my new python notebook

gistfile1.txt

{"nbformat_minor": 0, "cells": [{"execution_count": 1, "cell_type": "code", "source": "import pandas as pd", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 2, "cell_type": "code", "source": "import os as os", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 3, "cell_type": "code", "source": "a=os.getcwd()", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 4, "cell_type": "code", "source": "os.listdir(a)", "outputs": [{"execution_count": 4, "output_type": "execute_result", "data": {"text/plain": "['.continuum',\n '.enstaller4rc',\n '.idlerc',\n '.ipynb_checkpoints',\n '.ipython',\n '.matplotlib',\n '.RData',\n '.spyder2',\n 'AppData',\n 'Application Data',\n 'Canopy',\n 'Contacts',\n 'Cookies',\n 'Desktop',\n 'Documents',\n 'Downloads',\n 'Favorites',\n 'intermediate python notebook.ipynb',\n 'Links',\n 'Local Settings',\n 'Music',\n 'My Documents',\n 'my first R script file.R',\n 'My_First_Python_NB.ipynb',\n 'NetHood',\n 'ntuser.dat',\n 'ntuser.dat.LOG1',\n 'ntuser.dat.LOG2',\n 'NTUSER.DAT{6cced2f1-6e01-11de-8bed-001e0bcd1824}.TM.blf',\n 'NTUSER.DAT{6cced2f1-6e01-11de-8bed-001e0bcd1824}.TMContainer00000000000000000001.regtrans-ms',\n 'NTUSER.DAT{6cced2f1-6e01-11de-8bed-001e0bcd1824}.TMContainer00000000000000000002.regtrans-ms',\n 'ntuser.dat{aab24cb1-b794-11e4-a6c2-f04da2c4a5e3}.TM.blf',\n 'ntuser.dat{aab24cb1-b794-11e4-a6c2-f04da2c4a5e3}.TMContainer00000000000000000001.regtrans-ms',\n 'ntuser.dat{aab24cb1-b794-11e4-a6c2-f04da2c4a5e3}.TMContainer00000000000000000002.regtrans-ms',\n 'ntuser.ini',\n 'Pictures',\n 'PrintHood',\n 'Recent',\n 'Saved Games',\n 'Searches',\n 'SendTo',\n 'Start Menu',\n 'Templates',\n 'Untitled.ipynb',\n 'Videos']"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 5, "cell_type": "code", "source": "os.chdir('C:\\\\Users\\\\dell\\\\Desktop')", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 6, "cell_type": "code", "source": "os.listdir(a)", "outputs": [{"execution_count": 6, "output_type": "execute_result", "data": {"text/plain": "['.continuum',\n '.enstaller4rc',\n '.idlerc',\n '.ipynb_checkpoints',\n '.ipython',\n '.matplotlib',\n '.RData',\n '.spyder2',\n 'AppData',\n 'Application Data',\n 'Canopy',\n 'Contacts',\n 'Cookies',\n 'Desktop',\n 'Documents',\n 'Downloads',\n 'Favorites',\n 'intermediate python notebook.ipynb',\n 'Links',\n 'Local Settings',\n 'Music',\n 'My Documents',\n 'my first R script file.R',\n 'My_First_Python_NB.ipynb',\n 'NetHood',\n 'ntuser.dat',\n 'ntuser.dat.LOG1',\n 'ntuser.dat.LOG2',\n 'NTUSER.DAT{6cced2f1-6e01-11de-8bed-001e0bcd1824}.TM.blf',\n 'NTUSER.DAT{6cced2f1-6e01-11de-8bed-001e0bcd1824}.TMContainer00000000000000000001.regtrans-ms',\n 'NTUSER.DAT{6cced2f1-6e01-11de-8bed-001e0bcd1824}.TMContainer00000000000000000002.regtrans-ms',\n 'ntuser.dat{aab24cb1-b794-11e4-a6c2-f04da2c4a5e3}.TM.blf',\n 'ntuser.dat{aab24cb1-b794-11e4-a6c2-f04da2c4a5e3}.TMContainer00000000000000000001.regtrans-ms',\n 'ntuser.dat{aab24cb1-b794-11e4-a6c2-f04da2c4a5e3}.TMContainer00000000000000000002.regtrans-ms',\n 'ntuser.ini',\n 'Pictures',\n 'PrintHood',\n 'Recent',\n 'Saved Games',\n 'Searches',\n 'SendTo',\n 'Start Menu',\n 'Templates',\n 'Untitled.ipynb',\n 'Videos']"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 7, "cell_type": "code", "source": "bigdiamonds=pd.read_csv('BigDiamonds.csv')", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 8, "cell_type": "code", "source": "bigdiamonds.info()", "outputs": [{"output_type": "stream", "name": "stdout", "text": "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 598024 entries, 0 to 598023\nData columns (total 12 columns):\ncarat           598024 non-null float64\ncut             598024 non-null object\ncolor           598024 non-null object\nclarity         598024 non-null object\ntable           598024 non-null float64\ndepth           598024 non-null float64\ncert            598024 non-null object\nmeasurements    597978 non-null object\nprice           597311 non-null float64\nx               596209 non-null float64\ny               596172 non-null float64\nz               595480 non-null float64\ndtypes: float64(7), object(5)\nmemory usage: 47.9+ MB\n"}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 9, "cell_type": "code", "source": "bigdiamonds[\"newdata\"]=bigdiamonds[\"price\"]/bigdiamonds[\"carat\"]\nbigdiamonds.info()", "outputs": [{"output_type": "stream", "name": "stdout", "text": "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 598024 entries, 0 to 598023\nData columns (total 13 columns):\ncarat           598024 non-null float64\ncut             598024 non-null object\ncolor           598024 non-null object\nclarity         598024 non-null object\ntable           598024 non-null float64\ndepth           598024 non-null float64\ncert            598024 non-null object\nmeasurements    597978 non-null object\nprice           597311 non-null float64\nx               596209 non-null float64\ny               596172 non-null float64\nz               595480 non-null float64\nnewdata         597311 non-null float64\ndtypes: float64(8), object(5)\nmemory usage: 52.5+ MB\n"}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 10, "cell_type": "code", "source": "newdiamonds=bigdiamonds[[\"carat\", \"cut\"]]", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 11, "cell_type": "code", "source": "newdiamonds.info()", "outputs": [{"output_type": "stream", "name": "stdout", "text": "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 598024 entries, 0 to 598023\nData columns (total 2 columns):\ncarat    598024 non-null float64\ncut      598024 non-null object\ndtypes: float64(1), object(1)\nmemory usage: 11.4+ MB\n"}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 12, "cell_type": "code", "source": "newdiamonds.info()", "outputs": [{"output_type": "stream", "name": "stdout", "text": "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 598024 entries, 0 to 598023\nData columns (total 2 columns):\ncarat    598024 non-null float64\ncut      598024 non-null object\ndtypes: float64(1), object(1)\nmemory usage: 11.4+ MB\n"}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 13, "cell_type": "code", "source": "bigdiamonds.head(6)", "outputs": [{"execution_count": 13, "output_type": "execute_result", "data": {"text/plain": "   carat     cut color clarity  table  depth cert        measurements  price  \\\n0   0.25  V.Good     K      I1     59   63.7  GIA  3.96 x 3.95 x 2.52    NaN   \n1   0.23    Good     G      I1     61   58.1  GIA  4.00 x 4.05 x 2.30    NaN   \n2   0.34    Good     J      I2     58   58.7  GIA  4.56 x 4.53 x 2.67    NaN   \n3   0.21  V.Good     D      I1     60   60.6  GIA  3.80 x 3.82 x 2.31    NaN   \n4   0.31  V.Good     K      I1     59   62.2  EGL  4.35 x 4.26 x 2.68    NaN   \n5   0.20    Good     G     SI2     60   64.4  GIA  3.74 x 3.67 x 2.38    NaN   \n\n      x     y     z  newdata  \n0  3.96  3.95  2.52      NaN  \n1  4.00  4.05  2.30      NaN  \n2  4.56  4.53  2.67      NaN  \n3  3.80  3.82  2.31      NaN  \n4  4.35  4.26  2.68      NaN  \n5  3.74  3.67  2.38      NaN  ", "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>carat</th>\n      <th>cut</th>\n      <th>color</th>\n      <th>clarity</th>\n      <th>table</th>\n      <th>depth</th>\n      <th>cert</th>\n      <th>measurements</th>\n      <th>price</th>\n      <th>x</th>\n      <th>y</th>\n      <th>z</th>\n      <th>newdata</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.25</td>\n      <td>V.Good</td>\n      <td>K</td>\n      <td>I1</td>\n      <td>59</td>\n      <td>63.7</td>\n      <td>GIA</td>\n      <td>3.96 x 3.95 x 2.52</td>\n      <td>NaN</td>\n      <td>3.96</td>\n      <td>3.95</td>\n      <td>2.52</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.23</td>\n      <td>Good</td>\n      <td>G</td>\n      <td>I1</td>\n      <td>61</td>\n      <td>58.1</td>\n      <td>GIA</td>\n      <td>4.00 x 4.05 x 2.30</td>\n      <td>NaN</td>\n      <td>4.00</td>\n      <td>4.05</td>\n      <td>2.30</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.34</td>\n      <td>Good</td>\n      <td>J</td>\n      <td>I2</td>\n      <td>58</td>\n      <td>58.7</td>\n      <td>GIA</td>\n      <td>4.56 x 4.53 x 2.67</td>\n      <td>NaN</td>\n      <td>4.56</td>\n      <td>4.53</td>\n      <td>2.67</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.21</td>\n      <td>V.Good</td>\n      <td>D</td>\n      <td>I1</td>\n      <td>60</td>\n      <td>60.6</td>\n      <td>GIA</td>\n      <td>3.80 x 3.82 x 2.31</td>\n      <td>NaN</td>\n      <td>3.80</td>\n      <td>3.82</td>\n      <td>2.31</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.31</td>\n      <td>V.Good</td>\n      <td>K</td>\n      <td>I1</td>\n      <td>59</td>\n      <td>62.2</td>\n      <td>EGL</td>\n      <td>4.35 x 4.26 x 2.68</td>\n      <td>NaN</td>\n      <td>4.35</td>\n      <td>4.26</td>\n      <td>2.68</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>0.20</td>\n      <td>Good</td>\n      <td>G</td>\n      <td>SI2</td>\n      <td>60</td>\n      <td>64.4</td>\n      <td>GIA</td>\n      <td>3.74 x 3.67 x 2.38</td>\n      <td>NaN</td>\n      <td>3.74</td>\n      <td>3.67</td>\n      <td>2.38</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 14, "cell_type": "code", "source": "Sorted = bigdiamonds.sort(['price'], ascending=True)", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 15, "cell_type": "code", "source": "Sorted.head(4)", "outputs": [{"execution_count": 15, "output_type": "execute_result", "data": {"text/plain": "     carat     cut color clarity  table  depth cert        measurements  \\\n493   0.24  V.Good     G     SI1   61.0   58.9  GIA  4.09 x 4.10 x 2.41   \n494   0.31  V.Good     K     SI2   59.0   60.2  GIA  4.40 x 4.42 x 2.65   \n495   0.26    Good     J     VS2   56.5   64.1  IGI  4.01 x 4.05 x 2.58   \n496   0.24   Ideal     G     SI1   55.0   61.3  GIA  4.01 x 4.03 x 2.47   \n\n     price     x     y     z      newdata  \n493    300  4.09  4.10  2.41  1250.000000  \n494    300  4.40  4.42  2.65   967.741935  \n495    300  4.01  4.05  2.58  1153.846154  \n496    300  4.01  4.03  2.47  1250.000000  ", "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>carat</th>\n      <th>cut</th>\n      <th>color</th>\n      <th>clarity</th>\n      <th>table</th>\n      <th>depth</th>\n      <th>cert</th>\n      <th>measurements</th>\n      <th>price</th>\n      <th>x</th>\n      <th>y</th>\n      <th>z</th>\n      <th>newdata</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>493</th>\n      <td>0.24</td>\n      <td>V.Good</td>\n      <td>G</td>\n      <td>SI1</td>\n      <td>61.0</td>\n      <td>58.9</td>\n      <td>GIA</td>\n      <td>4.09 x 4.10 x 2.41</td>\n      <td>300</td>\n      <td>4.09</td>\n      <td>4.10</td>\n      <td>2.41</td>\n      <td>1250.000000</td>\n    </tr>\n    <tr>\n      <th>494</th>\n      <td>0.31</td>\n      <td>V.Good</td>\n      <td>K</td>\n      <td>SI2</td>\n      <td>59.0</td>\n      <td>60.2</td>\n      <td>GIA</td>\n      <td>4.40 x 4.42 x 2.65</td>\n      <td>300</td>\n      <td>4.40</td>\n      <td>4.42</td>\n      <td>2.65</td>\n      <td>967.741935</td>\n    </tr>\n    <tr>\n      <th>495</th>\n      <td>0.26</td>\n      <td>Good</td>\n      <td>J</td>\n      <td>VS2</td>\n      <td>56.5</td>\n      <td>64.1</td>\n      <td>IGI</td>\n      <td>4.01 x 4.05 x 2.58</td>\n      <td>300</td>\n      <td>4.01</td>\n      <td>4.05</td>\n      <td>2.58</td>\n      <td>1153.846154</td>\n    </tr>\n    <tr>\n      <th>496</th>\n      <td>0.24</td>\n      <td>Ideal</td>\n      <td>G</td>\n      <td>SI1</td>\n      <td>55.0</td>\n      <td>61.3</td>\n      <td>GIA</td>\n      <td>4.01 x 4.03 x 2.47</td>\n      <td>300</td>\n      <td>4.01</td>\n      <td>4.03</td>\n      <td>2.47</td>\n      <td>1250.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 16, "cell_type": "code", "source": "Sorted.head(10)", "outputs": [{"execution_count": 16, "output_type": "execute_result", "data": {"text/plain": "     carat     cut color clarity  table  depth     cert        measurements  \\\n493   0.24  V.Good     G     SI1   61.0   58.9      GIA  4.09 x 4.10 x 2.41   \n494   0.31  V.Good     K     SI2   59.0   60.2      GIA  4.40 x 4.42 x 2.65   \n495   0.26    Good     J     VS2   56.5   64.1      IGI  4.01 x 4.05 x 2.58   \n496   0.24   Ideal     G     SI1   55.0   61.3      GIA  4.01 x 4.03 x 2.47   \n497   0.30    Good     H      I1   57.0   62.2      GIA  4.21 x 4.24 x 2.63   \n498   0.34    Good     F      I1   66.0   55.0      GIA  4.75 x 4.61 x 2.57   \n512   0.24  V.Good     E     VS1   62.0   62.0      GIA  3.98 x 3.92 x 2.45   \n511   0.29  V.Good     F     SI1   60.0   61.9  EGL USA  4.22 x 4.24 x 2.62   \n510   0.21   Ideal     G     VS1   58.0   62.2      GIA  3.79 x 3.81 x 2.36   \n509   0.23  V.Good     G     SI1   62.0   58.6      GIA  4.00 x 4.02 x 2.35   \n\n     price     x     y     z      newdata  \n493    300  4.09  4.10  2.41  1250.000000  \n494    300  4.40  4.42  2.65   967.741935  \n495    300  4.01  4.05  2.58  1153.846154  \n496    300  4.01  4.03  2.47  1250.000000  \n497    300  4.21  4.24  2.63  1000.000000  \n498    300  4.75  4.61  2.57   882.352941  \n512    301  3.98  3.92  2.45  1254.166667  \n511    301  4.22  4.24  2.62  1037.931034  \n510    301  3.79  3.81  2.36  1433.333333  \n509    301  4.00  4.02  2.35  1308.695652  ", "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>carat</th>\n      <th>cut</th>\n      <th>color</th>\n      <th>clarity</th>\n      <th>table</th>\n      <th>depth</th>\n      <th>cert</th>\n      <th>measurements</th>\n      <th>price</th>\n      <th>x</th>\n      <th>y</th>\n      <th>z</th>\n      <th>newdata</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>493</th>\n      <td>0.24</td>\n      <td>V.Good</td>\n      <td>G</td>\n      <td>SI1</td>\n      <td>61.0</td>\n      <td>58.9</td>\n      <td>GIA</td>\n      <td>4.09 x 4.10 x 2.41</td>\n      <td>300</td>\n      <td>4.09</td>\n      <td>4.10</td>\n      <td>2.41</td>\n      <td>1250.000000</td>\n    </tr>\n    <tr>\n      <th>494</th>\n      <td>0.31</td>\n      <td>V.Good</td>\n      <td>K</td>\n      <td>SI2</td>\n      <td>59.0</td>\n      <td>60.2</td>\n      <td>GIA</td>\n      <td>4.40 x 4.42 x 2.65</td>\n      <td>300</td>\n      <td>4.40</td>\n      <td>4.42</td>\n      <td>2.65</td>\n      <td>967.741935</td>\n    </tr>\n    <tr>\n      <th>495</th>\n      <td>0.26</td>\n      <td>Good</td>\n      <td>J</td>\n      <td>VS2</td>\n      <td>56.5</td>\n      <td>64.1</td>\n      <td>IGI</td>\n      <td>4.01 x 4.05 x 2.58</td>\n      <td>300</td>\n      <td>4.01</td>\n      <td>4.05</td>\n      <td>2.58</td>\n      <td>1153.846154</td>\n    </tr>\n    <tr>\n      <th>496</th>\n      <td>0.24</td>\n      <td>Ideal</td>\n      <td>G</td>\n      <td>SI1</td>\n      <td>55.0</td>\n      <td>61.3</td>\n      <td>GIA</td>\n      <td>4.01 x 4.03 x 2.47</td>\n      <td>300</td>\n      <td>4.01</td>\n      <td>4.03</td>\n      <td>2.47</td>\n      <td>1250.000000</td>\n    </tr>\n    <tr>\n      <th>497</th>\n      <td>0.30</td>\n      <td>Good</td>\n      <td>H</td>\n      <td>I1</td>\n      <td>57.0</td>\n      <td>62.2</td>\n      <td>GIA</td>\n      <td>4.21 x 4.24 x 2.63</td>\n      <td>300</td>\n      <td>4.21</td>\n      <td>4.24</td>\n      <td>2.63</td>\n      <td>1000.000000</td>\n    </tr>\n    <tr>\n      <th>498</th>\n      <td>0.34</td>\n      <td>Good</td>\n      <td>F</td>\n      <td>I1</td>\n      <td>66.0</td>\n      <td>55.0</td>\n      <td>GIA</td>\n      <td>4.75 x 4.61 x 2.57</td>\n      <td>300</td>\n      <td>4.75</td>\n      <td>4.61</td>\n      <td>2.57</td>\n      <td>882.352941</td>\n    </tr>\n    <tr>\n      <th>512</th>\n      <td>0.24</td>\n      <td>V.Good</td>\n      <td>E</td>\n      <td>VS1</td>\n      <td>62.0</td>\n      <td>62.0</td>\n      <td>GIA</td>\n      <td>3.98 x 3.92 x 2.45</td>\n      <td>301</td>\n      <td>3.98</td>\n      <td>3.92</td>\n      <td>2.45</td>\n      <td>1254.166667</td>\n    </tr>\n    <tr>\n      <th>511</th>\n      <td>0.29</td>\n      <td>V.Good</td>\n      <td>F</td>\n      <td>SI1</td>\n      <td>60.0</td>\n      <td>61.9</td>\n      <td>EGL USA</td>\n      <td>4.22 x 4.24 x 2.62</td>\n      <td>301</td>\n      <td>4.22</td>\n      <td>4.24</td>\n      <td>2.62</td>\n      <td>1037.931034</td>\n    </tr>\n    <tr>\n      <th>510</th>\n      <td>0.21</td>\n      <td>Ideal</td>\n      <td>G</td>\n      <td>VS1</td>\n      <td>58.0</td>\n      <td>62.2</td>\n      <td>GIA</td>\n      <td>3.79 x 3.81 x 2.36</td>\n      <td>301</td>\n      <td>3.79</td>\n      <td>3.81</td>\n      <td>2.36</td>\n      <td>1433.333333</td>\n    </tr>\n    <tr>\n      <th>509</th>\n      <td>0.23</td>\n      <td>V.Good</td>\n      <td>G</td>\n      <td>SI1</td>\n      <td>62.0</td>\n      <td>58.6</td>\n      <td>GIA</td>\n      <td>4.00 x 4.02 x 2.35</td>\n      <td>301</td>\n      <td>4.00</td>\n      <td>4.02</td>\n      <td>2.35</td>\n      <td>1308.695652</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 17, "cell_type": "code", "source": "bigdiamonds.tail(15)", "outputs": [{"execution_count": 17, "output_type": "execute_result", "data": {"text/plain": "        carat     cut color clarity  table  depth cert  \\\n598009   5.02    Good     J     VS2   57.0   62.3  GIA   \n598010   5.01  V.Good     H     SI1   57.0   59.6  GIA   \n598011   3.05  V.Good     D     VS1   60.0   60.1  GIA   \n598012   5.59   Ideal     I     VS2   61.0   60.4  HRD   \n598013   2.57   Ideal     E      IF   59.0   60.9  GIA   \n598014   5.24   Ideal     I     SI1   60.0   59.5  GIA   \n598015   5.03   Ideal     H     SI1   58.0   62.2  HRD   \n598016   3.05   Ideal     D     VS2   59.0   61.3  GIA   \n598017   3.01    Good     E     VS1   61.0   62.6  GIA   \n598018   3.01   Ideal     D     VS2   58.0   62.0  GIA   \n598019   3.02   Ideal     E    VVS2   58.0   59.8  HRD   \n598020   5.01  V.Good     I    VVS2   63.5   61.5  IGI   \n598021   3.43   Ideal     F     VS2   54.0   62.7  GIA   \n598022   3.01  V.Good     E     VS1   58.0   62.9  GIA   \n598023   4.13   Ideal     H      IF   56.0   62.5  IGI   \n\n                    measurements  price      x      y      z       newdata  \n598009      10.77 x 10.84 x 6.73  99806  10.77  10.84   6.73  19881.673307  \n598010  11.11  x  11.17  x  6.64  99810  11.11  11.17   6.64  19922.155689  \n598011         9.4 x 9.34 x 5.63  99870   9.40   9.34   5.63  32744.262295  \n598012      11.52 x 11.57 x 6.97  99890  11.52  11.57   6.97  17869.409660  \n598013        8.82 x 8.88 x 5.39  99896   8.82   8.88   5.39  38870.038911  \n598014      11.35 x 11.43 x 6.78  99910  11.35  11.43   6.78  19066.793893  \n598015      6.82 x 10.94 x 10.98  99913   6.82  10.94  10.98  19863.419483  \n598016        5.73 x 9.33 x 9.36  99916   5.73   9.33   9.36  32759.344262  \n598017        9.16 x 9.25 x 5.76  99920   9.16   9.25   5.76  33196.013289  \n598018         9.25 x 9.2 x 5.72  99920   9.25   9.20   5.72  33196.013289  \n598019        9.43 x 9.51 x 5.66  99930   9.43   9.51   5.66  33089.403974  \n598020  10.78  x  10.89  x  6.68  99942  10.78  10.89   6.68  19948.502994  \n598021        9.66 x 9.61 x 6.05  99960   9.66   9.61   6.05  29142.857143  \n598022        9.15 x 9.19 x 5.77  99966   9.15   9.19   5.77  33211.295681  \n598023       10.27 x 10.19 x 6.4  99990  10.27  10.19   6.40  24210.653753  ", "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>carat</th>\n      <th>cut</th>\n      <th>color</th>\n      <th>clarity</th>\n      <th>table</th>\n      <th>depth</th>\n      <th>cert</th>\n      <th>measurements</th>\n      <th>price</th>\n      <th>x</th>\n      <th>y</th>\n      <th>z</th>\n      <th>newdata</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>598009</th>\n      <td>5.02</td>\n      <td>Good</td>\n      <td>J</td>\n      <td>VS2</td>\n      <td>57.0</td>\n      <td>62.3</td>\n      <td>GIA</td>\n      <td>10.77 x 10.84 x 6.73</td>\n      <td>99806</td>\n      <td>10.77</td>\n      <td>10.84</td>\n      <td>6.73</td>\n      <td>19881.673307</td>\n    </tr>\n    <tr>\n      <th>598010</th>\n      <td>5.01</td>\n      <td>V.Good</td>\n      <td>H</td>\n      <td>SI1</td>\n      <td>57.0</td>\n      <td>59.6</td>\n      <td>GIA</td>\n      <td>11.11  x  11.17  x  6.64</td>\n      <td>99810</td>\n      <td>11.11</td>\n      <td>11.17</td>\n      <td>6.64</td>\n      <td>19922.155689</td>\n    </tr>\n    <tr>\n      <th>598011</th>\n      <td>3.05</td>\n      <td>V.Good</td>\n      <td>D</td>\n      <td>VS1</td>\n      <td>60.0</td>\n      <td>60.1</td>\n      <td>GIA</td>\n      <td>9.4 x 9.34 x 5.63</td>\n      <td>99870</td>\n      <td>9.40</td>\n      <td>9.34</td>\n      <td>5.63</td>\n      <td>32744.262295</td>\n    </tr>\n    <tr>\n      <th>598012</th>\n      <td>5.59</td>\n      <td>Ideal</td>\n      <td>I</td>\n      <td>VS2</td>\n      <td>61.0</td>\n      <td>60.4</td>\n      <td>HRD</td>\n      <td>11.52 x 11.57 x 6.97</td>\n      <td>99890</td>\n      <td>11.52</td>\n      <td>11.57</td>\n      <td>6.97</td>\n      <td>17869.409660</td>\n    </tr>\n    <tr>\n      <th>598013</th>\n      <td>2.57</td>\n      <td>Ideal</td>\n      <td>E</td>\n      <td>IF</td>\n      <td>59.0</td>\n      <td>60.9</td>\n      <td>GIA</td>\n      <td>8.82 x 8.88 x 5.39</td>\n      <td>99896</td>\n      <td>8.82</td>\n      <td>8.88</td>\n      <td>5.39</td>\n      <td>38870.038911</td>\n    </tr>\n    <tr>\n      <th>598014</th>\n      <td>5.24</td>\n      <td>Ideal</td>\n      <td>I</td>\n      <td>SI1</td>\n      <td>60.0</td>\n      <td>59.5</td>\n      <td>GIA</td>\n      <td>11.35 x 11.43 x 6.78</td>\n      <td>99910</td>\n      <td>11.35</td>\n      <td>11.43</td>\n      <td>6.78</td>\n      <td>19066.793893</td>\n    </tr>\n    <tr>\n      <th>598015</th>\n      <td>5.03</td>\n      <td>Ideal</td>\n      <td>H</td>\n      <td>SI1</td>\n      <td>58.0</td>\n      <td>62.2</td>\n      <td>HRD</td>\n      <td>6.82 x 10.94 x 10.98</td>\n      <td>99913</td>\n      <td>6.82</td>\n      <td>10.94</td>\n      <td>10.98</td>\n      <td>19863.419483</td>\n    </tr>\n    <tr>\n      <th>598016</th>\n      <td>3.05</td>\n      <td>Ideal</td>\n      <td>D</td>\n      <td>VS2</td>\n      <td>59.0</td>\n      <td>61.3</td>\n      <td>GIA</td>\n      <td>5.73 x 9.33 x 9.36</td>\n      <td>99916</td>\n      <td>5.73</td>\n      <td>9.33</td>\n      <td>9.36</td>\n      <td>32759.344262</td>\n    </tr>\n    <tr>\n      <th>598017</th>\n      <td>3.01</td>\n      <td>Good</td>\n      <td>E</td>\n      <td>VS1</td>\n      <td>61.0</td>\n      <td>62.6</td>\n      <td>GIA</td>\n      <td>9.16 x 9.25 x 5.76</td>\n      <td>99920</td>\n      <td>9.16</td>\n      <td>9.25</td>\n      <td>5.76</td>\n      <td>33196.013289</td>\n    </tr>\n    <tr>\n      <th>598018</th>\n      <td>3.01</td>\n      <td>Ideal</td>\n      <td>D</td>\n      <td>VS2</td>\n      <td>58.0</td>\n      <td>62.0</td>\n      <td>GIA</td>\n      <td>9.25 x 9.2 x 5.72</td>\n      <td>99920</td>\n      <td>9.25</td>\n      <td>9.20</td>\n      <td>5.72</td>\n      <td>33196.013289</td>\n    </tr>\n    <tr>\n      <th>598019</th>\n      <td>3.02</td>\n      <td>Ideal</td>\n      <td>E</td>\n      <td>VVS2</td>\n      <td>58.0</td>\n      <td>59.8</td>\n      <td>HRD</td>\n      <td>9.43 x 9.51 x 5.66</td>\n      <td>99930</td>\n      <td>9.43</td>\n      <td>9.51</td>\n      <td>5.66</td>\n      <td>33089.403974</td>\n    </tr>\n    <tr>\n      <th>598020</th>\n      <td>5.01</td>\n      <td>V.Good</td>\n      <td>I</td>\n      <td>VVS2</td>\n      <td>63.5</td>\n      <td>61.5</td>\n      <td>IGI</td>\n      <td>10.78  x  10.89  x  6.68</td>\n      <td>99942</td>\n      <td>10.78</td>\n      <td>10.89</td>\n      <td>6.68</td>\n      <td>19948.502994</td>\n    </tr>\n    <tr>\n      <th>598021</th>\n      <td>3.43</td>\n      <td>Ideal</td>\n      <td>F</td>\n      <td>VS2</td>\n      <td>54.0</td>\n      <td>62.7</td>\n      <td>GIA</td>\n      <td>9.66 x 9.61 x 6.05</td>\n      <td>99960</td>\n      <td>9.66</td>\n      <td>9.61</td>\n      <td>6.05</td>\n      <td>29142.857143</td>\n    </tr>\n    <tr>\n      <th>598022</th>\n      <td>3.01</td>\n      <td>V.Good</td>\n      <td>E</td>\n      <td>VS1</td>\n      <td>58.0</td>\n      <td>62.9</td>\n      <td>GIA</td>\n      <td>9.15 x 9.19 x 5.77</td>\n      <td>99966</td>\n      <td>9.15</td>\n      <td>9.19</td>\n      <td>5.77</td>\n      <td>33211.295681</td>\n    </tr>\n    <tr>\n      <th>598023</th>\n      <td>4.13</td>\n      <td>Ideal</td>\n      <td>H</td>\n      <td>IF</td>\n      <td>56.0</td>\n      <td>62.5</td>\n      <td>IGI</td>\n      <td>10.27 x 10.19 x 6.4</td>\n      <td>99990</td>\n      <td>10.27</td>\n      <td>10.19</td>\n      <td>6.40</td>\n      <td>24210.653753</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 18, "cell_type": "code", "source": "bigdiamonds.columns", "outputs": [{"execution_count": 18, "output_type": "execute_result", "data": {"text/plain": "Index([u'carat', u'cut', u'color', u'clarity', u'table', u'depth', u'cert',\n       u'measurements', u'price', u'x', u'y', u'z', u'newdata'],\n      dtype='object')"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 19, "cell_type": "code", "source": "Sorted.describe()", "outputs": [{"execution_count": 19, "output_type": "execute_result", "data": {"text/plain": "               carat          table          depth          price  \\\ncount  598024.000000  598024.000000  598024.000000  597311.000000   \nmean        1.071297      57.631077      61.063683    8753.017974   \nstd         0.812696       4.996892       7.604342   13017.567760   \nmin         0.200000       0.000000       0.000000     300.000000   \n25%         0.500000      56.000000      61.000000    1220.000000   \n50%         0.900000      58.000000      62.100000    3503.000000   \n75%         1.500000      59.000000      62.700000   11174.000000   \nmax         9.250000      75.900000      81.300000   99990.000000   \n\n                   x              y              z        newdata  \ncount  596209.000000  596172.000000  595480.000000  597311.000000  \nmean        5.990771       6.198671       4.033430    5789.394414  \nstd         1.530936       1.485891       1.240951    4569.329246  \nmin         0.150000       1.000000       0.040000     525.000000  \n25%         4.740000       4.970000       3.120000    2669.696970  \n50%         5.780000       6.050000       3.860000    4174.257426  \n75%         6.970000       7.230000       4.610000    7436.666667  \nmax        13.890000      13.890000      13.180000   49519.402985  ", "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>carat</th>\n      <th>table</th>\n      <th>depth</th>\n      <th>price</th>\n      <th>x</th>\n      <th>y</th>\n      <th>z</th>\n      <th>newdata</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>598024.000000</td>\n      <td>598024.000000</td>\n      <td>598024.000000</td>\n      <td>597311.000000</td>\n      <td>596209.000000</td>\n      <td>596172.000000</td>\n      <td>595480.000000</td>\n      <td>597311.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>1.071297</td>\n      <td>57.631077</td>\n      <td>61.063683</td>\n      <td>8753.017974</td>\n      <td>5.990771</td>\n      <td>6.198671</td>\n      <td>4.033430</td>\n      <td>5789.394414</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.812696</td>\n      <td>4.996892</td>\n      <td>7.604342</td>\n      <td>13017.567760</td>\n      <td>1.530936</td>\n      <td>1.485891</td>\n      <td>1.240951</td>\n      <td>4569.329246</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.200000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>300.000000</td>\n      <td>0.150000</td>\n      <td>1.000000</td>\n      <td>0.040000</td>\n      <td>525.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>0.500000</td>\n      <td>56.000000</td>\n      <td>61.000000</td>\n      <td>1220.000000</td>\n      <td>4.740000</td>\n      <td>4.970000</td>\n      <td>3.120000</td>\n      <td>2669.696970</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>0.900000</td>\n      <td>58.000000</td>\n      <td>62.100000</td>\n      <td>3503.000000</td>\n      <td>5.780000</td>\n      <td>6.050000</td>\n      <td>3.860000</td>\n      <td>4174.257426</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>1.500000</td>\n      <td>59.000000</td>\n      <td>62.700000</td>\n      <td>11174.000000</td>\n      <td>6.970000</td>\n      <td>7.230000</td>\n      <td>4.610000</td>\n      <td>7436.666667</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>9.250000</td>\n      <td>75.900000</td>\n      <td>81.300000</td>\n      <td>99990.000000</td>\n      <td>13.890000</td>\n      <td>13.890000</td>\n      <td>13.180000</td>\n      <td>49519.402985</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 20, "cell_type": "code", "source": "import numpy as np\nimport urllib", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 21, "cell_type": "code", "source": "# URL for the Pima Indians Diabetes dataset (UCI Machine Learning Repository)\nurl = \"http://goo.gl/j0Rvxq\"\n# download the file\nraw_data = urllib.urlopen(url)\n# load the CSV file as a numpy matrix\ndataset = np.loadtxt(raw_data, delimiter=\",\")", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 22, "cell_type": "code", "source": "bigdiamonds.shape", "outputs": [{"execution_count": 22, "output_type": "execute_result", "data": {"text/plain": "(598024, 13)"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 23, "cell_type": "code", "source": "dataset.shape\n", "outputs": [{"execution_count": 23, "output_type": "execute_result", "data": {"text/plain": "(768, 9)"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 24, "cell_type": "code", "source": "print(type(dataset))\nprint(type(bigdiamonds))", "outputs": [{"output_type": "stream", "name": "stdout", "text": "<type 'numpy.ndarray'>\n<class 'pandas.core.frame.DataFrame'>\n"}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 25, "cell_type": "code", "source": "dataset", "outputs": [{"execution_count": 25, "output_type": "execute_result", "data": {"text/plain": "array([[   6.   ,  148.   ,   72.   , ...,    0.627,   50.   ,    1.   ],\n       [   1.   ,   85.   ,   66.   , ...,    0.351,   31.   ,    0.   ],\n       [   8.   ,  183.   ,   64.   , ...,    0.672,   32.   ,    1.   ],\n       ..., \n       [   5.   ,  121.   ,   72.   , ...,    0.245,   30.   ,    0.   ],\n       [   1.   ,  126.   ,   60.   , ...,    0.349,   47.   ,    1.   ],\n       [   1.   ,   93.   ,   70.   , ...,    0.315,   23.   ,    0.   ]])"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 26, "cell_type": "code", "source": "#dataset.head()", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 27, "cell_type": "code", "source": "dataset2=pd.DataFrame(dataset[0:,0:])", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 28, "cell_type": "code", "source": "dataset2.head()", "outputs": [{"execution_count": 28, "output_type": "execute_result", "data": {"text/plain": "   0    1   2   3    4     5      6   7  8\n0  6  148  72  35    0  33.6  0.627  50  1\n1  1   85  66  29    0  26.6  0.351  31  0\n2  8  183  64   0    0  23.3  0.672  32  1\n3  1   89  66  23   94  28.1  0.167  21  0\n4  0  137  40  35  168  43.1  2.288  33  1", "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>6</td>\n      <td>148</td>\n      <td>72</td>\n      <td>35</td>\n      <td>0</td>\n      <td>33.6</td>\n      <td>0.627</td>\n      <td>50</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>85</td>\n      <td>66</td>\n      <td>29</td>\n      <td>0</td>\n      <td>26.6</td>\n      <td>0.351</td>\n      <td>31</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>8</td>\n      <td>183</td>\n      <td>64</td>\n      <td>0</td>\n      <td>0</td>\n      <td>23.3</td>\n      <td>0.672</td>\n      <td>32</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>89</td>\n      <td>66</td>\n      <td>23</td>\n      <td>94</td>\n      <td>28.1</td>\n      <td>0.167</td>\n      <td>21</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>137</td>\n      <td>40</td>\n      <td>35</td>\n      <td>168</td>\n      <td>43.1</td>\n      <td>2.288</td>\n      <td>33</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 29, "cell_type": "code", "source": "dataset2.columns= [\"Number of times pregnant\",\"Plasma glucose concentration a 2 hours in an oral glucose tolerance test\"\n   ,\"Diastolic blood pressure (mm Hg)\",\n \"Triceps skin fold thickness (mm)\",\n  \"2-Hour serum insulin (mu U/ml)\",\n  \"Body mass index (weight in kg/(height in m)^2)\",\n\"Diabetes pedigree function\",\n \"Age (years)\",\n\"Class variable (0 or 1)\"]", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 30, "cell_type": "code", "source": "dataset2.head()", "outputs": [{"execution_count": 30, "output_type": "execute_result", "data": {"text/plain": "   Number of times pregnant  \\\n0                         6   \n1                         1   \n2                         8   \n3                         1   \n4                         0   \n\n   Plasma glucose concentration a 2 hours in an oral glucose tolerance test  \\\n0                                                148                          \n1                                                 85                          \n2                                                183                          \n3                                                 89                          \n4                                                137                          \n\n   Diastolic blood pressure (mm Hg)  Triceps skin fold thickness (mm)  \\\n0                                72                                35   \n1                                66                                29   \n2                                64                                 0   \n3                                66                                23   \n4                                40                                35   \n\n   2-Hour serum insulin (mu U/ml)  \\\n0                               0   \n1                               0   \n2                               0   \n3                              94   \n4                             168   \n\n   Body mass index (weight in kg/(height in m)^2)  Diabetes pedigree function  \\\n0                                            33.6                       0.627   \n1                                            26.6                       0.351   \n2                                            23.3                       0.672   \n3                                            28.1                       0.167   \n4                                            43.1                       2.288   \n\n   Age (years)  Class variable (0 or 1)  \n0           50                        1  \n1           31                        0  \n2           32                        1  \n3           21                        0  \n4           33                        1  ", "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Number of times pregnant</th>\n      <th>Plasma glucose concentration a 2 hours in an oral glucose tolerance test</th>\n      <th>Diastolic blood pressure (mm Hg)</th>\n      <th>Triceps skin fold thickness (mm)</th>\n      <th>2-Hour serum insulin (mu U/ml)</th>\n      <th>Body mass index (weight in kg/(height in m)^2)</th>\n      <th>Diabetes pedigree function</th>\n      <th>Age (years)</th>\n      <th>Class variable (0 or 1)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>6</td>\n      <td>148</td>\n      <td>72</td>\n      <td>35</td>\n      <td>0</td>\n      <td>33.6</td>\n      <td>0.627</td>\n      <td>50</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>85</td>\n      <td>66</td>\n      <td>29</td>\n      <td>0</td>\n      <td>26.6</td>\n      <td>0.351</td>\n      <td>31</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>8</td>\n      <td>183</td>\n      <td>64</td>\n      <td>0</td>\n      <td>0</td>\n      <td>23.3</td>\n      <td>0.672</td>\n      <td>32</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>89</td>\n      <td>66</td>\n      <td>23</td>\n      <td>94</td>\n      <td>28.1</td>\n      <td>0.167</td>\n      <td>21</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>137</td>\n      <td>40</td>\n      <td>35</td>\n      <td>168</td>\n      <td>43.1</td>\n      <td>2.288</td>\n      <td>33</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 31, "cell_type": "code", "source": "dataset3=np.array(dataset2)", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 32, "cell_type": "code", "source": "dataset3", "outputs": [{"execution_count": 32, "output_type": "execute_result", "data": {"text/plain": "array([[   6.   ,  148.   ,   72.   , ...,    0.627,   50.   ,    1.   ],\n       [   1.   ,   85.   ,   66.   , ...,    0.351,   31.   ,    0.   ],\n       [   8.   ,  183.   ,   64.   , ...,    0.672,   32.   ,    1.   ],\n       ..., \n       [   5.   ,  121.   ,   72.   , ...,    0.245,   30.   ,    0.   ],\n       [   1.   ,  126.   ,   60.   , ...,    0.349,   47.   ,    1.   ],\n       [   1.   ,   93.   ,   70.   , ...,    0.315,   23.   ,    0.   ]])"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 33, "cell_type": "code", "source": "b=len(bigdiamonds)", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 34, "cell_type": "code", "source": "b", "outputs": [{"execution_count": 34, "output_type": "execute_result", "data": {"text/plain": "598024"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 35, "cell_type": "code", "source": "len(bigdiamonds.columns)", "outputs": [{"execution_count": 35, "output_type": "execute_result", "data": {"text/plain": "13"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 36, "cell_type": "code", "source": "bigdiamonds.index.values", "outputs": [{"execution_count": 36, "output_type": "execute_result", "data": {"text/plain": "array([     0,      1,      2, ..., 598021, 598022, 598023], dtype=int64)"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 37, "cell_type": "code", "source": "rows = np.random.choice(bigdiamonds.index.values,0.00001*b)\nprint(rows)", "outputs": [{"output_type": "stream", "name": "stdout", "text": "[ 18336 387063 336467 563134 545950]\n"}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 38, "cell_type": "code", "source": "sampled_df = bigdiamonds.ix[rows]", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 39, "cell_type": "code", "source": "sampled_df", "outputs": [{"execution_count": 39, "output_type": "execute_result", "data": {"text/plain": "        carat     cut color clarity  table  depth cert        measurements  \\\n18336    0.23   Ideal     E     VS2     59   60.9  GIA  3.98 x 3.97 x 2.42   \n387063   1.33   Ideal     J     VS2     61   60.3  GIA  7.15 x 7.12 x 4.31   \n336467   1.02  V.Good     H     SI2     56   63.6  GIA  6.31 x 6.33 x 4.02   \n563134   2.01  V.Good     I     VS1     56   63.6  GIA  7.95 x 8.00 x 5.07   \n545950   2.08   Ideal     I     VS2     57   62.6  GIA  8.11 x 8.15 x 5.09   \n\n        price     x     y     z       newdata  \n18336     550  3.98  3.97  2.42   2391.304348  \n387063   6480  7.15  7.12  4.31   4872.180451  \n336467   4540  6.31  6.33  4.02   4450.980392  \n563134  21493  7.95  8.00  5.07  10693.034826  \n545950  16790  8.11  8.15  5.09   8072.115385  ", "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>carat</th>\n      <th>cut</th>\n      <th>color</th>\n      <th>clarity</th>\n      <th>table</th>\n      <th>depth</th>\n      <th>cert</th>\n      <th>measurements</th>\n      <th>price</th>\n      <th>x</th>\n      <th>y</th>\n      <th>z</th>\n      <th>newdata</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>18336</th>\n      <td>0.23</td>\n      <td>Ideal</td>\n      <td>E</td>\n      <td>VS2</td>\n      <td>59</td>\n      <td>60.9</td>\n      <td>GIA</td>\n      <td>3.98 x 3.97 x 2.42</td>\n      <td>550</td>\n      <td>3.98</td>\n      <td>3.97</td>\n      <td>2.42</td>\n      <td>2391.304348</td>\n    </tr>\n    <tr>\n      <th>387063</th>\n      <td>1.33</td>\n      <td>Ideal</td>\n      <td>J</td>\n      <td>VS2</td>\n      <td>61</td>\n      <td>60.3</td>\n      <td>GIA</td>\n      <td>7.15 x 7.12 x 4.31</td>\n      <td>6480</td>\n      <td>7.15</td>\n      <td>7.12</td>\n      <td>4.31</td>\n      <td>4872.180451</td>\n    </tr>\n    <tr>\n      <th>336467</th>\n      <td>1.02</td>\n      <td>V.Good</td>\n      <td>H</td>\n      <td>SI2</td>\n      <td>56</td>\n      <td>63.6</td>\n      <td>GIA</td>\n      <td>6.31 x 6.33 x 4.02</td>\n      <td>4540</td>\n      <td>6.31</td>\n      <td>6.33</td>\n      <td>4.02</td>\n      <td>4450.980392</td>\n    </tr>\n    <tr>\n      <th>563134</th>\n      <td>2.01</td>\n      <td>V.Good</td>\n      <td>I</td>\n      <td>VS1</td>\n      <td>56</td>\n      <td>63.6</td>\n      <td>GIA</td>\n      <td>7.95 x 8.00 x 5.07</td>\n      <td>21493</td>\n      <td>7.95</td>\n      <td>8.00</td>\n      <td>5.07</td>\n      <td>10693.034826</td>\n    </tr>\n    <tr>\n      <th>545950</th>\n      <td>2.08</td>\n      <td>Ideal</td>\n      <td>I</td>\n      <td>VS2</td>\n      <td>57</td>\n      <td>62.6</td>\n      <td>GIA</td>\n      <td>8.11 x 8.15 x 5.09</td>\n      <td>16790</td>\n      <td>8.11</td>\n      <td>8.15</td>\n      <td>5.09</td>\n      <td>8072.115385</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 40, "cell_type": "code", "source": "bigdiamonds.newdata.describe()", "outputs": [{"execution_count": 40, "output_type": "execute_result", "data": {"text/plain": "count    597311.000000\nmean       5789.394414\nstd        4569.329246\nmin         525.000000\n25%        2669.696970\n50%        4174.257426\n75%        7436.666667\nmax       49519.402985\nName: newdata, dtype: float64"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 41, "cell_type": "code", "source": "ajay=bigdiamonds.groupby(\"cut\")", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 42, "cell_type": "code", "source": "ajay", "outputs": [{"execution_count": 42, "output_type": "execute_result", "data": {"text/plain": "<pandas.core.groupby.DataFrameGroupBy object at 0x0BD58F10>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 43, "cell_type": "code", "source": "ajay.max().newdata", "outputs": [{"execution_count": 43, "output_type": "execute_result", "data": {"text/plain": "cut\nGood      43410.000000\nIdeal     49481.592040\nV.Good    49519.402985\nName: newdata, dtype: float64"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 44, "cell_type": "code", "source": "ajay.max().carat", "outputs": [{"execution_count": 44, "output_type": "execute_result", "data": {"text/plain": "cut\nGood      8.90\nIdeal     9.25\nV.Good    8.90\nName: carat, dtype: float64"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 45, "cell_type": "code", "source": "ajay2=pd.crosstab(bigdiamonds.color,bigdiamonds.cut)\najay2\n", "outputs": [{"execution_count": 45, "output_type": "execute_result", "data": {"text/plain": "cut    Good  Ideal  V.Good\ncolor                     \nD      6604  45435   21591\nE      9733  55547   28203\nF      9141  58148   26284\nG      8923  62067   25214\nH      7600  56026   22993\nI      7380  43000   19902\nJ      5357  29440   13912\nK      3467  14729    7672\nL      1475   5056    3125", "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>cut</th>\n      <th>Good</th>\n      <th>Ideal</th>\n      <th>V.Good</th>\n    </tr>\n    <tr>\n      <th>color</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>D</th>\n      <td>6604</td>\n      <td>45435</td>\n      <td>21591</td>\n    </tr>\n    <tr>\n      <th>E</th>\n      <td>9733</td>\n      <td>55547</td>\n      <td>28203</td>\n    </tr>\n    <tr>\n      <th>F</th>\n      <td>9141</td>\n      <td>58148</td>\n      <td>26284</td>\n    </tr>\n    <tr>\n      <th>G</th>\n      <td>8923</td>\n      <td>62067</td>\n      <td>25214</td>\n    </tr>\n    <tr>\n      <th>H</th>\n      <td>7600</td>\n      <td>56026</td>\n      <td>22993</td>\n    </tr>\n    <tr>\n      <th>I</th>\n      <td>7380</td>\n      <td>43000</td>\n      <td>19902</td>\n    </tr>\n    <tr>\n      <th>J</th>\n      <td>5357</td>\n      <td>29440</td>\n      <td>13912</td>\n    </tr>\n    <tr>\n      <th>K</th>\n      <td>3467</td>\n      <td>14729</td>\n      <td>7672</td>\n    </tr>\n    <tr>\n      <th>L</th>\n      <td>1475</td>\n      <td>5056</td>\n      <td>3125</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 46, "cell_type": "code", "source": "bigdiamonds.groupby('color').mean().price", "outputs": [{"execution_count": 46, "output_type": "execute_result", "data": {"text/plain": "color\nD    8266.345758\nE    7282.990286\nF    8234.729744\nG    8984.200296\nH    9941.795159\nI    9541.318901\nJ    9423.581170\nK    9694.256675\nL    7109.228314\nName: price, dtype: float64"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 47, "cell_type": "code", "source": "\nlastdiamonds=bigdiamonds[bigdiamonds['color']==\"D\" ]\nlastdiamonds2= lastdiamonds[lastdiamonds['cut']==\"Good\"]\nprint(lastdiamonds.shape)\nprint(lastdiamonds2.shape)\n\n", "outputs": [{"output_type": "stream", "name": "stdout", "text": "(73630, 13)\n(6604, 13)\n"}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 48, "cell_type": "code", "source": "#np.ix??", "outputs": [{"output_type": "stream", "name": "stdout", "text": "Object `np.ix` not found.\n"}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 50, "cell_type": "code", "source": "#.ix", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": null, "cell_type": "code", "source": "", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": null, "cell_type": "code", "source": "", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 51, "cell_type": "code", "source": "lastdiamonds.describe().price", "outputs": [{"execution_count": 51, "output_type": "execute_result", "data": {"text/plain": "count    73563.000000\nmean      8266.345758\nstd      12856.623236\nmin        301.000000\n25%       1050.000000\n50%       2690.000000\n75%      10613.500000\nmax      99920.000000\nName: price, dtype: float64"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 52, "cell_type": "code", "source": "\nclarity=bigdiamonds.groupby(\"clarity\")", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 53, "cell_type": "code", "source": "clarity.count().price", "outputs": [{"execution_count": 53, "output_type": "execute_result", "data": {"text/plain": "clarity\nI1       14355\nI2        2284\nIF       31156\nSI1     116468\nSI2     104104\nVS1      97677\nVS2     110997\nVVS1     54790\nVVS2     65480\nName: price, dtype: int64"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 54, "cell_type": "code", "source": "clarity.mean().price", "outputs": [{"execution_count": 54, "output_type": "execute_result", "data": {"text/plain": "clarity\nI1       2465.762382\nI2       2611.824869\nIF      11559.865419\nSI1      7909.712840\nSI2      6986.413317\nVS1     10270.489491\nVS2      9808.277449\nVVS1     8467.331922\nVVS2     9505.287538\nName: price, dtype: float64"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 55, "cell_type": "code", "source": "import matplotlib as mt\nimport pylab as pl\n%matplotlib inline\n", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 56, "cell_type": "code", "source": "pl.plot(bigdiamonds.carat,bigdiamonds.price)\n", "outputs": [{"execution_count": 56, "output_type": "execute_result", "data": {"text/plain": "[<matplotlib.lines.Line2D at 0x697a370>]"}, "metadata": {}}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0xa4b3550>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 57, "cell_type": "code", "source": "pl.show() ", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 58, "cell_type": "code", "source": "  \npl.scatter(bigdiamonds.carat,bigdiamonds.price)\npl.show()  ", "outputs": [{"output_type": "display_data", "data": {"image/png": 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ybZ0Ssacq/Rqeu5HCup96SDNOrBSYzufXkwYXrJNqv4nfe88IX23XXp2RHs0l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"text/plain": "<matplotlib.figure.Figure at 0xa4dae50>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 59, "cell_type": "code", "source": "bigdiamonds.describe()", "outputs": [{"execution_count": 59, "output_type": "execute_result", "data": {"text/plain": "               carat          table          depth          price  \\\ncount  598024.000000  598024.000000  598024.000000  597311.000000   \nmean        1.071297      57.631077      61.063683    8753.017974   \nstd         0.812696       4.996892       7.604342   13017.567760   \nmin         0.200000       0.000000       0.000000     300.000000   \n25%         0.500000      56.000000      61.000000    1220.000000   \n50%         0.900000      58.000000      62.100000    3503.000000   \n75%         1.500000      59.000000      62.700000   11174.000000   \nmax         9.250000      75.900000      81.300000   99990.000000   \n\n                   x              y              z        newdata  \ncount  596209.000000  596172.000000  595480.000000  597311.000000  \nmean        5.990771       6.198671       4.033430    5789.394414  \nstd         1.530936       1.485891       1.240951    4569.329246  \nmin         0.150000       1.000000       0.040000     525.000000  \n25%         4.740000       4.970000       3.120000    2669.696970  \n50%         5.780000       6.050000       3.860000    4174.257426  \n75%         6.970000       7.230000       4.610000    7436.666667  \nmax        13.890000      13.890000      13.180000   49519.402985  ", "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>carat</th>\n      <th>table</th>\n      <th>depth</th>\n      <th>price</th>\n      <th>x</th>\n      <th>y</th>\n      <th>z</th>\n      <th>newdata</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>598024.000000</td>\n      <td>598024.000000</td>\n      <td>598024.000000</td>\n      <td>597311.000000</td>\n      <td>596209.000000</td>\n      <td>596172.000000</td>\n      <td>595480.000000</td>\n      <td>597311.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>1.071297</td>\n      <td>57.631077</td>\n      <td>61.063683</td>\n      <td>8753.017974</td>\n      <td>5.990771</td>\n      <td>6.198671</td>\n      <td>4.033430</td>\n      <td>5789.394414</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.812696</td>\n      <td>4.996892</td>\n      <td>7.604342</td>\n      <td>13017.567760</td>\n      <td>1.530936</td>\n      <td>1.485891</td>\n      <td>1.240951</td>\n      <td>4569.329246</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.200000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>300.000000</td>\n      <td>0.150000</td>\n      <td>1.000000</td>\n      <td>0.040000</td>\n      <td>525.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>0.500000</td>\n      <td>56.000000</td>\n      <td>61.000000</td>\n      <td>1220.000000</td>\n      <td>4.740000</td>\n      <td>4.970000</td>\n      <td>3.120000</td>\n      <td>2669.696970</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>0.900000</td>\n      <td>58.000000</td>\n      <td>62.100000</td>\n      <td>3503.000000</td>\n      <td>5.780000</td>\n      <td>6.050000</td>\n      <td>3.860000</td>\n      <td>4174.257426</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>1.500000</td>\n      <td>59.000000</td>\n      <td>62.700000</td>\n      <td>11174.000000</td>\n      <td>6.970000</td>\n      <td>7.230000</td>\n      <td>4.610000</td>\n      <td>7436.666667</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>9.250000</td>\n      <td>75.900000</td>\n      <td>81.300000</td>\n      <td>99990.000000</td>\n      <td>13.890000</td>\n      <td>13.890000</td>\n      <td>13.180000</td>\n      <td>49519.402985</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 60, "cell_type": "code", "source": "bigdiamonds.price.describe()", "outputs": [{"execution_count": 60, "output_type": "execute_result", "data": {"text/plain": "count    597311.000000\nmean       8753.017974\nstd       13017.567760\nmin         300.000000\n25%        1220.000000\n50%        3503.000000\n75%       11174.000000\nmax       99990.000000\nName: price, dtype: float64"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": null, "cell_type": "code", "source": "", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": null, "cell_type": "code", "source": "", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": null, "cell_type": "code", "source": "", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": null, "cell_type": "code", "source": "", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 61, "cell_type": "code", "source": "bigdiamonds.corr()", "outputs": [{"execution_count": 61, "output_type": "execute_result", "data": {"text/plain": "            carat     table     depth     price         x         y         z  \\\ncarat    1.000000  0.036533  0.009846  0.856328  0.860246  0.960807  0.792051   \ntable    0.036533  1.000000  0.448772  0.023378  0.027504  0.044542  0.030344   \ndepth    0.009846  0.448772  1.000000 -0.001006 -0.003279  0.007669  0.031801   \nprice    0.856328  0.023378 -0.001006  1.000000  0.719778  0.796765  0.645317   \nx        0.860246  0.027504 -0.003279  0.719778  1.000000  0.894203  0.483102   \ny        0.960807  0.044542  0.007669  0.796765  0.894203  1.000000  0.820211   \nz        0.792051  0.030344  0.031801  0.645317  0.483102  0.820211  1.000000   \nnewdata  0.685399  0.012662  0.003913  0.885976  0.647908  0.716541  0.578021   \n\n          newdata  \ncarat    0.685399  \ntable    0.012662  \ndepth    0.003913  \nprice    0.885976  \nx        0.647908  \ny        0.716541  \nz        0.578021  \nnewdata  1.000000  ", "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>carat</th>\n      <th>table</th>\n      <th>depth</th>\n      <th>price</th>\n      <th>x</th>\n      <th>y</th>\n      <th>z</th>\n      <th>newdata</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>carat</th>\n      <td>1.000000</td>\n      <td>0.036533</td>\n      <td>0.009846</td>\n      <td>0.856328</td>\n      <td>0.860246</td>\n      <td>0.960807</td>\n      <td>0.792051</td>\n      <td>0.685399</td>\n    </tr>\n    <tr>\n      <th>table</th>\n      <td>0.036533</td>\n      <td>1.000000</td>\n      <td>0.448772</td>\n      <td>0.023378</td>\n      <td>0.027504</td>\n      <td>0.044542</td>\n      <td>0.030344</td>\n      <td>0.012662</td>\n    </tr>\n    <tr>\n      <th>depth</th>\n      <td>0.009846</td>\n      <td>0.448772</td>\n      <td>1.000000</td>\n      <td>-0.001006</td>\n      <td>-0.003279</td>\n      <td>0.007669</td>\n      <td>0.031801</td>\n      <td>0.003913</td>\n    </tr>\n    <tr>\n      <th>price</th>\n      <td>0.856328</td>\n      <td>0.023378</td>\n      <td>-0.001006</td>\n      <td>1.000000</td>\n      <td>0.719778</td>\n      <td>0.796765</td>\n      <td>0.645317</td>\n      <td>0.885976</td>\n    </tr>\n    <tr>\n      <th>x</th>\n      <td>0.860246</td>\n      <td>0.027504</td>\n      <td>-0.003279</td>\n      <td>0.719778</td>\n      <td>1.000000</td>\n      <td>0.894203</td>\n      <td>0.483102</td>\n      <td>0.647908</td>\n    </tr>\n    <tr>\n      <th>y</th>\n      <td>0.960807</td>\n      <td>0.044542</td>\n      <td>0.007669</td>\n      <td>0.796765</td>\n      <td>0.894203</td>\n      <td>1.000000</td>\n      <td>0.820211</td>\n      <td>0.716541</td>\n    </tr>\n    <tr>\n      <th>z</th>\n      <td>0.792051</td>\n      <td>0.030344</td>\n      <td>0.031801</td>\n      <td>0.645317</td>\n      <td>0.483102</td>\n      <td>0.820211</td>\n      <td>1.000000</td>\n      <td>0.578021</td>\n    </tr>\n    <tr>\n      <th>newdata</th>\n      <td>0.685399</td>\n      <td>0.012662</td>\n      <td>0.003913</td>\n      <td>0.885976</td>\n      <td>0.647908</td>\n      <td>0.716541</td>\n      <td>0.578021</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 62, "cell_type": "code", "source": "q=bigdiamonds.corr()", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 63, "cell_type": "code", "source": "q.to_csv(\"testnewdayoutput.csv\")", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 64, "cell_type": "code", "source": "cleandiamonds=bigdiamonds.dropna().reset_index(drop=True) ", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 65, "cell_type": "code", "source": "cleandiamonds.info()", "outputs": [{"output_type": "stream", "name": "stdout", "text": "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 593784 entries, 0 to 593783\nData columns (total 13 columns):\ncarat           593784 non-null float64\ncut             593784 non-null object\ncolor           593784 non-null object\nclarity         593784 non-null object\ntable           593784 non-null float64\ndepth           593784 non-null float64\ncert            593784 non-null object\nmeasurements    593784 non-null object\nprice           593784 non-null float64\nx               593784 non-null float64\ny               593784 non-null float64\nz               593784 non-null float64\nnewdata         593784 non-null float64\ndtypes: float64(8), object(5)\nmemory usage: 52.1+ MB\n"}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 66, "cell_type": "code", "source": "from ggplot import *\n", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 67, "cell_type": "code", "source": "p = ggplot(aes(x='price', y='carat'), data=cleandiamonds)\np", "outputs": [{"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x26b23330>"}, "metadata": {}}, {"execution_count": 67, "output_type": "execute_result", "data": {"text/plain": "<ggplot: (40575763)>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 68, "cell_type": "code", "source": "p + geom_point()\n", "outputs": [{"output_type": "display_data", "data": {"image/png": 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TShBqhWJWCYIgCIIgCNVCYpUgCIKIKFT7kyAIX1AYAEEQBBExejr9JkEQvR/yrBIEQQQB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\ntEjf+ta3+OkfAMSeVQCYFWOMd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"text/plain": "<matplotlib.figure.Figure at 0x1ab92150>"}, "metadata": {}}, {"execution_count": 68, "output_type": "execute_result", "data": {"text/plain": "<ggplot: (40575751)>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 69, "cell_type": "code", "source": "p + geom_point() +facet_grid('cut')\n", "outputs": [{"output_type": "display_data", "data": {"image/png": 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333/fo7YrlRKC4a7/Oz4R+dSQIUMcHvvqq6+8ri3srgQSSVnTFayTDEORbSUV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"text/plain": "<matplotlib.figure.Figure at 0x2689a2f0>"}, "metadata": {}}, {"execution_count": 69, "output_type": "execute_result", "data": {"text/plain": "<ggplot: (40419825)>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 70, "cell_type": "code", "source": "p + geom_point() +facet_grid('color')\n", "outputs": [{"output_type": "display_data", "data": {"image/png": 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qyIG97QCdOy/ZygudTofCwkIwDMM1lKKjo7Fx40bMmjXLqgLBpk2bAMCqYsEL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efPW7E4+vx+97vfYceOHTaPOcqlPXv2YMKECaioqBBP2O3atUNERATUajUWLlwIAJg0aRLOnj2L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\nisVkmIiIi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PrE/M0332DBggWorKz0U+uot3nooYeQk5OD4uJiCIKAVatW4fPPP8eNGzcw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\n", "text/plain": "<matplotlib.figure.Figure at 0x26b3adb0>"}, "metadata": {}}, {"execution_count": 70, "output_type": "execute_result", "data": {"text/plain": "<ggplot: (40575855)>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 71, "cell_type": "code", "source": "p + geom_point() +facet_grid('clarity')\n", "outputs": [{"output_type": "display_data", "data": {"image/png": 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sxNmzZ3Hq1Cmr21lazf9UVFQgIyOjQ2ZffcGdmeyMjAwcOHAAwPXqGbfeeqvV\njIJarcZPP/3UpnJnREREnsTBsJtqa2vFBfdNTU3Iy8vD4MGDbQ6fymVmmGVnnGtZcmbJkiVYvXq1\nr7vUJqGhoYiLi8O5c+eQmJiIBQsWYPLkyeI6X5VKhby8PDz++OOSV13UaDTYv3+/Vck0nU6HZ599\nFiUlJUhMTERubq5HL4QhB3IpX9QRuF+Sxjy5hlmSJpcssbSaB9XU1OCTTz6BxWKBxWLBgAEDcMst\nt9i0Y2k1/7FhwwZMnz7d191ol8ceewyLFi2yum3v3r02J6EVFBSIt82ePVs8aa7lv1euXImEhASr\n3CQkJGD//v1Wa/OYK/vkXL7IU7hfch3z5Byz5Do5Z4mDYTdVV1ejqakJFosFaWlpyMjI8HWXyIce\neOABuyeHdSX9+vXD888/b3N7UlKSTQ3g1re1XArEZUFERNQVcTDsBrPZjM8++wxTpkxBaGgo1q9f\nj9TUVLtT76wzLH+zZ8/u1APhwMBAxMbGWl2cA7i+XvfBBx9Ejx49EBgYiBkzZnTI1eiYJef8oZan\npzBL0pgn1zBL0vwhS/J8VV5SWlqKiIgIsXLELbfcguPHj9sdDMtlDRLLzjiWl5fn6y6gb9++eO+9\n9/DRRx+hvr4egiAgICBALAP2xhtvYO3atVb3mTFjBl588UWr2zrid8wsOadWqxEWFoba2lrZHor0\nFGZJGvPkGmZJmlyy1LrqV0scDLuhurra6nKsoaGhKC0tRXV1NWpqaqzayuUEOpad6RyaB7mbNm3C\n7bffLl5AY/r06YiMjHRY/mvGjBnYs2eP1YUjZsyY4ZPfKbPknD+UL/IUZkka8+QaZkmaP2SJ1STc\ncOzYMeh0OowbNw4AcPjwYZSWliIwMBCFhYVWbVlaTf5iYmJQXl7e7sfp27cvHnroISxYsABRUVGo\nqKjAK6+8gp07dyI2Nhbvvfdeu0qQVVRUICcnBwAwZ84cREVFtbvPREREcsHBsBvOnz+P/fv3Y/Lk\nyQCAr7/+GoIg4LbbbpPtzDDLzjim0+lw9913O22jVCoxdepUzJkzp0PW5XZmzJJzcilf1BGYJWnM\nk2uYJWlyyRJLq3lIbGwsKisrUVVVhZCQEPz888+YMGECQkNDERoaatWWpdXkLyEhAd9++y3uuece\nmM1mAEDPnj1RVFRkt72/v4/MkmvkXL7IU5gl1zFPzjFLrpNzljgYdoNSqcSYMWOQn58Ps9mMtLQ0\np980SP7b2kEBAAAgAElEQVSSk5NhMpm6/DXbiYiI/BWXSXiJXq+XTWm15llPsiUIAjQaDQwGg8uX\nKvZXzJJzzJLrmCVpzJNrmCVpcskSq0n4gFzWILHsjHNyKTnTEZgl55gl1zFL0pgn1zBL0uSSJWeD\n4a4/dUlERERE1EYcDBMRERGR3+KaYaJ2qK6uRlFREdLT020qihC5g1kiT2KeyFP8IUucGSZqh5qa\nGhQWFtrUmSZyF7NEnsQ8kaf4Q5Y4GCYiIiIiv8XBMBERERH5LQ6GiYiIiMhv8QQ6L1mwYAEMBoOv\nu0FeZjKZcO3aNYSEhECpVPq6O9SFMUvkScwTeYpcsqTRaLBs2TK723jRDS8xGAx44oknfN0NIiK3\n3H///Vi8eDEuXbqE1157DQEBAeK2rKwsvPTSSz7sHRFR22zZssXhNg6GiYhIJAiC+N/tt9+OrVu3\n+rpLRERexTXDRERkgyvoiMhfcDBMRERERH6LyySIiMiuI0eO4O677xZ/zs3Nxa233urDHhEReR4H\nw0REZNdtt93GNcNEJHtcJkFEREREfouDYSIissKT54jIn3AwTEREVppLqxER+QOuGSYiItGePXvE\nf48fP96HPSEi6hicGSYiIiIiv8WZYTd9//33OHToECwWC9LT0zFkyBC77YxGI9avX9/BvfO8rKws\nbN++3dfd6LTi4+Mxb948LF++HOfPn/d1dzo1Zsk5Zsl1zJI05sk1zJI0uWQpODjY4TbBwjMlXFZW\nVoZt27Zh2rRpUCqVyM/Px9ixYxEREWHTVq/XQ6Ho+hPvCoUCZrPZ193otARBgEajgcFg4ElHEpgl\n55gl1zFL0pgn1zBL0uSSpfDwcIfbODPshoqKCsTFxUGtVgMAEhIS8MsvvyAjI8OmbWNjY0d3zysC\nAwNRX1/v6250Wmq1GmFhYaitrUVTU5Ovu9OpMUvOMUuuY5akMU+uYZakySVLzgbDXX/qsgPFxMTg\n3LlzqKurg8FgQHFxMaqrq33dLSIiIiJqI84MuyE6OhoZGRnIy8uDRqNBz549IQgCqqurUVNTY9XW\nYDAgKCjIRz31HKVSKc6Eky2VSmX1f3KMWXKOWXIdsySNeXINsyTNH7LENcPtUFBQgO7du6OmpgaF\nhYVW24YPH46RI0f6qGdERERE5Ar5DvO9pKamBsHBwbhy5QqOHz+OqVOnwmAwIDU11aqdwWBAeXm5\nj3rpOVqtVjbrn71BpVIhPDwcVVVVMBqNvu5Op8YsOccsuY5ZksY8uYZZkiaXLEVHRzvcxsGwm/72\nt7/h8uXLMJlMCAoKQnl5OeLj4xEaGmrV7sKFC116oXkzlUoli9fhbUajke+TBGbJNcySNGbJdcyT\nc8yS6+ScJQ6G3RQZGYm0tDSkpaXBZDI5DIZWq5VNabXAwEBfd6PTEgQBdXV1UKvVsl5P5QnMknPM\nkuuYJWnMk2uYJWn+kCV5viovaWhowNmzZ/HQQw8BuL7wXqlU2m0rl8MuLDvjnFxKznQEZsk5Zsl1\nzJK0js5TSUkJ5s6dCwBYuXIlkpKSvP6cnsAsSZPLvol1hj2kqqoKQUFB2L59Oy5duoTY2FiMHj0a\nGo3G110jIiLyiZKSEmRmZsJgMAAAMjMzUVBQ4NaAuKsOpkkeOBh2g9lsxsWLFzFmzBjExcVh9+7d\nOHDgAAYOHMjSan7KH0rOeAqz5Byz5DpmSVpH5Umv1+Oxxx4TB8LA9c+/7Oxs7Ny506XH0Ol0NoPp\n/fv3Izk52WH7OXPmAABycnIctnMFsyTNH/ZNLK3mhmvXrmHjxo3iH+HZs2dx4MABxMbGsrQaEVEX\nU1FRgZycHADA2LFjkZ2dDQDYuHGjTYUgTzzHnDlzEBUV5fR2dx9z8uTJyMvLa9PjtMWJEycwceJE\n6HQ63HDDDaitrUVJSYlNu/j4eJw7dw4AsGfPHjz88MNoaGhA//798fHHH4vv74kTJ3D77bejoaHB\n6v4ZGRk4cOCA1fM+/fTTKC8vx8mTJ8Xb1Wo1kpOT8euvvyIlJQXvv/8+UlNTrfrZ8nYiezgYdtOm\nTZswbtw4REVFYd++fTAajbjzzjtlOzPMsjPOyaXkTEdglpzzVpbaOoum1+uxfv166PV6/Pzzz1Cp\nVFaDN3cer/mxAGD69OmIjIxsV1t3suTo8fR6PcaPH281sGqmUCgwadIkvPTSS077KkWn02Hs2LGo\nrKwEAPTr1w+ffvopqqqq8MADD6Cqqkq8PScnB4sWLQLg/H1t3W+1Wi2u40xKSsLOnTsRGRnplTzp\ndDoMGzbM5cebP38+7rjjDkycONHqdpVKha+++goAcM8998BsNtvcd/DgweLMsk6nw4gRI6xmnx1R\nqVTIy8vD5MmTrfrZ/Jyt31ful6TJ5XPOWWk1DobddOnSJezYsQMmkwnh4eHIyspCQECATbsLFy74\noHeex5MLnFOr1YiOjkZ5eXmXPrGgI8glS5WVldiwYQMAYNq0aYiIiPDI43oiS63XXZ47dw6TJ09G\ny918SkoKNm7cKK7JtPd6Kisr8fDDDzscKDYPXjQaDTZt2oQ1a9agqakJt956K8LDwzFt2jQAwIYN\nG1BVVYUdO3bg6tWrAK4P2DZv3oyPPvrI5jnXrl2Lbdu2WQ0et23bZvMeBwYGorS0VOz34MGDMXv2\nbFRVVSEkJASxsbEIDg7GK6+8guzsbPF19OvXDytWrMCSJUvw66+/4uLFi07fT0fP78r7/8orr+Dx\nxx8XB7zNbrzxRpw8eRImk8np++poze0bb7yBtWvXOuzDI488gjNnzkAQBLz33ns4deoUnn76aQBA\nbm4uwsLCJNfmOsp4VlYWDh486NJ70SwoKAi1tbU2t3fr1g0Gg8Hh4Oqrr74S++bu8zp6zkGDBmH7\n9u0A/v/flSAIWLFiBdcoOyGXz7nY2FiH2zgYdlNTUxO2bNkCo9EIk8mE/v37IzMz06YdB8P+QS47\niY4ghyy1HiQ2D5YA2B08uDNwbp0lZ/dtHjj+97//xR133IGxY8diwYIFOHr0KFzZpatUKuzduxfh\n4eFWrycgIAD/+Mc/UFBQ4HTA1ZIgCDbP2TywsHf4HADCwsJw5coVANffw3fffRdTp061O/h+/vnn\n8eKLL1rdVldXhwceeMBu+5ZaDjCd3ebM5MmTMX/+fKv3e9asWTh8+DCmTZuGxsZGJCUl4bXXXsNT\nTz0lzl66+zyt3X777di1a5fN7VKDYSkqlUocgLYcdDfnraqqCv/85z/F309zJtLT0/HAAw/gv//9\nr1vP15a/e6VSKS6xADw/GC4pKcG9994rvg/Nfw8cENsnl885DoY9zGAwQKPRwGQyYdOmTRg1ahQS\nEhKs2uj1etnUGW7PDl3uBEGARqOBwWBwaRDiD/R6Pd5++21UVlbiyJEjUKlUWLduHVJTU2E2m1Fc\nXIxZs2YBANatW4eUlBS79weAGTNmoLKy0qZ96+ewWCwYMGCAWC+0qqoKX3zxBaqqqqBQKKDVasUP\nx/j4eISEhECv1yM4OBjnz5+HwWBAaGgoevfuLZ4kEhAQgCVLlmDz5s345JNP2nQoteWgT6VSQalU\nwmQyoXfv3qitrYVCocBvf/tbzJgxA1u2bBEHW48//jimTJmCEydOWD2eQqFAnz59cOHCBZcOGTvj\n7PBwVFQUKioq2vX47nC2nwkMDERwcDCio6Oh1WrRq1cv/Otf//Lp4VpHgy1PEgQBf//73/H9998D\nAB577DH8/e9/R319PXbt2mU1WGyPm266CeXl5ZJXTJ02bRo2b97cIe+7UqlEbm4uDhw4gF27dqG6\nutrlvz+FQoG//e1v+P3vf2/T1+blIw0NDeKRima33XYb9u/f76mXICty+ZxzVlqNg+F2MBgM2LJl\nC7KyshATE2O1jTPD/kEu35g9dejf2eH1iIgIhIeH49SpU+IOtXlmCri+nlOn08FkMonbg4KCUF9f\nb3X4+KOPPrI69C1HzR881H5yeS9bzuiSczt27EBYWBiefvpp6HQ6lwZwQUFBst6ntIdcPuc4M+xh\nZrMZubm5qKqqwsCBAzFkyBCeQOenuuKJBXq9HmvWrMEPP/yAmpoanDlzpkvv4IikNJfOYs79Q69e\nvbBq1Sr8/ve/d3kmMyUlBd98842Xe9Y1dcXPOXt4Ap2XNDQ0IC8vD1FRUTh8+LDVNpZWo45WUVGB\nZcuWYf/+/bh06RLi4+OxePFivPbaazh9+jRqampgsVhsShgREclJZGQk9Hq9W/dJS0tDUVGRl3pE\nnR0Hw+1UWFgonkjXEmeG/YMnvjHr9XrMnDkTX375JQD7JyQREXVVXeHck5al3MiaP8wMy/dyIl7S\nfNJLYGAgmpqaUFJSghEjRtisRblw4YIsDsmpVCpZvA5vMxqNDt+nyspKLFy4EP/85z9dGuRyIExE\nHSUgIMDrR4tuuukmCIKAn376yavP01bN5dX4Weecs8+5ro6DYTcdPXoUn3/+OYDrJ5fdddddSExM\n9HGvqDOIi4vzdReIiNyiVCo9Wh3D3mM1V3nprG6++WaWVfNzHAy7wWw247vvvsPMmTMRGhqK9evX\no1+/fnbbarVa2ZRW6+w7Ml+65ZZbZFM5hIj8z+bNmwEAjz76qEvt+/TpA4VCgTNnzthsU6lU2Lx5\nMyZNmiRW8NBoNHjzzTcBAEOHDnW5skf37t1typ8pFAoolUrJ2cnm8ojNh/TVajX++te/YunSpThy\n5IhVW7VajY0bN/JzzglBEFBXVwe1Wi2+t3Ijz1flJaWlpWJ5KOD6QOj48eN216HIZZ0tS6s5lp6e\njkuXLvm6G0TkAe6UYFOr1RAEwaZ9UFAQjEYjjEaj+O/u3buLV7rr1asXVCoVzp8/D0EQcMMNN2Dg\nwIEoLi5GWVmZ+MU6NjYWERERaGxsRGlpKbRaLVQqFaKioqBSqRAQEIDZs2djzZo1AIDZs2fjz3/+\nM3Q6HZqamqBSqRAcHIykpCQ89thjeOWVV2AwGHDDDTcgMTERX3/9NQRBQG5uLjIyMgAA+fn5eOaZ\nZwBcv1IdADzzzDMwm83o1asX6uvrkZmZifnz5wMA1q5di6+//hpnzpyB0WhESkoKcnNzkZSUhIKC\nAqur3PXu3RsAxNubmpqQkJAg1lAeNWoU7r77bmRnZ4vPP2DAADz//PPYt28fACAhIQF5eXkAID5G\nz549UVhYiKamJvTp0wdBQUEICAjAypUrxXbNfUhKSsLu3btRUlKC5557DqdPn0ZiYiLeffdd9O7d\nm59zTqjVaoSFhaG2trZLL5NgnWEPOXr0KEpKSjBu3DgAwOHDh1FaWooxY8bYtJXLbCEHw45xWQR1\nZd26dUNdXV27HiM4ONimrKQgCOjTpw80Gg1Onz4tzs5FRkbCbDajpqYGycnJWLhwIRYvXoySkhJo\ntVps2LABffr0wdy5c1FfXw9BEBAQEIBHH30UixYtAnB9kNSnTx9kZ2fDYrE4vJywIy0vl9xyMNny\ncVpf0rqrHj6XS21Yb+NnnDS5ZIl1hj3k2LFj0Ol0NoPhe+65h3WG/VDrC62Qf9JoNOJV7qqrq61O\ngMzMzERwcDD27NkDg8GAyMhI8Upfffv2xfvvv4/k5GTodDrMmTMHarUas2fPxooVKwAAOTk5SE5O\nBgCxTevb/RH3S9LkUgHA25glaXLJEusMe8j58+exf/9+TJ48GQDEQ01NTU0oLCy0ass6w/LXu3dv\nlJaW+robbdb8Tb+ZWq0Wv/WrVCokJiZCqVTi119/Re/evREYGIiGhgacOnUKBoMB/fv3x8cff4zU\n1FScOHECTz/9NOrq6tDQ0IBff/0VKSkpeP3117F06VIAwMaNG5GamuqT10pEROQIB8NuMJlMePPN\nNzFlyhSEhIRgw4YNmDBhArRaLWeG/dSAAQPE9YCeMGzYMOTm5iIyMtJjj9lZMEvOyWX2pSMwS9KY\nJ9cwS9LkkiXWGfYQpVKJMWPGID8/H2azGWlpaeKbGxoaatWWdYb9w+HDh72ylkqO7zmz5Bo51/L0\nFGbJdcyTc8yS6+ScJc4Me4ler5dNabXOfuUgXxIEQTwLnX9KzjFLzjFLrmOWpDFPrmGWpMklS86q\nSXBm2EvkctiFZ9o6J5eSMx2BWXKOWXIdsySNeXINsyRNLllyNhju+lOXRERERERtxMEwEREREfkt\nrhkmaofq6moUFRUhPT3d5iRKIncwS+RJzBN5ij9kiTPDRO1QU1ODwsJCm9J6RO5ilsiTmCfyFH/I\nEgfDREREROS3OBgmIiIiIr/FwTARERER+S2eQOclCxYsgMFg8HU3yMtMJhOuXbuGkJAQKJVKX3eH\nujBmiTyJeSJPkUuWNBoNli1bZncbL7rhJQaDAU888YSvu0FEJOnQoUNYtWoVTp06BYVCgcTERLz4\n4osoLi7GJ598gq1btwIA3n//fXz66afQ6XT47W9/i6VLl/q450RErtmyZYvDbRwMExH5sZqaGsyc\nOROvvvoq7r//fhgMBhQVFUGtVkMQBKu2PXr0wDPPPINvv/0WDQ0NPuoxEZFncTBMROTHzp49CwAY\nPXo0AECr1eLuu+8GABw7dsyq7X333QcAOHr0KAfDRCQbPIGOiMiP9e3bF0qlEgsXLsSBAwdw9epV\nyfvwVBMikhMOhomI/FhQUBC2bt0KQRCwaNEijBgxArNmzYJer3d4n9bLJ4iIujIOhomI/FxiYiKW\nLl2KgoICfPzxxygvL8cbb7zhcNDLmWEikhMOhomISHTDDTdg3Lhx0Ol0DttwZpiI5ISDYSIiP3b6\n9Gls3boVZWVlAIBLly5h9+7dGDBggE1bk8mExsZGmEwmmM1mGAwGmEymju4yEZFHsZoEEZEfCwoK\nwk8//YT33ntPLKw/YsQIzJ07F1988YVV29zcXLzzzjvizzt37sSMGTMwY8aMju42EZHH8Ap0XpKd\nnc2LbhARERF1Alu2bMGKFSvsbuMyCSIiIiLyW1wm4SVGoxHr16/3dTfaLSsrC9u3b/d1Nzqt+Ph4\nzJs3D8uXL8f58+d93Z1OjVlyjllyHbMkjXlyDbMkTS5ZCg4OdriNyyTc9P333+PQoUOwWCxIT0/H\nkCFD7La7cOFCB/fMOwIDA1FfX+/rbnRaarUa0dHRKC8vR1NTk6+706kxS84xS65jlqQxT65hlqTJ\nJUuxsbEOt3Fm2A1lZWU4dOgQpk2bBqVSifz8fPTr1w8RERE2bbVaLRSKrr8KRaFQIDAw0Nfd6LQE\nQUBdXR3UajVUKv45OcMsOccsuY5ZktZZ81RQUIAnn3wSALB582ZkZmYCAIqLizFr1iwAwLp165CS\nkiLep3lb8yXAAwICbNq0FbMkrbNmyZM4M+yGo0ePQqfTYfz48QCAwsJCqFQqZGRk2LTlzLB/kMs3\n5o7ALDnHLLmOWZLWGfO0b98+TJo0yeq2/Px89OnTB5mZmTAYDAAAjUaDgoICJCUloaSkxGpbs5Zt\n2oNZktYZs9QWnBn2kJiYGOzduxd1dXVQqVQoLi5GXFwcqqurUVNTY9XWYDAgKCjIRz31HKVSCbVa\n7etudFrN35Ll+m3Zk5gl55gl1zFL0jpjnp555hm7t918881Wg12DwYDs7Gzs3LkT2dnZNgPh1m3a\ng1mS1hmz5GnyfWVeEB0djYyMDOTl5UGj0aBnz54QBAFFRUUoLCy0ajt8+HCMHDnSRz2ljhYeHu7r\nLpBMMEvkSd7K04kTJ/D0008DADZu3IjU1FTJ+9hbOqhQKOwORptnI50NVJvbUMeQ876JyyTaoaCg\nAN27d0dqaqpsZ4a1Wi0aGxt93Y1OS6VSITw8HFVVVTAajb7uTqfGLDnHLAE6nQ5z5swBALzwwgtY\nvXo1ACAnJwfJyclimxdeeAEWi8XqdrLmzTzpdDqMGDHCalnD/v37JX8XX375JSZOnGh12wcffICE\nhASHj9f6uZq5+pxSuF+SJpd9k7MvTpwZdlNNTQ2Cg4Nx5coVHD9+HFOnTkVAQABCQ0Ot2l24cKFL\nr61pplKpZPE6vM1oNPJ9ksAsucZfs9R6bWjLQdOIESNQUFAAAFZtmm9vuW60srISGzZsAABMmzYN\nVVVVmDt3LgBg5cqV7V5j6g0lJSUu99FR25KSEjz33HM4ffo0EhMTkZubi7vuugtGoxHHjx93+z1w\n1qfZs2fbLGuYPXu2ZImyYcOGIT8/X1wukZubi2HDhgEANm3aZHV7QkICmpqaYDQa0a9fP5SUlMBs\nNgO4PpucnJyMU6dOYfbs2W69rta4X3KdnPdNnBl207vvvovLly/DZDIhKCgIjzzyCOLj423a8QQ6\n/yCXEws6ArPkXGfJUstB0OzZs/HnP/9ZHGC99dZbXhtMjh49Gj/99JPD7YGBgWhoaEDrj6xBgwaJ\ng7DKyko8/PDDOHnyJACgb9++KC0tFd9PT5105UmtvwQ466OjtgBw7733Ws3aqVQq/Pzzz6isrLSZ\ndZV6D+w9z6ZNm7BmzRoAQENDg83vquXvwVPvAQC7J8/ZI/W6HA3uuV+S1ln2Te3l7AQ6Dobd9Mkn\nnyAhIQFpaWkwmUxoampCQECATTu9Xi+b0mrN38bJliAI0Gg0MBgMNh/SZI1Zcq4zZKm4uBhDhw51\nOPhQq9U4cOCAyyWt9Ho9Xn/9dXz++ecIDw+3OgGnZXms4uJi3HnnnW3q8+DBg7Fnzx4AwNKlS7Fq\n1SqX27eXK+XAWm7T6/V4++23AQAzZsxAZGQkRo8ejX//+99WjxsYGIj9+/fbvM/22gYFBcFsNtsd\n0AUHB8NkMtlsc/QeNPf56NGjqK2ttdomCIKYS5VKBUEQrL5kfP311+Lv0tF74oi91zV48GAAsLnd\nGWevq2WuW/aX+yVpnWHf5AnO1jxzmYQbGhoacPbsWTz00EMArp+FqlQq7baVyxokfmt2Tq1WIyws\nDLW1tV36G3NH8OcsuXIYvCOyJNWPmTNnOp2Fa2pqwsyZM12aAaysrMSDDz6IM2fOAAAuXrxo02bo\n0KEoKCgQ+9QWqampKC0tRUREhEtXx7JYLB7JYevZzKFDh4qzp/X19Th+/Lg4U3vnnXciICAAFotF\n/GxYvXo1+vfvb3cNZn19PYYMGYLJkydj3rx5Yi17ewOR1oPWllqfy9LMYrHg559/tsoC4HwWtuVz\nN/dZoVAgKSkJGzduRGNjI+6++24cP35cbHfnnXeKpdPmzp2La9euQa/XQ6VSITc3F+np6Q5f19Gj\nR5GYmOjwtTnqo73fbetcGwwGZGVl4ZFHHsGsWbPQrVs3t57H38jlc87ZYJgzw264ePEidu7ciejo\naFy6dAmxsbEYPXo0NBqNTVsuk/APcjl81BH8NUuuHAYvKSlBdnY21Go1li9fDqPR2O51rq2XOyxe\nvBjFxcXidpVKhb1794qPXVRUhP/5n/+RPEGm+XB48xrVU6dOISgoCKNHj8YjjzyCJUuWAAD69++P\nvLw8yX6mpKRAp9O1a8apb9++WLt2LcaNGyfZ9t5778WiRYvw0UcfAbi+rjgiIgL79u2zWrNqrxpQ\ny3W5jmZjPS02NhZXrlyBIAhYtGgRFi5c6NKyAXep1eo278OWL1+OP/7xjzCZTG7db8eOHUhPT3dY\nS9gd9v6umn+n9fX1Dmd/AwIC8I9//EMcmJMtuXzOcZmEh5SWlmLjxo14+umnERcXh927d0Or1WLg\nwIGsJuGn5HKWbUdomSW9Xo/169cDAKZPn47IyEiPP1/LygTerDrw5ZdfYurUqTAajRAEAUqlEu++\n+y7uu+8+AMDYsWNtDvUGBAQgJCQEKpUK2dnZmDdvnvhh3byUoOXsW/MVLf/f//t/eOutt2A2mxEe\nHo5Lly5Bq9Xij3/8I3JyclBVVQWNRgO1Wu10xrCl8PBwmM1mXL161aX2gYGBCA4ORnl5uUvtO7u4\nuDhcvnzZ5kM+ODgYK1euxDvvvINz586hqqqKh9M9TBAEcZlhjx49cPbs2XYfhlepVAgKCkJdXZ3L\nAzdBEPDNN9+wMokDcvmcc1ZNgoNhN1y7dg0bN24UP2DPnj2LAwcOIDY2lnWGiVxUUVGB4cOH49ix\nYwCAm266CYWFhYiKinLrcZzVOT1x4gQGDBggDr61Wi0OHz7sUi1UZ89VVVUFnU4Hg8EArVaLmJgY\nh4fmBUGAIAiIiYnBpUuX2vS8RNQxbrzxRnGfRP6Hg2E3bdq0CePGjUNUVBT27dsHo9GIO++8kzPD\nfkou35i9peUM8D333INnn30WFRUVNrM/U6ZMwUsvvYQ//elP2L17N65cuSLO6mRmZmLdunUAgD/9\n6U/44osvEBYWhhMnTljN1KlUKiiVShiNRpjNZskZpvj4eCiVSnFNKwCEhYXhypUrnnjpRNSFKBQK\nfml1QC6fc5wZ9qBff/0VW7duhcVigUKhQFpaGkaPHm3TjmuG/YNc1lJ5Q+syV1JUKpXTHS3P+iYi\nbwkMDIROp/N1NzoluXzOcc2whxkMBmg0GphMJmzatAmjRo1CQkKCVRuWVvMPna3kjL3STa23tfxy\nU1VVhcLCQkRHRyM5ORnfffcdevXqhbffftuqJFJBQQEef/xxNDQ0ICQkBEqlEnV1dVZHDZRKpdsn\n0BARdQb/+Mc/kJmZ6etudEqd7XOurVhNwksMBgO2bNmCrKwsxMTEWG3jzLB/8MY35tZX0GouqwRc\nP5v9+eefR1lZGTIzMzF//nxxe+uZ2PDwcDzyyCOYNGkS8vLysG3bNlRWVnqkj0QkL927d8fjjz+O\nPXv2uHw0p6MIguC1K8UFBgZiw4YNPMfHCc4Mk11msxm5ubmoqqrCwIEDMWrUKJs2HAz7B0/vJOxd\nQSskJAQVFRUYNGgQPvvsM5urTN1www24cuUKrl696pWSS0Qkf80l87KysnDw4EGf9qXlBT6cXWWv\nNaVSCYVCIbkvzs/PFwe//IyTxsEwOdXQ0IC8vDzcfffdNtPvPIHOP3jixIKWJ5mdPn26zZc0JSL/\nljJXJ5sAACAASURBVJiYiFOnTtndFh8f77DyiUajwf79+5GcnAydTmd1+WYAYsnAjjh5SqPRYOvW\nrVi9ejUA67KIOp0Ozz77LEpKSsSBza+//goASE5ORm5uLq5cuYIHHnjA6nB+SEgImpqabMoeAvyM\ncwVPoCNJhYWFOHv2rM0OiKXVqKWKigq88sor+PTTT2E2mxEcHIwzZ85wjS0ReUxGRgZ+/PFHm8Gd\nRqPBkSNHcOutt4oze0qlEgMGDEBgYKDd0oStyxa2LokoJSwsDL/73e8QGBgIAOjWrRvGjh2L7Oxs\n1NXVQRAE8bkBOCyT2BbOyi4S2cPBsJtqa2uhUCgQGBiIpqYm5OXlYfDgwVbrOgHODPuLlt+Yy8rK\nsH79etTX1+PixYsoKCgQTzpw9QIIRNR5xcXF4dKlSw6/xAqCgB49erhcoqv1CcrOZm9bi4yMhF6v\nF39Wq9Xi5MzEiROt2n7wwQe477772n0hmuajWPX19bBYLOjWrRsGDx6MP/zhDwCAVatWiReY8dbF\ndDyNn3HSODNMNsrKyvDJJ5/AYrHAYrFgwIAByMjIsGnHNcP+oXkt1fHjx3HjjTf6ujtEXUavXr2g\n1+vbtc69R48euHz5st0z3ENCQnDt2jUAwLx58xATE4N58+YBAIKCghAdHY1z587BbDZDoVBAo9Eg\nJSUFr7/+urhGNTMzE6+++qrVCatVVVXiJZkTExPx4osvYs2aNQCuXzo7PDwcGzZswJUrV3DkyBEA\nQJ8+ffDdd9/h6tWraGpqQkBAADZs2IA+ffrYXHa7qKgI06ZNQ3V1Nbp164aYmBioVCpYLBbxim3N\nz7N8+XJ88cUX6NGjB9auXSteirj5MsQKhQLr16/HsGHD2vweyx0/46RxzTDZKC4uxp49e2CxWJCW\nloZ77rnHbjsOhv3Dk08+iX/961++7gbJjFKpRFBQEGpra20uINJ8uyOCIEChUCA0NBQ9e/YULyvc\nPFCSA+6XpMllAONtzJI0uWTJ2WBY1YH96PLMZjM+++wzTJkyBaGhoVi/fj1SU1PtTr1rtVrZ1Blu\nXvNF1iZOnMiBMIlUKhV69+6NsrIycfAqCAJSUlKsSuWVlJQgLi4OAQEBCAgIEK+u9/zzz0OhUGDN\nmjVuH772N9wvSRMEAXV1dVCr1VCp+FHvCLMkzR+yJM9X5SWlpaWIiIgQK0fccsstOH78uN3BsFzW\nIPFbs2Off/65r7sgayqVCunp6Th8+DAaGhoQEBCAlJQUm8PSLWc8S0pKbA47dya7d+92uO2f//yn\nOPvCvznnuF+SplarERYWhtra2i49m+dtzJI0uWTJ2UU3OBh2Q3V1Nbp37y7+HBoaitLSUlRXV6Om\npsaqrVxOoFMqlVCr1b7uBnURAQEB2Lx5s1XpIr1ej/Hjx4u1k/v164dPP/20XSfX2KvtDQD9+/fH\nrl272vy4vtQ84yLXmRdP4n5JGvPkGmZJmj9kSb6vzAsEQbB7e1FREQoLC61uY2k18iWtVouEhARc\nu3YN0dHR4g4/IyMDCxYsQFRUVIf1JTo6Gt988w1ycnIAAHPmzOnQ5+9qnM1eELmLeSJPkXOWOBh2\nQ0hICK5evSr+XF1djdDQUNx22202dQwNBgPKy8s7uosex7Iz3hcQEIC9e/c6XCfa8qIcnihXZLFY\nfJLN+fPno7Gx0WfP39nJpXxRR+B+SRrz5BpmSZpcsuSstBoHw26IjY1FZWUlqqqqEBISgp9//hkT\nJkxAaGgoQkNDrdpeuHChS6+taeat68HLQWlpKeLi4pxud4ej9zk0NBTZ2dmS7To7Zsk1RqOR75ME\nZsl1zJNzzJLr5JwlDobdoFQqMWbMGOTn58NsNiMtLc3pNw2Sv8uXL8ui5AwREZG/Yp1hL9Hr9bIp\nrdbyCklkTRAE8Spz/FNyjllyjllyHbMkjXlyDbMkTS5ZYjUJH5DLGiSWnXFOLiVnOgKz5Byz5Dpm\nSRrz5BpmSZpcsuRsMNz1py6JiIiIiNqIg2EiIiIi8ltcM0zUDtXV1SgqKkJ6erpNRREidzBL5EnM\nE3mKP2SJM8NE7VBTU4PCwkKbKxASuYtZIk9inshT/CFLHAwTERERkd/iYJiIiIiI/BYHw0RERETk\nt3gCnZcsWLAABoPB190gLzOZTLh27RpCQkKgVCp93R3qwpgl8iTmiTxFLlnSaDRYtmyZ3W286IaX\nGAwGPPHEE77uBhGRpEOHDmHVqlU4deoUFAoFEhMT8eKLL6K4uBiffPIJtm7diqamJixZsgQ//PAD\nrl69ivj4eMyePRv33HOPr7tPRCRpy5YtDrdxMExE5Mdqamowc+ZMvPrqq7j//vthMBhQVFQEtVoN\nQRDEdkajEb169cKWLVvQq1cvfPXVV8jOzsbHH3+M2NhYH74CIqL24ZphIiI/dvbsWQDA6NGjIQgC\ntFot7r77bvTr1w8tV9EFBgZixowZ6NWrFwBg2LBhiIuLwy+//OKTfhMReQoHw0REfqxv375QKpVY\nuHAhDhw4gKtXr7p0v4qKCpw9exZJSUle7iERkXdxMExE5MeCgoKwdetWCIKARYsWYcSIEZg1axb0\ner3D+zQ1NeGPf/wjxo8fj759+3ZcZ4mIvICDYSIiP5eYmIilS5eioKAAH3/8McrLy/HGG29YrRlu\nZjabsWDBAmi1WixYsMAHvSUi8iwOhomISHTDDTdg3Lhx0Ol0NtssFgteffVVVFVVYfXq1V26zBIR\nUTMOhomI/Njp06exdetWlJWVAQAuXbqE3bt3Y8CAATZtlyxZgtOnT2Pt2rXQaDQd3VUiIq9gaTUi\nIj8WFBSEn376Ce+9955YWH/EiBGYO3cuvvjiC7HdhQsX8NFHH0Gr1WLkyJHi7a+99hrGjBnji64T\nEXkEB8NERH4sJiYGK1assLtt/PjxGD9+PAAgNjYWR44c6ciuERF1CC6TICIiIiK/xZlhN33//fc4\ndOgQLBYL0tPTMWTIELvtjEYj1q9f38G987ysrCxs377d193otOLj4zFv3jwsX74c58+f93V3OjVm\nyTlmyXXMkjTmyTXMkjS5ZCk4ONjhNsHS8hJD5FRZWRm2bduGadOmQalUIj8/H2PHjkVERIRNW71e\nD4Wi60+8KxQKmM1mX3ej0xIEARqNBgaDAfxTco5Zco5Zch2zJK11noqLizFr1iwAwLp165CSkmL3\ntpaktssBsyRNLvum8PBwh9s4M+yGiooKxMXFQa1WAwASEhLwyy+/ICMjw6ZtY2NjR3fPKwIDA1Ff\nX+/rbnRaarUaYWFhqK2tRVNTk6+706kxS84xS65jlqS1zNPx48eRmZkJg8EAABg6dCg2bdqEp556\nyuq2goIC8YqCJSUlNvdpuV0umCVpctk3ORsMd/2pyw4UExODc+fOoa6uDgaDAcXFxaiurvZ1t4iI\niByaO3euOKgFAIPBgGeeecbmtrlz5zq9T8vtRHLCmWE3REdHI+P/Y+/Ow6K40v7hf6v3FlkFl2ZQ\nBFk0giOuGTWuMSRRwCdmTObnFhWXJEacaCaLmTBZNBOXqDxGUSEmMokm8VERgySioiY6MTIxahSh\nVTSgCM0mS9Pr+4cvNTS9SzcN1ffnuriUqlPdp7pvqk6dOueuUaOwe/duiEQi9OzZEwzDoLa2FnV1\ndQZlVSoVPDw8XFRTx+Hz+WxPODEmEAgM/iXmUSxZRrH0X0VFRUhKSgIAbNy4Ef369TNY35liydy+\nWNvHtmoZT6aeJGgKwzDs52pqm5bruaIzxZKruMOxicYMt8HRo0fh7e2Nuro65OXlGawbO3asQS5O\nQggh1hUUFGDQoEHsUDOxWIwLFy4gIiLCxTWzX+t9EYlEmDdvHgAgPT2d7Xl19j6a+kwPHDiAhIQE\ns59zR/oeCgoKMH/+fABAWlqaTXUwtU1BQQGef/55FBUVISwsDF988UWnjCvieNQYtlNdXR26du2K\n6upqZGRkYMGCBVCpVJztGRaLxZwZ/+wMAoEAvr6+qKqqgkajcXV1OjSKJcucHUvO7ol0lClTpuCn\nn34yWDZ8+HBkZWWxvzsylix9Lm39zEztizmt9xEAcnNzsWDBAgDAzp07MXHiRIuv0bK+KSkpGD58\nOBtPpval5bLly5djzZo1kMvlCAkJQWpqKoqLi02+f/N2SqUSer0eDMNAr9dDKpU6PLaKioowbtw4\n9sJBJBLhxIkTFt+j9TZCoRBPP/00Dh48aDABTCAQ4OzZs+jdu7fD6stFXDnPBQQEmF1HjWE77dy5\nE/fu3YNWq4WHhweeffZZBAUFGZUrLS11Qe0cjyYXWCYUChEQEIDy8vJOPbGgPVAsWebMWGo9GUok\nErGToSorK7Fjxw4AQGJiosnsOO0pISEB586dM1g2bNgwg/RXjoql1p8LAISEhGDjxo3YuXMnMjMz\n2eUtPzO5XM6On12/fr3ZSWWm9sWc1vt4/PhxzJw506BMRkaG2TuOpr7jX3/9FX5+fmw8mfuu5XI5\nJkyYYNDQ4fP5YBiGXda8/wCMPrOWWn5OjmAuHtavX2/wHdy6dQuLFi0C8OABMYWFhTa9/vDhw7F/\n/36H1JWruHKek8lkZtdRY9hO+/fvR58+fRATEwOtVgu1Wg2JRGJUjlKruQeupJxpDxRLlllLhQXg\nodNcxcbGGvVQenh4IDw8HNXV1bhx4wYAICIiAllZWejWrZvRaxQWFmLx4sW4e/cunnjiCbz11ltm\nyz1sPQsLC/HCCy/gt99+Y5cJhUJMmTIFZ8+eRa9evbB161ZERESYjSWFQoGtW7cCAJ544gm8/fbb\nbF2AB41AuVzONtbseaqeRCLB559/jr/85S8GDcfo6GisWrWKfZLfihUrsG7dOiiVSly5csVqA4LH\n4yE+Ph7FxcUQCARISUnBhAkTUF9fb1COYRgMGzbM5Odq6jt+5JFH0LVrV+j1erz33nt45ZVXUFBQ\nAMDwuza1rSnDhw8HAKtlhw8fjiNHjrC/f/bZZwYN1zlz5gCwHiuFhYUYP348GhoaDJZHR0fj6tWr\nbIO8LceWESNGIDs7+6G2dRdcOc9ZyiZBjWE7KJVKbNu2jb2tZAn1DLsHrlwxtwd3iSW5XI4XX3wR\nN27cgJ+fH+7cuQOGYbB582Y88sgjZnsUW8ZS61RYAoEAer0eWq2WLZubm2tz75s9PZQBAQH4+OOP\nsWnTJjQ2NqKpqQm3bt0yGpYgFArh5+eHyZMn49lnn8Wbb74JuVxu8B2LRCKkp6dj06ZN7D4DDzIV\nqNVqREVFQSKRgGEYKJVKZGRk2NSoGTBgADw8PPD222/j66+/xpEjR3D//n0AgE6nM9tr6Qo8Hg8i\nkQhqtRp6vd5g//7whz+goqICSqXSYJvmYQfmMAwDHx8fNDU1ITQ0FFu2bMGrr75q83fc7JVXXsHf\n/vY3m+Nj4MCBkEqlVstGRkbC09MTADBu3DisXbvWYP3atWsxYsQIs3crgAd/RxMnTjQ6rgqFQkRG\nRuLixYs276c5DMPgyJEjGDhwYJtfi8u4cp6jnmEHuXPnDrKyshAQEIC7d+9CJpMhNjYWIpHIqCw1\nht0DVw4S7aGtsWTrrWlnO378OHs79sUXX8Qnn3wCAEhNTUXv3r2NbjebIxAI0L9/f0gkErz99ts4\nfvw4GhsbkZeXh4KCAquNwpCQEJSVlRnVY8aMGfj000+h1+vh7e0NmUzGjhmlwz03CQQCrFq1CsnJ\nyXZtN3v2bFy5cgWNjY24cuUKe7EFPBgmAcBgmUAgwJo1a7By5co219nDw8Oo57vlUBFzDfSAgAD4\n+/vjypUrba4D8KCH/JtvvnH58KCOjCvnOWoMO0hJSQnS0tIwf/58BAYGIjs7G2KxGEOHDqUJdG6K\nKxML2kNbYqn1hBjgQYO4f//+BjPGa2pqDCb89OnTx2DSkK+vL/uY9McffxzJyclQq9WIjo6GRCJh\nb2sPHjwYSUlJRsMAcnNz8fzzz5utZ3BwMG7evPlQ+0iIqzWnU5NIJEhPT8fq1auNemBNNWIdaf36\n9di7dy8uXrzYbh0xs2fPZoe4EGNcOc/RBDoHuX//PtLS0tiTa3FxMU6fPg2ZTEap1YjbOHLkCP78\n5z8DAL766ivExsa26fVsSZs0evRo/PDDD3a/tkAgMJgAJJPJbG6sent749///jdbn4qKCshksk7d\nM0KIrcRiMR555BHk5+cbLBcKhZz7G+Dz+bh8+TKlWXNj1Bi2U3p6OuLi4uDv74/jx49Do9FgxIgR\n1DPspjr7FbNCocCHH36Ib7/9FvX19QgNDUVqaqrZtEWmeka//PJLm1I+LV++HHq9Hhs3bgQALFq0\nCEVFRUa9P+Hh4di4cSMyMzORm5vr8tv7QqEQq1atwurVq+lvgbiVsLAwm7MydGS2TLAzldqOPNDZ\nz3PNqGfYge7evYvMzExotVr4+voiISHBZDYJGjPsHjrzWKrKykpMnTrVqKdUIBDg2LFjJsfkhoeH\nG90i9fDwwLVr19jfm8f23r9/HwqFgn2v5rGHfD4fOp2Oxq8S0sE5e0hEe7FlP1qntiP/1ZnPcy1Z\nGjPM3WfrOUm3bt3YcVUVFRU4ffo0Jk2a5OJaEWK75lyjZ86cMTlkQKPRIDY2FhERERg2bBhmzpyJ\nnTt34tChQyZPKPX19QgMDLT5/VtOyCGEdEwikQghISEOydrgbJbuYAoEAqSmpmLu3LlmezVFIhGb\n6YS4J5t6hv38/FBZWWm0vHv37rh3755TKtaRqVQqiEQiaLVapKenY/LkyejTp49BGcoz7B4clX+x\nZW7UJUuWmMzfamqbDz74ADk5OWz+1eY8nQqFAhs2bMDZs2fR1NSEO3fuoLa21ii1EyGkYxEIBJg+\nfTpyc3NRW1vL9sRZ+rvl8/lGF5kymQxbtmzBtGnTLL6fqTRu0dHR7MM5xowZY5D+7J///CeWL19u\n8/4wDINHH30UP/74o8HyxMREXLhwAffu3WMvykUiEebMmcO+d7PAwEC2DdKjRw+Di3iBQIDDhw8j\nJycHpaWl+P7771FdXQ2RSISwsDDs2LGDHe6RmJjIDvsICgqCRCKBVCrF5s2b7cqH7W4oz/D/z9PT\nk83h2EytVqNnz57sbVB3pFKpsGvXLiQkJKB79+4G62iYhHtwxO2jyspKPPPMM+xQA19fX0ydOhVK\npZKdmDl58mS89tprqKqqwosvvoiioiKj3KSEkPYxcOBA5OTkAHiQR9eR42rNPWWuOX/19evX4eHh\ngXHjxsHX1xcSiQTTp0/HggUL2GNIeHg4Dh48iMjISOzZswd/+ctf2EaMSCTCN998wz5NLjExEVVV\nVWbTFppKadicXrCxsdGokW7q6XD2pkFsmb4wNTXV6PNwZJpFOsdZ5w7DJCw2hseMGQMAOHPmDB59\n9FGDdb///jseeeQRtxxwrtPpkJqaiqqqKgwdOhQjR46kCXRuqi0TCxQKBTZt2oSDBw/izp07Tqoh\nIeRh9erVy+hvUyQS4cSJE+wk06KiIowePdqoURgWFobr16+zPbY8Hg88Hs/gOBEeHo5Fixbh73//\nO4AH6QCtTUY1R6FQsGkDFy5ciB49erDHpqtXrxqkGDQ3QdZerVMetv5sOgM6x1nn9hPodu3aBeDB\nbdtt27axV5YMw6BHjx6YOHEihEKhY2vbiSiVSuzevRv+/v64cOGCwTpKrUbMOXv2LOLi4lBeXu7q\nqhDSKQUHB6O4uNjkLVuhUAiNRgOxWAy1Wm0wfEAkEoHP58PLywvTpk3DnDlzsGLFCgAP0vr98ssv\nmDVrFgBg9+7dmDhxIjZu3AiFQoH8/HwIhUKT6f8KCgrw/PPPo6ioCGFhYfjiiy8QERFhlDYQgNU0\ngp2NLakRCenobBomceXKFfTv37896tPp5OXlQavVIjIy0mA59Qy7B3NXzM0py7777jv4+Pjg1q1b\nnJiVTYgzJCQkwMvLC7/88guuXbvGDgGSSCTYtGkTtm3bhrt372Ly5Ml4/fXXIZPJcPnyZau9nUVF\nRU7pEe0MuNKb52x0jrOOK7HkkNRqZWVl+Pe//w2FQmFwNT5v3ry217ATqa+vB4/Hg1QqhVqtxu7d\nuzFu3DiEhIQYlKMxw+6h5Viq7777DvPmzTN4ShohHZGPjw9qa2shFosxb948bNu2DVqtFj169EBV\nVRVUKhV4PB66dOmChoYGdggAwzDo27cvRo0aBYlEAoZhIJFIMGnSJLz33nu4f/8+ysrKUFNTA51O\nB4ZhEBgYyDY2fHx8UF1dDaVSibq6OkgkEuzYscPgLlpzthPgwXhWU4/JpeOSdVwZ5+lsFEvWcSWW\n2vw45gMHDmDmzJkICwvDpUuXMHDgQFy6dAmjR4/G8ePHHVrZju6nn35iJ05IpVI8+uijGDVqlFE5\nagy7B6FQiMjISJPZVoj74PF4+Pzzz40uhsLCwiCRSCCRSPD222/j6NGjqK6uxq+//gqhUGgw+Ycr\nJ5z2QMcl6yiebEOxZB1XYqnNeYbfeustpKen489//jN8fX3xn//8B59++ikuXbrksEp2BjqdDmfO\nnMHLL78MLy8vbN++HeHh4SbLisVizqRWk0qlrq5Gh2Wq14pwg7+/P/z8/FBSUoLQ0FA2RRMANk2T\nXC43WHfq1CksXboUAJCSkmKUrmn06NFm349hGDQ0NEAoFEIgoBTwltBxyTqKJ9tQLFnnDrFkU8+w\nl5cXamtrATxI+1RZWQmdToeePXu61SSg27dv48SJE+wEi1OnTgH4b9aNlqhnmPvsedAEcayW+a99\nfX0xZcoUvPbaa1YvTprTU924cQMhISHYsmVLm9IyORJXel/aAx2XrKN4sg3FknVciaU29wx3794d\nd+/eRc+ePREcHIwzZ87A39/f7ZL319bWwtvbm/3dy8sLJSUlLqwRIa4RGRmJjz76CFlZWfjPf/6D\nyMhISKVSSCSSDj3OMzQ0lB3mRAghhAA2NoYXLFiA06dPY/r06Vi+fDkmTJgAhmHYpNfuovkxzK3V\n1tZyNs8wn8936/R5XPHYY4/hww8/xNdffw3gQR5SW55yZ83IkSNtLkuxZFnz7Ueu3oZ0JIol6yie\nbEOxZJ07xJJNwyS0Wi34fD77e3FxMerr6zFgwACnVq6jMTVMgmEYqNVq9klhzSjPMPeZuzhypO7d\nu+PkyZM25e6sqKjAxo0bAQBJSUnw9/d3dvUIIYSQTs9qY1ij0cDT0xPV1dUQi8XtVa8OSavV4n//\n938xe/ZseHp6YseOHZg+fTrEYjFne4YpB6NlrR/DbYpEIsGtW7faoTYdG8WSZVzJ5dkeKJaso3iy\nDcWSdVyJJUt5hq32eQsEAoSFhaGiosLtJwzx+Xw89dRTyMjIgE6nQ0xMDPvhenl5GZQtLS3t1APN\nmwkEAk7sh7Pcu3fPpokF9BlSLNlKo9HQ52QFxZLtKJ4so1iyHZdjyaZhEh999BH27NmDV155BUFB\nQQa3hydMmODUCnZWCoWCM6nV3G2ipD0YhoFIJIJKpTL5aFjyXxRLllEs2Y5iyTqKJ9tQLFnHlVjy\n9fU1u86mxnBwcPCDwibGSN64cePha8ZhlFrNPXAl5Ux7oFiyjGLJdhRL1lE82YZiyTquxFKbU6vd\nvHnTUXUhhBBCCCGkw+j89/EJIYQQQgh5SDYNk6ipqUFycjLy8vKgUCjY8TUMw9AseeLWamtrcf78\neQwZMsRoEiUh9qBYIo5E8UQcxR1iyaae4Zdeegn5+fn4+9//jsrKSqSkpKB3795ISkpydv0I6dDq\n6uqQl5dnlFqPEHtRLBFHongijuIOsWTTmOGcnBxcuXIF/v7+4PF4SEhIwLBhwzB16lT89a9/dXYd\nCSGEEEIIcQqbeob1ej28vb0BgH0AR69evVBYWOjUyhFCCCGEEOJMNvUMR0dH4+TJk5g4cSJGjx6N\nl156CR4eHjY9IpYQQgghhJCOyqYJdNevX4der0doaCjKysrw5ptvoq6uDu+88w4GDBjQHvXsdN58\n802oVCpXV4M4mVarxf379+Hp6Qk+n+/q6pBOjGKJOBLFE3EUrsSSSCTC6tWrTa6zqWf4448/xnPP\nPYfQ0FD06NEDaWlp+OGHH7B9+3Zs3LjRoZXlCpVKhblz57q6GoQQYmTx4sWIiorCSy+9ZLD82LFj\neO+997Bnzx58+OGHOH/+PDQaDXr06IG5c+ciPj4eAJCcnIzz58/j1q1bePfdd9nlhBDSUe3atcvs\nOpvGDH/55ZcYOnSowbIhQ4bgX//6V5sqRgghpP3Fx8cjKyvLaHlWVhamTJmCN954A7169cJ3332H\n06dPY82aNejWrRtbLjIyEqtWrUL//v1NPpmUEEI6E5saw6ae3a3T6Tr1M6oJIcRdjR8/HjU1NTh/\n/jy7rKamBidPnsSUKVNw+fJlxMfHQyKRgMfjITIyEqNHj2bLPvfccxgxYgTEYrErqk8IIQ5lU2N4\n9OjRWLVqFdsg1mq1eOeddzBmzBinVo4QQojjSSQSTJ48GYcOHWKX5eTkICQkBBEREYiOjsYHH3yA\nI0eO4M6dOy6sKSGEOJ9NjeFNmzbh6NGj6NmzJ4YNGwaZTIbvv/8emzdvdnb9CCGEOEF8fDy+++47\nqNVqAMChQ4cQFxcHAFi/fj1iYmKQmpqKJ598Es8++ywuX77syuoSQojT2DSBLigoCPn5+fjpp59w\n+/ZtBAUFYcSIEeDxbGpLE0II6WAGDx4MX19f5Obm4pFHHsGlS5ewadMmAICXlxeSkpKQlJSE6upq\nrFu3Dq+88gpyc3NdXGtCCHE8mxrDAMDn8/Hoo4/i0UcfdWZ9CCGEtJOpU6ciMzMTN27cwOjRo+Hn\n52dUxsfHB3PmzEFmZiZqamrYBzARQghXUNcuIYS4qbi4OJw5cwb79u1jh0gAwIYNG1BUVASNRoP6\n+nrs3bsXffr0YRvCarUaTU1N0Ol07P9pQjUhpLOyuWeYEEIIt8hkMgwePBjXrl3DuHHj2OVNWGck\nVgAAIABJREFUTU1ISkpCeXk5JBIJoqOjDeaILFy4EOfPnwfDMLhw4QL+8Y9/ID093SgFJyGEdAbU\nGCaEEDeWnp5utOyNN96wuM2nn37qrOoQQki7o2EShBBCCCHEbVHPsJNoNBps377d1dVos4SEBBw4\ncMDV1eiwgoKCsHLlSqxduxa3b992dXU6NIolyyiWbEexZB3Fk20olqzjSix17drV7DpGT7Me7HL2\n7Fnk5+dDr9djyJAhGDlypMlypaWl7Vwz55BKpWhsbHR1NTosoVCIgIAAlJeXs/laiWkUS5ZRLNmO\nYsk6iifbUCxZx5VYkslkZtdRz7AdysrKkJ+fj8TERPD5fGRkZCA8PNxkOiKxWMyJPMw8Hg9SqdTV\n1eiwGIZBQ0MDhEIhBAL6c7KEYskyiiXbUSxZR/FkG4ol69whlri5V05SUVGBwMBACIVCAECfPn1w\n5coVjBo1yqhsU1NTe1fPKeiq2TKhUAgfHx/U19d36ivm9kCxZBnFku0olqyjeLINxZJ1XIklX19f\ns+uoMWyH7t2749ixY2hoaIBAIEBhYSECAwNRW1uLuro6g7IqlQoeHh4uqqnj8Pl8tvFPjDVfJXP1\natmRKJYso1iyHcWSdRRPtqFYss4dYonGDNspPz8f586dg0gkQkBAAAQCAcRiMfLy8gzKjR07FuPH\nj3dRLQkhhBBCiC2oMdwGR48ehbe3NyIiIjjbMywWizkz5MMZBAIBfH19UVVVBY1G4+rqdGgUS5ZR\nLNmOYsk6iifbUCxZx5VYCggIMLuOu33eTlJXV4euXbuiuroaV69exYIFCyCRSODl5WVQrrS0tFOP\nrWkmEAg4sR/OptFo6HOygmLJNhRL1lEs2c4V8SSXy/Hqq68CANavX4/Q0NB2fX97UCzZjsvHJmoM\n22nPnj24d+8etFotPDw8UF5ejqCgIFdXixBCOKEzNaS47GG/B7lcjkmTJkGlUgEAJk2ahKNHj9L3\nSDo0GiZhp/3796NPnz6IiYmBVquFWq2GRCIxKqdQKDiTWk2n07m6Gh0WwzAQiURQqVSgPyXLKJYs\no1gCCgsLMWbMGLYhJRKJcOrUKYSFhRmUo1iyri3xZOv3YEpsbCx++ukng2XDhw/HkSNH7KqDLRQK\nBbZu3QoAWLJkCbp164bCwkIsXboUAJCSkmK1zhRL1nHl2ETZJBxEqVSiuLgY06ZNA/BgFiqfzzdZ\nlitjkCjtjGVcSTnTHiiWLKNYAl5++WW2AQY8mHvx8ssvGz0hjGLJOqFQiLKyMsyePRt6vd6u3l1b\nvgdzPcemGkt6vd4h31dlZSV27NiBxsZGNDU1ISsrC5WVlQCArKwsrFu3Dv/zP//DjmsdMWIEMjIy\nMGjQIGzevBm//PILBg8ejJkzZ+Kbb74BACxduhRdunRpc924jCvHJmoMO0hVVRU8PDxw4MAB3L17\nFzKZDLGxsRCJRK6uGiGEuK3mRhIAJCYmmnwQki1sHRrQXE6tViMqKgq+vr42v297DQMpKirC+PHj\n2Y4Zc8MVHqY+crkcEydOZBtGEydORG5uLkJDQ7F+/XqDYRIikQjr169v8/5UVlbimWeewbVr10yu\nv3btGubPn280wWvmzJkICgpiHyN87tw5fPrpp2zdc3Jy8M033zx0zBBuoGESdigpKUFaWhrmz5+P\nwMBAZGdnQywWY+jQoZRNwk1xZZZte6BYssyRsVRUVISkpCQAwMaNG9GvXz+zZRUKBbZv3w4AWLhw\nIbp162bz+7Tc9tlnn8XXX3/Nvs63336LlStXAgDWrl2L/v37Y/78+QCAtLQ0+Pj4ICkpCWq1GtHR\n0aipqcG3335r0CMJAAMGDMDOnTsN9kEsFqO0tBTbt29HY2MjDh06hJKSEgBA7969kZOTY3U/Wn5G\ny5cvx5o1a3Dx4kWDnk2pVIqgoCCIxWIwDAO9Xg+lUonCwkKj1wsODkZ2djb7vq2/AwBYvHgxfv31\nV4PtpFIp+vXrh9TUVHYfi4qKMGfOHMjlcojFYqSnp2PixIlW90WpVEKv10MqlUKpVBq9V/MjdQHA\n29sb48ePx+HDhw16+8LCwqBWq3Hz5k12mUgkwoEDB/D9998DAA4fPmzUKBWJRBAIBPD09MS7776L\ntLQ0g89WLpcjJCTEYD9Nyc3NxYIFCwAAO3fuBAAsWLAAKpXKaq9k83dkr+XLl+ONN96wezt3wZXz\nnKVsEtATm9XW1uo//vhj9vebN2/qMzIy9MeOHdO/8847Bj/Hjh1zYU0JIe7q6tWrerFYrAegB6AX\ni8X6q1evGpQpLy/Xv/XWW/rly5frIyIi2LIDBgzQl5eX2/Q+5eXl+gEDBhi8T/P/e/bsyf7f3I9A\nILBapmXZ5n24evWqfvjw4XqpVGq2/Ny5c+36jBz1M2PGDL1er9dnZ2frGYZhlwuFQpv2d/v27frB\ngwebXJednW1yX1q/lzN+9uzZY/Bd2/ITFRWlz87O1guFQoPlfD5fHxMTox81apRRXGZnZz90HVu/\njz0/ixcvtinmCXdRz7Cd0tPTERcXB39/fxw/fhwajQYjRoygnmE3xZUr5vbg7Fgy1Ts2f/58JCcn\nA3jQGzl06FCz25vrIf35558xZ84cVFVVwdvbG1OmTMGMGTPY123u9XvppZfw+++/AwB69eqFwYMH\nw8/PD48//jhef/11XLlyhe3Z8vLyQm1tLQDAw8MD9fX1AAx7tkaOHIk7d+6guLiY3WbQoEE4c+YM\nAODxxx9H79690djYiHPnzuH69esO/XxbTixiGAbdu3dHWVkZGIZBWFgYBgwYYDSW15l69eoFf39/\nXLx40WpZHo+HRx55BBqNBtXV1fjjH/+I/Px8qFQq1NfXG/U+dwZSqRRRUVEG8f3000/jnXfecXXV\nHhqfz8fcuXPZXmRnEQgEFo/PMTExTpngxxVcOc9Z6hmmxrCdfv/9d3z22WfQ6/Xg8XiIiYlBbGys\nUbnS0lIX1M7xaKKKZUKhkL312JknFrSHlrHk6HGTcrkcEyZMsHqg9vb2hkwmQ9euXbFs2TJ8+OGH\nuHHjBtuobL41HBwcDB8fH5SUlLC3lc152FuzhJCOYdiwYe16YdfZcOU8J5PJzK6jxvBDUKlUEIlE\n0Gq1SE9Px+TJk9GnTx+DMpRazT1wJeVMe2iOJUtpm5rTIlVXV6O4uBhNTU3g8/nQarVGr9ejRw9k\nZmaiuroaTzzxRHvvDiGEI3JycjBs2DBXV6PD4sp5jrJJOFhz9gitVsvermqNK0MLqGfYMq6knGnt\n+PHjWLBgAZRKJTw8PKDVasHn85Gamorx48eb3KbljP7+/ftjxYoV0Ol06N69O+7evWv2b0KlUmHE\niBFm62KqIQwAZWVlFrcjhBBbzJw5E8eOHaOMEmZw5TxHjWEH0+l0SE1NRVVVFYYOHYru3bu7ukqE\nGDl//jxeeOEFKBQKg+UikQhSqRRqtZp9iqIlzeNZgQcnDXs1j3klhJCOqLy8HMuWLcPu3btdXRXi\nIjRMog2USiV2796NP/3pT0ZXHDSBzj2058QChUKBTZs2IT8/H35+fsjLy4NKpQKfz++UE4IIIaQj\nuXfvnqur0CHRBDpiVV5eHoqLi3H9+nWD5WPHjjV7O5mQZhUVFWw2gtGjR+O5555DTU2Ni2tFCCHu\nhcfjmR2SRbiPhknYqb6+Hjwej73NLJfLMXz4cEyaNMmgnEqlsnr7uTOgnmHLzF0xt34YwUcffUSz\nlQkhpINau3YtJ87ZzuAOPcPUGLZTXV0d9u/fD71eD71ej0GDBmHgwIFG5UpLSzv1QPNmAoGAE/vh\nbGVlZdiwYQM7gayljz/+2AU1IoQQx1q5ciVCQkKwZMkSp7+XuZSFvXr1wp07d9jfAwIC0KNHD1y5\ncsWgZ5fP5yM4OBg8Hg/FxcUGQ8la59D+6KOP8Nxzz9G5zgqNRsPZz4gaw3aqra2FWq2GXq9HTEwM\nRo0a5eoqERdjGMbVVSCE2MiZ6SLDwsIAALdu3WLvqPH5fLz99tvIyMhAUVERW1YkErEPMunatSt6\n9uwJPp8PhmGgVCoNGnASiQTvvfcevvrqKwDAsmXLsGnTJgDm83Q7MzdsXFycxfUtM8skJiaiqqrK\noXnFTXnY3OWUMYkANGbYLjqdDikpKZg9eza8vLywfft2TJ8+3WTXO+UZdg+UiocQ52AYBp6entBo\nNPjDH/4AgUCAqqoqPPHEE3j77bfh6+uLzz77DK+++ir0ej2io6Nx6dIlaLVaSCQShIeHY9WqVXj/\n/fchl8sRGhqKHTt2sA3W5pzWAJCSkgI/Pz9s3boVADBjxgzs2rULP/zwA0pKSlBfXw+GYRAUFASJ\nRAKJRIKUlBQAMHiN5tfuCLiSG9bZ6BxnHVdiyVJqNWoM2+H27ds4ceIEZs2aBQA4deoUAGDMmDFG\nZekJdNwXGBjo6iqQDoBhGKxYsQLZ2dmQy+UAHjzBbujQofD19UViYiL8/PzYnqvGxkY0NTWhtLQU\nISEh2LJlC0JDQznzlKf2QMcl6yiebEOxZB1XYsnSE+homIQdamtr4e3tzf7u5eWFkpIS1NbWoq6u\nzqAsV1Kr8fl8CIVCV1eDELMkEgmUSqXZ9V26dIFarUa/fv2QlpaGfv36AQCKioowZ84cyOVyiMVi\nvP/++9i7dy8AYOPGjWy55rJJSUkm1zVbuXKlxXpGRkbi8OHDZtcLBAKDf4l5dFyyjuLJNhRL1rlD\nLFHPsB1+++03FBUVseOlLly4gJKSEkilUuTl5RmUpdRq3OfOY4WlUin69++PL774AhEREezygoIC\nPP/88ygqKkJYWJjR+pap5JKSkuDv79/udSeEEEJaosawHUwNk2AYBtHR0ZztGabUauY5+8mDkyZN\nQkpKCqqqqqz2SnYGFEuWcSV9UXugWLKO4sk2FEvWcSWWKLWag8hkMlRWVqKqqgqenp64dOkSpk+f\nDi8vL3h5eRmUpdRq3FdSUmJx3HBmZiaGDBnS5vfx8vLC/v372d876/dBsWQbLqcvchSKJdtRPFlG\nsWQ7LscSNYbtwOfz8dRTTyEjIwM6nQ4xMTEWrzQI9927d48TEwsIIYQQd0XDJJyEUqu5B66knGkP\nFEuWUSzZjmLJOoon21AsWceVWLKUWo16hp2EK2OQKO2MZUKhED4+Pqivr6eeYSsoliyjWLIdxZJ1\nFE+2oViyjiuxZKkx3Pm7LgkhhBBCCHlI1BgmhBBCCCFui8YME9IGtbW1OH/+PIYMGWKUUYQQe1As\nEUeieCKO4g6xRD3DhLRBXV0d8vLyjPJME2IviiXiSBRPxFHcIZaoMUwIIYQQQtwWNYYJIYQQQojb\nosYwIYQQQghxWzSBzknefPNNqFQqV1eDOJlWq8X9+/fh6ekJPp/v6uqQToxiiTgSxRNxFK7Ekkgk\nwurVq02uo4duOIlKpcLcuXNdXQ1CCDGyePFiREVF4aWXXjJYfuzYMbz33nvYs2cPPvzwQ5w/fx4a\njQY9evTA3LlzER8fj5s3b2L9+vX49ddfodVqMXDgQLz++usIDg52zc4QQogNdu3aZXYdDZMghBA3\nEx8fj6ysLKPlWVlZmDJlCt544w306tUL3333HU6fPo01a9agW7duAB7MLJ8wYQIOHTqEEydOYODA\ngXjllVfaexcIIcRhqDFMCCFuZvz48aipqcH58+fZZTU1NTh58iSmTJmCy5cvIz4+HhKJBDweD5GR\nkRg9ejQAYODAgZg2bRq8vLwgEAgwa9Ys3Lx5EzU1Na7aHUIIaRNqDBNCiJuRSCSYPHkyDh06xC7L\nyclBSEgIIiIiEB0djQ8++ABHjhzBnTt3LL7Wzz//jICAAHh7ezu72oQQ4hTUGCaEEDcUHx+P7777\nDmq1GgBw6NAhxMXFAQDWr1+PmJgYpKam4sknn8Szzz6Ly5cvG73G3bt3sXr1aqxcubJd604IIY5E\njWFCCHFDgwcPhq+vL3Jzc3H79m1cunQJTz31FADAy8sLSUlJ2L9/P06cOIGIiAijccGVlZVYtGgR\nnn/+ecTGxrpiFwghxCGoMUwIIW5q6tSpyMzMRFZWFkaPHg0/Pz+jMj4+PpgzZw7Ky8vZccE1NTVY\ntGgRJkyYgAULFrR3tQkhxKGoMUwIIW4qLi4OZ86cwb59+9ghEgCwYcMGFBUVQaPRoL6+Hnv37kWf\nPn3g7e2Nuro6LF68GIMHD8ayZctcWHtCCHEMyjNMCCFuSiaTYfDgwbh27RrGjRvHLm9qakJSUhLK\ny8shkUgQHR2NzZs3AwByc3Nx+fJlyOVyHDx4EADAMAwOHDiAnj17umI3CCGkTagxTAghbiw9Pd1o\n2RtvvGG2fHx8POLj451ZJUIIaVc0TIIQQgghhLgt6hm209mzZ5Gfnw+9Xo8hQ4Zg5MiRJstpNBps\n3769nWvneAkJCThw4ICrq9FhBQUFYeXKlVi7di1u377t6up0aBRLllEs2Y5iyTqKJ9tQLFnHlVjq\n2rWr2XWMXq/Xt2NdOrWysjLs27cPiYmJ4PP5yMjIwJQpU0zOwFYoFODxOn/HO4/Hg06nc3U1OiyG\nYSASiaBSqUB/SpZRLFlGsWQ7iiXrKJ5sQ7FkHVdiydfX1+w66hm2Q0VFBQIDAyEUCgEAffr0wZUr\nVzBq1Cijsk1NTe1dPaeQSqVobGx0dTU6LKFQCB8fH9TX17MPLyCmUSxZRrFkO4ol6yiebEOxZB1X\nYslSY7jzd122o+7du+PWrVtoaGiASqVCYWEhamtrXV0tQgghhBDykKhn2A4BAQEYNWoUdu/eDZFI\nhJ49e4JhGNTW1qKurs6grEqlgoeHh4tq6jh8Pp/tCSfGBAKBwb/EPIolyyiWbEexZB3Fk20olqxz\nh1iiMcNtcPToUTYJfV5ensG6sWPHYvz48S6qGSGEEEIIsQV3m/lOUldXh65du6K6uhpXr17FggUL\noFKpEBERYVBOpVKhvLzcRbV0HLFYzJnxz84gEAjg6+uLqqoqaDQaV1enQ6NYsoxiyXaujCWFQsFm\nClq4cCG6deuG3NxczJs3D01NTejbty/7aOvm9QBQVFSEpKQkAMDGjRvRr18/u97X3u1tjSdT+2PP\n+7Z1vx6GI9+TjkvWceXYFBAQYHYd9QzbKT09HY2NjeDxeIiNjUXfvn1NlistLW3nmjkHTS6wTCgU\nIiAgAOXl5Z16YkF7oFiyjGLJdq6KpcrKSjzzzDO4du0aACA8PBzLly/HkiVLTJYPDg6Gj48PNBoN\nrl69yjYkRCIRjh49itDQUJveVy6XY9KkSVCpVGa3r6ysxI4dOwAAiYmJ6NGjh9V4MrU/+/btYzMk\nWXtfW+rVVnK5HK+++ioA4O2338Y333yDjIwMNgOEQCBA//79IZFIsH79eovvLZfL8corr6CsrAyT\nJk3Ca6+9hsDAQDouWcGVY5NMJjO7jhrDdmpsbERmZibb6xsfH4+goCCjcpRazT1wJeVMe6BYsoxi\nyXauiqX3338fGzZsMFgmFAofqoEwfPhwHDlyxKaysbGx+Omnn8xur1AoMGXKFBQUFAAAIiIicPjw\nYfTq1ctiPJnan7/+9a9YtWqVTe9rbX2zwsJCLF26FACQkpKCsLAwm/a7sLAQY8aMYRvb1ohEIpw6\ndcrk6xcWFmL06NEG31Xfvn1x9OhRi1kGCHeOTZRazYGOHDmCsLAwzJgxA1qt1uxBkCu3Xag3zzKu\npJxpDxRLlrljLLXuzTSVs92UlrHU3HNYXV2N33//HTweD8nJyfjqq6/Q2NgIhmHYXkMAePHFF3Hj\nxg2EhIRgy5YtbE9i67pcuHABiYmJaGpqQmhoKNLS0kwOfXvY70qv1xv8PbTsAW3dw2nq76bl9ikp\nKWxDGAAKCgqwZcsWbNiwwWI8mbrlrdFo2Nc11QhVqVRITk42u75lveRyOV588UVcvnyZbUSNHj0a\nzz33HBiGwY8//oji4mL4+PggLS0NQ4YMMXitl19+2eaGcHN9Xn75ZYOHaBw/fhyLFi1CY2Oj0QXU\njRs3sGXLFvZzJ6Zx5dhEjWEHUSqVKC4uxrRp0wA8mIXK5/NdXCtCCHl4lhphznzPV155BVevXoVS\nqQQA7N69G88++yyWLl2KqqoqzJ8/H3K5HGKxGDt27DA5Ibn1bfpmK1euNCr72GOPGfQoX7x4EY89\n9hjWrl2L2NhYTJ06FTdv3gQA7Ny5Ew0NDey2hYWFGDduHBiGccj+i0QitnFuqsE4adIkdriBXC7H\nb7/9ZvQa8+bNs/gee/bswaJFi4wuMJobhwCwbt06hIeHs8MkGIZBdnY2qqqqIJFIoFAojF5XLpfj\nl19+YfdDIBAYDP9YtmwZEhIS0NjYaDA0pJlarcbu3bsNlpWXlyMuLg6ZmZlGDeKH0Xxhc/PmTWRm\nZlos++WXXyIhIaFd4p50XDRMwg537txBVlYWAgICcPfuXchkMsTGxkKpVHI2tRpNLrCMKxML2gPF\nkmXNsfTTTz+xt5STk5Px/fffAzA9uamtioqKMG7cOIMxnydOnDA7IanlxKXk5GQcPHgQZ86cQVlZ\nGWQyGbZs2YJ+/fqxk7IaGxuh1+vRpUsXLFy4EACwZs0agzGftpJIJODz+di5cydGjBiB5ORk/Otf\n/4JWq23DJ/DAxIkTkZub2+bXsUYkEkEoFGLDhg24evUqFAoFvvzyS7O9bb6+vmhsbGQvGFqLiorC\nn/70J8TExOCll14yeQxq/tzeffddbNu2DYWFhQbr//GPfyA5OblNt78ZhgHDMFixYgU2bNjQpmMh\nwzDg8/kICwvD1KlT8dFHH9m8rUAgwJYtW7Bs2TKzn5kp1uLe3XHlPEcT6BykpKQEaWlpmD9/PgID\nA5GdnQ2xWAyGYSi1GiGkzQoKChAVFWWycTRgwADk5eXB39/fIe8zf/58/Oc//zHoAQUe3BI9efIk\nfH19MXv2bNy+fRu9evVCVFQUvvjiC6u3ST09PdHY2GjypMkwjEPGHD7sOF1CzBk1ahROnz7t6moQ\nF6HGsB3u37+PtLQ0tmekuLgYp0+fxtSpU6ln2E1x5Yq5PTgzlnJzc7FgwQIAD25xT5w4EYBhT+by\n5cuxZs0aFBYWQqfTQafTQa1Wg8fjITAwEJWVlQgJCUFqaqrFHqLm11QqldDr9ZBKpWx6p9YpnwDY\nlAKqqKgIixcvxsWLFy02FmfPno1169aZ3LaoqAhCoRAqlQpeXl7YuHEj9u7di8OHD4PP56N3795Q\nqVTsUABCyH9FRUW1y92Bzogr5znqGXag9PR0xMXFwd/fH8ePH4dGo8Hjjz9uVI5Sq7kHrqScaQ/2\nxFLLyUyTJk3Ce++9B8B4TKtcLseMGTNw584dg+15PB769u2L4uLihz54C4VCaLVa6PV6h82gjoqK\nAp/Ph5+fH06ePMk2pltfTBNC2tfAgQORk5Pj6mp0SFw5z1lKrUYT6Ow0efJkpKamQq/Xg8fjISYm\nxtVVIqTTaZ7Eo9fr8fjjj8Pb2xtnzpzBjRs3wOPxoNFo2DGlmzdvZrd77LHH8Ic//AF37tyxOFZU\np9NBLpe3qY7OOOhfvHjRaBk1hAlxPalU6uoqEBeinuGHoFKpIBKJoNVqkZ6ejsmTJ6NPnz4GZSjP\nsHvgSv5FWygUCmzduhUAMHLkSCxcuBDV1dVsL6xWq6Vb8ISQTiknJwfDhg1zdTU6JK6c5yi1moOJ\nRCIAYG+hmrqi5Mo4WxomYVlHzL/YPMSgsbERNTU1yMvLg06ng6enJzucoE+fPggODsaJEyfQ1NSE\nLl26oL6+/qHezxG9sIQQ4kqffPIJPvnkE1dXo0PqiOe5h2GpMUw9ww9Bp9MhNTUVVVVVGDp0KEaO\nHEkT6NxUWyYWNKefAmxPm2VqG4VCgQ8//BDffvstKioqOvWVOyGEuIJQKERJSYmrq9Eh0QQ6YpFS\nqcTu3bvh7++PCxcuGKyj1GrEkoqKCowdO5ZNpt+cNkuhUGD+/PloaGiAVqtFSUmJycT3hBBCHMfD\nw4PG77sxagy3UV5eHrRaLSIjIw2WU8+we7D1irnlQwiuXbuGY8eOtWMtCSGEWPLll1+yKRmJIXfo\nGaYxw3aqr68Hj8eDVCqFWq2GXC7HuHHjjFJ2lJaWduqxNc0EAgEn9sPZNBoN1Go1zp8/j8TERJSX\nl9PEQ0KIy3l4eDz0fIDOwtJEbx8fH3z++ecAgDlz5qCqqspgvVAoxKefforHHnuMznVWNJ/nuIga\nw3a6fPkym4tQKpXi0UcfRUhIiItrRVypoKDA6M4AIcR2PB4PDMOw6fIEAgH+7//+D0ePHkV1dTW+\n+OILgx4phmHQr18/vPPOO9i0aROABzmoAeDVV19FY2MjmpqaUFpaCplMBolEAr1ezy4LCQnBli1b\nDHJWAw/yVr/66qvs67Veb2sZa9s019Oe17CXM3PDPsxn0BFcunTJaBlNEicADZOwi06nQ0pKCmbP\nng0vLy9s374d06dPN9n1TqnV3IOfn5+rq0CI0zEMAx8fH/Tt2xezZs3CqlWroNPp4Ovri7t370Is\nFmPZsmXYtGkTmpqa0Lt3b4hEIhQXF0OlUkEoFAJ4MHxMIpGgd+/eAB484j40NJR9wMrSpUsBACkp\nKQgLC2Pfv7Cw0GBdREQEHZes4Eo6LGejc5x1XIklyibhILdv38aJEycwa9YsAMCpU6cAAGPGjDEq\nS0+g477AwEBXV4G4MYZhDE5MIpEIPB4Pnp6eiIqKwtmzZ6HVasEwDBiGQa9evdDY2Ihhw4bh1q1b\n0Gg07GuY+lcikXTYXj86LlnHlaeGORvFknVciSV6Ap2D1NbWwtvbm/3dy8uLUrEQ0oH5+vpi2bJl\nWLNmDZqamiAWi+Hj44OysjIAQHBwMA4dOtQhevi5csIhhJDOhhrDdmAYxuTy2tpazuYZ5vP57C1O\nQloKDg7G0qVLsWrVKjQ1NaFfv37YtWsX+vXrZ7K8KzOTvPjii+z/Hya/c3sQCAQG/xKP+yhOAAAg\nAElEQVTz6LhkHcWTbSiWrHOHWKJhEnYwNUyCYRio1Wrk5eUZlKU8w9xn7uKoPUmlUsyYMQNr166F\nv7+/zduZy3Nsz2sQQgghXMDdZr4TyGQyVFZWoqqqCp6enrh06RKmT58OsViMiIgIg7IqlQrl5eUu\nqqnjUJ5h8+7du4fu3bvbvZ25fJZt6bHU6/V2x9u+ffsM3u9hXsMeFEuWcSWXZ3ugWLKO4sk2FEvW\ncSWW6Al0DlRYWIgjR45Ap9MhJibG5OQ5gCbQuQsa52k7iiXLKJZsR7FkHcWTbSiWrONKLNEEOgcK\nCwszSPlDCCGEEEI6L+oZdhLKM+weuJJ/sT1QLFlGsWQ7iiXrKJ5sQ7FkHVdiyVKeYeoZdhKujEGi\nW0iWCYVC+Pj4oL6+vlPfPmoPFEuWUSzZjmLJOoon21AsWceVWLLUGO78XZeEEEIIIYQ8JBomQUgb\n1NbW4vz58xgyZAi8vLxcXR3SiVEsEUeieCKO4g6xRD3DhLRBXV0d8vLyjB66Qoi9KJaII1E8EUdx\nh1iixjAhhBBCCHFb1BgmhBBCCCFuixrDhBBCCCHEbdEEOid58803oVKpXF0N4mRarRb379+Hp6cn\n+Hy+q6tDOjGKJeJIFE/EUbgSSyKRCKtXrza5jvIMO4lKpcLcuXNdXQ1CCMHixYsRFRWFl156yWD5\nsWPHsHLlSgwYMAC7d+82WFdVVYUJEybgm2++Qe/evbFx40bk5OTg/v378PX1xfjx4/G3v/0NAPDF\nF1/g4MGDKCoqwpNPPon333+/3faNEEJssWvXLrPraJgEIYRwXHx8PLKysoyWZ2VlYcqUKbh06RJK\nSkoM1mVnZyMiIgKhoaHYuXMnrly5gj179uDf//430tPTMWDAALZsjx49sGjRIkybNs3p+0IIIY5G\njWFCCOG48ePHo6amBufPn2eX1dTU4OTJk/h//+//Yfjw4Th06JDBNocOHcLUqVMBAJcvX8aECRPg\n7+8PAJDJZOw6AJg4cSImTJgAb2/vdtgbQghxLGoME0IIx0kkEkyePNmgwZuTk4OQkBCEh4cjLi7O\noOf4xo0bKCgowNNPPw0AiI6Oxueff469e/fi2rVrMDfVhKagEEI6I2oME0KIG4iPj8d3330HtVoN\n4EHPb1xcHABgwoQJUCgU+OWXX9h1Y8aMgY+PDwBgwYIFmDdvHg4fPoznn38ekyZNQmZmptF7MAzT\nTntDCCGOQ41hQghxA4MHD4avry9yc3Nx+/ZtXLp0CU899RQAQCqVGvQcHz58mG0oAwCPx8Nzzz2H\nzz//HGfOnEFiYiL+/ve/4/r16wbvQT3DhJDOiBrDhBDiJqZOnYrMzExkZWVh9OjR8PPzY9fFxcUh\nJycHP/74IxoaGjB27FiTryESifDcc8/By8sLN27cMFhHPcOEkM6IGsOEEOIm4uLicObMGezbt8+g\n5xcAhgwZAk9PT7z77rt48sknIRD8N/NmRkYGzp07B6VSCY1Gg4MHD6KhoQGRkZEAHuQhbWpqglar\nhU6ng0qlglarbdd9I4SQh0V5hgkhxE3IZDIMHjwY165dw7hx44zWx8XFYdu2bUYNZYlEgnXr1uH2\n7dsAgL59+2LDhg0IDAwEAKSmpmLbtm1s+aysLCxZsgRLlixx3s4QQoiD0BPonGTFihX00A1CCCGE\nkA5g165dWLduncl1NEyCEEIIIYS4LRom4SQajQbbt293dTXaLCEhAQcOHHB1NTqsoKAgrFy5EmvX\nrmVvIRPTKJYso1iyHcWSdRRPtqFYso4rsdS1a1ez62iYhJ3Onj2L/Px86PV6DBkyBCNHjjRZrrS0\ntJ1r5hxSqRSNjY2urkaHJRQKERAQgPLycjZ/KzGNYskyiiXbUSxZR/FkG4ol67gSSzKZzOw66hm2\nQ1lZGfLz85GYmAg+n4+MjAyEh4cbpCdqJhaLweN1/lEoPB4PUqnU1dXosBiGQUNDA4RCocHse2KM\nYskyiiXbUSxZR/FkG4ol69whlri5V05SUVGBwMBACIVCAECfPn1w5coVjBo1yqhsU1NTe1fPKeiq\n2TKhUAgfHx/U19d36ivm9kCxZBnFku0olqyjeLINxZJ1XIklX19fs+uoMWyH7t2749ixY2hoaIBA\nIEBhYSECAwNRW1uLuro6g7IqlQoeHh4uqqnj8Pl8tvFPjDVfJXP1atmRKJYso1iyHcWSdRRPtqFY\nss4dYonGDNspPz8f586dg0gkQkBAAAQCAcRiMfLy8gzKjR07FuPHj3dRLQkhhBBCiC2oMdwGR48e\nhbe3NyIiIjjbMywWizkz5MMZBAIBfH19UVVVBY1G4+rqdGgUS5ZRLNmOYsk6iifbUCxZx5VYCggI\nMLuOu33eTlJXV4euXbuiuroaV69exYIFCyCRSODl5WVQrrS0tFOPrWkmEAg4sR/OptFo6HOygmLJ\nNhRL1lEs2Y7iyTKKJdtxOZaoMWynPXv24N69e9BqtfDw8EB5eTmCgoJcXS1CCCGEEPIQqDFsp27d\nuiEmJgYxMTHQarVmr5IotZp7cIeUM45CsWQZxZLtKJaso3iyDcWSde4QS9zcKydRKpUoLi7GtGnT\nADyYhcrn802W5coYJEo7YxlXUs60B4olyyiWbEexZB3Fk20olqzjSixZSq3W+bsu21FVVRU8PDxw\n4MABbNu2DZmZmVCpVK6uFiGEEBvJ5XIkJCQgISEBcrnc1dUhhHQA1DNsB51Ohzt37uCpp55CYGAg\nsrOzcfr0aQwdOpSz2SQoB6Nl7pB/0VEolizr7LFUVFSEpKQkAMDGjRsBwOD3fv362bytpbLAw8VS\nUVERFi9ejIsXL6I5idKkSZNw4sQJq+/XFj///DPmz58PAEhOTkZaWhqUSiX0ej2kUim7v/Z+Bta0\njqe2vn7z9q3r7uvri02bNiE/Px+DBw/GnDlz8PXXX0OhUODSpUsQCARG7+fofW0LOi5Z19mPTbag\n1Gp2uH//PtLS0tg/4uLiYpw+fRoymYzyDBNCHK6iosKgYenv79+hXq9ZQUEBBg0axA4Pa25cNN9S\nFYvFuHDhAiIiIqxua6mso+rX0qhRo3D69GmHvVeziooKrFixAp999pnFcgKBAGFhYbh69SrbSHf0\nZ9DWz9jc5ycSiSCTyXDz5k2DZa3vmLZ8v/b4vgmxFzWG7ZSeno64uDj4+/vj+PHj0Gg0GDFiBGd7\nhikHo2Vcyb/YHiiWLGsdSwqFAvHx8bh27RoAIDw8HAcPHkS3bt0AAAqFAtu3b0dJSQny8vLA4/HY\nnkfgQY9bdXU12yu5YcMGvPPOO2Zfr7Xm1weAhQsXAgC2b9+OxsZG6PV6dOnSBQsXLkS3bt0QGxuL\n/Px8i/sXHR2NDz/8EPPnz4dSqURDQwPUajV0Op1RWT6fj0GDBmHlypX4+OOP2f1p7kE0FUumehub\n92HPnj24c+eOyXoNGDAAXbt2hVqtRnh4OG7cuAGZTIYzZ86Ax+MhLS0NQ4cONXiP6upq3L59Gzwe\nDzt37sTEiRMNXvPnn3/GtGnT2hTvQqEQf/nLX/D666/b/B21LNcynmJjY/HTTz8ZbDt48GCMGzfO\n5LatXzsvL8/q92vN8OHDkZWVhSlTphjVpXkdYPg9zp8/H8nJydBoNPD394enp6fZXua6ujqUl5dD\nIBCw31nL11q+fDnWrFkDuVyOkJAQpKamol+/fnRcsgFXznOW8gxTY9hOv//+Oz777DPo9XrweDzE\nxMQgNjbWqFxpaakLaud4NLnAMqFQiICAAJSXl3fqiQXtwd5YksvlePHFF9nGiUQigUQiwfr16xEa\nGurEmrZNZWUlduzYAQBITEyEn58fu6y6uhq//vorhEKh0X60jqV//vOf2Lx5s8FrBwQEYNq0aZg5\ncyZmz55t0CP3MHr16oW9e/ca1OP8+fOYP38+Kioq2J7K5vWtx9iGh4dj3bp1mDZtGrRabZvqYo1A\nIED//v3B5/MRGRkJuVwOPz8/nDp1CjqdDk1NTWh5Otu6dSs+/vhjtvHfFpmZmaitrcWsWbNg6pSZ\nkZHB3gmUy+UYO3asyXIPIzg4GIcOHQIAg7gCgGeeecbg4mbfvn3w8/MDANTW1iIjIwMNDQ04evQo\nLl68aPC6EokESqXS5LaVlZUGr83n89v8/Q4cOBBffvklJk+ebHRhMmzYMBw4cAByuRyTJk2yOBdH\nJBLh6NGjCA0Nxfnz55GQkGDygmrt2rX429/+ZnJd8z4dP34cAwcOpHOcFVw5z8lkMrPrqDH8EFQq\nFUQiEbRaLdLT0zF58mT06dPHoIxCoeBMajVzBxPyIOVM821B+lOyzJ5YKiwsxKhRo0z2QohEIpw6\ndQphYWEAHvytbd26FaWlpfj+++9RW1uLsLAwJCcnY926dQCAlJQUhIWFsWUrKyvx66+/QiAQsOta\nUigU+OCDD5CdnQ0AePLJJ/HWW2+Z7KE7d+4cZs6cierqavTt2xdqtRo3btwA8KDBERISgpKSEtTU\n1Bht6+XlBZFIBD6fj6eeegrJyclQq9X45JNPcOTIEfz22282fV5tIRAIMGDAAABAfX293ZPK3OEY\nYW0fGYbBjBkzUFBQgIKCAjQ0NDj0/ZcsWYJjx46hoKAAAODp6QmNRmPUiPvrX/+KVatW4dy5c0hI\nSGDXCwQCqz16S5Yswdy5c7F06VLcvn3bbE96SwzDoEuXLqivr7dpPzw8PEyWDQ0NhYeHBwoLC21q\nmAoEAnTr1g1VVVVtmsTOMAyioqKwY8cOo2MA+S+unOcsZZOgxnAbqFQq7Nq1CwkJCejevbvBOuoZ\ndg9cuWK2RC6X49VXXwUAu3plKysrsXnzZvzyyy8IDg6GXC6HTqdje/MYhgGPxwOPx8PIkSNx5coV\ndOvWDSEhIfj222+tnrylUilefvllrF+/3qbGWNeuXaFSqUyePIcMGYL6+noUFxezvYyWMAzTqU8K\nhLTWPDnK2t9d165dodFojHriWxIKhZ3qeCgQCHDs2LEOfcfJlbhynqOeYQfT6XRITU1FVVUVhg4d\nismTJxuVocawe+DKQaL5Nv7du3dx/Phx1NXVsYnWW+Pz+ZBKpUbj5AkhpLP64x//iMOHD7u6Gh0S\nV85zlhrD3M2T4UQ8Hg9LliyBUqnE7t27cfnyZaPud65MoKO0M5a1d8qZ1hNmqqqqkJSUBLVajZ49\ne+LkyZPo0qULoqOj8eOPPzrlQkar1VJDmBDCKWVlZXSuM4NSqxGr8vLyUFxcjOvXrxssp9Rq5GFV\nVFRg9erV+OGHHwA8SP00YsQIzJkzh2Y9E0KIEyxevBhbt251dTWIi1Bj2E719fXss8zVajV2796N\n4cOHs7Nwm3GlZ5jSzlj2MClnlixZgn379jm5ZoQQwj1BQUG4ffu23duNGTMGp06dMrkuODgY2dnZ\nZlPYuTt3SK3G3T5vJ6mrq8P+/fuh1+uh1+sxaNAgDBw40KhcaWlppx5b00wgEHBiP5xNo9FArVZj\n5cqV+OKLL1xdHUIIsZsjJoYyDANvb29UV1cbLBcKhXjyySdx9OhRq9k2+vTpg99//90gnVv//v2R\nmpqK0NBQg7SLAQEBKC0ttZhVYuvWrYiLi8Px48eRmJiIpqYmBAcHY9SoUQgICMALL7wALy8vOtdZ\n0Xye4yJqDNuptrYWarUaer0eMTExGDVqlKurRFyMYRhXV4EQwmEymQx37951aAq7rVu34v3330dJ\nSQkAIDAwEFu3bsV7770HAPjzn/+M1157zWrjWCAQYNeuXdi0aROABxlnABjkC26ZG7h1LmEej4ct\nW7YgPT2d3b65nLksNqGhocjJyTGoh6kcxVFRUdiyZQu77fjx41FUVGSwHU0SJwANk7CLTqdDSkoK\nZs+eDS8vL2zfvh3Tp0832fVOeYbdQ+vhMYQQ+wmFQgiFQqMeQ2vDtNpyfDLVCyqVSvHmm29i48aN\nqKqqgoeHB4RCIRobGyEQCNDQ0AChUAgej8fW1dvbG4GBgRAIBJBIJFixYgXWrVsHtVoNqVSKH3/8\nEcCDBmFkZCR27NiB4uJivPDCCwCATz/9FADwwgsvQKfToUePHigrK4Ner4enpyeefvppvPXWW6is\nrMSsWbNQVFQEsViM1atX49NPP4VcLkdoaChWrVrF5tVesWIF3n//fVy/fh0hISHswzqWLl0KwDjv\nNvBg+FbrYQKFhYXsNs37pVQqoVQqUVJSgtDQULM5eltu2zqXt6V1bfEwr0vnOOsozzAxcPv2bZw4\ncQKzZs0CAHb80ZgxY4zKUmo17gsMDHR1FQiHDRkyBBcvXjR7+9fT0xONjY2QSCQms3uIxWL07t0b\nPj4+WLZsGdb9f+3de1SUdf4H8PfMMAwDDDAIJndkQNBSVyizo+YlYs0Q3KJzqF3drbSslhN2N7vY\nr5un3NWtbZNSt1V3u6GmYWR5Q0201pRdVBTwws28wMhwH+by+8PDs4wDMwMMDMzzfp3j8TDP8zDf\nZ3w78+HL9/k8K1bg4sWLSE5OxnPPPYfAwMAhe5c/vi/Z5y7tsPobs2Sfu2SJrdWcRKfTwd/fX/ja\nz88P1dXV0Ol0Vh9G7nIBHVurEXVPo9EgOjoae/fuhclkQlRUFFQqFSQSCS5fvizcxcvT0xMajQbx\n8fH44Ycf0NTUBI1Gg5ycHMTGxgLo/iKVsrIyZGdnAwBWrVol7N+ZI/t01Q89ISEBu3fv7vsLMcD4\nvmSfGNphOQOzZJ8YssSZ4R44ceIEysrKkJaWBgAoKipCdXU1lEolCgoKLPZlazX3x7XCfePj44MR\nI0YIt//18PBATEwMfH19oVQqsXbtWsTHx7t4lERE5O7ct8zvByqVCvX19cLXOp0Ofn5+GDdunNWH\ntl6vx+XLlwd6iE7H1mqDg1KphEQiQXR0NKZOnYrs7Owh1waoN1lyh/9DjnKX9kUDge9L9jFPjmGW\n7HOXLLG1mpOEhoairq4OWq0WKpUKxcXFyMjIgJ+fH/z8/Cz2ZWs191ddXW133fCyZcuwcOHCfnn+\nofbvwiw5xp3bFzkLs+Q45sk2Zslx7pwlFsM9IJPJMHv2bGzcuBEmkwmJiYk2f9Ig93fp0iW3uLCA\niIhIrLhmuJ+wtZo4uEvLmYHALNnGLDmOWbKPeXIMs2Sfu2TJVms1zgz3E3dZg8S2M7bJ5XIEBASg\nqamJM8N2MEu2MUuOY5bsY54cwyzZ5y5ZslUMD/2pSyIiIiKiXmIxTERERESixTXDRH2g0+lw5MgR\nJCUlWXUUIeoJZomciXkiZxFDljgzTNQHjY2NKCgo6PJ2uEQ9wSyRMzFP5CxiyBKLYSIiIiISLRbD\nRERERCRaLIaJiIiISLR4AV0/efHFF6HX6109DOpnRqMRDQ0NUKlUkMlkrh4ODWHMEjkT80TO4i5Z\n8vT0xFtvvdXlNt50o5/o9Xr84Q9/cPUwiIiwaNEijB07Fk888YTF47t378azzz6LMWPGYMOGDRbb\ntFotZs6cidzcXERGRmLVqlXYsWMHGhoaoFarMWPGDDz//PNob2/H66+/jsOHD6O+vh4RERF48skn\nMWXKlIE8RSIimz755JNut3GZBBGRm0tPT0deXp7V43l5eUhNTUVxcTGqq6sttuXn5yM+Ph4ajQZr\n1qzByZMn8dlnn+Hw4cNYt24dxowZAwAwGAwICQnBJ598gkOHDiErKwvPPPMMampqBuTciIj6isUw\nEZGbmzFjBurr63HkyBHhsfr6euzbtw+//e1vMXHiRHz99dcWx3z99deYM2cOAOD48eOYOXMmgoKC\nAAChoaHCNqVSicceewwhISEAgNtvvx1hYWE4efLkQJwaEVGfsRgmInJzXl5eSElJsSh4d+zYgZiY\nGIwaNQppaWkWM8dnz57FqVOncPfddwMAxo0bh/Xr1+Pzzz/H6dOnYetSkytXruD8+fPQaDT9d0JE\nRE7EYpiISATS09Px3Xffob29HcC1md+0tDQAwMyZM1FbW4tjx44J26ZOnYqAgAAAwIIFC/DQQw9h\n+/btuP/++5GcnIxt27ZZPUd7ezteeOEFpKenIzo6emBOjIioj1gMExGJwIQJE6BWq7Fr1y5UVlai\nuLgYs2fPBnBtqUPnmePt27cLhTIASKVSZGZmYv369SgsLMTChQvxyiuv4MyZM8I+JpMJL774IhQK\nBV588cWBPTkioj5gMUxEJBJz5szBtm3bkJeXhylTpiAwMFDYlpaWhh07duDgwYNobm7GtGnTuvwe\nnp6eyMzMhJ+fH86ePQsAMJvNeOWVV6DVarFy5coh3X6JiMSHxTARkUikpaWhsLAQmzZtspj5BYCk\npCSoVCr83//9H+666y54ePyv8+bGjRvx008/obW1FQaDAVu3bkVzczMSEhIAAK+//jrOnj2L9957\nD56engN6TkREfcU+w0REIhEaGooJEybg9OnTmD59utX2tLQ0rF692qpQ9vLywooVK1BZWQkAGDly\nJP785z8jLCwMNTU1yM3NhUKhwIwZM4RjXn31VWEZBhHRYMY70PWTZ555hjfdICIiIhoEPvnkE6xY\nsaLLbVwmQURERESixWUSPXTo0CH8/PPPMJvNSEpKwqRJk7rcz2Aw4KOPPhrg0Tnf3Llz8dVXX7l6\nGINWREQEnn32Wbz77rvCr5Cpa8ySbcyS45gl+5gnxzBL9rlLlnx9fbvdxmUSPXDx4kVs2rQJCxcu\nhEwmw8aNG5GammpxRXaH2tpaSKVDf+JdKpXCZDK5ehiDlkQigaenJ/R6vc0bERCzZA+z5DhmyT7m\nyTHMkn3ukiW1Wt3tNs4M98CVK1cQFhYGuVwOAIiKisLJkycxefJkq33b2toGenj9QqlUoqWlxdXD\nGLTkcjkCAgLQ1NQk3MyAusYs2cYsOY5Zso95cgyzZJ+7ZMlWMTz0py4H0PDhw1FRUYHm5mbo9XqU\nlpZCp9O5elhERERE1EucGe6B4OBgTJ48GRs2bICnpydGjBgBiUQCnU6HxsZGi331ej18fHxcNFLn\nkclkwkw4Wevoxdq5Jyt1jVmyjVlyHLNkX3/mqba2Vrgm5pFHHsGwYcMc2jYYMUv2ieG9iWuG+2Dn\nzp3w9/dHY2MjCgoKLLZNmzbNoucmERHRUHflyhVMmzYNJ06cAACMGTMGBQUFCAoKsrmNaDBz3zK/\nnzQ2NsLX1xdXr15FSUkJFixYAL1ej/j4eIv99Ho9Ll++7KJROo9CoXCb9c/9wcPDA2q1GlqtFgaD\nwdXDGdSYJduYJccxS/b1V57efvttodgFgBMnTuCtt97CkiVLbG4brJgl+9zlvSk4OLjbbSyGe+iL\nL75AS0sLpFIp7r77bnh5ecHLywt+fn4W+9XU1AzpheYdPDw83OI8+pvBYODrZAez5BhmyT5myXHO\nzpPRaOzysfb2dpvbBitmyXHu/N7EZRI91NLSgm3btgmzvunp6YiIiLDaj63VxMFdWs4MBGbJNmbJ\nccySff2Vp9raWqSmpuLUqVMAgPj4eOTl5WHYsGFW26KjoxEYGAgPDw+8//77iIuLc9o4nIVZss9d\n3ptsdZNgMdxDW7ZsQVRUFBITE4WfeL28vKz2q6mpccHonI9tZ2yTy+UIDg7G5cuX3fYnZmdhlmxj\nlhzHLFkqLy/H008/DQD405/+BI1GA7lcjrq6OsyfPx9ms1l4HADq6urw7rvv4vvvv8cNN9yA9957\nz2Lbxx9/DABYuHBhl330be3TsU2r1eKzzz4Tsuzp6YmdO3cKzzNQ9uzZg0cffRQAkJOTY3UtD7Nk\nn7u8N4WGh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"text/plain": "<matplotlib.figure.Figure at 0x26b3a750>"}, "metadata": {}}, {"execution_count": 71, "output_type": "execute_result", "data": {"text/plain": "<ggplot: (28108727)>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": null, "cell_type": "code", "source": "p = ggplot(aes(x='price', y='carat',color=\"cut\"), data=bigdiamonds)\np + geom_point()", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 73, "cell_type": "code", "source": "certi=bigdiamonds.groupby(\"cert\")", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 74, "cell_type": "code", "source": "certi.mean()", "outputs": [{"execution_count": 74, "output_type": "execute_result", "data": {"text/plain": "               carat      table      depth         price         x         y  \\\ncert                                                                           \nAGS         1.541315  56.559060  61.510615  14041.455375  6.771208  7.099966   \nEGL         1.555178  58.553025  62.042944   9922.133965  6.186729  7.104222   \nEGL ISRAEL  1.575869  58.162295  61.877356   9781.358464  7.151370  7.202344   \nEGL Intl.   1.472878  57.748214  61.611462   8964.943653  6.971325  6.997338   \nEGL USA     1.561697  57.485727  60.936625  10999.778843  7.093143  7.143957   \nGIA         0.997418  57.676309  61.655088   8681.642926  5.883144  6.069219   \nHRD         1.679021  57.830968  57.581864  16951.687500  6.995237  7.185487   \nIGI         0.877305  56.772684  54.794428   5309.890866  5.690697  5.741698   \nOTHER       1.212394  54.122195  58.471844   8008.277440  6.369640  6.412408   \n\n                   z      newdata  \ncert                               \nAGS         4.694529  7274.423876  \nEGL         5.362767  4974.516180  \nEGL ISRAEL  4.446176  5034.203046  \nEGL Intl.   4.327850  4696.446285  \nEGL USA     4.409165  5431.419306  \nGIA         3.928074  6058.856624  \nHRD         4.656997  7353.717449  \nIGI         3.618823  3840.667020  \nOTHER       3.954081  4719.449448  ", "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>carat</th>\n      <th>table</th>\n      <th>depth</th>\n      <th>price</th>\n      <th>x</th>\n      <th>y</th>\n      <th>z</th>\n      <th>newdata</th>\n    </tr>\n    <tr>\n      <th>cert</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>AGS</th>\n      <td>1.541315</td>\n      <td>56.559060</td>\n      <td>61.510615</td>\n      <td>14041.455375</td>\n      <td>6.771208</td>\n      <td>7.099966</td>\n      <td>4.694529</td>\n      <td>7274.423876</td>\n    </tr>\n    <tr>\n      <th>EGL</th>\n      <td>1.555178</td>\n      <td>58.553025</td>\n      <td>62.042944</td>\n      <td>9922.133965</td>\n      <td>6.186729</td>\n      <td>7.104222</td>\n      <td>5.362767</td>\n      <td>4974.516180</td>\n    </tr>\n    <tr>\n      <th>EGL ISRAEL</th>\n      <td>1.575869</td>\n      <td>58.162295</td>\n      <td>61.877356</td>\n      <td>9781.358464</td>\n      <td>7.151370</td>\n      <td>7.202344</td>\n      <td>4.446176</td>\n      <td>5034.203046</td>\n    </tr>\n    <tr>\n      <th>EGL Intl.</th>\n      <td>1.472878</td>\n      <td>57.748214</td>\n      <td>61.611462</td>\n      <td>8964.943653</td>\n      <td>6.971325</td>\n      <td>6.997338</td>\n      <td>4.327850</td>\n      <td>4696.446285</td>\n    </tr>\n    <tr>\n      <th>EGL USA</th>\n      <td>1.561697</td>\n      <td>57.485727</td>\n      <td>60.936625</td>\n      <td>10999.778843</td>\n      <td>7.093143</td>\n      <td>7.143957</td>\n      <td>4.409165</td>\n      <td>5431.419306</td>\n    </tr>\n    <tr>\n      <th>GIA</th>\n      <td>0.997418</td>\n      <td>57.676309</td>\n      <td>61.655088</td>\n      <td>8681.642926</td>\n      <td>5.883144</td>\n      <td>6.069219</td>\n      <td>3.928074</td>\n      <td>6058.856624</td>\n    </tr>\n    <tr>\n      <th>HRD</th>\n      <td>1.679021</td>\n      <td>57.830968</td>\n      <td>57.581864</td>\n      <td>16951.687500</td>\n      <td>6.995237</td>\n      <td>7.185487</td>\n      <td>4.656997</td>\n      <td>7353.717449</td>\n    </tr>\n    <tr>\n      <th>IGI</th>\n      <td>0.877305</td>\n      <td>56.772684</td>\n      <td>54.794428</td>\n      <td>5309.890866</td>\n      <td>5.690697</td>\n      <td>5.741698</td>\n      <td>3.618823</td>\n      <td>3840.667020</td>\n    </tr>\n    <tr>\n      <th>OTHER</th>\n      <td>1.212394</td>\n      <td>54.122195</td>\n      <td>58.471844</td>\n      <td>8008.277440</td>\n      <td>6.369640</td>\n      <td>6.412408</td>\n      <td>3.954081</td>\n      <td>4719.449448</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 75, "cell_type": "code", "source": "cleandiamonds.columns = ['a', 'b','c','d','e','f','g','h','i','j','k','l','m']", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 76, "cell_type": "code", "source": "cleandiamonds.info()", "outputs": [{"output_type": "stream", "name": "stdout", "text": "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 593784 entries, 0 to 593783\nData columns (total 13 columns):\na    593784 non-null float64\nb    593784 non-null object\nc    593784 non-null object\nd    593784 non-null object\ne    593784 non-null float64\nf    593784 non-null float64\ng    593784 non-null object\nh    593784 non-null object\ni    593784 non-null float64\nj    593784 non-null float64\nk    593784 non-null float64\nl    593784 non-null float64\nm    593784 non-null float64\ndtypes: float64(8), object(5)\nmemory usage: 52.1+ MB\n"}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 77, "cell_type": "code", "source": "import seaborn as sns", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 78, "cell_type": "code", "source": "sns.jointplot(bigdiamonds[\"price\"], bigdiamonds[\"carat\"])", "outputs": [{"execution_count": 78, "output_type": "execute_result", "data": {"text/plain": "<seaborn.axisgrid.JointGrid at 0x2ed646d0>"}, "metadata": {}}, {"output_type": "display_data", "data": {"image/png": 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ISEKBE8K+4/zpKrCywt0vfI3ZYgt7e0/Mm1WCQXuiB2DQqpj/vVNYMHsiJSMH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\n37LkKvkWGbmu7m7OOmkWTU1NsXTX+VZQoeRUZl3dtTnAKZN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"text/plain": "<matplotlib.figure.Figure at 0x2ed64e30>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 79, "cell_type": "code", "source": "sns.kdeplot(bigdiamonds[\"price\"], shade=True)", "outputs": [{"execution_count": 79, "output_type": "execute_result", "data": {"text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x2d6dfcb0>"}, "metadata": {}}, {"output_type": "display_data", "data": {"image/png": 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VMY9DwcKkn8O6jRqWKSIihwuiqNpuDFNO1IzVVX973y9Y8/J2/uyTZzBwYLCm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\n9zjnXjCzq8zsqmSfB4BXk46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"text/plain": "<matplotlib.figure.Figure at 0x26951830>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 80, "cell_type": "code", "source": "c1, c2, c3 = sns.color_palette(\"Set1\", 3)", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 81, "cell_type": "code", "source": "sns.kdeplot(bigdiamonds[\"table\"], shade=True, color=c1)\nsns.kdeplot(bigdiamonds[\"depth\"], shade=True, color=c3)", "outputs": [{"execution_count": 81, "output_type": "execute_result", "data": {"text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x2e8b0150>"}, "metadata": {}}, {"output_type": "display_data", "data": {"image/png": 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KOon5M2093fhuVolGJY7TWC9uf6Jim0+AtJsBIFvINnTZsnwoQIuIVJIsEgsS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\nJN+fyYqTjUrqzKBL/bgLBQ6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"text/plain": "<matplotlib.figure.Figure at 0x2dfdfc10>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 82, "cell_type": "code", "source": "import statsmodels.api as sm", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 83, "cell_type": "code", "source": "model = sm.OLS(bigdiamonds[\"price\"],bigdiamonds[\"carat\"])", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 84, "cell_type": "code", "source": "results = model.fit()", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 85, "cell_type": "code", "source": "model", "outputs": [{"execution_count": 85, "output_type": "execute_result", "data": {"text/plain": "<statsmodels.regression.linear_model.OLS at 0x2ea0e770>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 86, "cell_type": "code", "source": "print(results.summary())", "outputs": [{"output_type": "stream", "name": "stdout", "text": "                            OLS Regression Results                            \n==============================================================================\nDep. Variable:                  price   R-squared:                         nan\nModel:                            OLS   Adj. R-squared:                    nan\nMethod:                 Least Squares   F-statistic:                       nan\nDate:                Fri, 26 Jun 2015   Prob (F-statistic):                nan\nTime:                        13:29:33   Log-Likelihood:                    nan\nNo. Observations:              598024   AIC:                               nan\nDf Residuals:                  598023   BIC:                               nan\nDf Model:                           1                                         \nCovariance Type:            nonrobust                                         \n==============================================================================\n                 coef    std err          t      P>|t|      [95.0% Conf. Int.]\n------------------------------------------------------------------------------\ncarat             nan        nan        nan        nan           nan       nan\n==============================================================================\nOmnibus:                          nan   Durbin-Watson:                     nan\nProb(Omnibus):                    nan   Jarque-Bera (JB):                  nan\nSkew:                             nan   Prob(JB):                          nan\nKurtosis:                         nan   Cond. No.                         1.00\n==============================================================================\n\nWarnings:\n[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 87, "cell_type": "code", "source": "cleandiamonds2=bigdiamonds[[\"price\",\"carat\"]]\ncleandiamonds2.describe()", "outputs": [{"execution_count": 87, "output_type": "execute_result", "data": {"text/plain": "               price          carat\ncount  597311.000000  598024.000000\nmean     8753.017974       1.071297\nstd     13017.567760       0.812696\nmin       300.000000       0.200000\n25%      1220.000000       0.500000\n50%      3503.000000       0.900000\n75%     11174.000000       1.500000\nmax     99990.000000       9.250000", "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>price</th>\n      <th>carat</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>597311.000000</td>\n      <td>598024.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>8753.017974</td>\n      <td>1.071297</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>13017.567760</td>\n      <td>0.812696</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>300.000000</td>\n      <td>0.200000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>1220.000000</td>\n      <td>0.500000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>3503.000000</td>\n      <td>0.900000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>11174.000000</td>\n      <td>1.500000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>99990.000000</td>\n      <td>9.250000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 88, "cell_type": "code", "source": "#example of help is sm.OLS?", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 89, "cell_type": "code", "source": "X = sm.add_constant(X)\n\nmodel = sm.OLS(cleandiamonds2[\"price\"],cleandiamonds2[\"carat\"])\nresults = model.fit()\nprint(results.summary())\n", "outputs": [{"ename": "NameError", "evalue": "name 'X' is not defined", "traceback": ["\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)", "\u001b[1;32m<ipython-input-89-6484046dc3dc>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_constant\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOLS\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcleandiamonds2\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"price\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcleandiamonds2\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"carat\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mNameError\u001b[0m: name 'X' is not defined"], "output_type": "error"}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": null, "cell_type": "code", "source": "import sklearn as sk\nfrom sklearn import linear_model\n", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": null, "cell_type": "code", "source": "regr = linear_model.LinearRegression()\n", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": null, "cell_type": "code", "source": "regr.fit(cleandiamonds2[\"price\"], cleandiamonds2[\"carat\"])\n", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": null, "cell_type": "code", "source": "import sklearn as sk\nBoston=sk.load_boston()", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": null, "cell_type": "code", "source": "from sklearn.datasets import load_boston\nBoston=sk.datasets.load_boston()\n", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": null, "cell_type": "code", "source": "Boston", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": null, "cell_type": "code", "source": "Boston.columns()", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": null, "cell_type": "code", "source": "type(Boston)", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": null, "cell_type": "code", "source": "Boston2=np.array(Boston)\nBoston2", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": null, "cell_type": "code", "source": "print(Boston)\n", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": null, "cell_type": "code", "source": "Boston2=pd.DataFrame(Boston2[0:,0:])", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": null, "cell_type": "code", "source": "Boston2=pd.DataFrame(Boston2[0:,0:],index=data[:,0])", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": null, "cell_type": "code", "source": "result = sm.ols(formula=\"A ~ B + C\", data=df).fit()", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": null, "cell_type": "code", "source": "print result.summary()", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": null, "cell_type": "code", "source": "from pandas.stats.api import ols\nres = ols(y=bigdiamonds['price'], x=bigdiamonds[['carat','table']])\n", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": null, "cell_type": "code", "source": "res", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": null, "cell_type": "code", "source": "res2 = ols(y=bigdiamonds['price'], x=bigdiamonds[['carat','table','depth']])", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": null, "cell_type": "code", "source": "res2", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": null, "cell_type": "code", "source": "#what is the error here?\n\nnp.corr(bigdiamonds)", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": null, "cell_type": "code", "source": "bigdiamonds.corr()", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": null, "cell_type": "code", "source": "", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}], "nbformat": 4, "metadata": {"kernelspec": {"display_name": "Python 2", "name": "python2", "language": "python"}, "language_info": {"mimetype": "text/x-python", "nbconvert_exporter": "python", "version": "2.7.9", "name": "python", "file_extension": ".py", "pygments_lexer": "ipython2", "codemirror_mode": {"version": 2, "name": "ipython"}}}}