957 1985.0\n",
"138958 1985.0\n",
"138959 1985.0\n",
"138960 1985.0\n",
"138961 1985.0\n",
"138962 1985.0\n",
"138963 1971.0\n",
"138964 1986.0\n",
"138965 1983.0\n",
"138966 1983.0\n",
"138967 1983.0\n",
"138970 1978.0\n",
"138976 1986.0\n",
"138977 1946.0\n",
"138978 1905.0\n",
"138979 1905.0\n",
"138980 1986.0\n",
"138981 1869.0\n",
"138982 1948.0\n",
"138985 1976.0\n",
"138986 1976.0\n",
"138987 1925.0\n",
"138988 1941.0\n",
"138989 1941.0\n",
"138990 1899.0\n",
"138991 1945.0\n",
"138998 1976.0\n",
"138999 1983.0\n",
"139000 1979.0\n",
"139001 1972.0\n",
"Name: Birth, Length: 106062, dtype: object"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"master_copy['Birth'].dropna()"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.0 4673\n",
"1857.0 4573\n",
"1911.0 3678\n",
"1886.0 2730\n",
"1934.0 2131\n",
"1928.0 2022\n",
"1901.0 1912\n",
"1941.0 1887\n",
"1923.0 1841\n",
"1887.0 1791\n",
"1867.0 1775\n",
"1938.0 1612\n",
"1930.0 1460\n",
"1943.0 1459\n",
"1935.0 1436\n",
"1885.0 1390\n",
"1940.0 1365\n",
"1937.0 1298\n",
"1927.0 1249\n",
"1933.0 1246\n",
"1881.0 1163\n",
"1947.0 1148\n",
"1924.0 1141\n",
"1955.0 1130\n",
"1931.0 1107\n",
"1939.0 1098\n",
"1898.0 1077\n",
"1936.0 1073\n",
"1944.0 1057\n",
"1926.0 1050\n",
" ... \n",
"1842.0 4\n",
"1812.0 4\n",
"1804.0 4\n",
"1847.0 4\n",
"2003.0 3\n",
"1800.0 3\n",
"1846.0 3\n",
"2004.0 3\n",
"1808.0 3\n",
"1799.0 3\n",
"2010.0 3\n",
"1809.0 2\n",
"1998.0 2\n",
"1817.0 2\n",
"1835.0 1\n",
"1996.0 1\n",
"1765.0 1\n",
"2000.0 1\n",
"1838.0 1\n",
"2012.0 1\n",
"1787.0 1\n",
"1795.0 1\n",
"2007.0 1\n",
"1850.0 1\n",
"1782.0 1\n",
"1789.0 1\n",
"1994.0 1\n",
"1810.0 1\n",
"1993.0 1\n",
"1731.0 1\n",
"Name: Birth, Length: 215, dtype: int64"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"master_copy['Birth'].value_counts(ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dtype('float64')"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# convert back to float\n",
"master_copy['Birth'] = master_copy['Birth'].astype(float)\n",
"master_copy['Birth'].dtype"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Death"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.0 39828\n",
"1927.0 4623\n",
"2010.0 3685\n",
"1985.0 3102\n",
"1969.0 2508\n",
"1954.0 1930\n",
"1947.0 1683\n",
"1976.0 1628\n",
"2007.0 1139\n",
"1973.0 1110\n",
"2014.0 1081\n",
"1959.0 1002\n",
"1984.0 1002\n",
"2013.0 972\n",
"1997.0 959\n",
"1941.0 941\n",
"1944.0 921\n",
"1965.0 902\n",
"1994.0 874\n",
"2012.0 872\n",
"1958.0 866\n",
"1998.0 828\n",
"1987.0 817\n",
"1964.0 793\n",
"2002.0 782\n",
"1953.0 774\n",
"2008.0 731\n",
"1956.0 715\n",
"1983.0 674\n",
"1999.0 654\n",
" ... \n",
"1885.0 13\n",
"1878.0 12\n",
"1922.0 10\n",
"1915.0 8\n",
"1871.0 7\n",
"1877.0 7\n",
"1872.0 5\n",
"1868.0 5\n",
"1866.0 5\n",
"1795.0 5\n",
"1895.0 5\n",
"1891.0 5\n",
"1886.0 4\n",
"1876.0 3\n",
"1873.0 3\n",
"1912.0 3\n",
"1875.0 3\n",
"1892.0 2\n",
"1874.0 2\n",
"1908.0 2\n",
"1858.0 2\n",
"1893.0 1\n",
"1851.0 1\n",
"1852.0 1\n",
"1890.0 1\n",
"1859.0 1\n",
"1888.0 1\n",
"1881.0 1\n",
"1905.0 1\n",
"1899.0 1\n",
"Name: Death, Length: 162, dtype: int64"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"master_copy['Death'].value_counts(ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.0 39828\n",
"1927.0 4623\n",
"2010.0 3685\n",
"1985.0 3102\n",
"1969.0 2508\n",
"1954.0 1930\n",
"1947.0 1683\n",
"1976.0 1628\n",
"2007.0 1139\n",
"1973.0 1110\n",
"2014.0 1081\n",
"1959.0 1002\n",
"1984.0 1002\n",
"2013.0 972\n",
"1997.0 959\n",
"1941.0 941\n",
"1944.0 921\n",
"1965.0 902\n",
"1994.0 874\n",
"2012.0 872\n",
"1958.0 866\n",
"1998.0 828\n",
"1987.0 817\n",
"1964.0 793\n",
"2002.0 782\n",
"1953.0 774\n",
"2008.0 731\n",
"1956.0 715\n",
"1983.0 674\n",
"1999.0 654\n",
" ... \n",
"1885.0 13\n",
"1878.0 12\n",
"1922.0 10\n",
"1915.0 8\n",
"1871.0 7\n",
"1877.0 7\n",
"1872.0 5\n",
"1868.0 5\n",
"1866.0 5\n",
"1795.0 5\n",
"1895.0 5\n",
"1891.0 5\n",
"1886.0 4\n",
"1876.0 3\n",
"1873.0 3\n",
"1912.0 3\n",
"1875.0 3\n",
"1892.0 2\n",
"1874.0 2\n",
"1908.0 2\n",
"1858.0 2\n",
"1893.0 1\n",
"1851.0 1\n",
"1852.0 1\n",
"1890.0 1\n",
"1859.0 1\n",
"1888.0 1\n",
"1881.0 1\n",
"1905.0 1\n",
"1899.0 1\n",
"Name: Death, Length: 162, dtype: int64"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"master_copy['Death'].value_counts(dropna=False).head(1000)"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [],
"source": [
"master_copy['Death'] = master_copy['Death'].astype(str)\n",
"master_copy = master_copy[~master_copy['Death'].str.contains(\"[a-zA-Z]\").fillna(False)]"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.0 39828\n",
"1927.0 4623\n",
"2010.0 3685\n",
"1985.0 3102\n",
"1969.0 2508\n",
"1954.0 1930\n",
"1947.0 1683\n",
"1976.0 1628\n",
"2007.0 1139\n",
"1973.0 1110\n",
"2014.0 1081\n",
"1959.0 1002\n",
"1984.0 1002\n",
"2013.0 972\n",
"1997.0 959\n",
"1941.0 941\n",
"1944.0 921\n",
"1965.0 902\n",
"1994.0 874\n",
"2012.0 872\n",
"1958.0 866\n",
"1998.0 828\n",
"1987.0 817\n",
"1964.0 793\n",
"2002.0 782\n",
"1953.0 774\n",
"2008.0 731\n",
"1956.0 715\n",
"1983.0 674\n",
"1999.0 654\n",
" ... \n",
"1885.0 13\n",
"1878.0 12\n",
"1922.0 10\n",
"1915.0 8\n",
"1871.0 7\n",
"1877.0 7\n",
"1795.0 5\n",
"1872.0 5\n",
"1895.0 5\n",
"1891.0 5\n",
"1868.0 5\n",
"1866.0 5\n",
"1886.0 4\n",
"1912.0 3\n",
"1876.0 3\n",
"1873.0 3\n",
"1875.0 3\n",
"1908.0 2\n",
"1892.0 2\n",
"1874.0 2\n",
"1858.0 2\n",
"1881.0 1\n",
"1890.0 1\n",
"1851.0 1\n",
"1899.0 1\n",
"1852.0 1\n",
"1888.0 1\n",
"1893.0 1\n",
"1905.0 1\n",
"1859.0 1\n",
"Name: Death, Length: 162, dtype: int64"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"master_copy['Death'].value_counts(ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 1918.0\n",
"1 1918.0\n",
"2 1918.0\n",
"3 1918.0\n",
"4 0.0\n",
"5 0.0\n",
"6 0.0\n",
"7 0.0\n",
"8 0.0\n",
"9 0.0\n",
"10 0.0\n",
"11 0.0\n",
"12 0.0\n",
"13 0.0\n",
"14 0.0\n",
"15 0.0\n",
"16 0.0\n",
"17 0.0\n",
"18 0.0\n",
"19 0.0\n",
"20 0.0\n",
"21 0.0\n",
"22 0.0\n",
"23 0.0\n",
"24 0.0\n",
"25 1957.0\n",
"26 1957.0\n",
"27 0.0\n",
"28 0.0\n",
"29 0.0\n",
" ... \n",
"138957 0.0\n",
"138958 0.0\n",
"138959 0.0\n",
"138960 0.0\n",
"138961 0.0\n",
"138962 0.0\n",
"138963 0.0\n",
"138964 0.0\n",
"138965 0.0\n",
"138966 0.0\n",
"138967 0.0\n",
"138970 0.0\n",
"138976 0.0\n",
"138977 0.0\n",
"138978 1990.0\n",
"138979 1990.0\n",
"138980 0.0\n",
"138981 1962.0\n",
"138982 0.0\n",
"138985 0.0\n",
"138986 0.0\n",
"138987 2019.0\n",
"138988 0.0\n",
"138989 0.0\n",
"138990 1963.0\n",
"138991 0.0\n",
"138998 0.0\n",
"138999 0.0\n",
"139000 0.0\n",
"139001 0.0\n",
"Name: Death, Length: 106062, dtype: object"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"master_copy['Death'].dropna()"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dtype('float64')"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# convert back to float\n",
"master_copy['Death'] = master_copy['Death'].astype(float)\n",
"master_copy['Death'].dtype"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.0 39828\n",
"1927.0 4623\n",
"2010.0 3685\n",
"1985.0 3102\n",
"1969.0 2508\n",
"1954.0 1930\n",
"1947.0 1683\n",
"1976.0 1628\n",
"2007.0 1139\n",
"1973.0 1110\n",
"2014.0 1081\n",
"1959.0 1002\n",
"1984.0 1002\n",
"2013.0 972\n",
"1997.0 959\n",
"1941.0 941\n",
"1944.0 921\n",
"1965.0 902\n",
"1994.0 874\n",
"2012.0 872\n",
"1958.0 866\n",
"1998.0 828\n",
"1987.0 817\n",
"1964.0 793\n",
"2002.0 782\n",
"1953.0 774\n",
"2008.0 731\n",
"1956.0 715\n",
"1983.0 674\n",
"1999.0 654\n",
" ... \n",
"1885.0 13\n",
"1878.0 12\n",
"1922.0 10\n",
"1915.0 8\n",
"1871.0 7\n",
"1877.0 7\n",
"1872.0 5\n",
"1868.0 5\n",
"1866.0 5\n",
"1795.0 5\n",
"1895.0 5\n",
"1891.0 5\n",
"1886.0 4\n",
"1876.0 3\n",
"1873.0 3\n",
"1912.0 3\n",
"1875.0 3\n",
"1892.0 2\n",
"1874.0 2\n",
"1908.0 2\n",
"1858.0 2\n",
"1893.0 1\n",
"1851.0 1\n",
"1852.0 1\n",
"1890.0 1\n",
"1859.0 1\n",
"1888.0 1\n",
"1881.0 1\n",
"1905.0 1\n",
"1899.0 1\n",
"Name: Death, Length: 162, dtype: int64"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"master_copy['Death'].value_counts(ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1967.0 1798\n",
"1966.0 1695\n",
"1965.0 1588\n",
"1968.0 1491\n",
"1973.0 1477\n",
"1969.0 1435\n",
"1970.0 1372\n",
"2003.0 1360\n",
"1964.0 1334\n",
"1962.0 1245\n",
"1971.0 1245\n",
"1963.0 1235\n",
"1991.0 1190\n",
"1972.0 1153\n",
"1930.0 1110\n",
"1928.0 1096\n",
"1980.0 1074\n",
"1931.0 1056\n",
"2001.0 1054\n",
"1976.0 1053\n",
"2002.0 1046\n",
"1961.0 1016\n",
"1974.0 954\n",
"1990.0 940\n",
"1977.0 911\n",
"2004.0 899\n",
"1984.0 892\n",
"1960.0 888\n",
"1994.0 860\n",
"1999.0 858\n",
" ... \n",
"1886.0 15\n",
"1879.0 14\n",
"1885.0 14\n",
"1855.0 12\n",
"1864.0 11\n",
"1880.0 11\n",
"1851.0 10\n",
"1861.0 9\n",
"1883.0 9\n",
"1837.0 7\n",
"1863.0 6\n",
"1870.0 6\n",
"1850.0 5\n",
"1844.0 5\n",
"1768.0 5\n",
"1862.0 5\n",
"1878.0 5\n",
"1882.0 5\n",
"1884.0 5\n",
"1849.0 4\n",
"1841.0 2\n",
"1840.0 2\n",
"1832.0 2\n",
"1848.0 1\n",
"1847.0 1\n",
"1845.0 1\n",
"1842.0 1\n",
"1811.0 1\n",
"1809.0 1\n",
"1805.0 1\n",
"Name: Date, Length: 185, dtype: int64"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"master_copy['Date'].value_counts(ascending=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Age"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [],
"source": [
"master_copy['AcquiredAge_Artwork'] = master_copy['DateAcquired_Year'] - master_copy['Date']"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [],
"source": [
"master_copy['AcquiredAge_Artwork'] = master_copy['AcquiredAge_Artwork'].astype(str)\n",
"master_copy = master_copy[~master_copy['AcquiredAge_Artwork'].str.contains(\"-\").fillna(False)]"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"NaN 22319\n",
" 1.0 10880\n",
" 0.0 5420\n",
" 2.0 5395\n",
" 3.0 3589\n",
" 4.0 2553\n",
" 5.0 2351\n",
" 6.0 2090\n",
" 7.0 1882\n",
" 8.0 1608\n",
" 9.0 1442\n",
" 10.0 1301\n",
" 34.0 1239\n",
" 12.0 1147\n",
" 11.0 1144\n",
" 20.0 1067\n",
" 15.0 1057\n",
" 43.0 1032\n",
" 36.0 1017\n",
" 44.0 968\n",
" 41.0 950\n",
" 14.0 908\n",
" 21.0 897\n",
" 13.0 885\n",
" 40.0 866\n",
" 42.0 864\n",
" 28.0 862\n",
" 45.0 848\n",
" 29.0 834\n",
" 16.0 827\n",
" ... \n",
" 109.0 6\n",
" 161.0 6\n",
" 157.0 6\n",
" 135.0 6\n",
" 142.0 6\n",
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" 141.0 5\n",
" 186.0 5\n",
" 143.0 4\n",
" 140.0 4\n",
" 156.0 4\n",
" 116.0 4\n",
" 118.0 3\n",
" 154.0 3\n",
" 149.0 2\n",
" 137.0 2\n",
" 139.0 2\n",
" 151.0 2\n",
" 147.0 2\n",
" 138.0 2\n",
" 163.0 1\n",
" 120.0 1\n",
" 175.0 1\n",
" 173.0 1\n",
" 166.0 1\n",
" 148.0 1\n",
" 153.0 1\n",
" 133.0 1\n",
" 152.0 1\n",
" 177.0 1\n",
"Name: AcquiredAge_Artwork, Length: 165, dtype: int64"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"master_copy['AcquiredAge_Artwork'] = master_copy['AcquiredAge_Artwork'].astype(float)\n",
"master_copy['AcquiredAge_Artwork'].value_counts(dropna=False).head(1000)"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 83671.000000\n",
"mean 24.491915\n",
"std 27.355292\n",
"min 0.000000\n",
"25% 2.000000\n",
"50% 14.000000\n",
"75% 40.000000\n",
"max 186.000000\n",
"Name: AcquiredAge_Artwork, dtype: float64"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"master_copy['AcquiredAge_Artwork'].describe()"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [],
"source": [
"def drop_acquired_values(row):\n",
" # start date contains the current value for the Date column\n",
" start_value = row['AcquiredAge_Artwork']\n",
" \n",
" # if larger than 4 digits, then drop above four digits\n",
" # if less than 4 digits, then keep the 4 digit format\n",
" # anything else, keep the same 4 digit format\n",
" \n",
" if start_value >= 205:\n",
" final_value = np.NaN\n",
" elif start_value <= -205:\n",
" final_value = np.NaN\n",
" else:\n",
" final_value = start_value\n",
" \n",
" return final_value"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1.0 10880\n",
"0.0 5420\n",
"2.0 5395\n",
"3.0 3589\n",
"4.0 2553\n",
"5.0 2351\n",
"6.0 2090\n",
"7.0 1882\n",
"8.0 1608\n",
"9.0 1442\n",
"10.0 1301\n",
"34.0 1239\n",
"12.0 1147\n",
"11.0 1144\n",
"20.0 1067\n",
"15.0 1057\n",
"43.0 1032\n",
"36.0 1017\n",
"44.0 968\n",
"41.0 950\n",
"14.0 908\n",
"21.0 897\n",
"13.0 885\n",
"40.0 866\n",
"42.0 864\n",
"28.0 862\n",
"45.0 848\n",
"29.0 834\n",
"16.0 827\n",
"17.0 807\n",
" ... \n",
"109.0 6\n",
"135.0 6\n",
"161.0 6\n",
"157.0 6\n",
"142.0 6\n",
"145.0 5\n",
"186.0 5\n",
"141.0 5\n",
"143.0 4\n",
"140.0 4\n",
"156.0 4\n",
"116.0 4\n",
"154.0 3\n",
"118.0 3\n",
"139.0 2\n",
"138.0 2\n",
"151.0 2\n",
"149.0 2\n",
"137.0 2\n",
"147.0 2\n",
"163.0 1\n",
"166.0 1\n",
"173.0 1\n",
"148.0 1\n",
"153.0 1\n",
"133.0 1\n",
"120.0 1\n",
"152.0 1\n",
"177.0 1\n",
"175.0 1\n",
"Name: AcquiredAge_Artwork_2, Length: 164, dtype: int64"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"master_copy['AcquiredAge_Artwork_2'] = master_copy.apply(lambda row: drop_acquired_values(row), axis=1)\n",
"master_copy['AcquiredAge_Artwork_2'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 83671.000000\n",
"mean 24.491915\n",
"std 27.355292\n",
"min 0.000000\n",
"25% 2.000000\n",
"50% 14.000000\n",
"75% 40.000000\n",
"max 186.000000\n",
"Name: AcquiredAge_Artwork_2, dtype: float64"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"master_copy['AcquiredAge_Artwork_2'].describe()"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 100.0\n",
"1 76.0\n",
"2 74.0\n",
"3 102.0\n",
"4 8.0\n",
"5 NaN\n",
"6 NaN\n",
"7 NaN\n",
"8 NaN\n",
"9 NaN\n",
"10 NaN\n",
"11 NaN\n",
"12 NaN\n",
"13 NaN\n",
"14 NaN\n",
"15 NaN\n",
"16 NaN\n",
"17 NaN\n",
"18 NaN\n",
"19 NaN\n",
"20 NaN\n",
"21 NaN\n",
"22 NaN\n",
"23 NaN\n",
"24 NaN\n",
"25 94.0\n",
"26 94.0\n",
"27 15.0\n",
"28 NaN\n",
"29 NaN\n",
" ... \n",
"138957 1.0\n",
"138958 5.0\n",
"138959 1.0\n",
"138960 6.0\n",
"138961 5.0\n",
"138962 1.0\n",
"138963 5.0\n",
"138964 8.0\n",
"138965 NaN\n",
"138966 NaN\n",
"138967 NaN\n",
"138970 5.0\n",
"138976 12.0\n",
"138977 51.0\n",
"138978 73.0\n",
"138979 61.0\n",
"138980 4.0\n",
"138981 119.0\n",
"138982 24.0\n",
"138985 0.0\n",
"138986 0.0\n",
"138987 62.0\n",
"138988 1.0\n",
"138989 NaN\n",
"138990 40.0\n",
"138991 51.0\n",
"138998 1.0\n",
"138999 0.0\n",
"139000 0.0\n",
"139001 1.0\n",
"Name: AcquiredAge_Artwork_2, Length: 105990, dtype: float64"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"master_copy['AcquiredAge_Artwork_2']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Age: First Piece of Work Only Per Artist (_2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## AcquiredAge_Artwork_2"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [],
"source": [
"group = master_copy.groupby('Artist')"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [],
"source": [
"master_copy_3 = group.apply(lambda x: x['AcquiredAge_Artwork_2'].unique())"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [],
"source": [
"master_copy_3 = master_copy_3.apply(pd.Series)"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"minimum value in each row : \n",
"Artist\n",
"A. Becker 10.0\n",
"A. E. Gallatin 59.0\n",
"A. F. Gangkofner 2.0\n",
"A. G. Fronzoni 0.0\n",
"A. Gisiger 0.0\n",
"A. Graves & Cie. NaN\n",
"A. Gromov 72.0\n",
"A. K. Barutchev 71.0\n",
"A. Karra 70.0\n",
"A. Lawrence Kocher 70.0\n",
"A. M. Cassandre 0.0\n",
"A. Michael Noll 27.0\n",
"A. Paramonov 78.0\n",
"A. Portier 98.0\n",
"A. R. de Ycaza 1.0\n",
"A. Radishchev 65.0\n",
"A. Richard Ranft 62.0\n",
"A. Rozanova 71.0\n",
"A. Smolianskii 65.0\n",
"A. Strachov 84.0\n",
"A. Stuart-Hill 5.0\n",
"A. Vabbe 80.0\n",
"A. Wagner 2.0\n",
"A.A.P. 22.0\n",
"A.K. Burns 1.0\n",
"A.R. Penck (Ralf Winkler) 0.0\n",
"AA Bronson 0.0\n",
"ACT UP (AIDS Coalition to Unleash Power) 28.0\n",
"AT&T Bell Laboratories, Murray Hill, NJ 4.0\n",
"Aarne Aho 6.0\n",
" ... \n",
"a.r. Group 86.0\n",
"assume vivid astro focus 2.0\n",
"caraballo-farman NaN\n",
"frogdesign, Sunnydale, CA 10.0\n",
"herman de vries 16.0\n",
"interware SARL 1.0\n",
"matali crasset 2.0\n",
"raumlaborberlin 1.0\n",
"unknown 0.0\n",
"Álvaro Barrios 0.0\n",
"Álvaro Siza NaN\n",
"Édgar Negret 0.0\n",
"Éditions Surréalistes, Paris 68.0\n",
"Édouard Boubat 7.0\n",
"Édouard Manet 89.0\n",
"Édouard Molinaro 34.0\n",
"Édouard Pignon 0.0\n",
"Édouard Vuillard 25.0\n",
"Édouard-Denis Baldus 119.0\n",
"Édouard-Wilfred Buquet 50.0\n",
"Émile Berchmans 61.0\n",
"Émile Bernard 28.0\n",
"Émile-Antoine Bourdelle 58.0\n",
"Émile-René Ménard 61.0\n",
"Éric Chahi 21.0\n",
"Étienne Carjat 102.0\n",
"Étienne Hajdu 0.0\n",
"Étienne-Jules Marey NaN\n",
"Öyvind Fahlström 0.0\n",
"Øistein Thurman 1.0\n",
"Length: 10074, dtype: float64\n"
]
}
],
"source": [
"minValues_AcquiredAge_Artwork_2 = master_copy_3.min(axis=1)\n",
" \n",
"print('minimum value in each row : ')\n",
"print(minValues_AcquiredAge_Artwork_2)"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [],
"source": [
"master_copy_3['minValues_AcquiredAge_Artwork_2']=minValues_AcquiredAge_Artwork_2"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 9469.000000\n",
"mean 16.418101\n",
"std 26.047022\n",
"min 0.000000\n",
"25% 1.000000\n",
"50% 3.000000\n",
"75% 24.000000\n",
"max 186.000000\n",
"Name: minValues_AcquiredAge_Artwork_2, dtype: float64"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"master_copy_3['minValues_AcquiredAge_Artwork_2'].describe()"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [
{
"data": {
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" <tr>\n",
" <th>A. E. Gallatin</th>\n",
" <td>59.0</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>A. G. Fronzoni</th>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
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],
"text/plain": [
" 0 1 2 3 4 5 6 7 8 9 \\\n",
"Artist \n",
"A. Becker 10.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"A. E. Gallatin 59.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"A. F. Gangkofner 2.0 45.0 NaN NaN NaN NaN NaN NaN NaN NaN \n",
"A. G. Fronzoni 2.0 0.0 3.0 15.0 14.0 13.0 31.0 34.0 32.0 27.0 \n",
"A. Gisiger 0.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"\n",
" ... 66 67 68 69 70 71 72 \\\n",
"Artist ... \n",
"A. Becker ... NaN NaN NaN NaN NaN NaN NaN \n",
"A. E. Gallatin ... NaN NaN NaN NaN NaN NaN NaN \n",
"A. F. Gangkofner ... NaN NaN NaN NaN NaN NaN NaN \n",
"A. G. Fronzoni ... NaN NaN NaN NaN NaN NaN NaN \n",
"A. Gisiger ... NaN NaN NaN NaN NaN NaN NaN \n",
"\n",
" 73 74 minValues_AcquiredAge_Artwork_2 \n",
"Artist \n",
"A. Becker NaN NaN 10.0 \n",
"A. E. Gallatin NaN NaN 59.0 \n",
"A. F. Gangkofner NaN NaN 2.0 \n",
"A. G. Fronzoni NaN NaN 0.0 \n",
"A. Gisiger NaN NaN 0.0 \n",
"\n",
"[5 rows x 76 columns]"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"master_copy_3.head()"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [
{
"data": {
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>74.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>6210</td>\n",
" <td>Armchair</td>\n",
" <td>Otto Wagner</td>\n",
" <td>Male</td>\n",
" <td>Austrian</td>\n",
" <td>1841.0</td>\n",
" <td>1918.0</td>\n",
" <td>1902.0</td>\n",
" <td>Beech wood and aluminum</td>\n",
" <td>30 7/8 x 22 1/4 x 20 1/4\" (78.5 x 56.5 x 51.5 ...</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>74.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>6210</td>\n",
" <td>Stool</td>\n",
" <td>Otto Wagner</td>\n",
" <td>Male</td>\n",
" <td>Austrian</td>\n",
" <td>1841.0</td>\n",
" <td>1918.0</td>\n",
" <td>1904.0</td>\n",
" <td>Bent beech wood, molded plywood, and aluminum</td>\n",
" <td>18 1/2 x 16 x 16\" (47 x 40.6 x 40.6 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>74.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>6210</td>\n",
" <td>Railing</td>\n",
" <td>Otto Wagner</td>\n",
" <td>Male</td>\n",
" <td>Austrian</td>\n",
" <td>1841.0</td>\n",
" <td>1918.0</td>\n",
" <td>1899.0</td>\n",
" <td>Painted cast-iron</td>\n",
" <td>28 1/4 x 46 1/2 x 3\" (72.4 x 117.5 x 7.6 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>74.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>7470</td>\n",
" <td>City of Music, National Superior Conservatory ...</td>\n",
" <td>Christian de Portzamparc</td>\n",
" <td>Male</td>\n",
" <td>French</td>\n",
" <td>1944.0</td>\n",
" <td>0.0</td>\n",
" <td>1987.0</td>\n",
" <td>Paint and colored pencil on print</td>\n",
" <td>16 x 11 3/4\" (40.6 x 29.8 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>7470</td>\n",
" <td>LVMH Tower, New York, NY (Study model)</td>\n",
" <td>Christian de Portzamparc</td>\n",
" <td>Male</td>\n",
" <td>French</td>\n",
" <td>1944.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>Paper and cardboard</td>\n",
" <td>39 3/8 x 7 1/2 x 11 13/16\" (100 x 19 x 30 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>7470</td>\n",
" <td>LVMH Tower, New York, NY (Study model)</td>\n",
" <td>Christian de Portzamparc</td>\n",
" <td>Male</td>\n",
" <td>French</td>\n",
" <td>1944.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>Paper and cardboard</td>\n",
" <td>39 3/8 x 7 1/2 x 11 13/16\" (100 x 19 x 30 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>7470</td>\n",
" <td>LVMH Tower, New York, NY (Study model)</td>\n",
" <td>Christian de Portzamparc</td>\n",
" <td>Male</td>\n",
" <td>French</td>\n",
" <td>1944.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>Paper and cardboard</td>\n",
" <td>39 3/8 x 7 1/2 x 11 13/16\" (100 x 19 x 30 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>7470</td>\n",
" <td>LVMH Tower, New York, NY, Study model</td>\n",
" <td>Christian de Portzamparc</td>\n",
" <td>Male</td>\n",
" <td>French</td>\n",
" <td>1944.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>Paper and cardboard</td>\n",
" <td>39 3/8 x 7 1/2 x 11 13/16\" (100 x 19 x 30 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>7470</td>\n",
" <td>LVMH Tower, New York, NY (Study model)</td>\n",
" <td>Christian de Portzamparc</td>\n",
" <td>Male</td>\n",
" <td>French</td>\n",
" <td>1944.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>Paper and cardboard</td>\n",
" <td>39 3/8 x 7 1/2 x 11 13/16\" (100 x 19 x 30 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>7470</td>\n",
" <td>LVMH Tower, New York, NY (Site model)</td>\n",
" <td>Christian de Portzamparc</td>\n",
" <td>Male</td>\n",
" <td>French</td>\n",
" <td>1944.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>Paper and cardboard</td>\n",
" <td>16 1/8 x 17 5/16 x 9 13/16\" (41 x 44 x 25 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>7470</td>\n",
" <td>LVMH Tower, New York, NY (Perspective sketch)</td>\n",
" <td>Christian de Portzamparc</td>\n",
" <td>Male</td>\n",
" <td>French</td>\n",
" <td>1944.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>Ink on paper</td>\n",
" <td>8 1/4 x 11 11/16\" (21 x 29.7 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>7470</td>\n",
" <td>LVMH Tower, New York, NY (Perspective sketch, ...</td>\n",
" <td>Christian de Portzamparc</td>\n",
" <td>Male</td>\n",
" <td>French</td>\n",
" <td>1944.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>Ink on paper</td>\n",
" <td>11 1/4 x 8 1/4\" (28.5 x 21 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>7470</td>\n",
" <td>LVMH Tower, New York, NY (Perspective sketch)</td>\n",
" <td>Christian de Portzamparc</td>\n",
" <td>Male</td>\n",
" <td>French</td>\n",
" <td>1944.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>Pencil on paper</td>\n",
" <td>8 1/4 x 11 11/16\" (21 x 29.7 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>7470</td>\n",
" <td>LVMH Tower, New York, NY (Perspective sketches...</td>\n",
" <td>Christian de Portzamparc</td>\n",
" <td>Male</td>\n",
" <td>French</td>\n",
" <td>1944.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>Pencil on tracing paper</td>\n",
" <td>8 1/4 x 11 11/16\" (21 x 29.7 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>7470</td>\n",
" <td>LVMH Tower, New York, NY (Perspective sketches...</td>\n",
" <td>Christian de Portzamparc</td>\n",
" <td>Male</td>\n",
" <td>French</td>\n",
" <td>1944.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>Ink on paper</td>\n",
" <td>7 9/16 x 15 3/4\" (19.2 x 40 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>7470</td>\n",
" <td>LVMH Tower, New York, NY (Perspective sketches...</td>\n",
" <td>Christian de Portzamparc</td>\n",
" <td>Male</td>\n",
" <td>French</td>\n",
" <td>1944.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>Pencil and ink on paper</td>\n",
" <td>11 11/16 x 16 9/16\" (29.7 x 42 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>7470</td>\n",
" <td>LVMH Tower, New York, NY (Perspective sketch)</td>\n",
" <td>Christian de Portzamparc</td>\n",
" <td>Male</td>\n",
" <td>French</td>\n",
" <td>1944.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>Pencil on paper</td>\n",
" <td>7 9/16 x 15 3/4\" (19.2 x 40 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>7470</td>\n",
" <td>LVMH Tower, New York, NY (Perspective sketches...</td>\n",
" <td>Christian de Portzamparc</td>\n",
" <td>Male</td>\n",
" <td>French</td>\n",
" <td>1944.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>Felt-tipped pen on tracing paper</td>\n",
" <td>8 1/4 x 15 13/16\" (21 x 40.2 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>7470</td>\n",
" <td>LVMH Tower, New York, NY (Perspective sketch, ...</td>\n",
" <td>Christian de Portzamparc</td>\n",
" <td>Male</td>\n",
" <td>French</td>\n",
" <td>1944.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>Colored pencil on paper</td>\n",
" <td>11 11/16 x 16 9/16\" (29.7 x 42 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>7470</td>\n",
" <td>LVMH Tower, New York, NY (Perspective sketch, ...</td>\n",
" <td>Christian de Portzamparc</td>\n",
" <td>Male</td>\n",
" <td>French</td>\n",
" <td>1944.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>Pencil on paper</td>\n",
" <td>8 1/4 x 11 11/16\" (21 x 29.7 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>7470</td>\n",
" <td>LVMH Tower, New York, NY (Perspective sketches...</td>\n",
" <td>Christian de Portzamparc</td>\n",
" <td>Male</td>\n",
" <td>French</td>\n",
" <td>1944.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>Pencil on paper</td>\n",
" <td>11 11/16 x 13 3/16\" (29.7 x 33.5 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>7470</td>\n",
" <td>LVMH Tower, New York, NY (Elevation sketch, vo...</td>\n",
" <td>Christian de Portzamparc</td>\n",
" <td>Male</td>\n",
" <td>French</td>\n",
" <td>1944.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>Pencil on tracing paper</td>\n",
" <td>16 9/16 x 11 11/16\" (42 x 29.7 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>7470</td>\n",
" <td>LVMH Tower, New York, NY (Elevation)</td>\n",
" <td>Christian de Portzamparc</td>\n",
" <td>Male</td>\n",
" <td>French</td>\n",
" <td>1944.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>Print</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>7470</td>\n",
" <td>LVMH Tower, New York, NY (Elevations)</td>\n",
" <td>Christian de Portzamparc</td>\n",
" <td>Male</td>\n",
" <td>French</td>\n",
" <td>1944.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>Print</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>7605</td>\n",
" <td>Villa near Vienna Project, Outside Vienna, Aus...</td>\n",
" <td>Emil Hoppe</td>\n",
" <td>Male</td>\n",
" <td>Austrian</td>\n",
" <td>1876.0</td>\n",
" <td>1957.0</td>\n",
" <td>1903.0</td>\n",
" <td>Graphite, pen, color pencil, ink, and gouache ...</td>\n",
" <td>13 1/2 x 12 1/2\" (34.3 x 31.8 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>94.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>7605</td>\n",
" <td>Villa, project, outside Vienna, Austria, Exter...</td>\n",
" <td>Emil Hoppe</td>\n",
" <td>Male</td>\n",
" <td>Austrian</td>\n",
" <td>1876.0</td>\n",
" <td>1957.0</td>\n",
" <td>1903.0</td>\n",
" <td>Graphite, color pencil, ink, and gouache on tr...</td>\n",
" <td>15 1/8 x 7 1/2\" (38.4 x 19.1 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>94.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>7056</td>\n",
" <td>The Manhattan Transcripts Project, New York, N...</td>\n",
" <td>Bernard Tschumi</td>\n",
" <td>Male</td>\n",
" <td>NaN</td>\n",
" <td>1944.0</td>\n",
" <td>0.0</td>\n",
" <td>1980.0</td>\n",
" <td>Photographic reproduction with colored synthet...</td>\n",
" <td>20 x 20\" (50.8 x 50.8 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>7056</td>\n",
" <td>The Manhattan Transcripts Project, New York, N...</td>\n",
" <td>Bernard Tschumi</td>\n",
" <td>Male</td>\n",
" <td>NaN</td>\n",
" <td>1944.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>Gelatin silver photograph</td>\n",
" <td>14 x 18\" (35.6 x 45.7 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>7056</td>\n",
" <td>The Manhattan Transcripts Project, New York, N...</td>\n",
" <td>Bernard Tschumi</td>\n",
" <td>Male</td>\n",
" <td>NaN</td>\n",
" <td>1944.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>Gelatin silver photographs</td>\n",
" <td>Each: 14 x 18\" (35.6 x 45.7 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105960</th>\n",
" <td>40166</td>\n",
" <td>Time Is Up 2 (Darkroom Manuals)</td>\n",
" <td>Sara Cwynar</td>\n",
" <td>NaN</td>\n",
" <td>Canadian</td>\n",
" <td>1985.0</td>\n",
" <td>0.0</td>\n",
" <td>2018.0</td>\n",
" <td>Chromogenic color print</td>\n",
" <td>30 × 24 in. (76.2 × 60.96 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105961</th>\n",
" <td>40166</td>\n",
" <td>Display Stand, No. 66 WIRE H. 20 1/2 W. 24 D. ...</td>\n",
" <td>Sara Cwynar</td>\n",
" <td>NaN</td>\n",
" <td>Canadian</td>\n",
" <td>1985.0</td>\n",
" <td>0.0</td>\n",
" <td>2014.0</td>\n",
" <td>Chromogenic color print</td>\n",
" <td>30 × 36 in. (76.2 × 91.44 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105962</th>\n",
" <td>40166</td>\n",
" <td>Color Bars 2 (Darkroom Manuals)</td>\n",
" <td>Sara Cwynar</td>\n",
" <td>NaN</td>\n",
" <td>Canadian</td>\n",
" <td>1985.0</td>\n",
" <td>0.0</td>\n",
" <td>2018.0</td>\n",
" <td>Chromogenic color print</td>\n",
" <td>30 × 24 in. (76.2 × 60.96 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105963</th>\n",
" <td>40166</td>\n",
" <td>Man and Space (Books 2)</td>\n",
" <td>Sara Cwynar</td>\n",
" <td>NaN</td>\n",
" <td>Canadian</td>\n",
" <td>1985.0</td>\n",
" <td>0.0</td>\n",
" <td>2013.0</td>\n",
" <td>Chromogenic color print</td>\n",
" <td>30 × 24 in. (76.2 × 60.96 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105964</th>\n",
" <td>40166</td>\n",
" <td>Corinthian Column (Plastic Cups)</td>\n",
" <td>Sara Cwynar</td>\n",
" <td>NaN</td>\n",
" <td>Canadian</td>\n",
" <td>1985.0</td>\n",
" <td>0.0</td>\n",
" <td>2014.0</td>\n",
" <td>Chromogenic color print</td>\n",
" <td>30 × 24 in. (76.2 × 60.96 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105965</th>\n",
" <td>40166</td>\n",
" <td>Girl From Contact Sheet 3 (Darkroom Manuals)</td>\n",
" <td>Sara Cwynar</td>\n",
" <td>NaN</td>\n",
" <td>Canadian</td>\n",
" <td>1985.0</td>\n",
" <td>0.0</td>\n",
" <td>2018.0</td>\n",
" <td>Chromogenic color print</td>\n",
" <td>30 × 24 in. (76.2 × 60.96 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105966</th>\n",
" <td>131860</td>\n",
" <td>Windows on the World (Part 2)</td>\n",
" <td>Ming Wong</td>\n",
" <td>NaN</td>\n",
" <td>Singaporean</td>\n",
" <td>1971.0</td>\n",
" <td>0.0</td>\n",
" <td>2014.0</td>\n",
" <td>24-channel video (color, sound)\\r\\n</td>\n",
" <td>Duration variable</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105967</th>\n",
" <td>131861</td>\n",
" <td>Workers Leaving the Googleplex</td>\n",
" <td>Andrew Norman Wilson</td>\n",
" <td>NaN</td>\n",
" <td>American</td>\n",
" <td>1986.0</td>\n",
" <td>0.0</td>\n",
" <td>2011.0</td>\n",
" <td>Seven-channel and single-channel video (color,...</td>\n",
" <td>11:03 min. \\r\\n</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105968</th>\n",
" <td>131882</td>\n",
" <td>Think Evolution #1 : Kiku-ishi (Ammonite)</td>\n",
" <td>Aki Inomata</td>\n",
" <td>Female</td>\n",
" <td>Japanese</td>\n",
" <td>1983.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>Resin, ammonite fossil</td>\n",
" <td>4 3/4 × 3 1/8 × 4 3/4\" (12 × 8 × 12 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105969</th>\n",
" <td>131882</td>\n",
" <td>Think Evolution #1 : Kiku-ishi (Ammonite)</td>\n",
" <td>Aki Inomata</td>\n",
" <td>Female</td>\n",
" <td>Japanese</td>\n",
" <td>1983.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>HD video (color, sound)</td>\n",
" <td>2 min.</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105970</th>\n",
" <td>131882</td>\n",
" <td>Think Evolution #1 : Kiku-ishi (Ammonite)</td>\n",
" <td>Aki Inomata</td>\n",
" <td>Female</td>\n",
" <td>Japanese</td>\n",
" <td>1983.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>Resin, ammonite fossil sculpture and digital v...</td>\n",
" <td>see child records</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105971</th>\n",
" <td>131903</td>\n",
" <td>How to Make Money Religiously</td>\n",
" <td>Laure Prouvost</td>\n",
" <td>NaN</td>\n",
" <td>French</td>\n",
" <td>1978.0</td>\n",
" <td>0.0</td>\n",
" <td>2014.0</td>\n",
" <td>Video (color, sound)</td>\n",
" <td>8:44 min.\\r\\n</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105972</th>\n",
" <td>131907</td>\n",
" <td>VVEBCAM</td>\n",
" <td>Petra Cortright</td>\n",
" <td>NaN</td>\n",
" <td>American</td>\n",
" <td>1986.0</td>\n",
" <td>0.0</td>\n",
" <td>2007.0</td>\n",
" <td>Webcam video (color, sound)</td>\n",
" <td>1:43 min.</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>12.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105973</th>\n",
" <td>131944</td>\n",
" <td>The Black Panther Coloring Book (James Teemer)</td>\n",
" <td>Mark Teemer</td>\n",
" <td>NaN</td>\n",
" <td>American</td>\n",
" <td>1946.0</td>\n",
" <td>0.0</td>\n",
" <td>1968.0</td>\n",
" <td>Ink on newsprint</td>\n",
" <td>8 1/2 × 11\" (21.6 × 27.9 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>51.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105974</th>\n",
" <td>131916</td>\n",
" <td>Object</td>\n",
" <td>Yente (Eugenia Crenovich)</td>\n",
" <td>female</td>\n",
" <td>Argentine</td>\n",
" <td>1905.0</td>\n",
" <td>1990.0</td>\n",
" <td>1946.0</td>\n",
" <td>Painted board</td>\n",
" <td>9 1/16 × 10 5/8 × 5 1/8\" (23 × 27 × 13 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>61.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105975</th>\n",
" <td>131916</td>\n",
" <td>Tapestry no. 6</td>\n",
" <td>Yente (Eugenia Crenovich)</td>\n",
" <td>female</td>\n",
" <td>Argentine</td>\n",
" <td>1905.0</td>\n",
" <td>1990.0</td>\n",
" <td>1958.0</td>\n",
" <td>Wool and paint on canvas</td>\n",
" <td>47 5/8 × 18 7/16\" (121 × 46.9 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>61.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105976</th>\n",
" <td>131929</td>\n",
" <td>Cherry Lips</td>\n",
" <td>Pacifico Silano</td>\n",
" <td>Male</td>\n",
" <td>American</td>\n",
" <td>1986.0</td>\n",
" <td>0.0</td>\n",
" <td>2015.0</td>\n",
" <td>Pigmented inkjet print</td>\n",
" <td>32 × 40\" (81.3 × 101.6 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105977</th>\n",
" <td>131934</td>\n",
" <td>Untitled</td>\n",
" <td>Nancy Ford Cones</td>\n",
" <td>Female</td>\n",
" <td>American</td>\n",
" <td>1869.0</td>\n",
" <td>1962.0</td>\n",
" <td>1900.0</td>\n",
" <td>Kallitype</td>\n",
" <td>Approx. 8 × 10\" (20.3 × 25.4 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>119.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105978</th>\n",
" <td>37030</td>\n",
" <td>Highway</td>\n",
" <td>Barbara Ess</td>\n",
" <td>NaN</td>\n",
" <td>American</td>\n",
" <td>1948.0</td>\n",
" <td>0.0</td>\n",
" <td>1995.0</td>\n",
" <td>Chromogenic color print</td>\n",
" <td>40 × 60\" (101.6 × 152.4 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>24.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105979</th>\n",
" <td>131948</td>\n",
" <td>Disease Thrower #5</td>\n",
" <td>Guadalupe Maravilla</td>\n",
" <td>Male</td>\n",
" <td>Salvadoran</td>\n",
" <td>1976.0</td>\n",
" <td>0.0</td>\n",
" <td>2019.0</td>\n",
" <td>Glass, steel, wood, cotton, plastic, loofah, p...</td>\n",
" <td>91 × 55 × 45\" (231.1 × 139.7 × 114.3 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105980</th>\n",
" <td>131948</td>\n",
" <td>Circle Serpent</td>\n",
" <td>Guadalupe Maravilla</td>\n",
" <td>Male</td>\n",
" <td>Salvadoran</td>\n",
" <td>1976.0</td>\n",
" <td>0.0</td>\n",
" <td>2019.0</td>\n",
" <td>Maguey leaves</td>\n",
" <td>Dimensions variable</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105981</th>\n",
" <td>131958</td>\n",
" <td>Work</td>\n",
" <td>Tsuruko Yamazaki</td>\n",
" <td>Female</td>\n",
" <td>Japanese</td>\n",
" <td>1925.0</td>\n",
" <td>2019.0</td>\n",
" <td>1957.0</td>\n",
" <td>Aniline dye on tin</td>\n",
" <td>28 7/8 × 32 1/2\" (73.3 × 82.6 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>62.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105982</th>\n",
" <td>28285</td>\n",
" <td>Raja Ampat</td>\n",
" <td>Helen Marden</td>\n",
" <td>Female</td>\n",
" <td>American</td>\n",
" <td>1941.0</td>\n",
" <td>0.0</td>\n",
" <td>2018.0</td>\n",
" <td>Resin, acrylic, powered paint, and abalone she...</td>\n",
" <td>50 × 32\" (127 × 81.3 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105983</th>\n",
" <td>71841</td>\n",
" <td>Post-Partum Document: Documentation IV, Transi...</td>\n",
" <td>Mary Kelly</td>\n",
" <td>NaN</td>\n",
" <td>American</td>\n",
" <td>1941.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>Plexiglass, white card, plaster, cotton, ink, ...</td>\n",
" <td>Each 14 × 11\" (35.6 × 27.9 cm), overall instal...</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105984</th>\n",
" <td>116470</td>\n",
" <td>Plate (folio 16) from Les Biches, vol. I</td>\n",
" <td>Francis Poulenc</td>\n",
" <td>NaN</td>\n",
" <td>French</td>\n",
" <td>1899.0</td>\n",
" <td>1963.0</td>\n",
" <td>1924.0</td>\n",
" <td>One from an illustrated book with eighteen col...</td>\n",
" <td>plate: 11 × 8 11/16\" (28 × 22 cm); page (each ...</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105985</th>\n",
" <td>69941</td>\n",
" <td>Untitled (First White Light Series)</td>\n",
" <td>Mary Corse</td>\n",
" <td>Female</td>\n",
" <td>American</td>\n",
" <td>1945.0</td>\n",
" <td>0.0</td>\n",
" <td>1968.0</td>\n",
" <td>Glass microspheres and acrylic on canvas</td>\n",
" <td>78 × 78\" (198.1 × 198.1 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>51.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105986</th>\n",
" <td>131890</td>\n",
" <td>Anatomy of an AI System</td>\n",
" <td>Kate Crawford</td>\n",
" <td>Female</td>\n",
" <td>Australian</td>\n",
" <td>1976.0</td>\n",
" <td>0.0</td>\n",
" <td>2018.0</td>\n",
" <td>digital image file and newsprint</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105987</th>\n",
" <td>132134</td>\n",
" <td>Full of Surprises from Pulled in Brooklyn</td>\n",
" <td>Sheryl Oppenheim</td>\n",
" <td>Female</td>\n",
" <td>American</td>\n",
" <td>1983.0</td>\n",
" <td>0.0</td>\n",
" <td>2019.0</td>\n",
" <td>One from a portfolio with six screenprints</td>\n",
" <td>composition and sheet: 30 × 22 1/16\" (76.2 × 5...</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105988</th>\n",
" <td>132135</td>\n",
" <td>Pink Figures from Pulled in Brooklyn</td>\n",
" <td>Ruby Sky Stiler</td>\n",
" <td>Female</td>\n",
" <td>American</td>\n",
" <td>1979.0</td>\n",
" <td>0.0</td>\n",
" <td>2019.0</td>\n",
" <td>One from a portfolio with six screenprints</td>\n",
" <td>composition (irreg.): 18 3/4 × 16 9/16\" (47.7 ...</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105989</th>\n",
" <td>131931</td>\n",
" <td>Dyslympics 2680</td>\n",
" <td>Sachiko Kazama</td>\n",
" <td>Female</td>\n",
" <td>Japanese</td>\n",
" <td>1972.0</td>\n",
" <td>0.0</td>\n",
" <td>2018.0</td>\n",
" <td>Woodcut</td>\n",
" <td>95 × 251\" (241.3 × 637.5 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>105990 rows × 110 columns</p>\n",
"</div>"
],
"text/plain": [
" UniqueID Title \\\n",
"0 6210 Ferdinandsbrücke Project, Vienna, Austria (Ele... \n",
"1 6210 Armchair \n",
"2 6210 Stool \n",
"3 6210 Railing \n",
"4 7470 City of Music, National Superior Conservatory ... \n",
"5 7470 LVMH Tower, New York, NY (Study model) \n",
"6 7470 LVMH Tower, New York, NY (Study model) \n",
"7 7470 LVMH Tower, New York, NY (Study model) \n",
"8 7470 LVMH Tower, New York, NY, Study model \n",
"9 7470 LVMH Tower, New York, NY (Study model) \n",
"10 7470 LVMH Tower, New York, NY (Site model) \n",
"11 7470 LVMH Tower, New York, NY (Perspective sketch) \n",
"12 7470 LVMH Tower, New York, NY (Perspective sketch, ... \n",
"13 7470 LVMH Tower, New York, NY (Perspective sketch) \n",
"14 7470 LVMH Tower, New York, NY (Perspective sketches... \n",
"15 7470 LVMH Tower, New York, NY (Perspective sketches... \n",
"16 7470 LVMH Tower, New York, NY (Perspective sketches... \n",
"17 7470 LVMH Tower, New York, NY (Perspective sketch) \n",
"18 7470 LVMH Tower, New York, NY (Perspective sketches... \n",
"19 7470 LVMH Tower, New York, NY (Perspective sketch, ... \n",
"20 7470 LVMH Tower, New York, NY (Perspective sketch, ... \n",
"21 7470 LVMH Tower, New York, NY (Perspective sketches... \n",
"22 7470 LVMH Tower, New York, NY (Elevation sketch, vo... \n",
"23 7470 LVMH Tower, New York, NY (Elevation) \n",
"24 7470 LVMH Tower, New York, NY (Elevations) \n",
"25 7605 Villa near Vienna Project, Outside Vienna, Aus... \n",
"26 7605 Villa, project, outside Vienna, Austria, Exter... \n",
"27 7056 The Manhattan Transcripts Project, New York, N... \n",
"28 7056 The Manhattan Transcripts Project, New York, N... \n",
"29 7056 The Manhattan Transcripts Project, New York, N... \n",
"... ... ... \n",
"105960 40166 Time Is Up 2 (Darkroom Manuals) \n",
"105961 40166 Display Stand, No. 66 WIRE H. 20 1/2 W. 24 D. ... \n",
"105962 40166 Color Bars 2 (Darkroom Manuals) \n",
"105963 40166 Man and Space (Books 2) \n",
"105964 40166 Corinthian Column (Plastic Cups) \n",
"105965 40166 Girl From Contact Sheet 3 (Darkroom Manuals) \n",
"105966 131860 Windows on the World (Part 2) \n",
"105967 131861 Workers Leaving the Googleplex \n",
"105968 131882 Think Evolution #1 : Kiku-ishi (Ammonite) \n",
"105969 131882 Think Evolution #1 : Kiku-ishi (Ammonite) \n",
"105970 131882 Think Evolution #1 : Kiku-ishi (Ammonite) \n",
"105971 131903 How to Make Money Religiously \n",
"105972 131907 VVEBCAM \n",
"105973 131944 The Black Panther Coloring Book (James Teemer) \n",
"105974 131916 Object \n",
"105975 131916 Tapestry no. 6 \n",
"105976 131929 Cherry Lips \n",
"105977 131934 Untitled \n",
"105978 37030 Highway \n",
"105979 131948 Disease Thrower #5 \n",
"105980 131948 Circle Serpent \n",
"105981 131958 Work \n",
"105982 28285 Raja Ampat \n",
"105983 71841 Post-Partum Document: Documentation IV, Transi... \n",
"105984 116470 Plate (folio 16) from Les Biches, vol. I \n",
"105985 69941 Untitled (First White Light Series) \n",
"105986 131890 Anatomy of an AI System \n",
"105987 132134 Full of Surprises from Pulled in Brooklyn \n",
"105988 132135 Pink Figures from Pulled in Brooklyn \n",
"105989 131931 Dyslympics 2680 \n",
"\n",
" Artist Gender Nationality Birth Death \\\n",
"0 Otto Wagner Male Austrian 1841.0 1918.0 \n",
"1 Otto Wagner Male Austrian 1841.0 1918.0 \n",
"2 Otto Wagner Male Austrian 1841.0 1918.0 \n",
"3 Otto Wagner Male Austrian 1841.0 1918.0 \n",
"4 Christian de Portzamparc Male French 1944.0 0.0 \n",
"5 Christian de Portzamparc Male French 1944.0 0.0 \n",
"6 Christian de Portzamparc Male French 1944.0 0.0 \n",
"7 Christian de Portzamparc Male French 1944.0 0.0 \n",
"8 Christian de Portzamparc Male French 1944.0 0.0 \n",
"9 Christian de Portzamparc Male French 1944.0 0.0 \n",
"10 Christian de Portzamparc Male French 1944.0 0.0 \n",
"11 Christian de Portzamparc Male French 1944.0 0.0 \n",
"12 Christian de Portzamparc Male French 1944.0 0.0 \n",
"13 Christian de Portzamparc Male French 1944.0 0.0 \n",
"14 Christian de Portzamparc Male French 1944.0 0.0 \n",
"15 Christian de Portzamparc Male French 1944.0 0.0 \n",
"16 Christian de Portzamparc Male French 1944.0 0.0 \n",
"17 Christian de Portzamparc Male French 1944.0 0.0 \n",
"18 Christian de Portzamparc Male French 1944.0 0.0 \n",
"19 Christian de Portzamparc Male French 1944.0 0.0 \n",
"20 Christian de Portzamparc Male French 1944.0 0.0 \n",
"21 Christian de Portzamparc Male French 1944.0 0.0 \n",
"22 Christian de Portzamparc Male French 1944.0 0.0 \n",
"23 Christian de Portzamparc Male French 1944.0 0.0 \n",
"24 Christian de Portzamparc Male French 1944.0 0.0 \n",
"25 Emil Hoppe Male Austrian 1876.0 1957.0 \n",
"26 Emil Hoppe Male Austrian 1876.0 1957.0 \n",
"27 Bernard Tschumi Male NaN 1944.0 0.0 \n",
"28 Bernard Tschumi Male NaN 1944.0 0.0 \n",
"29 Bernard Tschumi Male NaN 1944.0 0.0 \n",
"... ... ... ... ... ... \n",
"105960 Sara Cwynar NaN Canadian 1985.0 0.0 \n",
"105961 Sara Cwynar NaN Canadian 1985.0 0.0 \n",
"105962 Sara Cwynar NaN Canadian 1985.0 0.0 \n",
"105963 Sara Cwynar NaN Canadian 1985.0 0.0 \n",
"105964 Sara Cwynar NaN Canadian 1985.0 0.0 \n",
"105965 Sara Cwynar NaN Canadian 1985.0 0.0 \n",
"105966 Ming Wong NaN Singaporean 1971.0 0.0 \n",
"105967 Andrew Norman Wilson NaN American 1986.0 0.0 \n",
"105968 Aki Inomata Female Japanese 1983.0 0.0 \n",
"105969 Aki Inomata Female Japanese 1983.0 0.0 \n",
"105970 Aki Inomata Female Japanese 1983.0 0.0 \n",
"105971 Laure Prouvost NaN French 1978.0 0.0 \n",
"105972 Petra Cortright NaN American 1986.0 0.0 \n",
"105973 Mark Teemer NaN American 1946.0 0.0 \n",
"105974 Yente (Eugenia Crenovich) female Argentine 1905.0 1990.0 \n",
"105975 Yente (Eugenia Crenovich) female Argentine 1905.0 1990.0 \n",
"105976 Pacifico Silano Male American 1986.0 0.0 \n",
"105977 Nancy Ford Cones Female American 1869.0 1962.0 \n",
"105978 Barbara Ess NaN American 1948.0 0.0 \n",
"105979 Guadalupe Maravilla Male Salvadoran 1976.0 0.0 \n",
"105980 Guadalupe Maravilla Male Salvadoran 1976.0 0.0 \n",
"105981 Tsuruko Yamazaki Female Japanese 1925.0 2019.0 \n",
"105982 Helen Marden Female American 1941.0 0.0 \n",
"105983 Mary Kelly NaN American 1941.0 0.0 \n",
"105984 Francis Poulenc NaN French 1899.0 1963.0 \n",
"105985 Mary Corse Female American 1945.0 0.0 \n",
"105986 Kate Crawford Female Australian 1976.0 0.0 \n",
"105987 Sheryl Oppenheim Female American 1983.0 0.0 \n",
"105988 Ruby Sky Stiler Female American 1979.0 0.0 \n",
"105989 Sachiko Kazama Female Japanese 1972.0 0.0 \n",
"\n",
" Date Medium \\\n",
"0 1896.0 Ink and cut-and-pasted painted pages on paper \n",
"1 1902.0 Beech wood and aluminum \n",
"2 1904.0 Bent beech wood, molded plywood, and aluminum \n",
"3 1899.0 Painted cast-iron \n",
"4 1987.0 Paint and colored pencil on print \n",
"5 NaN Paper and cardboard \n",
"6 NaN Paper and cardboard \n",
"7 NaN Paper and cardboard \n",
"8 NaN Paper and cardboard \n",
"9 NaN Paper and cardboard \n",
"10 NaN Paper and cardboard \n",
"11 NaN Ink on paper \n",
"12 NaN Ink on paper \n",
"13 NaN Pencil on paper \n",
"14 NaN Pencil on tracing paper \n",
"15 NaN Ink on paper \n",
"16 NaN Pencil and ink on paper \n",
"17 NaN Pencil on paper \n",
"18 NaN Felt-tipped pen on tracing paper \n",
"19 NaN Colored pencil on paper \n",
"20 NaN Pencil on paper \n",
"21 NaN Pencil on paper \n",
"22 NaN Pencil on tracing paper \n",
"23 NaN Print \n",
"24 NaN Print \n",
"25 1903.0 Graphite, pen, color pencil, ink, and gouache ... \n",
"26 1903.0 Graphite, color pencil, ink, and gouache on tr... \n",
"27 1980.0 Photographic reproduction with colored synthet... \n",
"28 NaN Gelatin silver photograph \n",
"29 NaN Gelatin silver photographs \n",
"... ... ... \n",
"105960 2018.0 Chromogenic color print \n",
"105961 2014.0 Chromogenic color print \n",
"105962 2018.0 Chromogenic color print \n",
"105963 2013.0 Chromogenic color print \n",
"105964 2014.0 Chromogenic color print \n",
"105965 2018.0 Chromogenic color print \n",
"105966 2014.0 24-channel video (color, sound)\\r\\n \n",
"105967 2011.0 Seven-channel and single-channel video (color,... \n",
"105968 NaN Resin, ammonite fossil \n",
"105969 NaN HD video (color, sound) \n",
"105970 NaN Resin, ammonite fossil sculpture and digital v... \n",
"105971 2014.0 Video (color, sound) \n",
"105972 2007.0 Webcam video (color, sound) \n",
"105973 1968.0 Ink on newsprint \n",
"105974 1946.0 Painted board \n",
"105975 1958.0 Wool and paint on canvas \n",
"105976 2015.0 Pigmented inkjet print \n",
"105977 1900.0 Kallitype \n",
"105978 1995.0 Chromogenic color print \n",
"105979 2019.0 Glass, steel, wood, cotton, plastic, loofah, p... \n",
"105980 2019.0 Maguey leaves \n",
"105981 1957.0 Aniline dye on tin \n",
"105982 2018.0 Resin, acrylic, powered paint, and abalone she... \n",
"105983 NaN Plexiglass, white card, plaster, cotton, ink, ... \n",
"105984 1924.0 One from an illustrated book with eighteen col... \n",
"105985 1968.0 Glass microspheres and acrylic on canvas \n",
"105986 2018.0 digital image file and newsprint \n",
"105987 2019.0 One from a portfolio with six screenprints \n",
"105988 2019.0 One from a portfolio with six screenprints \n",
"105989 2018.0 Woodcut \n",
"\n",
" Dimensions \\\n",
"0 19 1/8 x 66 1/2\" (48.6 x 168.9 cm) \n",
"1 30 7/8 x 22 1/4 x 20 1/4\" (78.5 x 56.5 x 51.5 ... \n",
"2 18 1/2 x 16 x 16\" (47 x 40.6 x 40.6 cm) \n",
"3 28 1/4 x 46 1/2 x 3\" (72.4 x 117.5 x 7.6 cm) \n",
"4 16 x 11 3/4\" (40.6 x 29.8 cm) \n",
"5 39 3/8 x 7 1/2 x 11 13/16\" (100 x 19 x 30 cm) \n",
"6 39 3/8 x 7 1/2 x 11 13/16\" (100 x 19 x 30 cm) \n",
"7 39 3/8 x 7 1/2 x 11 13/16\" (100 x 19 x 30 cm) \n",
"8 39 3/8 x 7 1/2 x 11 13/16\" (100 x 19 x 30 cm) \n",
"9 39 3/8 x 7 1/2 x 11 13/16\" (100 x 19 x 30 cm) \n",
"10 16 1/8 x 17 5/16 x 9 13/16\" (41 x 44 x 25 cm) \n",
"11 8 1/4 x 11 11/16\" (21 x 29.7 cm) \n",
"12 11 1/4 x 8 1/4\" (28.5 x 21 cm) \n",
"13 8 1/4 x 11 11/16\" (21 x 29.7 cm) \n",
"14 8 1/4 x 11 11/16\" (21 x 29.7 cm) \n",
"15 7 9/16 x 15 3/4\" (19.2 x 40 cm) \n",
"16 11 11/16 x 16 9/16\" (29.7 x 42 cm) \n",
"17 7 9/16 x 15 3/4\" (19.2 x 40 cm) \n",
"18 8 1/4 x 15 13/16\" (21 x 40.2 cm) \n",
"19 11 11/16 x 16 9/16\" (29.7 x 42 cm) \n",
"20 8 1/4 x 11 11/16\" (21 x 29.7 cm) \n",
"21 11 11/16 x 13 3/16\" (29.7 x 33.5 cm) \n",
"22 16 9/16 x 11 11/16\" (42 x 29.7 cm) \n",
"23 NaN \n",
"24 NaN \n",
"25 13 1/2 x 12 1/2\" (34.3 x 31.8 cm) \n",
"26 15 1/8 x 7 1/2\" (38.4 x 19.1 cm) \n",
"27 20 x 20\" (50.8 x 50.8 cm) \n",
"28 14 x 18\" (35.6 x 45.7 cm) \n",
"29 Each: 14 x 18\" (35.6 x 45.7 cm) \n",
"... ... \n",
"105960 30 × 24 in. (76.2 × 60.96 cm) \n",
"105961 30 × 36 in. (76.2 × 91.44 cm) \n",
"105962 30 × 24 in. (76.2 × 60.96 cm) \n",
"105963 30 × 24 in. (76.2 × 60.96 cm) \n",
"105964 30 × 24 in. (76.2 × 60.96 cm) \n",
"105965 30 × 24 in. (76.2 × 60.96 cm) \n",
"105966 Duration variable \n",
"105967 11:03 min. \\r\\n \n",
"105968 4 3/4 × 3 1/8 × 4 3/4\" (12 × 8 × 12 cm) \n",
"105969 2 min. \n",
"105970 see child records \n",
"105971 8:44 min.\\r\\n \n",
"105972 1:43 min. \n",
"105973 8 1/2 × 11\" (21.6 × 27.9 cm) \n",
"105974 9 1/16 × 10 5/8 × 5 1/8\" (23 × 27 × 13 cm) \n",
"105975 47 5/8 × 18 7/16\" (121 × 46.9 cm) \n",
"105976 32 × 40\" (81.3 × 101.6 cm) \n",
"105977 Approx. 8 × 10\" (20.3 × 25.4 cm) \n",
"105978 40 × 60\" (101.6 × 152.4 cm) \n",
"105979 91 × 55 × 45\" (231.1 × 139.7 × 114.3 cm) \n",
"105980 Dimensions variable \n",
"105981 28 7/8 × 32 1/2\" (73.3 × 82.6 cm) \n",
"105982 50 × 32\" (127 × 81.3 cm) \n",
"105983 Each 14 × 11\" (35.6 × 27.9 cm), overall instal... \n",
"105984 plate: 11 × 8 11/16\" (28 × 22 cm); page (each ... \n",
"105985 78 × 78\" (198.1 × 198.1 cm) \n",
"105986 NaN \n",
"105987 composition and sheet: 30 × 22 1/16\" (76.2 × 5... \n",
"105988 composition (irreg.): 18 3/4 × 16 9/16\" (47.7 ... \n",
"105989 95 × 251\" (241.3 × 637.5 cm) \n",
"\n",
" ... 66 67 68 69 70 71 72 73 74 \\\n",
"0 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"1 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"2 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"3 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"4 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"5 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"6 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"7 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"8 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"9 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"10 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"11 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"12 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"13 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"14 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"15 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"16 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"17 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"18 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"19 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"20 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"21 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"22 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"23 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"24 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"25 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"26 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"27 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"28 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"29 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"... ... .. .. .. .. .. .. .. .. .. \n",
"105960 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105961 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105962 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105963 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105964 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105965 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105966 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105967 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105968 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105969 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105970 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105971 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105972 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105973 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105974 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105975 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105976 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105977 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105978 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105979 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105980 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105981 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105982 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105983 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105984 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105985 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105986 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105987 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105988 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105989 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"\n",
" minValues_AcquiredAge_Artwork_2 \n",
"0 74.0 \n",
"1 74.0 \n",
"2 74.0 \n",
"3 74.0 \n",
"4 8.0 \n",
"5 8.0 \n",
"6 8.0 \n",
"7 8.0 \n",
"8 8.0 \n",
"9 8.0 \n",
"10 8.0 \n",
"11 8.0 \n",
"12 8.0 \n",
"13 8.0 \n",
"14 8.0 \n",
"15 8.0 \n",
"16 8.0 \n",
"17 8.0 \n",
"18 8.0 \n",
"19 8.0 \n",
"20 8.0 \n",
"21 8.0 \n",
"22 8.0 \n",
"23 8.0 \n",
"24 8.0 \n",
"25 94.0 \n",
"26 94.0 \n",
"27 2.0 \n",
"28 2.0 \n",
"29 2.0 \n",
"... ... \n",
"105960 1.0 \n",
"105961 1.0 \n",
"105962 1.0 \n",
"105963 1.0 \n",
"105964 1.0 \n",
"105965 1.0 \n",
"105966 5.0 \n",
"105967 8.0 \n",
"105968 NaN \n",
"105969 NaN \n",
"105970 NaN \n",
"105971 5.0 \n",
"105972 12.0 \n",
"105973 51.0 \n",
"105974 61.0 \n",
"105975 61.0 \n",
"105976 4.0 \n",
"105977 119.0 \n",
"105978 24.0 \n",
"105979 0.0 \n",
"105980 0.0 \n",
"105981 62.0 \n",
"105982 1.0 \n",
"105983 NaN \n",
"105984 40.0 \n",
"105985 51.0 \n",
"105986 1.0 \n",
"105987 0.0 \n",
"105988 0.0 \n",
"105989 1.0 \n",
"\n",
"[105990 rows x 110 columns]"
]
},
"execution_count": 75,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"master_copy.merge(master_copy_3, left_on='Artist', right_on='Artist', how='inner')"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [],
"source": [
"master_copy = master_copy.merge(master_copy_3, left_on='Artist', right_on='Artist', how='inner')"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [],
"source": [
"# change column name \n",
"master_copy.rename(columns={'minValues_AcquiredAge_Artwork_2_y':'MinAcquiredAge_Artwork'}, inplace = True)"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>UniqueID</th>\n",
" <th>Title</th>\n",
" <th>Artist</th>\n",
" <th>Gender</th>\n",
" <th>Nationality</th>\n",
" <th>Birth</th>\n",
" <th>Death</th>\n",
" <th>Date</th>\n",
" <th>Medium</th>\n",
" <th>Dimensions</th>\n",
" <th>...</th>\n",
" <th>66</th>\n",
" <th>67</th>\n",
" <th>68</th>\n",
" <th>69</th>\n",
" <th>70</th>\n",
" <th>71</th>\n",
" <th>72</th>\n",
" <th>73</th>\n",
" <th>74</th>\n",
" <th>minValues_AcquiredAge_Artwork_2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>6210</td>\n",
" <td>Ferdinandsbrücke Project, Vienna, Austria (Ele...</td>\n",
" <td>Otto Wagner</td>\n",
" <td>Male</td>\n",
" <td>Austrian</td>\n",
" <td>1841.0</td>\n",
" <td>1918.0</td>\n",
" <td>1896.0</td>\n",
" <td>Ink and cut-and-pasted painted pages on paper</td>\n",
" <td>19 1/8 x 66 1/2\" (48.6 x 168.9 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>74.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>6210</td>\n",
" <td>Armchair</td>\n",
" <td>Otto Wagner</td>\n",
" <td>Male</td>\n",
" <td>Austrian</td>\n",
" <td>1841.0</td>\n",
" <td>1918.0</td>\n",
" <td>1902.0</td>\n",
" <td>Beech wood and aluminum</td>\n",
" <td>30 7/8 x 22 1/4 x 20 1/4\" (78.5 x 56.5 x 51.5 ...</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>74.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>6210</td>\n",
" <td>Stool</td>\n",
" <td>Otto Wagner</td>\n",
" <td>Male</td>\n",
" <td>Austrian</td>\n",
" <td>1841.0</td>\n",
" <td>1918.0</td>\n",
" <td>1904.0</td>\n",
" <td>Bent beech wood, molded plywood, and aluminum</td>\n",
" <td>18 1/2 x 16 x 16\" (47 x 40.6 x 40.6 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>74.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>6210</td>\n",
" <td>Railing</td>\n",
" <td>Otto Wagner</td>\n",
" <td>Male</td>\n",
" <td>Austrian</td>\n",
" <td>1841.0</td>\n",
" <td>1918.0</td>\n",
" <td>1899.0</td>\n",
" <td>Painted cast-iron</td>\n",
" <td>28 1/4 x 46 1/2 x 3\" (72.4 x 117.5 x 7.6 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>74.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>7470</td>\n",
" <td>City of Music, National Superior Conservatory ...</td>\n",
" <td>Christian de Portzamparc</td>\n",
" <td>Male</td>\n",
" <td>French</td>\n",
" <td>1944.0</td>\n",
" <td>0.0</td>\n",
" <td>1987.0</td>\n",
" <td>Paint and colored pencil on print</td>\n",
" <td>16 x 11 3/4\" (40.6 x 29.8 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>7470</td>\n",
" <td>LVMH Tower, New York, NY (Study model)</td>\n",
" <td>Christian de Portzamparc</td>\n",
" <td>Male</td>\n",
" <td>French</td>\n",
" <td>1944.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>Paper and cardboard</td>\n",
" <td>39 3/8 x 7 1/2 x 11 13/16\" (100 x 19 x 30 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>7470</td>\n",
" <td>LVMH Tower, New York, NY (Study model)</td>\n",
" <td>Christian de Portzamparc</td>\n",
" <td>Male</td>\n",
" <td>French</td>\n",
" <td>1944.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>Paper and cardboard</td>\n",
" <td>39 3/8 x 7 1/2 x 11 13/16\" (100 x 19 x 30 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>7470</td>\n",
" <td>LVMH Tower, New York, NY (Study model)</td>\n",
" <td>Christian de Portzamparc</td>\n",
" <td>Male</td>\n",
" <td>French</td>\n",
" <td>1944.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>Paper and cardboard</td>\n",
" <td>39 3/8 x 7 1/2 x 11 13/16\" (100 x 19 x 30 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>7470</td>\n",
" <td>LVMH Tower, New York, NY, Study model</td>\n",
" <td>Christian de Portzamparc</td>\n",
" <td>Male</td>\n",
" <td>French</td>\n",
" <td>1944.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>Paper and cardboard</td>\n",
" <td>39 3/8 x 7 1/2 x 11 13/16\" (100 x 19 x 30 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>7470</td>\n",
" <td>LVMH Tower, New York, NY (Study model)</td>\n",
" <td>Christian de Portzamparc</td>\n",
" <td>Male</td>\n",
" <td>French</td>\n",
" <td>1944.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>Paper and cardboard</td>\n",
" <td>39 3/8 x 7 1/2 x 11 13/16\" (100 x 19 x 30 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>7470</td>\n",
" <td>LVMH Tower, New York, NY (Site model)</td>\n",
" <td>Christian de Portzamparc</td>\n",
" <td>Male</td>\n",
" <td>French</td>\n",
" <td>1944.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>Paper and cardboard</td>\n",
" <td>16 1/8 x 17 5/16 x 9 13/16\" (41 x 44 x 25 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>7470</td>\n",
" <td>LVMH Tower, New York, NY (Perspective sketch)</td>\n",
" <td>Christian de Portzamparc</td>\n",
" <td>Male</td>\n",
" <td>French</td>\n",
" <td>1944.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>Ink on paper</td>\n",
" <td>8 1/4 x 11 11/16\" (21 x 29.7 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>7470</td>\n",
" <td>LVMH Tower, New York, NY (Perspective sketch, ...</td>\n",
" <td>Christian de Portzamparc</td>\n",
" <td>Male</td>\n",
" <td>French</td>\n",
" <td>1944.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>Ink on paper</td>\n",
" <td>11 1/4 x 8 1/4\" (28.5 x 21 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>7470</td>\n",
" <td>LVMH Tower, New York, NY (Perspective sketch)</td>\n",
" <td>Christian de Portzamparc</td>\n",
" <td>Male</td>\n",
" <td>French</td>\n",
" <td>1944.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>Pencil on paper</td>\n",
" <td>8 1/4 x 11 11/16\" (21 x 29.7 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>7470</td>\n",
" <td>LVMH Tower, New York, NY (Perspective sketches...</td>\n",
" <td>Christian de Portzamparc</td>\n",
" <td>Male</td>\n",
" <td>French</td>\n",
" <td>1944.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>Pencil on tracing paper</td>\n",
" <td>8 1/4 x 11 11/16\" (21 x 29.7 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>7470</td>\n",
" <td>LVMH Tower, New York, NY (Perspective sketches...</td>\n",
" <td>Christian de Portzamparc</td>\n",
" <td>Male</td>\n",
" <td>French</td>\n",
" <td>1944.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>Ink on paper</td>\n",
" <td>7 9/16 x 15 3/4\" (19.2 x 40 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>7470</td>\n",
" <td>LVMH Tower, New York, NY (Perspective sketches...</td>\n",
" <td>Christian de Portzamparc</td>\n",
" <td>Male</td>\n",
" <td>French</td>\n",
" <td>1944.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>Pencil and ink on paper</td>\n",
" <td>11 11/16 x 16 9/16\" (29.7 x 42 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>7470</td>\n",
" <td>LVMH Tower, New York, NY (Perspective sketch)</td>\n",
" <td>Christian de Portzamparc</td>\n",
" <td>Male</td>\n",
" <td>French</td>\n",
" <td>1944.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>Pencil on paper</td>\n",
" <td>7 9/16 x 15 3/4\" (19.2 x 40 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>7470</td>\n",
" <td>LVMH Tower, New York, NY (Perspective sketches...</td>\n",
" <td>Christian de Portzamparc</td>\n",
" <td>Male</td>\n",
" <td>French</td>\n",
" <td>1944.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>Felt-tipped pen on tracing paper</td>\n",
" <td>8 1/4 x 15 13/16\" (21 x 40.2 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>7470</td>\n",
" <td>LVMH Tower, New York, NY (Perspective sketch, ...</td>\n",
" <td>Christian de Portzamparc</td>\n",
" <td>Male</td>\n",
" <td>French</td>\n",
" <td>1944.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>Colored pencil on paper</td>\n",
" <td>11 11/16 x 16 9/16\" (29.7 x 42 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>7470</td>\n",
" <td>LVMH Tower, New York, NY (Perspective sketch, ...</td>\n",
" <td>Christian de Portzamparc</td>\n",
" <td>Male</td>\n",
" <td>French</td>\n",
" <td>1944.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>Pencil on paper</td>\n",
" <td>8 1/4 x 11 11/16\" (21 x 29.7 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>7470</td>\n",
" <td>LVMH Tower, New York, NY (Perspective sketches...</td>\n",
" <td>Christian de Portzamparc</td>\n",
" <td>Male</td>\n",
" <td>French</td>\n",
" <td>1944.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>Pencil on paper</td>\n",
" <td>11 11/16 x 13 3/16\" (29.7 x 33.5 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>7470</td>\n",
" <td>LVMH Tower, New York, NY (Elevation sketch, vo...</td>\n",
" <td>Christian de Portzamparc</td>\n",
" <td>Male</td>\n",
" <td>French</td>\n",
" <td>1944.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>Pencil on tracing paper</td>\n",
" <td>16 9/16 x 11 11/16\" (42 x 29.7 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>7470</td>\n",
" <td>LVMH Tower, New York, NY (Elevation)</td>\n",
" <td>Christian de Portzamparc</td>\n",
" <td>Male</td>\n",
" <td>French</td>\n",
" <td>1944.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>Print</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>7470</td>\n",
" <td>LVMH Tower, New York, NY (Elevations)</td>\n",
" <td>Christian de Portzamparc</td>\n",
" <td>Male</td>\n",
" <td>French</td>\n",
" <td>1944.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>Print</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>7605</td>\n",
" <td>Villa near Vienna Project, Outside Vienna, Aus...</td>\n",
" <td>Emil Hoppe</td>\n",
" <td>Male</td>\n",
" <td>Austrian</td>\n",
" <td>1876.0</td>\n",
" <td>1957.0</td>\n",
" <td>1903.0</td>\n",
" <td>Graphite, pen, color pencil, ink, and gouache ...</td>\n",
" <td>13 1/2 x 12 1/2\" (34.3 x 31.8 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>94.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>7605</td>\n",
" <td>Villa, project, outside Vienna, Austria, Exter...</td>\n",
" <td>Emil Hoppe</td>\n",
" <td>Male</td>\n",
" <td>Austrian</td>\n",
" <td>1876.0</td>\n",
" <td>1957.0</td>\n",
" <td>1903.0</td>\n",
" <td>Graphite, color pencil, ink, and gouache on tr...</td>\n",
" <td>15 1/8 x 7 1/2\" (38.4 x 19.1 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>94.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>7056</td>\n",
" <td>The Manhattan Transcripts Project, New York, N...</td>\n",
" <td>Bernard Tschumi</td>\n",
" <td>Male</td>\n",
" <td>NaN</td>\n",
" <td>1944.0</td>\n",
" <td>0.0</td>\n",
" <td>1980.0</td>\n",
" <td>Photographic reproduction with colored synthet...</td>\n",
" <td>20 x 20\" (50.8 x 50.8 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>7056</td>\n",
" <td>The Manhattan Transcripts Project, New York, N...</td>\n",
" <td>Bernard Tschumi</td>\n",
" <td>Male</td>\n",
" <td>NaN</td>\n",
" <td>1944.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>Gelatin silver photograph</td>\n",
" <td>14 x 18\" (35.6 x 45.7 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>7056</td>\n",
" <td>The Manhattan Transcripts Project, New York, N...</td>\n",
" <td>Bernard Tschumi</td>\n",
" <td>Male</td>\n",
" <td>NaN</td>\n",
" <td>1944.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>Gelatin silver photographs</td>\n",
" <td>Each: 14 x 18\" (35.6 x 45.7 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105960</th>\n",
" <td>40166</td>\n",
" <td>Time Is Up 2 (Darkroom Manuals)</td>\n",
" <td>Sara Cwynar</td>\n",
" <td>NaN</td>\n",
" <td>Canadian</td>\n",
" <td>1985.0</td>\n",
" <td>0.0</td>\n",
" <td>2018.0</td>\n",
" <td>Chromogenic color print</td>\n",
" <td>30 × 24 in. (76.2 × 60.96 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105961</th>\n",
" <td>40166</td>\n",
" <td>Display Stand, No. 66 WIRE H. 20 1/2 W. 24 D. ...</td>\n",
" <td>Sara Cwynar</td>\n",
" <td>NaN</td>\n",
" <td>Canadian</td>\n",
" <td>1985.0</td>\n",
" <td>0.0</td>\n",
" <td>2014.0</td>\n",
" <td>Chromogenic color print</td>\n",
" <td>30 × 36 in. (76.2 × 91.44 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105962</th>\n",
" <td>40166</td>\n",
" <td>Color Bars 2 (Darkroom Manuals)</td>\n",
" <td>Sara Cwynar</td>\n",
" <td>NaN</td>\n",
" <td>Canadian</td>\n",
" <td>1985.0</td>\n",
" <td>0.0</td>\n",
" <td>2018.0</td>\n",
" <td>Chromogenic color print</td>\n",
" <td>30 × 24 in. (76.2 × 60.96 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105963</th>\n",
" <td>40166</td>\n",
" <td>Man and Space (Books 2)</td>\n",
" <td>Sara Cwynar</td>\n",
" <td>NaN</td>\n",
" <td>Canadian</td>\n",
" <td>1985.0</td>\n",
" <td>0.0</td>\n",
" <td>2013.0</td>\n",
" <td>Chromogenic color print</td>\n",
" <td>30 × 24 in. (76.2 × 60.96 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105964</th>\n",
" <td>40166</td>\n",
" <td>Corinthian Column (Plastic Cups)</td>\n",
" <td>Sara Cwynar</td>\n",
" <td>NaN</td>\n",
" <td>Canadian</td>\n",
" <td>1985.0</td>\n",
" <td>0.0</td>\n",
" <td>2014.0</td>\n",
" <td>Chromogenic color print</td>\n",
" <td>30 × 24 in. (76.2 × 60.96 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105965</th>\n",
" <td>40166</td>\n",
" <td>Girl From Contact Sheet 3 (Darkroom Manuals)</td>\n",
" <td>Sara Cwynar</td>\n",
" <td>NaN</td>\n",
" <td>Canadian</td>\n",
" <td>1985.0</td>\n",
" <td>0.0</td>\n",
" <td>2018.0</td>\n",
" <td>Chromogenic color print</td>\n",
" <td>30 × 24 in. (76.2 × 60.96 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105966</th>\n",
" <td>131860</td>\n",
" <td>Windows on the World (Part 2)</td>\n",
" <td>Ming Wong</td>\n",
" <td>NaN</td>\n",
" <td>Singaporean</td>\n",
" <td>1971.0</td>\n",
" <td>0.0</td>\n",
" <td>2014.0</td>\n",
" <td>24-channel video (color, sound)\\r\\n</td>\n",
" <td>Duration variable</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105967</th>\n",
" <td>131861</td>\n",
" <td>Workers Leaving the Googleplex</td>\n",
" <td>Andrew Norman Wilson</td>\n",
" <td>NaN</td>\n",
" <td>American</td>\n",
" <td>1986.0</td>\n",
" <td>0.0</td>\n",
" <td>2011.0</td>\n",
" <td>Seven-channel and single-channel video (color,...</td>\n",
" <td>11:03 min. \\r\\n</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105968</th>\n",
" <td>131882</td>\n",
" <td>Think Evolution #1 : Kiku-ishi (Ammonite)</td>\n",
" <td>Aki Inomata</td>\n",
" <td>Female</td>\n",
" <td>Japanese</td>\n",
" <td>1983.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>Resin, ammonite fossil</td>\n",
" <td>4 3/4 × 3 1/8 × 4 3/4\" (12 × 8 × 12 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105969</th>\n",
" <td>131882</td>\n",
" <td>Think Evolution #1 : Kiku-ishi (Ammonite)</td>\n",
" <td>Aki Inomata</td>\n",
" <td>Female</td>\n",
" <td>Japanese</td>\n",
" <td>1983.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>HD video (color, sound)</td>\n",
" <td>2 min.</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105970</th>\n",
" <td>131882</td>\n",
" <td>Think Evolution #1 : Kiku-ishi (Ammonite)</td>\n",
" <td>Aki Inomata</td>\n",
" <td>Female</td>\n",
" <td>Japanese</td>\n",
" <td>1983.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>Resin, ammonite fossil sculpture and digital v...</td>\n",
" <td>see child records</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105971</th>\n",
" <td>131903</td>\n",
" <td>How to Make Money Religiously</td>\n",
" <td>Laure Prouvost</td>\n",
" <td>NaN</td>\n",
" <td>French</td>\n",
" <td>1978.0</td>\n",
" <td>0.0</td>\n",
" <td>2014.0</td>\n",
" <td>Video (color, sound)</td>\n",
" <td>8:44 min.\\r\\n</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105972</th>\n",
" <td>131907</td>\n",
" <td>VVEBCAM</td>\n",
" <td>Petra Cortright</td>\n",
" <td>NaN</td>\n",
" <td>American</td>\n",
" <td>1986.0</td>\n",
" <td>0.0</td>\n",
" <td>2007.0</td>\n",
" <td>Webcam video (color, sound)</td>\n",
" <td>1:43 min.</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>12.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105973</th>\n",
" <td>131944</td>\n",
" <td>The Black Panther Coloring Book (James Teemer)</td>\n",
" <td>Mark Teemer</td>\n",
" <td>NaN</td>\n",
" <td>American</td>\n",
" <td>1946.0</td>\n",
" <td>0.0</td>\n",
" <td>1968.0</td>\n",
" <td>Ink on newsprint</td>\n",
" <td>8 1/2 × 11\" (21.6 × 27.9 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>51.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105974</th>\n",
" <td>131916</td>\n",
" <td>Object</td>\n",
" <td>Yente (Eugenia Crenovich)</td>\n",
" <td>female</td>\n",
" <td>Argentine</td>\n",
" <td>1905.0</td>\n",
" <td>1990.0</td>\n",
" <td>1946.0</td>\n",
" <td>Painted board</td>\n",
" <td>9 1/16 × 10 5/8 × 5 1/8\" (23 × 27 × 13 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>61.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105975</th>\n",
" <td>131916</td>\n",
" <td>Tapestry no. 6</td>\n",
" <td>Yente (Eugenia Crenovich)</td>\n",
" <td>female</td>\n",
" <td>Argentine</td>\n",
" <td>1905.0</td>\n",
" <td>1990.0</td>\n",
" <td>1958.0</td>\n",
" <td>Wool and paint on canvas</td>\n",
" <td>47 5/8 × 18 7/16\" (121 × 46.9 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>61.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105976</th>\n",
" <td>131929</td>\n",
" <td>Cherry Lips</td>\n",
" <td>Pacifico Silano</td>\n",
" <td>Male</td>\n",
" <td>American</td>\n",
" <td>1986.0</td>\n",
" <td>0.0</td>\n",
" <td>2015.0</td>\n",
" <td>Pigmented inkjet print</td>\n",
" <td>32 × 40\" (81.3 × 101.6 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105977</th>\n",
" <td>131934</td>\n",
" <td>Untitled</td>\n",
" <td>Nancy Ford Cones</td>\n",
" <td>Female</td>\n",
" <td>American</td>\n",
" <td>1869.0</td>\n",
" <td>1962.0</td>\n",
" <td>1900.0</td>\n",
" <td>Kallitype</td>\n",
" <td>Approx. 8 × 10\" (20.3 × 25.4 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>119.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105978</th>\n",
" <td>37030</td>\n",
" <td>Highway</td>\n",
" <td>Barbara Ess</td>\n",
" <td>NaN</td>\n",
" <td>American</td>\n",
" <td>1948.0</td>\n",
" <td>0.0</td>\n",
" <td>1995.0</td>\n",
" <td>Chromogenic color print</td>\n",
" <td>40 × 60\" (101.6 × 152.4 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>24.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105979</th>\n",
" <td>131948</td>\n",
" <td>Disease Thrower #5</td>\n",
" <td>Guadalupe Maravilla</td>\n",
" <td>Male</td>\n",
" <td>Salvadoran</td>\n",
" <td>1976.0</td>\n",
" <td>0.0</td>\n",
" <td>2019.0</td>\n",
" <td>Glass, steel, wood, cotton, plastic, loofah, p...</td>\n",
" <td>91 × 55 × 45\" (231.1 × 139.7 × 114.3 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105980</th>\n",
" <td>131948</td>\n",
" <td>Circle Serpent</td>\n",
" <td>Guadalupe Maravilla</td>\n",
" <td>Male</td>\n",
" <td>Salvadoran</td>\n",
" <td>1976.0</td>\n",
" <td>0.0</td>\n",
" <td>2019.0</td>\n",
" <td>Maguey leaves</td>\n",
" <td>Dimensions variable</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105981</th>\n",
" <td>131958</td>\n",
" <td>Work</td>\n",
" <td>Tsuruko Yamazaki</td>\n",
" <td>Female</td>\n",
" <td>Japanese</td>\n",
" <td>1925.0</td>\n",
" <td>2019.0</td>\n",
" <td>1957.0</td>\n",
" <td>Aniline dye on tin</td>\n",
" <td>28 7/8 × 32 1/2\" (73.3 × 82.6 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>62.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105982</th>\n",
" <td>28285</td>\n",
" <td>Raja Ampat</td>\n",
" <td>Helen Marden</td>\n",
" <td>Female</td>\n",
" <td>American</td>\n",
" <td>1941.0</td>\n",
" <td>0.0</td>\n",
" <td>2018.0</td>\n",
" <td>Resin, acrylic, powered paint, and abalone she...</td>\n",
" <td>50 × 32\" (127 × 81.3 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105983</th>\n",
" <td>71841</td>\n",
" <td>Post-Partum Document: Documentation IV, Transi...</td>\n",
" <td>Mary Kelly</td>\n",
" <td>NaN</td>\n",
" <td>American</td>\n",
" <td>1941.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>Plexiglass, white card, plaster, cotton, ink, ...</td>\n",
" <td>Each 14 × 11\" (35.6 × 27.9 cm), overall instal...</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105984</th>\n",
" <td>116470</td>\n",
" <td>Plate (folio 16) from Les Biches, vol. I</td>\n",
" <td>Francis Poulenc</td>\n",
" <td>NaN</td>\n",
" <td>French</td>\n",
" <td>1899.0</td>\n",
" <td>1963.0</td>\n",
" <td>1924.0</td>\n",
" <td>One from an illustrated book with eighteen col...</td>\n",
" <td>plate: 11 × 8 11/16\" (28 × 22 cm); page (each ...</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105985</th>\n",
" <td>69941</td>\n",
" <td>Untitled (First White Light Series)</td>\n",
" <td>Mary Corse</td>\n",
" <td>Female</td>\n",
" <td>American</td>\n",
" <td>1945.0</td>\n",
" <td>0.0</td>\n",
" <td>1968.0</td>\n",
" <td>Glass microspheres and acrylic on canvas</td>\n",
" <td>78 × 78\" (198.1 × 198.1 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>51.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105986</th>\n",
" <td>131890</td>\n",
" <td>Anatomy of an AI System</td>\n",
" <td>Kate Crawford</td>\n",
" <td>Female</td>\n",
" <td>Australian</td>\n",
" <td>1976.0</td>\n",
" <td>0.0</td>\n",
" <td>2018.0</td>\n",
" <td>digital image file and newsprint</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105987</th>\n",
" <td>132134</td>\n",
" <td>Full of Surprises from Pulled in Brooklyn</td>\n",
" <td>Sheryl Oppenheim</td>\n",
" <td>Female</td>\n",
" <td>American</td>\n",
" <td>1983.0</td>\n",
" <td>0.0</td>\n",
" <td>2019.0</td>\n",
" <td>One from a portfolio with six screenprints</td>\n",
" <td>composition and sheet: 30 × 22 1/16\" (76.2 × 5...</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105988</th>\n",
" <td>132135</td>\n",
" <td>Pink Figures from Pulled in Brooklyn</td>\n",
" <td>Ruby Sky Stiler</td>\n",
" <td>Female</td>\n",
" <td>American</td>\n",
" <td>1979.0</td>\n",
" <td>0.0</td>\n",
" <td>2019.0</td>\n",
" <td>One from a portfolio with six screenprints</td>\n",
" <td>composition (irreg.): 18 3/4 × 16 9/16\" (47.7 ...</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105989</th>\n",
" <td>131931</td>\n",
" <td>Dyslympics 2680</td>\n",
" <td>Sachiko Kazama</td>\n",
" <td>Female</td>\n",
" <td>Japanese</td>\n",
" <td>1972.0</td>\n",
" <td>0.0</td>\n",
" <td>2018.0</td>\n",
" <td>Woodcut</td>\n",
" <td>95 × 251\" (241.3 × 637.5 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>105990 rows × 110 columns</p>\n",
"</div>"
],
"text/plain": [
" UniqueID Title \\\n",
"0 6210 Ferdinandsbrücke Project, Vienna, Austria (Ele... \n",
"1 6210 Armchair \n",
"2 6210 Stool \n",
"3 6210 Railing \n",
"4 7470 City of Music, National Superior Conservatory ... \n",
"5 7470 LVMH Tower, New York, NY (Study model) \n",
"6 7470 LVMH Tower, New York, NY (Study model) \n",
"7 7470 LVMH Tower, New York, NY (Study model) \n",
"8 7470 LVMH Tower, New York, NY, Study model \n",
"9 7470 LVMH Tower, New York, NY (Study model) \n",
"10 7470 LVMH Tower, New York, NY (Site model) \n",
"11 7470 LVMH Tower, New York, NY (Perspective sketch) \n",
"12 7470 LVMH Tower, New York, NY (Perspective sketch, ... \n",
"13 7470 LVMH Tower, New York, NY (Perspective sketch) \n",
"14 7470 LVMH Tower, New York, NY (Perspective sketches... \n",
"15 7470 LVMH Tower, New York, NY (Perspective sketches... \n",
"16 7470 LVMH Tower, New York, NY (Perspective sketches... \n",
"17 7470 LVMH Tower, New York, NY (Perspective sketch) \n",
"18 7470 LVMH Tower, New York, NY (Perspective sketches... \n",
"19 7470 LVMH Tower, New York, NY (Perspective sketch, ... \n",
"20 7470 LVMH Tower, New York, NY (Perspective sketch, ... \n",
"21 7470 LVMH Tower, New York, NY (Perspective sketches... \n",
"22 7470 LVMH Tower, New York, NY (Elevation sketch, vo... \n",
"23 7470 LVMH Tower, New York, NY (Elevation) \n",
"24 7470 LVMH Tower, New York, NY (Elevations) \n",
"25 7605 Villa near Vienna Project, Outside Vienna, Aus... \n",
"26 7605 Villa, project, outside Vienna, Austria, Exter... \n",
"27 7056 The Manhattan Transcripts Project, New York, N... \n",
"28 7056 The Manhattan Transcripts Project, New York, N... \n",
"29 7056 The Manhattan Transcripts Project, New York, N... \n",
"... ... ... \n",
"105960 40166 Time Is Up 2 (Darkroom Manuals) \n",
"105961 40166 Display Stand, No. 66 WIRE H. 20 1/2 W. 24 D. ... \n",
"105962 40166 Color Bars 2 (Darkroom Manuals) \n",
"105963 40166 Man and Space (Books 2) \n",
"105964 40166 Corinthian Column (Plastic Cups) \n",
"105965 40166 Girl From Contact Sheet 3 (Darkroom Manuals) \n",
"105966 131860 Windows on the World (Part 2) \n",
"105967 131861 Workers Leaving the Googleplex \n",
"105968 131882 Think Evolution #1 : Kiku-ishi (Ammonite) \n",
"105969 131882 Think Evolution #1 : Kiku-ishi (Ammonite) \n",
"105970 131882 Think Evolution #1 : Kiku-ishi (Ammonite) \n",
"105971 131903 How to Make Money Religiously \n",
"105972 131907 VVEBCAM \n",
"105973 131944 The Black Panther Coloring Book (James Teemer) \n",
"105974 131916 Object \n",
"105975 131916 Tapestry no. 6 \n",
"105976 131929 Cherry Lips \n",
"105977 131934 Untitled \n",
"105978 37030 Highway \n",
"105979 131948 Disease Thrower #5 \n",
"105980 131948 Circle Serpent \n",
"105981 131958 Work \n",
"105982 28285 Raja Ampat \n",
"105983 71841 Post-Partum Document: Documentation IV, Transi... \n",
"105984 116470 Plate (folio 16) from Les Biches, vol. I \n",
"105985 69941 Untitled (First White Light Series) \n",
"105986 131890 Anatomy of an AI System \n",
"105987 132134 Full of Surprises from Pulled in Brooklyn \n",
"105988 132135 Pink Figures from Pulled in Brooklyn \n",
"105989 131931 Dyslympics 2680 \n",
"\n",
" Artist Gender Nationality Birth Death \\\n",
"0 Otto Wagner Male Austrian 1841.0 1918.0 \n",
"1 Otto Wagner Male Austrian 1841.0 1918.0 \n",
"2 Otto Wagner Male Austrian 1841.0 1918.0 \n",
"3 Otto Wagner Male Austrian 1841.0 1918.0 \n",
"4 Christian de Portzamparc Male French 1944.0 0.0 \n",
"5 Christian de Portzamparc Male French 1944.0 0.0 \n",
"6 Christian de Portzamparc Male French 1944.0 0.0 \n",
"7 Christian de Portzamparc Male French 1944.0 0.0 \n",
"8 Christian de Portzamparc Male French 1944.0 0.0 \n",
"9 Christian de Portzamparc Male French 1944.0 0.0 \n",
"10 Christian de Portzamparc Male French 1944.0 0.0 \n",
"11 Christian de Portzamparc Male French 1944.0 0.0 \n",
"12 Christian de Portzamparc Male French 1944.0 0.0 \n",
"13 Christian de Portzamparc Male French 1944.0 0.0 \n",
"14 Christian de Portzamparc Male French 1944.0 0.0 \n",
"15 Christian de Portzamparc Male French 1944.0 0.0 \n",
"16 Christian de Portzamparc Male French 1944.0 0.0 \n",
"17 Christian de Portzamparc Male French 1944.0 0.0 \n",
"18 Christian de Portzamparc Male French 1944.0 0.0 \n",
"19 Christian de Portzamparc Male French 1944.0 0.0 \n",
"20 Christian de Portzamparc Male French 1944.0 0.0 \n",
"21 Christian de Portzamparc Male French 1944.0 0.0 \n",
"22 Christian de Portzamparc Male French 1944.0 0.0 \n",
"23 Christian de Portzamparc Male French 1944.0 0.0 \n",
"24 Christian de Portzamparc Male French 1944.0 0.0 \n",
"25 Emil Hoppe Male Austrian 1876.0 1957.0 \n",
"26 Emil Hoppe Male Austrian 1876.0 1957.0 \n",
"27 Bernard Tschumi Male NaN 1944.0 0.0 \n",
"28 Bernard Tschumi Male NaN 1944.0 0.0 \n",
"29 Bernard Tschumi Male NaN 1944.0 0.0 \n",
"... ... ... ... ... ... \n",
"105960 Sara Cwynar NaN Canadian 1985.0 0.0 \n",
"105961 Sara Cwynar NaN Canadian 1985.0 0.0 \n",
"105962 Sara Cwynar NaN Canadian 1985.0 0.0 \n",
"105963 Sara Cwynar NaN Canadian 1985.0 0.0 \n",
"105964 Sara Cwynar NaN Canadian 1985.0 0.0 \n",
"105965 Sara Cwynar NaN Canadian 1985.0 0.0 \n",
"105966 Ming Wong NaN Singaporean 1971.0 0.0 \n",
"105967 Andrew Norman Wilson NaN American 1986.0 0.0 \n",
"105968 Aki Inomata Female Japanese 1983.0 0.0 \n",
"105969 Aki Inomata Female Japanese 1983.0 0.0 \n",
"105970 Aki Inomata Female Japanese 1983.0 0.0 \n",
"105971 Laure Prouvost NaN French 1978.0 0.0 \n",
"105972 Petra Cortright NaN American 1986.0 0.0 \n",
"105973 Mark Teemer NaN American 1946.0 0.0 \n",
"105974 Yente (Eugenia Crenovich) female Argentine 1905.0 1990.0 \n",
"105975 Yente (Eugenia Crenovich) female Argentine 1905.0 1990.0 \n",
"105976 Pacifico Silano Male American 1986.0 0.0 \n",
"105977 Nancy Ford Cones Female American 1869.0 1962.0 \n",
"105978 Barbara Ess NaN American 1948.0 0.0 \n",
"105979 Guadalupe Maravilla Male Salvadoran 1976.0 0.0 \n",
"105980 Guadalupe Maravilla Male Salvadoran 1976.0 0.0 \n",
"105981 Tsuruko Yamazaki Female Japanese 1925.0 2019.0 \n",
"105982 Helen Marden Female American 1941.0 0.0 \n",
"105983 Mary Kelly NaN American 1941.0 0.0 \n",
"105984 Francis Poulenc NaN French 1899.0 1963.0 \n",
"105985 Mary Corse Female American 1945.0 0.0 \n",
"105986 Kate Crawford Female Australian 1976.0 0.0 \n",
"105987 Sheryl Oppenheim Female American 1983.0 0.0 \n",
"105988 Ruby Sky Stiler Female American 1979.0 0.0 \n",
"105989 Sachiko Kazama Female Japanese 1972.0 0.0 \n",
"\n",
" Date Medium \\\n",
"0 1896.0 Ink and cut-and-pasted painted pages on paper \n",
"1 1902.0 Beech wood and aluminum \n",
"2 1904.0 Bent beech wood, molded plywood, and aluminum \n",
"3 1899.0 Painted cast-iron \n",
"4 1987.0 Paint and colored pencil on print \n",
"5 NaN Paper and cardboard \n",
"6 NaN Paper and cardboard \n",
"7 NaN Paper and cardboard \n",
"8 NaN Paper and cardboard \n",
"9 NaN Paper and cardboard \n",
"10 NaN Paper and cardboard \n",
"11 NaN Ink on paper \n",
"12 NaN Ink on paper \n",
"13 NaN Pencil on paper \n",
"14 NaN Pencil on tracing paper \n",
"15 NaN Ink on paper \n",
"16 NaN Pencil and ink on paper \n",
"17 NaN Pencil on paper \n",
"18 NaN Felt-tipped pen on tracing paper \n",
"19 NaN Colored pencil on paper \n",
"20 NaN Pencil on paper \n",
"21 NaN Pencil on paper \n",
"22 NaN Pencil on tracing paper \n",
"23 NaN Print \n",
"24 NaN Print \n",
"25 1903.0 Graphite, pen, color pencil, ink, and gouache ... \n",
"26 1903.0 Graphite, color pencil, ink, and gouache on tr... \n",
"27 1980.0 Photographic reproduction with colored synthet... \n",
"28 NaN Gelatin silver photograph \n",
"29 NaN Gelatin silver photographs \n",
"... ... ... \n",
"105960 2018.0 Chromogenic color print \n",
"105961 2014.0 Chromogenic color print \n",
"105962 2018.0 Chromogenic color print \n",
"105963 2013.0 Chromogenic color print \n",
"105964 2014.0 Chromogenic color print \n",
"105965 2018.0 Chromogenic color print \n",
"105966 2014.0 24-channel video (color, sound)\\r\\n \n",
"105967 2011.0 Seven-channel and single-channel video (color,... \n",
"105968 NaN Resin, ammonite fossil \n",
"105969 NaN HD video (color, sound) \n",
"105970 NaN Resin, ammonite fossil sculpture and digital v... \n",
"105971 2014.0 Video (color, sound) \n",
"105972 2007.0 Webcam video (color, sound) \n",
"105973 1968.0 Ink on newsprint \n",
"105974 1946.0 Painted board \n",
"105975 1958.0 Wool and paint on canvas \n",
"105976 2015.0 Pigmented inkjet print \n",
"105977 1900.0 Kallitype \n",
"105978 1995.0 Chromogenic color print \n",
"105979 2019.0 Glass, steel, wood, cotton, plastic, loofah, p... \n",
"105980 2019.0 Maguey leaves \n",
"105981 1957.0 Aniline dye on tin \n",
"105982 2018.0 Resin, acrylic, powered paint, and abalone she... \n",
"105983 NaN Plexiglass, white card, plaster, cotton, ink, ... \n",
"105984 1924.0 One from an illustrated book with eighteen col... \n",
"105985 1968.0 Glass microspheres and acrylic on canvas \n",
"105986 2018.0 digital image file and newsprint \n",
"105987 2019.0 One from a portfolio with six screenprints \n",
"105988 2019.0 One from a portfolio with six screenprints \n",
"105989 2018.0 Woodcut \n",
"\n",
" Dimensions \\\n",
"0 19 1/8 x 66 1/2\" (48.6 x 168.9 cm) \n",
"1 30 7/8 x 22 1/4 x 20 1/4\" (78.5 x 56.5 x 51.5 ... \n",
"2 18 1/2 x 16 x 16\" (47 x 40.6 x 40.6 cm) \n",
"3 28 1/4 x 46 1/2 x 3\" (72.4 x 117.5 x 7.6 cm) \n",
"4 16 x 11 3/4\" (40.6 x 29.8 cm) \n",
"5 39 3/8 x 7 1/2 x 11 13/16\" (100 x 19 x 30 cm) \n",
"6 39 3/8 x 7 1/2 x 11 13/16\" (100 x 19 x 30 cm) \n",
"7 39 3/8 x 7 1/2 x 11 13/16\" (100 x 19 x 30 cm) \n",
"8 39 3/8 x 7 1/2 x 11 13/16\" (100 x 19 x 30 cm) \n",
"9 39 3/8 x 7 1/2 x 11 13/16\" (100 x 19 x 30 cm) \n",
"10 16 1/8 x 17 5/16 x 9 13/16\" (41 x 44 x 25 cm) \n",
"11 8 1/4 x 11 11/16\" (21 x 29.7 cm) \n",
"12 11 1/4 x 8 1/4\" (28.5 x 21 cm) \n",
"13 8 1/4 x 11 11/16\" (21 x 29.7 cm) \n",
"14 8 1/4 x 11 11/16\" (21 x 29.7 cm) \n",
"15 7 9/16 x 15 3/4\" (19.2 x 40 cm) \n",
"16 11 11/16 x 16 9/16\" (29.7 x 42 cm) \n",
"17 7 9/16 x 15 3/4\" (19.2 x 40 cm) \n",
"18 8 1/4 x 15 13/16\" (21 x 40.2 cm) \n",
"19 11 11/16 x 16 9/16\" (29.7 x 42 cm) \n",
"20 8 1/4 x 11 11/16\" (21 x 29.7 cm) \n",
"21 11 11/16 x 13 3/16\" (29.7 x 33.5 cm) \n",
"22 16 9/16 x 11 11/16\" (42 x 29.7 cm) \n",
"23 NaN \n",
"24 NaN \n",
"25 13 1/2 x 12 1/2\" (34.3 x 31.8 cm) \n",
"26 15 1/8 x 7 1/2\" (38.4 x 19.1 cm) \n",
"27 20 x 20\" (50.8 x 50.8 cm) \n",
"28 14 x 18\" (35.6 x 45.7 cm) \n",
"29 Each: 14 x 18\" (35.6 x 45.7 cm) \n",
"... ... \n",
"105960 30 × 24 in. (76.2 × 60.96 cm) \n",
"105961 30 × 36 in. (76.2 × 91.44 cm) \n",
"105962 30 × 24 in. (76.2 × 60.96 cm) \n",
"105963 30 × 24 in. (76.2 × 60.96 cm) \n",
"105964 30 × 24 in. (76.2 × 60.96 cm) \n",
"105965 30 × 24 in. (76.2 × 60.96 cm) \n",
"105966 Duration variable \n",
"105967 11:03 min. \\r\\n \n",
"105968 4 3/4 × 3 1/8 × 4 3/4\" (12 × 8 × 12 cm) \n",
"105969 2 min. \n",
"105970 see child records \n",
"105971 8:44 min.\\r\\n \n",
"105972 1:43 min. \n",
"105973 8 1/2 × 11\" (21.6 × 27.9 cm) \n",
"105974 9 1/16 × 10 5/8 × 5 1/8\" (23 × 27 × 13 cm) \n",
"105975 47 5/8 × 18 7/16\" (121 × 46.9 cm) \n",
"105976 32 × 40\" (81.3 × 101.6 cm) \n",
"105977 Approx. 8 × 10\" (20.3 × 25.4 cm) \n",
"105978 40 × 60\" (101.6 × 152.4 cm) \n",
"105979 91 × 55 × 45\" (231.1 × 139.7 × 114.3 cm) \n",
"105980 Dimensions variable \n",
"105981 28 7/8 × 32 1/2\" (73.3 × 82.6 cm) \n",
"105982 50 × 32\" (127 × 81.3 cm) \n",
"105983 Each 14 × 11\" (35.6 × 27.9 cm), overall instal... \n",
"105984 plate: 11 × 8 11/16\" (28 × 22 cm); page (each ... \n",
"105985 78 × 78\" (198.1 × 198.1 cm) \n",
"105986 NaN \n",
"105987 composition and sheet: 30 × 22 1/16\" (76.2 × 5... \n",
"105988 composition (irreg.): 18 3/4 × 16 9/16\" (47.7 ... \n",
"105989 95 × 251\" (241.3 × 637.5 cm) \n",
"\n",
" ... 66 67 68 69 70 71 72 73 74 \\\n",
"0 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"1 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"2 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"3 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"4 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"5 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"6 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"7 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"8 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"9 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"10 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"11 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"12 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"13 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"14 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"15 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"16 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"17 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"18 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"19 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"20 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"21 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"22 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"23 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"24 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"25 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"26 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"27 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"28 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"29 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"... ... .. .. .. .. .. .. .. .. .. \n",
"105960 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105961 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105962 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105963 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105964 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105965 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105966 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105967 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105968 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105969 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105970 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105971 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105972 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105973 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105974 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105975 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105976 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105977 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105978 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105979 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105980 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105981 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105982 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105983 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105984 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105985 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105986 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105987 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105988 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"105989 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"\n",
" minValues_AcquiredAge_Artwork_2 \n",
"0 74.0 \n",
"1 74.0 \n",
"2 74.0 \n",
"3 74.0 \n",
"4 8.0 \n",
"5 8.0 \n",
"6 8.0 \n",
"7 8.0 \n",
"8 8.0 \n",
"9 8.0 \n",
"10 8.0 \n",
"11 8.0 \n",
"12 8.0 \n",
"13 8.0 \n",
"14 8.0 \n",
"15 8.0 \n",
"16 8.0 \n",
"17 8.0 \n",
"18 8.0 \n",
"19 8.0 \n",
"20 8.0 \n",
"21 8.0 \n",
"22 8.0 \n",
"23 8.0 \n",
"24 8.0 \n",
"25 94.0 \n",
"26 94.0 \n",
"27 2.0 \n",
"28 2.0 \n",
"29 2.0 \n",
"... ... \n",
"105960 1.0 \n",
"105961 1.0 \n",
"105962 1.0 \n",
"105963 1.0 \n",
"105964 1.0 \n",
"105965 1.0 \n",
"105966 5.0 \n",
"105967 8.0 \n",
"105968 NaN \n",
"105969 NaN \n",
"105970 NaN \n",
"105971 5.0 \n",
"105972 12.0 \n",
"105973 51.0 \n",
"105974 61.0 \n",
"105975 61.0 \n",
"105976 4.0 \n",
"105977 119.0 \n",
"105978 24.0 \n",
"105979 0.0 \n",
"105980 0.0 \n",
"105981 62.0 \n",
"105982 1.0 \n",
"105983 NaN \n",
"105984 40.0 \n",
"105985 51.0 \n",
"105986 1.0 \n",
"105987 0.0 \n",
"105988 0.0 \n",
"105989 1.0 \n",
"\n",
"[105990 rows x 110 columns]"
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"master_copy"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(array([ 0., 1000., 2000., 3000., 4000., 5000., 6000., 7000., 8000.]),\n",
" <a list of 9 Text yticklabel objects>)"
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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99trcdddd2XffffNf//VfqaioyPjx4/OPf/xDHCqgysrK1NbWZsiQIfnRj36UtddeO3379s28efMyZsyYPPfcc1ljjTUyePBgEbggHOf8TwjAH2LixIm57rrr0q9fvxx88MFpampKVVVVSqVSyuVySqVSNthggwwcODAvv/xyZsyYkRkzZqSqqipDhw4tzAtsU1NTamtr861vfSsHH3xwkqS6ujo77LBDJk+eLAIXVLlcTo8ePXLIIYfk0EMPTUVFRWpra/PZz34206dPz+OPPy4CF0xjY2O6dOmSQw89NAceeGCSpEOHDtlxxx0zbdo0KwQLyJyvfsz56qtbt27Zfvvtc+CBB6ZUKmXNNddMnz59snDhQiuBC6hcLqdPnz4ZMWJEjjjiiFRWVqZnz54ZMGBAJk6cmEmTJuXJJ58UgQumVCplvfXWyzbbbJMTTjghtbW16d27d8rlciZOnJgpU6akqalJHCqYddddN5tvvnmSpKGhIb169crAY5QuCQAAIABJREFUgQPz2muvZcyYMS0rgUXgYnCc8z8hAH+ImTNn5sYbb0xdXV2++tWvplu3bi23LR+Be/XqlQ033DDPPvtsnnrqqSxYsCADBw7MBhts0Iqj/9/T/FybLxjR2NjYEgO33357Ebigmud9jTXWSPLOyUT79u3Tq1evDB48WAQuoIqKipTL5XTq1CnJfx/rXbp0EYcKqqKiIk1NTeZ8NWLOV0/N56ydO3dOqVRKQ0NDKioqsu6662aDDTYQgQuo+Zhda621krxzHtehQ4f06dMn/fv3z8SJE/PYY4+JwAW0xhprZODAgUnemfc111wz/fv3T2NjYx599FFxqOCaA2/Pnj1F4AJznPNxCcAfomfPnhk9enTmzJmTddddN1tsscW7Dp7l3wz16tUr6623Xu69997MnDkz1dXV+fKXv9waw/7/1vwmYXnL/375bxgicLGtaN6bP/QQgYtpZce6OFQ8za/15nz1Y86L773ncqVS6V1z2PyBX6lUEoFXE83HemVlpQi8Gmme99raWnGoAFb0Pv2DiMCrB8c5H4UA/CEaGhoyefLkTJ06NQsXLsz222+/wjdBzS/Effv2Tc+ePXPHHXfksccey+c///n069evFZ/Bx7f8N5WnnnoqpVIp1dXVH/g1InCxNM/ZB51gNEfgjTfeONOmTcvjjz+esWPHisBtyMc9gUzeiUPDhg1riUM33XRTdt9996y55pqO9Tbgw4LQipjz4mhe6Vsul5NkpXNnztu+5Y/1J598MrNnz06vXr1W+OG+CFw8H+U4FYGLbWX/BsShtm351/YHH3wwnTp1SufOnT/060Tg4vgo798c56yMAPwh2rdvn8GDB+f666/P888/n1mzZmXfffdNZWVlyxup5N0n0P37988//vGPzJw5M1tttVWGDBnSys/io1v+BeX3v/99Tj311Fx//fXZdddd07Vr1w/8WhG47XrvN5L3/tte/vfLe28Enjx5sgjcRiw/57Nnz87kyZPz8MMPp1wup6qq6gM/9HnvCkFxqG1Yfs5POOGETJ06Ndtuu+1HjsDmvO1572v7okWL0qFDh5TL5Q99w2fO267l5/2SSy7JGWeckVtuuSW9evXKoEGD3nd/EbjtW9GHe8tb2XmcCNy2vXfe586dm2XLlqVTp04f+BotDrVdzfN6zDHH5NRTT826666bfv36fehirUQEbqv+p6/vjnNWRAD+CLp3755+/fpl9OjRmT59ep588snsu+++LXvoNR+Ezb+2a9cukyZNyqRJk9KrV6985Stfac3hf2TLv7j8+Mc/zplnnpk333wzX/jCFzJo0KD07t37Qx9DBG57lp/3Rx55JHfeeWcuuuiiPPzww3nyySfzmc98JtXV1Sv9tFEEbnuWn8uLL744Z511Vi688MLcdddduemmm/LCCy+kT58+WXvttVf6GOJQ27P8m4Yrr7wy48aNS9euXTN06FARuICWP86vueaaXHHFFfnRj36UO+64I3fccUe6devWctXw996/mTlve957Lnf22Wdn3rx5+eY3v5ktttgi66+//gq/TgRuu5af80mTJmXUqFG57LLL8vjjj2fmzJn53Oc+967tPt5LBG6b3vsa/9vf/jbHHXdcrrvuutx3330ZNGhQevbsudLXaXGo7TrttNPy+9//Psk7P+HRuXPn9O3bt+VaPR9EBG5blj/Ox48fn/vvvz+33HJLnn322cyfPz/9+/f/wHlznPNeAvBH1Ldv33Ts2DETJkzIk08+mSeeeCLDhw9PVVXVu+63ZMmSVFVVZcKECRkzZky233777Lzzzq006o+n+cXlzDPPzIUXXpjOnTvnqquuyvHHH/+R4m8zEbjtWP6byjnnnJMf//jHuf322/Pss89mypQpefjhh3PPPfdk2LBh6dGjh5XABbD8nJ9yyik577zzMnfu3PTr1y/rrLNOXn755Tz33HNpaGjItttum3bt2n3gj4mLQ23L1KlTc/zxx6eqqioNDQ0ZNWpUunXrJgIXzPLH+U9+8pOcdtppmTJlSurq6jJ37tw8/fTTGTlyZCZNmtRywRDHeTG891yupqYmf/3rX3P44YevNP4u/7UicNuy/LF+3nnn5ac//WluuOGGTJ06NWPHjs0999yTRx99NLvsskuqq6utBC6I5ef9tNNOy09/+tNMnz49y5YtS2NjY5555pk8+eST2XHHHVNbW7vS+C8OtU1Tp07N/fffny5duuSNN97ItGnT0qVLFxG4YJY/bs8666yccsopueWWWzJu3Ljce++9+dvf/pbx48dnrbXWSm1tbTp16rTCY91xzvIE4I+oqqoqG2+8caqqqlpOih5//PEMHjw4Xbp0Sbt27dLY2Jh27dolSX7xi19kzpw5OfLIIzN48OD/0V6breHOO+/MKaecklKplFtvvTXDhg1reYP3cd7oicCrvveGwIsuuij19fUZMWJEhg0bls6dO2fZsmV5/vnn8/DDD+drX/vaB/54kQjcNjTP+RlnnJGLL744NTU1ueaaa3LCCSfkqKOOSlNTU8aPH5+XX345Bx988IfuKyYOtT3XXntt2rdvn6233jqzZs3K6NGjReCCWf6DvQsuuCCdOnXKhRdemEMOOST77rtvFi5cmDlz5uSZZ57JDTfckN69e2ezzTZb6eOZ87Zl1KhR+c///M+USqXcfPPN2WGHHT7S3v6JCNyWvPc87le/+lXq6uqy0047Zeutt0779u2zePHiPPXUU3nyySez//77f2DcEYHbjuZ5P/XUU/PrX/86FRUVOe+883LggQdm+PDheeSRRzJjxowsXLgwe+65p+0gCubVV1/NXXfdle985zvvCv4icLEs/9M8F154YZYtW5YDDzwwn//859O5c+csWLAgTz/9dCZMmJDXX389/fv3T7du3URgPpAA/DF06NAhm222WdZYY41MnDgxTz31VB577LG8/vrrWW+99VJVVZW33norxx57bO6+++5suummOe6449K5c+c28+bo2muvzZgxY/LLX/4yw4cPT2NjY8tegfX19amrq8vUqVOTvHOBvE6dOq30sUTgVVvzf/+LLroo559/fmpqanLzzTfn+9//fnbdddfsuuuuGTBgQB5//PGWFaFf/OIX3/W1K3pMEXjVd+utt+bUU09tiQPDhg1Lu3bt0q5du+y4446577778tRTT2XvvfdOr169kvz3G00/Jt52NTU1pUuXLnn00Ucze/bsHHrooamoqMhzzz0nAhfQY489lp/85Cd5++23c9ttt2X33XfPRhttlP79+2e//fZLz549U19fn1mzZuXOO+9Mt27dsuWWWyaxHURbd8stt+S+++7LT3/60+y3337vOpdbtGhRFi9enCeeeCJz587N2muvnaampndtayYCtw3Nx9wFF1yQCy64IDU1Nbnxxhtz4oknZq+99sqwYcPSt2/fjB07Nk899VSqq6vz+c9//gMfUwRuOy6//PKcddZZqa6uzq233pq99torgwcPzqBBg7LNNtvk2muvTU1NTfbee++WuXNhuGLo1atXLr300vTp0ydXXnll7r///jz77LMicAH97W9/y49//OPU1NTk9ttvz3e+8518+ctfzh577JHddtstEydOzLPPPpvp06dn5syZ2XTTTdOjRw8RmJUSgD+mDh065HOf+1w222yzPPDAA3n++eczZsyYXH311bn22mvz+9//PhMmTEivXr1y3XXXZYMNNmjtIX8kjY2Nqa+vzw9/+MMsXLgwP/jBD9KrV6+WC0I988wzOe2003L++efn4osvzk033ZR77rkntbW1K7ygSLMVReC99947Xbt2XemPofHpeeihh3L66aenrq4uN9xwQ3bYYYeWeamurk6vXr3y6quv5tFHH0337t3z9a9//UPf5K8sAu+8884feiFBPh3XXXddxo8fn+OOOy4HHXRQmpqaUlVV1bKFzahRo1JTU9OyauTNN9/MsmXLUl1d/bF+THyPPfZIjx49xKFVRPMcPPPMMxk1alROPvnkDB06NK+88kqef/7590XghoaGd71Gv/dk0pyv2iZMmJCrr746I0aMyDHHHJNly5alsrIyDQ0NqayszCabbJIBAwakvr4+06ZNy6hRo7LWWmtlyJAhH+vDHnO+aqmvr8/pp5+el19+Od/73vfSr1+/ltf4p59+OieeeGJ++ctf5uKLL85VV12VcePGZfbs2dl0003TqVOnlnMAEbhtGD16dH72s5+lvr4+N954Y3bYYYeW1+7a2tqsv/76eemll/LEE09k/fXXzx577PGhjykCr/oWLFiQX/3qV5k1a1YuueSS7Lrrrmlqakryzvf6JUuW5JprrsnWW2+dvffe+33Xq1kRcahtaP7A7vbbb8+UKVPyve99L/vtt1/uv//+zJgx4wMj8Iq+TzdH4Llz52bs2LEi8Crmr3/9ayZMmJDTTz89I0aMaDmXa9euXXr16pU99tgj8+bNy4wZMzJt2rTMnDkzQ4cOTffu3UVgVkgA/h+orKxM//79M2LEiFRWVqZUKmXWrFmpq6tLr169sv322+fyyy/Phhtu2NpD/cgqKipSWVmZK664IlVVVTn55JNTXV2dioqKTJ06NV/96lczYcKE1NfXp1OnTpk3b15efvnl3HLLLenYsWMGDx6cjh07rvCxa2trs912270rAg8fPtyK0FXAlVdemZEjR+a4447Lt771rfddGb5Dhw6prKzMX//618ydOzf7779/unTp8pEj8KBBgzJ9+vRMnjw548aNyxe/+EXz3sreeuutnH766Zk7d26GDx+erbbaKo2NjWlqakr79u3T1NSU3/zmN5kxY0aeeOKJnH/++bniiity9913p7a2NoMHD06y8hWC2223XaZPn57p06fn5ptvzm677Za11lqrNZ4q79E8Z42NjbnuuuvSs2fPHHTQQamtrc0///nPzJw5810RuPm14MUXX0zXrl1XeNyb81VP8zzfeuuteeSRR9KnT5/su+++LecrzReDqqioaPmwrq6uLk888URGjhyZgQMHZuONN/7AD3vM+apr2bJl+dOf/pQFCxbk29/+dtZdd91UVFTkiSeeyN57750pU6akVCqlpqYm9fX1eeGFFzJhwoQ88cQT+dKXvpTq6uqVrgRef/31s3DhwowdOzbPPfdcOnbs+IFbh/DJu/zyy/PQQw/l5JNPzoEHHphyufyuN/E1NTV58803c+edd2bu3Lk58MAD07Fjxw89j2uOwP369cujjz4qAq9iZs6cmTPOOCNrrLFGjjvuuKy55popl8stcXDBggW544470r59+0yfPj2XXXZZ7rnnnjQ0NKRr167p0qXLB8ahZcuWZdKkSZkyZUrK5XKGDBnyvuve0HqqqqryyiuvZOTIkdl+++0zYMCA7L///u+KwDU1NRk0aFA6duyY0047LR06dFjpwrSePXtmwIABWbRoUR588MHMnj07nTp1yqabbvopPzOWt2zZspx22ml57bXXst9++2WTTTZp+d7c/FM7a6yxRr7whS9k4cKFee655/LMM89k3rx5GTJkyEoXXjnOV28+1vn/0Ldv35x22mm555578vDDD+e+++7Lvffem9/85jfp27dvaw/vY6uoqEjHjh0zf/78TJkyJUny/PPP51vf+lZee+21HHDAAbn55ptzzz335Iwzzshee+2VJPnZz36Wq6++eqWP29jYmH79+uXmm2/OoEGDMnv27Oy2225ZunTpp/K8WLHXXnstN954Y5Jkm222SfL+lQFNTU3ZcMMN07Vr1yxatChvvfXWR17hVVFRka233jqXXXZZ+vfvn8mTJ+eQQw7JkiVL/nefCB9LqVRq+eb+1FNPJXnnRLL5z77//e9n0qRJefvttzNz5sy0a9cu9fX1mTJlSg477LBcdtllLY9TLpff9diNjY1Zf/31c+ONN2bTTTfNvHnz8pWvfCX19fWf4jNkZZqP3c033zw9evTItGnTkiS77bZbjjjiiHzpS19KuVzOySefnD/84Q9JkqOPPjrHHHNMJk+evMLHNOernuZ5bv4Quq6u7l0xb/n7JMlGG22UH/zgB9lnn32SJMcff3zGjh270sc356u29u3bZ80118yyZcvy8ssvJ0lmz56df/u3f8u8efNy4IEH5t57782oUaPyt7/9LV/+8pfTsWPH3HPPPTn22GNTV1f3rg+Cl/93M3To0Jx22mkZMmRIpk2blksvvTTz5s1rlefJO/N60003paKiYoXncc3ztvnmm6e6ujr19fVZunTpRz6Pa9++fXbZZZdcdtll6dmzZ+65554ceuihLStNaT2LFy/OkiVLsnjx4syZMyfJO+fdzedyZ5xxRmbOnJmxY8fmoosuyl133ZUbbrghJ5xwQk466aQ8++yzK/130Ldv3xxxxBE5+uijs3Dhwvzxj3/Mxf+PvfOOy6r+Hvj7Wew9FBEVEVARFFGGKDhINGeiucqVplnqN7Msy7SszIaamqNy4VYUt7hKMQkXIjJlKyogoDjZPL8/fN3bwzBHJY/97vsfinufD8/13HM+53M+53POkiU1fD6JukGQm6OjIwCJiYkAGBgYsG/fPtq2bUtOTg5Lly5l8+bNjB8/nkWLFjF58mTu3btXQ46CPru5uYmnOmJiYmjTps3zeiSJWhBOYRsbGwNQUFAAUGV+FoLAVlZWTJ8+nYEDB6JSqfj111/ZunXrX/plkp7//0UKAP9NhInW1dWVtm3bYmJi8kQ1d7QNIQPQyckJtVrNoUOHADh16hQZGRkMGzaMFStW4OXlRfPmzZkwYQLz5s1j+PDhwMPi5EePHq11bCET4fDhw1y6dAm5XM7KlSulDII6pl69eowcOZKRI0fSvXv3Wu+Ry+XY2dlhZ2eHQqF46qC9QqEQawgDfP/99+jq6v7t7y7x7Ojr6zNkyBBkMhnr169n5syZxMbGEh4ezujRo9m4cSPGxsasXbuWAwcOEBcXx08//cSgQYMA+PDDD9m9ezdQc8NA0PW9e/cSGxuLXC5n48aNf1krXOL5UlFRga6uLo6Ojhw7dozMzEwAunfvzltvvUW3bt1Qq9V88MEH9OzZk82bN3Py5MlHNoCUZK69NG/eHJlMxh9//MHSpUuB2jduAJydnRk1ahTt27ensLCQ0NBQiouLa71Xkrl2o1Qqad68OWq1ml9++YW8vDxSU1NJSUlh8ODBLF26lGbNmmFvb09AQABff/21eLrn+PHjhISEAFQJ8mm+N+fPn+fs2bMAzJ8/Hysrq+f/kBIANGrUiL59+/L666/j7+9f47owR9vZ2Ym1uouLi5/qb+jq6nL58mVu3LgBwAcffCAdC9cC6tevj6urK+Xl5WzcuJGoqCjgYWmIt99+m9DQUIyMjPjoo49YuXIl27dvx9/fn5KSEsLCwvjmm2/IycmpMa6g5/b29lhZWVFSUkJWVhbdu3eXSvxoGR06dMDQ0JDffvsNQCzVtnv3brG81xdffEFISAgqlYqffvqp1r5Egj6HhoYye/ZsAI4cOYKLi8vzfSCJKggb935+fgDs27eP9PT0GvcJp7osLS2ZPn06Xbp04datW4SGhpKbmwtQY9NO0vP/30glIP4m/xUlkcvlyOVyVCoVO3fuJCoqCmdnZy5evEhycjLLly/HwsJCrA0nl8sxNjbGzc2N1NRU0tPT0dHRITAwUDRYmhw8eJARI0YADyeVxzWhkPh3EY53+vr64uLigpmZWa11odRqNWVlZWzatIns7GwGDx6MnZ2deP1xdZz379/PmDFjgIddyT09Pf+dB5J4KvT19blz5w5JSUmcOXOGHTt2sHnzZhISEjAwMGDLli0EBARgbGyMXC7H0dERR0dHLl++TEZGBnfu3KFHjx7o6urWeGeOHDnCa6+9Jv63pOvahWDnr169yvHjx3n11VfFRn8ODg7Y2NiQn59PWloa165dQ6VSceLECVq2bFmjJrCAJHPtQ61WY2Njw507d4iKiiI/Px8HBwfs7e1r9VtkMhkNGzYkNzeXkydPkp2dTf/+/TE3N691fEnm2olwpFupVPLbb79x8+ZNbG1tOXv2LGfPnuWnn36iXr16Yg1BAHNzcxwcHIiKiiItLU1sGlX9PZHJZJw7d46BAwcCD2vPCk0DJZ4/gs8WGBiIs7NzFR9dE7VaTVFREevXr+fGjRuMGDGCevXq1RjnUezcuZM333wTkPw4bcLU1JSMjAwxUefAgQPs37+fVatWcfz4cYyNjdm+fTtBQUG4uLjg4OBA9+7dyc/PJzo6mjt37uDl5YW9vX2VUhDCz9DQUCZPngw8tPEeHh519qwStaNSqdi3bx/Z2dmMGTMGHR0diouLMTQ0ZPDgwaxdu5aSkhIqKysZN24cI0eOfORYt2/fZsWKFcTHx3PkyBHJtmsBgl7eu3ePo0ePcuPGDZycnGjTpk2N8i1C82YjIyO8vb3Zs2cP6enp3L59mz59+tQ6n4Ok5/9fkbZwJarg6+tL7969gYd1xY4ePSoGDIAaxcHt7OzEHcK4uDjKy8trdSSdnJxo0KABhw8fpl27dv/yU0g8DmG3EKBJkybi76ojLAyErN2ysjLxWkVFBQqFglu3bvHHH3/UmlVib2+PmZkZBw8elOSuRbRq1Yp33nmHGTNmYG5uLjoMzZo1Y8SIEXTq1EmsIyj8bNmyJe7u7gCkpqY+ctHo6uqKg4ODpOtaSnW9P3jwIIB4TKxz585UVFSI95eXlxMbGws8zCzUvCYgyVz7EHSzc+fO2NraEhsby+bNm4mPj6/1frVajUqlYsqUKbi5uZGdnS2eBKotC1iSuXYiyN3DwwMXFxdycnJYtmwZGRkZYoM3oEqNP7VajaOjIxMmTAAgIiKCwsLCWuVubGzM4MGDpYWiFqDpxzVr1gyo6aPDn8eIhQz98vJy8ZoQMC4sLBR9+OoYGRkBcOjQIUnXtQQhm2/OnDmMHz+eBg0akJOTw6lTpygoKEAmk/Hll1/i7e0t3lteXi4eE7e3t+fatWts3boVqJnMVFxcLJaIOnz4sBQM1EIqKyvR19enXbt2ZGZmcurUKQD09PSorKxk5syZFBQUUFpailqtZt++fSxbtkzM5K+OqampWO5Lkrd2IOjlyy+/TNeuXbl79y5z5swhOjpa7OehiVwuF0t0zZ49GwMDAy5evMjNmzdrHV/S8/+/SAFgiSpYWFjQt29fLC0tOXPmDCkpKZSXl4u1QqsfIZDL5fj5+SGXyykuLubBgwc1xqyoqKBZs2ZER0dLmQNaxJNkrysUCnR0dGosHITsofz8fHr37k3v3r05f/58lc9WVlbSqlUrEhISpOwwLUJYMLq7u/P+++8TFRXFuXPn2LhxI4WFhWIjJ81aoULDqHbt2iGTySgsLKzVoaioqKBBgwacOnVK0nUtRTMwaGlpKdaOE3R8ypQpHDt2DIVCQfv27VGr1UyaNIlffvkFqBlgkGSu3QQGBvLGG2+gVqvZtm0bwcHBXLp0SbyuqecVFRXo6+uLTb1u374tXtNEkrl2IzSFWbRoEba2tiQmJnLo0CGKiorEki+aCPK1tLQEHm4GaW72atK8eXMWLVokLRS1hCfx4+RyOTo6OuJGvhA00PTjevXqRf/+/UVfX0CtVtO9e3cyMzPx8vL65x9A4pkQAj0A33zzDcHBwWzZsoVdu3bx008/0aBBAzE5R0juUCqVVFZWYmtrS0BAAECtAX94GEQcO3YsCQkJko3XUgS5CvLRtO1TpkwRm7rv3LkTb29vrl27xrJly1i3bh2FhYVVxhL8gNatW4vJARLagaDn8+fPp3379uTn5zNs2DDS0tJQKBQ1gsCCj96yZUt0dHRISEgQa4RXR9Lz/79IAWAJEWECGDJkCGPHjhULi9+9e5c9e/YAfxYbhz+NUn5+PpWVlbRu3RoLC4sa4wrGSKr9+mKiecxEyPJVqVTk5+fz+uuvk5iYiKura43FgeCcSHUhtYvaOj7r6+tz/vx5CgoKxKCPZnaRoPNZWVmo1Wo6duxI06ZNa4wt6LpwYkBCe9HV1cXU1JTIyEixidOkSZNYv349SqVSrBEYEBBARUUF06dPF2s/ayLJXHsR9Hbq1KlMnDgRtVrNypUrWblypdjUTzOLRNB3wXYLjUeqI8lcuxGCQ02aNOGHH37A2tq6yiIyISGhSk1fobZ/SUkJSqWSLl26YG1t/cjgop6e3vN5EIl/DM1s7up+3IgRI0hMTMTGxoaWLVtW+ZzwDjyqk7xE3aFQKKo07+rRowedO3fmwIEDYvPH2lCr1WIWqGZJt+o0aNCABg0a/LNfWuIfo3r2/2+//UZlZSWTJk1i48aNKJVKdu3aRefOnQkJCaF9+/Zcu3aNqKioGvr8Xyln+V9EOIlpYWHBjBkzaNWqFbm5ufTp04fk5ORaT30ANGzYUIzJ/FXTTknP/38iBYAlRIT6MQAzZsxg4sSJoqO/adMmPvnkE+DPxaFCoSAvL4+FCxcCSEfDtJC/28mzsrJSPBoMDxeIgBj8PX36ND4+Phw7duyRx8MltBvB8ROafO3bt49ff/1VvCaU+sjNzWXFihUAeHt7P9LpkHgxsLS0pFu3bhQXF3P58mWmT58uLhr27NmDp6cnHh4evPXWW7Rt2xYPDw969epV119b4inQzBKbO3cu48aNQ61Ws2rVKr777jtxY1fQZaVSSV5eHuHh4RgbG9OiRQvg788jEs8fQaZdunThu+++E2u+ZmRk8O2334pBYAAdHR0KCwv59NNPKS8vp23btnX2vSX+eQS/Xmi8LGR3C37cqVOn6NChA+Hh4bVmlEloL7WVbhPWbWfOnKkS+CktLUUul3P58mXOnDmDiYmJlMlfx/yduVWw3+7u7jg5OXHlyhXeeOMN0Y/bt28fHTt2pKSkBGNjY3bt2sXrr7/OunXrHtkMVkI7EWTt4+PD1KlTadmyJTmYoapwAAAgAElEQVQ5OfTt25fw8HCxhBv8ad9zc3MpKCjA3d2dxo0b18n3ltBepCZwElUQAj5yuZyuXbtSWVnJ1atXKSws5OzZs6Snp3Pr1i1KSkqIj49nwoQJXLp0icDAQL766iupM3AdU1tR+Oq/exqEhn67du0iJSWFcePGYWVlxauvvsqZM2fw8fFh7969KJVKysvLq9QVlHixkMvlnDx5kpSUFAoKCpDL5bi4uHDnzh0yMjIYOnQoqamp9OzZk3nz5okZwlLmwItLSkoKhw4d4siRI4SHh4uLBh8fH7Hhm4ODAy4uLkyfPl3Uc8nOvzgIQWC5XE5gYCDFxcWcPn2a1NRUwsPDuXHjBpaWlhQWFpKZmcmoUaNIS0ujT58+TJ48udamrhIvDgqFAkdHR9zd3Tlx4gQFBQVcvnyZ3377DQMDA7KyskhOTmb8+PEkJSXRvXt3vvvuO0nH/0MI+rtlyxauXr3KxIkTMTAwYMiQIZIf9x8kOjqaEydOEBsbS4sWLbCyskJPTw+FQkFOTg6DBw8mMzOTfv36MXXqVMm+1xGa/vPBgwexsbERN2meFCHAf+zYMSIiIrh06ZLoxwn1n1UqFeXl5ejp6dGrVy8UCkWVJqASLw4qlYrGjRtja2tLeno66enpHD58mPLyclQqFXZ2digUCrKzsxk5ciRZWVn079+/1iZwEv+/kRUWFkpbQP+PeNKAjZD1B7Bt2zYOHjzIzp07xesqlQq1Wk15eTmBgYHijqPm5ySeL5qynTt3LiYmJkyaNOkfGXv48OGEhYXx2WefcfDgQU6dOiUtGrSAyspKcaH+4MEDMYv3Wfnxxx/57LPPxMxvX19f8vLyKCgo4ObNm3Tu3Jnt27dLul6HVLfhzyIHYYzk5GT+97//cerUKRQKBfv37xcXDbUF+DXfN4m652k2YDTfk1WrVhESEsLp06eBh7X/hWPhDx48ICAggC1btog1IyWZ/ze4cuUK7777LomJieTk5NS43qNHDzZs2CDZdy3hn9xgLSsro1+/fpw/f55vv/2WrVu3EhkZKflx/0GKioro378/Z8+exczMjG7duuHo6Mjt27fZt28f165dw8/Pj9DQUMnG1xGauj158mQ2bNjAmjVreOWVV55pvOjoaIKCgigsLOTAgQN06NBBkqsWoin3vzPHlpSUEBsby6xZs4iMjESlUqFUKgkICOD27dukpKSQk5NDly5dCAkJQalUSgk7ElWQMoD/Y2gqeFlZGdeuXSM/Px8jIyMUCoVY5uFxRkCo9SuTyWjVqhW+vr54eHhQVFSEvr4+SqUSPz8/Xn/9debNmyc5j3WMptynTp3KsmXLUKlU9OvX728ZfuFz+/btIykpibi4OJKTk6VFgxYgNGYDmD17NvHx8bRs2fKZ6jMKuu7l5YWuri5ZWVncunWLrKwsbt68iaWlJf369WP16tWSzOsQTT3+8ccfadKkCcbGxmKG55MijGFiYoKpqSnXr19n2bJl+Pr6Vlk0VLcZkvP4/Kluu8vKypDL5U+dmauZCezh4YG7uztt2rQhMzMTpVLJ7du38fT0ZODAgSxevFjUcykI+N+goqICc3NzXn75Zdq0aYOpqSlyuRy5XE6vXr0YPnw4c+fOlex7HVLbCa5/kh07dpCenk5MTAyJiYmSH6cl1Lap+6wnLyorK9HR0cHFxYXExETS09NJTEwkIiKCqKgo7t27R9++fdm0aZNk4+sITXlPmzaN4OBgAK5evUpAQAAmJiZPPV6DBg3w8/Nj1KhReHl5ScFfLURT7r///jsnT57E0NCw1t5Jj0OpVGJra8uQIUMoLi6mvLycy5cvk5yczJUrVzA2NqZPnz6sWbNG3NCV3gcJTaQM4P8QmsZl3bp1hIeH8+uvv1JRUcFLL71EYGAgw4YNe+YxAbHOTGFhYZWi4VK2SN2hKaP333+fVatWidd+/vlnXn311b/9N6ZOncratWuBh/Vf9+7dKx4rkhYNzx9NmU+fPp1ffvkFZ2dn9u3bh7W19TONqanD586dIyEhgZSUFBo0aICnpyft2rVDLpdLMtcC/ve//7Fu3To6dOjAunXrsLKyemYbXFpayoMHDzAzM5MWDVqGpp4fOnSI2NhY9u3bh4mJCS+99BKvvPLKU9d2qz6nFxQUiEeD7e3txQ0kaU6vO/6pLKHqVNdvtVpNUVFRlZMjktzrBk2ZR0VFcfXqVRISEnB0dMTJyQl3d3fx3mex05WVlYwePZq9e/cCkh+nLWjKPSwsjPj4eH777TdMTEwYNmwY3bt3f+aTXbdu3WLWrFlkZmaSkpJCt27d8PLyYsSIESgUCknudYCmvD/44ANWrlyJoaEhZWVlmJmZsWLFCrp27frUdlhzXCnTU/vQlMmCBQtYsmQJlZWVTJgwgUmTJj110B+qztW5ublER0dz9+5dysvLcXNzw8XFRVqzSTwSKQD8H0HTuMyaNYslS5aI2R3l5eUAtGjRgo8++oj+/fv/I39Hou6pzZmwsrKiVatWhIeH89JLL/Hjjz9Sv379v/V3Fi9ezOzZs6VFgxZQm8wbNGjA6tWr8fHx+VtjP25hKQUI656dO3fyxhtviM16OnbsyNq1a/9WEFhC+9DU86+//prly5dz//79Kk19hg4dyrRp03B0dPzbc7O0gNQONP/t33nnHczNzZk1a9ZT14b8KwQ7LpT6EbqMSzKvG6rrenBwMLm5uVXumThxIv7+/vTs2RN4tkD9jBkzWLFiheTHaQnVy7YtX76cBw8eVLHxs2fP5t133/1bY8PDjT5LS0vx/yVf4flTm+9ev359li9fzvz584mIiOCll14iJCSkjr+pxD+Jptw/+eQTli1bhlwuZ8aMGfTs2RNXV9d/ZOzakNZsEo9CKgHxH0EwAJ9++ik//vgjurq6fP/997z11lv4+/tz9epV4uPjMTc3p0ePHo8dr3qZCOH4gLRA0B5qcybq1avH+vXrcXJyYteuXdy+fZsePXpga2v7RKU/qt8jNArw9vbG1taWr7/+Wlo01CF/JXOhduvT6qjmZ2QyWZUmX//2kVSJpyc5OZldu3bRrFkzdHV1SUhI4Pz58wQGBmJkZPRER70eZd8ltANNvZsxYwaLFi2itLSU8ePH061bN9q0acO5c+eIi4vD3NwcPz+/p7bt1WUu6bl2IPzbf/zxx6xZs4azZ8+io6ODp6fnMwVsapsTNO39o8q9SDw/NP33hQsXUlJSwoABA2jatCk2NjZcuXKFc+fOcfr0aR48eICvr2+Vki7VqT5vC3P6Sy+9hEqlYtGiRZIfV8doyuijjz5i8eLFlJWVMX78eDp06ECjRo1ITEwkPDycDh06YG9v/1RjVvfldHV1q9T1l+b758ujfPfg4GD8/PwwNDTk6NGjZGdn07Jlyyfa1NW07Xfu3EFXV/e5PIvE0yHIaOHChXz//fcYGRmxfft2hg4dSr169YBn33R/3GekeV3iUUgB4P8Qq1ev5ssvv0SlUrFnzx569+5NkyZNcHV1xdbWlu3bt3Pt2jVGjBiBUql8pAOgWVv03Llz2Nra/qWzKfH8eVwg0N7enmPHjpGens6VK1fo27fvY50DTbnHxMRgY2ODQqGguLgYpVJJmzZtpOMkdYjmTq6mzDds2ICnp6eYNfI0E76mzCMjI2nUqFEVXZecB+2jQYMGbNq0CSsrKwYOHEhKSgqJiYlPHAR+nMwl6h5B777//nsWLlyIqakpO3bs4I033sDPz4+AgADkcjknT54kMzOTYcOGoaOj80QyF2y7JHPtZd26dXzxxRdig6bff//9mYLAmnI/ffo0xcXFz1RvUOLfZ9OmTXz22WcYGBiwd+9eJk6cSFBQEMOHD8fMzIz8/HzS0tI4ceIEN2/epHv37rXqsKafkJKSgqWlJXK5XPTjfH19peP/WoBmUGjBggUYGxsTGhrKmDFj6NatG/369SM/P5/o6GhsbW3x9/f/S3utKfe8vDwMDQ1rDfhKPt3z51HrtQ0bNuDt7S3ed+TIEbKzs6lXrx4BAQFPHNxbs2YNu3btwsHBAXNz83/vQSSemaSkJD799FNu377Njh076NSpk3gC56/qfdfWiFkK+kv8E0gB4P8IZWVlLFiwgPT0dJYtW0aPHj2orKwUnQJbW1tCQ0Pp0KEDgwcPrnEsQHOnWDAuq1ev5vXXX6eoqIiuXbtKC0Ut4lHOhJeXF6WlpeJu/8mTJykrK8PX1/exWcDC71etWsXo0aORyWT4+vrWWCRI70HdUL3Os2bwt6KiQjzKe//+fW7dusWvv/5Kbm4uOTk52NnZieNUz/iFh7o+atQoCgoKCAwMlGSsxVRWVrJ7925KS0v5+OOPkclkpKWlkZSU9ERBYEnmLwY7d+7kk08+obKyku3bt9OxY0cqKiqAhzJ88OABO3bsoGHDhkyaNKmG/Kpng0FN2y7JXPuIiYlh8eLFZGVlMXHiRNq1a8e5c+eeKQisGWR65513UKvVNGnSpMpRcAntYPXq1Vy4cIEFCxbw8ssvi6evANq3b4+DgwMAiYmJnDt3jpycHHr27FkjCKyp62+++SaWlpa0adNG8uO0kKSkJL788ksKCgrYunUrfn5+oi8nl8spKysjNDSUXr164e3t/UQ2fs2aNaxYsQI7OzsaNmwoBXy1AEEGU6ZMYe3atTUSN2QyGVZWVqjVao4ePcr58+fx8fGhSZMmjx376NGjjBs3jvPnz1NRUYGrqyvGxsb/9iNJPCUREREEBwczduxYxo0bV2XNlpOTQ3x8PNu2bSM+Pp67d+9iYmKCvr4+Mpms1jWbFPSX+LtI27//Ea5cuUJYWBhyuRx7e3vRMRCyAvPz86msrCQjI4NPPvmEyMhIHB0dcXBw4KOPPkKpVFZxJi5fvsyaNWuAh/VfmzRpwhtvvFFnzydRk1mzZok1pDZs2ED79u1Rq9VirUAvLy90dHTIyMggJCREbOL1V6SlpTF37lxKS0v56quvsLOzY+jQoc/jcSSegFWrVrFq1SqUSiXvvfdeleCvUqkkNTWVBQsWEBUVRXJyMjo6OpSWlvLaa6/Rs2dP+vTpI9Z+FN6Fq1evsmHDBgBWrlxJ06ZNefvtt+vyMSUegVqtxsDAgICAABYuXEhxcTETJ05ELpezfv16IiIiGD16tFgTWKCoqAh9fX3x/yWZazdXrlxh1apVlJWVsWLFCjp27CguFoQgsDCnN2jQgIyMDE6fPo2JiQmtW7fGzs5OnP8FPZdsu/ZTUVHBkSNH+OOPP3B3d6dv3754e3tjaGjI/PnzmTt3LvCwCeST1gQuLy9n48aNlJaW8tNPP9G0aVOcnJz+zceQeAoqKip48OABR44cARAztFUqFfBnkM/f3x9ra2vMzMz45ZdfCA4OxtDQkK+++gqFQlFF12NjY5k5cybFxcXMmDGDevXqPVHpN4nnS1paGsnJybRv354OHTpQVlYmluaAh5u9DRs25ObNm4SGhpKQkEDr1q1p1aoVzZo1q2Hjw8PDee+994CHej9r1ixatmxZZ88n8ScXLlxg/fr1GBkZsWnTJtq1aycG+gUdDwwMJCQkhIsXLxIREYG/v/9ja7i6u7vTo0cPDh06REJCAjY2Ns/xqSQehyDbc+fOAVQp5aJUKklISOC9994jLS2N/Px88Vq/fv3o06cPr776ao0129GjR3nvvfeQyWRUVFQwdepUbG1tn+tzSbz4SBnA/xHy8vJYv349+vr6jBw5EhsbGyoqKsRd/w8//JCIiAjy8/OJi4vjypUrJCYmcvLkSaKioujevTv6+vpiJoGZmRn16tUjMTERR0dH5s2bJzUM0CIKCwvZtGkTarWaX375BU9PzxpHRaysrNDT0+Po0aOkp6fTrl27x3aMt7CwoH79+pw4cQJ3d3e++uorKVNEi/Dw8CAmJobk5GTOnTuHq6srjo6OyOVyYmNjGTRoEJGRkZSWlmJhYYGFhQWFhYXExsaSkpKCXC7H3d29yntiYmKCra0tiYmJODg48P3330u6rqUIcsvOzmb//v24u7vj6+srOpVCJnBUVJRY9mXWrFmkpKTQvHlz9PT0AEnm2k5xcTE7duzA2dmZ9957Dz09PbGmo1KppLS0lC+//JKMjAyUSiVLlixh586dhIaGEhkZSUFBAb6+vshkMnFekGy79lNRUUFUVBTh4eFMmDCBQYMGAdChQwcqKiqIjIx8qkxgYdNg+PDh7NixA2NjY3766SdJ7lqEXC5HV1eX/fv3k5OTw4ABA3BychJP5WnqsLW1NY0bN6ayspKYmBjOnDmDubk57du3rzKn169fHx0dHVHXZ86cKclcixDkuXv3bn7//Xfq16/P6NGjUSqVlJSUiJs7n3/+OefPnyc1NZUtW7YQGRnJrl27iIuLo7y8XPTlhPHs7e1JS0sjISEBa2trpkyZImUAawk2Nja4ubkxevToGus14ae5uTkxMTFERUWRlpZGUFAQJiYmfzmugYEBXbp0QaFQsHTp0hrJXBJ1iyCH8PBwTp8+zYABA8RyigkJCfTp04fU1FQcHBxo0KABjRs35vr161y6dImYmBhUKhUeHh411mzJycmkpqaip6fH+PHjJXlLPDVSAPg/gomJCXv37uXq1aucPn2awMBAlEold+/e5d1332Xbtm0YGxsza9YsxowZQ9++fbG0tCQ1NZXExEQSEhLEnSYhCOzs7IyLiwvTp09HqVRWKRMhUbfo6enRoUMHgoKCaNWq1V9O+L///jt5eXm4urqKWcK13SscM3F1daV169Z8/PHHKJVKqU6kliDo36BBg0hISODixYscPnwYX19fysrK6NOnD7m5uQwYMIC5c+fyv//9j6FDh9KiRQtiYmJIT08nNzeXRo0aicdJhXehWbNmuLm5MW3aNEnX65DquvkoXdXR0WHjxo2UlZUxaNAgTE1NadasGZWVlWIQ+Ny5cxw/fpw1a9aIdWINDQ0lmb8AGBkZ4enpiZeXF82aNQMe2mch4Dd27FjCwsLQ09MjKCiIwMBAunXrRnp6OmlpaVy8eBGZTIa3t3eVI4SSbdduFAoFjRs35qWXXmLYsGHAQ7krlUo6depEeXn5UwWBhU0DPT09Ro8ezTvvvCPJXUs5cOAAly5d4ubNmwQFBaFSqWqtD2lpaUm9evXIy8sjJSWF27dv07ZtW7GZkCBbb29vmjVrJtaSlmSuPWjW8Dxw4AD37t3DyMiIdu3aiUk7kydPJjQ0FAMDA/r160fnzp1p0aIFKSkpZGZmcvHiRUxNTWndunWVhm/9+vVDLpezaNEisYa4FByqWzTX1HZ2drX6dcLv3N3dOXz4MJcvX0ZXV5eOHTs+Vm81g8CapWMktIfTp08TERGBQqEgKCiIu3fv8uabb5KamsqoUaNYt24dQ4cOZdSoUVhZWZGZmUlmZiZpaWnY2tri7OwsjiUF/SX+CaQA8H8AYYEgk8lITEwkLS2NLVu2EBoaytq1a4mIiBC7Tr7yyis0a9aM5s2b4+HhQb169YiMjCQpKQkzMzPat29f5UiKZoMgqWGE9qBWqzE2NsbMzAx4dGMHW1tbUlJSiIqK4vz58/Tu3fuR9f80AwUODg6i3CVn4vmjOZkLMhEa8MnlcgYMGEBiYiIXL15k7969REZGkpKSwujRo1m+fDmNGjXC2NgYExMT2rRpg4ODA7/99huZmZkYGxsTGBgIUCV7pGHDhpKu1yGaMk9NTcXCwuKReq2vr8+ePXu4ffs2I0eORKFQYGJiQtOmTZHL5VU29lQqFTt37qRp06aAJPMXBSsrKxo2bAhUbeb19ttvs337duzs7Ni5cyevvfYaPj4+eHt74+bmRkZGBsnJyZSUlNC1a1eMjY0l2/4CYWxsLOqqpozkcvkTBYGrLwQFWQtNAiW5axeCvMzNzTl69CjZ2dlYWVnh6ur6yMCPjY0Nenp6HD9+nNTUVFxdXWnbti1AlSQOFxcXSeZajFqtJjw8nKysLJKTk4mMjCQyMpIffviB/fv3Y2xszObNmxk3bhwBAQG8/PLLNGnShNTUVNLT0yktLaVz584YGRlV8Q87duwoBgOlef35oml/i4uLUSgUVdbUUPt6TdMvi4+PJzY2FpVKVWMz6HFIeq6dGBgYsGfPHu7cuUPr1q0BmDdvHj169ODnn39GqVSiUqlQKBS0a9cOS0tLLly4QGZmJmZmZvTo0aPKOyAF/SX+LlIA+D+AYBDs7OwwNDQkOzubrKwsCgoKMDExobKykjlz5tCnTx+xJrBMJsPAwAALCwsiIiLIzs6mcePGYp2w6hONlDlQt2ga/id1BIQFf9OmTfn999/Jzc3F1tYWb2/vxzaIEpDk/vypLl+hzpNcLn9kEPjatWv06NGDlStXAn9mHAjvgJOTEzo6Ovz6668kJSXRs2dPMWNIknndoynzadOmsWvXLvz8/DAyMqpxb0VFBbq6ukRGRnLixAn8/f1p3Lgx5eXlmJub4+TkxP79+7lx4wYArVq14qOPPgKocqxYE0nm2o0gr7KyMnbu3Imenh7r16/HxcWFiooKMUuwcePGmJqaEhoaytWrV/Hy8qJFixZVxhCQZK79VJfR44LAgh1JTk4mIyNDrAuoOY4k97pFmJOrZ/cqlUrOnTvHpUuXKCoqwtnZuda6jsLnnJycuHXrFqdOneLKlSu88sorGBgYALW/NxLPn8f56paWljRr1oyzZ89y9epVEhMTOX/+PFlZWchkMjZs2EDXrl3F+2UyGS4uLqjVao4cOUJ6ejpdunQRT3RVl7MUFHq+aMp79+7dhIaGUl5ejp2d3RMF4mUyGTo6OlhYWLBx40auXLmCqampeJJHQvupbt8FysvLOXToECkpKWIvj9DQUCZOnEjbtm1rrNlcXFwoKyvj+PHjxMfH06dPH6ytrWv9m5KeSzwLklfwAqBWq5/oPmtra8aOHcvBgwfZuXMnhw8fZuXKlajVarFGZPXFf9OmTfHw8AAgJSWF4uJiscmMRN2iKXeZTEZubi7379+nsLCwxvXaEJxBGxsbHBwcKCsrIywsDEBcLEpoF5pOw9tvvy1m6mpO8MJxfYDg4GACAwPR1dUVg3zVs8YEffbx8cHS0hJdXV2xwYxE3aMp8/fff5/Vq1dz9OhRLl68WOv9gmz9/f0ByM3NBRAXGN988w1xcXHI5XLMzc2JjY2lT58+5OXliUdCJeqWx9ne2q6r1WpUKhUrVqxg27ZtODs7i2UhNBu+9u7du0rpBwnt4Z+YcxUKBR9//LHY7Gnu3LksWrSIoqIi8RRYYGAg3bt3JzY29m//PYm/R3WZP3jwAECcw4X5uVGjRkydOhWZTEZ4eDjLly8nOTm5xnjChjDAgAEDsLS0pKioSAoCaBma8/pvv/3GrVu3alwH6NKlC2vXrmX+/PlMnDiROXPm8PHHH9O8eXNcXV2rbNoK9nzs2LHiuu3atWvP8akkHoWmvOfNm8ebb77JwoULOXLkSJXmXk8yTocOHRg3bhwymYzjx4+LPp6E9lHdvt+9exeo3b5Pnz4dgM2bN7Ns2TKAGjWeNddsr732Go6OjqhUKkpKSv69h5D4f4kUANZyNCcVIaPrr1AoFBgbG9OlSxc8PT05ceIE9+7do0GDBgBVgrulpaUAFBQUAA+7ierp6UmOpBagKfeQkBD+97//4evrS7du3QgKCiI8PFzcZXzcOMbGxkybNg0DAwMiIyP58ccfgUeXjZCoGzRl/sEHH7B582ZiYmJITU2tca9mEHjr1q189tln2NnZATV3g4X/Ly8v586dO9y5c0dchErULdVlvmrVKvT19amsrOS7776r1fEXdF44Jr5nzx7x2qRJk1i7di1KpZLg4GCmTJmCjY0NERER9OvXj8LCQikbrI7RlPnvv//OqlWr+OCDD/jwww/Zt28fqampNepAw58lenR1dbG2tq5SFgL+3PDLzMwkIyNDDBhLaAe1+XLPGhCuLQj8yy+/EBMTw7Bhw7h9+zZubm60bNnyn/nyEs+EpswPHjzIV199Rc+ePRkyZAgzZszg+vXr4hFeeBgM/OmnnwAIDQ3l+++/JzExUXxPhJ/CnK6vr09JSQnXr18nJyfneT+exF8gyH3KlCkMHDiQdevWickbwnVBnm5ubowcOZK5c+cyefJkIiIiuHv3LjY2NlUyR4VSAjk5OaJvIDSMk6hbBHl//PHHfPPNN1RUVLBkyRLGjRtXayb/48Zp27YtarWaY8eOERMT8698Z4m/R/WM748//piuXbsSFBTEzJkzycnJqWLf+/fvz/vvvw/A+fPnAdi2bZu4gVfdvpeVlXH//n0ePHggBpYlJP4ppOJAWkz1bMC8vDy++uqrKsXAq1N9cV9cXAzAihUrmDNnjlgSQq1Wo6Ojw9WrVzl16hSGhoZ4enrW+LsSzx/Nf//PP/+cH374Qbx27949SktLCQoKYuvWrbz00kt/OZbgZDo5OdGzZ09CQ0OJjo6mpKQEXV1dSdZaQvVA4MqVK6lfvz4bNmzA0dGx1s8IjV0UCgVvvfVWjXEEhHtSU1OprKykR48euLm5/bsPJPFYapO5tbU1gwYNYtOmTeTk5HD58mXq169fJatb+Iyvry/29vZidsm7777Lxo0bUSqV7N27Fx8fH7Kysrh//z7ff/89MpkMY2PjunlYCaCqzL/88kt+/vnnKo79hg0b0NXV5YsvvsDPz4/GjRtXOU6oOb8/Ss8vX77M/fv38fHxoUOHDs/nwST+kupZ/hEREQQHB+Ps7PzMc7AQBFar1SxcuJDZs2djYWHBzZs38fX1Zffu3VXmCInni6Zcv/rqK5YuXUpRUREA8fHxAPz666/s3bsXOzs7UU6vvvoqd+/eZdq0aYSEhFBcXMyYMWPw9fVFV1eXiooK1Go1SqWSO3fuoFar8fLyEk/5SWgPX3/9NevXrwce2nuZTMbIkSP/sndHeXk5hYWFlPAk3jgAACAASURBVJaWEhUVhYeHh3if8I7k5uZy7949nJycaNeu3fN7IIm/5Oeff2b58uWYmJiwefNmfH19xWvl5eViML+ysvKxG/HDhw/nwIED7N+/n/nz5+Pu7i6WbZOoezTt+xdffMEPP/wgZuhnZGRw7NgxLl68SHBwcBW5DR8+nBs3brBu3ToAEhISWLduHaNGjUJPT4/Kykqxr9OtW7e4f/8+bdu2xcXF5fk/pMR/GqkGsBYjGJdPP/2U1atXk56ezt27d3F2dn5kI6/q3Lt3j7CwMDFI0Lx5cwwMDJDL5WRlZTFkyBAyMjLo27cv77//fo2OwxLPF81J5dNPP2Xx4sXI5XI+//xz3njjDfz8/CgqKiIzM5M//viD/v371zhCUh2ZTIauri5FRUXs37+fxMRE3N3dcXZ2lmStBdQWCKxXrx4bNmzA09PzLwME1Z3IRwWF8vLymDhxIjdv3mTkyJH4+Pj8Ow8j8UQ8Subbt29n+PDhrF27lqysLO7cucOAAQNqyFk4yREWFkZERATh4eHs378fpVLJvn378PHxoaKiAjMzM5o2bYqzszMLFy6UusHXMYLMZ86cyaJFi6isrGTcuHG8/PLLKJVKFAoFWVlZHDp0iDt37mBsbEyTJk1qrSmniaaejxw5ktzcXF577TW6d+8u2XgtQDM77Oeff+bWrVskJCTg6emJpaXlMweB5XI5nTt3Jicnh4sXL1JUVISPjw979uwRT4lITaCeP5ry/OSTT1i0aBHl5eVMmjSJ/v3706RJE27fvk1mZibZ2dn06NGjSiZn27ZtsbW15eDBgyQnJ5OSkkJOTo54Sk8ul4u6fv36dQYOHPjYZACJ58/ly5c5fPgw1tbW3L17l/DwcCwsLGjevDl6eno17ler1RQXF7N7924uXbqEUqnEy8sLfX194KG+37hxg2HDhnH9+nUGDBjAkCFDJBuvBeTl5TFv3jyuXbvGpk2b8Pf3F4N5Qv8Ogcf1cxF8tJKSEk6cOEFFRQWdO3cWm8JK1D2ac/qSJUsA+PDDD3nllVdo0qQJBQUFxMfHY21tjbe3d5Vmn/b29lRWVnLhwgXu3LlDXl4excXFuLq6is1a8/LyeP3118nKymLAgAH06dNH0nOJfxQpAKzlbNmyhdmzZ4vHA+Lj47l3794TB4FVKhUREREkJSURGxvL0aNHycnJYdu2bXzxxRekp6fj5+fHhg0bUCgUUnCgjhEM/KJFi/j2228xNDRk165dDBw4EGdnZ9zc3DA3N+fEiROUl5czePDgx74HwsTj6urKpUuXSEpK4vbt23Tr1k1sGiJRNzwqC3Tjxo14enpWadr4uM/X9ju5XE5ubi7Dhg0jMTGR3r17M3fu3BpdiSWeH38V8Bfq+pmYmHDs2DFyc3NxdXWladOmNeQql8t58OABx44d48qVK+jq6rJ37168vb3FRYdMJsPU1BR3d3epG7yWsGHDBj7//HMMDAzYvXs3o0ePxtfXl/79+9O6dWtycnJIS0sjLi6O7OxsjI2NcXJyqhEErv4+CIGBhIQEevTowfz58yU91yK2bNnCnDlzgIfNn5KSkoiPj8fLy+uZgsBCs5ikpCS+++47bt++jZeXF3v37kWlUknB3zpEkOP8+fOZP38+RkZGhIaGMmrUKDw9PfH29ubBgwdERESgUqkYMmRIjYBgmzZtcHFx4eTJk6SnpxMREcHBgweJiopi//79fPnll6SlpdGtWzeWLFki6boWcuPGDUJCQhg+fDiurq5cuHCB48ePPzIILDQBE+by6Ohorly5Ql5eHnfv3iUyMpJJkyaRmppKly5d+Pnnn1EoFKItkKg7YmJi+Oabb+jSpQvTpk0DEDP1s7Ky2L17N8uXL2fv3r1ERETQoEEDzM3Na/XHNPu37N27l/T0dExMTOjWrdtzfSaJv2bp0qV8++236Ovrs2fPHoYOHYqHhwdeXl5UVlZy4sQJLC0t6devXxX/zdrampYtW2JqakpERAS5ublER0fz22+/kZOTw6FDh5g5cybJycl06dKF5cuXV2nyKiHxTyAFgLWYS5cusXTpUtLT03nvvffo378/R48eJS4u7omDwGZmZrRp04Zz585RUFBAZmYmJ0+eJCYmhuLiYvr16yceG5YWDNpBWFgYs2fPpqioiJCQEDp16iSW7RAcg7Vr1/LgwQNGjhyJlZVVjTE0JwrNCSM3N5ejR4/i7OzMiBEjpMmkjhH+/d955x2Cg4OxtrZm586duLu7i9kDCoWCwsJCcnJy2L9/P9HR0dy+fRsAc3PzR46Zk5PDmTNnGDFiBElJSfj5+bF582YpC7SOEeQzbdo0Vq1aVSXbW3Mhd+TIEa5fv469vT1+fn616mq9evUoKioiISGB7du34+vrWyX4Wx1J5nWHWq1GrVazbNky4uPjWbx4MT179hRtu0qlws7ODkdHR5KTk8nKyuLy5ctkZ2dTv359HBwcqshUs57s2bNnGT58OElJSfj7+7N161ZJz7WIxMRElixZwuXLl3nttdfo2rUrV69eJTY29pmDwDKZjNjYWEaNGkVmZia+vr5S8FeL2LVrF59++illZWXs2LGDTp06UVFRgUwmQ19fH7VaTUhICHp6euLxXwGh8Vfz5s3p0KEDurq65Obmkp6eTlxcHHFxcajVavr27UtwcLCk61qKpaUl27Zto7i4mAULFpCbm0t8fHytQWDNWu/NmzdHoVBw8uRJkpKSCA8PZ8uWLYSFhVFQUED37t2r2HhpU7fuOX78OGFhYXTt2pVevXqJx/jj4+MZMWIEmzdvFu19VFQU27dvR61WU69ePSwsLGqMV1lZiZGRERUVFZSXl7N06VJJzlrE1atXmTdvHtnZ2QQHB9O1a9cq9h1g06ZNdO3aVQzca87tpqamdOrUCRcXF/F0d0pKCr///junT5+mqKiIvn37sn79esm+S/wrSB6iFnP8+HGOHj1K8+bN8fPzo3PnzpiZmfHWW2+xZcsWAKZOnfqXNYHVajXu7u6sXLmSQ4cOceTIEe7du4ebmxs+Pj4MHToUhUIhLRi0hMLCQvbt28fNmzdZsGABnTt3FgO/wrHvnJwcVCoVNjY2XL9+neDgYGQyGZ07d8bd3Z369es/chE5cOBAoqOjWbx48WOPFUs8H8LDw9m0aRMA/v7+tGrVCnjYpFFPT4/k5GQ+//xz4uLiuHLlCvCw+YuBgQHz58+nZ8+e6OrqVhnzxIkT/PDDD5w+fZoHDx4wcOBAVqxYIW30aAkLFixg9erVWFlZsXHjRtq3b19FF93c3Hj99df58ssvWbx4MS+99JJYo10TOzs73nnnHSZPnkyTJk2eqLacRN0gk8nIycnh+PHjyOVyHBwcxN9rbta1atWKTp06cerUKdRqNZGRkVhaWtKwYcMaTb2OHTvGokWLOHPmDEVFRQwdOpQlS5ZIeq5FCDI8duwYTZs2ZdSoUbRs2RIjIyN++eUX/vjjD959911++OEHnJycnnhOLikpYcKECaSlpeHv78+OHTskuWsJ2dnZbN26lQcPHrBs2TI6deqEWq0WT9kpFApu3LiBUqnEzs6O69evs3XrVgwMDOjUqZPY4BPA09MTV1dXJk+eTFhYGPfu3cPIyIgWLVrg5+eHXC6XZK6lmJqa4uDgQGpqKjo6OixcuJDKykp27NjB7NmzARgxYgRmZmZVdF6hUDBx4kTq16/PvHnzKCsro6CgAD8/Pzp06MD06dOldZuWINhroSmzkJyhVCpJTEykf//+FBQU0KlTJ1q2bEl+fj7JycniJnBeXh4TJkwQ/QEBwY/r06cP48ePRy6XU1ZWJjV21RKuXLlCVFQUjRo1EutwC+t0pVKJTCajUaNGFBQUsHr1apKSkjA3N2fAgAE4OTmJwfx+/frRunVrMUtcrVZjbW1N69at6dmzp2TfJf41pAxgLaagoIDt27czZswYRowYAYCrqyv29vbs37//iTKBhSCftbU1Pj4+DBkyhKCgIIKCgmjdunUVgyVR95SUlBATE4ONjQ1jxowRHUPNCeDDDz/k4sWLlJWVceDAAU6cOMG5c+fYsWMHd+7cwdrautaus5WVlRgaGtKnTx+xM6m0o1z36OnpYWNjQ1xcHNHR0RQUFNCxY0f09fW5cOECgwYNIjo6Gn19fWxsbLCwsBAXkHv27MHKyko84i9QUlLC3r17qV+/PmPHjuWLL76QggNaRGFhIXl5eSxevLhK8FdzU8bKyorIyEgKCgpo0qSJWNe3eoDX3Nz8L5vKSGgPFRUVbNy4kfv379O9e3ecnZ2rBO2FTGALCwvWrFmDo6MjN2/eJDU1lcaNG1epJQd/6rmzszMTJ07kk08+kfRcy5DJZPzxxx+cOXOGiRMnEhQUhI6ODk5OTujo6JCenv5MmcBKpRI3Nzeys7PZtm2blPmrRRQUFBAWFkbbtm0ZP348hoaGVfy4srIy5syZQ0pKCjKZjPXr17Nnzx7CwsLYuXMnhoaGNGzYEENDQ+ChrM3MzGjfvj2+vr60b98ee3t7ZDKZeEpIQrsQ5uqsrCzCwsJo164drq6udO7cmWvXromZwObm5ri5uaFSqfjyyy+5du0abm5u6Orq4u7uTu/evRkxYgQDBw7krbfeIiAgQFq3aRGCnS4tLWXLli2o1Wq6dOmCrq4uM2fO5Pz584wZM4bg4GC6d+9O//79efXVV0lOTiYuLo6UlBRsbW1p3759raU8TExMRL9Q0nPt4dKlS4SEhFC/fn0GDBggrtUFX+7TTz/l9OnTXL58mYMHD3L27FkiIiLYvXu3aN+NjIyAhye1mzRpQmBgID169KBTp05i2S/Jvkv8W0gBYC2mWbNm9OvXj4EDBwKIAbtnCQIDoiGpXndKyhjTHvT09GjWrBleXl44OTmJvxdkNG7cOPbu3YupqSnTpk3j1VdfZdy4cahUKi5cuEBcXBxKpRJvb+9a64tpIk0q2oGRkREODg4YGhpy/vx5/vjjD8rLy6lfvz6vv/46165dY9CgQSxdupQpU6bwxhtv4Onpyd27d7l06RK//vorbdq0EbPH1Go1VlZWBAYGEhAQQK9evaQFg5bh5ORE7969a9T2hT/11NzcnLNnz3Lx4kWuXr3K4MGDpZrdLzBqtZq7d+8SEhLCjRs3sLOzo2vXrsjlcnHhJ7wLsbGxbN++nTlz5tCwYUOioqI4deoUPXv2FDtKCxu7vXr14uWXXxazASU91z68vLzw8PBg6NChwMMj/gYGBjg7O6NSqZ4pCFxRUUGjRo0YPHiwlA2oZQgLeh8fHxwdHQGqBHAmTJjA/v37MTY2ZtCgQQQEBBAQEMD9+/dJTk7m3Llz2NnZ0aZNmxobRNXfCWnTTzsRZFZSUsK2bdto164dXl5e6Onp4e/vLwaBw8PDMTMz45dffmHVqlWkp6czYMAAdHV1xaZR5ubmNGzYUGwUqFkOTkI7KC8v58CBA6SmptKqVSs8PT35/PPPsbe3Z/PmzeI9arUaPT09goKCiImJIT4+ngsXLhAUFISpqekjx5f0XLvIyspiy5Yt3Lx5k4YNG+Li4iLOw1OmTCEkJARjY2OGDRtG37598fDwoLS0lLS0NKKiomjYsCHu7u5Vkjok+y7xPJECwFqMUCwcEBd1wkLxSYLAgjHRzC6T0H5MTU1FuWtOCBMnTiQkJIRmzZqxfft2+vTpQ4sWLWjcuDE9e/akuLiYyMhIoqOj6dixY40jRRLai4GBAQ4ODhgbGxMdHc2JEyc4duwYmZmZjBgxguXLl2NhYYFKpUKlUtG4cWP8/f1JS0sjOTmZs2fP8sorr2BsbCy+L4aGhmJtMWnBoD0IOi2U7ajNLguL/pYtWxIWFkZmZiYGBgb4+PhIcnxBkclkGBoaUlRUxLFjxzh9+jTW1tZ4eHjUqNe+Zs0aTp8+zeTJkxk1ahRnzpwRs4B9fHyqBIUMDAzETEFJz7UPYYFnb28P/LkRLwQCmjdvjkqlIiMjg9jYWOLi4vD09MTKyqrGxoDmmNU3cCW5aweCrOzs7MSTWJryGzFiBLt378be3p7t27czbNgwvL298fLyYvDgwZw9e5akpCROnz5NUFCQeLoDpGDAi4ZQB3b16tXcv3+foKAg5HI5+vr6dOrUiezsbDETOCEhAZlMxoYNG8RNg0fJW3oPtA8zMzPy8/OJjIzk4MGD6OnpcfHiRby8vOjZsyclJSXo6OiIR/rlcjkdO3bk0KFDYta3m5ubVJbvBcHe3p4bN25w4cIFjh07RmJiIjt27GD16tUcOnQIExMTduzYwahRo/D29sbf358BAwYQFRVFUlISp06don///lXqP0tyl3ieSAHgFwTBuReOBDxJEFjoEH306FEaNWpUIyNUQvvRnBDq16/PxYsXWb16NS1bthQXlkJmeJcuXYiIiODKlSuYmJgQGBgodQd+gdAMAl+4cIHr168TGBjI6tWrgT8bw2jer6Ojw9GjRykuLuaVV16hfv36tY4tvQPaw5PIQrhHqVQSFxdHYmIiBgYGvPLKK1I34BcUzQ7Q2dnZJCcnc/jwYfT19WnUqBE6OjrcunWLpUuXsmDBAlq0aMHIkSOxsLDg+vXrnDhxAgMDAwYOHCgFBl4gqgdmNYP9mkFgpVIpBoHj4+PFIHB5eTkKhYLk5GSCg4OlTSAtpzYd1PxdVlYWaWlpbNu2DRcXlyp+nI6ODu3bt+fIkSPk5OTQokUL2rRpI9n7FxSZTIapqSm///47V65cYcKECWKpFkNDQ7p27cquXbu4f/8+lZWVjB07ljFjxoiflXgxENZZDg4OJCcnk5GRQXR0NDk5OTRq1IigoKAqpzME+61UKtm3bx9Xr17F29sbb29vSe4vAII9dnFx4d69e1y8eJG0tDTS0tK4e/cuarWaH374gcDAQNG+l5eXo6+vj4eHB8eOHSM7OxsrKyt8fX0l+y5RJ0gB4BeQxwWBHR0dsbKyIjk5mT59+hASEkK7du1o3rx5XX91iWekoqICOzs7hg0bho2NTZW6QAqFQnwfDh8+zKVLl2jbti09evQAJEfyRcLAwICmTZtiZGREXl4ekydPxsnJ6ZHHuuvVq8fGjRvJz8+nd+/eUtb3fwxdXV2MjY3ZunUraWlp2Nra0rZtW0mnX0A0S3sYGRlx+/Zt0tLSOH78OMeOHWPjxo1s2rSJnTt3YmRkxIIFC8TGf8bGxqxduxYzMzOxH4CEdlB98fY0i7nHBYHbtWtHvXr1iIuLY8iQIezdu5d69erRtm3bf+txJJ6Av7Ng9/HxYfjw4TRs2LCGHwcPy4Dt2LGD3NxcAgICqpwQkKhbnlbuFRUVyGQyoqKiCA8Px8PDAwcHB9GXe//99wkPDxdLdwm9HpydndHX1/+3HkPiCdFMoLl7926NZssCmqfu1Go1ycnJZGdni9fbtWtHgwYNqnymvLwcXV1dDhw4QEpKitiXR0L7EeRtamrKyy+/jKOjI/7+/vTq1Qs/Pz+Sk5N5++23sbKyEoP9wk9DQ0Mx6O/m5kZAQIBk3yXqBCmN4AVFLpejVqsBGDp0KMuXLwdgy5YtLFu2jLCwMF599VXy8/Np27Yt/8feecZFdaZ9+JpGR4oIKhasgN2oMIIKlth7Yq8xmri2+EZNMdGYrJuyxmyMWXeTqIld7FjQRGyoqKyKhd4RKQIiFqQO837wd06GYkuMjPhcXwTmzJM5+c/T/s997rtfv35V+XEFfxJpcyDlACsfAaTX67l//z63bt0CoHnz5oAwf19EatWqxahRo1i0aBFarRZ4eL7m7OxscnNzAUR+2GqKj48PY8aMAeDXX3/l1q1b8tgveLGQdHv11Vd59913+dvf/gZATEwMV69eJTU1FW9vb37++Wf5AA8eFA3U6/UUFxeTn5+PTqerks8vKIuhIRQREUFJSYls6j4p0vU1atRg/PjxTJ06lUaNGnHmzBkWLFjAwYMHmTRpEikpKXTu3JmJEyf+VbcjeAIMNc/MzJT/9jRIuT4rW8fl5eWRl5cH8NC6HoLnj6Hu0jr7cbqrVCoUCgW9e/cGIC4uTl7LzZo1iw0bNqBWq9m+fTsTJkxAr9ezdOlSfvjhB+7evfsX3o3gcRimU/r000/54osvyMrKeuR7VCoVw4cPZ+zYsXLKn5iYGHbs2MH169fl6woLC1Gr1aSlpXHx4kXq1q1Lq1at/rJ7ETw5paWl8s+P64NS/3/99dd58803mTRpEqdOneLmzZtyrQbD9soXca5du/az/OgCwVMhKka8wEiRwEqlktGjR6PX65kxYwYbNmzA39+fO3fu4OXlhb+/v6gMXk2ozNCV8gHGx8dz9epVmjZtipeXF/DnIlUEVYejoyO9evWSD3rKayhpnpycjE6no2vXriIqrJqiVCrp1KkTW7ZskXONicfGXkwM87lKj3wOGDCAGzducO3aNbp06ULt2rWpV68e8GCjaGpqSmJiIgBdunQRkWFGgmH/mzFjBpcuXeKTTz6hZ8+eqNXqPxQJLJnAer2etWvXcurUKUJDQ8nLy8Pb25s9e/aItVwVYqjp/PnzOX36NOvWraN58+Z/ejyW5vSYmBhu3LhBhw4d6Ny587P66II/gaG2CxcuJDExka+++ooGDRo8ke4ODg4AnDx5khkzZjB79my2bNmCWq1m3759aLVa2rdvLxeMO378OO+///5ffl+ChyNpumDBAlavXk3dunWpUaMGb775plyjpTx6vR5TU1PefvttlEol27ZtIzw8nP/85z+UlpYycOBAunTpgqmpKenp6YwbN46MjAzGjh0ron+NgPKmf2FhIf/3f//3UL0r6/d3797l9u3bnD17lv79+8tze0lJiVzwNSYmhjp16sh7NrGWF1QFIgXEC45kAuv1etq0aYOpqSknT56koKAArVbL3r17xYahGiNtGrKzsxk7dizXr19nxIgRjB07VhT+e8EpXxhKQtI8KyuLyZMnk5WVxZgxY+jevXtVfEzBX4i0MGzfvj0hISHEx8eTkZFBnz59hBH4glK+Pzdo0AB3d3e0Wq28yQTknKCZmZlMnz6d+/fvM3PmzGdiNgn+PNL//08//ZTVq1eTnZ1NRkYGjo6ONGzY8KlzdRumg+jYsSMZGRlcvnyZ/Px8sZYzEgxNwB9//JFbt24RERFBp06dqFmz5h/ul4bruAkTJpCRkcGYMWNkA0FQtUgavP/++/zwww9kZmaSlZVFmzZtsLGxeazuderU4ciRI1y/fp2QkBB27Ngh53/VarXodDosLCzo2rUrZmZmrFy5UuT6NwJ++eUXvvjiCxQKBXfv3iUuLg6dToebm5tceNUQaT9uYmJCu3btsLe3p6CggISEBC5cuMChQ4c4ceIEmzdvZtmyZSQkJNCtWzd+/vnnMqn8BFWDoem/atUq0tLS0Ol0uLq6Vqp3ZVy+fJnz588TGhpK69atadCgAQqFApVKxY0bNxg3bhzJyckMGTKEt956S+zTBVWGMICrCUqlkoiICD799FNycnLw8PBg3759csEBsWGoPhguCpVKJenp6YwePZqwsDB69uzJf/7zH5RKpVhMVCPKa37jxg1Gjx5NREQEffr0Yfny5Q+NFha8uCgUCvmxsby8PIKDg7lx4wZ9+/YVj49VM8r3XZVKRWpqKqNHj5ZzBL777rtiw2BE7N69m08//ZTS0lKsra2JjY0lPT39D5vA0rXJycksXryYnJwcPD09xVrOiNi6dSufffYZ8CBFQ1RUFOHh4Xh4eDyVCVx+Ts/MzGT06NGEh4fTp08fvv32WzGnGxEbNmxg6dKlKBQK1Go1ERERZGVl0bZt28eawKWlpQQGBhISEkJUVJRs/np6espPcSoUCszMzOjSpQsqlUou7iyoGkJDQ/n2229JS0tj4sSJODo6ysW+HmcC6/V61Go1rq6udOvWDXNzc5KTk8nMzCQpKYnk5GRq1qzJoEGD+Pnnn+WDPaF31fO0pr+E1P/VajWXL18mNjaWvXv3YmJiQlRUFMHBwcyaNYv4+Hi6du0qTH9BlSMM4GqAQqEgIiKCsWPHkpSUhLe3t9gwVGOkySIyMpIzZ84wefJkYmNj6dGjB35+fqjVajmiRFA9kDRPSEjg3LlzTJw4kejoaHx8fNi6dausuagOX/2QNHV0dOT777+nZs2aLF68WGhdzTDcBCQlJbFz507mzJlDTEwMvr6+rFu3TmwYjIjExES+//57oqKiePvtt5k0aRJnzpwhOjr6D5vACoWC8PBwhg0bRmpqKl5eXmItZ0RERkaycuVKkpOTGTduHN27d+f69ety0b6nMYGl11NTU7l48SJjx44lKiqKbt26lVnHiXG+6rl69SorVqzg+vXrTJgwgZYtWxIXF0d4ePhjTWDpsfLu3bvj5+fHvXv3OHDgAFqttoz5Wx6xfq86iouLWbt2Lbt27aJNmzZ89tlnDB06lKSkJEJDQ5/IBIYHazdra2u6detG7969GTx4MD4+PgwbNozZs2czbtw4VCqVGNuNhD9q+sPvmtepU4fCwkIyMjJIS0vj2LFjHDp0iKNHj3Lnzh369+8v79mE6S+oSsSIUw3Q6XR89dVXJCcnizxxLwnx8fH07NmT/Px8LC0tmThxIt98841YTFRjoqKiGDFiBKmpqej1ekaMGMGqVatEX38JKC0txdnZmR07duDl5YVKpRKHPNWUoqIiLl++zNdff01GRgbjx4/nX//6l+jnRsbp06cJCAjAxcWF7t2707t3bxwdHZk6dSqnT5+Wr+vRo8cT5wQuLCxky5YtpKWl4eHhIeo3GBF6vZ4zZ85w7NgxGjVqxKRJk3B3d8fKyoqffvqJ4OBg5s6dy7fffkuzZs2eSO+LFy8yceJEsrOzKSwsZMyYMXz33XdCcyNCp9Nx9OhRgoODcXV15c0336R+/fqYm5uzZcsW9uzZAzxIC1K/fv0KuisUCkpKSrCzsyM4eV+eiwAAIABJREFUOJjk5GTatWsnm78C40Oj0ZCSkkJpaSljx46Vi2p/+eWX6PV6AgICWL9+PQDTpk17aI5YQNa5adOmNG3atNLXRT+veoqLi9m/fz/nz5+nTZs2zJgxgxo1ajB//nwOHDjwRHpLOaDfeust6tevz/79+/ntt98oKiqiV69edOrUidmzZ4t9usAoEBHA1QCpSFBxcTGrV68Wi8eXAHt7e2xtbdFoNMybN4933nlHNoWE7tUTOzs77t69S1FREbNmzWLx4sVylJDQvHojbShdXFzE4rGao1KpsLOzw93dnWHDhomx3UixsrJi/fr1jB49mmnTpgEP+mebNm04cuTIH4oEVqvVWFpa4uLiIoxAI0OhUBAcHExISAh/+9vfGD58OCYmJjRr1gwTExMSEhKeOhLYxsaGqKgorKysmDdvHh9++KHo60aGUqnkwIEDhISEMHfuXAYNGoS5uTlt2rTh3r17REZGPjYSWKlUUlJSgqWlJbVr1y5TbEpgXEjaDRo0iPr16/PGG28ADwxCOzs7tFotSUlJXLp06akiQx+GeJrHOFCpVGzatInw8HDeffddevfujZWVFR4eHiQnJz+R3lL6D41Gg7u7O0OGDJHXB2+88QZarRalUinGd4FRoMjNzdVX9YcQ/DnKbxDEhqF6Y7i4zMvLkyciEVHwcpCRkSHnfxVRoAJB9cRwnBdju3Eh6ZGdnY2DgwNQdt11/Phxpk2bRnZ2Nt7e3syZM+exkcCVaSzWcsbH8ePH8fX1BX7X5/bt22zatImffvqJpKQkvLy8njgSuLS0lJycHPl7JOZ042Tv3r0MHjwY+F337Oxsli9fzpYtWygsLGTo0KEPjQQWvDiUH3elPimN0Wlpabz33nscOHAAJycnJk6cKEeGSrqX11/M4caLoVabNm1i3LhxwAPTX6PRkJqaynvvvUdAQEAFvR+G0Ftg7IgI4L+A5z3xlx9kxKBTNTwv3Q3/GyYmJpX+XVB9sbKyAhBRJEaAyNH4cmA4tlfFOC/GduNC0sPCwgJ4sNkzjPB9kkhgqR2p2FNlpoEYW4wHaax3cXEBympuZmaGq6srGo2GxMRErl69SlhYGJ06dcLBwUHO211eXykHpPQ9EnO68SHp7urqKv8uHeRYWlrSrl077t69K0cCZ2Zm0qZNG2xtbR9qBop1g3HzsD21QqGgtLSUGjVqoNVqSU5OLpMT2NXVFSsrK1nfpKQkzp8/T+PGjcUcbsRIaVqUSiVt2rQBfu/npaWl2NjY0Llz5wp6S5HAlfVz6bsidBcYK8IAfsYYDgBBQUFcuXIFR0dHzM3Nq/iTCf5KDHU/ceIEFy9epFatWvLCXlB9qcwceh4mkVhYPH8etoGXEBu76oeh5gUFBWg0GqGzoAyGm74nNYEVCgWRkZGsW7cOGxsbHB0dxZhuxJTv7+U1l0xgtVotm8Dh4eGyCSzNFTExMaxbtw6tVlsh0lfob3w8ygx8mAmclZUlm8CS7gkJCQQEBNC6dWsxd7zAPM4EbtasGTVq1CA8PJyhQ4dy4MAB2rVrR4MGDar6owsegTD9BS8bwgB+hhhuFFesWMHChQvZvHkzDRs2xN3dXTzKV00x1P3bb79l4cKF+Pn54ezsTMuWLdFoNFX8CQXPmvInvTdu3KC0tJR79+5hYWEhJv5qiKHmgYGB7N69m7///e+EhoaSlJREx44d5fxeYoNXPTDUfO7cuSxfvpzhw4djbm4udBZUyuNMYAcHB5o2bUpcXBxDhgzh0KFDuLq60rFjx6r+6II/yONM4A4dOuDo6EhYWBijRo1i3759ODo60r59+6r+6II/weNM4FatWmFvb09YWBivvfYafn5+uLu74+bmVtUfXfAneJQpKEWNvv3226SkpNCyZUs++OADsVZ4gRGmv6A6IgzgZ4ThRvHDDz/k66+/pqioiPfeew9PT08aNmxYxZ9Q8FdQXvfly5dTXFzMwoUL6dy5s9C9GmKo+fbt21m9ejWLFi1i06ZN7Nq1C7VaLW8CBdUDQ80///xzPv74YwIDA0lLS+PSpUsEBgaSlZVFnz59xEK/GmFo/q5bt46MjAzCw8Pp06ePMIEFD+VRJnB2djZ5eXnMmzePtLQ0OnbsyPLly8X36AXnUSZwdHQ0dnZ2zJkzh+TkZDp37syXX34pNK8GPMoEvn37NmZmZsyYMYNr166h1WpZsmSJ0L0aUN4UlArDRUZGsmvXLtLT0+nSpQt79+6VizUL3V9chOkvqG6IInDPmH/961989tln2NjYsG7dOnx8fOTXniYpuCgg8GLx7bff8umnn2Jra8u6devo1q2b/JqUB+hJ9BS6GzeG+ixZsoQVK1ZUet27777L/PnzMTc3f6ymVZFfVPDkGGrywQcf8MMPPwAwdepUzM3NiY2NJSgoiMLCQlavXs3QoUOfqk2BcZORkcGQIUOIi4vDysqKO3fu0LNnT9auXUuNGjWeuGiT6OcvH4Y6Hz16lOnTp5OVlYWlpSV5eXl4eXnh7++PWq0WBd+qCZLmd+7cYcOGDaxdu5aEhARZc29vb/bs2SM0r2ZIumdnZ/P111/j5+dHQUEBJiYm3Llzhy5durB7927ZDBSF/qoH0r4+NzeXyZMnc/r0aUpKStBqtezbt0/082qGYSHABQsWEBAQgIODAzqdjlu3bol+LnhhEBHAz5DLly+zePFi7ty5w86dO+natSulpaXywqCyDV9lp4KGRrGUmFxgvFy9epUlS5aQm5vL9u3bK9W9vPaP072oqEhMHEaIpOPHH3/MypUrUavVLFq0iMmTJ9O1a1dsbGyIiIjgzJkzODs70759+8dWADf8HhgWIxAYB4bpXZYvX46VlRV79uxhypQpdO/enVdeeYW8vDxCQ0Np1aoVXl5ej2zPUPOcnBxMTU2FGWjEWFlZERQURHR0NAMHDuT27dtcuXKFq1ev0rdv3yeKBBb9/OVEig4sLS2lSZMm2NjYcOzYMfLz89FqtXJ0mDAIqg+GkcAdO3YkIyODy5cvC82rOYaRwN7e3sTFxREZGcn9+/fRarXioKeaIu3zbt68yXfffcft27fx9PRk//79Qu9qiGEkcM+ePbl06RKxsbGinwteOMTu4xkSGRlJSkoK06dPx9vbG51OJ2/8srKyOHv2LCtXrmTr1q38+uuvAJWafNKm8LvvvmPKlCncvn0beLCJFBgf0dHRJCUlMXXqVLp06VJG98zMTE6ePMk333zDL7/8wt69e4FH675ixQrGjBnDzZs3AaG7sbFmzRr+/e9/Y2Zmxr59+5g7dy5Dhw7lzTff5Ouvv2bq1KnAAx2vX7/+yLYMNX/jjTcA5MeJBMZDeHg4W7ZsAWDdunV07tyZkpISABo3boynpycAJiYmFd5bXktDzUePHk1oaCjwYCMhMC4k7Tp06IBKpaJly5aMHDkSOzs7jhw5wpQpU7hz5w4qlQqdTvfQdkQ/f7lRqVRERESwYsUKCgsL8fT0FEZgNUYay69du8auXbsoKCgQmr8ESLpnZWVx+vRpioqK8PT0FJGg1RilUklERAR9+/YlNTUVLy8vYf6+JOTl5REXF0dJSYno54IXDvENfQZIZt/Ro0cBaNq0KfBgMaBWq4mMjGTevHlER0eTk5Mjv69Pnz4sWLCAVq1aYWpqWqbNM2fO8MknnwDg5OTEsmXLRLSQkSHpHhQUBECTJk3kv2s0GiIiIpg3bx5RUVHk5ubK7+vatSsffPAB7du3x9zcvEybISEhLFmyBIBFixaxatUqobsRERERwaZNm1AoFKxevRqtVoter0ev16NUKqlRowYDBw5k27Zt3Lp1i7y8vMe2efz4cVnz2rVr88UXXwjNjYykpCRiYmLo1KkT3t7ewANTp6ioCBMTE8zNzalfvz737t1j3759pKSkYGdnx9ChQyv0cXhgKEuaf/PNN2zcuFFEARshUj/UarXodDosLCx444030Ol0bN68WTaBpXQQ0vehMkQ/fzlRKBRER0czatQorl+/LlIAvAQolUrCw8MZOXIkaWlpItXHS4JkBo4cOVI2A4Xu1Zvc3FwWL17M9evXRQToS4LUz0eNGiX6ueCFRew+ngHlN+4WFhbAgwif8PBwBgwYwJkzZ3BwcMDDw4OGDRtiZmbGr7/+yocffsiRI0coLi4u04atrS1vvvkmarUaPz8/rly58tzuR/BkSLpLUVzSxl+j0RAeHs7AgQM5e/YsderUwcvLiyZNmmBtbc3Jkyf54IMP+PXXXyksLCzTpr29PTNmzMDc3Jw9e/Zw4cKF53tTgkdy/vx5QkNDGTZsmJzfW6FQlDFyunXrRv369blz547cbx8V3WliYsLQoUPlMSElJeWvvQnBU3Pt2jX55/z8fOBBv5f6vJ+fHykpKfzyyy9MnjyZjz76iBkzZjB8+HB+/fXXCgcBzs7OLF26FAsLCwICAjh06NDzuxnBU6HX63F2dsbCwoI9e/ZgY2PDG2+8wdixY6lZs6ZsAt+6dQsTExP0ej3/+9//5Cd3JEQ/fznR6/Vs2rRJNgiE+Vv9KSwsZMuWLaSlpeHh4SHMgZeEe/fusWLFClJTU4UZ+JJga2tLly5dGDt2rIgAfUkQpr+gOiByAD8DJCMwIiKCU6dOUadOHXr37k1GRgYzZswgLi6OyZMn88svvzBlyhR69+5Nx44dCQwMJCkpSV4sSInElUoltWrVom7duqSkpBAVFYWPjw9ubm5VfKcCQyTd4+PjOXbsGLVr16Zfv35kZmYyc+ZMYmNjmTx5MuvXr2fSpEn06dMHrVbL8ePHSUpKIikpCQ8PD5ycnGTd7e3tqVevHikpKURERKDVamndunUV36lAr9dTUlLC+vXruXz5MpMnT6Zz584VrisuLkalUnHw4EESEhLw9vamY8eOj4zurF+/PrVq1eLSpUtER0fj4+MjP0UgMA4iIyM5dOgQ+fn5tG/fnkaNGslpXGbPns2uXbuwtLSkZ8+edOnShWbNmnH9+nXi4uKIjo6mWbNmNGrUSO7nZmZmNG7cmHv37nHx4kVatmxZ6fdJUPUoFApsbGwICQkhNjaW8ePHU7t2bRo3bkxJSQnx8fGEh4cTERFBv379+L//+z8+/vhj3NzccHd3l/u+6OcvJwqFgvbt22Nra8u3334rNoovAWq1GktLS1xcXPjuu++E5i8JJiYmFBUVUb9+fX788UehezVHyv+r1Wrp378/KpVK6P0SYGZmRlpaGs7Ozqxfv170c8ELiTCAnwHSJJCens6+ffvIyMhg5MiR6HQ6PvnkE/r06cNPP/2EiYkJCoUCe3t73N3d5ZOjxMREbt26xeDBg1EqlXJ7jo6O5Ofnc/DgQW7evMnAgQMrpIoQVB2SThkZGezZs4eUlBSGDRuGiYkJixcvpkePHqxevRqNRgM8OClu1qwZ3bp1Y9++fSQlJZGens7rr79eJoJUOgjYt28fmZmZDBo0CDMzs6q6TQEPNvEqlYqmTZtSt25dOTrfsMo7PHg0SKFQEBoayvnz5+natSuenp4VrpMoLS1FoVDQoEEDCgoKOHLkCHq9nr59+4oigEZE8+bNOXXqFHFxcZw4cYLk5GQOHz7M999/z/79+7G2tmbHjh289dZb9OnTh379+tG9e3f2799PUlIS165dY8yYMWU0tbCwQKVSERgYSFhYGMOGDcPa2roK71JQGVIfvXTpEkePHqVz5840adIEW1tbGjduTHFxMfHx8YSFhbFt2zaCg4MpLS1l7ty51K1bF/h9rhD9/OVDSh2i1WpRKpVio/iSUK9ePby8vITmLwnSGN+iRQt69OghdH8JkAr/SYW+9Xq9mM+rOcL0F1QXRAqIZ4Bk7AwbNox27dqRlpbGW2+9xd69ezExMWHAgAEAFBUVye/R6/V4eXnx+eefY2pqyunTp4mNjZXbkx4ZnzhxIr169SIxMVHkiDQyJD2GDBlC586dycnJ4a233mL79u2o1Wr69u0LVNS9Xbt2fP7551haWnLhwgUiIyPLvA4wYsQIhgwZwvXr10WeSCNBr9fTtGlT3nzzTfnR//J9UvpdMuzv3LkD/J4mRCoUJf2rVCrln//2t7/RuXNnLl++LIpDGRGlpaWYm5uzdOlSOnToQFZWFmvXrmXNmjWcPHkSlUrFqlWr5EJw8Hs///7777G2tubs2bPs3LmzQts9evRg2LBh5ObmPjR3rKBqkcbf/v37Aw+e9JFo2LAhM2fOZNy4cZiZmZGRkYFKpWLnzp288sor8tgvVY4G0c9fNsobAmKj+PwxTMEk/fw8+57QvPpjuG+TELo/Xx6Vau1RRVr/DIZ7ALFHr/4Y9nPpZ9HPBS8i4lv7BJSP3svLy0OpVJYp7lNcXIxGo2HatGksWbKEs2fPkpGRQV5enlwt3nCDL7UnFYDLzMzk7t27ZV7X6XSoVCqWLVsGgJWV1V96n4KnR9JoypQpJCcnExoaSm5uLvfu3ZPz+1ame8uWLTE1NSU7O7tMgThD3ZcuXYpOp6NGjRrP96YElSJp96goTakwoJQHXFp0qlQqWdesrCzWrFmDr68vWq0WlUolv2/JkiU4ODiIiG8jQjIAO3bsyK5du/j+++/Jz8+nRo0a3Lt3j1OnTtGhQwfg9w2f9G/Lli2pU6cOd+/elXMHS0iaL1u2jLlz51KzZs3neFeCp8XBwQG1Ws2pU6eYO3cuBQUFmJmZUbt2ba5du0ZBQYG8IVizZg2vvPIKtra2cnSIUqkU/byKediTGE/6uuDFw1DT4OBgkpKSGDBgADY2NlX8yQRVwV/Zx8XYUXUY6hoeHk5ycjJxcXHUq1ePHj16YGtrK6/BBdUHQ92lNdlfjTD9BdUBYQA/BsPBxd/fn9DQUE6cOIFarWbMmDF07twZd3d3+TH/bt264evry549e4iKigIgJCSE4cOHV2ocNWjQADs7O+7cuVOhEJw0Ubm4uPyFdyj4M0gade3alR49erBt2zaio6MBuHjxIrdv3650o1GvXj3s7e3JycmpEIkitVmvXr2/+NMLnjXlI4Clwx/pgCg7O5vRo0dz8eJF+TEi+N1k9PDwqIJPLXgSSktLqVGjBgsXLpT/1q9fP+7evYuDg0OlG0sLCwu5f5ePEjBM9+Ps7PzX34DgT+Hq6oqnpyehoaHcuHEDJycnAGbNmsWePXtQqVQMGzaMEydOEBAQwNtvv80PP/yAra2t3Ibo51WHYf9MS0sjNTVVjt5p1aqVnKJLmATVB0PNv/32W/773/9y//59EhMTee+99+R1u6B6UX4uloIsbG1tyxRvFk/XVQ8M9V6xYgVbtmwhJiYGvV6PUqnE3d2dXbt24ejoKHSvRhjqvmDBAurUqcPUqVNF0JRA8AQIA/gRGA4uS5cuZcWKFeh0Ojn8PzIykqFDhzJ//nzZpK1Xrx6zZ88mPT2dkJAQioqKOHLkCBcuXMDX11duu6ioCBMTEzIyMrh16xYdOnSgefPmz/sWBc8IJycn5syZQ3p6OqdPn6agoIDDhw8THBxMv3795Osk3dPT08nKyqJdu3ZC92pEeQPw9u3bALL5O27cOC5evEinTp149913q+IjCv4g5TcNhYWF5OTkkJubS2pqKi4uLnK0p/RvSkoKmZmZNGvWjHbt2lVoU0QPvFg0btyY06dPk5KSgpOTEzNmzGDLli2o1WoOHz6Mq6srX3zxBX5+fvz2228sW7aMf/zjH1X9sV96DNdy//rXv9ixYwcRERFyxParr76Kp6cn8+bNE+ZvNcFQ848++ohVq1bJP/fq1UuYv9UUQ923b9/O//73P/z9/VGr1bRp0wZfX1/Gjh2LtbW1MAOrAYZ6f/jhh/z3v/8FoFOnTuTn53Pr1i3Cw8MZN24cu3fvFk/SViMk3WfPns3GjRvRaDRYWVkxevRoYQILBI9BFIF7CIaTysKFC/nuu+9QKBS88847DBs2DDs7Oy5fvkxUVBQdOnTA3d1dfo+TkxMtWrQgPj6enJwccnJyOHXqFK6urtjZ2WFubo5KpSI7O5tJkyZx7do1hg8fTv/+/YUh8IKi1+upWbMmrVu3JjExkezsbG7fvs2JEydo0qQJ9vb2ctGn7OxsJk6cyLVr1xg6dCiDBg0Sulchz/KRQKmtS5cuERgYSIsWLRg4cCAZGRlMnDiRkJAQtFotBw4cQKPRUFJSIjYgLyhqtZq9e/cSFRVFTEwM3bt3l5/yUCqVZGZmMmHCBFJTUxk2bBjjxo0T/fwFRerXeXl57Nu3j4YNG7J79265AvS+ffvo2LEjGo2GRo0akZOTg4ODAytXrhSGohFgaAR+/fXXZGVl0bx5cxo3bkxaWhpJSUmcOHGCs2fP0rp1a+zs7B6pm0gVYfxI+ixfvpzly5djZWXFjh07GDNmDLVr1waeTkehufFjqNFnn33GRx99xKVLl7h37x53794lLi6O0NBQUlNT8fb2Fil4qgGS3v/4xz/47rvvsLa2ZsuWLbz//vsMGzaMZs2acfXqVRITExk0aBCOjo6PbE8q+goPAnbE/G3cpKen8/bbb8tP7wQFBeHo6EizZs0wNTWt6o8nEBgtwgB+CNIEsHLlSpYtW4a5uTl79uxh/PjxdOjQgUGDBpGbm8u5c+coKSlh+PDhZcycOnXq0Lp1a+7du0d2djZpaWkcP36ckJAQkpOTOXbsGO+//z4xMTH4+Pjwn//8p8wjwYKq5Wl1kCKJatWqRfv27cnLy+PmzZukp6dz9OhRzp07R3x8PIcPH2bhwoXExsbi4+PDf//7X1QqldC9ijD8/379+vVncmqsUCiIjIzk4MGD9OnTB29vb0aMGCGbv/v27SsTJSp4vpTva4YL/ifF8D3nz58nPDyc48ePU79+feLj47l48SLTpk0jPj4eHx8ffvzxRznXs+jnLx6SZoWFhWzatImgoCAuXLiAWq1m//79aLVadDodCoUCOzs72rdvz6RJk+R+Lg55qp4ff/yRL774QjYCP/74Y9544w08PDxwcHAgJiaG6OhoLl26hJOTE87OzpVGiRpGDebk5GBqair6tJFy+fJlFi1aRF5eHjt37qRLly6UlpbKc0B53aS/VzZHSJrn5uYK49AIKR/xvXLlShQKBYsXL2bEiBEMHjyY5ORk0tPTiY+Px8rKio4dOwIPfxLHcL6+d++eKNRqpBw6dIhFixZRXFzMnj176NatG0qlEktLSxo2bMjFixe5cuUKw4cPp0GDBvL7KluPSb/7+flx+vRpGjduLNf0EBgXpaWlaDQaAgMDUSqVuLm5kZKSwsmTJ5/KBBamv+BlRBjAjyA1NZV//OMf3Lhxg02bNuHj40NpaSk6nQ6lUkndunXZtGkTgwYNolu3bqhUqjKLSkdHR9q2bUvTpk25du0aCQkJJCQkcPLkSc6cOUN+fj6DBg1i48aNqNVquV3B86f8gv+PbOikjUPNmjVp3749zZs3JyMjg7i4OJKTkzlz5oycFmTQoEFs2LBB6F6FGGo+f/58Vq5cSc+ePf9UcRipvUuXLnHo0CFMTExYu3Yt58+fF+avEWCo+blz56hXr94f7usANWvWJCcnh9TUVBITEwkICGDz5s38+uuv5OTk0Lt3b/z8/OR+LhaWz5/KDtf+6IGbtbU1hYWFnDt3DoADBw6g1WopLS2V5394ULBVmg+E5lVPfn4+3377LYmJiaxZs4ZevXqhVCpRKBQ0atSIDh060K5dO44dO0ZCQgIxMTE4OTnRsGHDCiawYa7JL774gpYtW1K3bl1xiGuEnDx5ks2bNzN16lTefPNNOYWbVIw1ISGBgIAAwsLC0Gg0KBQKLC0tK5jA0r/r16/nm2++wd3d/bGRhILni6TRTz/9xOeff46VlRX+/v68/vrrtGnThlatWuHh4UF0dDTR0dGo1WpGjBjxyD4rvbZ582a2bdtGs2bNyuR0FxgHu3fv5ujRo8ycOZNJkybJh646nQ5TU1NOnTpFfn4+ffv2pbCwkGvXrmFqavpQY3fbtm1Mnz6diIgIWrZsSbNmzcThvRGiUCjQaDSEhYVx/vx51q9fT15eHleuXHmqSGBh+gteRoQD8Qji4+O5dOkSDRo0oH379nIhJ6moT1ZWFvb29qSkpLB+/XpCQkLo2LEjjRs3plu3bgA4Ozvz+uuv079/f/bs2UN8fDyZmZnUrVuX9u3b8+qrr6JSqYQhVIUYLvQTEhJITEwkJiaG1q1b06hRI5ydnZ94c2do/g8ePJj+/fvj7+9PYmIiGRkZ1K5dm3bt2tGjRw+USqXQvYow1HPevHmsXbuWmjVrEhsbS4MGDf60WSe1HRISQn5+vjB/jQBDzd999138/f354osvGDly5B9us27dusydO5eGDRuydetWIiMjAdBqtXh4ePDBBx+I8b0KKZ8f0N3dnYkTJ1Ya5fckWFlZMWbMGIqLixk8eLBs/j7sAE9sGI2DrKwszpw5g42NDa6urvLfpe+AjY0Nffv2xd/fn2HDhhEWFsY333yDjY0N3bt3r/BdCQ8PZ8mSJQB88803bNy4UWhtZOj1ekJCQgDkOgtS0b+IiAjmz59PQkICN27cAMDOzg4vLy+mTJlCjx49KowRBw8e5J133kGj0eDi4sLixYsxNzcXh/dGxNWrV9mwYQPwwAiWxmdJw5YtW/Luu+8SFBTEb7/9xpkzZ+jcufMj29yxYwczZ87E1tYWFxcXpk+fLh/4CaoWKSArICAA+H2+VavVcr0VgLCwMGJjY1m8eDEREREUFxdTu3Zt5s2bh6+vLw0bNizTbpMmTahbty5paWmsXLmSV199VeQMN0Kk8blFixbcv3+fK1eu8OWXX3Lnzh1+/fVXFi1aBMCoUaOoUaNGhXnc8HfJ9K9duzYuLi70799f5AgXVGvEjvQRZGZmAg82fQ4ODsCDR0Cl06RffvmF9PR0Dhw4gL+/P/n5+fj5+WFlZcWCBQuYMWMG8OA1VelCAAAgAElEQVSRAgsLC8aOHQtUrD6r0+mEOVBFGE4A3333Hdu3bycsLAx4UDG4Q4cOLFmyhFatWj1125Kur732WoX/luHrgudL+cjftWvXolaruXnzJqtWraJnz55/enFfu3ZtLC0tycvLw9PTk7179wrztwopXy34559/xs7OjpYtW/7ptuvVq8esWbN4++23iYmJoaSkBHd3d3meEP286pA0nzlzJps3b8ba2hpLS0tee+21P2wCt2jRgiVLlmBmZiY2CC8IJSUllJaWUlRUxM2bN2natGkF3UpLS2nVqhX79+9n4MCBXL16la+++orWrVvj4OBQ5lDQ2dmZpUuX8vnnnxMQEMChQ4fKFHsVVD0KhYLCwkIAeSxWq9WEh4czaNAgbt26RfPmzbG3t6ewsJCEhAQOHDjA+fPnWbZsWYXaDFqtFm9vb06fPk1gYCAffvghlpaWVXJvgsoJDg7m6tWrjBw5El9fX/R6fZl+rtfrad26NS1atCAmJoa8vLzHtikZhMnJyWzevJk33nhDpAAxEpRKJUqlEldXVy5dukRmZiapqak4OzvL5u+cOXM4e/YsGo2GoqIiGjZsSEFBAYmJiXz22WfExsYyffp06tWrJ7fboUMH1q5dy5tvvkl4eDjR0dG0atVKPOVhZEha+Pr6Ym5uzpUrVxg/fjyrVq1izpw5HDhwgEWLFqHX6xk5cqQcvR8REUGLFi3KaClMf8HLhti5PIImTZqgVCqJiIjgk08+AR4sJHU6HTNmzGDPnj1yRNDChQuZOnUqrVu3Jjc3l48++ogffvgBoELeqPITiDhJrhrKR4d98sknhIWF4e7ujpubG8XFxRw5coSvvvqKnJycp27/cboK3Z8/5Y3ANWvW4OTkxLx587C1teXcuXP4+/vL1/5RGjVqRK1atfDy8mLfvn1ywTdhBD5/ymu+evVqateujZ+fHy1btvxTOkuUlpZiYmJCq1ataNeunWw4iBQAVc/58+fZvHkzGo2Gu3fvMmfOHHbu3An8nrbnaZEMAGH+vhg4OjrKhXojIiKABwczhkiPDLu6urJ161bs7Ow4d+4c8+fPB8rO17a2towaNYrx48ejUCiIiop6fjcjeGJq1qwJQGhoKPAgf++cOXO4desWkyZN4siRI/z666+cOHGCDz74AHd3d27cuMGHH37IyZMn5XZ0Oh12dnZs2rQJd3d3EhIS2LZtG/Dn1gmCZ8uVK1cA5AJvleV3tbe3p27dupSUlJCUlPTI9kpKSnBwcODAgQO4uLgQFhbG0aNHAeQnQQVVj3SQv23bNlatWsW2bds4ePAg48aNY8OGDVhbW7Nu3Tq2bt1KYGAgAQEB9OrVi5ycHLZu3cqZM2eAsnNC69atGTFiBHfv3iUwMBAQT/QYK2ZmZtjb23PixAnu3r2Lvb09K1asYMCAARQWFrJ48WJ2797NvXv3mDNnDr1795b7sYRk+js7O8umP4jxXVB9ETmAH4GZmRmpqanExcURHBzMuXPnOHDgAP/973/57bffsLa2xs/PjylTpuDh4UHv3r1p1qwZBQUFREZGEhkZSefOnalTp06ZdsUkYhxIOixdupSVK1fK1WPfe+89Jk+ejIWFBREREaSnpzNu3Disra0f2V75HFHlc/sK3auWyoxAR0dHNm/eLKdoSUtLo06dOvTs2fOJ9KpMcwAHBwf69u3L9OnTReRvFfIwzTds2ECnTp3kCE7D66QF35MUhpEQKQCMl8LCQtavX4+trS2tW7cmMTGRwMBAXFxc5CiQx0X2lNdc5AN8sTAxMSEkJITQ0FDCwsIYOnQoNjY2FXSUxoK6detSq1YtgoKCiIuLo2nTpri5uZX5nlhYWKBSqQgMDCQsLIxhw4Y9do0geL7odDp27NjBjRs3aN++PUqlkn/+85/069ePH374ARMTE9RqNWZmZnh5eWFqakp8fLycI7R79+4oFApUKhU6nQ5zc3MaNmzI4cOH0Wg0DBkyRIwDRkT37t2xtbVl+PDhWFhYPLTg6/79+4mJicHT0xMvL6+HticdCtnY2GBiYsKxY8dwcHB44vWh4K9F0tfT05MbN25w6dIl/ve//7F//3527txJbGwspqam7Nixg+7du2NtbY1Go8HKyorhw4dz/Phx4uLiiI6OZuzYsWVyxWo0Guzt7dm6dSuFhYUMHz68TJ5/gfFgbW3N1atXCQkJYdSoUdjb22NhYYGvry9xcXFERUVx/PhxTpw4wcGDBwH429/+hpOTU5l27O3tyc3NJSgoiAYNGqDVaoXegmqLMIAfgbm5OTVr1uT27dskJiYSHx9PTEwMubm5KBQKfvzxR3r27Iler5cnovr16wMQGBjIzZs38fb2fiaPGQv+Gvbu3cuiRYsoLS1l165dcvVYMzMzOnTowJEjR4iIiGDQoEFlHhGqDGmiWLduHaWlpTg7O4sCb0bCw4zAjRs30rFjR0xMTOSI7/Pnz9O5c2dcXFwe267U5s6dO9Hr9Tg5OaHT6VAoFNjZ2ckbCGH+Pn8eZf56eHjI1eClYlASlVWHN8SwnycnJ+Pm5vbX3ojgD6PX67Gzs+P06dNkZ2czadIkCgoKSEhIqGACP8rUrayfCxP4xUDSqVWrVgQFBcnFeH19fbG0tKygo/RzjRo1iIiIICYmBhcXF3x9fSvo3ahRI1JSUjh//jwzZ84UhWOMDKVSSUhICDExMVhaWnLr1i0OHTrEe++9R4sWLSguLkatVssHgW3atOH69eucPXuWjIwMxo8fL6d5kNZxNjY2XLx4kQsXLjBhwgRMTU3FOGAkaDQa2rZtS40aNYCKB7DS72fPnuXChQu8+uqreHh4yK+XX68bHtzb2dlx+PBhEhMTGTt2rFw0UFB1GM7bffv2lQ/wzMzM6NatG/b29nh4eDBt2rQy6UCkej729vYEBgZSVFQk54k1xMnJiXr16jF+/Hjq1Kkj9DZCpHV+WFgYx48fx9PTE3d3d3Q6HZaWlvTp04fY2FgiIyNJTU1FpVKxdetWtFpthQMiYfoLXiaEM/UQpCgwLy8vli5dyrZt2/jkk09YsWIFf//733FwcMDNzU2efKSJB2DIkCFyzljxGIFxc+XKFYqLi5k7dy5eXl7odDo5V5RarcbZ2Zk2bdpgZmZGSkoKycnJZGVlPbS9HTt2MHfuXPr168f58+fF499GgmHxL0Pzt1OnTpSUlAAwePBgPDw8UKlUnDhxAqj4mHBlbNq0ialTpzJp0iTCwsJQq9VlFgziO1A1GOZ5Xr16NXZ2dqxfvx4PDw85J6hKpSIjI4OAgADmzJnD9OnTmTZtGgcOHCA5ORmofOz29/dn7ty5TJs2jT179jzX+xI8OdJ3oHnz5ty6dYuuXbsyc+ZMPD09uX//fpl0EFJRzodRvp+Lg70XA0knR0dHRowYgZ2dHUFBQSxbtozc3FyUSmWlj3M3bNiQAQMGAHDgwAEKCgrKjAXSe5YtW8bZs2fldAMC48HFxYWhQ4cCD4qCHT58GPh9Tpa+G9J3QKFQMHPmTOrUqcPNmzflAnGG1KxZk/nz53Pw4EFsbGyEOWBkPEl+Xim3Z1FRkfw3Kcd3VlYWCxYsIDMzs8zBvYuLC3//+9/ZsGEDlpaWQncjwXD8fu+999i2bRvHjx9n1apVJCYmYmZmhlqtLrMOl/RXq9XcuXOH7OxscnNzy4zv0s+jR4+mRYsWz/GOBH+Enj17Ag8OdwyxsrLC3Nxc/l2pVMp6V9aH27Rpw7/+9S+++uqrStPICATVhZdyB/MkuZsMcwPWr18fHx8f5s6dy8SJEwkODubOnTtyIRGpPYVCQXFxMXfv3uX27dvAg2JQ0msC46KoqIjDhw9TWloqP/qjUCgoKSmR8zbHxcURGxvLwoUL6dKlCz4+Prz66qvs2bOH4uLiCu0lJyfTuHFjioqK+Oyzz577PQkezo8//sjPP/+Mvb09mzZtolOnTnJVcIC6devStm1bdDod27ZtIyMj47HmbX5+PmfPnsXOzo74+HhWrFjxPG5F8IQkJCTw22+/oVQqady4MZaWlvLhjlqtJjIykilTpjBjxgw2bNiAn58fO3bs4J133mHWrFlcuHChQp5YnU5HcXExrVu3pri4mDVr1lThHQoehTQ3d+vWjcLCQk6cOEG/fv14++230Wq1FUxgaSzIyMgo047o5y8+ZmZmvPbaa/To0YPCwkJ27NjBsmXLuHXrVgUTWOrvTZo0AR7oXz5ayPDQ39nZ+TneieBJkLSZPXs248ePB5DzPkr/qlQq+TrpO1BQUEB+fj7FxcXk5+dX2ranpyeNGjX6q29B8IyR+nhBQQHwICc0PIgIValUZGdnM2HCBFavXs3XX38tv0/6jvTp04fmzZs/508teByGY7GFhQUWFhbExcWRnp4uz+k6nU6+Rjrozc3NRaPR0LNnT9zd3St9EkRg3Eg61alTR04FUVBQIO/dZs2axfbt21GpVHTu3FkO+Nq+fbvc/yWE6S94mXjpUkAYPgby7rvvkpSURMeOHSu9trIJQKfTsWHDBqKjo2ncuDEtW7aUDYLS0lLUajXJycn88MMP1KxZU44mEBgfer2eXbt2kZKSQtOmTenbt68czQ0PNg6HDh2S/yZFFqSmpuLv74+1tTWdOnWS9Ver1bRo0YLCwkKcnJxYv369iP40Iq5fv45CoeDLL7+UzV+pjxs+Jvzbb7+RlJSEXq+nW7duj9RQo9HQoUMHUlNTqVu3LmvWrBGaGxF2dna0bduWiIgILl68SHx8PPXr18fFxYWoqCj69+9PXFwcrq6ueHh40L9/fwoKCsjOziYuLo5Tp07h4eFRZgxXKpU0atQIW1tbbG1tWbt2rdDcSJH6t6mpKevWrcPe3p4BAwbg5uaGlZUVN27ckHMCS3leZ82axd69e2nZsiUODg6A6OfVBRsbG9zc3IiNjSUqKorY2FgyMjJ45ZVXsLKyoqSkBKVSKR8SRUdHs3v3bnr06MHrr79eoT1hEhgvho+Ht2vXjmvXrhETEwM8WAvY2trSrl07FAoFOp1OTtV0/fp1NmzYQPv27Zk5c2aFIs6CFxuFQiHXdPHy8sLX11c2f8ePH8+5c+fo3LkzP/30kzzGi35u/JTXKD09nXXr1hEaGoqXlxeNGjWSr1EqlWRlZfH222+Tk5PDqFGj6Nq1a1V8bMEzoLS0FBsbG06ePElISAjjxo3DxsaGWbNmsWnTJtRqNYcOHWLBggWEhYURGRnJ4cOHad++fZkUbqKfC14mXjoDWOrgH3zwAatXryYwMJD69evTpk2bx75XMo+l/F9KpRInJyfq168vm4Q3btxg9OjRpKSkMHjwYCZPniwGFSNFqVRy//59Dh8+zKVLlygoKEChUJCQkMAnn3zCjh07sLa25ueff2bOnDnMnz+fV155hZKSEiIiIjh27Bht27alWbNmsglsbm5OmzZteP3111GpVHJkgaDqcXNzw9fXlyZNmlSI5jI08ePj4wkNDcXExITBgwdjZmb20Hyfer0eKysrvLy8GDdunFzwTTweXvUY5mV3dXWVi0RkZ2ejUqmYP38+aWlpTJgwgTVr1jBixAi6d+/O8OHDsbS0JD09ndjYWNLS0vD29i5T4Emj0dC0aVMGDhwo+nkVUr4fV1bMTXq0NyAggCtXrjBy5Eisra1xc3PD2tqajIwMEhMTOXz4MCdOnGD//v3Ex8czffp07Ozs5HZFP3/x0ev1ODg40LZtW5KTk4mPj+fChQucPXsWT09P7OzsUKlUqNVqbt26xVtvvUVmZiajRo1Cq9VW9ccXPCXSWGBtbU27du24ceMG0dHR5Ofnk5qaSmlpKa+88gpKpRKVSkVOTg4TJkwgJSWFYcOG0bt3b7F+r0ZIWl64cIGgoCA8PDzo3r07WVlZjB8/npCQELRaLfv27UOj0Ygx/gWmdu3ahIeHExMTw4EDB2jevDn29vbcvXuXmJgYJkyYQFxcHL1792b58uUVigELXhwkzWJjYzl16hR9+vThm2++YePGjajVavbv3y/n+vbx8eHChQvo9XpZd4HgZeSlM4AB1q5dy+eff45Go6G0tJSDBw9Sr169x5rA0iBz//599uzZQ3R0NElJScTFxZGXl8exY8eYO3cucXFx+Pj4yJFholiM8SFN9E5OThQUFHDp0iXOnTvH7t272bZtG1FRUZiZmeHn50ePHj2wsrJCpVLRoEEDPDw8iImJkYsCDho0qExOMKkoiF6vF6aQkSDpLUVxV9YfFQoFGo0GOzs7Nm3axLVr17Czs8PT0/ORxaEk41+KOBKaP38qW7hL2igUCurVq4e7uztXr17lzJkznDt3jmvXrjFy5EhWrVol91mp0nuLFi3Iy8vj7Nmz3L17l+7du1coAmmY61lo/vwx1DwoKIhatWpVGq2nVCoxNTUlKiqK4OBgJkyYIOdrbdq0KTVr1iQ9PZ2EhASSk5MxMTEhKCiI5s2by0WBRD+vHkg61qpVi1deeYWCggKysrLkSN/IyEhiY2M5efIkCxYsIC4ujl69erFs2TKxUXzBsbOzo2vXrty+fZvLly+TmZnJ0aNHiY6OJi4ujjNnzrBgwQKio6Px9fXl3//+t5wiQqzfqweSlkePHuXMmTP4+PjQsmVLxo0bV8b8lQ74RPHeqqGyPvc0/VC61sTEhOTkZJKTk9m1axfHjx/n559/5ueffyY1NRUfHx/8/PxQq9WiYHcVYqitpMPT+CbS++/du8euXbsIDg7m5MmTsvnr6ekpp3+xsLBg4MCBzJkzRxzkC15qXjoD+OLFi6xYsYLU1FSmT59O3bp1iYqKemITGJAjh44ePSpXDN69ezeBgYHk5ubSt29ftm7dKg8uYqNofBhW+nZ1daVu3brExsZiZWVFp06dMDMzY8CAAUyZMqWCkWthYUFGRgYnTpzA3NycyZMnV1r9W2waqo7KInyfZEGh1+upV68eOTk5XLhwgcLCQnx9fStUBzZE5A2rWgy1Pn36NLVq1ZKLfFRmAoeHhxMfH49Wq2Xjxo0olUp50SlFgZiamuLm5sa+fftISUnBxsZGLjIhqHoMNZ89ezbfffcdXbp0wdnZucJGTro2ISGBw4cP06BBAzw9PSkoKMDExITmzZvj5+fHtWvXgAfR3a+++iqNGzcusxER/dx4eRpzQBoT7O3tad++PU2bNiUjI4PY2FgiIiIICgri1KlT5ObmMnDgQDZs2CAMgmqChYUFffv2xcrKinv37nH9+nUiIyMJCgoiKCiIO3fuMGjQIDlyTGhevZDGieDgYE6fPo2dnR0bNmwQ5q8RYDiG37lzh8TERLKzs7l9+zb29vZPvIaH3+fnBg0aYGdnR15eHomJiWRmZpKbm4uzszNDhw7lp59+EnpXMYa6T58+neDgYHr16vVUayzpWnt7ew4cOEBSUhJmZmb4+/uj1WopLS2VD/LhQZCWtLYT/ozgZeWlGvGKiooICAggJCSETp06MWLECJo1a4apqSnbtm1j9uzZAIwbN+6hbUgDycyZM7GxsWHv3r2EhoaSn5+Pt7c3np6evPPOO6hUKjGpVCFPsyF0cXFhzpw5TJw4EXNzc0pLS2ndujU1atQoU1xAQqlU4urqil6vJz09nczMTFEB3Agw1DwzM5Nr165x+/Zt1Go1vr6+T7SRk97fqVMnfvzxR86dO0d4eDjOzs4iEsgIKW8Ebty4kZUrVzJu3Dj574YmsFarZenSpXz11Ve8/vrrqNXqCotAaaNRs2ZN2rZtS3x8fKUHPIKqwVDzefPmsXHjRgC2bdtGp06dKizopWu7d++OlZUVsbGxwO/V4t955x1OnjyJSqXCzc2N8PBwJk2axIoVK3jttdeEAWQkGOpeVFREUlIS9+/fp0GDBlhZWT1VrlapHUdHR4YNG8bAgQPZsmULycnJJCYm0rBhQ9q2bcugQYPEWq4KedZzrrR+nzVrFv379ycyMpKAgADy8vJo2rQprVq1YvDgwSiVSqF5FVJe98LCQjmi82HXPAnSWC6l9jly5Aj5+fnC/K1iDLVcs2YNBw8elANsatSowTvvvMO0adOeKk2DXq/HzMyMIUOG0KtXL3bv3k1+fj4ajYaOHTvSunVr+fBf6F01GGo5f/58/Pz8UCqV9OvXDx8fn6dqS6fTUbNmTfz9/RkwYAArVqwoY/5WhljbCV5mXqpRT6fTcf/+fQCGDh1Ku3btAPjqq6/Q6/Vs3779sSawdGqkVCoZP348AwcOJC8vj7t379KkSRM58kxMKlWH4aQSGBiIm5tbhce3K7teMnwvXLjAzZs3KSwsBH43hAw3BRkZGSgUCnx9fWnWrNlzuS/BwzHU/Mcff8Tf35/g4GD59REjRvDhhx/SqFGjJ1pAvv766/j7+7N//37++c9/0rZtW5ycnP7SexA8HYY6LliwgI0bN2JpaYmbm9sj00F4e3vzz3/+k1q1agGVLwIVCgW3b98mOTkZeJBPTlD1lNd87dq1WFtbc//+fXbs2MHQoUPx9vau9L1WVlZYWFhw7Ngxbt++XaFIyL59+ygsLOSrr77izJkzTJ06FXt7e7p37/48b1FQCYa6//LLLxw+fJiAgAAUCgUNGjRg+PDhTJw4ERcXl6duW6fTodFomDhxIkAFA0is5aoGQ82zsrLk8frPYGggNW7cmMaNGzNgwIAK1wnNqw5D3Xfu3Mnly5c5f/48KpUKX19fWrRoQb9+/eRr/ogRLO3TJPN37969wvytIgz1+/jjj/n3v/8NPJivFQoFqampvPfee2g0mqeqqWOYnqtGjRpMmjSpwjUiArTqKL+WW7NmDWq1Go1Gw7lz5/Dx8XmkeVse6aDW2dmZ//3vf3KKT2HyCgSV8//s3Xl8TGf7+PHPZDJZyCYrERJLhBC7JCSoImLX1r5U9am2tLT4qocuWm3pog8PtfShRVUVERLa0lbVFktIREJEElmtETSxZf/94TenM1nszESu9+vVV51ZzpyTa+4z97nOfe6rSk0BodFoqFu3LoGBgYwcORK43dGrVq0anTt35syZMxw/fvyu00HoJhPMzc2xsbHB0dFR+SHRFosTT57uj8q4ceOYOXMmjo6OeHh46BVx0qU7UhBujx5dtWoVCQkJeHl56RV5U6vVXLx4kQkTJnDlyhWGDx9eYcJBPBm6MX/33XeZM2cOGRkZuLq64uLiwuXLlzlx4gQXLlxgwIABd+1A6s5BtWvXLvLy8vD398fDw0NuCTUSpTuPy5cvx9nZmQ0bNtC2bdsK5wTWcnR0pFq1ahUWDTMxMSExMZHFixdTt25dxo8fj7Oz8+PfMVGhimI+ffp0MjIyyMzMpGnTprRr167cuFpbWxMbG0tCQgLPPfccn332GatWrcLU1JTw8HDat2+Pm5sbNjY2xMfH4+bmxvvvvy/t3cB0Yzlz5kxmzZrF6dOn0Wg02NnZce7cOU6ePMnNmzfx9fVVRnbfq4ouAN3pefF46cb81Vdf5fPPP6dr166P5E6r0scF7dyQuv16iblhlG7r7777LocOHSIzM5P09HT27dtHSEgIubm5ODg44OLicl9TA2jXb29vT2RkJPb29mzfvl0p+CbJ3ydPN95ff/015ubmLF26lEmTJhEUFES1atU4evQo2dnZPPfcc5ibm99xfeV9Fyq6SCB39BlGRX05f39/EhISSExMpH///tjZ2d3XerXHbe10DxJfISpWpRLAAA4ODnh5eQH/VAeH23PC3EsSWHvgKp001CUHHcPR/u0///xzli5dCkB8fDwWFhZ3TALrsra2JioqiqSkJJKSkjA3N8fHx4dLly6RnJzMsGHDSE5OpkePHnzxxRdSPdaAdP/us2bNYuHChVhYWLB8+XKmTp3K4MGDadSoEb/99hsnT56kTp06d53nW9uJcHJyYuvWraSmpnLt2jWef/55OTE0AhV1Hn/44QfatWundwKgPanX/fed5nLV/iZcunSJkSNHkpGRwcCBAxk2bJi0bwOqKOZr166lT58+xMfHEx0dTUxMDD179iyTKNK+PzY2lh07dvD777+zc+dOpUiIv78/BQUFaDQaGjduTKNGjXjvvfekSIiB6cb9gw8+YOHChahUKhYuXMjkyZMZN24cycnJHD9+nIsXLzJo0KA7ztcOZRMEpS/qSTs3PN2Yr1ixgqtXrxIbG4ufn98DJYHvlBS6W39ePDnav/+nn37KvHnzsLCwYObMmYwYMQJPT09sbW1JTEwkMjKStLQ0VCoVTZo0qbDYdunHtP+2tbWlS5cueoWgJPlrOGvWrGHmzJmYmJiwZcsWgoKCcHZ2xsPDAzc3N3bs2MG5c+cYPXo0VlZWyvtK9+d0B18dOnQIExMTrK2tpV0bkfL6ck5OTmzevJnXXnuNzZs3k56ejkajITAwUG/u3opU1M6FEBWrcglgXaVP6u6WBNYeuFJSUvj777/v++qUeDK2bdvGnDlzuH79Ok2bNiU1NZXjx49jaWl5T0lgMzMzrl27RkxMDElJSfz888/s2rWLpUuXsnLlSqV6rLbQn4wKNRzdaR8+/vhjzM3N2bx5M926dcPW1hZbW1s8PT2Ji4tTkvYtWrRQ3l9R4r64uBgrKyucnZ35448/iIuLo3379g90i7F4tMpLBK5evRpfX1+Ki4uVkwDt6woKCu55RICJiQlpaWkMHTqU48eP06VLF5YuXXrfVYnFo3OnhH/btm0BcHV1JSIigvPnz1O7dm38/PzKTexZW1tz+PBhEhMT0Wg0ehWiTU1Nlc9yd3eX+QGNgDbuixYt4rPPPsPa2pqwsDB69uyJk5MTdnZ2dOvWjZCQEDIzM+nQoQONGjUCyk/66a5z1apVFBcXl1s4UBjexo0b+eijj4DbxXqTk5OJjY3F398fe3v7+1qXNubbtm3jxIkTeHl5lXtBUBje/v37mTFjBoWFhYSHh/P888/j7e1Np06dCA4Opnbt2oNpAhQAACAASURBVPzxxx+kpKSQnJxMcXExrVq1Qq1WV3iBd9euXWRnZ+Pq6qo8bmtrK4WgDEwbr0WLFnH8+HHmzp1L3759lX6cSqWiWrVqbNiwgYYNG/Lcc89RUlLCtWvXsLS0LDMNiHb566+/5uWXXwb+KdouDE93SgbdvtyaNWto2bIlubm5nDhxgri4ODQaDYMGDdLrl5WnoqS/EOLOpMdbio2NDXPnzmXQoEGUlJQwYcIE1qxZQ1FRESqVivj4eDp37oyfnx+ZmZmG3lxRyvnz5wkNDeXy5csMHjyY7777jr59+5Kdnc3ixYtZu3Yt586dq/D92tGCr7zyChMnTqRdu3YAHDhwgJSUFGUuqZCQEGXkgHQeDSsiIoL//e9/mJiY8MMPP+Dv709JSYkSS1NTU27evAnc7vgXFhZy9epVZRn+ibuWtkPRuHFjioqKaNWqFYGBgU9ql8RdvPrqqyxfvhx7e3u2bduGr68vBQUFyslcZmYm69at45VXXqF///6MGjWKTz75hLNnz1JQUACUjfnRo0f5+uuv6d27N7GxsXTr1o3169ejVqslQWRAukVCli9fjouLizLaWxtDDw8PGjZsSEFBAVu2bFGOy7qjv+F2ex43bhze3t6EhIQoyV9tbEufZMix3fC0F1/hdtJWN2YlJSWYm5tja2sLoJcYrOjYDhASEsLbb79Nz549lflFhfFISkpi/fr1FBUV8fLLL7NgwQKcnZ05cOAAEyZMICkp6b7XuXDhQoYNG8Z3331HWFgYICPFjFFycjJ///03AwcOVIo4FRUVAVC9enVefvllVq9eTb169YiPj2f58uWsXLmywgs+CxcuZMCAAXzzzTccPny4zPPyu244KpWKc+fO8euvvwJQq1Yt5XHt1CyFhYUUFRVx5swZ3n33XXr06EH//v35+OOPiYuLU5KD2uN8bm4u0dHRwO0Lhz///LMB9kyUR9vWpk2bVu6de7a2trz00ksA7N27V/ndv9NxWjfp36NHDxYtWsTZs2cf744I8RSQoS3l0CaBAaUwnIWFBU2aNGH48OHk5ubi7e2t/FgJ45GSksKGDRuwsrIiODiYRo0a8e233/Lyyy+zdetWFi9eDMCwYcPKjZ+242FiYsK4cePo2rUrhw8fJjMzEzs7O3x9fWnevLmMDjMSRUVFxMTEkJyczNSpU+nWrZvSIdTezn/hwgWuXLmCpaUlmZmZ9O7dm8uXL9OsWTP69+9P165dsba2Lvcqc6NGjVi0aBF9+vRREoGSLDAs7QkBwOXLl8nMzFSK+2k0Gk6cOMH48eM5deqUkvgH+OWXXzh48CAjRoygX79+VKtWTXkuPT2dhQsXEhoaioWFBS+99BJffvml3B5qJNavX8+3336Lo6Mjq1ev1pvnt6SkBCsrK9555x327NlDZGQkS5cu5c033yzTni0sLBgwYADBwcE4ODhIkRAjd/PmTTZu3EhmZiaffPIJXbp0UUb8aI/FaWlpXLt2DUdHR5KTk1myZAkFBQV4eXnx3HPP0bJlS71je35+PmlpadSvX5/Tp08za9YswsPDDbynQtfhw4f57bff8PDwoGfPnjz77LM4OjoyZswYJQm8cOFCGjZseM/r1BaA3rVrF97e3vTv3/9xbb54COnp6QB6d1jq1ldRqVT06tULCwsLpk+fzqlTp1i9ejUuLi706dNHb10lJSVkZGQAt8/lPD09lbtGhHGwsbGhVq1a5Ofnc/78eaW/pe1zzZgxg/j4eDQaDVlZWeTk5FBYWEhsbCxHjx5l9OjR9O3bVzm+W1tb8/7771NYWEhCQoKSUBTGYcaMGfzvf//D1dWVlStXlunL+fr68q9//YsVK1awZ88ehg4detcC3KWT/u7u7owdO/ZJ7I4QlVaVngLiTkpPB7F161bCw8M5e/YsAQEBbN++XW7/N0J16tQhJSWF1q1b89ZbbwG3O4+9e/cmISGBo0eP3nU6CN2iEg4ODvj4+BAQEECbNm2oWbOm8rwkAg3PxMSEwsJCbGxs6NOnD66ursooX20HcuLEiezduxdASQafPXuW48ePc/ToUW7cuEGrVq3KFJfQfge0c8xJItA4mJiY0KNHD3Jzczly5Ahr166lSZMmNG3alNjYWPr06UNaWhpt2rShR48eBAcH4+XlRUxMDGlpaaSlpWFvb4+Xl5dy7La1tUWtVtOgQQOGDx/OpEmTlIS/xNzw3NzcyMvLY+bMmWWK/GlPHCwtLYmPjychIQFbW1t69+5d7jHazMxMSf7LCEDjptFouHXrFhkZGbz++us4OzuXOb6//fbbHD58mOLiYiIiIpQLggcPHiQ0NJTWrVvj4eGhfGfUajXe3t7k5eXh4uLC999/L7/lRubSpUscOHCAF154QUng1KlTh9atW/PHH3+QkJBw39NBBAYGUlxczMWLF/nhhx8k5kbqwIED7Nmzh/z8fAYMGKB3oVZ32o569epRr1499u7dS1JSEoWFhQQEBFC9enW9KQG6dOlCdnY2165dY9myZRJ3I3Pz5k3CwsLIyMjgypUrODk5YWJiQk5ODv/3f//H+vXrsba25tNPP+W1117j5ZdfplGjRvz555+kpKRw48YN2rRpQ40aNZTzcTs7O9q3b683x7OcpxuH3Nxcjh49yrJly/Dz8yt3LvbLly8r9VcCAwPveqHP3NycFi1aKHf3LliwQNq5EHehunr1atn744SiuLiY4cOHs337dgD8/f3ZsmWLjAwzAleuXMHa2lqZIwjKr/BcUlKCWq0mLy+PsWPHsmXLFhwcHBg/fnyFI4G1ZI4441I65tpk/OXLl3F0dCzz+jFjxrB582acnJyYP38+jRo1wt3dnaioKKZPn050dDSenp589NFH9OzZU0YEGqHyYg5w69YtPvjgA5YtWwbA/PnzWbduHfv37+fFF1/kP//5j14ncP/+/YwZM4YLFy7wzDPPsHLlSmxtbfVGdd+4cUM54ZTvguHoxlxbnE33tv+Kjsnr16/ntddeA25PF9CvX78nudniMTl79qwyf6du/EeOHMnPP/+MtbU18+bNo0GDBnh4ePDTTz8RFhbGgQMHsLe3Z+PGjbRs2VLv/bm5uVhZWaFSqZTvmDAOeXl5pKamKgWbdY/F+/bt4+WXX+bixYv4+/vf00hg3WO8Nv7SfzdOR44cYfjw4Wg0Gr7++mueeeaZMnde6R4DNm3apMz3+tFHHzFx4sQy69RO4acdMCBxNxzd2Gn/HRUVxYABA8jNzcXZ2ZnCwkIsLS05c+YM1tbWhIaGlhm5HRISoozynD59Ou+88065nyf9OONz9epV7Ozs7tiXGzVqFFu3bqVdu3bKCP+7OX/+PM7OztLOhbgHMgK4AtrRf4mJiSxatIicnBx8fX3ZsmULGo1GDi4GdvbsWdq2bYuDgwMtW7Yst8CT9lZR7e2iGo2GXr163XEksPYK8vXr15XRQsI4lBdzQCkUAfqdy7Vr1/LVV1/RoEEDQkJCaN++Pfb29qjVatzc3GjTpg2//vor6enpmJiY6N1GJoxDRTGH23M7d+zYkZycHKKioti2bRuZmZm88MILLFmyROkEakcN1a1bl1atWvHjjz+SmpqKvb09fn5+eicHukkg+S4YRumYa4/Buu29NG27b9q0KQkJCZw8eZKCggK6dOmCpaXlE91+8eho46r9fdY9vq9YsYKvv/4aNzc3tmzZQqdOnXBxccHS0pJmzZrh6OhIfHw858+fx9XVlQ4dOgD/fH/Mzc2VY4P8zhsXU1NT5YJu6QRO3bp17zoSuPR8sLpFPCXmxs3U1JSff/6Z5ORkLl26xODBg5U+vO5c7dpjQZMmTdBoNOzevZvExESCg4OpUaOG3jq1RWAl7k9eeYX5dNticXGxcnzev38/+fn5XLp0CQ8PD4qLixk3bhyDBg1S3qP9Hnh7e5OVlUV0dDS3bt1i+PDh5Z4HSj/OMO6U3LWwsADKj402znl5eezcuZPCwkI6d+6Mm5vbXe+41l7QlTt0hbi7p+6ymHYkaHmFP+6HiYkJcXFxDB8+nIyMDDp06MDPP/8syV8jkZSURG5uLvHx8QDKnKC6dH9ctLdzm5ubs2zZsgoLw6nVamJjYxk3bhxhYWEUFhY+mR0Sd3W/MR82bBjvv/8+a9euxcvLSykqUVJSQlFREc2aNePtt98GUDqRD3vcEI/W3WJuYWHBrFmzlBFAvr6+LFiwQHmtqampMvKnqKiIgIAA+vbtC0BaWtoT3BNxr+6lnZemO6KodevWAERGRiqFWrVtX1QupY/Husf35s2bM2XKFEJDQ5Vindokj4WFBV26dKFu3boUFBSwd+/eCr8DkiAwbuWd8AcEBPDdd9/pFYZLTEwE/rnwHxcXx/z585U+nO56JObGRdvOi4uLcXZ2Zs6cOVSvXp0dO3Ywbtw44J8+vJZu4a9u3brh5ubGuXPnyMrKqvBzJO5Plm4ScNy4cUrfTHtBRvvvkpIS/Pz8+OWXX9i6dSu7d+9m6dKlqFQq6tatq7wO0Cvuqr3ok52drRT3FYanG/fjx4+Tmpp6T/04+CfOPXr0oHbt2pw5c4ZFixYB916UV0Z8C3F3lbKV6J4UaK8ARkREkJ6ezt9//w08/A99bm4ugwcPJjk5mU6dOhEWFibTPhiRDh060KpVK7Zu3crFixfv6YfhTkngNWvWkJOTQ3JyMiNHjmTLli0cOHBAYm1E7ifm2pO+yZMn4+npqTeKSHdkt/axwsJCCgsLJQFsZO4l5hYWFnz66aeMHz+ejh07Ym5uXu5IH+2yg4MDcHuaAZDkoLF5kGO7lkql4tVXX8XHx4eLFy/y+eefk5eXJycERkr3eFtUVMSFCxe4cOEC165dA/45Ppd3XG7Tpg3vvPOOcnzXHSleUlJC9erVad++PXB73ud7PQEVj1fpWD7o8bd0EnjixIkkJCSgUqmIj49n6NChfPTRR6xevfpRbLZ4SLpxz8/P59SpUxw9epTLly8ryTvt6L127doxZcoULCws+Omnn5g6dSpQfhIYoEWLFspFfu2FAGF42viMHz+en376iZkzZ/Ldd98B+klg7THb2dkZT09PfHx82LdvH1lZWcpoUd0Er7Z/r30sMDAQS0tLSfAbAd3k79KlSxk5ciTDhw8nJibmns+viouLsbe3Z8qUKVSrVo39+/ezc+dOZf1CiIdX6c6KdA8u//3vfxkyZAjPPvssvXv3pmPHjgwaNIiQkBDy8vL03nO/rK2tee+99/D19SUkJESSv0bG1NSUpk2bcvbsWZYvX37PI3UrSgJ/8803zJkzhwEDBpCenk6HDh2YM2fOY94LcT/uJ+al22npBJD2vWfPngVuX222srKSRJGRudeYW1hY8MEHHzBhwgTUanW5JwIFBQXk5+cro0KbN28OyGgBY/Ogx3ZAObaPGDECS0tLEhMTSUpKAiTRb2x0+3Lff/89r7/+Ol26dKFLly4MHjyYmTNnkpqaSl5enpIYKs3MzAwo24a1d3nExcUB4OPjI3P8GgHdmO/btw/QTwTdr9JJ4MmTJ7N9+3aGDRvGmTNn8PPzY+TIkY9s+8WD0Y37ypUrGTNmDH5+fjz77LM8++yzfPbZZ6Smpip365ibm9OvXz+GDx+OmZkZy5cvZ/LkyUDZUYC3bt0C/kk2ahOGwjikpqaybt065Vg9ZcoUVqxYAZRNAuvSPr5+/XoApQ5AYWEhGo2G8+fPs2HDBlQqFW3atAEkOWhouu38vffeY/r06aSnp9O8efMy0/Lcifb3vGnTpjg5OXHp0iUiIiIAGcUvxKNS6eYA1jb+6dOnM3fuXC5cuICnpycuLi5cuXKFtLQ0tmzZQnZ2NtWqVcPd3V1vzqF7of0Rad68OaNGjUKtVkvy14hof2RcXV0JDw/n+vXrDBw4UOkg3C3OunMC9+jRg5SUFKKiojh27BhXrlyhQ4cOhIWFKdN9SILI8B425rq0UwOkp6fz73//Wxk12LhxYyn6Z0TuN+ampqYVnvxpY56amsrcuXOpUaMGEyZMoHbt2hJzI/Ioju1we37XdevWcfbsWaytrXnmmWckxkak9InirFmzOHnyJCUlJVy9epXMzEwOHjxIdHQ0Fy5cwNvbm2rVqt3Td0BbMCo1NZXZs2djbW3N+PHj8fDweAJ7JiqiG/PJkyczbdo0HBwcaN269X330XXVrVuXli1b8ueff3Ly5El+/fVXsrKy6NChg17NDunHGYZu3GfOnMmsWbM4ffo0Go0GOzs7zp07x8mTJ7l58ya+vr6YmZmhUqmwt7enVq1a5OXlkZCQwOHDh0lISCAwMBAzMzNljn8zMzPlbg8bGxvGjx+Ps7OzgfdaaBUWFioJ4ICAAE6fPs327dtxcXGhVatWFbZ9U1NTQkJCOHHiBGfPniU4OFip6ZKWlsawYcNITk6mX79+vP/++8o8z8JwtH//OXPmMH/+fKysrFizZg0TJ05UirjeD2dnZ27dusWuXbuIioqiY8eOuLm5PerNFqJKqnQJYIAlS5YoP/YbNmxg2rRpvPbaa/j4+GBnZ0dUVBRHjx4lIyMDMzMzvL29y/2R0V2+evWqXvKg9A+JdB6NhzY2pqamREREEBERgZmZGYGBgXrFIe7ExMSE/Px8LCwscHd3JzQ0lJs3b+Ln58eWLVtkxLeReRQxB5RbhbOyspQO5AsvvMCkSZPKLSAhDOdxxTw1NZUXXniBl156SWJuZB5VzF1cXDA1NWXnzp1kZmbi7+9PrVq1Hvfmi3ukjeHs2bOVE8XFixczadIkevbsSbNmzdi5cydnzpwhISGBuLg4OnTogLW19R0LwWiTv1lZWQwfPpyUlBQGDhzIq6++Ku3cwLR//48//pglS5YAEBMTg5WV1R0TQXdTXFyMh4cH9vb27Nixg5s3b+Lv7094eLj04wxM93j9wQcfsHDhQlQqFQsXLmTy5MmMGzeO5ORkjh8/zsWLFxk0aBC2trbK+11cXKhbty6mpqacOHGC2NhYIiMjuX79OjVr1qSoqIgzZ84watQoTp8+Te/evZXfdWF42ql4Tp48SVxcHJMmTcLS0pITJ07cMQmsfd+NGzc4duwYUVFR/Pnnn+zevZtNmzYxe/ZsUlJSCAwM5Mcff8TU1PSuBcLEk7Fjxw6mT59OUVERYWFhdOzYUe8Oj/sZiKe9EHTgwAGys7Px8PDA39//gS8WCiH+UekSwPn5+cydO5fU1FR++OEHunTpgomJCaampjRo0ICgoCDs7Ow4ePAgSUlJpKamYm5ujo+PT5kTSO3/ly9fzk8//US9evVwdHSUA0slUa1aNezs7AgNDSUmJoaGDRvi5eV1T/ErKSnB1NSU2NhYxowZw4ULF/D399cbMSInDcbnYWIOt9v86dOnGTx4MMePH6dz586sWLECtVotnQoj9SAxL32c18b8xIkTPPPMMyxbtkxibsQetp3D7cTQnj17yMzMpGPHjnh7ez/GLRb3KyIighkzZlBQUEBYWBjdu3fHycmJ+vXr4+fnh6+vL+vXr+fGjRucPn2aw4cP8+yzz2JjY1NhuzUxMSE9PZ0hQ4YQFxdH586dWb58ubR1IxEeHs6nn35Kfn4+TZo0IS0tjUOHDmFjY/NASWDt3P4nTpxg8uTJZGdnKxfxpR9neNo4Llq0iM8++wxra2vCwsLo2bMnTk5O2NnZ0a1bN0JCQsjMzKRDhw40atQIQBm17ezsTOPGjalfvz4REREkJyezY8cOfvrpJ77//nuWL19ORkYGnTt3ZuXKldLWjYg2BidPnuSPP/7gpZdeonPnzly+fJlTp06VSQJrY65SqTA3N8fBwUGZ0zstLY0TJ05w8uRJTExM6NevH6tXr5aLPEZmw4YN7Ny5k9mzZzNgwACKioqUmhzXrl3jypUrHDx4kJKSEm7evImNjU2569F+d+zt7YmKiiI6Opq4uDhGjx6NpaXlk9wlIZ5KlS4BnJGRwbRp07C2tmbKlClYWVnpze2mUqlo27Ytbm5uREdHk5SUxNmzZ7G3ty/3BHL37t28/PLLHDt2jBs3btCsWTPs7Oye9G6JB+Tm5saFCxc4fPgwBQUFNG7cGCcnp7u+T6VSkZyczNChQ0lNTSUgIIDw8HA5aagEHjTmu3btYvPmzbz99tukpaXRs2dP1q5dq3Qg76fYlHiy7jfm2uP87t272bhxI5MnTyY1NZWgoCB++uknZcSIxNx4PWg716pTpw5//fUXt27dYv78+TI6yMj8/vvvhIeHM2LECMaOHasUd9JeqK9fvz6enp6Eh4dTXFxMRkYG8fHxdO3alerVq5cZDR4dHU1oaCiTJk0iOTmZoKAg1q1bJ23dSKSlpbFo0SKOHz/OkCFDGDFiBPn5+cTFxT1wElilUnHs2DFGjRpFWlpamWkfpB9neLt27WLWrFnk5OTw008/ERgYqFeUt6SkhPXr15OVlcUrr7xCnTp1APTuzrGxsaFFixb06tWLnJwcVCoV6enpXLt2DR8fH/r27cs333wjfTkjoz1GV6tWjZUrV+Li4sKwYcNwdXXl2rVrnDx5Ui8JrP1OJCQk4OjoSM2aNfHx8cHX15ecnBzq1KlDr169ePnll5k6dapMz2hEioqKyM/PZ8aMGVy6dIk333wTDw8PiouLMTU1JSEhgXfffZf58+fzzTffsGnTJtatW4e9vT2urq7lJnW1x4lmzZrx7bffUrt2bcaNGyd9OSEegUqXAP77779ZuXIl1atX55VXXtG7XUi349i0aVNcXFyIjIwkKSmJmzdv0qhRI1xcXPTWZ29vz5kzZzh+/DiWlpa88cYbcuW4EtFoNKjVavbt20dMTAzm5uY0atSowquKuvbu3cvKlSsJDAxk8+bNciW5kniQmOfn5zNp0iS+//57atSowYsvvsiCBQsk5pXEg8S8oKCAqVOnsmrVKiwsLBgxYgRLliyRmFcSD3Ns194O2rlzZyZPnqzEXE4cDE9bY2HVqlXExMTQuXNn5U4u3VH7RUVF2NrasmPHDrKzs7G2tiYhIYHCwkL8/PwwNzdX1pmamspnn32mtO/Ro0ezePFiaetGZO/evcybNw8rKyumTZtGnz59cHFxITs7m+PHjz9wEvirr77izz//JCAggLCwMIm5Ebl58yYLFixg7969fPLJJwwePJiSkhKl+JeJiQlJSUn8+OOPaDQamjdvzty5c1m/fj1xcXHUqFGDmjVrKkUdnZ2dCQoK4qWXXuK5557jpZdeYty4cfTs2VMp8CxxNx7a9mtmZsa6desoKipi6NCh1K5dm1q1auklgV1dXWnRogVvvvkm3333HfXq1cPDwwMrKys8PT0ZOHAgw4YNo2vXrjRp0kQ5Rkiy3zho78T+4YcfuH79ujJQz8TEhNjYWAYMGEBUVBRwu1DjrVu3yM7OZuvWrcDtudzt7e311qm9GFxUVIS9vb3y+y7TfQjx8CpdAtjS0pKNGzeSkZGBi4sLrVu31vsB0O04ent7U6NGDX755ReSk5NxcnKiY8eOeuszNzfnmWeeoXr16kpCSG4fqlwaNmzI9evX2b17N0ePHsXe3p769etjZWUFUOG8kV5eXvj6+vLvf/9bThoqmfuNuVqtJjg4GGdnZ8aOHcuLL76oFAOUmFcODxLzHj16UL16dUaNGsUbb7whMa9kHvTYrj05qF69upJskBNF46Ad2bd//34OHTqEiYkJffr0KVPA0cTEBBsbG0JCQigoKKBz584kJCSQk5ND27ZtqV27ttJXs7Ozw9TUlBYtWjBmzBjGjh0rCSEjk5uby4EDB3jxxRcZNWoUAO7u7koB5wcdCdy9e3dUKhX//e9/ZeSvkdFoNNy6dYuMjAxef/11nJ2dlVv9tTF6++23OXz4MMXFxURERBATE0NycjIHDx4kNDSU1q1b4+HhoXwP1Go1arUaJycnnJ2dlaJx2sSyMC5FRUVYWlqyZ88edu3aRf/+/XF0dMTNzQ1XV1dyc3NJSEhg27Zt7Nq1SyniOHHiRBwdHfXWpTtHsNRvME4//vgjKSkpNGvWDB8fH9LS0hgxYgSZmZkMGzaMefPm8dJLL9GmTRvMzc05ceIEBw8exNramrZt2+rd0Q0oI8j9/PyU/rv05YR4eJUqAaztDB47dozY2FhMTEwIDg7G0tJS74qQbsfRx8cHMzMzdu/eTUREBP7+/mWqQZubm9OhQwfUajUFBQXSeaxEtB2BgIAAsrOzOXz4MPv370etVuPq6qrMIVVRAcB69epJUqiSeZCYFxUVUa1aNdq1a0edOnVk9EAl8zAxb9++vTL9j8S88njQY3t55ETRcK5cuYJGo9G75VulUpGRkcG2bdsAaNu2Le7u7sp7tP25nJwcvvnmGxo3bsyMGTPYsWMHycnJaDQaevTooVfXwcvLi3bt2uHp6Slt3Qi5urrStWtXevfuDdy+Q0OtVuPu7o6zs/MDJYG16wgMDJTbwY1UkyZN6N69Ow0aNABQ5gMFGDlyJL/88gvW1tYsWLCA119/nQ8//JCaNWty/fp1Tp8+zW+//Ubnzp2pVavWHZN+cow3Tto7O1JSUti9ezf9+/enbt26ANSsWZP69euTk5PDyZMnOX/+PGq1moiICLy8vPTu2tGNr8Ta+GiP03FxcRw5cgR7e3t69uzJrl27+O677xg6dChLlizB2dkZBwcHmjRpQt++fcnKyiI6Opr9+/fTpk0bPD097/g5cpFHiEejUiWAVSoVJiYm1KxZkx9//JHk5GRyc3Pp0aOHksQrLwncqlUr4uPjSUxMpG7dunTs2LHCkUNywlC56MY5KCiIc+fOERUVRWRkJDk5OVhbW+Pu7l7mRKJ07OVHpfJ4kJiXF1/pRFYeDxNzOXGonB702C6Mx9mzZ2nbti0ODg60bNkS+KcNuru78+uvv5KSkkJMTAzNmjXD3t5eL1m8cOFCwsLC6Nu3L4MHD6Zu3bqEhoZy+5to4AAAIABJREFU6dIl+vbti42NjV7cdY/z8n0wHtr+tra+hnZeSO3j7u7uODk53TEJDLdjmpeXp7y3dH9d+nHGRRtfa2trvWWAFStW8PXXX+Pm5saWLVvo1KkTLi4uWFpa0qxZMxwdHYmPj+f8+fO4urrSoUMHQ+6KeEDamGdnZ7N582ZsbGzo1q0beXl5aDQavfP54uJiiouL8ff3x9vbW7l7R47lxk8bIxMTE9avX8+xY8eoU6cOZ86c4eDBg8yfP5+aNWsqeRrt96Jr166cPHmSU6dOkZ2dTZ8+fdBoNBJzIR6zSpUAhts/Jq6urlhbW7Njxw6io6MpLCykU6dO5SaB4XZSNyEhgYiICAoLCxkyZIiMEniK6CYAevbsSX5+PtHR0Rw9epRDhw5RVFREu3btlO+DdCgqP4l51SMxr3ok5pVbdHQ0q1atwt3dne7duyv9M+3o/KZNm7J3716Sk5M5cOAA586do7i4mNjYWFatWsW8efOwt7dn2rRpuLm5YWVlxS+//EJ6ejpDhgyhZs2aht5FcQ9Kt0ndC/HaRICHh8cdRwKrVCri4+OZPn06tWrVws3NzRC7Iu5DRXGH28dqJycnPv74Y7y8vPQSQxqNhtq1a/PXX3+RmJiIiYkJgwcPlmO7Eaho8FRFtK+1tbVl3bp1VKtWjYEDByrn4BMnTmTTpk3KSP709HTCw8OVwnAS88rFw8ODCxcuEBMTQ1xcHImJiVy/fp3XX3+dGjVqlBmkp1arOX/+PDt27MDKyorRo0eXmQZCCPHoVboEsPbHwNnZmVu3bnH06FH2798PQGBgYJkksPbHqrCwkJCQEBwcHBgyZAhmZmYG2wfx6OkmCjp37oyzszP5+fkcOXKEHTt2kJiYSFFREXXq1NErHnO/nRlhPCTmVY/EvOqRmFdetWvX5o8//iA6OppBgwYpIwG1/bOaNWvi5eXFoUOHSElJITIyktDQUDZt2sThw4dxcnJiwYIFdOnSBQBra2u2b99ORkYGo0ePlgSwkXiYtqabBC5vOggrKytat25NYmIiAwcO5NChQ1haWtK9e/dHvBfifj1M3F1dXfH398fZ2Vlvuhbt98HMzIwzZ86wa9cuGjVqxHPPPSd3aBqYbrz/+OMPzMzM7qkoq3YEf2hoKMeOHWPgwIHY2dnx5ptv8sMPP2BqasrWrVt54YUXuHjxolIYrmnTpnh5eT3WfRJ3dz/tXHv33fHjxzl9+jSXLl0iLy+PJk2a0Lx583LvyLt16xbr1q1DrVYzaNCge/pOCSEeTqVLAGvZ2dnh5OREbm4uJ0+eZN++fdy6dYtnnnlGL/mrTQYfOXKEsLAwOnXqxAsvvGDgrRePg+6JRIsWLWjbti0NGjTgyJEjREdHEx4ezu7du7G1teXGjRvKnGKi8pKYVz0S86pHYl45mZiYcPjwYfbt24elpSXt27fXu01frVZTv359evXqRVZWllId3MXFhWHDhjF16lS6du0KQH5+PleuXGHhwoVcvnyZkSNH4urqaqhdE//fgyaFdN0pCRwZGcn169f56KOPSE9Px9/fn2+//VamezCwRxF33aRv6XUXFxfz3XffER8fz4ABA5SLQMIwdOM9btw4Zs6ciaOjIx4eHsqFvYqoVCrMzc3JyMjgyJEj9OvXj/nz57Nq1SpMTU0JDw+nffv22NjYULt2bTIzM7G0tOTTTz+VpL+BPUg7b9CgAfn5+cTHx5ObmwuApaUlHTt2pFq1asrrtPmZ2NhYNm/eTJcuXXjxxRcf384IIRSVNgEMt68gu7i4kJ+fr1SSTE1Nxd3dHRsbG2UuuaysLCZMmEB2djYjRoygXbt2ht508ZjonkjUqFGDNm3aEBwcjI+PjzLZ/KZNm9i4cSMeHh40bNhQOhiVnMS86pGYVz0S88pFGytXV1fCw8O5fv06AwcORKPR6E3VUVxcjJ2dHb169WLgwIEMGjSI119/nV69einFgvLz8zEzMyMxMZFvv/0Wf39/3nzzTYmvgT1MUqi00klg7QCP2NhYIiMjyc7OJiAggPDwcExNTfUKRIkn61HGvbSioiLUajWpqanMnj0ba2trxo8fj0ep4t3iydLG+/PPP2fp0qUAxMfHY2Fhcde4a78vycnJ/PLLL/z222/s2bNHGfnr7++vFHCsXbs2zZs3Z+rUqdLODexB2rn2t71du3bk5+dz+vRppchfbm4uLVu2pHr16spI4aysLMaPH09WVhYjRozAz8/vSe6iEFWWwRPApStE3wvdkwc3Nzfc3NywsbHh0KFDHD9+nMjISPbs2UNOTg5//fUX77zzDklJSQQFBTFnzhz5MXnKlR5N4OjoSIsWLRg2bBjBwcE0adIEb29v+vfvj62trYG2UjxKEvOqR2Je9UjMKw9trExNTYmIiCAiIgIzMzMCAwP1kn3af6vVaszNzbGzs8PCwoLi4mJlSi+NRqOcIJ49e5bhw4fTqVMnA++heJikUEXr050T+Pr16+zdu5ebN2/i5+enl/yVOh6G86jjrqVN/mZlZTF8+HBSUlIYOHAgr776qtzRYQS2bdvGnDlzuH79Ok2bNiU1NZXjx49jaWl5x7hrY1e7dm0iIyNJSEhAo9GwdetW/Pz8lClAtG3fyclJKQAnF/kM50Haue6UXe3bt8fU1JQrV65w9uxZjh49SkpKCsnJyRQVFREVFcUbb7xBQkIC3bt35/PPP5f8jBBPiEETwOVViL4b7UkBQGJiIg4ODtSqVYtWrVrh4+PDgQMHuHDhAsePH2f79u3s2rWLK1eu0LNnT2WuId05gkXVoK06XatWLdq2bUtAQIDMM/SUk5hXPRLzqkdibtyqVauGnZ0doaGhxMTE0LBhQ7y8vMqdC1CX7lReycnJDBo0iISEBIKCgvjqq6/0KokLw3nQpNCdqFQqEhMTmTx5MhcvXsTf358tW7ag0Wgk+WskHkfcTUxMlAKPcXFxdO7cmeXLl6NWq6XAp4GdP3+exYsXEx0dzcCBA/n888+5cOECR48evee4W1lZUa9ePZKSkli4cCEBAQF65/R3KhooDONB2rluErhNmza4urpia2tLVFQUiYmJ7N27l5CQEDZt2kR2djZBQUH8+OOPkp8R4gkyaAK4ogrRFdH9oVi5ciUTJkygevXqtGzZEgsLC7y9vQkODqZNmzZoNBo8PT155plneOWVV3jvvfdk5EAVVrojIT8wTz+JedUjMa96JObGz83NjQsXLnD48GEKCgpo3LgxTk5Od31feno677//Ph9++CGZmZkEBQWxZs0aOVE0Eo8iKVSaNvnbs2dPzp07p0z7IMlf4/E44h4dHU1oaCiTJk0iOTmZoKAg1q1bp7R1GQlqWLGxsbz77rtUq1aNCRMmEBgYSJ8+fYiPjycmJuae4+7m5sbzzz9Po0aN9M7phfF5mHaumwSuX78+QUFBtGjRAmtra1QqFXZ2djzzzDOMGjWK2bNnS35GiCfMoAngiipEl0f3h2LVqlVMmjSJa9euMXz4cKVKaElJCQ4ODjRp0oR+/frx3HPP0a1bN5o1a6bcSigHFyGEEEKIJ0Oj0aBWq9m3bx8xMTGYm5vTqFGju47UNjc3Z+fOnaSlpfHKK6/w3//+V04UjcijSgrpKi4u5vfffyckJIRWrVrx888/S8yNzKOOe2pqKp999hlLlizB1NSU0aNHs3jxYom7EalTpw4pKSm0bt2at956C7hdxK93794kJCTcc/JfpVJhZmam/FsYr4dt56Xj6+npSXBwMEOHDmXw4MEMGjSItm3bSn5GCAMwaAL4bhWitUpKSvSSv2+//TYAixcvZuDAgcrrtAebim4LlCuNQgghhBBPVsOGDbl+/Tq7d+/m6NGj2NvbU79+faysrIDy+22mpqZ0796d7t278/zzz8uJopF5VEkhXSqVCmdnZ3x9fZk1a5YkAY3Qo467nZ0dpqamtGjRgjFjxjB27FjUarW0dQPRrc1TUlIC3G6Xffv2JSgoCLh9oaa4uBiNRkOvXr0euL0L4/U4ju8lJSWYmppiYWGh97jkZ4R4sgyWAL7XCtHwT2L3+++/10v+Dhs2TG9dpV8vhBBCCCEMR9tHCwgIIDs7m8OHD7N//37UajWurq44ODjo3TKqy8TEBEdHR+V5uRXcMJ5EUkj7PbGyssLLy0sS/kbgSSUDvby8aNeuHZ6entLWDah0bR5toU5d2kFZukU67xZ37ZQ9169fR6VSSWyNzJNq55KfEcI4GCwBfK8VorWys7OZN28ep0+f5uuvv2b48OEAMoeQEEIIIYSR0k3uBgUFce7cOaKiooiMjCQnJwdra2vc3d0rTALrrkc8eU8yKVS67y/9e8N50slA3VhLWzeMe6nNU7p93kvcTUxMiI2NZcaMGQA0atRI2raRkKS/EFWPQaeAgHurEK19nYeHB4MGDaJPnz6A/tQQQgghhBDC+Ogmd3v27El+fj7R0dEcPXqUQ4cOUVRURLt27ZS+350SweLJepJJIUkSGA9JBlY991ObR+tOcTc3N6dZs2ZkZmYyZMgQIiMjcXZ2Jjg4+AnsjbgX0s6FqHoMngCGe68QXbt2bdzd3YGK5/kVQgghhBDGRTcJ3LlzZ5ydncnPz+fIkSPs2LGDxMREioqKqFOnDubm5sr7pL9nWJIUqpok7lXPvdbmKe995cU9Pj6ey5cvM2vWLDIyMujQoQPLly+XCz1GRNq5EFWPUSSAH6RCtJwMCCGEEEJUHrpTfLVo0YK2bdvSoEEDjhw5QnR0NOHh4ezevRtbW1tu3LhBrVq1pL9nYJIUqpok7lXL/dTmKY9u3Hv06EFKSgpRUVEcO3aMK1eu0KFDB8LCwtBoNBQWFspoUCMh7VyIqscoEsDwYBWihRBCCCFE5aGbBK5RowZt2rQhODgYHx8fsrKyiI6OZtOmTWzcuBEPDw8aNmwoJ48GIkmhqkniXvXcb22e8piYmJCfn4+FhQXu7u6EhoZy8+ZN/Pz82LJlC6amphQWFkphRyMh7VyIqskoEsAPUyFaCCGEEEJUHqX7co6OjrRo0YJhw4YRHBxMkyZN8Pb2pn///tja2hpoK4UkhaomiXvVda+1ecpTUlKCqakpsbGxjBkzhgsXLuDv78+WLVuUJKDE23hIOxeiajKKBPCjqhAthBBCCCEqn+LiYkxNTalVqxZt27YlICDgjlOBiSdHkkJVk8S9arrX2jylqVQqkpOTGTp0KKmpqQQEBBAeHi7xNnLSzoWoWowiAQxSIVoIIYQQoqoq3aeT20WNiySFqiaJe9XzILV5tPbu3cvKlSsJDAxk8+bNMgK0kpB2LkTVYTQJYJAK0UIIIYQQQhgbSQpVTRL3qulBa/N4eXnh6+vLv//9b4l3JSLtXIiqw6gSwCAVooUQQgghhDA2khSqmiTuVcuD1ubRLterV08pECbxrjyknQtRNRhdAhikQrQQQgghhBDGQpJCVZPEvep50No8Mo1P5SXtXIiqwygTwCAVooUQQgghhDAGkhSqmiTuVZPU5qlapJ0LUXUYbQK4PFIhWgghhBBCiCdPkkJVk8S9apLaPFWLtHMhqoZKlQCWq0xCCCGEEEIYhiSFqiaJe9UktXmqFmnnQjz9VFevXi0x9EYIIYQQQgghKgfdE/7Tp0/zxx9/8Nlnn3HlyhUA2rRpw4QJE6hTpw6tW7c25KaKR0jiXjWVTvAlJCRw8OBBvv/+e6KjoykuLqZ69eosWLCAvn37otFoDLi14mFJOxfi6SUJYCGEEEIIIcR9kaRQ1SRxF1oFBQXExcVx6NAhsrKyeOWVV6hZs6ahN0s8AtLOhXg6SQJYCCGEEEII8UhIUqhqkrhXLcXFxXrTMRYVFaFWqw24ReJJkHYuROUmCWAhhBBCCCHEQ5OkUNUkcRfi6SftXIjKT6qoCSGEEEIIIR5a6QLNkhyoGiTuQjz9pJ0LUflJAlgIIYQQQgghhBBCCCGeUpIAFkIIIYQQQgghhBBCiKeUJICFEEIIIYQQQgghhBDiKSUJYCGEEEIIIYQQQgghhHhKSQJYCCGEEEIIIYQQQgghnlKSABZCCCGEEEIIIYQQQoinlCSAhRBCCCGEEEIIIYQQ4iklCWAhhBBCCCGEEEIIIYR4SkkCWAghhBBCCCGEEEIIIZ5SkgAWQgghhBBCCCGEEEKIp5QkgIUQQgghhBBCCCGEEOIpJQlgIYQQQgghhBBCCCGEeEpJAlgIIYQQQgghhBBCCCGeUpIAFkIIIYQQQgghhBBCiKeUJICFEEIIIYQQQgghhBDiKSUJYCGEEEIIIYQQQgghhHhKSQJYCCGEEEIIIYQQQgghnlKSABZCCCGEEMIIpKam8tZbb9G6dWtq1qyJnZ2d8t/WrVsNvXmiHGlpaXpxsrOzIy0tzdCbJYQQQgihx9TQGyCEEEIIYQhTpkzh22+/LfP4wYMH8fLyMsAWPV7Xr19n48aN7N69m6NHj3Lp0iVyc3OxtLSkRo0aNGjQgDZt2vDss8/SoUMHVCqVoTe5SklKSqJr1678/fffj3S958+fp2nTphQVFek9PmDAAFauXPlIP2vr1q3ExsYqyz4+PvTp0+eRfoYQQgghhLh/kgAWQgghRJVTUFDApk2byn1u3bp1fPDBB094ix6fkpISlixZwhdffMHVq1fLPH/t2jWuXbtGRkYGf/31F1999RW+vr789ttvBtjaqmvevHmPPPkLEBISUib5C7Bt2zZycnKwsbF5ZJ/1888/s3btWmV52LBhkgAWQgghhDACMgWEEEIIIaqc33//ncuXL5f73IYNGygpKXnCW/R4FBYWMmrUKGbMmFFu8rciWVlZj3GrRHmio6P1lnv27ElERAQxMTHExMTw7LPPPtB6169fX+7jt27dIiws7IHWKYQQQgghKhcZASyEEEKIKqeipBhARkYGERERBAQEPMEtejymTZtWZu5YCwsLRo4cSVBQEHXr1sXExISLFy9y7Ngxfv/9d3bv3m2gra3arl27prfcu3dvvL29H2qdCQkJHDt2rMLn169fz6hRox7qM4QQQgghhPGTEcBCCCGEqFJycnLYtm2b3mM9evTQW75TglgrKSmJsWPH0qhRI2rWrEmbNm346KOPyM3NZc2aNXpFoXx8fCpcz/79+3njjTdo27Ytbm5uuLi44OPjw9ixY9m7d++D7SQQFRVVZo7j2rVr89dffzF37lyCgoJo3LgxjRo1IjAwkPHjx7Np0yZiYmIYPXp0mfX17t1bb5/mzJnDjRs3mDNnDn5+ftSqVQs7O7sy7zt16hTvvPMOHTp0oG7dujg5OeHp6Um/fv1YvHhxmcSn1rhx4/Q+b9y4cff9morisHPnTgYOHEi9evWoVasWgYGBfPPNN+VOlXA/MjMz+eijj+jSpQv16tXDycmJBg0aEBwczBdffEF2dnaZ9/j4+GBnZ0d6erre42+++abetj+IdevW6S0/++yzmJr+M/5j3759nDlzpsL3a7dN+9+aNWu4cuUKM2bMoFWrVjg7O+Pj46PEQXf6B4C1a9eWKZAG8OGHH+o9NnPmTL337dy5U+/5BQsW6D2/YcMGvedfeOGFMtuen5/P6tWrGTJkCE2aNMHZ2Zk6derQrl07JkyYwOHDhyvc79LbvGfPHjIyMpg4cSLNmjXD0dGR3r17V/j+0j755BO99Tk4OBAaGqo8n5OTw/z58+nZsycNGjTAyckJNzc3WrZsSa9evXj//fcJCwsjPz//nj9TCCGEEEKXjAAWQgghRJUSFhbGrVu3lOWWLVsydepUtm/frveaL774AnNz83LXsWvXLoYOHcrNmzeVx5KTk5k3bx6bN2++p1GV169f56233iIkJKTMcxkZGWRkZLBhwwZGjBjBvHnzMDMzu5/dZOHChWUeW758OY0bN77j+9zc3Hj77bfvuv6///6bbt26ceLEiXKfLykpYc6cOcydO5fi4mK957KyssjKymL37t0sWLCAVatW4efnd9fPfBTmzZvHRx99pPdYXFwc06ZN488//2T16tX3/bcGWLFiBdOmTSuTpMvOziY7O5sDBw7w9ddfs2TJkvtKHj6okpISNmzYoPfYq6++iomJCX/88QcAxcXFhISE8NZbb93TOjMyMggICODs2bMPtW0BAQHMnz9fWT506JDe8wcOHCizPHHiRGX54MGDes8HBgbqLZ86dYqRI0dy6tQpvcfz8/PJzc0lMTGR1atXM3r0aL788su7xjsmJoYRI0aQk5Nz950rZeHChcydO1dZVqvV/O9//+P5558HbhfpCw4OJjU1Ve99BQUFXLt2jdTUVCIiIpTtcHd3v+9tEEIIIYSQEcBCCCGEqFJKj+594YUXaNu2LR4eHspjV69e1UsI6zp//jwvvviiXvJXV0pKCnPmzLnjNpSUlPCvf/2r3ORvaWvWrGHKlCl3fZ2uoqIi/vzzT73H/P39ad++/X2t506WLVtWYfIX4IsvvuCLL74ok/wt7fz58wwcOJCTJ08+sm2ryLlz58okf3Vt376d2bNn3/d6165dy6RJk+46QjMnJ4fRo0c/kWk2IiIiyMjIUJZr1KhB165dy4yWLT1K+E6+/PLLh07+Avj5+aFWq5Xl6Ohovb9d6QRv6QTx/v379ZZ1p2u5cOEC/fv3L5P8Lc+qVauYPHnyXV83c+bMB0r+rlq1ivfff19Z1iZ/dWPw5Zdflkn+CiGEEEI8apIAFkIIIUSVcebMGfbt26csq1QqZSSe9v9aFU0D8Z///Ie///5b77E33niDffv28dtvv9GlSxcKCgruuB2hoaF601BYWloya9Ysdu3axb59+5g9ezYWFhbK86tXr1ZGAd6LjIyMMttYepQk3E50p6Wllfvf3YrGFRUVYW9vz7x58zh06BA7duzg3XffBSA1NZUvv/xS7/WNGzdm7dq17Nmzh08//VRvdHVubi7Tpk275/17UIWFhVhYWPDZZ5+xd+9evv/+e+rUqaP3mkWLFnHx4sV7XmdOTg7Tp0/Xe8zV1ZUVK1awb98+Fi5ciI2Njd42TJkyRZluYtu2bcTExODq6qq3jo8//lgpABcTE3O/u1rm+9uvXz80Gg29e/fW+26dOHGCuLi4e1pnUVERderUYdmyZURGRvLrr7/y2muv8cknnxATE0O/fv3KfKbuPmj3w8bGhubNmyuvu3XrlvJcUVFRmekZLl26RFJSEnD77x0fH688V61aNVq1aqUsf/zxx5w7d05ZVqlU/N///R9//fUXmzdvpkOHDnrr/uGHH8oklMvbb29vb3788UciIyMJCwtj0KBBd3zPxo0bmTRpkrKsVqv55ptvyiTgS3/2rFmz2LdvH0eOHOG3335jwYIFDB48WO87JIQQQghxv2QKCCGEEEJUGSEhIXojUtu3b0/t2rWB2yOB//Of/yjP/f7771y9erXM/KsbN27UW+7Vqxeffvqpsvzjjz/i4+PDpUuXKtyOFStW6C3Pnj2bMWPGKMtNmzblypUreknUlStXlkleVaS8uWZLJxgB5s+fr3crvq5p06aVSWyWtnLlSjp16qQst2nTBrg9armwsFB53MLCgvDwcJydnYHbc8uamJjorX/Xrl2kpqbqjcR+HD7++GPGjh0LQLNmzWjYsKHe37WgoICwsDDlNXcTFhZWJlm+bt06Zb7hpk2bYm9vz4gRI5TnExMT2bdvH506dVK+f7ojYgHs7e0f+Hb/vLw8wsLC9B7TJh5tbGzo3r07W7ZsUZ5bv349zZo1u+t6TU1N2bx5Mw0aNADA09NTGVXu4OBA9erV9V5fvXr1CvchICCA6OhoZfngwYO0a9eOuLg4ZV5oJycnsrKygNvTQDRs2JBDhw7ptWFfX180Gg0AN2/eLDPtxbhx43jvvfeU5fbt29O8eXMuXLigPLZq1ao7jo63tbXll19+UY4Fnp6edO7cucLX//bbb8yYMUPZTm3yd+DAgWVeq9tObGxsGDdunLI/DRo0wNfXlxdffJG8vDxMTGTsjhBCCCEejPQihBBCCFFllL7dXTch07RpU735cfPy8ti8ebPe69PS0sokV3UTe3B7NG95iR6toqKiMre0T5o0qUzhqdIjaEvPi2porVu31kv+6iq9rd26dVOSv1ql/27lve9xGDJkiN6yt7c3LVq00HssKirqntdXepubN29epuhfr169sLe3v+P7HqXt27frJaVr1qypNwK89CjUjRs33nWqDoA+ffooyd+HpTttA/wz7YPu9A+vvvpqmedL/9101xMVFUVeXp7e88OHD9dbNjc3L7P/d4vF6NGj76sQ37Rp05S7ANRqNUuXLq3wmKA7EjonJ4eAgACmTp3KsmXL2LVrF5cvX1a2W5sYFkIIIYS4X5IAFkIIIUSVEBcXpzdnrampKf3799d7zd3mR9WORtRV3gjHO41ivXz58l3nii3P/UxL4ODgUOYx3dviH4WmTZtW+FzpbS3vb2RjY1MmqaY7KvNxqFGjRrm30peeBqK8OFfkXvZVpVLh5uam99jj3NfS0z8MGDBAb/Rojx49sLKyUpbPnDnDnj177rreO8X8frVv315vmyIjIwH9+X5HjBihfJfvJQFcXhspLx5169bVW75bLO53v3WT6f369bvjdBFTpkzR+06eOnWKZcuWMXXqVPr370+DBg3o1q2b3ohtIYQQQoj7JQlgIYQQQlQJpZNihYWFNGjQQG/Ure5UDnA72ZSenn7H9apUqke+reW5devWPb+2Tp062Nra6j2mO/ex1ocffsjVq1f5f+3daUxTSxsH8H+hlFKwaoNS1+KCUvyAIOISFBfQiLhG5SqKYoJGvfdGo7jhnrCoiBGDIhqjxg0xYiLKNUoiGNQPoOZ6iRi3ugZvUAGNCyh9P5Ce9PQU2gJe8+L/lxCY6cyZOZ3y5TnTZ6qqqiQ7Mm3RarUOtW8pU85cc6bdkS31X63hf6GqqgpXrlwR1WXz6COjAAAKDUlEQVRmZoo+5126dBHSLJg0lvPaXGuueYcOHURpJyoqKmAwGIQAb48ePdC1a1cEBwcDaEib8ebNG9HubDc3NyHtyI/UkvvOzc2VpHwxp9frcf36dcTFxQnpQMwZjUaUlJRg3rx5dq0RERERkTUMABMREVGbV19fL8ndaw+j0SjKKdqpUydJmxcvXkjqDAZDo9fUaDSSr3JnZWVJDsuy9mMvZ2dnjB49WlR348YNh1Ib2NJUPlLLdA/Pnj2TtKmurpbkzvXy8hL+lsvFR1V8/vxZco0nT57YNVeT9+/fo6amRlJvGeS3ts6NsedejUYjXr58Kaozv9fWdP78eUkaBHtcuHDB5kOG1s5Ba/nQITc3V3ifhg4dKvptNBpx8OBBfPr0SWgfFBQkOkzQci0A6+thud621sLR+46IiBCVV65cKcnJbE6n02Hnzp0oKyvDw4cP8ddffyE9PR1jxowRtduzZ49D8yAiIiIyYQCYiIiI2rzr16/j1atXzeprHgDW6XTw9PQUvW6ZJuLz5884e/Zso9dzdnbG4MGDRXX5+fnQ6XSN/rx79w7V1dUOzfv333+X1MXGxjYZnG4tpqCdydWrVyVfzz958mST/Sx3MD969EhULi4uxsOHDx2em+V6lZeXS4LrgYGBdl/P8l7//vtv3Lt3T1R36dIlyW5ly36txfL+7FVTU4P8/PwWjW35YMNWQNkyAHzgwAHhb9POX9NvADh06FCT/QMCAkQBYUD6Ofv69avkYVBrr0VSUpIoCFxfX49FixZZTbNhmX6iU6dOGDp0KGJiYiQ7hy3/B4iIiIjsJbfdhIiIiOj/m+VXpydOnIikpCSrbT9+/IgRI0YIeTzLy8tx9+5dDBw4EEBDnmDzQFVubi68vLwQFRWFb9++ISkpCZWVlU3OZ+HChbhx44boGl++fEFsbCx0Oh3q6+vx8uVLlJaW4uLFi7h37x4yMjJEB0bZEhQUhJiYGBw7dkyoe/bsGUaMGIEFCxYgLCwM3bp1Q21tLQwGQ7MD5NZER0dj9+7d+PbtG4CGQOCUKVOwefNmdO/eHUVFRdi6dauoT2hoqCh3sl6vF71+//59JCQkYM6cOXj69CnWrFnTrLlt3LgR379/R0hICAwGA9avXy963cXFBZMnT7b7elOmTMHGjRtFu5mjoqKQmJiIfv364c6dO0hISBD18fHxcTjlhj2eP38uyZGbl5cnyXFskpaWhqNHjwrl7OxsTJs2rdnjWz4cKSoqwrVr1+Dt7Q2ZTAYPDw9Rfurhw4dDJpPBaDQCaEgDYTJkyBAADUFdhUKB2tpayY5xy/dQpVJh5syZOH78uFC3f/9+qFQqREZGorq6Gtu3b5cEXefPn9/se7ZGJpPh0KFDmDx5MkpKSgA0BJ6jo6Nx4cIF0aGD8fHxePz4MSZMmIDAwED07NkT7u7uqKyslASAVSpVq86TiIiIfh0MABMREVGb9uXLF8kBSpGRkVYPhzIJCAhAaWmpUD5z5owQAF6xYgVOnz4t2pGbmZmJzMxMoaxUKpvc/Th9+nTk5OTg8uXLQl1+fn6Ld2Ba2rVrF968eSMa58OHD9i7dy/27t3bqmOZ8/b2Rnx8PJKTk4W6+/fv47fffrPavl27dti+fbuobsKECXBzcxOlfsjIyEBGRoZQNg8e2sPV1RW1tbVYu3Zto22WLFniUHoGtVqN5ORkLFmyRKh7/fo1YmNjrbaXy+XYtWsXnJ2d7R7DXjk5OaL3Q6fTISQkpNH2kZGRogBwQUEB3r17B41G06zxg4KCROW3b99i6tSpQnnWrFnIysoSyhqNBnq9XnQ4I9DweTAdvKZUKuHv7y8cEmfi6uoq2UkPABs2bEBBQYFw6KHRaERqaipSU1Otznnu3LkYNmyYA3dpH5VKhdOnTyM8PBxPnz4F0LDLeubMmbh8+TJ69eoltC0rK0NZWZnNa4aHh7f6PImIiOjXwBQQRERE1Kbl5+eL8r7KZDKMHTu2yT6Wr587d044hEyr1eLYsWNwc3Oz2tfX11cSYFQoFKKyk5MTDh8+jFmzZtl1DwqFQrK70h4uLi44deoUNmzYAA8PD7v7+fr6tjgotnr1asTHx9s8YE2r1SInJwe+vr6ieo1Ggy1btjTab9y4caLgoj06d+6M1NTURnO6hoWFSXbr2mP27NlIS0uTrLMltVqNI0eOYOTIkQ6PYQ/zdCWA7YBhSEgIlEqlUK6rq8O5c+eaPX54eDj8/Pwc6mNtJ/SgQYNEAXLTbmBzgYGBormbaLVanD9/Hj4+PjbHjomJQVpamkPzdYSnpyfOnj0r2vX877//Ytq0aZJdyLb069cP27Zta+0pEhER0S+CAWAiIiJq0yxzovr7+1s9LMqcZeCsoqIChYWFQjk0NBRFRUWYMWMGPD094erqir59+2LVqlUoKCjAx48fRf2tHSrm7u6OrKwsXL16FQsWLIBer4darYazszPUajX8/PwQFRWFffv24cGDBxg/fryjtw6gIdi8atUq/PPPP0hJSUFERAR69uwJDw8PyOVytG/fHj4+PoiIiMDmzZtRWFiIW7duYdSoUc0az0QmkyEhIQE3b95EXFwc/Pz80K5dO8jlcnh6eiIkJASJiYkoKSlpNAfr4sWLcfToUQQHB0OlUsHDwwODBg1Ceno6srOzrQYAbVm4cCHy8vIwfvx4aDQaKJVKDBgwAMnJycjOzpbkkHXkuqWlpVi+fDkGDhyI9u3bQy6Xo2PHjggODsa6detw+/ZtREZGNuv6tty9exfl5eWiurCwsCb7uLm5SQKwlulSHOHi4oK8vDzExcXB29vbZkAcgNUdyuZ5fwHrAeCmUmj0798fxcXFSE9Px7hx4+Dl5QWFQgF3d3f06dMHc+fOxZUrV5Cenm7XHFuiT58+OHXqlOizajAYMGPGDNTU1CAlJQWZmZmIiYlBQEAAunfvDqVSCYVCAa1Wi9GjR2PHjh0oLCyEVqv9oXMlIiKitktWVVVl//fmiIiIiKhJdXV1CA0NFX2tfenSpY3mHKYf58SJE1i2bJlQ7tGjh+SANiIiIiKito45gImIiIgctGnTJqhUKkyaNAl6vV5IKfD48WMkJiaKgr8ymQxRUVE/a6pERERERPSLYwCYiIiIyEEVFRU4c+YMUlJS4Orqig4dOuDTp0/48OGDpO3ixYvh7+//E2ZJRERERETEADARERFRi3z9+tXqgU5OTk74888/sWnTpp8wKyIiIiIiogYMABMRERE56I8//kDv3r1RXFyM58+fo7KyEnV1dVCr1fDx8cHw4cMRHR2N3r17/+ypEhERERHRL46HwBERERERERERERG1UU4/ewJERERERERERERE9GMwAExERERERERERETURjEATERERERERERERNRGMQBMRERERERERERE1EYxAExERERERERERETURv0PswVZLbMp6oQAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 1440x1440 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(20,20))\n",
"#plt.style.use('seaborn-colorblind')\n",
"\n",
"acquired_age_artwork_bins_2 = pd.cut(master_copy_3['minValues_AcquiredAge_Artwork_2'], 10)\n",
"chart_acquiredage_artwork_2 = sns.countplot(\n",
" data=master_copy,\n",
" x=acquired_age_artwork_bins_2,\n",
")\n",
"\n",
"# set title and subtitle\n",
"chart_acquiredage_artwork_2.text(x=-0.3, y=7700, s=\"MoMA's Acquistions Trends by Age of Artworks\", fontweight='bold', fontsize='48')\n",
"chart_acquiredage_artwork_2.text(x=-0.3, y=7400, s=\"First Piece of Work Per Artist\", fontweight='bold', fontsize='48')\n",
"chart_acquiredage_artwork_2.text(x=-0.3, y=7140, s=\"Number of artworks by age of artworks\", fontsize='36')\n",
"\n",
"# set axis labels\n",
"plt.ylabel(ylabel='Number of Artworks', fontsize=24, fontweight='bold')\n",
"plt.xlabel(xlabel='Age Group of Artworks', fontsize=24, fontweight='bold', labelpad=15)\n",
" \n",
"plt.xticks(rotation=45, horizontalalignment='right', fontweight='medium', fontsize='24' \n",
")\n",
"\n",
"plt.yticks(\n",
" rotation=45, \n",
" verticalalignment='top',\n",
" fontweight='medium',\n",
" fontsize='24' \n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# The Bootstrap"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Population "
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" }\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Artist</th>\n",
" <th>PopulationAge</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>A. Becker</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>A. E. Gallatin</td>\n",
" <td>59.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>A. F. Gangkofner</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>A. G. Fronzoni</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>A. Gisiger</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>A. Gromov</td>\n",
" <td>72.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>A. K. Barutchev</td>\n",
" <td>71.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>A. Karra</td>\n",
" <td>70.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>A. Lawrence Kocher</td>\n",
" <td>70.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>A. M. Cassandre</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>A. Michael Noll</td>\n",
" <td>27.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>A. Paramonov</td>\n",
" <td>78.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>A. Portier</td>\n",
" <td>98.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>A. R. de Ycaza</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>A. Radishchev</td>\n",
" <td>65.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>A. Richard Ranft</td>\n",
" <td>62.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>A. Rozanova</td>\n",
" <td>71.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>A. Smolianskii</td>\n",
" <td>65.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>A. Strachov</td>\n",
" <td>84.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>A. Stuart-Hill</td>\n",
" <td>5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>A. Vabbe</td>\n",
" <td>80.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>A. Wagner</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>A.A.P.</td>\n",
" <td>22.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>A.K. Burns</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>A.R. Penck (Ralf Winkler)</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>AA Bronson</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>ACT UP (AIDS Coalition to Unleash Power)</td>\n",
" <td>28.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>AT&amp;T Bell Laboratories, Murray Hill, NJ</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>Aarne Aho</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>Aaron Curry</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9439</th>\n",
" <td>Zwelethu Mthethwa</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9440</th>\n",
" <td>Zwi Milshtein</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9441</th>\n",
" <td>Zühtü Müritoğlu</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9442</th>\n",
" <td>a.r. Group</td>\n",
" <td>86.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9443</th>\n",
" <td>assume vivid astro focus</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9444</th>\n",
" <td>frogdesign, Sunnydale, CA</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9445</th>\n",
" <td>herman de vries</td>\n",
" <td>16.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9446</th>\n",
" <td>interware SARL</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9447</th>\n",
" <td>matali crasset</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9448</th>\n",
" <td>raumlaborberlin</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9449</th>\n",
" <td>unknown</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9450</th>\n",
" <td>Álvaro Barrios</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9451</th>\n",
" <td>Édgar Negret</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9452</th>\n",
" <td>Éditions Surréalistes, Paris</td>\n",
" <td>68.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9453</th>\n",
" <td>Édouard Boubat</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9454</th>\n",
" <td>Édouard Manet</td>\n",
" <td>89.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9455</th>\n",
" <td>Édouard Molinaro</td>\n",
" <td>34.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9456</th>\n",
" <td>Édouard Pignon</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9457</th>\n",
" <td>Édouard Vuillard</td>\n",
" <td>25.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9458</th>\n",
" <td>Édouard-Denis Baldus</td>\n",
" <td>119.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9459</th>\n",
" <td>Édouard-Wilfred Buquet</td>\n",
" <td>50.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9460</th>\n",
" <td>Émile Berchmans</td>\n",
" <td>61.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9461</th>\n",
" <td>Émile Bernard</td>\n",
" <td>28.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9462</th>\n",
" <td>Émile-Antoine Bourdelle</td>\n",
" <td>58.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9463</th>\n",
" <td>Émile-René Ménard</td>\n",
" <td>61.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9464</th>\n",
" <td>Éric Chahi</td>\n",
" <td>21.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9465</th>\n",
" <td>Étienne Carjat</td>\n",
" <td>102.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9466</th>\n",
" <td>Étienne Hajdu</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9467</th>\n",
" <td>Öyvind Fahlström</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9468</th>\n",
" <td>Øistein Thurman</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>9469 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" Artist PopulationAge\n",
"0 A. Becker 10.0\n",
"1 A. E. Gallatin 59.0\n",
"2 A. F. Gangkofner 2.0\n",
"3 A. G. Fronzoni 0.0\n",
"4 A. Gisiger 0.0\n",
"5 A. Gromov 72.0\n",
"6 A. K. Barutchev 71.0\n",
"7 A. Karra 70.0\n",
"8 A. Lawrence Kocher 70.0\n",
"9 A. M. Cassandre 0.0\n",
"10 A. Michael Noll 27.0\n",
"11 A. Paramonov 78.0\n",
"12 A. Portier 98.0\n",
"13 A. R. de Ycaza 1.0\n",
"14 A. Radishchev 65.0\n",
"15 A. Richard Ranft 62.0\n",
"16 A. Rozanova 71.0\n",
"17 A. Smolianskii 65.0\n",
"18 A. Strachov 84.0\n",
"19 A. Stuart-Hill 5.0\n",
"20 A. Vabbe 80.0\n",
"21 A. Wagner 2.0\n",
"22 A.A.P. 22.0\n",
"23 A.K. Burns 1.0\n",
"24 A.R. Penck (Ralf Winkler) 0.0\n",
"25 AA Bronson 0.0\n",
"26 ACT UP (AIDS Coalition to Unleash Power) 28.0\n",
"27 AT&T Bell Laboratories, Murray Hill, NJ 4.0\n",
"28 Aarne Aho 6.0\n",
"29 Aaron Curry 0.0\n",
"... ... ...\n",
"9439 Zwelethu Mthethwa 1.0\n",
"9440 Zwi Milshtein 0.0\n",
"9441 Zühtü Müritoğlu 2.0\n",
"9442 a.r. Group 86.0\n",
"9443 assume vivid astro focus 2.0\n",
"9444 frogdesign, Sunnydale, CA 10.0\n",
"9445 herman de vries 16.0\n",
"9446 interware SARL 1.0\n",
"9447 matali crasset 2.0\n",
"9448 raumlaborberlin 1.0\n",
"9449 unknown 0.0\n",
"9450 Álvaro Barrios 0.0\n",
"9451 Édgar Negret 0.0\n",
"9452 Éditions Surréalistes, Paris 68.0\n",
"9453 Édouard Boubat 7.0\n",
"9454 Édouard Manet 89.0\n",
"9455 Édouard Molinaro 34.0\n",
"9456 Édouard Pignon 0.0\n",
"9457 Édouard Vuillard 25.0\n",
"9458 Édouard-Denis Baldus 119.0\n",
"9459 Édouard-Wilfred Buquet 50.0\n",
"9460 Émile Berchmans 61.0\n",
"9461 Émile Bernard 28.0\n",
"9462 Émile-Antoine Bourdelle 58.0\n",
"9463 Émile-René Ménard 61.0\n",
"9464 Éric Chahi 21.0\n",
"9465 Étienne Carjat 102.0\n",
"9466 Étienne Hajdu 0.0\n",
"9467 Öyvind Fahlström 0.0\n",
"9468 Øistein Thurman 1.0\n",
"\n",
"[9469 rows x 2 columns]"
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"minValues_AcquiredAge_Artwork_2 = minValues_AcquiredAge_Artwork_2.dropna()\n",
"population = minValues_AcquiredAge_Artwork_2.to_frame().reset_index()\n",
"population.head(5)\n",
"\n",
"#population.columns\n",
"\n",
"population.rename(columns={0:'PopulationAge'}, inplace = True)\n",
"population\n"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"16.418101172246278"
]
},
"execution_count": 81,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"population_observed_mean = np.average(population['PopulationAge'])\n",
"population_observed_mean"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PopulationAge</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>9469.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>16.418101</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>26.047022</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>3.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>24.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>186.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
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"text/plain": [
" PopulationAge\n",
"count 9469.000000\n",
"mean 16.418101\n",
"std 26.047022\n",
"min 0.000000\n",
"25% 1.000000\n",
"50% 3.000000\n",
"75% 24.000000\n",
"max 186.000000"
]
},
"execution_count": 82,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"population.describe()"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(array([ 0., 1000., 2000., 3000., 4000., 5000., 6000., 7000.]),\n",
" <a list of 8 Text yticklabel objects>)"
]
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Histogram of your population\n",
"plt.figure(figsize=(20,20))\n",
"population.hist(bins=np.arange(0, 186, 13))\n",
"\n",
"# set title and subtitle\n",
"plt.title(x=0.5, y=1.1,label=\"MoMA's Acquistions Trends by Age of Artworks (Population)\", fontweight='bold', fontsize='24')\n",
"\n",
"plt.ylabel(ylabel='Number of Artworks', fontsize=20, fontweight='medium')\n",
"plt.xlabel(xlabel='Minimum Acquired Age of First Work Per Artist', fontsize=20, fontweight='medium', labelpad=15)\n",
"\n",
"plt.xticks(rotation=45, horizontalalignment='right', fontweight='medium', fontsize='16' \n",
")\n",
"\n",
"plt.yticks(\n",
" rotation=45, \n",
" verticalalignment='top',\n",
" fontweight='medium',\n",
" fontsize='16' \n",
")"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [],
"source": [
"population['PopulationAge'] = population['PopulationAge'].astype(int)\n",
"population['Artist'] = population['Artist'].astype(str)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Standard error the mean given sample size = 9469"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### It can be seen from the formula that the standard error of the mean decreases as N increases. This is expected because if the mean at each step is calculated using many data points, then a small deviation in one value will cause less effect on the final mean. \n",
"\n",
"#### The standard error of the mean tells us how the mean varies with different experiments measuring the same quantity. Thus if the effect of random changes are significant, then the standard error of the mean will be higher. If there is no change in the data points as experiments are repeated, then the standard error of mean is zero. "
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.2676597641860606"
]
},
"execution_count": 85,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from math import sqrt\n",
"# standard error of the mean\n",
"# standard deviation of the observed / sqrt(sample size)\n",
"np.std(population['PopulationAge']) / (sqrt(9469))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Simulate SAME random sample size of 9469 times with Replacement"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Simulated average of acquired age of artwork "
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([16.32263175, 16.41535537, 16.7920583 , ..., 16.4294012 ,\n",
" 16.54461928, 16.59953533])"
]
},
"execution_count": 86,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"avg = []\n",
"n = 9469\n",
"simulations = 9469\n",
"for i in np.arange(simulations): \n",
" sample = np.random.choice(minValues_AcquiredAge_Artwork_2, n, replace=True)\n",
" avg = np.append(avg,np.average(sample))\n",
"avg"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {},
"outputs": [],
"source": [
"sample_avg_pd = pd.DataFrame(avg)"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {},
"outputs": [
{
"data": {
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" <tr>\n",
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" <td>16.425003</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.270116</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>15.488647</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>16.239941</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>16.425283</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>16.606505</td>\n",
" </tr>\n",
" <tr>\n",
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" <td>17.328335</td>\n",
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"text/plain": [
" 0\n",
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"25% 16.239941\n",
"50% 16.425283\n",
"75% 16.606505\n",
"max 17.328335"
]
},
"execution_count": 88,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sample_avg_pd.describe()"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"155528.3574823107"
]
},
"execution_count": 89,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sum(avg)"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"16.425003430384482"
]
},
"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sample_avg = np.average(avg)\n",
"sample_avg"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [
{
"data": {
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" 0\n",
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"execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sample_avg_pd.describe()"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(array([ 0., 200., 400., 600., 800., 1000., 1200., 1400., 1600.]),\n",
" <a list of 9 Text yticklabel objects>)"
]
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uu6SkBKNGjeIdQ3F27NiBOXPmyJ9wjoSEBGRmZjKvTUxM0LVrVwwZMoS1nrQmkvPnz8eZM2fELhMKhQgLC5NZM1nVx1mcSZMm4Y8//pC53u3btzFgwIAauS9z8zK9evWCqakpevfuzXpfkSaqs2bNktiqQxFdunRhvb58+TLz/4KCAt5Ac+VrfjMzM/Ho0SPmta6uLpydnZnX8fHx+Pbbb5Gfny81DYWFhfjhhx9kPitkPZskKS0txdixY1nPZTMzM8TExMDe3p55b9q0adXWdzclJYX1uk2bNnJ/tkmTJujRowfzunzeqnwtm6WlJe+cEiciIgJLliyR2FxX5NKlSwgICKjwb5Keni7zOQyU3Z/+/PNPictXr16N9PR0sctMTU3RqFEj5vXNmzflqqmvcJ+28sHOiRMnkJGRgcePH7NOLnl2OiUlhfdQ79SpE44cOYJz585h5syZrGrTjIwMufq0DBs2jHkgFxUVMdW75Uex6dSpE1q0aCFzWyLcG5Moc8JtdvD3339LfAgcP34cubm5vJs/98L46quvcPLkSVy7dg1JSUn4/fffMWnSJNZQoYq6dOkS60alra2NXr16oUGDBnLfhENDQ3kPiwEDBiA6OhqpqamIi4vDTz/9BAsLC9Y6q1ev5jWlCg4OxpkzZxAdHQ0PD48quZFXVkWOQ6NGjXDjxg2xfWdOnjzJ6o8l73D1eXl5vMyOhYUFdu7cieTkZKxfvx46OjrMsuLiYsyYMUPqzUwoFEJNTQ3Lli3DxYsXsX79el7HdG5Ts/LNKTQ0NLBu3TpcvHgRV69exYkTJxAREQFfX19oamrKtV/VSSgUorS0FH5+foiNjcWlS5ewe/duptnqggULWAU2dnZ22LZtGy5evIj4+HiMGDGCtb2QkBCZI9MJhUKMHTsW58+fR1RUFO+85z7ML126xDtP2rRpg0OHDuHcuXMICQmRGASKcJu4+Pj4IDY2ljlHd+/ejYkTJ8LBwUHqduRVUFCACRMm4OzZs4iOjkbnzp1Zy8+fP4/4+HjmdfkCM6Cs8KF8c5XXr18jOTmZea2trY1+/fopnK78/HycOHGC9Z7oXswtSBN3P2vYsCHve7nH6/jx46wAp1u3bjA3NwdQds1NnTqV1Qzcy8sLhw4dwpUrV3DkyBG4ubkxy96+fSszgBFdv2PHjsWZM2eQkpKCzZs3o3HjxgAAVVVVdO3aFeHh4Th48CASEhJw9epVxMXFISwsjHU9P3v2jDdIx88//8zLGEydOhUXLlxAbGws3N3dZd6H9+/fzwqCtLS0sHTpUiQkJCA5ORmLFy+Gmpoas/y3335jNclXBDdoGDhwIJSVldG/f3+oqqoy71+7dk3sYDTPnj3jjVzn4OCAqKgoXLlyBStXroSamprU0euq4zhznT17lleg6uHhgaNHj+LcuXOYNm0aKx/09OlTLF++HADQuHFj3LhxQ2w//TNnzjDPns2bNyuUJnFdJSRdX4qMnF1SUgJPT09ER0fj8uXL2L9/Pzw9PZm0ipq/i6xYsYL1DBXtR5cuXVi/yeXLl5mg6Nq1a7zz+NKlS2L/D5TlBUVNbj99+oRp06ax9kdfXx8bN25EcnIytm/fzjR3FJk5c6bUZ4WsZ5MkU6ZMYd2TzMzMcOzYMVbAJsp7i5iYmGD37t24cuUKLl26hOjoaCxevBjdu3dnXTPy+ueff1ivnZycFPp8+Tjg4sWL+Pfff5Gbm8vqC/ntt99CWVlZ6nbS09OxYsUK3rZPnz6NS5cu4bfffmMFQdevX2dVIgGApqYmevXqhTVr1uDIkSM4f/48rly5ghMnTuCHH35gNbu+cuUKUlNTpaZJKBRCX18fW7duxYULFzB37lzW8tLSUqmFW+V/y5KSEomVJeWpyFxDAn9/f8ybNw+5ubkoLi7Gzp07UVRUxJzoenp6GDJkCNauXSt1O9yRY8zMzHD06FHm4ePi4oL8/HzWDScyMhIrV66UmlHU1dVFQEAAdu7cCaCsxK9nz56sfkPyBJUi4kZQEt3A7O3t0bp1a6ZkR1Syq0gtHjezPWzYMFbfiZYtW6J///5YvHixzNIfSbgZl759+zKDXwwePJh1ER0+fBiLFy9mXUhPnz7FqVOnWNvw9/dnlZjY2dmhQ4cO+P7771kDa3D7CAwYMAArV65kXnfq1AmtW7fmjX5V0ypyHFRUVGBjYyN2exYWFhKXSRMdHc0L7A8cOMD0S2vRogUMDAwwfPhwZvm9e/eQnJwMDw8PidsNCQnBhAkTAJT1d/j3339ZNzZuLVT5jIqNjQ2CgoKYB6W9vT06d+6MsWPHVvicrGo9evTAjh07mNfNmjUDUJbBL39+Kykp4dChQ7C1tWXea9++PW7fvs1kMF+9eoWYmBgEBARI/D5vb2+mZsLZ2RkLFy5k3Vfu3buHjx8/MplYbqf9hg0b4siRI0zfSxcXF7x9+1bq6LbcDObIkSNZmcaWLVtiwIABWLp0aZUcl4CAACxbtox57erqipYtW+LVq1fMe1FRUejZsyezD+3bt2f6Ej979gwJCQnw8vICUBYIld+HAQMGoEGDBgqni9vywd7eHi4uLgDKatzCwsKY59Hly5fx6NEj1vEGgMDAQFZgcOTIEUyZMoV5zQ3iytcIJiQksEpRmzRpgqioKKiolD1WHRwc0LZtW9jZ2TH7e/r0aWRkZPCC+/K++eYbVm1X+Qd7QECAxPOxQ4cOeP78OeuZevHiRQwaNAhAWQaCWyjj5+eHBQsWMK///PNPtGzZUmqNErdVTEREBIYNG8a8btGiBbKzs1m1zLt27ULbtm0lblOcDx8+8IJOUQ2bgYEBvLy8WENlHzhwgDc365EjR1j3dIFAgL179zL3BQcHB+Tl5UktCK6u41weNx9kaWmJQ4cOMZls0X2h/L3tjz/+wLJly6CqqgobGxuxtaeNGjXiBRjySklJwdOnT5nXOjo68Pb2BlBWmKmrq8tMaSMaObt8/3hJmjdvjoMHDzL71rRpU9ZybubdyMhI7DPU0NAQjo6OzJQAubm5SEtLg6OjIysoMzY2RnZ2Nh4/fozMzEyYmZnxCr7K19qdPXuWtd9AWQ1R9+7dAZSd3zY2Nvjqq6+Y5dnZ2Thx4gSvBrg8Sc8mScLCwlh9yUxNTXHs2DFeYRz3edChQwcMGDCA9T2enp6YNGlShZ4H3D6GBgYGCn2+Z8+esLGxYQLLbdu2wcHBgbl3q6mpISgoCMeOHZO6nT179rD2ddiwYaz7ZLNmzZh8v8iuXbtYw+tPnToVU6dO5W3bwcEBnTt3xn///cdqWXHx4kW0a9dOarrWrFmDgQMHAig7t5OTk1nbkNayR19fn/VaVt9coBI1bZqamqwM4++//469e/cyr0eMGCFX6Tu3s7Kfnx+v9L/89wBlNWfc6F+c8kHTkydPWK9NTU3h6+srcxsi3BGU2rdvz8oAcGvbFB3RqnxzU6CshGXMmDFYtWoV/vrrL1YpYsOGDRXaNlAWSHIf2OVvMD169GAN2pGZmcmbmJZbCwVA4gTmWlpazM330aNHvJpHbnMoNTU1hUckqg7VfRzkxb0uWrVqxRuYpU+fPrwbqKzO/9988w3rdfkSOwC8eeXK/x5paWnw8vJCWFgYdu3aheTkZKbUvjp/C0WUv0GXd/XqVVbJa2lpKVxcXHid5Lk1ArJ+z1GjRrFec39PoVDIqtng3rdEncvLCwwMlPqd3HN0/PjxCA4OxurVq3Hs2DFW06yqOC7c9GhqajIPKRHufo0ePZr1unxrCu7DuaqaRpa/B5uamsLd3Z21XFyzdQ8PD17prGgUr9zcXNbDV0dHh1Uzx834paen8yaJtbGx4WWqZJ1T4jIV5d2/fx8//fQTevTogSZNmsDExIT5Pm7mv3zTwvv37/NaO3Cfrdra2kyQJ05hYSHvGpkwYQLvOuI2C67IoCTHjx9nZTKtra1ZzV65zwtxA2hxz8v27dvzMssjR46Umo7qOs7S1g0ICODVinCP1fv37/Hvv//K/R2KklbIq6amxss/yZvnmTBhQoVqfMThNpEUBWuif5WUlFj5Pu5ykfL3Cu6xMDc3ZwI2EXHnkazjLenZJM6DBw9YA6OYmpoiJiZGbOsJS0tLVu3k8ePH4evriwULFmDfvn24evUqE9BX5HlQvnAO4AcasigpKbFaXxw8eJBVUDxw4EAYGxvL3A73Ovzjjz949x1ugVZ6ejov6Lx+/TpmzpwJT09PZmA1SZOAl79/imNgYMC7DmTlqcqr0aANKGsGIyp1z8rKYjLm3D5l0nB/UHElKtbW1rz35BkmtEWLFqxakvKTNI4aNUqhGwf3hsR9YPj5+fGq6ss3RZTFy8uLKYkGgHfv3uHgwYNYvHgxgoKCmJvE4sWLKzSdQlxcHCtwMjQ0RLdu3ZjX4m7C3EwO9wTW1tbmlVyLI+5EFHdMraysZG6rulX3cZCXPNeFQCCApaUl6z1p14W2tjZvbhXuNAPcjMePP/7Iuk6uX7+OTZs2YerUqejbty9sbW3Rv39/nD9/XvoO1ZDy/RLKq2gNrqz7jJ2dHeu1uGkbyv+m3ONakevAx8eH1UQxLy8PkZGRWLRoEUaOHIl27dqhefPmWLp0qcymlvKQJ43c/fLz82MVKMTExCA3Nxdv377FuXPnWNvmZrzkIa3lgwi31Ftcpl5JSYn3oBcVbh07dowV6Pfv359VEFkd51TDhg2l3lP37dsHNzc3rF+/HqmpqXj9+rXUPq/l71HcYwSIv69I+/7s7GyZ/UnEqciw3rKeueWDCKCscJCbGZfnPmpqaiq1gLm67h0iQqGQ94yUNx8kK1NZUUVFRbzmlrKuL3lHzpZ0j64IcUGbUChk+rc5OjoytYNAWWDFDXa1tbVZBWHc4ybudxf3vqxjoch+c5ua/vTTTxKbuyspKWHevHms95KSkrB27VpMnDgRX331FaytrTFy5Mham6h8xIgRzLX6/v171gjo8rZIq4rrcOXKlfDy8sJvv/2GGzduMC0FJZGVx7O1teXVDMvKU1VWpYI2bidDka+++oppg1/bxDWBVFFRwbfffiv3NtLS0nDz5k3We2FhYawI39nZmdfJVJEBSQQCAdPss127dmLb92ZlZWHVqlW8Ejd5cB+AOTk5vNLC8lXxQFmJjaw+PfKQdyjXiswhw81AyOpQLk8aqvM41CZx0x/Iakfu6emJc+fOITAwEEZGRrzlJSUlSExMxIABA2o9cFNVVeX1h6gsWZkQbkmZrN9Tno7osq4DJSUlHD16FMuWLUObNm1Y7fBFXrx4gYiICF7NalWRlUYNDQ3W9VFYWIhDhw7h1KlTrCDj66+/rrK5o1xdXVn3M27J9v3798X2UeDW9IkGLeG2TJBVAyovaeeUtKZsz549w/Tp0xXKBMg63yry21eEogMCZGdn80q9165dyzq+VlZWvO3Kqu2pqf0FFN/nuuTUqVO8JrJDhgxh/f7c2nZ550Q0MzOrsnSKG4zk7t27ePPmDQCgY8eOaNmyJdP8+tKlS7h69SrrGnJzc5N5366syj6bwsLCJA5eBZRVQsTExKBfv37Q1tbmLS8qKsKxY8fQq1cvhSoTAPCe+6LfVhEGBgZiW1K1atWKVXteHUTXYWpqKpYtW6bQYDCy1hVX66jIucT9LcXlsbgq3KdNZOzYsaxO6KL35GViYsLqRClujiRx85LJ207b19cXZmZmrFKQfv36MZ3J5VGR0SCBspLd0NBQuddXVVVFcHAwgoODUVhYiPv37+PBgwe4evUqtm7dymR2zp07hxs3bsjsxCqSl5en0DxaIvn5+Th+/DgzWhr3Zvvu3Ts8fvxYZp8tcVXfT58+5Q0CI2v+OVEfgvI+fPjAuklJGqlHEdV1HBTBrRETd10IhUI8e/aM9V5F+y9I06JFC6ZPaUZGBh48eIC0tDT8+eefTCa4pKQEGzdu5DVJq0nighcR7u+ioaGB5ORksedUeVU9wIqJiQmrvwS37wQg+zoAymrGJ0yYgAkTJqCgoAD3799Heno60yFbVEN06tQppKWlyew/Ic2TJ094NYrcNHLPV6CsJcaGDRuYB9++fft4fXwqGghVdELlAwcO8PooNGvWDG3atGGa0t26dQuXL9U3N0oAACAASURBVF9m1eTZ2NjwBmDhnlNubm7YunWrzDSIKzwRkXYOnzp1itVvSUVFBbNnz8ZXX30FPT09CAQCrFy5UuLIouKO0dOnT3nHVlqmzsjICEpKSqyagN27dzN9CSVRNFg6dOhQhUqojxw5guXLlzOtA7j7LO56y8rKklojXR3HuTyBQABjY2NWHkXefFBVBkDlVeb6kta8FpB+jivKxMQETZs2Zea3vH//PqsfZMeOHaGiooJ27dohKSkJN2/e5BUGcJ9Z3OMt6X7MfV/asVB0ny0tLWFgYMBUFuTl5WHIkCGIjY2VWBPu7u4Od3d3lJaW4smTJ0hPT8d///2HnTt3MvmivLw87Ny5EwsXLpQ7LdxrqCJBG1A28Bx3dFRFYgVTU1PWdfHDDz/I9XnRceEOCKKlpYUFCxagU6dOTB5y6tSpvPOjOnF/S3maiVb66hF1MhSxtbVlddCUpXwHeqCslJPboZb7EFJXV5d72FFVVVVevxNFBgiRd7JpcSSV7IqTm5vLKpnT0NCAs7MzBgwYgMWLFzOd/MtvW17R0dEVLvUrf/PmZloAYOnSpWI/9/79e+ahW35CZBFuICyuzx2Xrq4u773yv0NJSQmvtlBRlTkO4prbVvR3514XN2/e5PVfOHHiBK9mkfu5yuI2+bCwsEDXrl0xZswY3iBDipyTNa1Dhw6sAK2wsBAXLlwQO3m3jY0NLC0tcf369SoP2riDMZw8eZLX5n3//v1St/HmzRtWbZWmpiYz+MiyZct4gwHcu3evUmnmDmlcWFjIazol7n5sa2vLaolx7do11kBGbm5uFWqRIa7lg7yOHDkiNhjg1rZNnDiRtd7QoUN5gQd3ku1//vkHhYWFEs8pAwMDpKamir2PyYN7Lbq4uCAkJAQuLi6wtbVFo0aNpI4+Zm9vzxpxFuCfa+/evZN6H9bU1OQd65MnT0rcZxsbG7x8+VLhSacrGjS8fv2aNYI1N62XL1/mFezJembUxHHm3rejoqJ4ox9y80FaWlq8fs5VITc3lzf1i7ykjZwtL+5zVFYTb25tW/n+Uh07dmT9W1xczBtIh/t57rF48eIFb8qIq1ev8qY9qspnr6amJiIjI1ndHzIzM+Hn5ye2u0n5e4OSkhJsbW3RvXt3TJo0iTeioaLPA+41JGvKHElEA1SJiAYrlBf3OoyLi2MGqRH3JxAI8ODBA2aMDO79s1evXhg7diycnZ1hY2MDfX39Cu9bRZWf8F1JSUlm4RdQBTVtSkpKWL58OTOEc5cuXRQqVRg1ahQrA5+ZmYmBAwciNDQU+vr6iImJwZYtW1ifCQgIUCgzNXr0aKZtaoMGDRSqDeCOoKSqqork5GTeYCkiISEhrBueuJJdcS5evIjx48ejT58+cHd3h4ODA/T19fHx40dcunSJNyiIIqOtcR+AY8eOxQ8//CB23QcPHrCqsc+ePYvs7GwYGxvDysoK3t7erBG7IiMj8enTJ3zzzTewsrLCmzdvkJKSgq1bt+L8+fNMaePgwYNZN9MjR47A1NQUQ4cORX5+PlatWoWMjAyp+2FnZwc1NTVWhnXKlClYvnw5NDU1pc6JIa/KHAd9fX1eKfTWrVsRHBzMnK+NGjWSWbsDlI2o99NPP7GaqAwdOhRLly5F06ZN8c8///AGgXFwcKhQ/yBpAgMDoaqqil69eqFVq1Zo1KgRNDQ08OLFC95gAxUZAbCmaGtrY8iQIaxM6owZM3Dnzh307t0bZmZmyM/Px4MHD3DhwgXExMTgxYsXSEtLE9vcpKIGDx7MGhny3bt38PPzw48//ghDQ0McP36cN0Q5V2JiIqZPn44+ffqgS5cucHBwgJ6eHoqKipCSksIaTh8Aay6yioiMjISRkRH8/f2Rl5eHlStX8jIOkh6+o0ePZrXEKJ8RregAJNwCH2k1H0KhEO7u7syAFtnZ2Thz5gyrn4so/XPnzmXSx83YiKsR9PLyQuPGjZmBS4qKitC/f39MmTIFHTt2hJ6eHt68eYPbt2/j3LlzOH36NMzNzSs84BK34OvmzZv4/fff4ebmhszMTKxbt46VCeBSUlKCn58fK9O6f/9+mJiYwM/PD0VFRViyZInMuci+/fZbVmHk/v378f79ewQFBTEDcjx9+hRXr17F8ePHcfv2bWzfvl3u6XXu37/PG+wkLi5ObE0hAISHh7Ou68jISPTp0wdA2SiiCxYsYJrSlpaWYsiQIViyZAns7e1x7tw5mfPS1cRxHjVqFGuk0qdPn2Lw4MGYMWMGdHV1ER0dzYyELTJs2LAqG9CjvKNHj7IKzq2srBATEyNx/cGDBzMFdhUZOZvL0NCQNTXL/v374ezszDRFMzIyYt3TunTpwvptRJlzExMTplBIFLQBYJ3fDRs25GWUvby8YGVlxcr3fffdd1i6dClat26NO3fu8J69xsbGzDlXVczMzBAVFQUfHx+mYC89PR1DhgxBTEwM67nk6uqK9u3bo0ePHnB2doaZmRlUVFTw6NEj3ryPij4PuIX18gxLL8n8+fOZgrvWrVsrlI8PCgrChg0bmGs5LS0NvXv3ZqZgUldXR1ZWFm7evIlTp04hOTkZQUFBzCAy3PvnmTNncPToUbRo0QIPHz7EihUrKtT3tqIyMzNZeV5nZ2deoZo4lQ7agLIR0OSZGE+czp07Y/jw4axSpAsXLvDaS4tYWFjwSg5kMTU1lWtuN3G4AY+bmxtviNryfHx8WEHbkSNHsGzZMrky6m/evMG+fftkTpyrpaXFK3WQ5Pnz57xM3MCBAyU2abS2toapqSlz8hYXF+PQoUP4/vvvAZTNmXL16lVWadqRI0dk1pJNmzYN+/fvZ41etmXLFlZArqmpKbVUTV1dHX369GE93G7evMm6WQoEAoUnsOSq6HHQ1NSEs7MzqxZgx44drGF+r127hiZNmshMg46ODsLDw1nzumVkZEjsi6miooJVq1ZVedt8Uafu8hOXSsKthaxr5s+fj8TEROZGWVRUhA0bNmDDhg01lgY3Nzf06tWLVeOUmprKyuCpqKjInPMoJycHe/bskVlLoKOjU+k+AyoqKti0aRNrNLPyOnfuLPHY+/j4wNLSkteMV0NDQ+I9XhpxLR969+4ttYl2165dWX1tIiMjeUGboaEhevbsyZv3DZBcI6iiooK1a9di8ODBTK1cVlYWwsLCFNoneXl7e+PHH39kMi2iSeDLMzExETvgiMiMGTNw6NAhVs3XL7/8wiqA0dDQkNpCIDAwEAcPHmQNKHPs2DGZQ3bLixuUN2vWDB06dJC4ft++fVlBW2xsLPLy8qCjowNLS0uMGTOGFdSnp6ezpihQVlaGqqqqxPnpauI4e3l5YciQIawpQRITE3mD7YhYWVlh9uzZVfb95XF/f29vb6nXV48ePVitLCIjIysVtHXo0IH1DD1//jxr0LStW7di6NChzGtJBZXl73sdOnQQmzdwdXXl5c1UVVWxevVqDB06lLkPv3nzhpkqR5yVK1dWS6Fl8+bNsXfvXgwePJgprL5x4waGDx+OgwcPMlPJlJSU4MyZMxInkS9PkZZwQFme0MHBgSnIunnzJgoKCirUCqVr167o2rWrwp8DygrtZ82ahfDwcOa9mzdvyt3E0sfHh/UMy83N5fX5lnX/rErcQZO4I5RKUnWNiythzZo1CAoKkrmeg4MDU0NTE8SNoCQrY8odmEVUsltVVFVVsX79ernbyx88eJCVAdTR0WGVOnEJBALW6IkAO3Bt3Lgx/vrrL96wprKYm5tj165dYkfXA8puTvI8BBcsWCCxQ6+hoSEWLVqkULoqStJxkDVctyICAwOxevVq1kS14ujo6GDXrl1S52erbh07dsS0adNq7fvlYW5ujr/++kvuEn8jIyOJNeqVsXnzZokTlKqoqGDdunVV8j3q6urYtGlTpWva1qxZI/G6bdy4MbZv3y6xz5KSkpLYgoY+ffrIfQ8rj9vyAZCdCeEuP3HihNj5iiTV/Enrd+fp6Yl9+/bJPXeRvPN2idOkSROpfaT9/f1l9hG0srLC9u3bJZ7Xzs7OmDFjBus9bm2OsrIy9uzZI3fQraGhodAgDNygXNbx9fT0ZKWR23x34cKFvGeaiJKSEtasWcMr4ebuc00c5w0bNshV+9y8eXMcPXq0ygddAsr6aXGHrlf0+lJ05Gyu8i1T5GFubi62ILR8PkdPTw+Ojo68dSQFfD179sSOHTtkDpGvoaGBDRs2yOzHVxldu3bFxo0bWffYxMREBAcHyz2hucjgwYOlzjsqSflpMQoKClitrWpSaGgo5s6dK1fhtEAgYPUz9PDwwIgRIySuP3369Brtk8+NLWRNPSJSJ4I2NTU1/PLLL4iPj8fIkSNhb28PLS0tqKqqwtTUFF999RV++eUXJCcnV6pDvaLEjaAk6wZma2vLG5pVnvb53bp1w+HDhxESEgIPDw/Y2dlBR0cHysrK0NHRQatWrTB+/HhcvHhRoWYX3FKzbt26yaz14wam165dY5WkOTs7IyUlBZs3b0bfvn1haWkJDQ0NNGjQADY2NujTpw9WrVrFu+F1794dCQkJGDx4MJMhtre3R0hICOLj4+V6INra2iI+Ph7+/v4wMjKCmpoarK2tMW7cOFy8eFGuNsHSVPY4+Pn5Ye/evXB3d4eurm6lRysbPXo0UlNTMXXqVLi4uEBXVxcqKirQ19eHq6sr5syZg2vXrrHmj6pKO3fuxLp16xAQEIBWrVrB3NwcampqUFdXh6WlJXr16oVNmzbhxIkTlQ4OaoK9vT0SEhKwY8cO9O/fH1ZWVmjQoAFUVVVhZGQEV1dXjBs3DpGRkfjf//5XocBCFgMDA8THx2PmzJmws7ODuro6jIyM0K9fP8THx/OG1ubq2bMnDh48iOnTp6Nr1668c9TFxQU//PADLl26VCXnhbe3NxISEuDv7w9TU1OoqanBxsYGkydPxrlz52QO6hQUFMQreKiqudksLCxkBuHce/aHDx/E1gr5+PjwRgOTp0awV69e+Oeff7B06VJ069aN+Y3U1dXRqFEjdOvWDaGhoThz5gzvQa2oWbNmYdeuXXB1dUWDBg3QsGFDtGnTBmvXrsWvv/4q1/3G29sb586dw6BBg2BoaAh1dXU4ODhg1qxZiIuL4/WxFNc5XltbG7t27UJsbCyCgoLQrFkz1jnYokULBAYGYsuWLbhz5w6rpkSaS5cu8TL8sgpKxdUmlz9PNDQ0EBUVhWXLlsHJyQkaGhowMDBAr169cPz4cQwaNAg5OTky97m6j7OGhga2bNmC2NhYDB8+HHZ2dtDS0oKamhrMzMzg7e2NjRs3IjExkTd4TFWJiopi1UapqanJLAx0d3fnFepUdPA2oKxm9eTJk/D19YWJiYlcGXRxwRe3cFpcYbW07gQDBw7EP//8g9mzZ6NDhw7Q09ODiooKdHV14eLigunTpyM1NVVqIFBV/P39ecP6Hz16FDNnzgRQVru8fPlyDBgwAE5OTjAxMYGKigo0NTXRuHFj+Pn54c8//5RawCaNqJuEiGiE3doQEhLC5InatWsHfX19qKioQEtLC3Z2dujXrx/Cw8Nx/fp1XiHX+vXrsXr1arRs2RLq6urQ0dFBp06dsHv3bt7vW50+fPjAam3j7u4u9zUtyM3NrVxbMkKqyL59+zBx4kTmtZWVVbVOHkoIqX4lJSVo3rw50+zE1NQUt2/frvZhtoniioqK4O7uzurTN3369BrN0NQ07nNHTU0N6enpVTIxPSFfismTJ+P3338HUFbA8O+//8o12iHh495zDhw4gF69esn12TpR00YIIeTLIxQKsX79elY/gREjRlDAVovmzJmDiIgI/O9//2M1r7p37x7GjBnDCtiUlZUr1JyqLklLS8OwYcMQExPDqkX88OEDIiMjMWfOHNb6vr6+FLARwjF79mym2WphYaFc014QPtEzUaRz585yB2xAFQ1EQgghhIisX78eO3bswOvXr1kZ5YYNGzKDGpHa8fz5c2zevBlLly6FhoYGdHV18eHDB7HD8otGZvuclZSU4MSJE8wgM6LmVK9fv+ZN0G5sbIz58+fXRjIJqdMsLCwQHBzM9Lnetm0bpkyZUqWjK9cHsbGxuHPnDvN6wYIFCn2egjZCCCFVKicnhxkiXUQgEODnn3+mJjV1SGFhodiRIlVUVDBt2rRqGwmzNkmaHNjJyQnbtm2DtbV1DaeIkM/DwoULFZqYm/D17t1b5rQq0lDQRgghpNoYGhoyoxLW5ginpMz06dPh6OiIlJQUPHnyBK9evUJxcTF0dXXh4OCAzp07Y8SIEbC1ta3tpFaJxo0bY8OGDUhKSsK///6Lly9fIjc3Fw0aNICJiQlcXFzg6+uLPn36VMu8Z4QQUlVoIBJCCCGEEEIIqcNoIBJCCCGEEEIIqcMoaCOEEEIIIYSQOoyCNkIIIYQQQgipwyhoI4QQQgghhJA6jII2QgghhBBCCKnDKGgjhBBCCCGEkDqMgjZCCCGEEEIIqcMoaCOEEEIIIYSQOoyCNkIIIYQQQgipwyhoI4QQQgghhJA6jII2QgghhBBCCKnDKGgjhBBCCCGEkDqMgjZCCCGEEEIIqcMoaCOEEEIIIYSQOoyCNkIIIYQQQgipwyhoI4QQQgghhJA6jII2QgghhBBCCKnDKGgjhBBCCCGEkDqMgjZCCCGEEEIIqcMoaCOEEEIIIYSQOoyCNkIIIYQQQgipwyhoI4QQQgghhJA6jII2QgghhBBCCKnDKGgjhBBCCCGEkDqMgjZCCCGEEEIIqcMoaCOEyO3evXu1nYQaQ/v6ZaJ9/TLRvhJCvnQUtBFCCCGEEEJIHUZBGyGEEEIIIYTUYRS0EUIIIYQQQkgdRkEbIYQQQgghhNRhFLQRQgghhBBCSB1GQRshhBBCCCGE1GEUtBFCCCGEEEJIHUZBGyGEEEIIIYTUYSq1nQBCCCGkoh7mFePZ+xK519fRMKjG1BBCCCHVg4I2Qgghn61n70vgG/tK7vUjPbWqMTWEEEJI9aDmkYQQQgghhBBShwlyc3OFtZ0IQkjNuXfvXm0ngZAq81DZGAEJ7+VeP9JTC41LsqsxRYTULAcHh9pOAiGkBlDzSELqmco84O/du1dvMgi0r5+HzBdFAOQP2oD6k8n9nI+romhfCSFfOmoeSchnRiikynFCCCGEkPqEatoIqeNKSkrw4cMHAIC2tjYEAkEtp4gQQgghhNQkqmkj9crjx49x+vRpvH///82piouLsWzZMnTp0gXe3t44duxYLaaQ7d27dxg3bhz69esHHx8fjBgxAmlpaSgqKqrtpBFCCCGEkBpCQRupV1asWIFx48ZBXV2deS8iIgIRERG4ffs2rly5gm+//RZXrlypxVSW+fDhA3r06IHnz5+jT58+6NKlC/73v/+hd+/e2LlzJ169kn+Yc0IIIYQQ8vmi5pGkXrly5Qo8PT2holJ26peWlmL79u1o2rQpDh8+jJcvX2LAgAHYtGkTdu7cWatp3b9/P9TU1LBx40Y0adIEAJCVlYXQ0FCEhYXh9evXCA4OhpGRUa2mkxBCCCGEVC+qaSP1ysuXL2FlZcW8vnnzJnJycjBmzBg0atQIbdq0QZ8+fXDt2rVaTGWZZ8+eIS8vDzY2NgDKBiAxNTXFrl27EBgYiIiICOzfvx8FBQW1nFJCCCGEEFKdKGgj9UpxcTFrII9Lly5BIBDAw8ODea9Ro0bIysqqjeQB+P/RIY2MjFBSUoK7d+9CKBRCIBCgpKQEALBx40YMGjQIP//8M9LS0lifI4QQQgghXxYK2ki9YmFhgf/++495HRcXB0NDQzRr1ox5Lzs7G9ra2rWRPABggkpvb2+8evUKe/fuZd5TUlJiArdVq1bBwsIC8+fPZ32OkM/Zw7xiJL0okvuvsIQKKwghhHz5qE8bqVd69eqFTZs2Ye7cudDQ0MDZs2cxfPhw1jr3799nNaGsKSUlJVBWVmZe29vbY9asWViyZAlsbGwQHBwMgUAAZWVllJaWQl9fHwMHDsS+ffuQlZUFU1PTGk8zIVXt2fsS+MbKP8jO3u4G1ZgaQgghpG6goI3UK1OmTMHx48exceNGAGU1b3PmzGGWZ2dn48qVKxg3blyNpSk/Px/Lli3D/fv3oaOjg+bNm2Pq1KlQVlZGYGAg7t69i9DQUAgEAowdOxZCoRBKSmWV5MbGxkzNGyGEEEII+TJR0EbqFWNjY6SkpCAhIQEA0KVLF1ZTyJycHCxatAg9evSokfTk5+fDy8sLWlpasLOzw/Pnz3Hy5EnExMQgPDwcbm5uCA0NhVAoxKxZs5CRkYFRo0bB1tYWOTk5uHDhAmxsbNCgQYMaSS8hhBBCCKl5FLSRekdTUxM+Pj5ilzk6OsLR0RFv3rypkbSsWbMGurq62LFjB6ytrVFUVISUlBTMmTMHY8aMQUREBHr37o158+bB1tYWa9euRXR0NBo2bAgdHR3cunULMTExtdoHjxBCCCGEVC8aiITUKzNnzpS5Tm5uLgYNGlQDqQHS09PRsGFDWFtbAwDU1dXh5eWFyMhIGBsbIyQkBOfPn4elpSXCwsJw5swZDBgwAE5OTujcuTPi4+Ph7OxcI2klhBBCCCG1g2raSL2ybds2WFlZYfLkyWKX5+fnY8iQIbh582a1pqO0tBRCoRBqamooKipCUVERVFRUoKSkBIFAAGtra+zZswcBAQGYM2cO4uLioKGhAWdnZ7Ro0QICgYCZBoAQQgghhHzZqKaN1Cu+vr5YuHAhDh06xFv24cMH+Pv7IzU1FQsXLqy2NIgGElFWVkafPn1w8eJFnDhxAsrKyqy51iwtLREREYHHjx8jPDy82tJDCCGEEELqNgraSL3y22+/wdXVFRMmTEBSUhLzfmFhIb7++mtcvHgRYWFhmDRpUpV/d2FhITIyMphJsoVCIbp3746+ffti4sSJSElJgZKSElMLBwDt27eHu7s7bt26heLiYgD/Px8b1bIRQgghhNQPFLSRekVdXR1//vknbG1tMWLECNy+fRufPn3C8OHDkZSUhBkzZsjV701RBQUF8PT0xPDhw5Gens7Mx6atrY3g4GA0a9YMY8eOxYULF5jATZReGxsbvHz5Eh8/fqzydBFCCCGEkLqPgjZS7+jp6SEqKgqamprw9/fHsGHDcObMGYwfPx5z586tlu9MTEzE3bt3kZeXhxkzZuDBgwdMTZmHhwemT58OY2Nj+Pv7IyYmhpmHLTc3F48ePYKDgwNUVKgLKiGEEEJIfUS5QFIvWVtbIyoqCn379sXff/+N7777DsuWLau272vatCkMDAzQtGlT3L59GzNnzsTPP/+MJk2aACjra6etrY1NmzZh5MiR8Pb2hra2NgoKCpCcnIzY2FioqalVW/oIIYQQQkjdRUEb+aKtWLFC6vK2bdvi33//hZGREWtdgUCAWbNmVUkaSkpKYG5ujjZt2mD8+PFIS0vD6tWrERISghUrVsDBwQEA0K1bN7Rt2xbHjx/HoUOHkJGRAVtbW5w+fRqOjo5VkhZCCCGEEPL5oaCNfNGWL18u13rc4K4qgzZlZWUoKytDW1sbkZGR2LBhA968eYPdu3dj9uzZWLNmDf744w+4uLjA29sbgYGBGDRoEDQ0NPDx40eqYSOEEEIIqecoaCNftGPHjtV2ElBaWgolJSW4ubkhOjoaADBr1iyoqqpi+/bt6NmzJ968eYPk5GRmXXV1dQCAqqpqbSadEEIIIYTUARS0kS+au7t7bSeBGVSkR48eWLduHW7cuIHWrVtj2rRpiIqKwr179+Dk5AQlJSWoqKgwgRtAw/oTUtUaqKsi6UWR3OtbaimjsQ49KgkhhNQuehKResXX1xcdO3asllEi8/PzsWHDBkyaNAlaWlqsZUKhEFpaWlBSUkJ+fj4A4LvvvkNmZib8/f2RnJyMcePGYdu2bbC1ta3ytBFCyrz+CASdeyX3+sd8jChoI4QQUutoyH9Sr1y9epWZA60qFRQUoF+/flixYgWmTZuGwsJC1nKBQABzc3O4ubkhLi4Oo0aNwt9//409e/Zg06ZN8Pf3x/v375n52wghhBBCCBGhoI3UK02aNMGzZ8+qdJvFxcVYvHgxcnJyMGTIEMTExCA4OJgVuAmFQgCAhYUF1q1bh9TUVOzatQuurq4AgLlz5yImJgZWVlZVmjZCCCGEEPL5o6CN1CtBQUE4ffo0nj59WmXbfP78OeLj4+Hs7IwlS5YgPDwccXFxGDduHK/GbdGiRQgKCsKqVavQtWtXqKqqoqSkBABgaGhYZWkihBBCCCFfDmqoT+oVHx8fnD17Fj4+PpgyZQratm0LExMTsQN+yFvrZWFhgdDQUHTv3h36+vro378/Pn36hJ9++gnjxo3D1q1boaGhgaKiIqirq2PdunUQCoXMd1KTSEIIIYQQIg0FbaRecXFxgUAggFAoxOzZsyWuJxAIkJOTI9c2VVVV4efnB4FAgOLiYujr68Pf3x8AmMBty5Yt0NTUBAC8efMG+vr6ld8ZQgghhBBSL1DQRuqVr7/+ulqG0RdtU0Wl7JLS1dVlBW7jx4/H1q1bkZmZiaVLl8La2rpaRrCUx71792r1858T2teaV6BsrND61TGwUHkFBQW4d+9JtX5Hdaorx7Um1Nd9dXBwqMWUEEJqCgVtpF7ZvHlzjX2Xrq4uhgwZAoFAgLlz5yIoKAgCgQAJCQmIj4+vsXRwVeYBf+/evXqTQaB9rR2ZL4oAvJd7fdGchtVFU1MTDuZ147dRVF06rtWN9pUQ8qWjoI2QaiIUCqGnpwd/f3/k5OQgPDwcurq6OHXqFFq0aFHbySOEEEIIIZ8JCtpIvfX8+XPcvHkTb9++hY6ODlq3bo1GjRpV2fZFTSZzc3Pxzz//QFtbG7GxsXB0dKzUdssPYkIIIYQQQr58FLSReufJkyeYNm0azp49y1vm5eWF1atXw8bGpkq+6+PHj1iyRwepcQAAIABJREFUZAliY2ORlJRUoYCtpKQEHz58AABoa2tTwEYIIYQQUs9Q0EbqlaysLPTu3RsZGRmwtrZG586dYWZmhszMTFy4cAFnzpxB7969cfbsWZiamlb6+9TU1DB9+nRMmTKlQk0i3717h2nTpuHevXsoLi5G48aN8dNPP8HW1hbq6uqVTh8hhBBCCKn7KGgj9UpERAQyMjKwcOFCTJw4kTVHWklJCTZt2oR58+bh559/RkRERJV8Z0WbQ3748AE9evSAoaEh+vTpg5ycHPz999/o3bs3Zs2ahSFDhsDIyKhK0kgIIYQQQuouCtpIvXLq1Cl0794dkydP5i1TVlbGpEmTcO7cOcTGxlZZ0FZR+/fvh5qaGjZu3IgmTZoAKKspDA0NRVhYGF6/fo3g4GAK3AghhBBCvnDVO1YyIXXMy5cv4eLiInUdFxcXvHz5soZSJNmzZ8+Ql5fH9K8TCoUwNTXFrl27EBgYiIiICOzfvx8FBQW1nFJCCCGEEFKdKGgj9YqOjg6ePn0qdZ2nT59CR0enhlLEJxQKAQBGRkYoKSnB3bt3mREjS0pKAAAbN27EoEGD8PPPPyMtLY31OUIIIYQQ8mWhoI3UK25uboiOjsalS5fELr969Sqio6Ph5uZWwyn7f6LRIb29vfHq1Svs3buXeU9JSYkJ3FatWgULCwvMnz+f9TlCCCGEEPJloaCN1CszZswAAPTt2xfBwcHYs2cP4uLisHfvXnz//ffo3bs3a72aJArGROzt7TFr1ixs2rQJv/76K4CywExZWRmlpaXQ19fHwIED8ejRI2RlZdV4egkhhBBCSM2ggUhIveLi4oLdu3dj/PjxiIqKwsGDB5llQqEQ+vr62LBhg8x+b1UpPz8fy5Ytw/3796Gjo4PmzZtj6tSpUFZWRmBgIO7evYvQ0FAIBAKMHTsWQqEQSkpl5S3Gxsa8YI8QQgghhHxZKGgj9Y6Pjw9u3bqFEydO4MaNG8jLy4OOjg5atWqFvn37QktLq8bSkp+fDy8vL2hpacHOzg7Pnz/HyZMnERMTg/DwcLi5uSE0NBRCoRCzZs1CRkYGRo0aBVtbW+Tk5ODChQuwsbFBgwYNaizNhBBCCCGkZlHQRuolLS0t+Pv7w9/fv1bTsWbNGujq6mLHjh2wtrZGUVERUlJSMGfOHIwZMwYRERHo3bs35s2bB1tbW6xduxbR0dFo2LAhdHR0cOvWLcTExEBbW7tW94MQQgghhFQf6tNG6pWkpCR8/PixtpPBSE9PR8OGDWFtbQ0AUFdXh5eXFyIjI2FsbIyQkBCcP38elpaWCAsLw5kzZzBgwAA4OTmhc+fOiI+Ph7Ozcy3vBSGEEEIIqU5U00bqlf79+0NTUxMdO3aEh4cHPDw80LZt2xofebG0tBRCoRBqamooKipCUVERVFRUoKSkBIFAAGtra+zZswcBAQGYM2cO4uLioKGhAWdnZ7Ro0QICgYCZBoAQQgghhHzZqKaN1CuTJ09Gs2bNkJiYiEWLFqFnz56wtbXFsGHDsGXLFty+fbtG0qGkpARlZWX06dMHFy9exIkTJ6CsrMyaa83S0hIRERF4/PgxwsPDayRdhBBCCCGk7qGaNlKvLFy4EACQm5uLpKQkJCYmIiEhASdPnsTJkychEAhgZGQEDw8PbNu2rdrT061bN/Tt2xcTJ06EqakpOnfujNLSUggEAggEArRv3x7u7u64desWiouLoaKiwtSuUS0b+Vw8zCvGs/fyjXJaWEKTxBNCCCFcFLSReklPTw++vr7w9fUFAGRmZuLw4cNYu3YtsrOzcfjw4SoN2goLCxEfH487d+7A0tISLVu2RIsWLaCrq4uxY8fi+fPnGDt2LLZt24ZOnTqhpKQEysrKUFdXh42NDc6fP4+PHz9CRYUuWfL5efa+BL6xr+Rad293g2pODSGEEPL5oRwgqbcKCgqQkpKChIQEnDt3Dv/99x9KS0vRoEEDuLm5Vdn3vHv3DgEBAXj58iVycnLw9u1bdOjQAXPmzIGXlxc8PT0xffp0rFq1Cv7+/tiyZQv69u0LoKxG8NGjR3BwcKCAjRBCCCGknqJcIKlXLl68iISEBCQkJCA1NRUfP36Eqqoq2rVrh5CQEHh4eMDV1RWqqqpV8n0fPnyAr68vDAwM8Ouvv6Jly5Y4c+YMgoODsX//fnh5eQEAfH19oa2tjc2bN2PkyJHw9vaGtrY2CgoKkJycjNjYWKipqVVJmgghhBBCyOeFgjZSr/Tu3RsCgQAtW7bEuHHj4OHhgc6dO1fL5NRCoRAbNmyAqqoqli5diubNmwMom9z7hx9+wKpVqzB//nxYWFgAKOvf1rJlS8TFxeHgwYPIyMiAra0tTp8+DUdHxypPHyGEEEII+TxQ0EbqHaFQiIyMDDx//hwZGRnIzs6GjY1NlX/Pu3fv8PDhQ9jb28PBwQFA2VD/SkpKaNGiBUpLS/Hu3TvW+4aGhvj6668xcOBAaGho4OPHj1TDRgghhBBSz1HQRuqV69evIyEhAYmJiUhMTMThw4chEAhgZWUFT09PeHp6wsPDA8bGxpX+Lh0dHXh7e6NDhw5QUVGBUCiEklLZLBtNmjSBsrIysrOz0axZM+Z9EVH/tapqpkkIIYQQQj5fFLSResXGxgZBQUEICgoCANy+fZsJ4qKjo7F3714AgKOjI1JSUir8PaKas0GDBjHvlR+iX1VVFR8/fmRq2oCy/m+3bt2Cq6srE7TRsP6EEEIIIYQm1yb1mpOTE77//nuEhIRgwoQJMDIyglAoxJ07dxTeVn5+PpYvX47379/zas64lJWVoaWlxayXl5eHsLAwTJo0CW/evKnQvhBCCCGEkC8T1bSReiktLY0ZRTI5ORl5eXnMMmdnZ3h6eiq0vYKCAvTr1w83btxAeno6fvnlF2hoaEhcX01NDWpqanj//j0+fvyIuXPn4sCBAzh9+jT09fUrvF+EEEIIIeTLQ0EbqVeCg4ORlJSErKwsCIVCAICdnR38/Pzg6emJrl27wsBAscl9i4uLsXjxYuTk5GDIkCGIiYlBYWEhfv31V4mBm0AgQFFRETIzM7Fo0SJERUXh9OnTaN26daX3kRBCCCGEfFkoaCP1SlRUFCwsLBAQEMAMOtKoUaNKbfP58+eIj4+Hs7MzlixZAnd3d8yePRvjxo3D1q1bxQZuKioqMDAwwIoVK1BSUoKTJ09SwEYIIYQQQsSioI3UK1euXIG9vX2VbtPCwgKhoaHo3r079PX10b9/f3z69Ak//fQTK3ATCoXMwCJqamrQ0NDA27dvcfr0aTg5OVVpmgghhBBCyJeDgjZSr1y5cgWFhYVwdnaWuM7t27dx48YNBAYGyrVNVVVV+Pn5QSAQoLi4GPr6+vD39wcAJnDbsmULNDU1AQC5ubnQ09PDrFmz0LZtWzRp0qTyO6aAe/fu1ernPye0r1WjQFn+KTRKS0sV2rai6yuqoKAA9+49qdbvqE50Dn+Zyu+raB5QQsiXjYI2Uq9MmDABs2fPlhq0nThxAsuWLZM7aAP+f2h+0VD9urq6rMBt/Pjx2Lp1KzIzM7F48WI4OjoiJCSkEntScZV5wN+7d6/eZBBoX6tO5osiAO/lWlfWyKuVXV9RmpqacDD/PM8DOoe/TPVpXwkh/4+CNkI4SkpKqmR+NF1dXQwZMgQCgQBz585FUFAQBAIBEhISMG3atCpIKSGEEEIIqQ8oaCOE48GDB9DT06v0doRCIfT09PB/7N15XNTl+v/x18ywiqyCCyjiGp20sNRcc993D1h43L6ZWpmVS2CamaWZWqamaW64pWXuW6Se1KPZcio1NStK0QDFRJBFkGXm94c/pjxZYToMMO/n4+EjnblnPtfVPcrnmnuLiIggJSWF6dOn4+3tzUcffcQ999xzW++rQ7dFREREHIeKNinzRo4cecOfd+7cyblzv1+jUlBQQEJCAp9++ikdO3a87esWFlZpaWkcOXIET09PYmNjCQ0NvaX3KSgo4OrVqwB4enqqYBMRERFxMCrapMxbu3at9fcGg4Hjx49z/Pjxm7Y1GAw0bNiQ6dOn35Fr5+bmMnXqVGJjYzl48OAtF2wZGRmMHj2auLg48vPzqVGjBpMmTSIkJARXV9c7EqOIiIiIlGwq2qTMO3bsGHB9WmFYWBhPPPEEjz/++O/amUwmfHx88PDwuGPXdnFxYcyYMTzzzDO3PCXy6tWrtGvXjgoVKtC1a1dSUlL497//TZcuXYiKiiI8PBx/f/87FquI/J6TAQ6ev1aktlU9TNTw0o9VERG58/TTRcq84OBg6++jo6Np2bLlDY/Z2q2OrhV67733cHFxYcGCBdZjAZKTk4mOjmbChAlcvnyZ4cOHq3ATsaGUa2YGfHy5SG23d/ZX0SYiIjZh272SRUqYtWvXsnnzZnuHUSQJCQmkp6dTvXp14PpIYaVKlVixYgWRkZHMmjWL9957j+zsbDtHKiIiIiK2pKJNHMrly5fx8vKydxh/ymKxAODv709BQQE//PCDdcfIgoICABYsWECfPn14/fXX+f777294nYiIiIiULSraxKGEhoZy5swZe4fxpwp3h+zYsSOXLl1izZo11seMRqO1cHvjjTcIDAxk8uTJN7xORERERMoWFW3iUEaMGEFsbCwnTpywdyi/U1iMFapduzZRUVG8/fbbLF68GLhemJlMJsxmM76+vvTu3Zv4+HiSk5PtEbKIiIiIFAOtmBaHEhgYSKtWrejcuTNDhgzh/vvvp2LFijcdpWrevHmxxJSZmcmrr77Kjz/+iJeXF3fffTfPPvssJpOJyMhIfvjhB6KjozEYDAwbNgyLxYLReP37loCAgN8VeyIiIiJStqhoE4fSvXt3DAYDFouFBQsW/OmUwsuXi7Zj3O3IzMykTZs2eHh4UKtWLRITE/nwww/ZsWMH06dPp0mTJkRHR2OxWIiKiiIpKYnBgwcTEhJCSkoKn376KdWrV6dcuXI2j1VERERE7ENFmziUqKioErX2680338Tb25vly5cTHBzMtWvXOHz4MM8//zyPPfYYs2bNokuXLrz44ouEhIQwZ84ctm7dSvny5fHy8uLEiRPs2LEDT09Pe6ciIiIiIjaiok0cyvPPP2/vEG5w+vRpypcvbz03ztXVlTZt2rB+/XoGDx7MuHHj8PT0pEWLFkyYMIGePXuyceNGzp8/T3BwMHPmzKF27dp2zkJEREREbElFm8j/MJvNfPjhh3Tr1s2m17BYLLi4uHDt2jWuXbuGk5MTRqMRg8FAcHAwq1evpl+/fjz//PPs2bMHNzc36tWrxz333GOd4lmSRg1FRERExDa0e6TI/3fu3DmmTp1KvXr1GDhwoE2vZTQaMZlMdO3alc8++4xdu3ZhMpluOGutatWqzJo1i7NnzzJ9+nSbxiMiIiIiJZdG2sShFRQUsHPnTlauXMn+/fsxm80YDAZat25dLNdv1aoV3bp1Y+TIkVSqVIlmzZpZYzAYDDRs2JAWLVpw4sQJ8vPzcXJyso6uaZRNRERExDGoaBOHFB8fz8qVK1m7di2//PILABUqVGDIkCEMHDjQusbsTsnKyiImJob4+HiCg4OpW7cunTt3xsfHh+HDh5OQkMCwYcNYunQpTZs2paCgAJPJhKurK9WrV+fQoUPk5ubi5KS/siIiIiKORneA4jDy8/PZsWMHK1as4ODBg5jNZlxcXOjRowfbtm2ja9euTJw48Y5fNzMzk7Zt22IwGHB3d2f79u2kp6fTp08fZs6cyUMPPcRzzz3HzJkziYiIYNGiRdb1dGlpacTHx1OnTh0VbCIiIiIOSneBUub99NNPrFy5knXr1pGSkoLFYiEsLIz+/fsTERGBj48Pvr6+Nrm22WwmOjqagIAAFixYQEhICPHx8WzevJk33niDpKQk5s+fT7du3fDw8GDhwoUMHDiQjh074unpSXZ2Np988gmxsbG4uLjYJEYRERERKdlUtEmZ17BhQwwGAxUrVmTkyJH079+fu+++u1iubTQaOX36NPXq1SMkJASAkJAQhg8fTq1atRg9ejQjR45k1apVtG7dmrvvvpt9+/axYcMGkpKSCAkJYffu3YSGhhZLvCIiIiJS8qhoE4dgMBho3749PXv2LLaCzWw2k5mZSWZmpnVXyNzcXFxcXPDw8KBbt24YjUaefPJJnnvuORYvXkylSpV45JFH6N27N25ubtb2IiIiIuK4tOW/lHkTJ06katWqvPvuu3Tq1IkHH3yQuXPncuHCBZte12g04uXlRYcOHVi9ejU//vgjLi4uFBQUAGAymWjXrh3R0dFs2bKFDRs2WF/r6uoKgLOzs01jFPk7zqTnc/D8tSL/yimw/PWbioiIyB/SSJuUeePGjWPcuHH8+9//ZuXKlcTGxvLSSy/xyiuv0LZtWyIjI21y3cLDr/v27cvOnTt58skniYmJISgoyLo7pLu7Oz179mT58uWcOHGC8PBwAG3rLyVaQlYBPWIvFbn9mrZ+NoxGRESk7NNImziMdu3asWrVKk6ePMmLL75ItWrV2LNnD0OHDsVgMHD8+HGOHj36t98/MzOT1157jaysLODXgqtevXoMGTKEpKQkxowZQ2JiIiaTidzcXACqVatGjRo1OHXq1A2Ha4uIiIiIgIo2cUABAQGMHj2aI0eOsGXLFnr16oWzszNHjhyhbdu2tGzZkiVLltzSe2ZnZ9O9e3dmzJjB6NGjycnJAbBOhXziiSeIjIzk5MmTPPbYY5w5c8a6Vu3y5ctkZWVRt25djayJiIiIyO9oeqQ4tFatWtGqVStSUlJYu3Ytq1at4sSJE0RHRzNs2LAivUd+fj6vvPIKKSkphIeHs2PHDnJycli8eDFubm7WqZATJ07Ey8uLlStX0qJFC5566inr7pLffvstc+fOtXG2IiIiIlIaaaRNBKhQoQKjRo3iv//9L9u2bbOuLSuKxMRE9u7dS7169Zg6dSrTp09nz549jBgxgpycHEwmk3XEbdSoUSxcuJD+/fuzbt06NmzYwC+//MLOnTupW7eurdITERERkVJMI20i/6Nly5a0bNmyyO0DAwOJjo6mbdu2+Pr60rNnT/Ly8pg0aRIjRozgnXfewc3Njfz8fJycnGjUqBGNGjVizJgx+Pj4UFBQQPny5W2YkYiIiIiUZhppE7lNzs7O9O3bF19fX/Lz8/H19SUiIoJXXnmF3bt3M2LECLKzs3Fyuv4dSUpKCgBVqlTB3d1dBZuIiIiI/CmNtIncAYUbiBQWZt7e3kRERAAwadIknnjiCd555x0uXLjAtGnTCA4O5oUXXrBLrHFxcXZ9fWmiXG8u2xRwS+9tNptt0vbvtL9Vt/L+2dnZxMWds2E0t06f4bLpt7nWqVPHjpGISHFR0SZiI97e3oSHh2MwGHjhhRcYNGgQBoOBAwcOsHfvXrvFdTs/4OPi4hzmBkG5/rEL568BWUVubzQWfVLHrbT9O+1v1a28v7u7O3WqlJzPjD7DZZMj5Soiv1LRJmIjFosFHx8fIiIiSElJYfr06Xh7e/PRRx9xzz333Nb76mgAEREREcehok3ERgoLq7S0NI4cOYKnpyexsbGEhobe8nsVHhtQ+L4q3EREREQchzYikTItJCTkhvPPZsyYwSeffFJs18/NzWXq1KnExsaya9euWy7YMjMzmTBhApGRkTz22GO88cYbFBQUqGATERERcSAq2qRMu3LlCjk5OdY/v/baaxw6dKjYru/i4sKYMWM4dOgQ9erVu6XXZmZm0qZNGw4fPoynpyeJiYnMnj2bDh068Omnn5KXl2ejqEVERESkJFHRJmVaxYoVSUpKsmsMoaGhf2sN25tvvom3tzerVq1i2bJlbNmyhTVr1nD16lWGDx/O7t27yc3NtUHEIiIiIlKSaE2blGkNGzbk/fffx2QyUalSJYAijbQZDAaioqJsHd6fOn36NOXLlyc4OBgAV1dX2rRpw/r16xk8eDDR0dF4eXnRsmVLrXETERERKcNUtEmZ9sorr/DTTz8RExNjfezQoUN/WbjZs2gzm81YLBZcXFy4du0a165dw8nJCaPRiMFgIDg4mNWrV9OvXz8mTpzI7t27cXNzU+EmIiIiUkapaJMyrWbNmhw+fJizZ8+SlJRE9+7d6d+/P5GRkfYO7Q8VngvVtWtXhgwZwq5du+jTpw9ms9lalFWtWpVZs2YRGRnJ9OnTmTJligo2ERERkTJKRZuUeUajkRo1alCjRg0AgoODadGihZ2j+mutWrWiW7dujBw5kkqVKtGsWTNr4WYwGGjYsCEtWrTgxIkT5Ofn4+Skv84iIiIiZZHu8sShpKam2juE38nKyiImJob4+HiCg4OpW7cunTt3xsfHh+HDh5OQkMCwYcNYunQpTZs2tZ7Z5urqSvXq1Tl06BC5ubkq2kRERETKKN3licNKTEzkm2++4cqVK3h5eXHfffcRFBRUrDFkZmbStm1bDAYD7u7ubN++nfT0dPr06cPMmTN56KGHeO6555g5cyYREREsWrSIbt26AdcP7Y6Pj6dOnToq2ERERETKMN3picM5d+4co0ePZt++fb97rk2bNsyePZvq1avbPA6z2Ux0dDQBAQEsWLCAkJAQ4uPj2bx5M2+88QZJSUnMnz+fbt264eHhwcKFCxk4cCAdO3bE09OT7OxsPvnkE2JjY3FxcbF5vCIiIiJiHyraxKEkJyfTpUsXkpKSCA4OplmzZlSuXJkLFy7w6aef8vHHH9OlSxf27dtnPSLAVoxGI6dPn6ZevXqEhIQAEBISwvDhw6lVqxajR49m5MiRrFq1itatW3P33Xezb98+NmzYQFJSEiEhIezevZvQ0FCbxikiIiIi9qWiTRzKrFmzSEpKYsqUKYwcORKTyWR9rqCggLfffpsXX3yR119/nVmzZtksjoKCArKyssjMzMRisQCQm5uLi4sLHh4edOvWDaPRyJNPPslzzz3H4sWLqVSpEo888gi9e/fGzc3N2l5EREREyjajvQMQKU4fffQRbdu25emnn76hYAMwmUyMGjWKtm3bEhsba9M4TCYTXl5etGnThtWrV/Pjjz/i4uJCQUGB9fl27doRHR3Nli1b2LBhg/W1rq6uADg7O9s0RhG5NU4GOHj+WpF/nUnPt3fIIiJSSmikTRzKxYsX6dev35+2CQsL+8vDt/+OnJwc9u3bR3JyMv7+/nTv3p2hQ4eyc+dOnnzySWJiYggKCrLuDunu7k7Pnj1Zvnw5J06cIDw8HMB6HpvOZRMpWVKumRnw8eUit9/e2Z8aXvoxLCIif00/LcSheHl58fPPP/9pm59//hkvL687et2MjAz69OlDWloaZ86cwcfHhw8//JAFCxYwYsQI5s2bx5gxY5g9ezZBQUHWqY/VqlWjRo0anDp1CovFokJNRERExAFpeqQ4lCZNmrB161Y+//zzmz7/5ZdfsnXrVpo0aXLHrpmdnU2vXr3w8fFh3rx5fPXVV4SHh7N//37i4+MZPnw4kZGRHD9+nMcee4wzZ85Y16pdvnyZrKws6tatq4JNRERExEFppE0cytixY9m9ezfdunWjb9++tGzZksqVK5OcnMyhQ4fYuHEjRqORsWPH3rFrvv/++6SnpzNv3jxCQ0NxcnJi4MCBrF69mitXrgAwceJEqlatypw5c2jRogVPPfWUdXfJb7/9lrlz596xeERERESkdFHRJg4lLCyMlStX8sQTT/DBBx/csMGHxWLB19eX+fPnExYWdseu+cMPP5CXl0e9evWsjzk7OxMYGMjcuXNJTEwkNDSUKVOm0LBhQ5YtW8a6detwdXWlWrVq7Ny5k7p1696xeERERESkdFHRJg6nc+fOnDhxgl27dnHs2DHS09Px8vLi3nvvtR5kfSfVqlWLc+fO8e9//5t27dpx4cIF/u///o+8vDz8/f1xdnZm69atHD16lN27dzN79mwuXLiAt7c3BQUFlC9f/o7GIyIiIiKli4o2cUgeHh5EREQQERFh82v16NGDTZs2ER4ezt13301qair+/v5s3brVeqj2pk2bGDZsGLNmzeKFF14gICDgd0cSiIiIiIhjUtEmYmMVK1Zk9erVHDp0iPLlyzNz5ky6detG9erVrdv7d+jQAT8/PzIyMgBUsImIiIiIlYo2kWLg5+dHz549ycjI4PTp05QvXx6DwYDJZKKgoICkpCQCAgKsa9e0vb+IiIiIFNKW/yLFqFy5ctx9992sW7eOo0ePApCYmMiiRYvIyMigQ4cOgA7OFhEREZFfaaRNpBiZTCamTJlC3759iYyMJCQkhJycHH755RfWrVtHcHCwvUMUERERkRJGI20ixSwsLIzt27fz0EMP4ebmRuvWrdm2bRv169e3d2giIiIiUgJppE3EDu655x7efvttjMbr35toOqSIiIiI/BGNtIlD6dGjB1OnTrV3GMD1qZIGg0EFm4iIiIj8KY20iUP58ssvadSokb3DsKu4uDi7vr40Ua43l20KuKX3NpvNNmn7d9rfKlvGnp2dTVzcuVsN6ZboM1w2/TbXOnXq2DESESkuKtrEodSsWZOEhAR7h2FXt/MDPi4uzmFuEJTrH7tw/hqQVeT2hdOA73Tbv9P+Vtkydnd3d+pUsd1nTJ/hssmRchWRX2l6pDiUQYMGsXv3bn7++Wd7hyIiIiIiUiQaaROH0rlzZ/bt20fnzp155plnuP/++6lYseJN15VVq1bNDhGKiIiIiNxIRZs4lLCwMAwGAxaLhfHjx/9hO4PBQEpKSjFGdmsKCgowmUz2DkNEREREioGKNnEojzzySKnarTErK4t58+YRHx9PQEAA99xzD5GRkSrYRERERByIijZxKAsXLrR3CEWWmZlJmzZtcHZ2pmLFinzxxRcsX76cDRs2MHfuXKpWrWrvEEVERESkGGgjEpESyGKxMGXKFPz9/Vm/fj1btmxh3759TJ8+nW+//Zb+/fvzzTff2DujNmfAAAAgAElEQVRMERERESkGKtrEYf3www9s376d9957z96h/I7BYOD06dNUrVrVOqLm4+NDZGQkS5YsITMzk5EjRxIfHw9cX+MmIiIiImWTpkeKw/nmm294+umnbxipeuSRRwA4dOgQERERLF++nC5dutglPrPZTG5uLjk5OeTn5wOQn5+PyWTCxcWF5s2bM3/+fIYNG8YzzzzD1q1btcZNbstVNz8Onr9W5PY5BRYbRiMiIiL/S0WbOJQff/yR7t27Yzabefzxx/npp5/Ys2eP9fnmzZvj6+vL1q1b7Va0GY1G3Nzc6N27N1FRUTz66KO0bNkSi8WCxWLBYDDQqFEjXnjhBcaOHcuiRYt4/PHH7RKrlA3JeSb67b1U5PZr2vrZMBoRERH5X5oeKQ5lxowZ5OXlsXfvXl599VUaNGhww/OFBdGRI0fsFOGv2rRpQ+PGjRk3bhzHjh2z7nppsVhwdnamS5cu1K1bV2vbRERERMo4FW3iUA4cOECPHj0IDQ39wzZBQUGcP3++2GK6evUqq1atYtq0aaxbt44vv/wSgNq1azN48GDy8vIYP34833zzDQaDwbp+zcfHh7p163L27FnrNEoRERERKXs0PVIcSlpaGoGBgX/axmKxkJeXVyzxZGRk0KFDB65du4bBYCApKYkKFSoQHh7OlClT6N+/PxkZGdYpkK+//jrNmjUD4MqVKyQnJ1O7dm2MRn3/IiIiIlJWqWgTh1KxYkVOnz79p22+++47goKCbB5LQUEBo0aNolKlSrz55pvUrFmTo0ePsnLlSubNm0diYiJLly5lxIgRlCtXjpiYGLp168aQIUNwc3MjOTmZI0eOMGPGDBVtIiIiImWYijZxKC1btmTjxo3ExcVRp06d3z3/9ddfc+DAAR577DGbx2I2m/npp5/o1q0bNWvWBCAsLIzAwEBq1arFtGnTGDp0KMuWLWPgwIGEhYWxd+9ePvjgA5ycnKhWrRqxsbF/OtVTREREREo/FW3iUMaMGcPWrVvp2rUr48eP58KFCwCcOnWKw4cPM2PGDMqXL8+oUaNsHsvly5e5cOECbm5uAOTm5uLi4kLFihUZMGAARqORiRMnUrlyZaZNm0b9+vWpX78+Q4cOxdPTk5ycHNzd3W0ep4iIiIjYl4o2cSh16tRh1apVDB06lOeeew64voatefPmWCwWvL29Wb16NdWqVbPJ9fPy8rh06RJVqlShUqVKtG/fnkWLFtGvXz8CAwMpKCjAZDLh4+NDREQEZ8+eZcOGDXTs2JFWrVoB4OnpicFgsBZ7IiIiIlK2aSGMOJz27dtz7Ngxpk2bRp8+fWjdujU9evTg5Zdf5siRI9bi6E7LzMzkX//6FwsXLuTkyZMA9OrVCycnJyZPnsylS5cwmUzW3SEDAgIYMGAAOTk5fPvtt9b3Kdz6v/C/IiIiIlK2aaRNHJKPjw9PPPFEsV0vMzOTtm3b4u/vT4MGDahduzYAnTt35sCBA6xfv56ZM2cSFRWFv7+/dapk/fr1qVWrFseOHSu2WEVERESkZFHRJmJjZrOZsWPHEhAQwMKFCwkKCsJkMpGXl4ezszPTp08nLy+P999/n/T0dCZPnkyVKlUAuHTpEgUFBdYiT0REREQcj4o2cUjr169nzZo1fPPNN2RkZODp6cm9997LgAED6Nev3x29VnZ2NqdPnyYiIoLg4GAAPvvsMw4cOEBycjL33nsvM2fOxNPTk/fff59OnToRFRVFbm4uX3/9NWfPnqV37953NCYRERERKT1UtIlDycvLY9CgQXz00UdYLBZMJhP+/v6kpKRw8OBBDh06xObNm1m1ahXOzs535Jrp6ekkJCRYz37bvHkzTzzxBLVr1+bSpUvExMTw3nvvsXTpUpo2bUpMTAzR0dH4+fkRGBjI9u3bNdImIiIi4sC0EYk4lNmzZxMbG0vDhg3Zvn07ycnJfP/99yQnJ7Nt2zYeeOABPvroI+bMmXPHrunu7o7BYOD48ePk5eUxadIkRo8ezaZNmzh27BgLFy4kISGBRx99lI4dO7Ju3ToOHTrEwYMHWb9+PfXr179jsYiIiIhI6aORNnEo7733HjVr1mTHjh24uLhYHzeZTLRs2ZIdO3bQtGlT1q5daz0S4Hb5+PjQr18/lixZgouLC5UrVyYiIoKKFSsC0KNHD/Ly8hgzZgzvv/8+Dz/8MNWrV8do1HcqIiIiIqKRNnEwSUlJdO3a9YaC7bdcXV3p2rUr58+fv6PXHTJkCP7+/ixevJjk5GR8fHyA6wdqe3h40KNHD5ydnUlOTgZQwSYiIiIiVrozFIdSuXJl8vLy/rRNXl4elStXvqPXDQkJYdGiRWRkZJCQkMDWrVsBrMXjxYsXqVq1qnXXSBERERGRQiraxKFERESwbds20tPTb/p8Wloa27ZtIyIi4o5fu0GDBuzcuRMvLy/GjBnDggULOHfuHMeOHWPevHmkp6fz4IMP3vHrioiIiEjppqJNHEpUVBRhYWG0a9eODz74gMTERPLy8khMTGT9+vV06NCB+++/n6ioKJtcPywsjL1799KxY0defPFFWrRowb/+9S/+85//sH79euuRACIiIiIihbQRiZRpvr6+GAyG3z1usVgYMWLETR//6aefqFy5MikpKTaJqU6dOqxYsYKTJ09y9OhRAgMDue+++6xHAoiIiIiI/JaKNinTmjVrdtOizd7c3d1p2LAhDRs2tHcoIiIiIlLCqWiTMm3nzp32DkFERERE5LaoaBNxMHFxcXZ9fWniMLmaAm6pudlstll7W77332HL2A3mfHafzi5y+0rOBZTLuXxL13CYzzCOm2udOnXsGImIFBcVbSIO5nZ+wMfFxTnMDYIj5XrmdNottb/VcwRvpb0t3/vvsGXsaflGBhzIKnL77Z39ua9OhSK3d6TPsHIVkbJORZs4pA8//JDjx4+TlJR003PbDAYD8+fPt0NkRWOxWErkWj0RERERufNUtIlDOXfuHI888gjfffcdFovlD9uVpKItOzubTz/9lISEBP7xj38QHBxMxYoVVbiJiIiIOAgVbeJQoqOjOXXqFAMGDOCRRx4hMDAQk8lk77D+UEZGBl26dCEjI4Pz58/j7u6Ov78/c+bMoWXLlvYOT0RERESKgYo2cSgHDx6kXbt2vPXWW/YO5S/l5eUxePBgAgICmD9/PnfddRdbtmxh3bp1hIeHM336dPr374+bm5u9QxURERERG7LtCm6REsbJyYl//OMf9g6jSC5evEh8fDwPP/wwYWFhuLu7ExkZyZw5c4iIiCAqKop3330X4E+neoqIiIhI6aaRNnEoDz74IKdOnbJ3GEWSlZXF5cuXrdM3r127hqurKzVr1uSll17CaDQyYcIEatasSZs2bTCbzTbfSU9EREREip/u8MShTJgwgcOHD7Nx40Z7h/KXatasSWBgIOvXrwfA1dWV/Px8APz9/RkzZgwtWrRg1KhRJCUlqWATERERKaM00iYO5b777mPr1q3069ePmJgY7rvvPry8vH7XzmAwEBUVZYcIrzObzTg5OfHUU08xduxYpk+fzvPPP4+TkxP5+fk4OTkREhLCoEGDePbZZzlx4gSBgYF2i1dEREREbEdFmziUK1eu8PLLL5Oamsonn3zCJ598ctN2xV20Xbt2jTNnzpCcnEy9evUoX748rq6uNGvWjC5durB69Wq8vLwYOXIkTk5O1qmSvXr14plnnuHo0aN07Nix2OIVERERkeKjok0cyoQJEzh48CCtW7fm4YcfpkqVKnbf8j8jI4O+ffty4cIFEhISCAoKolGjRrz22muEhIQwZswYXnjhBd5++22ysrKIiorC1dUVgLNnz1K5cmWCg4PtmoOIiIiI2I6KNnEoH330EQ8++CCbN2+2dyjA9RG2vn374uXlRXR0NCEhISxZsoQPP/yQ1q1bs27dOsLCwnjllVeYOXMmc+bM4dixYwwfPpzc3Fx27dpFSkoKTZo0sXcqIiIiImIjKtrEoeTk5NC4cWN7h2F16tQpzp8/z6RJk3jooYcAmDp1Km3btuXVV18lIiKC9evX06BBA15++WUeeughFixYwIABA/Dw8MDPz4+NGzcSEhJi30RERERExGZUtIlDqV+/PvHx8fYOwyolJYVLly7h5+cH/Lqtf8eOHfH29ub555/nkUceYf/+/dSoUYNhw4YxYMAAjh49io+PDwEBAfj7+9s5CxERERGxJRVt4lCioqJ4+OGH+fTTT2natKm9w+Ef//gHLi4ubNu2jXr16uHq6kpBQQEmk4kHH3yQiRMnMm7cOJ599llWrFiBu7s77u7uJSJ2KbnOpOeTkFVQ5PZmo34UiIiIlGT6SS0O5cKFC3Tq1ImePXsSHh5OWFjYTbf8B4iMjLzj18/Ozubzzz+ndevWAHh7exMREcHKlSu599576d69OyaTyVq4tWrVin/+85+sWbOG5ORkTYOUIknIKqBH7KUit1/V2seG0YiIiMjtUtEmDuXJJ5/EYDBgsVh47733eO+99zAYDDe0sVgsGAyGO1605eTk0KFDB3Jzc5k4cSK9evWiXLly9O3bl08++YS33nqL8uXL07p1a0wmE7m5ubi4uDBy5Ehmz57N119/raJNRERExAGpaBOHsmDBArtd+7vvvuPkyZMYDAZmzJiBwWCgZ8+eNG/enPHjxzN+/Hhee+01rl69SteuXXFxcQHg3LlzBAUFUblyZbvFLiIiIiL2o6JNHEr//v3tdu17772Xf/7znzRo0IC33nqLF198EYvFQq9evejduzcWi4Vp06YRFRXFqVOnGDBgAL/88gurV68G0CibiIiIiINS0SZSTIxGI8nJyVy9epXDhw/TuHFjXn75ZSwWC71796ZPnz74+Piwfv16Xn/9debMmYO3tzcGg4G1a9cSGBho7xRERERExA6M9g5AxBGYzWYA+vXrxxdffIGfnx+xsbFcuXKFqVOnsmvXLgYPHszly5eZPn06Bw8eZPr06cybN4/Y2FjuvfdeO2cgIiIiIvaikTZxKPfdd1+R2hkMBo4ePXrHrms0Xv9+JDg4mE8++YTvv/+eu+66i/3799OmTRuefPJJrly5wuDBg/Hx8cHHx4fatWvfseuLiIiISOmlkTZxKGazGYvF8rtfaWlpnDt3jnPnzpGXl2cdGbuTLBYLYWFh1K5dm0uXrm/HXrVqVRo0aEBGRgZBQUFkZWXd8euKiIiISOmmkTZxKMePH//D506fPk10dDRZWVls2rTpjl/bYDDg7e2Nh4cHsbGxNG/enMGDB/PVV1/x1ltv8corr/Dss8/i4uJCp06d7vj1RURERKR00kibyP9Xs2ZNVq9ezfnz55kxY8Ydf//C0bsWLVqQlJTE//3f//Gf//yHd955h/79+7Nr1y58fHyoW7fuHb+2iIiIiJReKtpEfsPNzY02bdqwYcOGO/7ehevaWrRowaZNmzhw4AAxMTG0a9cOi8VCjRo1+Oyzz6hRo8Ydv7aIiIiIlF6aHinyP0wmExcvXrTZ+7ds2ZJly5bh7+9P8+bNMRgM1uecnPRXUkRuzskAB89fK3J7Lzc/G0YjIiLFSXeIIr+RkpLCzp07CQoKstk1jEYjffr0uaFYK3Szx0REAFKumRnw8eUit1/fysOG0YiISHFS0SYO5Y/WquXn55OYmMiuXbtIT09n8uTJNo1DxZmIiIiIFJWKNnEor7322p8+7+npybhx43jmmWeKKSIRERERkT+nok0cyvbt22/6uNFotO7cWNbXlcXFxdn19aVJac012xRg0/e/1XMMb6W9Ld/77yhJsf+dXEvrZ/jvcNRc69SpY8dIRKS4lO27U5H/0aJFC3uHYHe38wM+Li7OYW4QSnOuF85fA2x3UHvhTqi2aG/L9/47SlLsfyfX0voZvlWl+e/rrXKkXEXkV9ryX0REREREpATTSJuUeX93+pStv8G/HQUFBZhMJnuHISIiIiLFQEWblHn+/v63/BqDwUBKSooNork1WVlZzJs3j/j4eAICArjnnnuIjIxUwSYiIiLiQFS0SZkXFBRU5C32s7KyuHy56Ocg2VJmZiZt2rTB2dmZihUr8sUXX7B8+XI2bNjA3LlzqVq1qr1DFBEREZFioKJNyrzjx4//ZZu8vDzeeecd3njjDQCCg4NtHdafslgsTJkyBX9/f5YsWULVqlVJS0tj69atvPbaa/Tv35/58+dz77332jVOEREREbG9krtoR6SYbNmyhcaNG/Piiy9isVh4+eWX+eKLL+wak8Fg4PTp01StWtU6oubj40NkZCRLliwhMzOTkSNHEh8fD1xf4yYiIiIiZZNG2sRhff7550yaNIkvv/wSJycnRowYQXR0ND4+PnaNy2w2k5ubS05ODvn5+QDk5+djMplwcXGhefPmzJ8/n2HDhvHMM8+wdetWrXEr486k55OQVfTCPKfAYsNoREREpLipaBOHc+bMGSZPnsyOHTuwWCz06tWLyZMnU6NGDXuHBlzftdLNzY3evXsTFRXFo48+SsuWLbFYLFgsFgwGA40aNeKFF15g7NixLFq0iMcff9zeYYsNJWQV0CP2UpHbr2nrZ8NoREREpLhpeqQ4jNTUVKKjo2nSpAnbt2+nUaNG7N69mxUrVpSYgu232rRpQ+PGjRk3bhzHjh2zbqZisVhwdnamS5cu1K1bl2+++cbOkYqIiIiILalokzIvNzeXuXPn0qBBAxYvXkxQUBArVqzgo48+olGjRvYOj6tXr7Jq1SqmTZvGunXr+PLLLwGoXbs2gwcPJi8vj/Hjx/PNN99gMBis69d8fHyoW7cuZ8+etU6jFBEREZGyR9Mjpcxr2LAhCQkJ+Pr6Mn36dIYNG1Zi1oBlZGTQoUMHrl27hsFgICkpiQoVKhAeHs6UKVPo378/GRkZ1imQr7/+Os2aNQPgypUrJCcnU7t27RJ9ELiIiIiI3B4VbVLm/fzzzxgMBiwWC/Pnz2f+/PlFet2JEydsGldBQQGjRo2iUqVKvPnmm9SsWZOjR4+ycuVK5s2bR2JiIkuXLmXEiBGUK1eOmJgYunXrxpAhQ3BzcyM5OZkjR44wY8YMFW0iIiIiZZiKNnEIFouF1NRUUlNT7R2Kldls5qeffqJbt27UrFkTgLCwMAIDA6lVqxbTpk1j6NChLFu2jIEDBxIWFsbevXv54IMPcHJyolq1asTGxhIaGmrnTERERETEllS0SZlXkgq1QmazmbS0NC5cuICbmxtwfe2di4sLFStWZMCAARiNRiZOnEjlypWZNm0a9evXp379+gwdOhRPT09ycnJwd3e3cyYiUlKVc3Xm4PlrRW5f1cNEDS/dFoiIlET611mkmJnNZoxGIwEBAbRr145FixbRr18/AgMDKSgowGQy4ePjQ0REBGfPnmXDhg107NiRVq1aAeDp6YnBYLAWeyIiN3M5FwbtL/pREds7+6toExEpobQQRqSYmM1mLBYLycnJ1sfCw8MxmUxMnjyZS5cuYTKZrLtDBgQEMGDAAHJycvj222+trync+r/wvyIiIiJStqloEykGmZmZPPfcc7Rv354HH3yQp556ih9++IH27dvTu3dv9u/fz8yZM62FW25uLgD169enVq1aHDt2zM4ZiIiIiIi9aB6EiI1lZmbSrl07/P39eeCBB2jatCkrVqwgKSmJjRs3Mm3aNC5dusSmTZtIT09n8uTJVKlSBYBLly5RUFBA7dq17ZyFiIiIiNiLijYRG8rNzWXEiBFUqVKFuXPnUr16dQAefPBBBg8ezJo1axg4cCDvvPMOUVFR7Ny5k06dOhEVFUVubi5ff/01Z8+epXfv3nbORERERETsRUWbiA0dOnSIhIQEnn32WapVqwZcP37g/vvvJzAwkO+//97adubMmTRt2pTNmzcTHR2Nn58fgYGBbN++XSNtIiIiIg5MRZuIDVWrVg1PT0/at2+P0WjEYrFgMBgICgoiJCSEuLg44Nft/vv06UOfPn04ffo0fn5+GAwGvL297ZyF2MKZ9HwSsgqK1DanwGLjaERERKQkU9EmYkN16tRh/fr1lCtXzrrVf+F/PTw8sFiu34y7uLgAkJ+fj5OTk/WwbSm7ErIK6BFbtO3Y17T1s3E0IiIiUpJp90gRGytXrhwARuP1v25msxkAd3d38vLyrO0yMzNZuHAhO3fuLP4gRURERKTE0kibSDFzcrr+187V1ZWMjAzy8/PJzs5m4sSJvPvuu3zxxRd2jlBEREREShIVbSLFrHBdm4uLC/n5+WRmZvLSSy+xadMm/v3vf1OrVi17hygiDsjJAAfPXytS26oeJmp46RZCRKS46F9ckWJWWLS5ublhNBp5/vnn2bJlC7Gxsdx33332Dk9EHFTKNTMDPr5cpLbbO/uraBMRKUb6F1ekmBWubfP19eXIkSP89NNPKtjKgFvZDRK0I6SIiIgUnYo2ETvp3r07MTEx7Nixg7vuusve4chtupXdIEE7QkrpditTKUHTKUVEbpchLS1NX/eK2El2djbu7u72DkNERERESjBt+S9iRyrYREREROSvqGgTEREREREpwVS0iZQSFovlpr8XERERkbJNRZtICZeXl0dqaipnzpyxPmYwGDCbzXaMSkRERESKi4o2kRIsIyODhx9+mA4dOtCwYUO6du3KW2+9BVw/OqCgoOhbzIuIiIhI6aSiTaSEys7OpnPnzlgsFkaNGsXKlSsxm8289dZb9OvXD4vFgslkUuEmIiIiUsbp0BSREurjjz8mIyODxYsXc8899wDQokULli5dyoIFC+jYsSO7d+/GZDJhNputh3aLiIiISNmiuzyREurChQtkZmYSEhICQG5uLr6+vjz55JO8+OKL/PDDD4SHhwPXp0re7uYkZXmNXHZ2Nl9++aW9wyh26tOySf1aNmmDKRH5M6bx48e/ZO8gROT3srKyiImJ4YEHHqBOnTrWqZCurq7cddddODs7s3PnTnJycmjevDkGg6HI752dnc2ePXv44osvSEpKonbt2rf0+tIkPT2dxo0b4+HhQZMmTTCZTPYOySbUp2WT+rVsunr1Ku+//z4HDhzg5MmT1KlTBxcXlzLbtyJy+wxpaWn6akekBLpy5Qr9+vXDxcWFWbNmERoaCmCdCpmWlsajjz5Kamoqe/bswcmpaLOdMzIy6Ny5M3l5eZw7dw4nJydCQkKYNGkSzZs3p3z58rZMq1ilp6fTqlUrAgMDWbJkCYGBgVgsljJ3Y6Q+LXt9CurXstyvHTp0wGg0cunSJXJycvDy8uKZZ56hd+/eBAQE2DtEESmBND1SpITy9vZmxowZfP311yxatIj4+Hjg+lTIvLw8fHx8eO655zh69CjHjx8v0nvm5+czaNAgfH19iYmJ4b///S/Lli3Dw8ODYcOGERMTQ2pqqg2zKj4ZGRm0aNGCGjVqsGjRIgIDAwF+dxNY2qckqU/LXp+C+hXKZr8WFBQwYsQI/Pz8ePfddzl8+DC7d++mXr16TJo0iZkzZ5KQkGDvMEWkBNJGJCIlWFhYGGvXrqV3794YDAYef/xx69RIgLS0NKpUqYKvr2+R3i8pKYnExESio6Otm5tUq1aNNm3aMGTIEKZMmQLAkCFD8PT0tE1SxSAnJ4devXpx9epVli5dip+fHwBHjx7l8OHD/Pzzz/j7+/P444/j4eFh52hvj/q07PUpqF/Lar9euXKFn3/+mYEDB1KjRg0A/P39ee+99xgzZgwffPABFouFqKgoKlasaOdoRaQk0UibSAnXqlUrtmzZwrp165gwYQJ79+4FICEhgd27d+Pj41PkmzaDwcDFixdJS0uzPmaxWHBxcWHt2rV06tSJGTNmcODAAaD0bnjwyy+/YLFYcHNzIyYmBoDY2Fh69uzJwoUL+eCDD5g6dSrt2rXj888/B0rvt/jq07LXp6B+Lav9ajQaSUlJ4eLFi9bH8vPzAZg9ezbh4eGsWbOGDz74gPz8/FKdq4jcWVrTJlJKfPXVV4wdO5ZvvvmGatWq4erqSmpqKhs3buTee+8t0nukpqbSvn176tWrx+LFi3F1dQWuT9kp3Oike/fupKam8umnn5bK9SSF62B+/PFHoqOjiYuLo3HjxuzevZvhw4cTERFBcHAw+/fvZ9q0aZjNZvbv34+Li4u9Q/9b1Kdlr09B/VpW+zU7O5tHHnmEnJwcli1bRtWqVYHrhVvhuuQBAwbw5ZdfcvjwYevIo4iIdo8UKSUCAwPp0aMHjRo1wtfXlw4dOjBp0iTuuuuuIr3eYrHg7u6Ol5cXb775JpUrV+b+++8Hrn/7azabMZlMVK9enZiYGKpUqUL9+vVtmZJNGAwGLBYLFSpU4IEHHuC///0vhw4d4rHHHmPs2LFUqVIFZ2dngoODqVSpEkuXLr3h/0Vpoj4te30K6tey2q8Azs7OVKpUiTfffBN3d3datWoFXO/X/Px8jEYjDRs2ZPny5Tg7O9OsWTM7RywiJYXWtImUIhUqVKB79+507979ll9b+E18p06diIyMZNy4cXh7e1vPeit8vn79+ri6ut4wfae0KbwZrF27Nq+//joLFiygc+fOlCtXDrg+lczV1ZUWLVrg6upKRkaGnSP+e9SnZa9PQf1aVvsVrhfkbdu2JSoqipkzZ+Lt7c3TTz8NYB1p8/f3x8/Pj8uXL9szVBEpYVS0iTgYPz8/nnjiCVJTUxk2bBiZmZkMGjQIo/H6EterV68SFBRk3dyktG67/dubwZdeeglvb2/g1yMTLBYL3333Hf7+/tYNAUprrurT0t2nhfH/r7LYrzfLtaz2680UxjxkyBBSU1OZPHkyGRkZPPbYY1SqVAm4vjmLr68vFSpUAEpvriJyZ6loE3FA9erVY8KECXh6ejJ69Gg+++wzOnfujK+vL+vXrycpKck6bac03ywUxl54E5iXl2fdeTMlJYXVq1fj5uZG48aNb2hf0hTlpq2s9Olf5VpW+hQgNz+2z0sAACAASURBVDcXFxcXa2Fys1jLSr/+Va5lqV/h18/xHxXkVapUYfTo0Xh5efH666/zxRdf0KlTJ4KCgti1axdxcXEsWrQIKPm5ikjx0EYkImVIRkYG+/fvp1u3bje9UYAbb4qTk5P58MMPee2118jKysLb2xsvLy/eeeedEr9Gpii5/pEdO3awY8cOdu3axc6dO0t0rlevXmXFihXUr1+fli1b3rRNWenTouT6R0pTnwJkZWXRpEkTBg8ezLhx427apqz0a1Fy/SOlrV8LCgq4du0aaWlp1vPm/qhwg+t9vHv3bl577TXOnj2Lh4cHAQEBzJ07t8TnKiLFS0WbSBmRkZFBWFgYDRo0YM2aNbi5uRX5tRcvXrSuiwkMDCzxO5bdTq779+9nwoQJGAwGFi9ebD0DqyTKyMigU6dO+Pn50a9fP/71r39hMpmK9NrS2Kd/N9fS1KcA6enptG/fnri4OFq2bMmyZcsICAgo0mtLW7/eTq6lrV8zMzMZP348x48fJyUlhR49ejB9+nTgj0eQCx9PT08nMzOTvLw8fHx8rCOOIiKFVLSJlAHp6em0aNGCWrVqMX/+fIKCgn7X5rc3DYXbht/suZLudnNNSUnh+++/JyQkxPpNeEmUk5NDt27d8PLyYurUqdStW9c6XaxQWenT2821tPQpXP/8tmzZkrvvvpsmTZowdepU1q1bR4cOHW7aZ6W5X28319LUr5mZmbRr1w4/Pz8aNGjAlStXWLt2LWPGjGHSpEm/a//bLf5v9mcRkf+lw7VFSrmMjAzat29P7dq1efvtt6lSpQpwfUpObm6u9XBWg8FAQUEBgPXGaN++fdbnSoPbyfXjjz8Gru/A2axZsxJ/E/jJJ5+Qm5vLxIkTueuuu3B2dubcuXPEx8dz5MgR4Nd+K9wCHkpfn8Lfz7W09Wl6ejqtWrUiODiYN998kz59+lCrVi1mzZrF5cuXb9pnpbVfbyfX0tav+fn5PPvss1SuXJmFCxfy6quvMnv2bPr27cuxY8d+195isVgLtE2bNgGoYBORv6SiTaQUKygooHXr1sTFxfHMM89QpUoVjEYj//nPf4iKiqJr164MGzaMZcuWAddvivLz8wEYN24cffv25d1337VnCkV2u7n+85//ZM2aNfZM4ZYcP36cjIwMGjZsiJOTEzt27KBfv360atWKLl26EBkZyVdffXXDepnS1qeF/m6upalPr169SpMmTQgMDGTJkiVUqVKF6tWr06FDB44dO8aPP/4IYP2y4bdKW7/ebq6lqV/h+pq9kydP0qxZM0JCQrBYLLi6ulKjRg0qVqzInj172Lp1K5mZmcCvhff06dMZOnQos2fPtmf4IlJK6HBtkVKs8AZ279695OTk8NBDD3HgwAH+9a9/kZ2djbe3N6dOneKDDz7gypUrtG/f3voaZ2dnTp48yahRo6xbhpdkdyLXp59+ulTkCvDDDz9w9OhRBg0axL59+xg4cCDh4eH069ePpk2bsnnzZg4ePEjjxo2tW4WXtj4tdDu5lpY+PXToEG5ubkydOtU6QgwQFhbGunXrSExMpE+fPjfdsKK09eudyLW09CvA+fPnmTlzJq1ataJp06YYDAYyMjIYN24c8fHxbNy4ka1bt/Lxxx8TGhpqndJdrlw5kpOTefzxx63b+4uI/BGtaRMppX476rB8+XLGjh1Lw4YNOX36NMOHD2fgwIEEBgby008/sWjRIpYuXcpbb73FgAEDrK/Nycm5pU087MWRci20Z88eHn74YdatW0dcXBynTp1i5syZeHh4AHD06FH69u1L69atWb58ufV1pS1PcJxcr127hqur6w2P5eTkMH78eDZt2sTq1aut2/cXKq2fX0fKFeCpp55i8+bNjBgxggoVKrBixQo8PT2ZMWMGAQEB/Pzzzzz++OOEhoayceNG6+sKj0IQEfkrGmkTKaUKzwAyGAw0aNCAihUrsmLFCsLDw3n++eet39z6+flRs2ZNtm3bhre3Nx07drS+3mQylYo1Mo6Ua6FatWrx9ddfs2DBAuLi4rjvvvvo1KkTcH0NTWBgINnZ2WzdupUePXrg5eVVKvOEsp9r4Wf3ZuuWnJycqFatGitXrsTPz482bdrc8Hxhfsq1ZCrcUKVOnTpkZ2ezbds28vPziY+PZ9myZdx///34+PhQvXp1nJ2dWbJkCe3bt6dKlSrWz7CISFFoTZtIKZKZmWk9pwmuTxk0m80APProo6xatYp27dpRvnx5AOtztWvXxs/Pj6SkJODXm6OSfGPkKLn+b56/NXToUEJCQjh9+jQXL14kLS3thk0MnJyccHd3x9fX1zoSWVLzBMfN9c/OETSbzdxzzz08/PDDLF++nKNHj960nXItGf7oM1ynTh1mz57N119/Tb9+/fDw8KBu3brWzZHg+qhaYGAgQUFBJTpHESmZtF2RSCmRnZ1N9+7dOXbsGKdPn2bevHm4ublZixmj0UiPHj2s7X+7ffb3339Pbm4ujRs3tlf4t8RRcv2jPAu/ve/YsSMXLlxg/vz5bN26lQcffJA+ffrg4+PDL7/8wsmTJ6lVq9YtHy5uD8r15tP9CvNp164dK1euZM+ePYSFhf1u+/uSSrm63bBrrYuLC9nZ2SQmJpKYmEitWrWA68cXHDlyhNDQUMqVK2fPNESklFLRJlIK5Ofn88orr5CSkkJ4eDg7duwgJyeHxYsXW4uZ3/rtTdDFixd56623uHr1Kr169bJH+LfEUXL9qzwL8xo0aBDe3t4sWrSIMWPGEBsbi7e3N5cvX+aLL77gww8/xNPT097p/Cnl+muuf6R79+706tWLd955hyeffNK6nq8kU643z7V+/fpUq1aNiIgIXn/9dZKTk/nPf/7Dxx9/zK5du3Rwtoj8LVrTJlIK/Pzzz8yYMYPQ0FBmzpxJSEgIixcv5tSpU3Tp0uV360cKC5u1a9cyZ84c9u/fz4YNG7jrrrvsEf4tcZRc/ypPFxcXCgoKMBqNhIaG0rp1a6pUqcK3337LpUuXqFq1KgsWLOAf//iHvVP5S8r1jz+/8Os6sLS0NA4ePMjDDz+Ml5eXHaK/Ncr15rkGBARQrlw5jh07xvz58/n8888xGo2sXLmyVHyGRaRk0u6RIqVAXl4e27Zto23btvj6+pKamsrGjRuZNGkSHTt25J133rlhqhnAl19+ydtvv01ycjJvvPEGoaGhds6iaBwl16LmmZ+ff8MNYXp6Op6enuTl5ZWaXeeU659/fgtdvXqVK1eu3LBNfkmmXP/8M5yamsrRo0epWrUq/v7+peYIAxEpmTTSJlIKmEwm7r77btzd3cnPz8fDw4NatWpRqVIlli5dyqlTp+jcuTPOzs7A9ZvdGjVq0LhxYyIiIqhWrZqdMyg6R8m1qHkWbpuekpJCuXLlcHV1LXW7zinXP/78pqam4u7uDlw/o6ykT//8LeV688+wxWLhypUr+Pr6UqNGDSpUqGDNW0Tk71LRJlJKFH5TXTgd0M3N7Yabhu+++44uXbqQmJjICy+8wIkTJ+jatWupvFlwlFxvJc/Jkydz7NgxHnroIXuG/Lcp15vn+uKLLyrXUsKRchWRkkcbkYiUYt7e3oSHh2MwGHjhhRcYNGgQBoOBAwcOsHfv/2vv3uNyvP8Hjr9CYiY3opySRua0GcsKOUQHvkgOo8xxc5iN8XWeaHzNIWIOG7P5Kik5NEoHLTl0UMuM0fwcymmhySY5FtXvjx739e1233cqbbV5Px8Pj0eu67qv+/25ruu+7+t9fU4Hyzu8MvWylPVlKSdIWaWsf38vU1mFEOVLatqE+BvLz8+nevXqNG/eHENDQ3x9fbl9+zbh4eG0a9euvMMrUy9LWV+WcoKUVcr69/cylVUIUb6kpk2IvzF1c53MzExOnjxJzZo1OXDgwN9iII6SelnK+rKUE6SsUta/v5eprEKI8iVJmxB/czk5OSxZsoQDBw4QGxv7j75ZeFnK+rKUE6Ss/1RSViGEKFsy5L8Q/wDnzp0jNzeXNm3alHcof7qXpawvSzlByvpPJWUVQoiyI0mbEEIIIYQQQlRglco7ACGEEEIIIYQQ+knSJoQQQgghhBAVmCRtQgghhBBCCFGBSdImhBBCCCGEEBWYJG1CCCGEEEIIUYFJ0iaEEEIIIYQQFZgkbUIIIYQQQghRgUnSJoQQQgghhBAVmCRtQgghhBBCCFGBSdImhBBCCCGEEBWYJG1CCCGEEEIIUYFJ0iaEEEIIIYQQFZgkbUIIIYQQQghRgUnSJoQQQgghhBAVmCRtQgghhBBCCFGBSdImhBBCCCGEEBWYJG1CCFFOWrduzVtvvVWuMWzbtg2VSsXOnTvLNY6K6Msvv+Sdd97BzMwMlUrF5s2by2zfFy5cQKVSMX369DLb58viwIEDqFQq1q9fX96h/OPIsRWi4pKkTYg/yapVq1CpVKhUKi5evFje4fxjBQQEKMf56NGj5R3OSyU/P5/27dujUqno06dPeYdTpnbu3Mn8+fOpVq0aH374IXPmzKFjx45FvmbJkiXKtajr31+doFtZWWFtbV2i1wwcOBCVSkVMTIzO9W3btkWlUjFt2jSd6z09PVGpVKxdu7bE8f7Vzp49q3WOTExMaNmyJW5ubhXq+2TRokWoVCrq1KlDWlpaqfejLvOsWbPKMLr/2bx5MyqViuDg4D9l/0K8zKqUdwBC/BPl5+ezbds2DAwMyM/Px9fXlyVLlpR3WP9Ivr6+Gse5e/fu5R1SsYWFhWFgYFDeYZTakSNHuHLlCgYGBiQkJHDhwgWsrKzKO6wyERkZCcCePXuoV69eiV5rZ2dH586dtZbXrl1b+dvCwoKkpCRUKtWLBVrGunfvzpEjR4iJiaFbt24a6y5dukRaWhoGBgZ6kzp1ovN3+hyamJjw/vvvA/D48WN+/vlnIiIiiIiIYN26dYwaNapc43vy5AkBAQEYGBiQl5eHn58f8+bN+1Pey87OjqSkpBJf80KIP58kbUL8CQ4dOsS1a9dwd3cnOjqaHTt2sHDhQqpWrVreof2j/N///R8//PADvXv3Jj09ndDQUH7//Xfq1q1b3qEVS7Nmzco7hBfi4+MDwLRp01izZg0+Pj4sXbq0fIMqIzdv3qRy5cqlunnt1q3bc2syqlatWiETXHWiFhsbq7VOnai5uLiwb98+0tLSaNy4sbI+MzOT06dPU7t2bd54442/JuAyUK9ePa0k6Ouvv2bOnDksXLgQd3d3qlQpv9ul8PBwfvvtN8aNG0dQUBD+/v7Mnj2bypUrl/l71ahRo0Jel0IIaR4pxJ/C19cXgNGjRzN06FB+//13QkNDtbabMmUKKpVKear/rMTERFQqFePGjdNY/uDBA7y9venSpQsNGzakUaNGODo68t1332nt48iRI6hUKlauXMnx48cZOnQoFhYWqFQqrl+/DhQ8HZ8yZQqdOnWiSZMmNGjQAFtbW7y8vMjOztYZ282bN5k0aRKvvfYaZmZm2NnZsXPnTo33e9Yff/yBp6cn1tbWmJqaYm5uzsCBAzly5EiRx1Mf9XEeMWIE7u7u5OTkEBAQUORr9uzZQ//+/bGwsMDU1JR27drxwQcfcOrUKY3tsrKymDt3Lq1bt8bU1JROnTrx1VdfkZqaikqlYsqUKRrbOzs7600W9fUb09WnrfC233//PX379sXc3BwTExON7c6dO8fEiRNp3bo19erVw8rKivHjx5OamqozhtTUVEaNGkXTpk1p1KgRTk5OREVFFXmsipKRkUF4eDgtW7bk008/xcTEhMDAQL3XC0BUVBSOjo40aNAACwsLRowYQUpKChMmTNC4HgtLSkpi5MiRtGjRgnr16tGmTRumT59Oenp6ieJ9/Pgx3t7e2NraYmZmRpMmTejTp49WMy51E8eEhARyc3OVZnNl/SBAX5+2cePGoVKpuHHjBhs2bMDW1hZTU1MGDx4MQF5eHtu2baN3795YWlpiZmZG27ZtGTJkCCEhIQAcPHgQlUrFrVu3uHjxokbzv+f1oWvfvj3Gxsb89NNP3L9/X2NdTEwMxsbGfPTRR8r/C4uLiyMvLw87OzsqVdK8vUhKSsLNzQ1LS0vq16/Pm2++ydy5c7l9+7ZWDKNGjVLiX79+PTY2NpiamjJs2LAiY3/w4AHDhg1DpVKxcOFC8vPzi9y+KGPHjqVKlSpkZmZqfabCw8NxdXXFwsKC+vXr06FDBxYvXqx1vAAsLS2xtbXlzp07zJo1i7Zt21K3bt0S9Rnbtm0bUHBtDBo0iLS0NA4ePKhzWw8PD1QqFSdPnmT79u306NGDhg0bYmtri4eHh1ID/M0332hcF+rPgb4+bSkpKUyePJk333wTU1NTmjVrRufOnZk5cyZZWVkA9OjRg9mzZwMFv32F9//7778Xu7xCCN2kpk2IMnbr1i0iIiJo3rw577zzDjVr1mTDhg34+PgwaNAgjW3d3Nzw8/MjMDAQJycnrX3t2LEDAHd3d2VZZmYm/fr1Izk5mfbt2zNixAjy8/M5ePAg48aN4/z58zqbziQmJuLl5UXnzp0ZOXIkGRkZGBoaArB69WquXLmCtbU1zs7OPHr0iMTERJYuXUp8fDzfffedxlPd3377DQcHB9LS0ujatSvW1takp6czbdo07O3tdR6XK1eu0L9/f3799Vc6d+6Mg4MD9+/fJzIyEldXV9avX897771X7OP8+PFjdu7ciUqlom/fvty7dw9PT0+2bdumlVBBQZPViRMnsmvXLkxMTOjfvz8mJiZcv36d2NhYWrZsSfv27ZV9DxgwgFOnTtGuXTuGDh1KZmYmy5cv11kD8Wf47rvvOHjwIA4ODowdO1YjoYmMjGT06NHk5ubi7OxMs2bNuH79OiEhIURGRhIWFka7du2U7S9cuICTkxN37tzBycmJNm3akJqayvDhw3FwcChVfAEBATx58gR3d3cMDQ0ZMmQImzZtYv/+/QwZMkRr+127djFx4kSqV6+Oq6srpqamJCYm4uDgwOuvv67zPXx9fZk+fTrVq1enT58+NGzYkJSUFHx9fYmMjOTgwYM0bNjwubFmZ2fj6upKQkICr7/+OuPHj+fhw4fs27eP0aNHM2vWLObPnw8UNA+rXLky/v7+XL9+XbkJfTYJ+bNNnz6dH374AUdHR5ycnDAyMgIKbsq/+uorLC0tGTx4MK+++irp6emcOHGC0NBQBgwYgKWlJXPmzGHDhg0YGRkxfvx4Zb/P61dXuXJlunTpQkREBMeOHcPR0REo+PzExsbSpUsXOnTogLGxMUePHtX4btLXNHLv3r2MHz+eypUr4+LiQqNGjTh+/DibNm0iPDycyMhIGjRooBXLlClTSEpKwsHBAWdnZ6pXr6437oyMDIYNG8apU6dYsWIFEydOfM4RLlrhhK9wE+aFCxeybt066tWrR58+fTAxMeHUqVOsXr2a6OhoIiIieOWVVzT29fDhQ/r27UtOTg6Ojo5Uq1aNRo0aFSuOq1evcvjwYd544w3atm2Lu7s7W7duxcfHR+dvhpr6u8rZ2ZkePXrw+PFj7O3tefToEVu2bKFDhw4an/2WLVsWGYO9vT2PHz/GyckJV1dXHj58yJUrV9i+fTtTpkzB2NiYMWPGEBYWRlRUFAMHDtTYZ1HnTghRPJK0CVHG/P39lZtZKKhNad++PbGxsVy6dAlLS0tl286dO9OsWTMiIiLIzMzU6N/y+PFj9u7di5mZGT179lSWz549m+TkZJYsWcLHH3+sLH/06BFubm54eXkxYMAA2rRpoxFXdHS03v4Za9eupWnTplr9qxYtWsSaNWsIDQ3FxcVFWe7p6UlaWhozZsxgwYIFyvKJEyfqTQImTZpEWloaPj4+DBw4UFmemZlJ3759mT17Ns7Ozlo1SvoEBwdz584dPvjgA4yMjDAyMsLBwYHw8HDi4uLo2rWrxvZbtmxh165dWFtbExQUhLGxsbIuNzeXjIwMjeNx6tQpXF1d2bJli3LDPm3atL+sr05UVBRBQUEa5x4KaivHjx9PjRo1iIiI0GjKlJycjKOjI1OnTuXw4cPK8hkzZnDnzh1WrlypcQMfHBzM6NGjSxybus9m5cqVldoPd3d3Nm3ahI+Pj1bSdvfuXWbOnEnVqlWJiorSuDY9PDzYsGGD1nucP3+emTNnYmlpSWhoKGZmZsq6Q4cOMWTIEObNm6fUthZl7dq1JCQk4OzszPbt25WmbrNnz8be3p5Vq1bh5OTE22+/Tffu3enevTtHjx7l5s2bpeo7FBMTw9OnT7WWv/feezRp0qRY+/jll1+IjY3V2t7X1xdzc3OOHTtGtWrVNNapazMsLS2ZN28eW7dupVatWiUuQ/fu3YmIiCAmJkZJ2s6ePUtGRoaS1Nra2hIXF6fxOvUDjcKfkTt37jB16lQMDAw4cOCARtK4ZMkSVq1axaxZs9i+fbtWHOfOnSMuLu65CU5qaiqDBw8mPT0dHx8fBgwYUKLy6rJ161aePn1K7dq1le/sAwcOsG7dOrp164a/vz81a9ZUtt+8eTOzZ89m9erVeHh4aOzr6tWrODs74+Pjo3XOnsfPz4+8vDxGjBgBgLW1NVZWVnz//ffcvHlTZ7ILkJCQwKFDh7QeiJiZmbFlyxY6duxY7OsiKCiIrKwsnb8f9+7dUx4ojBkzhpycHKKionB1ddX4zRBCvDhpHilEGVLfzFaqVInhw4cry93d3ZWBMp41fPhwJUErLCwsjKysLN59912llisjI4M9e/ZgbW2tkbBBwZPMzz77jPz8fIKCgrTe56233tLbod7CwkLngBiTJ08GCm6S1dSxqlQq/v3vf2ts3759e4YOHaq1n1OnTpGYmMigQYM0EjYAlUrFnDlzePjwoc4mpPqoj2XhJ/3qv3Ud582bN2NgYMCaNWs0EjYoqF0onBT4+/tTuXJlPD09NWpYmjVrxoQJE4od44vo37+/VsKmji0rKwsPDw+tvidt27ZlxIgRnDx5kpSUFACuXbtGbGwslpaWymALai4uLtjY2JQ4ttjYWFJTU+nVq5dy3NQ1AXFxcVrNyUJDQ8nKymL48OFaDxNmz56tdT4Avv32W548ecKKFSs0zg2Avb09Dg4OhIWF8eDBg+fGu337dipVqsTSpUs1+iaZmpoyc+ZM8vPz8fPzK3b5nyc2NpYVK1Zo/SvJqH8zZszQStjUtT9Vq1bV2Z+prJpwqpOuws0f1X+r+7zZ2dlx/fp15Tq7desW586do1GjRjRv3lx53b59+7h37x7u7u5atXwzZ87E1NSU8PBwnc3nZsyY8dyE7ccff8TR0ZG7d++yb9++UiVsGRkZLFu2jGXLluHp6cnAgQOZO3cuUPDgSn3NbNq0CYD169drJGwAEyZMwNLSkt27d+t8j2XLlpU4YcvNzcXf35+qVatqfK+6ubmRm5tb5DU7YcIEvTXYpaWrtqxmzZrSV1uIv4jUtAlRhmJiYrh8+TK9evXSaLY1dOhQPDw8CAgIwMPDQ2mWCAU/wMuXL2fHjh2MHTtWWa6raeSJEyfIy8sjPz+fZcuWab1/Tk4OUFBL8ayihiu/f/8+GzduJCwsjNTUVO7fv6/RPOjmzZvK3+fPnyc7O5tOnTpRo0YNrX3Z2Nho9StLSkoCCmrVdMWtruXSFbcuFy9e5NixY7Rq1YoOHTooy52cnDAxMSEkJAQvLy9ltL67d+9y4cIFGjZsSNu2bYvcd2ZmJteuXaNp06ZYWFhore/atSurVq0qVpwvQt/5On78OACnT5/WeSwvX74MFBzL5s2b8/PPPwNga2urs4lf165dSUxMLFFshfsSFubu7s6nn36Kr68vixcvVpafPn0aQGeCaGxsTJs2bUhISNBYri5nbGyscv0U9scff/D06VMuXbqk0RT0Werz2aRJE41abjV1EqKOsSzMnz//hYdUL3xdqxkYGDBkyBB8fX2xsbHB1dUVW1tbrK2tdSa+pdWqVSvq169PcnIyd+7coXbt2sTExFC3bl0l6bazswMKvvOaN2+uldSpqa+/Z5cDVKtWjU6dOrF//36Sk5O1arGfN8XCkSNHWLZsGXXr1iUoKKjUA2jcvn2bFStWAAUPcOrUqYOTkxMffvghPXr0ULY7fvw4NWrUKLLf7NWrV8nOzlZqn6Bg1NDSDDp04MABbt68yYABA6hTp46yfNiwYfznP//Bz8+PmTNn6vxcP+/YlUT//v3x8vLio48+Ijw8nJ49e2JjYyMDlgjxF5OkTYgypB5Nr3CiBQU/2s7OzoSEhBAeHq7RbMTc3JyuXbsSGxtLSkoKzZs3Jz09ncOHD/PWW29pPC39448/gIKnyz/++KPeOHTVPtSvX1/ntjk5OfTr149Tp07RunVrBg0ahImJCVWqVCEvL4+VK1dqDC6h7nSub3+6lqvjjo6OJjo6ukRx66LvOBfuW7Vjxw6lpvDu3bsAepsSFabetiTl+zPoex/1sdy6dWuRr1cfy9Kcr6KoB9WpXbu21txs7777Lp6enuzYsYMFCxYoDyeeF4OuERrV5fziiy+KjOd514z6fD5bW6dmamqqsV1FoY7rWatWraJ58+bs2LFDeXhgaGiIk5MTn3/+OU2bNi2T9+/WrRt79uwhJiaGfv36ER8fT8+ePZUa+Xbt2ilzI44bN07pz1Y4yYH/nXt95VGfl8zMTK11+l6j9tNPP/Hw4UN69uypMyEvrlatWmk9NHhWdna2cq2pEzx9Hjx4oJG0Pa8c+uhqTQDQsGFDevbsSXR0NIcOHaJ3795ary3te+rSokULoqKi8PLyIioqSmkVYm5uzr///W/GjBlTZu8lhNBPkjYhysjt27cJCwsD4P3339dqiqbm4+Oj1dbfzc2N2NhYAgMD8fDwYNeuXeTm5uLm5qaxnfpp+tSpUzVqMopD33xg+/fv59SpU4wcOVJrxLC0tDStUSDVzYJu3bqlc3+6lqvjXrVqFR988EGJ4n5WZytRzQAADHdJREFUdnY2gYGBACxYsECjT11h27ZtU5K2WrVqAZo1hvqoty1J+QBlrri8vDytJ9+lSQj0nS/1sUxMTCxW8yf19iUtjz4BAQFkZ2eTnZ2tNwnLyMggNDQUV1dX4PnXTOH+hM/GfePGDa2BHUpCfT5/++03nevVy8uypqos6Dv/hoaGTJkyhSlTpnDr1i0SEhLYtWsXoaGhXLhwgWPHjpXJ8PTqpC02NpbGjRuTlZWl1K5BwcAsnTt3Ji4ujvz8fL01bc+7/tSjgKrPU2HPm8Pwk08+4dy5cwQGBjJq1Ch8fHz+tKZ66n6z9erVIzk5uUSvLc1cjIVHiCzc1P5ZPj4+OpO2sp7/sV27dvj5+fHkyRPOnDlDdHQ033zzDdOmTcPY2FhrkC0hRNmTpE2IMrJjxw5ycnJo37693uZaERERyoTEhZveubi4MHv2bAIDA5k/fz6BgYFUrVpVa0CHt99+W5nIuKxcunQJQGdfkPj4eK1lr7/+OkZGRpw5c4YHDx5oNZHU1dTO2toaKOgc/6JJm3ouNisrK9555x2d2xw9epRz586RmJiIjY0NtWrVwsrKiosXL5KcnFxkE0mVSoW5uTlpaWlcvXpVq+bi2cEXCr8uLy+PGzduaMxdBXDy5MkSllI/a2trwsPDlZEQn+fNN98ECs6LroRSX3n0UfejGTp0qM4+OpmZmezfvx9fX18laVPP2ZWYmKj1ICIrK4tffvlFaz/W1tYkJyeTkJBAr169ShRjYSqViiZNmpCWlqb1uYP/DZ6hPk5/J/Xr18fFxQUXFxecnJz44YcfuHjxIq1atQIKmvrl5uaWat/q5CsmJkbpV/ZsQmZnZ0d4eDjh4eFcvXqVli1batVmq899XFyc1o19dnY2x48fx8DAoMgmrvpUrlyZjRs3Uq1aNXx8fBgxYgR+fn4l7jtWXG+//Tbx8fE6vxfKmnoAEmtra72f85CQEA4cOMBvv/1W7Jo1dV/I0l4XhoaGdOjQgQ4dOih9mMPCwpRz+6L7F0LoJwORCFFG1E1ZvL29Wb9+vc5/Y8aM0TnoQY0aNRgwYABpaWls2LCBs2fP4uTkpNGPAQqaEg0ePJjjx4+zevVqnT+Mly5d4tq1a8WO29zcHNC+eb98+bLO2rxq1arh4uJCZmYmq1ev1lj3888/6+yIb21tTadOndi7d6/e/iBnzpwp1lw+6qaRHh4eeo+zei4q9bZQMLJlfn4+06dPV5psqeXm5mrUxIwYMYLc3Fw+++wz8vLylOWXL19m8+bNOuNS9yF5dhCUQ4cOsW/fvueWq7hGjhyJsbExS5cu1ZkM5ubmakxLYG5ujp2dHampqWzZskVj2+Dg4BL1Z4uPj+fChQu0bt2ab775Ruex9/HxoVGjRhw9epQrV64A0K9fP2rWrElgYCBnz57V2KeXl5fW+YCCgRSqVKnC3LlzlQcLheXk5BT74cV7771HXl4eCxYs0DifGRkZShPDkkw3UV4ePHigs1l0Tk6OUptbeLCI2rVrc+vWLaWva0lYWFhgbm7OhQsX2L17N2ZmZlp9mNQ1b59//jmgu9+aq6srr776Kv7+/pw5c0Zj3erVq0lPT6dv376lHkRFPbjQxIkTiYqKYtiwYTx8+LBU+3oe9fx0H3/8sc7a4Xv37nHixIkXfh/1ACSA3u+49evX4+7uztOnT5Vti0P9m1KSQXFOnDihcw46de1p4WuuNPsXQhSP1LQJUQbU/dFat25dZAfwkSNH4u3tjb+/P/PmzdNoxuTm5kZAQICSKD1bI6Hm7e3NpUuXWLx4MQEBAdjY2GBiYkJ6ejrnz5/n5MmTyrDgxdG3b1+aNm3K2rVrlVqoX3/9lcjISJycnHRO2L148WLi4+Px9vbmhx9+oFOnTqSnp7N3714cHR0JCwvTqtHZsmULLi4uTJ48mY0bN9KxY0dq1arFjRs3OHPmDOfOnePw4cNF3rylpqYSFxdH/fr1tfpTFTZ48GA8PDwIDg5m+fLlygTliYmJ7N69m44dOypzLN24cYPY2FjGjBmjDB4xdepUwsPD2bt3L6mpqdjb23Pnzh327t2rzGH1rJEjR7JhwwZWrlzJ6dOnadmyJRcuXCA6Opp+/fopEx+/KBMTE3x8fBg1ahT29vb06NGDli1bYmBgQFpaGklJSdy/f19jXjdvb28cHR2ZNWsWBw8eVOZpCw0NxdnZmQMHDhTrvdUJqb5RSKHgSbubmxurVq3C19cXT09PVCoVXl5eTJ48md69eyvztCUkJHDu3DlsbW1JSEjQuGZatWrFunXr+OSTT3jnnXfo3bs3r732GtnZ2aSlpZGQkECDBg2KlbhNmzaN6Oho9u/fT9euXendu7cyT9vt27eZMWOGUhtckd2/f5/evXvTvHlz3nzzTRo3bsyjR484dOgQKSkpyoTPat27d2fjxo0MHjwYGxsbqlatSvv27Ys9N1/37t3x8/Pj7NmzOkeFbdOmDXXq1FEScV3TYdSuXZsvvviCiRMn4ujoiIuLCw0bNuT48eNK08tnm2CXlIGBAStWrKBatWqsXbuWIUOGsGvXLl599dUX2u+z+vbty8yZM1m1ahUdOnSgV69eNG3alHv37nHt2jXi4+NxcHBQJsMuraioKNLS0ujUqVORtemjRo1i48aNbNu2jenTpxerSWS9evVo3bo10dHRTJo0iWbNmlGpUiVcXFz0Dizi4+NDUFAQNjY2NGvWDGNjY1JSUoiMjOSVV17RGFHX1taWqlWrsnr1aq5fv65M4fLxxx/LXG1CvCBJ2oQoA+of6aJuZgGaNm1Kjx49OHz4MBEREfTv319Z17VrV5o2bcrVq1cxMTHRe2NVq1YtIiIi2Lp1K0FBQYSEhJCdnU29evV47bXXWLZsmc4n3vrUrFmT0NBQFi1aRFxcHPHx8VhYWDB37lwmTJigM2kzMzPj+++/Z/HixURFRfHjjz/SokUL1qxZg6GhIWFhYVpDYjdp0oQjR47w9ddfs3//fnbv3k1eXh7169fn9ddf58MPPyxyglcoOM75+fkMHz5cYwTOZxkbGzNw4ED8/f3ZuXMnEydOxMDAgM2bN2Nvb8+2bdvYt28fOTk5mJqa0qVLF42JaqtXr05ISAhLly4lODiYr776SjkmTk5OOpM2U1NTwsLC8PT0JD4+nri4ON566y2Cg4NJSUkps6QNCoa8j4uLY/369Rw6dIhjx45hZGSEqakpPXv21GrqamVlxcGDB1m0aBFHjx4lNjaWtm3bEhgYyM2bN4uVtGVmZhISEoKRkZEyN5s+6ocTAQEBfPrppxgaGuLm5kbt2rXx9vbmu+++w8jIiM6dOxMVFcWcOXMAtK4Zd3d32rVrx5dffklcXBzR0dG88sorNGjQgEGDBinNL5/HyMiI4OBgNmzYQFBQEF9//TWGhoa0a9eOlStXFns/5U2lUuHp6UlsbCyJiYncvn2bmjVrYmlpydq1a7VG85w3bx4PHz4kMjKSY8eOkZuby9ixY0uctAEa/dnUDAwM6Nq1KyEhIVSqVElrbkS1IUOG0LhxY9asWUNkZCT379/HzMyMCRMmMGvWLJ0D0ZTGokWLMDIywsvLC1dXV/bs2aOzr9yL8PDwoFu3bmzevJljx44RFhZGrVq1aNiwIR988IHO5Lak1A9HRo4cWeR2rVq1olOnTiQlJXHkyBGdU4To8t///hcPDw8iIiLIysoiPz8fKysrvUmbm5sblSpV4vjx45w4cYKcnBwaNGjA8OHDmTJlCi1atFC2NTMzY+vWrXh7e+Pn56fUeo4bN06SNiFekEFmZmb+8zcTQoji8fT0ZO3atQQHB/9lE1H/lS5dukSHDh10DtwiSu7p06e88cYbGBgY6OzbJoQQQgjp0yaEKCVdIzGeOXOGb7/9lrp165Zq0mbxz5WZmcmjR480luXn57N8+XJu3LjBv/71r3KKTAghhKj4pHmkEKJU7OzssLKyonXr1lSvXp2UlBSioqLIy8tj/fr1GvMUCZGYmMiECROwt7fH3Nyc+/fvk5SURHJyMk2aNFGaSAohhBBCmyRtQohSGTt2LBEREezevZsHDx5Qq1YtevXqxdSpU+nSpUt5hycqmJYtW+Lo6EhiYiKRkZHk5ubSqFEjJk2axIwZM0o9eqAQQgjxMpA+bUIIIYQQQghRgUmfNiGEEEIIIYSowCRpE0IIIYQQQogKTJI2IYQQQgghhKjAJGkTQgghhBBCiApMkjYhhBBCCCGEqMAkaRNCCCGEEEKICuz/AdraEJ3EL8IjAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Histogram of your sampled means\n",
"plt.figure(figsize=(20,20))\n",
"sample_avg_pd.hist(bins=np.arange(15, 18, .1))\n",
"\n",
"# set title and subtitle\n",
"plt.title(x=0.5, y=1.1,label=\"MoMA's Acquistions Trends by Average Age of Artworks (Mean)\", fontweight='bold', fontsize='24')\n",
"\n",
"plt.ylabel(ylabel='Number of Artworks', fontsize=20, fontweight='medium')\n",
"plt.xlabel(xlabel='Average Acquired Age of First Work Per Artist', fontsize=20, fontweight='medium', labelpad=15)\n",
"\n",
"plt.xticks(rotation=45, horizontalalignment='right', fontweight='medium', fontsize='16' \n",
")\n",
"\n",
"plt.yticks(\n",
" rotation=45, \n",
" verticalalignment='top',\n",
" fontweight='medium',\n",
" fontsize='16' \n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Deviation of the simulated avg from the observed mean "
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([-0.13063681, -0.02872531, 0.0149963 , ..., -0.01742528,\n",
" -0.21184919, 0.13264336])"
]
},
"execution_count": 93,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"avg2 = []\n",
"n = 9469\n",
"simulations = 10000\n",
"for i in np.arange(simulations): \n",
" sample2 = np.random.choice(minValues_AcquiredAge_Artwork_2, n, replace=True)\n",
" avg2 = np.append(avg2,population_observed_mean-np.average(sample2))\n",
"avg2"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {},
"outputs": [],
"source": [
"avg2_pd = pd.DataFrame(avg2)"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>10000.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>-0.000107</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.265883</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>-1.001901</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>-0.178609</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>-0.001162</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>0.183124</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>0.904319</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0\n",
"count 10000.000000\n",
"mean -0.000107\n",
"std 0.265883\n",
"min -1.001901\n",
"25% -0.178609\n",
"50% -0.001162\n",
"75% 0.183124\n",
"max 0.904319"
]
},
"execution_count": 95,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"avg2_pd.describe()"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"(array([ 0., 200., 400., 600., 800., 1000., 1200., 1400., 1600.]),\n",
" <a list of 9 Text yticklabel objects>)"
]
},
"execution_count": 96,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"<Figure size 1440x1440 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Histogram of difference btwn simulated vs population avg\n",
"plt.figure(figsize=(20,20))\n",
"avg2_pd.hist(bins=np.arange(-1, 1, .1))\n",
"\n",
"# set title and subtitle\n",
"plt.title(x=0.5, y=1.1,label=\"Distribution of Difference between Simulated Values vs Population Average Age of Artworks (Mean)\", fontweight='bold', fontsize='24')\n",
"\n",
"plt.ylabel(ylabel='Number of Artworks', fontsize=20, fontweight='medium')\n",
"plt.xlabel(xlabel='Difference of Average Acquired Age of First Work Per Artist', fontsize=20, fontweight='medium', labelpad=15)\n",
"\n",
"plt.xticks(rotation=45, horizontalalignment='right', fontweight='medium', fontsize='16' \n",
")\n",
"\n",
"plt.yticks(\n",
" rotation=45, \n",
" verticalalignment='top',\n",
" fontweight='medium',\n",
" fontsize='16' \n",
")"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
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"22 1.871397\n",
"23 0.281321\n",
"24 -0.243443\n",
"25 2.173235\n",
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"27 -0.727963\n",
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"29 -0.136123\n",
"... ...\n",
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"\n",
"[10000 rows x 1 columns]"
]
},
"execution_count": 97,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# calculate the z-score\n",
"# using the z-score because sample size is larger than 30 \n",
"# and know standard deviation of population\n",
"# difference of sampled mean from population mean / (standard deviation of population/sqrt(sample size))\n",
"\n",
"z_score = (avg2_pd) /((np.std(population['PopulationAge']))/sqrt(n))\n",
"z_score"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(array([ 0., 500., 1000., 1500., 2000., 2500., 3000.]),\n",
" <a list of 7 Text yticklabel objects>)"
]
},
"execution_count": 98,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"<Figure size 1440x1440 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Histogram of your sample minimums\n",
"plt.figure(figsize=(20,20))\n",
"z_score.hist()\n",
"\n",
"# set title and subtitle\n",
"plt.title(x=0.5, y=1.1,label=\"Distribution of Z-Score (Standard Error of the Mean)\", fontweight='bold', fontsize='24')\n",
"\n",
"plt.ylabel(ylabel='Number of Artworks', fontsize=20, fontweight='medium')\n",
"plt.xlabel(xlabel='Difference of Average Acquired Age of First Work Per Artist', fontsize=20, fontweight='medium', labelpad=15)\n",
"\n",
"plt.xticks(rotation=45, horizontalalignment='right', fontweight='medium', fontsize='16' \n",
")\n",
"\n",
"plt.yticks(\n",
" rotation=45, \n",
" verticalalignment='top',\n",
" fontweight='medium',\n",
" fontsize='16' \n",
")\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### When you have multiple samples and want to describe the standard deviation of those sample means (the standard error), you use this z-score.\n",
"\n",
"### This z-score will tell you how many standard errors there are between the sample mean and the population mean.\n",
"\n",
"### z = (x – μ) / (σ / √n)\n",
"\n",
"### Since the z-score is on a normal distribution curve, we can use it to compute the probability of finding each sampled mean or the average acquired age. The z-score shows the sampling distribution of means as the standard error. For example, 99% vs 95% vs 68%/ of values fall within 3 vs 2 vs 1 standard deviations from the mean in a normal probability distribution."
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Bootstrapped 95% confidence interval for Average Acquired Age of First Work Per Artist[15.898078, 16.956521]\n"
]
}
],
"source": [
"diff_lower_bound = np.percentile(avg,2.5)\n",
"diff_upper_bound = np.percentile(avg,97.5)\n",
"print(\"Bootstrapped 95% confidence interval for Average Acquired Age of First Work Per Artist[{:f}, {:f}]\".format(diff_lower_bound, diff_upper_bound))"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.4635"
]
},
"execution_count": 100,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p_value = np.count_nonzero(avg < population_observed_mean)/10000\n",
"p_value"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Simulate minimum 10k times, same sample size with replacement"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0., 1., 48., ..., 0., 10., 0.])"
]
},
"execution_count": 101,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"minimum = []\n",
"for i in np.arange(10000): \n",
" minimum = np.random.choice(minValues_AcquiredAge_Artwork_2, size = 9468, replace=True)\n",
" minimum = np.append(minimum,min(minimum))\n",
"minimum"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"16.565635230752985"
]
},
"execution_count": 102,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"representative_sample_3_mean = np.average(minimum)\n",
"representative_sample_3_mean"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {},
"outputs": [],
"source": [
"minimum_pd = pd.DataFrame(minimum)"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>9469.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>16.565635</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>26.518263</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>3.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>24.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>186.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0\n",
"count 9469.000000\n",
"mean 16.565635\n",
"std 26.518263\n",
"min 0.000000\n",
"25% 1.000000\n",
"50% 3.000000\n",
"75% 24.000000\n",
"max 186.000000"
]
},
"execution_count": 104,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"minimum_pd.describe()"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(array([ 0., 1000., 2000., 3000., 4000., 5000., 6000., 7000.]),\n",
" <a list of 8 Text yticklabel objects>)"
]
},
"execution_count": 105,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"<Figure size 1440x1440 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Histogram of your sample minimums\n",
"plt.figure(figsize=(20,20))\n",
"minimum_pd.hist(bins=np.arange(0, 186, 13))\n",
"\n",
"# set title and subtitle\n",
"plt.title(x=0.5, y=1.1,label=\"MoMA's Acquistions Trends by Average Age of Artworks (Minimum)\", fontweight='bold', fontsize='24')\n",
"\n",
"plt.ylabel(ylabel='Number of Artworks', fontsize=20, fontweight='medium')\n",
"plt.xlabel(xlabel='Minimum Acquired Age of First Work Per Artist', fontsize=20, fontweight='medium', labelpad=15)\n",
"\n",
"plt.xticks(rotation=45, horizontalalignment='right', fontweight='medium', fontsize='16' \n",
")\n",
"\n",
"plt.yticks(\n",
" rotation=45, \n",
" verticalalignment='top',\n",
" fontweight='medium',\n",
" fontsize='16' \n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Using bootstrap is not a great estimation method for estimating the acquired age of artworks because:\n",
"#### - there are rare elements around the min and max value of the population\n",
"#### - majority of count of artworks are in the range of [0,20] years of age yet there is a long tail of data in the range of dataset is [20,186]\n",
"#### - probability distribution of the statistic is not roughly bell shaped (the shape of the empirical distribution will be a clue) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# The Hypothesis Test"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Nationality "
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"American 46869\n",
"French 19680\n",
"German 7536\n",
"British 4769\n",
"NaN 2511\n",
"Italian 2418\n",
"Japanese 2031\n",
"Spanish 2022\n",
"Swiss 1813\n",
"Russian 1566\n",
"Dutch 1367\n",
"Belgian 1249\n",
"Mexican 1042\n",
"Nationality unknown 794\n",
"Canadian 766\n",
"Brazilian 719\n",
"Colombian 712\n",
"Czech 641\n",
"Austrian 612\n",
"Argentine 572\n",
"Chilean 546\n",
"Ivorian 480\n",
"Polish 474\n",
"Venezuelan 419\n",
"Danish 381\n",
"Israeli 335\n",
"South African 327\n",
"Australian 250\n",
"Chinese 240\n",
"Swedish 215\n",
" ... \n",
"Czechoslovakian 4\n",
"Slovak 4\n",
"Ghanaian 3\n",
"Serbian 3\n",
"Beninese 3\n",
"Panamanian 3\n",
"Bahamian 3\n",
"Bolivian 3\n",
"Estonian 2\n",
"Senegalese 2\n",
"Bangladeshi 2\n",
"Welsh 2\n",
"Namibian 2\n",
"Palestinian 2\n",
"Salvadoran 2\n",
"Kazakhstani 2\n",
"Tajik 1\n",
"Singaporean 1\n",
"Sierra Leonean 1\n",
"Ugandan 1\n",
"Afghan 1\n",
"Mauritanian 1\n",
"Native American 1\n",
"Syrian 1\n",
"Kuwaiti 1\n",
"Vietnamese 1\n",
"Paraguayan 1\n",
"Guyanese 1\n",
"Tanzanian 1\n",
"Nicaraguan 1\n",
"Name: Nationality, Length: 117, dtype: int64"
]
},
"execution_count": 106,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# nationality\n",
"\n",
"master_copy['Nationality'].value_counts(dropna=False).head(500)"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 103479\n",
"unique 116\n",
"top American\n",
"freq 46869\n",
"Name: Nationality, dtype: object"
]
},
"execution_count": 107,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"master_copy['Nationality'].describe()"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 False\n",
"1 False\n",
"2 False\n",
"3 False\n",
"4 False\n",
"5 False\n",
"6 False\n",
"7 False\n",
"8 False\n",
"9 False\n",
"10 False\n",
"11 False\n",
"12 False\n",
"13 False\n",
"14 False\n",
"15 False\n",
"16 False\n",
"17 False\n",
"18 False\n",
"19 False\n",
"20 False\n",
"21 False\n",
"22 False\n",
"23 False\n",
"24 False\n",
"25 False\n",
"26 False\n",
"27 NaN\n",
"28 NaN\n",
"29 NaN\n",
" ... \n",
"105960 False\n",
"105961 False\n",
"105962 False\n",
"105963 False\n",
"105964 False\n",
"105965 False\n",
"105966 False\n",
"105967 True\n",
"105968 False\n",
"105969 False\n",
"105970 False\n",
"105971 False\n",
"105972 True\n",
"105973 True\n",
"105974 False\n",
"105975 False\n",
"105976 True\n",
"105977 True\n",
"105978 True\n",
"105979 False\n",
"105980 False\n",
"105981 False\n",
"105982 True\n",
"105983 True\n",
"105984 False\n",
"105985 True\n",
"105986 False\n",
"105987 True\n",
"105988 True\n",
"105989 False\n",
"Name: Nationality_Am, Length: 105990, dtype: object"
]
},
"execution_count": 108,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"master_copy['Nationality_Am'] = master_copy['Nationality'].str.contains('Amer' or 'American')\n",
"master_copy['Nationality_Am']"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {},
"outputs": [],
"source": [
"master_copy['Nationality_Am'] = master_copy['Nationality_Am'].replace({True:'American', False:'International'})"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 International\n",
"1 International\n",
"2 International\n",
"3 International\n",
"4 International\n",
"5 International\n",
"6 International\n",
"7 International\n",
"8 International\n",
"9 International\n",
"10 International\n",
"11 International\n",
"12 International\n",
"13 International\n",
"14 International\n",
"15 International\n",
"16 International\n",
"17 International\n",
"18 International\n",
"19 International\n",
"20 International\n",
"21 International\n",
"22 International\n",
"23 International\n",
"24 International\n",
"25 International\n",
"26 International\n",
"27 NaN\n",
"28 NaN\n",
"29 NaN\n",
" ... \n",
"105960 International\n",
"105961 International\n",
"105962 International\n",
"105963 International\n",
"105964 International\n",
"105965 International\n",
"105966 International\n",
"105967 American\n",
"105968 International\n",
"105969 International\n",
"105970 International\n",
"105971 International\n",
"105972 American\n",
"105973 American\n",
"105974 International\n",
"105975 International\n",
"105976 American\n",
"105977 American\n",
"105978 American\n",
"105979 International\n",
"105980 International\n",
"105981 International\n",
"105982 American\n",
"105983 American\n",
"105984 International\n",
"105985 American\n",
"105986 International\n",
"105987 American\n",
"105988 American\n",
"105989 International\n",
"Name: Nationality_Am, Length: 105990, dtype: object"
]
},
"execution_count": 110,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"master_copy['Nationality_Am']"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"International 56598\n",
"American 46881\n",
"Name: Nationality_Am, dtype: int64"
]
},
"execution_count": 111,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"master_copy['Nationality_Am'].value_counts(dropna=True).head(20)"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 103479\n",
"unique 2\n",
"top International\n",
"freq 56598\n",
"Name: Nationality_Am, dtype: object"
]
},
"execution_count": 112,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"master_copy['Nationality_Am'].describe()"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Percentage of American Artist in MoMA Collection :45.304844461195025\n",
"Percentage of International Artist in MoMA Collection :54.695155538804975\n"
]
}
],
"source": [
"Percentage_American = str((46881/103479)*100)\n",
"Percentage_International = str(100-((46881/103479)*100))\n",
"\n",
"print('Percentage of American Artist in MoMA Collection :' + (Percentage_American))\n",
"print('Percentage of International Artist in MoMA Collection :' + (Percentage_International))"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>UniqueID</th>\n",
" <th>Title</th>\n",
" <th>Artist</th>\n",
" <th>Gender</th>\n",
" <th>Nationality</th>\n",
" <th>Birth</th>\n",
" <th>Death</th>\n",
" <th>Date</th>\n",
" <th>Medium</th>\n",
" <th>Dimensions</th>\n",
" <th>...</th>\n",
" <th>67</th>\n",
" <th>68</th>\n",
" <th>69</th>\n",
" <th>70</th>\n",
" <th>71</th>\n",
" <th>72</th>\n",
" <th>73</th>\n",
" <th>74</th>\n",
" <th>minValues_AcquiredAge_Artwork_2</th>\n",
" <th>Nationality_Am</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>6210</td>\n",
" <td>Ferdinandsbrücke Project, Vienna, Austria (Ele...</td>\n",
" <td>Otto Wagner</td>\n",
" <td>Male</td>\n",
" <td>Austrian</td>\n",
" <td>1841.0</td>\n",
" <td>1918.0</td>\n",
" <td>1896.0</td>\n",
" <td>Ink and cut-and-pasted painted pages on paper</td>\n",
" <td>19 1/8 x 66 1/2\" (48.6 x 168.9 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>74.0</td>\n",
" <td>International</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>6210</td>\n",
" <td>Armchair</td>\n",
" <td>Otto Wagner</td>\n",
" <td>Male</td>\n",
" <td>Austrian</td>\n",
" <td>1841.0</td>\n",
" <td>1918.0</td>\n",
" <td>1902.0</td>\n",
" <td>Beech wood and aluminum</td>\n",
" <td>30 7/8 x 22 1/4 x 20 1/4\" (78.5 x 56.5 x 51.5 ...</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>74.0</td>\n",
" <td>International</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>6210</td>\n",
" <td>Stool</td>\n",
" <td>Otto Wagner</td>\n",
" <td>Male</td>\n",
" <td>Austrian</td>\n",
" <td>1841.0</td>\n",
" <td>1918.0</td>\n",
" <td>1904.0</td>\n",
" <td>Bent beech wood, molded plywood, and aluminum</td>\n",
" <td>18 1/2 x 16 x 16\" (47 x 40.6 x 40.6 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>74.0</td>\n",
" <td>International</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>6210</td>\n",
" <td>Railing</td>\n",
" <td>Otto Wagner</td>\n",
" <td>Male</td>\n",
" <td>Austrian</td>\n",
" <td>1841.0</td>\n",
" <td>1918.0</td>\n",
" <td>1899.0</td>\n",
" <td>Painted cast-iron</td>\n",
" <td>28 1/4 x 46 1/2 x 3\" (72.4 x 117.5 x 7.6 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>74.0</td>\n",
" <td>International</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>7470</td>\n",
" <td>City of Music, National Superior Conservatory ...</td>\n",
" <td>Christian de Portzamparc</td>\n",
" <td>Male</td>\n",
" <td>French</td>\n",
" <td>1944.0</td>\n",
" <td>0.0</td>\n",
" <td>1987.0</td>\n",
" <td>Paint and colored pencil on print</td>\n",
" <td>16 x 11 3/4\" (40.6 x 29.8 cm)</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" <td>International</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 111 columns</p>\n",
"</div>"
],
"text/plain": [
" UniqueID Title \\\n",
"0 6210 Ferdinandsbrücke Project, Vienna, Austria (Ele... \n",
"1 6210 Armchair \n",
"2 6210 Stool \n",
"3 6210 Railing \n",
"4 7470 City of Music, National Superior Conservatory ... \n",
"\n",
" Artist Gender Nationality Birth Death Date \\\n",
"0 Otto Wagner Male Austrian 1841.0 1918.0 1896.0 \n",
"1 Otto Wagner Male Austrian 1841.0 1918.0 1902.0 \n",
"2 Otto Wagner Male Austrian 1841.0 1918.0 1904.0 \n",
"3 Otto Wagner Male Austrian 1841.0 1918.0 1899.0 \n",
"4 Christian de Portzamparc Male French 1944.0 0.0 1987.0 \n",
"\n",
" Medium \\\n",
"0 Ink and cut-and-pasted painted pages on paper \n",
"1 Beech wood and aluminum \n",
"2 Bent beech wood, molded plywood, and aluminum \n",
"3 Painted cast-iron \n",
"4 Paint and colored pencil on print \n",
"\n",
" Dimensions ... 67 68 \\\n",
"0 19 1/8 x 66 1/2\" (48.6 x 168.9 cm) ... NaN NaN \n",
"1 30 7/8 x 22 1/4 x 20 1/4\" (78.5 x 56.5 x 51.5 ... ... NaN NaN \n",
"2 18 1/2 x 16 x 16\" (47 x 40.6 x 40.6 cm) ... NaN NaN \n",
"3 28 1/4 x 46 1/2 x 3\" (72.4 x 117.5 x 7.6 cm) ... NaN NaN \n",
"4 16 x 11 3/4\" (40.6 x 29.8 cm) ... NaN NaN \n",
"\n",
" 69 70 71 72 73 74 minValues_AcquiredAge_Artwork_2 Nationality_Am \n",
"0 NaN NaN NaN NaN NaN NaN 74.0 International \n",
"1 NaN NaN NaN NaN NaN NaN 74.0 International \n",
"2 NaN NaN NaN NaN NaN NaN 74.0 International \n",
"3 NaN NaN NaN NaN NaN NaN 74.0 International \n",
"4 NaN NaN NaN NaN NaN NaN 8.0 International \n",
"\n",
"[5 rows x 111 columns]"
]
},
"execution_count": 114,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"master_copy.head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Simulate null hypothesis 50%-50% between American vs International Artists "
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0.499, 0.501])"
]
},
"execution_count": 115,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def simulate():\n",
" share = [0.5, 0.5]\n",
" return sample_proportions (1000,share)\n",
"\n",
"simulate()"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0.52 , 0.513, 0.492, ..., 0.506, 0.488, 0.517])"
]
},
"execution_count": 116,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_statistics_under_null_am = make_array()\n",
"repetitions = 10000\n",
"\n",
"for i in np.arange(repetitions):\n",
" test_statistics_under_null_am = np.append(test_statistics_under_null_am, simulate().item(0))\n",
"\n",
"test_statistics_under_null_am"
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {},
"outputs": [
{
"data": {
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