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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Quick look at NY Phil concert program data\n",
"\n",
"Eamonn Bell, Columbia University `<epb2125@columbia.edu>`\n",
"\n",
"---\n",
"\n",
"\n",
"Work in progress. Some interesting questions worth asking are bolded if there's anything in the notebook that moves towards a solution.\n",
"\n",
"- What composers tend to get programmed together?\n",
"- **Where did the orchestra play?**\n",
"- **What does 'composer discovery' look like?** Can we spot faddish composers by the shape of their performance frequency?\n",
"- **Who played with whom over the course of the existence of the orchestra?** The social network of performers.\n",
"- What are the significant differences between tour and subscription concert programs in general?\n",
"- What conductors prefer which works?\n",
"- What are the genres of the most-programmed works?\n",
"- What time do concerts tend to start at?\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Acknowledgements\n",
"\n",
"Thanks to https://github.com/bmcfee for the parsing code. The dataset this notebook is based on was released under CC0 1.0 Universal."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import lxml\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import numpy as np\n",
"import folium\n",
"import collections\n",
"import glob\n",
"\n",
"from pprint import pprint\n",
"from IPython.display import HTML, Image\n",
"from lxml import etree, objectify"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cloning into 'PerformanceHistory'...\n",
"remote: Counting objects: 350, done.\u001b[K\n",
"remote: Total 350 (delta 0), reused 0 (delta 0), pack-reused 350\u001b[K\n",
"Receiving objects: 100% (350/350), 12.53 MiB | 7.66 MiB/s, done.\n",
"Resolving deltas: 100% (253/253), done.\n",
"Checking connectivity... done.\n"
]
}
],
"source": [
"!git clone https://github.com/nyphilarchive/PerformanceHistory.git"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Author: https://github.com/bmcfee/nycphil (Brian McFee)\n",
"\n",
"def parse_programs(programs):\n",
" \n",
" return [parse_program(x) for x in programs]\n",
"\n",
"def parse_program(program):\n",
" \n",
" dispatch = dict(concertInfo=parse_concertInfo,\n",
" worksInfo=parse_worksInfo)\n",
" data = dict()\n",
" \n",
" for child in program.getchildren():\n",
" if child.tag in dispatch:\n",
" data[child.tag] = dispatch[child.tag](child)\n",
" else:\n",
" data[child.tag] = child.text\n",
" \n",
" return data\n",
" \n",
"def parse_concertInfo(concertInfo):\n",
" data = dict()\n",
" \n",
" for child in concertInfo.getchildren():\n",
" data[child.tag] = child.text\n",
" \n",
" return data\n",
"\n",
"def parse_worksInfo(worksInfo):\n",
" \n",
" data = list()\n",
" \n",
" for child in worksInfo.getchildren():\n",
" data.append(parse_work(child))\n",
" \n",
" return data\n",
"\n",
"def parse_work(work):\n",
" \n",
" dispatch = dict(soloists=parse_soloists)\n",
" data = dict()\n",
" \n",
" for child in work.getchildren():\n",
" if child.tag in dispatch:\n",
" data[child.tag] = dispatch[child.tag](child)\n",
" else:\n",
" data[child.tag] = child.text\n",
" \n",
" return data\n",
"\n",
"def parse_soloists(soloists):\n",
" data = list()\n",
" for child in soloists.getchildren():\n",
" data.append(parse_soloist(child))\n",
" return data\n",
"\n",
"def parse_soloist(soloist):\n",
" data = dict()\n",
" \n",
" for child in soloist.getchildren():\n",
" data[child.tag] = child.text\n",
" \n",
" return data\n",
"\n",
"def flatten(d):\n",
" \n",
" works = d.pop('worksInfo', [])\n",
" concertInfo = d.pop('concertInfo', [])\n",
" \n",
" out = []\n",
" for w in works:\n",
" out.append(concertInfo.copy())\n",
" \n",
" # Added this to get soloist's names in. Dirty.\n",
" \n",
" soloists = w.get('soloists', None)\n",
" \n",
" if soloists is not None:\n",
" soloists_names = [s.get('soloistName') for s in soloists if s.get('soloistName') is not None]\n",
" soloists_tsv = \"\\t\".join(soloists_names)\n",
" out[-1].update({'soloists_tsv' : soloists_tsv})\n",
" \n",
" w.pop('soloists', [])\n",
" out[-1].update(d)\n",
" out[-1].update(w)\n",
" \n",
" return out\n",
"\n",
"def load_programs():\n",
" # We need this to handle badly formatted &'s in strings\n",
" parser = etree.XMLParser(recover=True)\n",
"\n",
" fd = []\n",
" globbed = sorted(glob.glob('./PerformanceHistory/Programs/*.xml'))\n",
" \n",
" for xmlfile in globbed:\n",
" obj = objectify.parse(xmlfile, parser=parser)\n",
" dix = parse_programs(obj.getroot())\n",
" for _ in dix:\n",
" if _['programID'] == '11451':\n",
" print _['programID']\n",
" fd.extend(flatten(_))\n",
" df = pd.DataFrame.from_records(fd)\n",
" df['oldDate'] = df['Date']\n",
" df['Date'] = pd.to_datetime(df['Date'])\n",
" del df['worksInfo']\n",
" del df['work']\n",
" del df['concertInfo']\n",
"\n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df = load_programs()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>Location</th>\n",
" <th>Time</th>\n",
" <th>Venue</th>\n",
" <th>composerName</th>\n",
" <th>conductorName</th>\n",
" <th>eventType</th>\n",
" <th>id</th>\n",
" <th>interval</th>\n",
" <th>orchestra</th>\n",
" <th>program</th>\n",
" <th>programID</th>\n",
" <th>season</th>\n",
" <th>soloists_tsv</th>\n",
" <th>workTitle</th>\n",
" <th>oldDate</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1842-12-07 05:00:00</td>\n",
" <td>Manhattan, NY</td>\n",
" <td>8:00PM</td>\n",
" <td>Apollo Rooms</td>\n",
" <td>Beethoven, Ludwig van</td>\n",
" <td>Hill, Ureli Corelli</td>\n",
" <td>Subscription Season</td>\n",
" <td>38e072a7-8fc9-4f9a-8eac-3957905c0002</td>\n",
" <td>NaN</td>\n",
" <td>New York Philharmonic</td>\n",
" <td>NaN</td>\n",
" <td>3853</td>\n",
" <td>1842-43</td>\n",
" <td>NaN</td>\n",
" <td>SYMPHONY NO. 5 IN C MINOR, OP.67</td>\n",
" <td>1842-12-07T05:00:00Z</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1842-12-07 05:00:00</td>\n",
" <td>Manhattan, NY</td>\n",
" <td>8:00PM</td>\n",
" <td>Apollo Rooms</td>\n",
" <td>Weber, Carl Maria Von</td>\n",
" <td>Timm, Henry C.</td>\n",
" <td>Subscription Season</td>\n",
" <td>38e072a7-8fc9-4f9a-8eac-3957905c0002</td>\n",
" <td>NaN</td>\n",
" <td>New York Philharmonic</td>\n",
" <td>NaN</td>\n",
" <td>3853</td>\n",
" <td>1842-43</td>\n",
" <td>Otto, Antoinette</td>\n",
" <td>OBERON</td>\n",
" <td>1842-12-07T05:00:00Z</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1842-12-07 05:00:00</td>\n",
" <td>Manhattan, NY</td>\n",
" <td>8:00PM</td>\n",
" <td>Apollo Rooms</td>\n",
" <td>Hummel, Johann</td>\n",
" <td>NaN</td>\n",
" <td>Subscription Season</td>\n",
" <td>38e072a7-8fc9-4f9a-8eac-3957905c0002</td>\n",
" <td>NaN</td>\n",
" <td>New York Philharmonic</td>\n",
" <td>NaN</td>\n",
" <td>3853</td>\n",
" <td>1842-43</td>\n",
" <td>Scharfenberg, William\\tHill, Ureli Corelli\\tDe...</td>\n",
" <td>QUINTET, PIANO, D MINOR, OP. 74</td>\n",
" <td>1842-12-07T05:00:00Z</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1842-12-07 05:00:00</td>\n",
" <td>Manhattan, NY</td>\n",
" <td>8:00PM</td>\n",
" <td>Apollo Rooms</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Subscription Season</td>\n",
" <td>38e072a7-8fc9-4f9a-8eac-3957905c0002</td>\n",
" <td>Intermission</td>\n",
" <td>New York Philharmonic</td>\n",
" <td>NaN</td>\n",
" <td>3853</td>\n",
" <td>1842-43</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1842-12-07T05:00:00Z</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1842-12-07 05:00:00</td>\n",
" <td>Manhattan, NY</td>\n",
" <td>8:00PM</td>\n",
" <td>Apollo Rooms</td>\n",
" <td>Weber, Carl Maria Von</td>\n",
" <td>Etienne, Denis G.</td>\n",
" <td>Subscription Season</td>\n",
" <td>38e072a7-8fc9-4f9a-8eac-3957905c0002</td>\n",
" <td>NaN</td>\n",
" <td>New York Philharmonic</td>\n",
" <td>NaN</td>\n",
" <td>3853</td>\n",
" <td>1842-43</td>\n",
" <td>NaN</td>\n",
" <td>OBERON</td>\n",
" <td>1842-12-07T05:00:00Z</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Location Time Venue \\\n",
"0 1842-12-07 05:00:00 Manhattan, NY 8:00PM Apollo Rooms \n",
"1 1842-12-07 05:00:00 Manhattan, NY 8:00PM Apollo Rooms \n",
"2 1842-12-07 05:00:00 Manhattan, NY 8:00PM Apollo Rooms \n",
"3 1842-12-07 05:00:00 Manhattan, NY 8:00PM Apollo Rooms \n",
"4 1842-12-07 05:00:00 Manhattan, NY 8:00PM Apollo Rooms \n",
"\n",
" composerName conductorName eventType \\\n",
"0 Beethoven, Ludwig van Hill, Ureli Corelli Subscription Season \n",
"1 Weber, Carl Maria Von Timm, Henry C. Subscription Season \n",
"2 Hummel, Johann NaN Subscription Season \n",
"3 NaN NaN Subscription Season \n",
"4 Weber, Carl Maria Von Etienne, Denis G. Subscription Season \n",
"\n",
" id interval orchestra \\\n",
"0 38e072a7-8fc9-4f9a-8eac-3957905c0002 NaN New York Philharmonic \n",
"1 38e072a7-8fc9-4f9a-8eac-3957905c0002 NaN New York Philharmonic \n",
"2 38e072a7-8fc9-4f9a-8eac-3957905c0002 NaN New York Philharmonic \n",
"3 38e072a7-8fc9-4f9a-8eac-3957905c0002 Intermission New York Philharmonic \n",
"4 38e072a7-8fc9-4f9a-8eac-3957905c0002 NaN New York Philharmonic \n",
"\n",
" program programID season \\\n",
"0 NaN 3853 1842-43 \n",
"1 NaN 3853 1842-43 \n",
"2 NaN 3853 1842-43 \n",
"3 NaN 3853 1842-43 \n",
"4 NaN 3853 1842-43 \n",
"\n",
" soloists_tsv \\\n",
"0 NaN \n",
"1 Otto, Antoinette \n",
"2 Scharfenberg, William\\tHill, Ureli Corelli\\tDe... \n",
"3 NaN \n",
"4 NaN \n",
"\n",
" workTitle oldDate \n",
"0 SYMPHONY NO. 5 IN C MINOR, OP.67 1842-12-07T05:00:00Z \n",
"1 OBERON 1842-12-07T05:00:00Z \n",
"2 QUINTET, PIANO, D MINOR, OP. 74 1842-12-07T05:00:00Z \n",
"3 NaN 1842-12-07T05:00:00Z \n",
"4 OBERON 1842-12-07T05:00:00Z "
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"## Number of works performed by composers over time"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Get, e.g., top 5 composers by performances of all time\n",
"\n",
"sample_list = list(df.composerName.value_counts()[1:5].index)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sample = df[df.composerName.isin(sample_list)]"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"all_works = df.groupby(df['Date'].map(lambda x:x.year)).count()\n",
"yearly_counts = pd.Series(all_works['id'], index=all_works.index)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 157.000000\n",
"mean 558.694268\n",
"std 567.675746\n",
"min 18.000000\n",
"25% 130.000000\n",
"50% 430.000000\n",
"75% 646.000000\n",
"max 2484.000000\n",
"Name: id, dtype: float64"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"yearly_counts.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Raw counts"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x110fd0350>"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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diqdPn/7f6erqaqqrq5toolEoUkW+ucnXYhf5fHvyqSIfR7imqkr9A61fD/36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\nQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x109a56c50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"yearly_counts.plot()\n",
"plt.title('# works performed in the NY Phil Program Archives')"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes.AxesSubplot at 0x110f0bb50>"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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5TMePt7nuwaCVZu66y1YSLMVwh8iKkiLFQgEvgJVWPvoo8Rzu5cvh3HMtqB3H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\nQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10d303e90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"all_programs = df.groupby(df['Date'].map(lambda x:x.year)).programID.nunique()\n",
"all_programs.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What's the deal with 1956? There's a bunch of phoney data somewhere. That peak of 900 or so should be distributed over the decade.\n"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes.AxesSubplot at 0x110e5dad0>"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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wARYvdh2JSPopIcSQWgh1y8+HOXMsMezd6zoakfRSQogh9SHUr39/OPNMmDHDdSQi6aWE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\nNJFFAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x110e5dfd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"all_programs.ix[1950:1970].plot()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df.Year = df.Date.map(lambda x:x.year)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Date datetime64[ns]\n",
"Location object\n",
"Time object\n",
"Venue object\n",
"composerName object\n",
"conductorName object\n",
"eventType object\n",
"id object\n",
"interval object\n",
"orchestra object\n",
"program object\n",
"programID object\n",
"season object\n",
"soloists_tsv object\n",
"workTitle object\n",
"oldDate object\n",
"dtype: object"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True 87715\n",
"dtype: int64"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# See if the issue is with the datetime parsing from before. Seems like it's not.\n",
"\n",
"df['Sanity'] = (df.Date.map(lambda x:str(x)) == df.oldDate.map(lambda x:str(x).replace('Z', '').replace('T', ' ')))\n",
"df.Sanity.value_counts()\n"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"del df['Sanity']"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Now, check if the application of x.year is OK\n",
"# First do a kludgy year parse on the string rep of the datetime, i.e. oldDate\n",
"\n",
"oldDate_example = '1842-12-07T05:00:00Z'\n",
"\n",
"def year_from_oldDate(oldDate):\n",
" return int(oldDate.split('-')[0])\n",
"\n",
"assert year_from_oldDate('1842-12-07T05:00:00Z') == 1842"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df['oldYear'] = df['oldDate'].map(year_from_oldDate)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes.AxesSubplot at 0x110519bd0>"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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xTMeMsbnuwaCVZu6/31YSLMVwh8iKkiLFQgEvgJVWPvww8Rzu5cvhoossqB3H\n5gU/9pitABgtPMAaFq7Dr11rYT9mTNvbjxhhO/8Eg7Zu9/TpWf21RHwt3wE/Bfgx0BV4CLgzz89f\nsrZssXLGqafaFMLKyuw99p49Nrd2wQI49lhb/nTTJiuv3HSTrTUyZYrVyy++2Hryl19uBzUYMcKm\nyJ1/vg0Qvvpq213WBw+2Oe0PPhgp0cQ64gjb3b1Xr+z9TiKS34DvCtwPnAk0AK8Bi4B38tiGpNXW\n1hLMx5EXkrB3r9Wmx4+3gcuRI2HYsFqOPjrY4eyFlhbrPYePVNO1qwXu0KH2U1Vl1z31lA1sbt1q\nAf3aaxb0775r9eQuXax8cuGF9rjl5VZu+eADOwzdK6/YEWvee8967+eeWwsEAXu+66+He+6xD4VE\n8rHAFhTX3zWeYm5fMbcNirt9hWpbPgN+HLAeeM+9/BvgPIow4B0HFi+uZdy4IGVl7afrOY7N446t\nQYcPqhxe2yMUsvrzxo22behQC936envMIUPark4XCllPdtkym0990kl2UIb77rODXNxzjz3O7Nlw\n0032D9PR8SO7drV5soMGWVubmy3wN260n/p6u+7WW+2AwIGA7UYffQDgK6+0oD/11LaPHQhYeA8e\nbIdNu+aayHU1NbWEA37GDDtYx9Spnb3q+VHMIQDF3b5ibhsUd/v8EPDVQPRhh+uBk2NvdPPNVh4I\nC4Xs8vvvWy9v8GA7ZNsHH1iZoro6uTm4zc1WH9661eYsDxrUdhW9/fst8MLht3+/BWpLi9Wl+/Wz\nUNuzxx6npcVWmjvkENu+c6e18aCDLPC6drVZId27R+rWGzda0A4ZYu3ZuLH9tLojj7T7b9pkoTl6\ntE0jvOyyyAfHsGF2gOmOesSJjB2b2u2rq1NfJTBa7AeDiORPPgM+qfmP+/bZUd+j9e9vgRwO0YoK\nu7xtm11O5ijo4fLEIYfY+h9NTZEeN1jZobo6Ur740Y9snerWVuuFb9lit+vRw25XXm4fMtu22fae\nPS24d++2Y0m2ttpBdvv3T+a3FhHJvnzOgz8FqMEGWgFmAyHaDrQuB7SqhYhIalYAx3d6qxzqBmwA\nDgXKsTAfVcgGiYhI9pwDrMEGW2cXuC0iIiIiIlLMfgE0ASujth0P/B14E5uXf1LUdccCfwNWAW9h\nZSWAE93HWAfcW6D2lQHz3HbVAdG7EOWiffHadhz2+ryF7c8QfTji2e7zrwYm57htqbbvLOB1d/vr\nwKQcty+MDIPyAAAFjUlEQVTV1w5gGLAbmFVkbSuG90Si9uX7PTEUWAq8jb0e4SO2VgGLgbXA80BF\n1H3y/b7wlQnACbT9Z3keONs9fw72BwMbL1gBHONeriSytPIybE4/wLNEBo3z2b6Lgfnu+R7Au1go\n5Kp98dr2mrsd4HLg++750dj4Shk23rKeyGB+Pl+7RO07HggfVmMMNl03rNCvXdgC4HHaBnyh21Ys\n74lE7cv3e2IgkUHNXljpeRRwF3C9u/0GwF1aryDvC1+tB/8XYFvMthDQxz1fge1hC/bp+haRf6xt\n7m0HYT2GZe72R4FpBWhfCOiJ7R3cEzgA7Mxh++K17Uh3O8ALwBfd8+dhb7RmbKe29dj+Dvl+7RK1\nbznQ6J6vw8KgLIftS6VtuM/5T7dtYcXQtmJ5TyRqX77fE43Y/xLYt613sH19pmLfJHBPw89ViPeF\nrwI+nu8CdwP/dk/DA79HYvP2nwP+AXzP3V5N2x5fg7stX+37T3f7AuAj4APsn+VuYHue2/c29k8L\ncD72lRVgcEwb6t02xG7P9WuXqH3Rvoj9fZspjteuF9b7q4m5fTG0bSTF8Z5I1L5CvicOxb5pvAoM\nwMpKuKfhY3QV5H3h94CfiYXoMOAarOYH1qM7FfvadyrwBeB0ktxZK4ft+z93+8lAC/bpPwK4zj3N\np6+57XsdC6YDeX7+znTWvjHY1+dv5LldkLhtNcA9WFAV6lgNidrWjeJ4TyRqX6HeE72A3wFXA7ti\nrnPI/+vTht+XC/4KkcGRBdgKl2BLKvwZ+NC9/CwwFvgVELUYLkOIlE3y2b6LsZ5UK7AZeBkbqPlr\nHtu3hsj4wEjgs+75Btr2lodgPZSGPLato/aFn/v3wKVYrZY8ty+2bee658dh3yruwkpyIWCv29ZC\n/12L5T2R6LUrxHuiDAv3XwJPuduasPp8I/ZhE154pVjeF552KG0HbOqAie75M7ABHLABpH9g9dlu\n2Kj4Oe51r2K9hQBZHhBJoX3XE/m20RP72ho+mmeu2hfbtvDhpbtgdcOvupfDg0nlWA9qA5HeaD5f\nu0Ttq8AGC+PVOQv92kW7Bbi2iNpWQXG8JxK1L9/viYD7/PfEbL8LG1wFm8kTO8ia7/eFb8wH3se+\n0m3ERuA/g33VW45NvToh6vZfxqY/rSTyR4LIlKb1wH0Fal9P4Am3fW8TfzpdNtsX27avYd8s1rg/\nt8fc/j/d519NpLeVq7al2r452KDYm1E/4UNlF8NrFxYb8MXQtkK/JzpqX77fE6di37CWE/k/moJN\nk3yB+NMk8/2+EBEREREREREREREREREREREREREREW/ZnWD7I9jepP+PtnO9h2M7p/TObbNERCRT\nseuGhD0MTAcOwnZIOcrd/hRwUYbP6fe1nyTP9A8nfnAttqfgSmxRqGgB4H4szBcD/d1t+7AF3n6C\nrXfSE9uz8nvY0q4raLvq45PYXsergCujtu8Gfojt8XhK9n4lERE5EVvHvAcW0quwAzWEe/DTsV3K\nA9jiUNvcbWELsAWjjsTWRP+Zu70L8DSRg09Uuqc9sA+S8OUQ8KVs/kIiyfL7apLifadiqzHudS//\nHjgt6vrTgMewZV0/AF6Muf9PsHLNOmxp4cnYuiNgHxhHYAeguJrIAmZDsQ+EZdjqhr/L2m8jkgIF\nvHidQ/u11Z1Oro+9bfTt7wAejLlNEFvt8xSstLMU+1DAvVzQNcHFv1SDF6/7C9azDpdovkDkkG9g\na5xfiL0XBtH2INyx/oStaNjTvVyNLV/bGyvt7MMGZVVrl6KgHrx43ZvY1MfwMS9/jg14hnvVT2JH\nJqrDDo34Ssz9o3vwi7EDK//NvbwLuAQ70MQ33cdYE3U9qPcuIiIiIiIiIiIiIiIiIiIiIiIiIiIi\nIiIiIiJ+9v8BaLNS5UGFfsYAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x110ceed50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Same spike! \n",
"\n",
"all_programs = df.groupby(df['oldYear']).programID.nunique()\n",
"all_programs.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"Here's one I know is messed up. See the season field."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>Location</th>\n",
" <th>Time</th>\n",
" <th>Venue</th>\n",
" <th>composerName</th>\n",
" <th>conductorName</th>\n",
" <th>eventType</th>\n",
" <th>id</th>\n",
" <th>interval</th>\n",
" <th>orchestra</th>\n",
" <th>program</th>\n",
" <th>programID</th>\n",
" <th>season</th>\n",
" <th>soloists_tsv</th>\n",
" <th>workTitle</th>\n",
" <th>oldDate</th>\n",
" <th>oldYear</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>40633</th>\n",
" <td>1956-07-12 04:00:00</td>\n",
" <td>Manhattan, NY</td>\n",
" <td>8:30PM</td>\n",
" <td>Lewisohn Stadium</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Stadium Concert</td>\n",
" <td>c7f0aeae-8de8-4f42-a040-f5a7e624311b</td>\n",
" <td>NaN</td>\n",
" <td>Stadium-NY Philharmonic</td>\n",
" <td>NaN</td>\n",
" <td>11451</td>\n",
" <td>1962-63</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1956-07-12T04:00:00Z</td>\n",
" <td>1956</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Location Time Venue \\\n",
"40633 1956-07-12 04:00:00 Manhattan, NY 8:30PM Lewisohn Stadium \n",
"\n",
" composerName conductorName eventType \\\n",
"40633 NaN NaN Stadium Concert \n",
"\n",
" id interval orchestra \\\n",
"40633 c7f0aeae-8de8-4f42-a040-f5a7e624311b NaN Stadium-NY Philharmonic \n",
"\n",
" program programID season soloists_tsv workTitle oldDate \\\n",
"40633 NaN 11451 1962-63 NaN NaN 1956-07-12T04:00:00Z \n",
"\n",
" oldYear \n",
"40633 1956 "
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df.programID == '11451']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here's the salient part of the original .xml. I've no idea what the hell is going on."
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" </work>\r\n",
" <work>\r\n",
" <composerName>Falla, Manuel de</composerName>\r\n",
" <workTitle>THREE-CORNERED HAT (EL SOMBRERO DE TRES PICOS), SUITE NO. 2</workTitle>\r\n",
" <conductorName>Rudel, Julius</conductorName>\r\n",
" </work>\r\n",
" </worksInfo>\r\n",
" </program>\r\n",
" <program>\r\n",
" <id>c7f0aeae-8de8-4f42-a040-f5a7e624311b</id>\r\n",
" <programID>11451</programID>\r\n",
" <orchestra>Stadium-NY Philharmonic</orchestra>\r\n",
" <season>1962-63</season>\r\n",
" <concertInfo>\r\n",
" <eventType>Stadium Concert</eventType>\r\n",
" <Location>Manhattan, NY</Location>\r\n",
" <Venue>Lewisohn Stadium</Venue>\r\n",
" <Date>1963-07-25T04:00:00Z</Date>\r\n",
" <Time>8:30PM</Time>\r\n",
" </concertInfo>\r\n",
" <worksInfo>\r\n"
]
}
],
"source": [
"!cat './PerformanceHistory/Programs/1955-56_TO_1962-63.xml' | grep -C 10 '11451'"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x10bf6b390>"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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7iIiIIC0tjenTpzeqKMmiRYsYO3YsRqORyZMnM2zYMPLy8rxKbD722GNER0fT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SYFLmJt1xP0Nu3DEs0ciAnG41haWyEvD4vBgFlRUAoL4emnxcfW2SkCJzrAjwLleXnkWLzkWA3Z\nlYLZSTQaoC8W44qSEvb37OeWmgepmH8ax6zTCgeGQiOvFAwGikwmzMNRCuXlGC02FkbKWNe0Ljke\nCOA3xil2llBoMmFUFJbm5mJQFKbYbFT7/ewPhdjU20ttYsWClPY+GOojXDiN18aVUG430aSZtrF4\nnO2BAMVmc//GsYjFi9rXRIEtvUZVkdXB/nAMj9FIU09Tv6VgNBjx2r2oIdkJb4wFcAxwH3W7A+TF\nLVy/eze1mlXTHRX3kd1sx26yozpbiO23EA6H8Wr7buxTNKVgVuiIRCTRoLYWPB4cqkq+TaXLXEC4\ndLrsU/D5xK1QU0NDfh59wSCV3sFuokqbjbqCI+c++hnwNpJtdCwSTxj6qtb5UBw8KBP4hAnQPLzC\nhoN6x6Zy0UVw2WVw4YXiv7RapVPXmjWDnxsOw6uvJhNuZs8WtxRIobJzz5XXeOUVGbvr1btYvXUz\nS5fKfHrf2vtZUysvXFMjSmnRIniq+in+uP6vQ36GcDyI3ZoINJvojXShoGAxWnAbi4hEoCQ/qRSM\nRqkwUbupnBmeT1J+38NUvPhOxtcORqP9loLNYBB/97Rpov1uuQWqqqR3ZEvLqLiOAMpLS+lR7Xis\neViyBJqdFie5gd18v6KCYLiX29bcxoppKxj3tevgySdFIQSDUqt6BMkxGCgyS9e8HFNm2QehKDB/\nPoubrekupN5eekxxShwlGBSFFYWFnKL5xafm5FDt97O6vZ0ik6l/ggcotBfSEQnjUlWMtgBlFgvN\nWiXchmAQh6oyI2XzWciURzRQh8eaPmkW25y0xAy4VJWm7iaKHcX9j5U5y2jvbSTXaESJ9OI0OyXQ\nrCmFrpw+PtlZzAVeL/c3NspYVLKPQFxQfeZ6InVWSjqb8bgKYcUKciukE53TormPbDb5LbrdnJqb\nS5XLysaCZfinnig7mlOUQmNhEaFwH5Wlg2twuY1GLMrwrN6hlMJ3gDLgJqCRZKmLbvQyF8OmoeHQ\nq//6erEKP4j7aCilMH/+4KZbCbdSissVgHfekWQcr1Ydec4cUQYgymHuXFEwjzwCfcEY5verqd6z\nh+nTRfbqxjrqOrWSw5pS8Hggx7eHtdv2ZZU/HocoQRyaUrCazHRFWiSwCrTXF2G3Q67dzobubiKa\nqTJnjriuzjWcRGF7iIK1m/s/VOO+fexvaCAejxMMh2VDmMmE1WDApCgSqb74YtEuU6fKSUxkHo0C\npT4fzSg4rXmYrJlX2xbVQnGwBhch7nj5Dub75rO8armsJCZPhuefT29aPULYVHX4lgLAggVU7Wxj\nfdN6wlHN1eP306VG+t02F3i9uDR33tScHLb5/azu6OBCrzdNKZhVMxid5Jh6RSlYbTRqSqHa72eK\nTXYtJ47xq26skY7BSsHioAcre1u3sr11O6XO0v7HSp2lNPc047NYINrTbyl0R6P4o1GCljAVdgtL\nPR4agkEagkECUenPDKIUupU6elpVPPv3kO/Og0su6TdOcywGuqNRUQoAHg/nFxbitZt413s2xsIy\nAtEocZ+v3310oKCQeCTM5PIMhRmBstDw2t0PpRQMgJVkiYvU29A9BnUG8eST8M9DFBxPKIXi4uEp\nhe5ucQVVVBz+MdOnS5bTwDjAqlXpCqSyUkrsbNsmlszkyXLspEnw6JMtfOof2/A0vUVFBZSPD7Gv\nbT/1XRIt37MnWf7H4ath/a56NHfuIMJhiBtC/QXxbGYL3dGkUqirLsLtllLTd9fV8YaWnjVHaww7\n7v138X7uUnLdXti2jWgkwl3PP89Tr79OJB7HEAph1PIELQlLIZUxoBSsOTl4gNZIBNPAzVoaiqLg\nsrj44Ss/pNhRzMUzL04++IUvSB5yT8+IWwqn5uZyots9fKUwdSqWrl6mxwqSpal7e2k3hPrdNqlU\n5eSw3e/HoCic7HZzMBwmlJLFYDC5MantGMwB5jjdvNLRQTAWo9rvZ2pOTppS6FRs5EQ7yR3gPnKb\nTJS5K4iFuznBd0J/G1JI9npeUViIM9SM0+LErm3eq+nr4xNFVk47VcFoMLDI4+FfLS3YVLU/3bXc\nVU6zvwGnEw50dlDkkoncZhMXr8OstZJNRKq1CrxGo+h5q0nBZDDQ5/PB3r1QX09bYQFKLJpVKZTG\nj5yloHMEqanJHIhNJdH1LzdXgrtDZN+lUV0tk/UhOj+mYTCItfDAA+m399+XXr+pz5s1S+IIM2dC\nIknjkkvgjSfextwVxWfchcEAzrIGwgEbdV1iKdTWygK2O9iNzenHbIny5L9lMt+zB958M/k+4TCg\nhkAx8kxrKxaTiZ5YUils3+Th3F0HMP3lKXpqa3lJSyUtLoay4iiejWuY8JkryDl9OaxaxarVq2mP\nx6kJBCQdNRzuTx63JSyFVKqqpHTA/v2jphQAyo1GiEQwZ1EKIGmpJtXEtxd8G4OS8qVPmiQn/Nln\nR9xSmG63U261YlbNg7quDYnBAPPmseyAM1kgz++nzRDstxRSyVFVfBYLSzwejAYDXrO5f0NbPB4H\nowOj2oJiDjDD6WGa3c5TLS1s9/uZkpNDucVCXV8fHeEwisEsBfwGWAouVaXYUcy5k5Zy6exL04os\nljhLaOppYo7TSSzYitPsRFUUbAYD7/f2MqvI2m+xL83N5cX2dlwpmU3l7nJ2tu6koDBGS08nxSnp\noqefTn9mnG2AUgBRHCYT2A0Gej0ecRMWFBC32TASI8/hzHiKyw3DWyDoSmEEiEYlQ6emZrC7JpX6\nelEKiiIupMONK2zfnt11NBTnnSexA58vebvxxsFlZubMEcWTWJWDVFlY6HqT9lABRWFRAuaCenK6\njqUr2EVnb4CmJrF8ajpqGO8Zz4mfKOdP/6zjjTfg5psl6SdhOQRDMVAjvO8P8rvGRoxmM/54Oy6L\ni+ZmCPdEuKrWQXtlFRPb2ti+YwdtWmbJ98/YiHtCvphKixcTeOst/rJ3L9+pqqImHpfMo1CoP1aQ\n0VKw22Wn0aZNo6oUfFrht2yWAsCV867kxpNuzJz2+YUvyA9phAPNCcyqOXPXtaFYsIDptT281fAW\nsXiMUFc73cYoeba8jE//Tnk5y7XvsjzVHRSLYTNaGDelGW9JBItq4ZLiYla2tLA/HKbSasVrNtMe\nibA9EKDSasWAgtuSvsJ2a64qT4YMtBJHCc09cmH2hHuwm2XydhqNbO7poTLle5tgtVJsNvfHE0BK\nUNjNdrZ4fkxP0I83Nz31OPG7tCY0QAalkChAiM8HlZWYzGasqpq+QEhhstGecTwbulIYARoaZD6y\n2TLvlUpQVyeTKIhSaGwUa+Ghh4ZWJlu3fjClYLfDWWdJY/XE7dhjBz9v9mxRVLNnp48fG97IhtKT\nKPV3STMcRx2hg+MocZTy7o5GSkvlR1zTXkNlbiXTfT580+q57z5RPiUl8MK6EE8dPEhPIIQRMxta\nW4k2NNBpNBGPg9viZv16WDihEc+4KuoXLGDivHmcsGkTa2prIRJh/KaVmE7Xqpjm5/P0/Pl8wmJh\n7kknYQsE2NfdjaWvD/JkkrEZDJgzmVVTp8oJH02loK0cjTnZg7VV+VXZg7lVVRL4yRKTONoM21IA\nmDULZ90B8sNmdrbupL21AYszN+skN8Fmw5bw0acohY5IhHyTmbboXjwOK4qi4DWbOTU3l0k2G0Zt\nL0WJ2czrnZ1U2V14rB5Ug5r2+omVvVtNHwctmN3XQSgakm5rZpnUHarKNr+fypTzrigKS3Jz+5VM\n4vzcevKtuOxmjFEXHnf6Z+xXCqoqv9eU7n39loLmrqKiQpSCxYx9iBTqvExNoIZg5JOxP4YkAq7B\noNz3egc/p6tL0jwT1mTCUnjiCYlHLFkyuBoDSPGtmprMfV6OFB6PpKemZWr29mJrrGPerV+h8PbV\n1HfWcTBYT4F5Hm6lkQ2766islAble9r3ML1oOt3BbswL6/nsJcl6c3/a1IHffgCfTcWomNlQXc3M\nzZtpO+GTxGLiKtmwAc4qqgdbOfXBIL7cXCZPmcID777L+X/6kxR/O+20ftHWLV3KF7xeMBiojMWo\nbmhIUwqWTO4jEKXwwgv9zxsNyr1e1H37MAyhFA7Jd74zYmWzB2JWzajK4Ml06IPMMHMmZ7R3srZ+\nLSe0NmLzHJ5iLrdY+pvetIfDFFns7D24N00xXez19mcHgSiStV1dfLWkhC+e+tNBr5kICmeyFAyK\ngSJ7ETXtNViMln5rzaGqhOJxxg9Qxp/Kz+ekAZOySTVxwbjv0vXqgUFN+yza92YzGODOO8U/qmG1\napaCwSAtOS+9FMxmJr37GD3B7Oe85KThlTzRLYUPQUsL3Hprchdygr4++PnPYeNG+T+hFCZMSG7m\nGkhNTdJ1BKIUNm8W9/C8eckMoIGsWSMxgIxxxaeeSnfcfwjKygYMbN7M3mIbEz4xjRxTDk311dR3\n1XOMz8fL//Lxvy/UM3Gi9tk6aqj0VFLuLmd/oK5/IX7SSbChpwd73MiTuzswm10EGhs5vbOTtq4u\n4jEwx11s2QJT7OJbqwsG8VksTDv7bILd3ewpK4Mbbuh3l0RiMXbH41Rp2nWC1Up1WxuWQKB/snep\nav+Fn8a0aeJCGoWNawkqxo3DFY8PL0A0EJdLNuCNAg6zoz8ONCwWLGDO3hBvNbxFd8d+nJ4MK6cM\nDLQUSqwOGrsb05SC0WAgNyXGUm61EojFqLTZMsYtjAYD+UZj2jGplDhK2NG6A4cp6fpxqirFZjM5\nA35XJoOBwgyuvKJCA9ZI8aD+TP2WgsEgk0CKcne5pBCk22jkvd5eLa0vB5PZiHuIxAJn6fB+C7pS\n+IDs2wfXXSfxgoceSo53dcFNN0mQNZFtlMjCqazMHmx+5RXZO5CguFhSQM8+W7KBEnsFUonHJa30\nlFOyCPnqq5k3IxwB4uvXs9lnosRVCj4f7Tu30NjdyA+u9PHdK8o55bx6zj4bIrEIDd0NjPOM6y8T\nkMBgjWIcF8DyQglP7OziOI+J2fE4U6dNoykUJBqHd193c+KJkNMqvrX6YJByiwWDzcYpZ53FquXL\nk9FvoKavD6/J1H9xVrrdbPf7sfT29iuFuU4nV/t8gz9UWZk0bhlFnB4Pv7/oolGV4cOwYtoKzp36\nAYooH388+bsaCPm7aWjegTO3+NDHIEqhIRgkFo/THongy3ETjUeH3Cvhs1hQESsjGw9UVaW5fVIp\ncZaws20nTktysnWoqrRpPUwKCmQdM1CENKUwgOuuk9IuF3u9vN3VxYNNTcTiceIGE07Tkcs2+8gp\nhd5eSd3SLMajwsqV0np3kB+/txe+9S3CnX5uuklS3G+9VRTE5s2SuHLddeKXv/tu8fV3dCSzcNKU\nwtNPS6QV2W/0xhvpFZorKuSYT39asn6qqwfXHdq9W8a0PiPphEKSsrZ5s2iuI0kwSPjN16idmIfD\n7MAyfiJN1e/gsrjIdVqZWemjM1aH0Qh1nXV47V7Mqhmv3Ut7XzvBSJDVNav51qu/Yn6ZDeseFxWz\nuulVi5gzaxYFkydj6+4hZLCzYa2Liy4C6uoIlJXRFYlQpK28Tiks5JXOzv49C0B/lkmCysJC/MEg\nlp6e/kCzQVEyxxRgcJR9FLB+GNfRKGM0GA+v7tFAnE6UiZM4o6eY1tY6PPmlhz4G2R/hNBrZ19fH\nG11dlNvsuC3uIYPdk2w2ptnt2X8DaD79LBQ7itnZurM/ngBQajYz3X74Ad3SUrm+B3r5hlIKFosY\nkAVmMz+ZMIHtfj/31tVhNrsptA0vbjAUHzml8PrrMtcldtRmoq9Pm9AjkcOrxKYRi8mq/9lnZTLf\nsSPDm9fUsO1vm/H5ZBI3mSQ984EHpLbQ8uWiLGw22TS2cqWkW+bnizXY2Qm9TV3E/voo0Zdeht5e\n1q6VlNL+jbSBAHl58KtfyevY7aJQEjWHOjpkH8Mzz4iVkPG3vWuXaJbCwsEfJBYTpXEYhDKdv5Ur\naZ7kwzp+MgDOSdOI1e3F55LVd5mrjObeZqKxaL/rKBKLEUGhxFHC7vbd/HHjQ3mOLgwAABOCSURB\nVGztCTDR08x998XJrVlFd14Zs2bNQpk6lWPq6wjn5HLmEhc5rgg0NtJQWEipxdKf811isVBmsbCu\np6dftEQ+eoISnw9LTw8Wvx+GWS1SZ4RZsID59XGswSi5+eWHfZjPYuGmmhrKzGaW5+dTZC8aMthd\nZrFw14QJWR8/FCUO6fWcqhTOKyzknGEkKHg8cM89g8fTYgpD4DQaubOykmA8zm5jBccXzzzs9z4U\nHzmlsGqVZMy89FLmx3fsgMsvh5ce2gtXXAHf/rYk3x8Gzz8vGYl33w3LliX7tqe9+YwZ1P9rfdoG\nrxNPFK1/xRWiFBIsXSpKobJSVgQGgwSLD973GP9qP4mVtTNpWfl6+oax114TLTPAXzRnjgy9/DL8\nv/8nFsrOnXDqqVk+TGKLc+qWZJDsmltukZzQoVKagB1+P1/ato2e1B1nHR10/+c/XL50KftyZhCL\nx/FMmkFRW5Byl1zIZtVMnjWPrQe38uS2J5lZPJO/HDjAJdu20eyYzb3v/A+f3goTmnLZsvMJVu1Z\nxadfXcs5pRW4TSbweJga7CPf6STvZLhw0yZqi4upQyaAVJbm5rIqpYjTQKVg8PkYv38/FqNxVGMF\nOofB/PmUVTcyXi0g9zAtBYD5TiefKijgm2VlqIpCkb1oeBvohklih7PTfOTjNqqiyK7hw4gpmQ0G\nvl9RwZn5+WlZTx+Wj5xSaGiQSb+lRVI4QXz269bBK4828PC31/H1Kaux/PAmAp//sjT4/tnP4N57\nB1WA6+2VXbogpRL++lepDup0ygr8tddkQR2PQ/07TdDQQNfnv4L1/fV88gRtQt23D2X9Oq49dSML\nF6Rv150xA8qsrUwuS+5C+0ReA3V/fpmWZRdQdvFS1t61ih07tHhCOAwPPyz9d++9VzSAxpw5khjz\n8MNwz9UN/P53cf7rv7TOTpnIoBT8ra103nyz2K6BQNYgdDwep6m7iQebmjD39fFqZ2fywb/+lcfP\nOguvJUjAVMC9dXVsKiqmN2ccfbYK1nd3E4hG8bl83P7y7Zwy/hSWVJ7KS+3tfK+iAo81jxdjk9lk\nPAaD2c6y6gP87dmfcVaHhSsXLep/m6kFeZi8PtYE/HwqFOKhOXNo0ILMqZzodrO5p4euSIT2cJje\nWIyy1OdYLEyIxbCM8A5fnQ+A14shv4ATQkUYnYfvDlleUMDni4pQtFW21+4d/l6JYVBkL0JBSbMU\njhSKomAxGA5pKSRQFYXLS0pYeASt4I/c0mnRIgnQLF4s1kJ+vjRymVraxaf+cx1XL5xIYVzlsc99\nl53Ns7j0TCRv+29/k2DE5z8PZ59NS7vKLbeIO+eGGyRTaPZs+jNmCgtl9b92rVQJ7XtwNSfOXMSB\nvZVMLIhga2+EXrP4jKqqROG43Wm7vww7qrlp/x3Ea+YA1wJwWvPD1F9wPpdd40aJzqX1mfuwnNmE\nxVICT/1bXD4rVkjK0Q03yGuXlDBxoqRwrpi3j/zbrpITcNVVmVe/8bgohcsvl+BqfT1s384vn3mG\n9iVLuPvMM1E2b5Y803nzBr3Gq/te5eZ1jzIpbwVX/uUxnjjnHM5cvhwef5ymbdt46StfYVrPauZ6\nZ1CjqvwzEqG7aj7ECtl34ADBWIzTK07hlMpTWDRuEeu6uykwmZjrdHLDpGP5+gu/Zm/l8cyZMYNT\nfvo0s0qLcJ93VtqGq6kTJrF872ZunDgRx7//zTfz8tjX0cFlxekBSLuqsiwvjzv37uW03Fym2Gz9\n7qUECw0G/IewinTGCAsWSODtQ8RVjis9jt5w76Gf+AExqSYKcwrTAs1HkrPz87MGuUeCsaYUzgB+\nCajAfwODkoiXLI7B2rdZcvLxXHOtSlGR+Oa8Kx+Dby6SRgLAaW2iA/LyQFVtzD39y5QsXQq/+x1d\nT77Az7uvYtkXq6ishPtub2PigTf52lfj8LT2RoWFLFkyn1/+Eo6dFuG6KS/xi/j3Wfewwn2LtdKh\nO3eKv+iiiySY+8ADMpGfeqq4aVauxHv7N+APf5DnhkKU9Oyi5GfXggIYjeSfv4hljQ/D0zPg73+X\n9mkgyuHcc6Xy3PXXYzBoH+2OP0q50+3bpTnCvHnil5o7t38DxI76eswOB2G1E0dfjJLp03n/nnvY\ndcEFOCoqeL2ri4WzZtFWXs6bf/4z8ZSNExGnnd90vog/dxFLHrufuSu+wm8OHKDhuuso6evjtouW\n44s3sq91K+dPXsYZhVqu6oEO2PA+8dxc/l5ZyT/UIm7XCh+91N7OEm0TzsTmIGw9wHFXLZccu9nz\nmfXGG/CDM9O+Z+u06dz9n2cks6iuji9Pnsxd4fAgSwHg0uJiHm5u5tcNDVyQwXQ6Njf38GuG6Iwu\nCxbAo4/CMIK2A/mE9yhu2tEocZYcFUsB4JLiw8u8OloMc5fJUUUFngFOA34C/BfwMpDa++u2X5Xa\nUP65Ek9zNc5lC7jsq0by+xrgv/9bVu2ab81mEy/Jrl3ianrkEZh2gpv9007h4SccfLPv5xx3no9i\nb5zFz91IZaVCsadPckq7uuCZZ/DltOGaW8UV+3+EuayQ4+88B7td4biZYZR/PCn+q+99jzWvv874\nCROkEUI8LuN9fbK5ZPZs+YE/+aT4uD73uaQ5AhJkqK0Vk+XUU6XQUIJJk6Sv5pQpYrps2gQvvgjX\nXismU2urpDwdPCjFiXbu5AWXi183NvJvl43fdmzm5d3PsGD6SdysWLnitNM40e3m901NfMJu5+a8\nPOjpoS8QoCsYpCsYpPadVzCFTXzRPQnna38jfsXlqGWT2W02c9esPN7va+NkUwfj3D5OHn9yMtPE\nbofGRpSODqb//e/Yuru5V1GozM3l8YMHuWr/fiy/+Y20Yfv61+Wzgbi4JkyAKVNYs2YN48ePl3G3\nW/x3mzdDfT2+hQvJKytjtsMxyBJQFIXZTifFZjPHO504Bq6yPB7ZKp7V1zY80uQco3xkZfR4ZLEw\nbdqobcAbSCY5XRYXk/MnHzXFMFwO9X3ffvvtALcfzmuNJUthHrALqNX+fww4F9iW+iRFNcgEeP/9\nnPXqDdA6TXI/P/1pBm4PPOEEuYGUhL7jDplArr17Ed6cYvjhDyEaxfn1y3AOrC3d1YXpjjtY3vS8\nrF6+8Q1sqsL55wPds+Dn90hbMpuNNWvWsHjxYlAUnp0/n/rUib2xUcqKbt4sq97Jk2UsldTo9MDH\nvvQlSV/dskXM6i99KRkIWbgQgI3NG+mdXYB5XwttrzzPt176N+urFM79xq/5V2sbpx14n9i6V9ix\nwIJBgQPBXFYc3MlicyeeiYG0t6uN1PCLt/JwvvB73rjyu9z3zq8p987jQZcdnxLnpZMvwmPOYNqf\nfLLcAL76VZa9/DKe557jhzU1zGlrw7lnj3TbOfHEdHdVXp4oOEieR5BzdeedYgbu2IFSUcEZuenV\nLAdySrbHP0SmSSbS5ByjfGRlVJT062EMkEnO40qPGx1hsnAkv++xpBTKgLqU/+uB+YOede214i65\n+moJxHZ1SapQ1jQc4fjjZY5R1US5iCpJM2pvh2OOGXyAyyVKY/160SypqxanU15sxoxBh7mNRoKZ\n/NfLl4vcww14zpghKaTBoLROO+aYQSuoUqudHkMcZbqbRWqIWNFZXHjieRSWTeaL5XH+01TA71eu\nwWuXOhUrrAqdsTjlRgeQvtL5+pLrcK6YAe+9x4K5c+jdU4o/7OeqnBy+OuU8rFl6IKdhNMLSpRy/\nZAk/27BBMn+uumr4Kz+zGb7/ffkO9HRSHZ0RYSwphcOLBCai8gbDEFt5MzNowVhcnFZbZBBWa3od\n6VRmZs4LPiFb8akPU2TtEArv3IKT0wdSXKqKonB26VTeKZoyvJ2mxx2HAVg2cdnhHzMQRWF8amnV\nD4KqikbX0dEZEcaG005YANyGBJsBbgBipAebNwJHbpeGjo6OzseDTcCsQz5rjGEEdgPjATOiADL4\ndXR0dHR0Pi6cCWxHAs43jLIsOjo6Ojo6Ojo6Ojo66TwI7Ae2pIzNAtYCG4B3gNTo57HAm8B7wGbE\n/QUwV3uNncCvRlFGE/CwJttW4PspxxxNGbPJORM5X5uBfwKpW0Nv0GSpRvasjIScw5FxGfCuNv4u\nkJrtMFZkTFAB9ADfHSEZP4icY+XaySbjaF075cBq4H3k3HxLG88DXgB2AM8DqWl5o3HtfGw4CZhN\n+o/meeB07f6ZyBcGEgvZRDLnJ5dkTam3kf0XAP8hGUQfaRkvBB7V7tuAGmTCONoyZpPzHW0c4FLg\nDu3+NCSWZEJiS7tIJkOM9LnMJuMsIJGyNh1JnU4wVmRM8ATwN9KVwlj6vsfStZNNxtG6dopJBoYd\niFv9GOBu4Dpt/Hpkoy8cwWvnI1cQb4R4FWgfMBYDEvmmHqBBu38asopI/MDateeWIKuNt7XxR4Dz\nRknGGGBHdo3bgRDQNQIyZpNzsjYO8CLwGe3+ucgFGEY2Me5C9qqMxrnMJuNGoFm7vxWZKExjTEa0\n996jyZhgrH3fY+naySbjaF07zchvDcTa24bs5ToHsVzQ/ibe84hdO7pSOHyuBu4B9ml/E4Hwycge\ni2eBdcD3tPEy0leRDdrYSMp4ozb+BOAHmpAfzD1AxyjJCGISJzZNfBYxlQFKB8hTr8kzcHwk5Mwm\nYyqfQb7zMKNzLrPJ6EBWk7cNeP5Y+76rGDvXTjYZx8K1Mx6xbN4CvIjrC+1vonDZEbt2dKVw+HwD\nmXQrgGsQvyTIKnEhYmYuBM4HlnC4m/GOroz/o43PByLIqqESKdlaOQryJbgMkfVdZAI7vI4/I8uh\nZJyOmO5fG2G5Uskm423AL5DJbCzsRcomp5Gxc+1kk3G0rx0H8L/At4GB/SbjHIVzNZZ2NI91LiYZ\n7HkCqeIKUprjFaBN+/8/wBzgz0BqI2AfSXfOSMt4IbIaiwIHgdeR4NNroyAjiH80EfuoAs7W7jeQ\nviL3IaucBkZezmwyJt7/SeBLiI+ZMSLjWdr9eYgVczfiRowBAUTmsfR9j6VrJ9u5HM1rx4QohD8B\nT2lj+5F4QzOiqA5o42Pp2vk/y3jSA1FbgUQ9iaVIYAokOLYO8S0bkcyARB3ot5CVhsLRCeodrozX\nkbRs7IipnCjcdLRlzCRnofbXgPg4v6z9nwiWmZHV2G6SK92RPpfZZPQgwdFMftmxImMqtwLfGUEZ\nYXjncqxcO9lkHK1rR9Hk+MWA8buRADNIJtTAQPNoXDsfCx4FGhETsg7JRjgRMS03Iqlrs1OefxGS\nNraF5JcEyVSwXUgp8NGS0Q48rsn4PplTFI+GjJnkvAyxZrZrt7sGPP9GTZZqkiu3oy3ncGS8CQn8\nbUi5JQpbjRUZUxmoFMba9z0Wrp2hZByta2chYuFtJPk7OwNJSX2RzCmpo3Ht6Ojo6Ojo6Ojo6Ojo\n6Ojo6Ojo6Ojo6Ojo6Ojo6Ojo6Ojo6Ojo6Ojo6Ojo6OiMRRSkGFnqpprPAs+MwHt/Ftn0t+oovPZU\nJM98HTCwQ7iOjo6OzhBMRyZnC1LvZQcfvMbMcMq3PAt88gO+z1CoyI7THwzjGIWxUatIR0dHZ0zw\nU2QH7t3ALUhBv7eA9UjJYJDyBK8gq+91wAna+GLE2liJ7EodyAUkyzQnds7eghQZq9beM5XF2vs8\nrT1+P8kJ+zTgDe39H0d2vYJU0fyJNn4BUlmznqQV8h3t/bcgBc4Sn2c7Ugb5PWCR9n4PaeN/Bk5F\nau3sINk4aZ4mw3qkHk+VNv5lpMbRM9rzf5rymc7QZNuI7IpFk/1BBp9nHR0dnVEnB5kQNyOlBi7S\nxj3IBJmD1MmxaOOTSdZ3WoyUnxiX4XVLgb1APrKCX0WyTPJqpAjbQBYjReTGI/VwnkeKzBUAL2ty\ngNShuVm7X4NU0UyQWmJirva5bMhE/B7SRGU8Umwt0QRlPFKKezqihN4lWdjwHOAf2n2n9llAlMYT\n2v0vI3VvnMh5qkVKJRciJdUT5ydRHiHbedb5mKJXSdUZS/iRTmE9wOeAT5GcZC1IFchm4NdI+8Qo\nohgSvI1M/gM5Hpn8W7X//4KsyFdq/2dz2byNTKog9XIWAn1I8bE3tHFzyn00+VNJvPZCZAUf0P5/\nEun09U9N5rdTjqlB6uyg/U1YGu8hSgNkAn8EmISUT069lleRLLO8VTsmD7F8EuenQ/t7GpnPcyZr\nS+djgK4UdMYaMe2mAJ9G+sqmchvilvkSslLuS3msN8trxkmf+BXS69Bnq0mfOp44RkGqeV6Y5ZgP\nIsPAY4Ip92Mka/vHSF6zdyKT//nI6n9NluOj2jFD1d3PdJ51PqboTXZ0xirPkewNAcmKry6S7TAv\nJulCGYp3kJLiCffRFxAX0KGYR9J99DkkZrEWqUY7UXuOnXRrJRuvIuW2E+6j87SxDxpYdiGVPkEq\n5A5FHJF7EUlLI0/7m+0863xM0ZWCzlgkjqyETYgf/j3gdu2x3wKXIMHSKYirKfW4TDQhmUCrtePe\nBf51GDK8g7iqtiL9jv8BtCB++0eRvgpvaHIM9TogpY//iLiJ1gJ/0I7PJPdQ/yfu3w38GAkOqynj\n2bpxtQBfRdxWG0k2o892nnV0dHR0UljMoRWHjs7/OXRLQUcnM0el/62Ojo6Ojo6Ojo6Ojo6Ojo6O\njo6Ojo6Ojo6Ojo6Ojo6Ojo6Ojo6Ojo6Ojo6Ozv9x/j8ucMIZYYC5nQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10bd7a850>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for composer in sample_list:\n",
" one_composer = df[df.composerName == composer]\n",
" aggregate = one_composer.groupby(one_composer['Date'].map(lambda x:x.year)).count()\n",
" composer_counts = pd.Series(aggregate['id'], index=aggregate.index, name=composer)\n",
" composer_counts.plot(legend=True, label=composer, alpha=0.7)\n",
"\n",
"plt.ylabel('Number of works performed that year')\n",
"plt.xlabel('Year of performance')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### As a proportion of all works played that year"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x109fcbad0>"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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W/OfNm6cdSwghrr32WruMn//+97+FwWDQxN+RUaNGiddff10IoYh/RESE3bYp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4AAAg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\n3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4/Vhr+tk7VrwXbOcbp8QJRClpppMZgZ0umSBzqYTAn9cAvthXQGw0jDTga7h8GSaGyd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\nOV32lnWa5QAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10ad0f050>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for name in sample_list:\n",
" one_composer = df[df.composerName == name]\n",
" aggregate = one_composer.groupby(one_composer['Date'].map(lambda x:x.year)).count()\n",
" composer_counts = pd.Series(aggregate['id'], index=aggregate.index, name=name)\n",
" composer_counts_prop = composer_counts.divide(yearly_counts) * 100\n",
" composer_counts_prop.plot(legend=True, label=name)\n",
"\n",
"plt.ylabel('% of works performed that year')\n",
"plt.xlabel('Year of performance')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Discovery of new composers"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Rubinstein, Anton 162\n",
"Ives, Charles 157\n",
"Goldmark, Karl 156\n",
"Dukas, Paul 154\n",
"Respighi, Ottorino 145\n",
"Massenet, Jules 142\n",
"Barber, Samuel 142\n",
"Hadley, Henry Kimball 141\n",
"Borodin, Alexander 137\n",
"Chabrier, Emmanuel 130\n",
"dtype: int64"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.composerName.value_counts()[50:60]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Who on earth is `Hadley, Henry Kimball`"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def composer_counts_by_name(name):\n",
" composer = df[df.composerName == name]\n",
" aggregate = composer.groupby(composer['Date'].map(lambda x:x.year)).count()\n",
" annual_composer_counts = pd.Series(aggregate['id'], index=aggregate.index, name=name)\n",
" return annual_composer_counts\n",
" \n",
"def plot_composer_by_name(name):\n",
" composer_counts_by_name(name).plot(legend=True, label=name)"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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KSgq9evVi7Nix3HLLLdxxxx0AXHbZZUyfPp1zzz2XCy+8kMmTJ7tdSLzrrru48sorGTJk\nCBdccAFTp051GHvo0CHGjBnjse32144ePdoWlrDP/c7KymL58uU899xzdOrUiWeffZbly5fbdkl3\nngfg/vvvZ8mSJWRmZvLAAw+QmZnp1RzByj1vi2A+m7RXDQAvffUSpeWlzL1aLXC4ZsE13DnsTq7p\nd02YLRPskfaqnjN06FDWrFlDRkZG+4PPYkLdXlUIAa5i2OJhC5HMpk2bwm1CxCIhEZ3jKoYti46C\nEJ2IYOsc+059IDFsQYhmRLB1jnNIRJo/CUL04o9gP4i6e3op8A6QEBCLBAckS0QQBA1fBbsr8Avg\nfGAwEAPMCJRRQgv2rVVBYtiCEM34kyUSCyQDzdaf3tewCu0ipemRQUZGRkS26xTChy9pjb4K9hHg\nOeAgYAY+Af7r41yCGxRFcV2aLjFs3WHfq1kQgoWvgp0BXAPkA6eAd4FbgPn2g4qLi233i4qKKCoq\n8vHpohNzkxmApLgk2zHxsAXh7KKkpISSkhKPxvr6He5G4ErgTuvjW4GRwD12Y6TS0U+OVB/hwlcu\n5OivWrqFPbHuCeoa63hifOt+CoIgRD5tVTr6uuh4AFWgk6wTXwZs93EuwQ3O4RAQD1sQohlfBftL\nYAnwLbDVeuyfAbFIsOFKsKU0XRCiF3+yRIqtNyFIOJelgxTOCEI0I5WOOsa5LB2kcEYQohkRbB1j\nMpvITGwdwxYPWxCiExFsHSMxbEEQ7BHB1jFV5irXMWwpTReEqEQEW8eYzrjwsCWGLQhRiwi2jnGX\nhy0xbEGITkSwdYxzpz6QwhlBiGZEsHWMc6c+sDZ/khi2IEQlItg6RkrTBUGwRwRbpzRZmqhpqKFj\nYkeH43FGaa8qCNGKCLZOOXnmJGkJaRgNjm+ReNiCEL2IYOsUV/FrkMIZQYhmRLB1iqv4NUjhjCBE\nMyLYOsVVpz6QwhlBiGZEsHWKq059IIUzghDNiGDrFFed+kBi2IIQzfgq2P2ATXa3U8B9gTJKkBi2\nIAit8XXHmV3AUOt9I3AE+HdALBIAVbB7dOzR6rjEsAUheglESOQyYC9wKABzCVbcxbDjYtTCGdmR\nXhCij0AI9gzgnQDMI9hhMpvYtTmTRqfoh9FgJNYYS5OlKTyGCYIQNvzZhBcgHpgMPOzqZHFxse1+\nUVERRUVFfj5d9GAym3jl75ncNgIGDHA8p5Wnx8XEhcc4QRACRklJCSUlJR6NNfj5XNcC/wdMcHFO\nka/tvjPwxYEcfO5dVi8YxIgRjufSn0pn/wP7SU9MD49xgiAEDYPBAG602d+QyE3AAj/nEFxgMpsw\nmzI5dar1OWmxKgjRiT+CnYK64PhegGwRrCiKgslswlKb4VKwpQGUIEQn/sSwa4FOgTJEaKG2sZZY\nYyyNTYmuPWxJ7ROEqEQqHXVIlbmKjvFqSp87D1vK0wUh+hDB1iEms4m0OFWwq6tbn5fydEGITkSw\ndYjJbCI1ph0PWxYdBSHqEMHWIVVnqkg2qq1VJYYtCIKGCLYOMZlNJCMxbEEQHBHB1iEms4kEJZMO\nHVzHsCWtTxCiExFsHWIym4hvzqRLFzchESmcEYSoRARbh1SZq4htzKBrV/chEfGwBSH6EMHWIaYz\nJmIa2vCwrc2fBEGILkSwdYjJbMJYrwq2xLAFQdAQwdYhVeYqMGfQqRM0NUGDkzZLDFsQohMRbB1i\nMpuw1GWSkgJpaa3DIvFG8bAFIRoRwdYhJrOJ5hpVsDt2bC3YUpouCNGJCLbOaGxupK6xjobTaW4F\nWwpnBCE6EcHWGSfPnCQ9MZ26WoNNsJ0XHqU0XRCiE38EOx1YAuwAtgMjA2JRlGMym8hMyqS2Fvcx\nbGn+JAhRiT8bGPwNWAncYJ0nJSAWRTnOgi0xbEEQNHz1sDsCY4HXrI+bABclHoK3mMwmMpIy2hRs\niWELQnTiq2AXAJXA68C3wCtAcqCMimaqzlS18rCdY9hSOCMI0Ymvgh0LDANesv6sBX4bKKOiGZPZ\nRGaiKtipqW5CIkYpnBGEaMTXGPZh6+0r6+MluBDs4uJi2/2ioiKKiop8fLroQYth19S0LDru2eM4\nRjxsQTh7KCkpoaSkxKOxvgr2D8Ah4BxgN3AZsM15kL1gC55RZa6ie4cCAOLj3S86SgxbEM4OnJ3Z\n2bNnux3rT5bIL4D5QDywF7jDj7kEK6YzJvp3PJ8Ua86Nu0VH8bAFIfrwR7C3ABcGyhBBxWQ2kUSm\ng2C7KpwRD1sQog+pdNQZ2vZgmmC7K5wRD1sQog8RbJ1RZa4ivimjzZCIFM4IQnQigq0zTGYTsU2Z\n7cawJa1PEKIPEWwdoSgKVWeqMNa3eNgdOkBdHTQ3t4yT5k+CEJ2IYOuImoYaEmISaDDH2wTbaFTz\nsU+fbhknpemCEJ2IYOsI58ZPGs5hEYlhC0J0IoKtIzwVbIlhC0J0IoKtI9oSbPtcbEnrE4ToRARb\nR1SdqXJorarRKiQihTOCEJWIYOsIrVNfTY3aqU/DuXhGPGxBiE5EsHWEN4uOEsMWhOhDBFtHVJk9\nC4mIhy0I0YkIto7wdNFRYtiCEJ2IYOsI0xnXgi0xbEEQQARbV0jhjCAIbSGCrSOqzFVkJHqY1tfc\niKIooTdSEISw4c8GBvuBaqAZaASGB8KgaMbTGHaMMQaDwUCz0kyswZ+3UBCESMKf/3YFKAJMgTFF\n8DQkAi3l6bFGEWxBiBb8DYkYAmKFQENzA/XN9aTGp7a76AjSYlUQohF/BFsBVgFfA3cFxpzoRYtf\nGwwGzz1sSe0ThKjCH8G+CDgfuAq4BxgbEIvOUu5ecTdby7e6PV9ZV0lWchZNTdDUBAkJLee0GLb9\nGmMoU/te/vplFpYtDMlzCYLgHn8CoMesPyuBf6MuOq63H1BcXGy7X1RURFFRkR9PF9msO7COwpxC\nzs091+X5ncd30r9Tf5t3bbALNsXFQXy8uvOM5nmHsjz9qyNfkZeax4zCGSF5PkGIJkpKSigpKfFo\nrK+CnQzEAKeBFOAKYLbzIHvBjnbKa8spqyhze760vJTC7EJqax0bP2locWxNsEPpYZfXlpMclxyS\n5xKEaMPZmZ09u5WU2vA1JJKL6k1vBr4AlqPGswUXNFmaOFF3gtKKUrdjyirLKMwppKbGMX6tEc4W\nqxW1FZyqP9X+QEEQgoqvgr0POM96KwSeDJhFZyEn6k4QHxNPWUWZ22KXsgpVsJ0XHDXC2QBKBFsQ\n9IFUOoaAitoK+mT2IT4mnqOnj7Y6b240c/DUQc7JOqdNwXZoABWiGLaiKKpgnxHBFoRwI4IdAipq\nK8hJyaEwp9BlWGTH8R30zexLXEycW8EOVwOo2sZazE1mquur2x8sCEJQEcEOAZpgD84Z7HLhUQuH\nAB6HREJVOFNRW4HRYJSQiCDoABHsEFBeW27zsF0Jdml5KYNzBgOeC3aoCmcqaivIT8+XkIgg6AAR\n7BBgHxJx6WFXeuZhO8ewQ+Fhl9eU0zezL6fqT0l3QEEIMyLYIaCitoLclFwGZQ9ix/EdNFuaHc77\nEhLRmj8Fm4raCrqldSPWGIu5yRz05xMEwT0i2CFA87A7JHQgJyWH76u+t507eeYkJ8+cpGd6T8C9\nYDsvOoYyhp2TkkNaQposPApCmBHBDgGa6AGtwiJlFWUMyh6E0aC+FXqMYeek5NAxoaPEsQUhzIhg\nhwB7wR6cM9ghtc8+HALeCXZIPOw6q2AndpRMEUEIMyLYIaA9D1vLEAEvFh2NoSmcEQ9bEPSDCHaQ\nqW2opVlpJjVe7ejkLNilFaWtPOy2mj9phMzDrm3xsCWGLQjhRQQ7yGgZIgZrv9R+Wf3Yd3If9U31\nKIric0gkLiY0zZ/Ka8rJTcklLSFNQiKCEGZkQ8AgYx8OAUiITaBXRi92Ht9JTkoOMYYYclNzbec9\n7dYXCg+72dJM1ZkqspKzJCQiCDpABDvIOAs2tIRFslOyHbxrcO9hJyVBczPU16u70YQihn3CfIL0\nxHRijbGqYIuHLQhhRUIiQcalYGergu0cDgH3gm0wOC48hsLDtre9Y6J42IIQbkSwg4wrwR6cO5iy\nSu8EGxwXHkNRmm5vuxTOCEL48VewY4BNwIcBsOWsRGv8ZE9hTiGl5aWUVpQ6pPRB24JtH8cOReGM\ng4ctIRFBCDv+Cvb9wHZAugK5QcsSsacgvYDKukq1yjFnkMO59gRbC4mEojRdyxABpHBGEHSAP4Ld\nDZgI/AswtDM2anEVEokxxjCg0wBbjw6N5mZobITERNdztfKwg7zo2MrDlhi2IIQVfwT7eeAhwBIg\nW85KXAk2qHFsV+GQ5GR1gdEVaWlw4AAcPQq1p+M4WdOA2csGehbF4rHQOy86RnMMu6kp3BYI0YDJ\n1PZ5X9P6rgYqUOPXRe4GFRcX2+47b+UeLbgT7MsKLsNkdnx32gqHAJx7Ljz9tHqr6xuPuUsj096B\nD71YQXhu43Mcqj7EnKvmtG97neOiY7SGREpLYcoU2LkTYmLCbY1wtlFSUkJJSQkA//5322N9FezR\nwDWoIZFEIA2YB9xmP8hesKMRi2LhhPkEnZI7tTp3y7m3tDrWnmA/9JB6A1i8LZ5XNjRweK53Ns3b\nOo/eGb09GishEZVPPoE9e6CkBMaPD7c1wtmGvTO7aRNs3Trb7VhfQyK/A7oDBcAMYA1OYi3AiboT\npCWkERcT59H49gTbnjhjHDHxjVRUeG5PWUUZ2yq2UVHr2UX2gp0cl0yjpTEkDaf0xurVMHo0LFwY\nbkuEs51T7fhEgcrDliwRF7jKEGkLbwQ7PiYeQ2wDlZXg6c5dC8sWMrnfZJ8E22AwRGVYpKEBNmyA\nl16C995THwtCsAiFYK9FDY8ITriLX7vDXac+V8TFxNGsNJKcDCdPtj9eURQWli3k/hH3eyTYdY11\nNFma6BDfwXYsGotnvvwS+vSBIUNg0CBYtSrcFglnM9Xt/HtJpWMQ8UWwvfGwG5obyMmB8vL2x399\n9GuMBiOX5l9KfXM95sa200s02w12KSvRGMdes6Ylbj1jBixYEF57hLObUIVEBBd4K9juOvW5Is6o\ntlfNzcWjOPaCsgXcVHgTBoOBnJScdr1sV7ZHY/HMmjUwbpx6/4YbYMUKqKsLr03C2YmiiGCHlVB5\n2O0JtkWxsGjbImYUzgDwXbCjzMOuq4Ovv4axY9XHOTkwYgQsXx5eu4SzE7MZYtvJ2xPBDiLBFGyt\n+ZMngr3+wHqyk7MZkD0A8EOwo6x4ZsMGOO88x3WFm26SsIgQHKqr1WrmthDBDiKuGj+1hbcedmNz\no0eCvbBsoc27Bi8EO9nR9rT46MoSWb26dd719derYRJPFnoFwRtOnVKrmdtCBDuIBDOtT2v+1J5g\nNzY3smTHEkfBTvYjhh1FIRH7+LVGx47qsfffD49NwtnLqVPiYYeVYMewGy3te9j//f6/9M3sS356\nvu1Ybmpuu4JdXlvusHUZRFeL1ZMnYccOGDmy9TkJiwjBQAQ7zIQihp2b23Za38JtjuEQsIZE6iSG\n3RZr18KoUep2bM5cfTV88YVn2TmC4Cki2GHE3GimvrneoX1qewQ6hm1uNLNs1zKmDZrmcDwnJYfy\nmraTt10JdjRVOq5e3TocopGcDJMmwZIlobVJOLuprpYYdtiorKtsVXjSHoFO6/toz0cM6zyMvNQ8\nh+OS1tc+9gUzrpCwiBBoxMMOI+U13mWIgPeLjo2WRtLT1YIbVz0utGIZZ9oTbIti4Xjd8VZdBqOl\ncKa8HI4cgWHD3I+54grYvh0OHQqdXcLZjQh2GPE2QwR887CNRsjOhspKx/PV9dWs2ruKKQOmtLo2\nOzmbyrpKLIrrvSeqzFV0iO9AfEy8w/Fo8bDXrIFLLmm793V8vNoje9Gi0NklnN2IYIcRbxccwbvm\nTzHGGBRFodnS7DIssmzXMi7ueTGZSZmtrk2ITSAlLoWTZ1wnE7uzPVoWHV2l87nippuk5aoQOCSG\nHUZ8FWxPPWxwTO1zzhRxFw7RaCu1z1VKH0TPoqOrghlXXHKJGjr57rvg2ySc/YiHHUZCIdhxMXE0\nNrduAHWi7gSfHfyMa/q573rbVhzbne0d4jtQ01DjNpRyNrBvn9pDZODA9sfGxMC0abL4KAQGEeww\nYr8foqe2qEHsAAAevklEQVR4060P3GeKLN2xlAl9JpAa7z6+0lZqn6uydFDDMClxKZyuP+25kRGG\nFg7xNLlHa7nq6SYSguCOYAp2IvAFsBkoA4p9nOesxVsPu7kZ6ushKcnz53BXnr6gbAEzBs1wfyFt\nl6e3ZfvZHsf2NH6tMXIknDkDW7cGzyYhOgimYJ8BLgXOs94mACN8nOuspLym3Ksskbo6tSDDi7Rt\nl+XpR08fZcsPW7iq71VtXutLSATO7ji2orSff+2MwSAbGwiBIdiLjlob93ggDjh7A5s+EMyydA1X\nLVYXb1vMtf2vJTE2sc1rfRXsszm1b8cOSEyEggLvrpsxQ80WkbCI4A+eeNjttMtuEyPwLdAb+Dvw\nlR9znVVYFAuVdZVkp2R7fI0vgu2qPH1h2UJmF81u83nWrIFDVblsqVrDhx9C585wwQUtY9oLibjy\nsDce2siJuhPevQCdEGOMYVzBOFavTvTKu9Y491z129Hnn6v9R/TOsdPHyEzKJCHWRaMUISw0Nqq3\n5OS2x/kj2BbUcEhH4N/AIGCb/YDi4mLb/aKiIoqKivx4usjhTNMZrut/XavCk7YoL4dOndofZ48W\nw9YaQJ08c5KyijLG93KvOrNmqRvJpg7KYVfXCl78CDZvhh9+aBlz4NQBuqV1c3m9Kw+7vqme8fPG\nc1mvy7x7ATrhcPVhBucMpnrNm0y70YuYlBUtLLJwYWQI9pTFU5g6YCq/Hv3rcJsiACUlJaxcWUJs\nLMx272sFlD8Cv3I6pgie849/KModd3h3zfn/OF/56shXSl2dosTHK8r6/Z8pF/7zQrfjm5sVpVs3\nRSkrU5QdlTuUc144R2lsVJTYWPWcoihKlblKSXkiRWm2NLuc465ldykvf/Wyw7HNxzYrA18c6J3x\nOqK2oVY596VzleSLX1KOHfNtjt27FSUvT1GamgJrW6D53vS9QjHKhLcnhNsUwY69exUlP1+9D7gN\nrvkaw+4EpFvvJwGXAzt8nEsAysqgsNC7a7S0vqQktQ3o1wfLGJwz2O34DRsgIwMGDWpJ64uNVeNm\nJ6zRjG0V2xiUMwijwfWfhque2GUVZRTmeGm8jkiOS6Z44FLqR8/iQNMXPs3Rty907QolJYG1LdAs\n2raIGwfeyIaDG2hodtGARggLnsSvwXfB7gysAbYAXwKrgJU+ziWgCvZg91rrEq1wBtQNYr8+VNqm\ncC5YoH51B0hPTKe2sZb6pnqHGHhpRWmbou9q15nSilIKsyNXsAG++6IPExr+xY3v3khlbWX7F7hA\nC4vomQVlC7jnwnvom9WXr47IspNeCLZglwLDgCHAYOBxH+cRULMLSkt997BBFextle493aYmtX+z\nJthGg9HWBMq+UrI9b9mdhz0418tPG52xejXcOfYabj33Vm5aehPNlmav55g+Hd57z3XnRD2wvXI7\nx+uOM6bHGMblj2P1vtXhNkmwEmzBFgJIRQVYLJCX1/5Ye7QWqwDZOQp7T7sXztWroVcv9aahpfbZ\ne9jtCraLwplID4k0NMDGjVBUBI9e+igGg4E/fvpHr+fp3l0taV+1KvA2BoKFZQuZPmg6McYYxvca\nz5p9a8JtkmDFkw14QQRbF2jhEG+KZsDRw07r/AMWC26LdezDIRpaAyhNsBVFaTck4lw4U11fTWVd\nJQXpXiYv64jPP4f+/SE9XU3xe2fKO8wvnc8HOz/wei69bmygKIpDQ7AxPcbw9dGvqWusa+dKIRRU\nV4uHHTH4Eg4Bxxi2JbuMbKXQ5Q43Z87ABx+ojYrscfawf6j5AaPB2GbBj3Na37aKbQzMHkiMsY3m\n0TrHuboxOyWbd298l7s+vIvvTnjXiu+GG2DFCrVyVU98e+xbFEXhgi5qwn1qfCpDOw9lw8ENYbZM\nAAmJRBS+ZIiAo4dt7lBGB7Nrz/ijj+C886BLF8fjWj8RTbC10EZb25o5F85EejgEXO/fOLzrcB67\n9DGmLJ5CbUOtx3Pl5MCIEbB8eYCN9JMFZQuYUTjD4b2VOLZ+EMGOIHzJEIGWwhmAqrgy4qpcC+fC\nhepXdWfsPezy8vYzRED1sO1j2JGeIVJbC5s2wZgxrc/99Pyfcn7n8/nZ8p+heFF3rrewiEWxsGjb\nImYUOsbEJI6tH0SwIwSLBbZtU3OjvUVr/gRwzFKK5YfWwllTAx9/DFOntr4+JyWH8tryVh52W6Ql\npDmERCLdw/7sMzj/fNclwQaDgZcmvURZRRkvffWSx3Ned50aZjnpekOfkLPh4AbSE9NbvU8juo5g\n5/GdbnceEkKHJ42fQAQ77Bw4oC52pae3P9YZzcO2KBYO1m2nbn9r1f/gA9V7zMpqfb3mYWtpfZ6I\nrxYS0TzOSE/pcxUOsSc5Lpml05by6LpH+d+h/3k0Z3q6Ouf77wfISD9xt/tQQmwCo7qPYu3+tWGw\nSrBHPOwIwddwCLQ0f9p/cj+ZSZmcONJa9d2FQ8AxS6S8wsL2yu3tCnZ8TDyxxljMTWYqaitosjTR\nObWzby9AB3jSTrV3Zm9eveZVpi2Z5nbTB2f0EhZpsjSxZPsSpg+a7vK8xLH1gQh2hODrgiO0LDqW\nlpcyOK+QU6fUjl8aJhOsWwfXXuv6es3D7tABGpL3kZmURVpC+9/LtEyR0vLSdhcp9YzJBLt3w/Dh\n7Y+9+pyrueO8O5ixdAZNlqb2x18NX3zRenPkULP6+9UUZBTQO7O3y/PjCsZJHFsHiGBHCL6m9IE1\nrc/SqIYlcgrJyoLjx1vOL10KV1wBHTq4vj47OdvaE1shrU8ZfdI8M0QrnlGfN3LDIWvXwkUXQbyH\nTRVnXTKL+Jh4fr/69+2OTU6GSZPU6tJwsnDbwjZ3HxrWeRhHTh/hh5of3I4Rgo8UzkQI/oZEGpob\nKKtUhdN5q7C2wiEASXFJJMQkUF1fTXz3UrrHe2aIVjwT6QuO7cWvndGKahZtW8R7O95rd3y4d6I5\n03SGD3Z+wPRC1+EQUF/TJT0v4dN9n4bQMsEZKZyJABob4bvv1Co7X4gzqoUzmnDaC/axY/Dtt3BV\n2zuF2cIiirXwxhNsIZGKtptN6R1vtwMDyErOYsm0Jfx8+c/ZdXxXm2OvvBK2b4dDh/ww0g8++u4j\nhuQNoUuHLm2OG18wXuLYYcRiUbO53H0TtkcEO4zs3g09eni38a498THx1DbWsse0h/6d+jsI9rvv\nwjXXtD+3ltqnFt54HhI5eeYk2yq3RaxgHz2q5p4PGeL9tRd0uYAnxj3B1MVTqWmocTsuPh6mTIFF\ni/ww1A/aC4doSBw7vJw+re42FeNBsbAIdhjxZ8ER1Bh2WUUZPTv2JCkuyUGwFyxoOxyikZOSw+Hq\nw9TE7SWmyjNXv2NCR0orSumY0JGMpAzfX0AY+fRTtdmTJ/8krrhz2J0M7zqcuz68q82imnC1XK1p\nqOHjPR9zw8Ab2h07MHsgdY117KvaFwLLBGc8jV+DCHZY8Sd+DaqH/e2xb21errZV2L59sGePZ1/3\nc1NyWX9gPZnGfKoq2964VyMtIY0NhzZErHcNajjEm/i1MwaDgRcnvsiu47t44csX3I4rKoIjR9TQ\nVyhZtmsZY3qMISvZRQK+EwaDQbzsMOJp/BpEsMOK3x62MY5T9adswql52IsWqU2I4uLanyMnJYc1\n+9fQM7nQ4xS0jgkd+fzw5xEr2IqiLjj6suGuPUlxSSydtpQn1j/htolSTAzceGPoFx8XlC3wKByi\nMb5gPGv2i2CHA09T+sB3we4OfIq66W4ZcJ+P80Q1/qT0AbZNfp0F21UrVXfkpOSw8/hO+mUUUu5Z\nTQgdEztS11gXsSl9+/apPbD79fN/roKMAt649g2mL5nuNjVOK6Lxoh2JX5jMJtYdWMe1/d0k4LtA\n87C96ZkiBIZQCHYj8CDqTukjgXuAAT7OFZXU1qoLX336+D5HXIzqQmvCmZOj9nY+cQLGjvVsDq2V\n6pC8wV552EDEetiadx2oep+r+l7FncPuZMYS10U1I0eC2Qxbtwbm+drjvR3vcXmvyz0qgtIoyCgg\nKTaJHcdla9ZQEwrB/gHYbL1fg7oBb9u5Q4IDO3aoHl5srO9zxMfEkxCTYKtiy8lRxXr6dDB6+M5q\ngj2iwIuQSGJHDBgYkB2Zn9H+xq9d8cglj5AUl8T/++//a3XOYAjt4qO73iHtMa5gHKu/l/S+UOPN\noqMfcmEjHxgK+LbddJTwwQdqG0+Nbdv8C4eAGsMekD2AWKP6NuZY9x3wNBwCqmAnxiZyQa/eVFaq\nOaHOYr95s7or+wCrPqclpNE7szfJcS5a3FlZt04VRj2yahU89VRg5zQajMyfMp/z/3k+I7qNaJWd\ncdNNahe/P/0pcJ69K46dPsa3x75lYt+JXl87vmA8i7cv5hcjfhEEywLD9srtmBvNnN/l/IDNqSgK\nf974Z+6+8G5S41MDNq+neLPo6K9gpwJLgPtRPW0HiouLbfeLioooKiry8+kil9/8BiZMgAxrFtyg\nQWrpsj+M7j6aP1/+Z9vjlBR45x244ALP5zgn6xzeuv4tkhJjSE1VW4JmZjqOef551e6//lV9PLLb\nSOZMmON2TkWBO+5Q88A9/UMMJcXF0LNn4OfNTMpk6bSlTHh7AoU5hfTv1JImee65kJiohqxGjQr8\nc2u8u/1dJp8zmaQ475P7Ly24lLtX3k2zpVmXOwgdOHmAcW+OY2zPsbx747sBm/ejPR/x5pY3+dWo\nXwVsTm/YurWEo0dLsJPLoBAHfAI84Oa8IqjU1SlKYqKi1NeH25K26ddPUXbsaH182DBFGT/e83m+\n/FJR+vZVFIslcLZFEq9++6oy4O8DlNP1px2Oz56tKPfdF9znHvmvkcrK3St9vn7giwOVr458FUCL\nAkOVuUoZ+OJA5c4P7lT6/71/wOZttjQrQ18eqizdvjRgc3rLPfcoypw5LY8Btyu/vsawDcCrwHbg\nr+4GnTnj4+xnGTt3qouLnjYZChfazjP2NDer5dVlZZ7Po2WpRGgTP7/58dAfc1H3i/jJsp84ZF3M\nmAGLF6u/02Cwr2ofe0x7uKzXZT7PMb5gvO7i2A3NDUxdPJXxBeN5cdKL7D+5nzNNgRGX93a8h9Fg\n5Pr+1wdkPl8IReHMRcBM4FJgk/U2wXnQrrZbLUQN/uZbhwrn5lEAe/dCXp764VtZ2f4cFouaB+5J\nleXZzAsTX2CvaS9/++JvtmPnnKPuq1lSEpznXLRtEVMHTLVlD/nCuIJxusrHVhSFny3/GSlxKTx/\n5fPEx8TTO6M3O4/v9HvuZkszj3z6CI+PezysLYJDUTjzmfXa81AXHIcCHzsPKi31cfazjNJS/yoa\nQ4UrwdaqMQcP9szLXr8esrNbFiijlcTYRJZMW8JTnz3F+gPrbcdvuil42SILyxb6lB1iT1F+ERsP\nbaS+qT5AVvnHE+ufoLS8lAVTF9ji6oU5hZRVePGVzw3zS+eTlZzFlb2v9HsufwhFWp9HePM1+mwm\nkj1szfbCQs/eT2+Kds528tPzefO6N5mxdAbHTh8D1JTL995TC3cCyY7KHVTWVTKmh4vdhL0gPTGd\n/p3688WR8Cd9zd86n399+y8+vOlDUuJTbMcDIdgNzQ0UlxTzxLgnwr4Bhwi2zohkwdaqMT0R7MZG\nddMEEewWruxzJT8//+dMWzKNxuZGuneHgQPV1MJAsqBsAdMHTQ9Idoce4thr96/lwU8eZPnNy+nc\nwXELusE5gymt8O/r+2ubXqNPZh8u7nmxX/MEAt00f5KQiJomZzJBfn64LWmf9kIi7b2f//0v9O0b\nGa81lPz+4t/TMaEjD//3YSDw+z0qihKQcIhGuOPYu47vYtqSabwz9R2X1bT+etjmRjOPr3ucx8c9\n7o+ZAUM3HvaJE2pAPZrZtk3Nufa08jCcaLuna5w5A/v3qxWZgwap4t1Wq4mFC8W7doXRYOSt69/i\ng10fsHjbYm64AVasgLq6wMz/7bFvsSgWLujiRQJ+G4zpMYZNxzZR21AbkPm8oaK2gonvTOSp8U+5\nzXYpyCjgeN1xqut9E5e5X8/lgi4XMLyrB5t5BhlF0VG3voEDVcGKZiIlHAKt0/p27oRevdR0xKws\nSE11v3uK2QzLlsG0aaGxNdLISMpgyY1LuHflvRw3bGfECFi+PDBzLyxbyIzCGQGLxSbHJXN+l/NZ\nf3B9+4MDiLnRzLULr+Xmwpu5Y+gdbscZDUYGZg/0ycs+XX+apzc8zWOXPuaPqQHDbFbbU3ia8htU\nwS4slLBIpAm2vYft3K+7rfdz5UoYNkxNARRcM7TzUJ65/BmmLp7KddNPByQsYlEs6s4yhYH9ajO+\nYHxI+2NbFAu3/vtWemX04tFLH213/OCcwT4J9pwv5jC+YDyDc/WRtuVNOARCINjRvvAYKSl9AOnp\n6tf0emtGl/OHTVupfe1t+Cuo3H7e7Vzc42I+Sfgxq9conDrl33wbDm4gPTE94J0TxxWMC+k+jw//\n52Eqait47ZrXPPqm4Escu8pcxV+/+Cuzi2b7ambA8WbBEUSwg4qiRJaHbTCoOdRagYxzv25372d1\ntZr1MGVKaOyMdOZcNYejdQfoMf0v/Pvf/s0VyMVGe4Z3Hc53J77DZDYFfG5n5n41l2W7l/H+jPdJ\niE3w6JrCnEKvM0We3fgs1/a7lr5ZfX0xMyh4E7+GIAu2llkQrT3RtXhwbm547fAG+7CIq5CIK8Fe\ntgwuvrh10yjBNQmxCSyZtoTDPf7MiyvW+jxPk6WJd7e/y/RB0wNonUp8TDwX9biIkv0lAZ/bnpXf\nreTRdY+y8uaVZCZ5/gc0OGcwpeWlHm+4UFFbwcvfvMwjlzziq6lBQVchkbw8tVTZ0z7LZxuahxpJ\nPTU0wa6uhuPHoaCg5dzAgWq7gSanHv2ebvgrtNCjYw/emvIW3+TfzNZ9R32aY82+NRRkFNj6oQea\ncfnB3edx8w+buf3923lv2ntev4a81DwUFCpqPROXJ9c/yS2Db6FHxx6+mBo0dCXYBkN0h0X83WQ3\nHGgb+ZaVqQJtn46YkqL2wtizp+XYiRPw2WdqK1XBOyYPvJzBZ+7m+nfUohpv8XbfRm8Z32t80OLY\nh6sPM3nBZF6a9BKjunvfb9ZgMHgcFjlcfZg3t7zJ78b+zhdTg4quYtjgWcHF2Uokxa81NA/b3YeN\n8wfw0qVqn+/U0Pd9Pyt49PL/x8ljmTz0n4e8uu5M0xk+2PkB0wsDHw7RGJI7hIraCo6e9u0bgDuq\n66uZ9M4k7ht+X6uNHrzB00yRx9Y+xl3D7iIvVX8pTLrysEE87EgWbFe2O6f2STjEP66aYMSydB7v\nb1/OglLP8/w+3vMxQ/KG0KVD8HbmizHGUJRfFNCwSGNzI9PencbobqP59ehf+zWXJ5kie017Wbpj\nKb+56Dd+PVew0NWiI0SvYFssgdkGLNRogu1uR3f71L6jR2HLFtXDFnwjPh6mTkpnSuNS7vv4PrZV\neFZpFuxwiEYg49iKonDvynsxGoy8MPEFvwt9PAmJFK8t5hfDf0FWcpZfzxUsdOlhb9umClg0sX+/\nmjWhxy2y2kKrdnSXP27/Abx4MVx7rbr1leA7N90E694dwnNXPMeUxVPaLbmuaajh4z0f+xVO8BQt\nju1pNkZb/Hnjn/niyBcsumGRbR9SfyjMKWRbxTYsimtx2VaxjU/2fMKDox70+7mCRShj2K8B5UCb\nH3Hp6ertwAE/nikCicRwCKiCraViuqpa7NsXDh5US2olHBIYiorgyBEYlXwb4wvGc8cHd7QpkMt2\nLWNMjzEh8Rr7ZfWjsbmR76u+92uexdsW8/cv/86Km1fQIaFDQGxLT0wnIymDAyddi8sjJY/w0OiH\nSEvwQhFDTCg97NdxscuMK3wNi5QEa2uOIOBsq7uQgh5o6/eak6OGOtylI8bHq9udrVihfosYNy5o\nZgKR/TfgKTExcOON6gfg81c+z+Hqwzy78Vm34wMRDvHUVoPB4He2yMZDG7l35b18eNOHdE3r6vX1\nbdnqLizyzdFv+Pzw59wz/B6vn88fvP0bCGUMez1Q5clAXzNFIvmfVc8pfe0JNrRt++DBMGsW3HCD\n2rgmmETy34A3aC1X42MSWHLjEv7y+V/4dN+nrcaZzCbWHVjHtf2v9cNS72z1J469x7SHqYunMu/6\neQzJG+LTHG3Z6i5T5A+f/oHfjfkdyXHJPj2nr3j7N6C7GDZE58JjpIZEEhPVmFpbthcWqhvzSivV\nwDFypBpm2roVunfsztvXv80t793C4erDDuPe2/Eel/e6PKRf88cVqILtLlbsjhN1J5g4fyKzi2Yz\noU9wVqZdZYp8dvAzdlTu4K7z7wrKcwYSbwU7yP6RyuDB8MADMHmyd9ft2gXffBMcmwKNs63ffQf9\n+4fPHn/Iy2tbsAcPhm7d4KKLQmfT2Y7BoH4AzpypbQAxno5599HvySJSz7RskFmdvJlBB//K5Lf8\nez7v/rd6UluYRu6vJmBUPOv1AVCbuJu8quv5cNVP+dAnK1XasvVU0mA+7/cAqz9rEZfTSWWcc3QW\nU6/zsGdpAPFWsw4e9E6w/S2azgc+BFx9gd4M+PYdSBAEIXrZgrrBeSuC6WG7fEJBEAQh9CwAjgL1\nwCHA/RYRgiAIgiAIgiAEB1fFNkOA/wFbgWWAllU/HNhkvW0GrrO75nzrHN8Bf9OBrRo9gBrgVzq2\nNR8w0/K7fUnHtgKcaz1XZj2vrSTpzdZbaPmdbgKarbbr0dY44E3r8e3Ab+2u0Zut8ag1H1tRdeCS\nENvaHfgU2Ib6N3if9Xgm8B9gN7AKSLe75v9ZbdoJXBFiewPKWGAojm/UV9bjoIZVtA3dkmhJO8xD\nfYO1x1+iCjrASjws4AmirRpLgEU4CrbebM3HfXWq3myNRV2A0Ra1M9D/3wBAIWDXhFZ3tt6MGtIE\n9f9sH6qzoUdb7wFetd7PBr62uyYUtubRsh6XCuwCBgDPAFpHqYeBp6z3B6J+sMSh/q/toSW5IxT2\nBpx8HN+ok3b3u6N+kjnTCziG+s/aGdhhd24G8HJgTbSRj+e2Xof6Js6iRbD1aKvzOA092joRcJXA\npkdb7fkToG3LrUdbZ6B6sTFAJ1QRSteprX8HZtqd+y9wIaG11Z73gctQvWdtL6k862NQveuH7cZ/\nDIwkQPaGpHCmHbYBWtnWjahvlsZw6/ktwM8BC9AVsK8mOGI9Fgrc2ZqK+mlb7DRej7YCFADfAiXA\nGOsxPdp6DqCg/tF/A2hNo/Voqz3TaPFg9WjrEqAO1QnaD/wZVTD1aOsW4BrUD5cC1LBCN8Jjaz7q\nN4MvUMXaugkg5bSIdxcnuw5b7XI+7pO9ehDsHwN3o37VSQUa7M59CQxC/UT9HeB51n5wcGdrMfA8\n6j+BXjYEc2frUdR/hmHAL4F3aB2LDzXubI1F/UC52frzemAcqoiHi7b+XgFGoP4dbA+xXa5wZ+sI\noAnV6ysAfm39GU7c2foaqtB9jfo/thF1fSDUfwOpwFLgfuC00zklVPaEpNKxHXYBV1rvnwNMcjFm\nJ+pi3iDUN6+b3bluqJ9WocDZ1onW+8OBqaghkXTUbwJm4D30Y6v2e22g5Z/hW2Av0Ndql95sPQSs\nA7Stu1eiftC8jf5s1ZiB+iGooaffq/b3ejPqt5ZmoBLYgOq5foZ+bNV+r82ojoXGBtSFvlOEztY4\nVLF+CzUkAqpXnQf8gPrBp20ueQTHb13dUDUrnH8HfpGPY+wq2/rTCMwDbrcbp32g9ER9cdq2yl+g\negkGghu899RWe2bh+AemN1s7oX69BHVt4DAtK9x6szUdNRSShPq38B/gKp3aqh07bL3GHr3Z+htU\nzxUgBTUcoTUj0JutSVYbAS5HDeNphMJWg9We552OP0NLrPq3tF50jEf91rKXlm/dofrdBgyt2KYB\n1Xv6MWqazC7r7U92Y2eiptFsQv2ntd/mVUuP2QPM0YGt9jgLtt5snYLj79XeQ9SbraCmy5VZ7XrK\n7rgebS1C/crujN5sTQEWo/5et+E6DVUvtuajfsPejpo+Z++9hsLWMajfmDfTkrI5AdV5/C+u0/p+\nZ7VpJy3fGkJlryAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiB4QzNqjmwZas7sL2m/TUBP\n4KYg2yUIgiA4Yd/fIRu1KrK4nWuKwK/9YQVBEAQfcG7IUwAct97PR+1J8o31Nsp6/HPULnWbUJv6\nGFE7132J2iXup0G1WBAEIUpxFmyAKlRvO4mWTo99UZvlg7p7ib2H/VPg99b7CdZx+YE2VBB8RQ/d\n+gQh2MSjNsIfghrr7ms97hzjvgJ1d5sbrI/TgD6oPaMFIeyIYAtnK71oaR9ajNqs/1bUToVn2rju\nXtT4tyDoDj1sYCAIgSYbdfulF6yP01D7FgPcRkt72dM4bt7wCWoTfc2ROQdIDqqlgiAIUUgT7tP6\n+qAuIm5GbdFabT0eC6y2Hr/fOv4J1B26S63n0kJjviAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAI\ngiAIgiAIgiAIgiAIQpTy/wHTki8XuYYzSgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10de595d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_composer_by_name('Milhaud, Darius')\n",
"plot_composer_by_name('Gould, Morton')"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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9VyI1KOyyHtTDLiXpCMmJwpzYu1fsg3JZIkE87EceyR2zoDX1hqgj7Lq6fP87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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10eb28f10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_composer_by_name('Ravel, Maurice')"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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cI24aNcpe5bpkiX0jycuL73lNmsCf/2yvHH39dXdjSrfLLrMXAA0cGHub0lJ7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\nAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10e6f3b90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_composer_by_name('Hadley, Henry Kimball')"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"workTitle\n",
"CULPRIT FAY RHAPSODY, OP. 62 60\n",
"SALOME, OP. 55 18\n",
"IN BOHEMIA 10\n",
"OCEAN, THE, OP. 99 8\n",
"CHINESE SKETCHES - STREETS OF PEKIN 6\n",
"SYMPHONY NO. 3, B MINOR, OP. 60 5\n",
"SILHOUETTES, OP.77 (ARR. Roberts) 5\n",
"SYMPHONY NO. 4, D MINOR, OP. 64, \"NORTH, EAST, SOUTH, WEST\" 5\n",
"LUCIFER, OP. 66 4\n",
"SYMPHONY NO. 2, F MINOR, OP.30 (FOUR SEASONS) 3\n",
"Name: id, dtype: int64"
]
},
"execution_count": 113,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hadley = df[df.composerName == 'Hadley, Henry Kimball']\n",
"hadley.groupby([df.workTitle], sort=True).count()['id'].order(ascending=False).head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"## The social network of NY Philharmonic Soloists"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"soloists = df.soloists_tsv[df.soloists_tsv.notnull()]"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"soloist_list = list(soloists)"
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 117,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len([s for s in soloists if ('\\t' in s) and (';' in s)])"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"tab_separated = [t.split('\\t') for t in [s for s in soloists if ('\\t') in s]]\n",
"semicolon_separated = [t.split(';') for t in [s for s in soloists if (';') in s]]"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"soloists_split = tab_separated + semicolon_separated"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"5250"
]
},
"execution_count": 120,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(soloists_split)"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from itertools import combinations\n",
"\n",
"played_with_pairs = []\n",
"\n",
"for collection in soloists_split:\n",
" for pair in combinations(collection, 2):\n",
" played_with_pairs.append(pair)"
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from collections import Counter\n",
"cnt = Counter(played_with_pairs)"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"top_ten_thou = cnt.most_common(n=10000)"
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"with open('edges.txt', 'w') as f:\n",
" f.write('source;target;weight\\n')\n",
" for edge, weight in top_ten_thou:\n",
" try:\n",
" f.write(\"{}\".format(\";\".join(edge)) + \";{}\\n\".format(weight))\n",
" except:\n",
" continue"
]
},
{
"cell_type": "code",
"execution_count": 125,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 9783 32217 333024 edges.txt\r\n"
]
}
],
"source": [
"!wc edges.txt"
]
},
{
"cell_type": "code",
"execution_count": 126,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"source;target;weight\r\n",
"Smith, Philip;Alessi, Joseph;209\r\n",
"Smith, Philip;Myers, Philip;179\r\n",
"Myers, Philip;Alessi, Joseph;163\r\n",
"Smith, Philip;Sullivan, Robert;162\r\n",
"Sullivan, Robert;Alessi, Joseph;161\r\n",
"Alessi, Joseph;Deck, Warren;142\r\n",
"Smith, Philip;Deck, Warren;141\r\n",
"Sullivan, Robert;Myers, Philip;140\r\n",
"Myers, Philip;Deck, Warren;130\r\n"
]
}
],
"source": [
"!head edges.txt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Process this with something like `networkx` or Gephi to get something like this:\n",
"\n",
"[pending]"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"---\n",
"## NY Phil on Tour"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Manhattan, NY 48285\n",
"Philadelphia, PA 1217\n",
"Brooklyn, NY 1096\n",
"Washington, DC 441\n",
"Greenvale, NY 277\n",
"Baltimore, MD 272\n",
"Bronx, NY 202\n",
"Princeton, NJ 183\n",
"Newark, NJ 174\n",
"Pittsburgh, PA 168\n",
"dtype: int64"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.Location.value_counts().head(10)"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def get_state(location_str):\n",
" splitted = location_str.split(', ')\n",
" if len(splitted) != 2:\n",
" return None\n",
" elif len(splitted[1]) == 2:\n",
" return splitted[1].strip()\n",
" else:\n",
" return None\n",
" \n",
"def test_get_state():\n",
" assert get_state('Manhattan, NY') == 'NY'\n",
" assert get_state('Dublin, IRELAND') is None\n",
" assert get_state('foobar,,') is None\n",
" return True\n",
"\n",
"def run_tests():\n",
" assert test_get_state()\n",
" return True\n",
"\n",
"assert run_tests()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df['State'] = df.Location.apply(get_state)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"in_usa = df[df.State.notnull()]\n",
"out_of_state = df[df.State != 'NY']"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"decade_state = out_of_state.groupby([(out_of_state.Date.apply(lambda x: x.year)//10)*10,\n",
" out_of_state.State]).count()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>Location</th>\n",
" <th>Time</th>\n",
" <th>Venue</th>\n",
" <th>composerName</th>\n",
" <th>conductorName</th>\n",
" <th>eventType</th>\n",
" <th>id</th>\n",
" <th>interval</th>\n",
" <th>orchestra</th>\n",
" <th>program</th>\n",
" <th>programID</th>\n",
" <th>season</th>\n",
" <th>soloists_tsv</th>\n",
" <th>workTitle</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th>State</th>\n",
" <th></th>\n",
" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>1890</th>\n",
" <th>MA</th>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
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" <td>1</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"9\" valign=\"top\">1900</th>\n",
" <th>DC</th>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
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" <td>0</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
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" <tr>\n",
" <th>IA</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
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" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>IL</th>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
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" <td>2</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>IN</th>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
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" <td>2</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>KS</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
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" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>KY</th>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
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" <tr>\n",
" <th>MI</th>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
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" <td>3</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
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" <tr>\n",
" <th>MN</th>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
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" <td>4</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MO</th>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
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" <td>3</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" Date Location Time Venue composerName conductorName \\\n",
"Date State \n",
"1890 MA 5 5 5 5 5 5 \n",
"1900 DC 2 2 2 2 2 2 \n",
" IA 1 1 1 1 1 1 \n",
" IL 8 8 8 8 8 8 \n",
" IN 2 2 2 2 2 2 \n",
" KS 1 1 1 1 1 1 \n",
" KY 2 2 2 2 2 2 \n",
" MI 3 3 3 3 3 3 \n",
" MN 4 4 4 4 4 4 \n",
" MO 3 3 3 3 3 3 \n",
"\n",
" eventType id interval orchestra program programID season \\\n",
"Date State \n",
"1890 MA 5 5 0 5 0 5 5 \n",
"1900 DC 2 2 0 2 0 2 2 \n",
" IA 1 1 0 1 0 1 1 \n",
" IL 8 8 0 8 0 8 8 \n",
" IN 2 2 0 2 0 2 2 \n",
" KS 1 1 0 1 0 1 1 \n",
" KY 2 2 0 2 0 2 2 \n",
" MI 3 3 0 3 0 3 3 \n",
" MN 4 4 0 4 0 4 4 \n",
" MO 3 3 0 3 0 3 3 \n",
"\n",
" soloists_tsv workTitle \n",
"Date State \n",
"1890 MA 1 5 \n",
"1900 DC 0 2 \n",
" IA 0 1 \n",
" IL 2 8 \n",
" IN 0 2 \n",
" KS 0 1 \n",
" KY 0 2 \n",
" MI 0 3 \n",
" MN 0 4 \n",
" MO 2 3 "
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"decade_state.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"nineties = decade_state.loc[1990]\n",
"# Move index to column for use in plotting package later\n",
"nineties.reset_index(level=0, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"twenties = decade_state.loc[1920]\n",
"twenties.reset_index(level=0, inplace=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use `folium` for chloropleth visualization"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"!wget https://raw.githubusercontent.com/python-visualization/folium/master/examples/us-states.json"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def inline_map(m, width=650, height=500):\n",
" \"\"\"Takes a folium instance and embed HTML.\"\"\"\n",
" m._build_map()\n",
" srcdoc = m.HTML.replace('\"', '&quot;')\n",
" embed = HTML('<iframe srcdoc=\"{}\" '\n",
" 'style=\"width: {}px; height: {}px; '\n",
" 'border: none\"></iframe>'.format(srcdoc, width, height))\n",
" return embed"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def state_concert_counts(state_data):\n",
" state_geo = r'us-states.json'\n",
" \n",
" f = folium.Map(location=[48, -102], zoom_start=3, max_zoom=4, min_zoom=3)\n",
"\n",
" f.geo_json(geo_path=state_geo, data=state_data,\n",
" data_out='data.json', \n",
" columns=['State', 'programID'],\n",
" key_on='feature.id',\n",
" fill_color='YlGn', fill_opacity=0.7, line_opacity=0.2,\n",
" legend_name='Concerts played')\n",
" return inline_map(f)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<iframe srcdoc=\"<!DOCTYPE html>\n",
"<head>\n",
" <meta http-equiv=&quot;content-type&quot; content=&quot;text/html; charset=UTF-8&quot; />\n",
" <link rel=&quot;stylesheet&quot; href=&quot;https://cdnjs.cloudflare.com/ajax/libs/leaflet/0.7.3/leaflet.css&quot; />\n",
" <script src=&quot;https://cdnjs.cloudflare.com/ajax/libs/leaflet/0.7.3/leaflet.js&quot;></script>\n",
" <script src=&quot;https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js&quot; charset=&quot;utf-8&quot;></script>\n",
" <script src=&quot;https://cdnjs.cloudflare.com/ajax/libs/queue-async/1.0.7/queue.min.js&quot;></script>\n",
"\n",
" \n",
" \n",
" \n",
" \n",
"\n",
" <style>\n",
"\n",
" html, body {\n",
" width: 100%;\n",
" height: 100%;\n",
" margin: 0;\n",
" padding: 0;\n",
" }\n",
"\n",
" .legend {\n",
" padding: 0px 0px;\n",
" font: 10px sans-serif;\n",
" background: white;\n",
" background: rgba(255,255,255,0.8);\n",
" box-shadow: 0 0 15px rgba(0,0,0,0.2);\n",
" border-radius: 5px;\n",
" }\n",
"\n",
" .key path {\n",
" display: none;\n",
" }\n",
"\n",
" </style>\n",
"</head>\n",
"\n",
"<body>\n",
"\n",
" <div id=&quot;map&quot; style=&quot;width: 100%; height: 100%&quot;></div>\n",
"\n",
" <script>\n",
"\n",
" queue()\n",
" .defer(d3.json, 'data.json')\n",
" .defer(d3.json, 'us-states.json')\n",
" .await(makeMap)\n",
"\n",
" function onEachFeature(feature, layer) {\n",
" // does this feature have a property named popupContent?\n",
" if (feature.properties && feature.properties.popupContent) {\n",
" layer.bindPopup(feature.properties.popupContent);\n",
" }\n",
" };\n",
"\n",
" function makeMap(error, data_1,gjson_1) {\n",
"\n",
" \n",
"\n",
" \n",
"\n",
" function matchKey(datapoint, key_variable){\n",
" if (typeof key_variable[0][datapoint] === 'undefined') {\n",
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" return parseFloat(key_variable[0][datapoint]);\n",
" };\n",
" };\n",
"\n",
" \n",
" var color = d3.scale.threshold()\n",
" .domain([4.0, 6.0, 20.0, 20.0, 20.0])\n",
" .range(['#FFFFCC', '#D9F0A3', '#ADDD8E', '#78C679', '#41AB5D', '#238443']);\n",
" \n",
"\n",
" var map = L.map('map').setView([48, -102], 3);\n",
"\n",
" L.tileLayer('https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png', {\n",
" maxZoom: 4,\n",
" minZoom: 3,\n",
" attribution: 'Map data (c) <a href=&quot;http://openstreetmap.org&quot;>OpenStreetMap</a> contributors'\n",
" }).addTo(map);\n",
"\n",
" \n",
" function style_1(feature) {\n",
" return {\n",
" fillColor: color(matchKey(feature.id, data_1)),\n",
" weight: 1,\n",
" opacity: 0.2,\n",
" color: 'black',\n",
" fillOpacity: 0.7\n",
" };\n",
"}\n",
" \n",
"\n",
" \n",
" gJson_layer_1 = L.geoJson(gjson_1, {style: style_1,onEachFeature: onEachFeature}).addTo(map)\n",
" \n",
"\n",
" \n",
" var legend = L.control({position: 'topright'});\n",
"\n",
" legend.onAdd = function (map) {var div = L.DomUtil.create('div', 'legend'); return div};\n",
"\n",
" legend.addTo(map);\n",
"\n",
" var x = d3.scale.linear()\n",
" .domain([0, 22])\n",
" .range([0, 400]);\n",
"\n",
" var xAxis = d3.svg.axis()\n",
" .scale(x)\n",
" .orient(&quot;top&quot;)\n",
" .tickSize(1)\n",
" .tickValues([4.0, 6.0, 20.0, 20.0, 20.0]);\n",
"\n",
" var svg = d3.select(&quot;.legend.leaflet-control&quot;).append(&quot;svg&quot;)\n",
" .attr(&quot;id&quot;, 'legend')\n",
" .attr(&quot;width&quot;, 450)\n",
" .attr(&quot;height&quot;, 40);\n",
"\n",
" var g = svg.append(&quot;g&quot;)\n",
" .attr(&quot;class&quot;, &quot;key&quot;)\n",
" .attr(&quot;transform&quot;, &quot;translate(25,16)&quot;);\n",
"\n",
" g.selectAll(&quot;rect&quot;)\n",
" .data(color.range().map(function(d, i) {\n",
" return {\n",
" x0: i ? x(color.domain()[i - 1]) : x.range()[0],\n",
" x1: i < color.domain().length ? x(color.domain()[i]) : x.range()[1],\n",
" z: d\n",
" };\n",
" }))\n",
" .enter().append(&quot;rect&quot;)\n",
" .attr(&quot;height&quot;, 10)\n",
" .attr(&quot;x&quot;, function(d) { return d.x0; })\n",
" .attr(&quot;width&quot;, function(d) { return d.x1 - d.x0; })\n",
" .style(&quot;fill&quot;, function(d) { return d.z; });\n",
"\n",
" g.call(xAxis).append(&quot;text&quot;)\n",
" .attr(&quot;class&quot;, &quot;caption&quot;)\n",
" .attr(&quot;y&quot;, 21)\n",
" .text('Concerts played');\n",
" \n",
"\n",
" \n",
"\n",
" \n",
"\n",
" \n",
"\n",
" \n",
"\n",
" \n",
"\n",
" \n",
"\n",
" };\n",
"\n",
" </script>\n",
"</body>\" style=\"width: 650px; height: 500px; border: none\"></iframe>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"state_concert_counts(nineties)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<iframe srcdoc=\"<!DOCTYPE html>\n",
"<head>\n",
" <meta http-equiv=&quot;content-type&quot; content=&quot;text/html; charset=UTF-8&quot; />\n",
" <link rel=&quot;stylesheet&quot; href=&quot;https://cdnjs.cloudflare.com/ajax/libs/leaflet/0.7.3/leaflet.css&quot; />\n",
" <script src=&quot;https://cdnjs.cloudflare.com/ajax/libs/leaflet/0.7.3/leaflet.js&quot;></script>\n",
" <script src=&quot;https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js&quot; charset=&quot;utf-8&quot;></script>\n",
" <script src=&quot;https://cdnjs.cloudflare.com/ajax/libs/queue-async/1.0.7/queue.min.js&quot;></script>\n",
"\n",
" \n",
" \n",
" \n",
" \n",
"\n",
" <style>\n",
"\n",
" html, body {\n",
" width: 100%;\n",
" height: 100%;\n",
" margin: 0;\n",
" padding: 0;\n",
" }\n",
"\n",
" .legend {\n",
" padding: 0px 0px;\n",
" font: 10px sans-serif;\n",
" background: white;\n",
" background: rgba(255,255,255,0.8);\n",
" box-shadow: 0 0 15px rgba(0,0,0,0.2);\n",
" border-radius: 5px;\n",
" }\n",
"\n",
" .key path {\n",
" display: none;\n",
" }\n",
"\n",
" </style>\n",
"</head>\n",
"\n",
"<body>\n",
"\n",
" <div id=&quot;map&quot; style=&quot;width: 100%; height: 100%&quot;></div>\n",
"\n",
" <script>\n",
"\n",
" queue()\n",
" .defer(d3.json, 'data.json')\n",
" .defer(d3.json, 'us-states.json')\n",
" .await(makeMap)\n",
"\n",
" function onEachFeature(feature, layer) {\n",
" // does this feature have a property named popupContent?\n",
" if (feature.properties && feature.properties.popupContent) {\n",
" layer.bindPopup(feature.properties.popupContent);\n",
" }\n",
" };\n",
"\n",
" function makeMap(error, data_1,gjson_1) {\n",
"\n",
" \n",
"\n",
" \n",
"\n",
" function matchKey(datapoint, key_variable){\n",
" if (typeof key_variable[0][datapoint] === 'undefined') {\n",
" return null;\n",
" }\n",
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" return parseFloat(key_variable[0][datapoint]);\n",
" };\n",
" };\n",
"\n",
" \n",
" var color = d3.scale.threshold()\n",
" .domain([5.0, 20.0, 30.0, 90.0, 100.0])\n",
" .range(['#FFFFCC', '#D9F0A3', '#ADDD8E', '#78C679', '#41AB5D', '#238443']);\n",
" \n",
"\n",
" var map = L.map('map').setView([48, -102], 3);\n",
"\n",
" L.tileLayer('https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png', {\n",
" maxZoom: 4,\n",
" minZoom: 3,\n",
" attribution: 'Map data (c) <a href=&quot;http://openstreetmap.org&quot;>OpenStreetMap</a> contributors'\n",
" }).addTo(map);\n",
"\n",
" \n",
" function style_1(feature) {\n",
" return {\n",
" fillColor: color(matchKey(feature.id, data_1)),\n",
" weight: 1,\n",
" opacity: 0.2,\n",
" color: 'black',\n",
" fillOpacity: 0.7\n",
" };\n",
"}\n",
" \n",