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kaggle-notebooks-outputs-filtered-0

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{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "1b1048b2-96fc-7920-299a-484e9c3d3dfd" }, "source": [ "This is a markdown cell. Click here to edit..." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "073a0d92-96b1-c5ac-9111-1a479cd2412c" }, "outputs": [], "source": [ "from time import sleep" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "6f17a155-ce7a-b7fa-89c9-cf7e80c2e455" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", "1\n", "2\n" ] } ], "source": [ "for i in range(3):\n", " print(i)\n", " sleep(0.1)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "a5df577b-2b0c-61ba-fffb-ac5ee82a7cc5" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "abbb74de-3093-5759-9d95-1ca4a4a46816" }, "outputs": [], "source": [] } ], "metadata": { "_change_revision": 180, "_is_fork": false, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/299/299160.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "008957cf-6ddf-bd50-6d69-bf64d2a4fe49" }, "source": [ "This is a markdown cell. Click here to edit..." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "3d4eb4b2-5e0e-08a6-9358-a431691c1c93" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/IPython/core/interactiveshell.py:2723: DtypeWarning: Columns (7) have mixed types. Specify dtype option on import or set low_memory=False.\n", " interactivity=interactivity, compiler=compiler, result=result)\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>location</th>\n", " <th>count</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Argentina-Buenos_Aires</td>\n", " <td>84</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Argentina-CABA</td>\n", " <td>84</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Argentina-Catamarca</td>\n", " <td>84</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Argentina-Chaco</td>\n", " <td>84</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Argentina-Chubut</td>\n", " <td>84</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>Argentina-Cordoba</td>\n", " <td>84</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>Argentina-Corrientes</td>\n", " <td>84</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>Argentina-Entre_Rios</td>\n", " <td>84</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>Argentina-Formosa</td>\n", " <td>84</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>Argentina-Jujuy</td>\n", " <td>84</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>Argentina-La_Pampa</td>\n", " <td>84</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>Argentina-La_Rioja</td>\n", " <td>84</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>Argentina-Mendoza</td>\n", " <td>84</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>Argentina-Misiones</td>\n", " <td>84</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>Argentina-Neuquen</td>\n", " <td>84</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>Argentina-Rio_Negro</td>\n", " <td>84</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>Argentina-Salta</td>\n", " <td>84</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>Argentina-San_Juan</td>\n", " <td>84</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>Argentina-San_Luis</td>\n", " <td>84</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>Argentina-Santa_Cruz</td>\n", " <td>84</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>Argentina-Santa_Fe</td>\n", " <td>84</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>Argentina-Sgo_Del_Estero</td>\n", " <td>84</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>Argentina-Tierra_Del_Fuego</td>\n", " <td>54</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>Argentina-Tierra_del_Fuego</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>Argentina-Tucuman</td>\n", " <td>84</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>Brazil</td>\n", " <td>7</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>Brazil-Acre</td>\n", " <td>170</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>Brazil-Alagoas</td>\n", " <td>171</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>Brazil-Amapa</td>\n", " <td>143</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>Brazil-Amazonas</td>\n", " <td>113</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>1643</th>\n", " <td>United_States-Missouri</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>1644</th>\n", " <td>United_States-Montana</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>1645</th>\n", " <td>United_States-Nebraska</td>\n", " <td>34</td>\n", " </tr>\n", " <tr>\n", " <th>1646</th>\n", " <td>United_States-Nevada</td>\n", " <td>20</td>\n", " </tr>\n", " <tr>\n", " <th>1647</th>\n", " <td>United_States-New_Hampshire</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>1648</th>\n", " <td>United_States-New_Jersey</td>\n", " <td>34</td>\n", " </tr>\n", " <tr>\n", " <th>1649</th>\n", " <td>United_States-New_Mexico</td>\n", " <td>14</td>\n", " </tr>\n", " <tr>\n", " <th>1650</th>\n", " <td>United_States-New_York</td>\n", " <td>34</td>\n", " </tr>\n", " <tr>\n", " <th>1651</th>\n", " <td>United_States-North_Carolina</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>1652</th>\n", " <td>United_States-Ohio</td>\n", " <td>34</td>\n", " </tr>\n", " <tr>\n", " <th>1653</th>\n", " <td>United_States-Oklahoma</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>1654</th>\n", " <td>United_States-Oregon</td>\n", " <td>34</td>\n", " </tr>\n", " <tr>\n", " <th>1655</th>\n", " <td>United_States-Pennsylvania</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>1656</th>\n", " <td>United_States-Pennsylvania††</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>1657</th>\n", " <td>United_States-Puerto_Rico</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>1658</th>\n", " <td>United_States-Rhode_Island</td>\n", " <td>6</td>\n", " </tr>\n", " <tr>\n", " <th>1659</th>\n", " <td>United_States-South_Carolina</td>\n", " <td>6</td>\n", " </tr>\n", " <tr>\n", " <th>1660</th>\n", " <td>United_States-Tennessee</td>\n", " <td>34</td>\n", " </tr>\n", " <tr>\n", " <th>1661</th>\n", " <td>United_States-Texas</td>\n", " <td>34</td>\n", " </tr>\n", " <tr>\n", " <th>1662</th>\n", " <td>United_States-US_Virgin_Islands</td>\n", " <td>34</td>\n", " </tr>\n", " <tr>\n", " <th>1663</th>\n", " <td>United_States-Utah</td>\n", " <td>20</td>\n", " </tr>\n", " <tr>\n", " <th>1664</th>\n", " <td>United_States-Vermont</td>\n", " <td>6</td>\n", " </tr>\n", " <tr>\n", " <th>1665</th>\n", " <td>United_States-Virginia</td>\n", " <td>34</td>\n", " </tr>\n", " <tr>\n", " <th>1666</th>\n", " <td>United_States-Washington</td>\n", " <td>34</td>\n", " </tr>\n", " <tr>\n", " <th>1667</th>\n", " <td>United_States-West_Virginia</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>1668</th>\n", " <td>United_States-Wisconsin</td>\n", " <td>6</td>\n", " </tr>\n", " <tr>\n", " <th>1669</th>\n", " <td>United_States_Virgin_Islands</td>\n", " <td>413</td>\n", " </tr>\n", " <tr>\n", " <th>1670</th>\n", " <td>United_States_Virgin_Islands-Saint_Croix</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>1671</th>\n", " <td>United_States_Virgin_Islands-Saint_John</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>1672</th>\n", " <td>United_States_Virgin_Islands-Saint_Thomas</td>\n", " <td>32</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>1673 rows × 2 columns</p>\n", "</div>" ], "text/plain": [ " location count\n", "0 Argentina-Buenos_Aires 84\n", "1 Argentina-CABA 84\n", "2 Argentina-Catamarca 84\n", "3 Argentina-Chaco 84\n", "4 Argentina-Chubut 84\n", "5 Argentina-Cordoba 84\n", "6 Argentina-Corrientes 84\n", "7 Argentina-Entre_Rios 84\n", "8 Argentina-Formosa 84\n", "9 Argentina-Jujuy 84\n", "10 Argentina-La_Pampa 84\n", "11 Argentina-La_Rioja 84\n", "12 Argentina-Mendoza 84\n", "13 Argentina-Misiones 84\n", "14 Argentina-Neuquen 84\n", "15 Argentina-Rio_Negro 84\n", "16 Argentina-Salta 84\n", "17 Argentina-San_Juan 84\n", "18 Argentina-San_Luis 84\n", "19 Argentina-Santa_Cruz 84\n", "20 Argentina-Santa_Fe 84\n", "21 Argentina-Sgo_Del_Estero 84\n", "22 Argentina-Tierra_Del_Fuego 54\n", "23 Argentina-Tierra_del_Fuego 30\n", "24 Argentina-Tucuman 84\n", "25 Brazil 7\n", "26 Brazil-Acre 170\n", "27 Brazil-Alagoas 171\n", "28 Brazil-Amapa 143\n", "29 Brazil-Amazonas 113\n", "... ... ...\n", "1643 United_States-Missouri 32\n", "1644 United_States-Montana 32\n", "1645 United_States-Nebraska 34\n", "1646 United_States-Nevada 20\n", "1647 United_States-New_Hampshire 32\n", "1648 United_States-New_Jersey 34\n", "1649 United_States-New_Mexico 14\n", "1650 United_States-New_York 34\n", "1651 United_States-North_Carolina 32\n", "1652 United_States-Ohio 34\n", "1653 United_States-Oklahoma 32\n", "1654 United_States-Oregon 34\n", "1655 United_States-Pennsylvania 30\n", "1656 United_States-Pennsylvania†† 4\n", "1657 United_States-Puerto_Rico 32\n", "1658 United_States-Rhode_Island 6\n", "1659 United_States-South_Carolina 6\n", "1660 United_States-Tennessee 34\n", "1661 United_States-Texas 34\n", "1662 United_States-US_Virgin_Islands 34\n", "1663 United_States-Utah 20\n", "1664 United_States-Vermont 6\n", "1665 United_States-Virginia 34\n", "1666 United_States-Washington 34\n", "1667 United_States-West_Virginia 30\n", "1668 United_States-Wisconsin 6\n", "1669 United_States_Virgin_Islands 413\n", "1670 United_States_Virgin_Islands-Saint_Croix 32\n", "1671 United_States_Virgin_Islands-Saint_John 32\n", "1672 United_States_Virgin_Islands-Saint_Thomas 32\n", "\n", "[1673 rows x 2 columns]" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "import seaborn as sns\n", "sns.set_style(\"whitegrid\")\n", "\n", "zika = pd.read_csv(\"../input/cdc_zika.csv\")\n", "\n", "zika.groupby(\"location\").size().reset_index().rename(columns={0: \"count\"})" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "6475edf2-6315-e6d3-0b0a-f07b4851e78d" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "90bf2062-9643-ac25-b4dc-08de22b3782a" }, "outputs": [], "source": [] } ], "metadata": { "_change_revision": 167, "_is_fork": false, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/303/303338.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "061ccc04-2038-8bed-0d61-a50d12943e2a" }, "source": [ "# This is currently a work-in-progress" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "6aee2813-1742-ccc2-c592-1b73364cc53f" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Id</th>\n", " <th>Title</th>\n", " <th>NumVotes</th>\n", " <th>NumNonSelfVotes</th>\n", " <th>HasNonSelfVotes</th>\n", " <th>NumVersions</th>\n", " <th>NumSuccessfulRuns</th>\n", " <th>NumErroredRuns</th>\n", " <th>NumChangedVersions</th>\n", " <th>Lines</th>\n", " <th>LinesAddedOrChanged</th>\n", " <th>Name</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>80455</td>\n", " <td>Initial loan book analysis</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " 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<tr>\n", " <th>35283</th>\n", " <td>105</td>\n", " <td>Random Forest Benchmark</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>R</td>\n", " </tr>\n", " <tr>\n", " <th>35284</th>\n", " <td>104</td>\n", " <td>Random Forest Benchmark</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>R</td>\n", " </tr>\n", " <tr>\n", " <th>35285</th>\n", " <td>103</td>\n", " <td>by time</td>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>R</td>\n", " </tr>\n", " <tr>\n", " <th>35286</th>\n", " <td>94</td>\n", " <td>Rentals By Time/Temp/Workingday</td>\n", " <td>5</td>\n", " <td>5</td>\n", " <td>1</td>\n", " <td>10</td>\n", " <td>14</td>\n", " <td>0</td>\n", " <td>8</td>\n", " <td>0.0</td>\n", " <td>8.0</td>\n", " <td>R</td>\n", " </tr>\n", " <tr>\n", " <th>35287</th>\n", " <td>93</td>\n", " <td>Bike Rentals By Time And Temperature</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>R</td>\n", " </tr>\n", " <tr>\n", " <th>35288</th>\n", " <td>92</td>\n", " <td>Installed R Packages</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>R</td>\n", " </tr>\n", " <tr>\n", " <th>35289</th>\n", " <td>90</td>\n", " <td>Bike Rentals By Time</td>\n", " <td>11</td>\n", " <td>10</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>11</td>\n", " <td>0</td>\n", " <td>11</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>R</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>35290 rows × 12 columns</p>\n", "</div>" ], "text/plain": [ " Id Title NumVotes \\\n", "0 80455 Initial loan book analysis 0 \n", "1 80443 Classification using scikit learn 0 \n", "2 80443 Classification using scikit learn 0 \n", "3 80443 Classification using scikit learn 0 \n", "4 80444 face recognition test 0 \n", "5 80444 face recognition test 0 \n", "6 80124 Calories, carbs, fats, and proteins 0 \n", "7 80124 Calories, carbs, fats, and proteins 1 \n", "8 80124 Calories, carbs, fats, and proteins 0 \n", "9 79939 Extracting Goal Times 0 \n", "10 80266 New Coder Age vs Job Role Interest 0 \n", "11 80266 New Coder Age vs Job Role Interest 0 \n", "12 80430 Plane Crash Analysis in Python 0 \n", "13 80430 Plane Crash Analysis in Python 0 \n", "14 80430 Plane Crash Analysis in Python 0 \n", "15 80408 testone 0 \n", "16 66097 List Files 0 \n", "17 66097 List Files 0 \n", "18 74216 List Files 2 0 \n", "19 80378 Looking at the Zika data 0 \n", "20 80377 Exploring the Zika data 0 \n", "21 80377 Exploring the Zika data 0 \n", "22 80376 Hello Kaggle 0 \n", "23 80073 Test Football 0 \n", "24 80361 Exploring Airplane Crashes 0 \n", "25 80361 Exploring Airplane Crashes 0 \n", "26 80361 Exploring Airplane Crashes 0 \n", "27 80348 kNN example 0 \n", "28 80343 'Clinton: Champion of the Primaries 0 \n", "29 78540 Visualizing Iris datasets with R ggplot2 0 \n", "... ... ... ... \n", "35260 167 Example 0 \n", "35261 166 Histogram Open/Closed by Length 0 \n", "35262 152 broken 0 \n", "35263 164 Random Forest Benchmark 0 \n", "35264 162 Random Forest Benchmark 0 \n", "35265 156 fiddling2 0 \n", "35266 159 Testing 0 \n", "35267 157 Random Forest Benchmark 0 \n", "35268 153 fiddling 0 \n", "35269 155 Random Forest Benchmark 0 \n", "35270 151 head of train 0 \n", "35271 150 Default R Script 0 \n", "35272 148 Random Forest Benchmark 0 \n", "35273 146 Random Forest Benchmark 0 \n", "35274 145 Random Forest Benchmark 0 \n", "35275 144 Input Files 0 \n", "35276 142 Random Forest Benchmark 0 \n", "35277 141 Installed R Packages 0 \n", "35278 126 Prueba 0 \n", "35279 125 by time 3 \n", "35280 122 Random Forest Benchmark 0 \n", "35281 121 Random Forest Benchmark 0 \n", "35282 120 Random Forest Benchmark 0 \n", "35283 105 Random Forest Benchmark 1 \n", "35284 104 Random Forest Benchmark 1 \n", "35285 103 by time 3 \n", "35286 94 Rentals By Time/Temp/Workingday 5 \n", "35287 93 Bike Rentals By Time And Temperature 1 \n", "35288 92 Installed R Packages 0 \n", "35289 90 Bike Rentals By Time 11 \n", "\n", " NumNonSelfVotes HasNonSelfVotes NumVersions NumSuccessfulRuns \\\n", "0 0 0 1 1 \n", "1 0 0 1 0 \n", "2 0 0 1 1 \n", "3 0 0 5 1 \n", "4 0 0 1 0 \n", "5 0 0 3 2 \n", "6 0 0 8 0 \n", "7 1 1 5 5 \n", "8 0 0 2 2 \n", "9 0 0 4 0 \n", "10 0 0 1 0 \n", "11 0 0 3 3 \n", "12 0 0 2 0 \n", "13 0 0 1 1 \n", "14 0 0 1 1 \n", "15 0 0 1 1 \n", "16 0 0 60 11 \n", "17 0 0 1 0 \n", "18 0 0 11 11 \n", "19 0 0 1 1 \n", "20 0 0 1 0 \n", "21 0 0 6 5 \n", "22 0 0 4 2 \n", "23 0 0 6 1 \n", "24 0 0 3 0 \n", "25 0 0 1 1 \n", "26 0 0 1 0 \n", "27 0 0 1 1 \n", "28 0 0 1 1 \n", "29 0 0 1 0 \n", "... ... ... ... ... \n", "35260 0 0 4 1 \n", "35261 0 0 9 7 \n", "35262 0 0 12 5 \n", "35263 0 0 1 0 \n", "35264 0 0 1 1 \n", "35265 0 0 13 3 \n", "35266 0 0 1 1 \n", "35267 0 0 1 0 \n", "35268 0 0 2 0 \n", "35269 0 0 1 1 \n", "35270 0 0 2 2 \n", "35271 0 0 1 1 \n", "35272 0 0 1 1 \n", "35273 0 0 1 1 \n", "35274 0 0 1 1 \n", "35275 0 0 2 1 \n", "35276 0 0 1 1 \n", "35277 0 0 2 2 \n", "35278 0 0 1 1 \n", "35279 3 1 20 17 \n", "35280 0 0 1 1 \n", "35281 0 0 1 1 \n", "35282 0 0 1 1 \n", "35283 1 1 1 1 \n", "35284 0 0 1 1 \n", "35285 3 1 1 3 \n", "35286 5 1 10 14 \n", "35287 1 1 1 1 \n", "35288 0 0 1 1 \n", "35289 10 1 1 11 \n", "\n", " NumErroredRuns NumChangedVersions Lines LinesAddedOrChanged \\\n", "0 0 0 0.0 0.0 \n", "1 1 0 NaN NaN \n", "2 0 0 0.0 0.0 \n", "3 4 5 -3.0 8.0 \n", "4 1 0 NaN NaN \n", "5 1 2 2.0 3.0 \n", "6 8 0 NaN NaN \n", "7 0 4 0.0 3.0 \n", "8 0 1 2.0 13.0 \n", "9 4 4 -3.0 15.0 \n", "10 1 0 0.0 0.0 \n", "11 0 1 1.0 1.0 \n", "12 2 0 NaN NaN \n", "13 0 1 2.0 2.0 \n", "14 0 0 0.0 0.0 \n", "15 0 0 0.0 0.0 \n", "16 49 8 2.0 21.0 \n", "17 1 0 0.0 0.0 \n", "18 0 1 0.0 1.0 \n", "19 0 0 0.0 0.0 \n", "20 1 1 0.0 0.0 \n", "21 1 5 -101.0 4.0 \n", "22 2 4 0.0 43.0 \n", "23 5 4 3.0 5.0 \n", "24 3 0 NaN NaN \n", "25 0 0 NaN NaN \n", "26 1 0 NaN NaN \n", "27 0 0 0.0 0.0 \n", "28 0 0 0.0 0.0 \n", "29 1 0 NaN NaN \n", "... ... ... ... ... \n", "35260 3 2 -18.0 3.0 \n", "35261 2 8 8.0 23.0 \n", "35262 7 7 -12.0 6.0 \n", "35263 1 0 NaN NaN \n", "35264 0 0 NaN NaN \n", "35265 10 12 15.0 23.0 \n", "35266 0 1 NaN NaN \n", "35267 1 0 NaN NaN \n", "35268 2 1 0.0 0.0 \n", "35269 0 0 NaN NaN \n", "35270 0 1 0.0 1.0 \n", "35271 0 1 NaN NaN \n", "35272 0 0 NaN NaN \n", "35273 0 0 NaN NaN \n", "35274 0 0 NaN NaN \n", "35275 1 1 0.0 1.0 \n", "35276 0 0 NaN NaN \n", "35277 0 0 0.0 0.0 \n", "35278 0 1 NaN NaN \n", "35279 4 18 0.0 25.0 \n", "35280 0 0 NaN NaN \n", "35281 0 0 NaN NaN \n", "35282 0 0 NaN NaN \n", "35283 0 0 NaN NaN \n", "35284 0 0 NaN NaN \n", "35285 0 3 NaN NaN \n", "35286 0 8 0.0 8.0 \n", "35287 0 0 NaN NaN \n", "35288 0 1 NaN NaN \n", "35289 0 11 NaN NaN \n", "\n", " Name \n", "0 RMarkdown \n", "1 IPython Notebook \n", "2 IPython Notebook HTML \n", "3 Python \n", "4 IPython Notebook \n", "5 Python \n", "6 IPython Notebook \n", "7 IPython Notebook HTML \n", "8 Python \n", "9 R \n", "10 Python \n", "11 R \n", "12 IPython Notebook \n", "13 IPython Notebook HTML \n", "14 Python \n", "15 Python \n", "16 Python \n", "17 R \n", "18 R \n", "19 RMarkdown \n", "20 R \n", "21 RMarkdown \n", "22 Python \n", "23 R \n", "24 IPython Notebook \n", "25 IPython Notebook HTML \n", "26 R Notebook \n", "27 R \n", "28 RMarkdown \n", "29 IPython Notebook \n", "... ... \n", "35260 R \n", "35261 Python \n", "35262 Python \n", "35263 Python \n", "35264 R \n", "35265 Python \n", "35266 R \n", "35267 R \n", "35268 Python \n", "35269 R \n", "35270 Python \n", "35271 R \n", "35272 R \n", "35273 R \n", "35274 R \n", "35275 Python \n", "35276 R \n", "35277 R \n", "35278 R \n", "35279 R \n", "35280 R \n", "35281 R \n", "35282 R \n", "35283 R \n", "35284 R \n", "35285 R \n", "35286 R \n", "35287 R \n", "35288 R \n", "35289 R \n", "\n", "[35290 rows x 12 columns]" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "\n", "import sqlite3\n", "\n", "\n", "\n", "con = sqlite3.connect('../input/database.sqlite')\n", "\n", "\n", "\n", "scripts = pd.read_sql_query(\"\"\"\n", "\n", "SELECT s.Id,\n", "\n", " cv.Title,\n", "\n", " COUNT(DISTINCT vo.Id) NumVotes,\n", "\n", " COUNT(DISTINCT CASE WHEN vo.UserId!=s.AuthorUserId THEN vo.Id ELSE NULL END) NumNonSelfVotes,\n", "\n", " CASE WHEN COUNT(DISTINCT CASE WHEN vo.UserId!=s.AuthorUserId THEN vo.Id ELSE NULL END)>0 THEN 1 ELSE 0 END HasNonSelfVotes,\n", "\n", " COUNT(DISTINCT v.Id) NumVersions,\n", "\n", " SUM(CASE WHEN r.WorkerStatus=2 THEN 1 ELSE 0 END) NumSuccessfulRuns,\n", "\n", " SUM(CASE WHEN r.WorkerStatus=3 THEN 1 ELSE 0 END) NumErroredRuns,\n", "\n", " SUM(CASE WHEN v.IsChange=1 THEN 1 ELSE 0 END) NumChangedVersions,\n", "\n", " SUM(v.LinesInsertedFromPrevious-v.LinesDeletedFromPrevious) Lines,\n", "\n", " SUM(v.LinesInsertedFromPrevious+v.LinesChangedFromPrevious) LinesAddedOrChanged,\n", "\n", " l.Name\n", "\n", "FROM Scripts s\n", "\n", "INNER JOIN ScriptVersions v ON v.ScriptId=s.Id\n", "\n", "INNER JOIN ScriptVersions cv ON s.CurrentScriptVersionId=cv.Id\n", "\n", "INNER JOIN ScriptRuns r ON r.ScriptVersionId=v.Id\n", "\n", "INNER JOIN ScriptLanguages l ON v.ScriptLanguageId=l.Id\n", "\n", "LEFT OUTER JOIN ScriptVotes vo ON vo.ScriptVersionId=v.Id\n", "\n", "WHERE r.WorkerStatus != 4\n", "\n", " AND r.WorkerStatus != 5\n", "\n", "GROUP BY s.Id,\n", "\n", " cv.Title,\n", "\n", " cv.Id,\n", "\n", " l.Name\n", "\n", "ORDER BY cv.Id DESC\n", "\n", "\"\"\", con)\n", "\n", "\n", "\n", "scripts" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "9b694c67-0f2e-177f-3d16-6835e7235856" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Score 0.921933\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/sklearn/cross_validation.py:43: DeprecationWarning: This module has been deprecated in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", " \"This module will be removed in 0.20.\", DeprecationWarning)\n" ] } ], "source": [ "from sklearn.pipeline import Pipeline, FeatureUnion\n", "\n", "from sklearn.cross_validation import train_test_split\n", "\n", "from sklearn.ensemble import RandomForestClassifier\n", "\n", "\n", "\n", "class RawColumnExtractor:\n", "\n", " def __init__(self, column):\n", "\n", " self.column=column\n", "\n", "\n", "\n", " def fit(self, *_):\n", "\n", " return self\n", "\n", " \n", "\n", " def transform(self, data):\n", "\n", " return data[[self.column]]\n", "\n", "\n", "\n", "features = FeatureUnion([(\"NumSuccessfulRuns\", RawColumnExtractor(\"NumSuccessfulRuns\")),\n", "\n", " (\"NumChangedVersions\", RawColumnExtractor(\"NumChangedVersions\"))\n", "\n", " ])\n", "\n", "\n", "\n", "pipeline = Pipeline([('feature_union', features),\n", "\n", " ('predictor', RandomForestClassifier())\n", "\n", " ])\n", "\n", "\n", "\n", "train = scripts\n", "\n", "target_name = \"HasNonSelfVotes\"\n", "\n", "\n", "\n", "x_train, x_test, y_train, y_test = train_test_split(train, train[target_name], test_size=0.4, random_state=0)\n", "\n", "\n", "\n", "pipeline.fit(x_train, y_train)\n", "\n", "score = pipeline.score(x_test, y_test)\n", "\n", "print(\"Score %f\" % score)\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "e313b544-9883-c965-02e4-bd81c6ea6c47" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Id</th>\n", " <th>Name</th>\n", " <th>AceLanguageName</th>\n", " <th>DefaultScriptFileName</th>\n", " <th>DockerImageName</th>\n", " <th>AllowWrite</th>\n", " <th>AllowView</th>\n", " <th>BaseScriptLanguageId</th>\n", " <th>RenderedScriptLanguageId</th>\n", " <th>NotebookKernelMetadata</th>\n", " <th>IsNotebook</th>\n", " <th>GlobalDefaultScriptId</th>\n", " <th>DisplayName</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>R</td>\n", " <td>r</td>\n", " <td>script.R</td>\n", " <td>kaggle/rstats</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>None</td>\n", " <td>0</td>\n", " <td>33153.0</td>\n", " <td>R</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>Python</td>\n", " <td>python</td>\n", " <td>script.py</td>\n", " <td>kaggle/python</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>None</td>\n", " <td>0</td>\n", " <td>33156.0</td>\n", " <td>Python</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>5</td>\n", " <td>RMarkdown</td>\n", " <td>markdown</td>\n", " <td>script.Rmd</td>\n", " <td>kaggle/rstats</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>None</td>\n", " <td>0</td>\n", " <td>33158.0</td>\n", " <td>R</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>6</td>\n", " <td>Julia</td>\n", " <td>julia</td>\n", " <td>script.jl</td>\n", " <td>kaggle/julia</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>None</td>\n", " <td>0</td>\n", " <td>33157.0</td>\n", " <td>Julia</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>7</td>\n", " <td>SQLite</td>\n", " <td>sql</td>\n", " <td>script.sql</td>\n", " <td>kaggle/python</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>None</td>\n", " <td>0</td>\n", " <td>33147.0</td>\n", " <td>SQLite</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>8</td>\n", " <td>IPython Notebook</td>\n", " <td>python</td>\n", " <td>script.xpynb</td>\n", " <td>kaggle/python</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>2.0</td>\n", " <td>9.0</td>\n", " <td>{\"display_name\":\"Python 3\",\"language\":\"python\"...</td>\n", " <td>1</td>\n", " <td>33156.0</td>\n", " <td>Python</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>9</td>\n", " <td>IPython Notebook HTML</td>\n", " <td>python</td>\n", " <td>script.ipynb</td>\n", " <td>kaggle/python</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>None</td>\n", " <td>0</td>\n", " <td>NaN</td>\n", " <td>Python</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>10</td>\n", " <td>IJulia Notebook HTML</td>\n", " <td>julia</td>\n", " <td>script.ijlnb</td>\n", " <td>kaggle/julia</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>None</td>\n", " <td>0</td>\n", " <td>NaN</td>\n", " <td>Julia</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>12</td>\n", " <td>R Notebook HTML</td>\n", " <td>r</td>\n", " <td>script.irnb</td>\n", " <td>kaggle/rstats</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>None</td>\n", " <td>0</td>\n", " <td>NaN</td>\n", " <td>R</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>13</td>\n", " <td>R Notebook</td>\n", " <td>r</td>\n", " <td>script.xrnb</td>\n", " <td>kaggle/rstats</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1.0</td>\n", " <td>12.0</td>\n", " <td>{\"display_name\":\"R\",\"language\":\"R\",\"name\":\"ir\"}</td>\n", " <td>1</td>\n", " <td>33153.0</td>\n", " <td>R</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Id Name AceLanguageName DefaultScriptFileName \\\n", "0 1 R r script.R \n", "1 2 Python python script.py \n", "2 5 RMarkdown markdown script.Rmd \n", "3 6 Julia julia script.jl \n", "4 7 SQLite sql script.sql \n", "5 8 IPython Notebook python script.xpynb \n", "6 9 IPython Notebook HTML python script.ipynb \n", "7 10 IJulia Notebook HTML julia script.ijlnb \n", "8 12 R Notebook HTML r script.irnb \n", "9 13 R Notebook r script.xrnb \n", "\n", " DockerImageName AllowWrite AllowView BaseScriptLanguageId \\\n", "0 kaggle/rstats 1 1 NaN \n", "1 kaggle/python 1 1 NaN \n", "2 kaggle/rstats 1 1 NaN \n", "3 kaggle/julia 1 1 NaN \n", "4 kaggle/python 1 1 NaN \n", "5 kaggle/python 1 0 2.0 \n", "6 kaggle/python 0 1 NaN \n", "7 kaggle/julia 0 1 NaN \n", "8 kaggle/rstats 0 1 NaN \n", "9 kaggle/rstats 1 0 1.0 \n", "\n", " RenderedScriptLanguageId \\\n", "0 NaN \n", "1 NaN \n", "2 NaN \n", "3 NaN \n", "4 NaN \n", "5 9.0 \n", "6 NaN \n", "7 NaN \n", "8 NaN \n", "9 12.0 \n", "\n", " NotebookKernelMetadata IsNotebook \\\n", "0 None 0 \n", "1 None 0 \n", "2 None 0 \n", "3 None 0 \n", "4 None 0 \n", "5 {\"display_name\":\"Python 3\",\"language\":\"python\"... 1 \n", "6 None 0 \n", "7 None 0 \n", "8 None 0 \n", "9 {\"display_name\":\"R\",\"language\":\"R\",\"name\":\"ir\"} 1 \n", "\n", " GlobalDefaultScriptId DisplayName \n", "0 33153.0 R \n", "1 33156.0 Python \n", "2 33158.0 R \n", "3 33157.0 Julia \n", "4 33147.0 SQLite \n", "5 33156.0 Python \n", "6 NaN Python \n", "7 NaN Julia \n", "8 NaN R \n", "9 33153.0 R " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.read_sql_query(\"\"\"\n", "\n", "SELECT *\n", "\n", "FROM ScriptLanguages\n", "\n", "LIMIT 100\n", "\n", "\"\"\", con)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "155b61fb-b2ce-afd7-7cd1-3c373dab449f" }, "outputs": [], "source": [] } ], "metadata": { "_change_revision": 26, "_is_fork": false, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/306/306027.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "97d902b5-5755-3ed4-5744-0957a4cb3174" }, "source": [ "This is a markdown cell. Click here to edit..." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "1abf315d-a154-49bd-bb0f-26a6f83f1fa2" }, "outputs": [], "source": [ "# Importing Libraries \n", "\n", "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "import networkx as nx\n", "import matplotlib.pyplot as plt\n", "\n", "# Input data files are available in the \"../input/\" directory.\n", "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n", "from subprocess import check_output\n", "\n", "# Read the input files\n", "comments=pd.read_csv(\"../input/comment.csv\")\n", "likes=pd.read_csv(\"../input/like.csv\")\n", "members=pd.read_csv(\"../input/member.csv\")\n", "posts=pd.read_csv(\"../input/post.csv\")" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "b68a1cc3-6673-bf49-e065-cbd367708346" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x7fce3daa4f28>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "## ANALYSIS 1 \n", "\n", "## We would like to see whether there are few people who are very active on the Group ( LIKES/COMMENTS)\n", "\n", "## Or there is a separate group which is very active on LIKEs but not very active on COMMENTS\n", "\n", "## We can see this quantitatively, but let us take a more graphical loop using networkx in the way\n", "\n", "\n", "\n", "# Let us first analyse the LIKES\n", "\n", "#like1=likes.loc[likes['gid']==117291968282998]\n", "\n", "#post1=posts.loc[posts['gid']==117291968282998,['pid','name']]\n", "\n", "likeResponse=pd.merge(likes.loc[likes['gid']==117291968282998],posts.loc[posts['gid']==117291968282998,['pid','name']],left_on='pid',right_on='pid')\n", "\n", "result=likeResponse.groupby(['name_y','name_x'])['response'].count()\n", "\n", "\n", "\n", "# We will create another clean dataframe from this with the appropriate results\n", "\n", "finalResult=pd.DataFrame(result.index.values,columns=['NameCombo'])\n", "\n", "finalResult['Weight']=result.values\n", "\n", "finalResult['From']=finalResult['NameCombo'].map(lambda x:x[0])\n", "\n", "finalResult['To']=finalResult['NameCombo'].map(lambda x:x[1])\n", "\n", "del(finalResult['NameCombo'])\n", "\n", "\n", "\n", "# Creating the networkx graph\n", "\n", "g = nx.Graph()\n", "\n", "plt.figure()\n", "\n", "g.add_edges_from([(row['From'],row['To']) for index,row in finalResult.iterrows()])\n", "\n", "d = nx.degree(g)\n", "\n", "spring_pos=nx.spring_layout(g)\n", "\n", "plt.axis(\"off\")\n", "\n", "nx.draw_networkx(g,spring_pos, with_labels=False,nodelist=d.keys(), node_size=[v * 10 for v in d.values()])\n", "\n", "plt.savefig('LIKE_PLOT_GROUP1.png')\n", "\n", "plt.clf()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "87f3d34a-12e4-7eea-963a-e783b1541ee8" }, "outputs": [], "source": [] } ], "metadata": { "_change_revision": 37, "_is_fork": false, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/309/309674.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "97d902b5-5755-3ed4-5744-0957a4cb3174" }, "source": [ "This is a markdown cell. Click here to edit..." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "1abf315d-a154-49bd-bb0f-26a6f83f1fa2" }, "outputs": [], "source": [ "# Importing Libraries \n", "\n", "\n", "\n", "import numpy as np # linear algebra\n", "\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "\n", "import networkx as nx\n", "\n", "import matplotlib.pyplot as plt\n", "\n", "\n", "\n", "# Input data files are available in the \"../input/\" directory.\n", "\n", "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n", "\n", "from subprocess import check_output\n", "\n", "\n", "\n", "# Read the input files\n", "\n", "comments=pd.read_csv(\"../input/comment.csv\")\n", "\n", "likes=pd.read_csv(\"../input/like.csv\")\n", "\n", "members=pd.read_csv(\"../input/member.csv\")\n", "\n", "posts=pd.read_csv(\"../input/post.csv\")" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "b68a1cc3-6673-bf49-e065-cbd367708346" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x7f43b4155eb8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "## ANALYSIS 1 \n", "## We would like to see whether there are few people who are very active on the Group ( LIKES/COMMENTS)\n", "## Or there is a separate group which is very active on LIKEs but not very active on COMMENTS\n", "## We can see this quantitatively, but let us take a more graphical loop using networkx in the way\n", "\n", "# Let us first analyse the LIKES\n", "#like1=likes.loc[likes['gid']==117291968282998]\n", "#post1=posts.loc[posts['gid']==117291968282998,['pid','name']]\n", "likeResponse=pd.merge(likes.loc[likes['gid']==117291968282998],posts.loc[posts['gid']==117291968282998,['pid','name']],left_on='pid',right_on='pid')\n", "result=likeResponse.groupby(['name_y','name_x'])['response'].count()\n", "\n", "# We will create another clean dataframe from this with the appropriate results\n", "finalResult=pd.DataFrame(result.index.values,columns=['NameCombo'])\n", "finalResult['Weight']=result.values\n", "finalResult['From']=finalResult['NameCombo'].map(lambda x:x[0])\n", "finalResult['To']=finalResult['NameCombo'].map(lambda x:x[1])\n", "del(finalResult['NameCombo'])\n", "\n", "# Creating the networkx graph\n", "g = nx.Graph()\n", "plt.figure()\n", "g.add_edges_from([(row['From'],row['To']) for index,row in finalResult.iterrows()])\n", "d = nx.degree(g)\n", "spring_pos=nx.spring_layout(g)\n", "plt.axis(\"off\")\n", "nx.draw_networkx(g,spring_pos, with_labels=False,nodelist=d.keys(), node_size=[v * 10 for v in d.values()])\n", "plt.savefig('LIKE_PLOT_GROUP1.png')\n", "plt.clf()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "87f3d34a-12e4-7eea-963a-e783b1541ee8" }, "outputs": [ { "data": { "image/png": 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4g+ii/gERrG8TW5i6EzcwnURMRd9KhOo+xJrwQGLqfAYR0nOINxmlucdumHuN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\nEEuSlND/B3bhfc6Upi6YAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f4393c730b8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "g.number_of_nodes()\n", "spring_pos=nx.spring_layout(g,scale=2)\n", "nx.draw(g, spring_pos,with_labels=False,nodelist=d.keys(), node_size=[v * 5 for v in d.values()])" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "5aadf03c-1613-8e51-e44c-0fafe2415949" }, "outputs": [ { "data": { "text/plain": [ "<function TextIOWrapper.close>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f = open(\"g.json\",\"w\")\n", "f.write(\"{\\\"nodes\\\":[\")\n", "str1=\"\"\n", "\n", "for i in finalResult['From'].unique():\n", " str1+=\"{\\\"name\\\":\\\"\"+ str(i) + \"\\\",\\\"group\\\":\" + str(1) +\"},\"\n", "f.write(str1[:-1])\n", "f.write(\"],\\\"links\\\":[\")\n", "\n", "str1=\"\"\n", "for i in range(len(finalResult)):\n", " str1+=\"{\\\"source\\\":\" + str(finalResult['From'][i]) + \",\\\"target\\\":\" + str(finalResult['To'][i]) + \",\\\"value\\\":\" + str(finalResult['Weight'][i]) + \"},\"\n", "f.write(str1[:-1])\n", "f.write(\"]}\")\n", "f.close\n", "\n", "h1 = \"\"\"\n", "<!DOCTYPE html>\n", "<meta charset=\"utf-8\">\n", "<style>\n", ".link {stroke: #ccc;}\n", ".node text {pointer-events: none; font: 10px sans-serif;}\n", "</style>\n", "<body>\n", "<script src=\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\"></script>\n", "<script>\n", "var width = 800, height = 800;\n", "var color = d3.scale.category20();\n", "var force = d3.layout.force()\n", " .charge(-120)\n", " .linkDistance(80)\n", " .size([width, height]);\n", "var svg = d3.select(\"body\").append(\"svg\")\n", " .attr(\"width\", width)\n", " .attr(\"height\", height);\n", "d3.json(\"g.json\", function(error, graph) {\n", " if (error) throw error;\n", "\tforce.nodes(graph.nodes)\n", "\t .links(graph.links)\n", "\t .start();\n", "\tvar link = svg.selectAll(\".link\")\n", "\t .data(graph.links)\n", "\t .enter().append(\"line\")\n", "\t .attr(\"class\", \"link\")\n", "\t .style(\"stroke-width\", function (d) {return Math.sqrt(d.value);});\n", "\tvar node = svg.selectAll(\".node\")\n", "\t .data(graph.nodes)\n", "\t .enter().append(\"g\")\n", "\t .attr(\"class\", \"node\")\n", "\t .call(force.drag);\n", "\tnode.append(\"circle\")\n", "\t .attr(\"r\", 8)\n", "\t .style(\"fill\", function (d) {return color(d.group);})\n", "\tnode.append(\"text\")\n", "\t .attr(\"dx\", 10)\n", "\t .attr(\"dy\", \".35em\")\n", "\t .text(function(d) { return d.name });\n", "\tforce.on(\"tick\", function () {\n", "\t link.attr(\"x1\", function (d) {return d.source.x;})\n", "\t\t.attr(\"y1\", function (d) {return d.source.y;})\n", "\t\t.attr(\"x2\", function (d) {return d.target.x;})\n", "\t\t.attr(\"y2\", function (d) {return d.target.y;});\n", "\t d3.selectAll(\"circle\").attr(\"cx\", function (d) {return d.x;})\n", "\t\t.attr(\"cy\", function (d) {return d.y;});\n", "\t d3.selectAll(\"text\").attr(\"x\", function (d) {return d.x;})\n", "\t\t.attr(\"y\", function (d) {return d.y;});\n", " });\n", "});\n", "</script>\n", "\"\"\"\n", "\n", "f = open(\"output.html\",\"w\")\n", "f.write(h1)\n", "f.close" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "273b1168-d165-13ec-fb48-64e67563c2c5" }, "outputs": [], "source": [] } ], "metadata": { "_change_revision": 270, "_is_fork": false, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/309/309683.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
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0000/311/311174.ipynb
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