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| { | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "execution_count": 52, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# %load poi_id.py\n", | |
| "#!/usr/bin/python\n", | |
| "\n", | |
| "import sys\n", | |
| "import pickle\n", | |
| "import pandas as pd\n", | |
| "import numpy as np\n", | |
| "from pandas.tools.plotting import scatter_matrix\n", | |
| "sys.path.append(\"../tools/\")\n", | |
| "from email_preprocess import preprocess\n", | |
| "\n", | |
| "\n", | |
| "from feature_format import featureFormat, targetFeatureSplit\n", | |
| "from tester import dump_classifier_and_data\n", | |
| "\n", | |
| "### Task 1: Select what features you'll use.\n", | |
| "### features_list is a list of strings, each of which is a feature name.\n", | |
| "### The first feature must be \"poi\".\n", | |
| "features_list = ['poi','salary', 'exercised_stock_options','total_payments'] # You will need to use more features\n", | |
| "\n", | |
| "### Load the dictionary containing the dataset\n", | |
| "with open(\"final_project_dataset.pkl\", \"r\") as data_file:\n", | |
| " data_dict = pickle.load(data_file)\n", | |
| "\n", | |
| "### Task 2: Remove outliers\n", | |
| "data_dict.pop(\"TOTAL\",0)\n", | |
| "\n", | |
| "### Task 3: Create new feature(s)\n", | |
| "### Store to my_dataset for easy export below.\n", | |
| "my_dataset = data_dict\n", | |
| "\n", | |
| "### Extract features and labels from dataset for local testing\n", | |
| "data = featureFormat(my_dataset, features_list, sort_keys = False)\n", | |
| "labels, features = targetFeatureSplit(data)\n", | |
| "\n", | |
| "### Task 4: Try a varity of classifiers\n", | |
| "### Please name your classifier clf for easy export below.\n", | |
| "### Note that if you want to do PCA or other multi-stage operations,\n", | |
| "### you'll need to use Pipelines. For more info:\n", | |
| "### http://scikit-learn.org/stable/modules/pipeline.html\n", | |
| "\n", | |
| "# Provided to give you a starting point. Try a variety of classifiers.\n", | |
| "from sklearn.naive_bayes import GaussianNB\n", | |
| "clf = GaussianNB()\n", | |
| "\n", | |
| "### Task 5: Tune your classifier to achieve better than .3 precision and recall \n", | |
| "### using our testing script. Check the tester.py script in the final project\n", | |
| "### folder for details on the evaluation method, especially the test_classifier\n", | |
| "### function. Because of the small size of the dataset, the script uses\n", | |
| "### stratified shuffle split cross validation. For more info: \n", | |
| "### http://scikit-learn.org/stable/modules/generated/sklearn.cross_validation.StratifiedShuffleSplit.html\n", | |
| "\n", | |
| "# Example starting point. Try investigating other evaluation techniques!\n", | |
| "from sklearn.cross_validation import train_test_split\n", | |
| "features_train, features_test, labels_train, labels_test = \\\n", | |
| " train_test_split(features, labels, test_size=0.3, random_state=42)\n", | |
| "\n", | |
| "### Task 6: Dump your classifier, dataset, and features_list so anyone can\n", | |
| "### check your results. You do not need to change anything below, but make sure\n", | |
| "### that the version of poi_id.py that you submit can be run on its own and\n", | |
| "### generates the necessary .pkl files for validating your results.\n", | |
| "\n", | |
| "dump_classifier_and_data(clf, my_dataset, features_list)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 53, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import matplotlib.pyplot as plt\n", | |
| "for f1, f2, f3 in features:\n", | |
| " plt.scatter( f1,f2 )\n", | |
| " plt.scatter( f1,f2 )\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 46, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 54, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "feat = pd.DataFrame(features, columns=features_list[1:])\n", | |
| "scatter_matrix(feat, alpha=0.2, diagonal='hist')\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 55, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>salary</th>\n", | |
| " <th>exercised_stock_options</th>\n", | |
| " <th>total_payments</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>count</th>\n", | |
| " <td>139</td>\n", | |
| " <td>139</td>\n", | |
| " <td>139</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>mean</th>\n", | |
| " <td>192117</td>\n", | |
| " <td>2150471</td>\n", | |
| " <td>2340318</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>std</th>\n", | |
| " <td>197336</td>\n", | |
| " <td>4865021</td>\n", | |
| " <td>8994791</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>min</th>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>25%</th>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>182110</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>50%</th>\n", | |
| " <td>211844</td>\n", | |
| " <td>651850</td>\n", | |
| " <td>999356</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>75%</th>\n", | |
| " <td>272161</td>\n", | |
| " <td>1741654</td>\n", | |
| " <td>1991740</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>max</th>\n", | |
| " <td>1111258</td>\n", | |
| " <td>34348384</td>\n", | |
| " <td>103559793</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " salary exercised_stock_options total_payments\n", | |
| "count 139 139 139\n", | |
| "mean 192117 2150471 2340318\n", | |
| "std 197336 4865021 8994791\n", | |
| "min 0 0 0\n", | |
| "25% 0 0 182110\n", | |
| "50% 211844 651850 999356\n", | |
| "75% 272161 1741654 1991740\n", | |
| "max 1111258 34348384 103559793" | |
| ] | |
| }, | |
| "execution_count": 55, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "np.round(feat.describe(),0)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Visualize features:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 57, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
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| " $(\"head\").append([\n", | |
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| "<div class='q-grid-container'>\n", | |
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| "var path_dictionary = {\n", | |
| " jquery_drag: \"https://cdn.rawgit.com/quantopian/qgrid/72d356cb123fab413dba73ec46616e4916fbd827/qgrid/qgridjs//lib/jquery.event.drag-2.2\",\n", | |
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| " var grid = new dgrid.QGrid('#69f331e6-ef52-455b-bfb4-013d61391ea2', 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[{\"field\": null, \"type\": \"Integer\"}, {\"field\": \"salary\", \"type\": \"Float\"}, {\"field\": \"exercised_stock_options\", \"type\": \"Float\"}, {\"field\": \"total_payments\", \"type\": \"Float\"}]);\n", | |
| " grid.initialize_slick_grid({\"fullWidthRows\": true, \"rowHeight\": 28, \"enableColumnReorder\": false, \"enableTextSelectionOnCells\": true, \"syncColumnCellResize\": true, \"forceFitColumns\": true});\n", | |
| " });\n", | |
| " });\n", | |
| "});\n" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "import qgrid\n", | |
| "qgrid.nbinstall(overwrite=True)\n", | |
| "qgrid.set_defaults(remote_js=True, precision=4)\n", | |
| "qgrid.show_grid(feat)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 6, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>365788</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1061827</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>267102</td>\n", | |
| " <td>6680544</td>\n", | |
| " <td>5634343</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>170941</td>\n", | |
| " <td>4890344</td>\n", | |
| " <td>211725</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>0</td>\n", | |
| " <td>651850</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>243293</td>\n", | |
| " <td>5538001</td>\n", | |
| " <td>288682</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>5</th>\n", | |
| " <td>267093</td>\n", | |
| " <td>0</td>\n", | |
| " <td>628522</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>6</th>\n", | |
| " <td>0</td>\n", | |
| " <td>493489</td>\n", | |
| " <td>1848227</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>7</th>\n", | |
| " <td>370448</td>\n", | |
| " <td>1104054</td>\n", | |
| " <td>4099771</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>8</th>\n", | |
| " <td>0</td>\n", | |
| " <td>5210569</td>\n", | |
| " <td>3131860</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>9</th>\n", | |
| " <td>197091</td>\n", | |
| " <td>880290</td>\n", | |
| " <td>1737629</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>10</th>\n", | |
| " <td>130724</td>\n", | |
| " <td>2282768</td>\n", | |
| " <td>3100224</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>11</th>\n", | |
| " <td>288589</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1130461</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>12</th>\n", | |
| " <td>248546</td>\n", | |
| " <td>765313</td>\n", | |
| " <td>2014835</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>13</th>\n", | |
| " <td>257486</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1545059</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>14</th>\n", | |
| " <td>0</td>\n", | |
| " <td>2218275</td>\n", | |
| " <td>966522</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>15</th>\n", | |
| " <td>0</td>\n", | |
| " <td>372205</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>16</th>\n", | |
| " <td>288542</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1490344</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>17</th>\n", | |
| " <td>251654</td>\n", | |
| " <td>1056320</td>\n", | |
| " <td>3202070</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>18</th>\n", | |
| " <td>288558</td>\n", | |
| " <td>185063</td>\n", | |
| " <td>551174</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>19</th>\n", | |
| " <td>63744</td>\n", | |
| " <td>0</td>\n", | |
| " <td>762135</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>20</th>\n", | |
| " <td>0</td>\n", | |
| " <td>1030329</td>\n", | |
| " <td>87410</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>21</th>\n", | |
| " <td>357091</td>\n", | |
| " <td>4346544</td>\n", | |
| " <td>1798780</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>22</th>\n", | |
| " <td>271442</td>\n", | |
| " <td>81042</td>\n", | |
| " <td>3471141</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>23</th>\n", | |
| " <td>0</td>\n", | |
| " <td>3285</td>\n", | |
| " <td>102500</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>24</th>\n", | |
| " <td>304110</td>\n", | |
| " <td>1441898</td>\n", | |
| " <td>3038702</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>25</th>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>189583</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>26</th>\n", | |
| " <td>187922</td>\n", | |
| " <td>0</td>\n", | |
| " <td>618850</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>27</th>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>228656</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>28</th>\n", | |
| " <td>213625</td>\n", | |
| " <td>1465734</td>\n", | |
| " <td>2047593</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>29</th>\n", | |
| " <td>249201</td>\n", | |
| " <td>1635238</td>\n", | |
| " <td>1099100</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>...</th>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>109</th>\n", | |
| " <td>0</td>\n", | |
| " <td>754966</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>110</th>\n", | |
| " <td>76399</td>\n", | |
| " <td>4160672</td>\n", | |
| " <td>394475</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>111</th>\n", | |
| " <td>262788</td>\n", | |
| " <td>8191755</td>\n", | |
| " <td>2368151</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>112</th>\n", | |
| " <td>0</td>\n", | |
| " <td>2604490</td>\n", | |
| " <td>15456290</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>113</th>\n", | |
| " <td>261809</td>\n", | |
| " <td>0</td>\n", | |
| " <td>477557</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>114</th>\n", | |
| " <td>248017</td>\n", | |
| " <td>825464</td>\n", | |
| " <td>1054637</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>115</th>\n", | |
| " <td>0</td>\n", | |
| " <td>192758</td>\n", | |
| " <td>55097</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>116</th>\n", | |
| " <td>229284</td>\n", | |
| " <td>400478</td>\n", | |
| " <td>812194</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>117</th>\n", | |
| " <td>231946</td>\n", | |
| " <td>636246</td>\n", | |
| " <td>1566469</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>118</th>\n", | |
| " <td>221003</td>\n", | |
| " <td>0</td>\n", | |
| " <td>319941</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>119</th>\n", | |
| " <td>158403</td>\n", | |
| " <td>8308552</td>\n", | |
| " <td>360300</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>120</th>\n", | |
| " <td>0</td>\n", | |
| " <td>30766064</td>\n", | |
| " <td>91093</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>121</th>\n", | |
| " <td>250100</td>\n", | |
| " <td>1550019</td>\n", | |
| " <td>1410464</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>122</th>\n", | |
| " <td>492375</td>\n", | |
| " <td>8831913</td>\n", | |
| " <td>550981</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>123</th>\n", | |
| " <td>1060932</td>\n", | |
| " <td>10433518</td>\n", | |
| " <td>17252530</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>124</th>\n", | |
| " <td>261879</td>\n", | |
| " <td>15364167</td>\n", | |
| " <td>3123383</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>125</th>\n", | |
| " <td>239671</td>\n", | |
| " <td>0</td>\n", | |
| " <td>827696</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>126</th>\n", | |
| " <td>0</td>\n", | |
| " <td>176378</td>\n", | |
| " <td>50591</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>127</th>\n", | |
| " <td>304588</td>\n", | |
| " <td>940257</td>\n", | |
| " <td>3676340</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>128</th>\n", | |
| " <td>309946</td>\n", | |
| " <td>671737</td>\n", | |
| " <td>1587421</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>129</th>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>3750</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>130</th>\n", | |
| " <td>85274</td>\n", | |
| " <td>2165172</td>\n", | |
| " <td>1979596</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>131</th>\n", | |
| " <td>247338</td>\n", | |
| " <td>591250</td>\n", | |
| " <td>399393</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>132</th>\n", | |
| " <td>349487</td>\n", | |
| " <td>2070306</td>\n", | |
| " <td>8407016</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>133</th>\n", | |
| " <td>330546</td>\n", | |
| " <td>2542813</td>\n", | |
| " <td>2355702</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>134</th>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>119292</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>135</th>\n", | |
| " <td>415189</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1868758</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>136</th>\n", | |
| " <td>265214</td>\n", | |
| " <td>3181250</td>\n", | |
| " <td>1092663</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>137</th>\n", | |
| " <td>278601</td>\n", | |
| " <td>765920</td>\n", | |
| " <td>875760</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>138</th>\n", | |
| " <td>274975</td>\n", | |
| " <td>384728</td>\n", | |
| " <td>1272284</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "<p>139 rows × 3 columns</p>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " 0 1 2\n", | |
| "0 365788 0 1061827\n", | |
| "1 267102 6680544 5634343\n", | |
| "2 170941 4890344 211725\n", | |
| "3 0 651850 0\n", | |
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| "6 0 493489 1848227\n", | |
| "7 370448 1104054 4099771\n", | |
| "8 0 5210569 3131860\n", | |
| "9 197091 880290 1737629\n", | |
| "10 130724 2282768 3100224\n", | |
| "11 288589 0 1130461\n", | |
| "12 248546 765313 2014835\n", | |
| "13 257486 0 1545059\n", | |
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| "15 0 372205 0\n", | |
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| "17 251654 1056320 3202070\n", | |
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| "20 0 1030329 87410\n", | |
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| "23 0 3285 102500\n", | |
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| "25 0 0 189583\n", | |
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| "27 0 0 228656\n", | |
| "28 213625 1465734 2047593\n", | |
| "29 249201 1635238 1099100\n", | |
| ".. ... ... ...\n", | |
| "109 0 754966 0\n", | |
| "110 76399 4160672 394475\n", | |
| "111 262788 8191755 2368151\n", | |
| "112 0 2604490 15456290\n", | |
| "113 261809 0 477557\n", | |
| "114 248017 825464 1054637\n", | |
| "115 0 192758 55097\n", | |
| "116 229284 400478 812194\n", | |
| "117 231946 636246 1566469\n", | |
| "118 221003 0 319941\n", | |
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| "120 0 30766064 91093\n", | |
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| "122 492375 8831913 550981\n", | |
| "123 1060932 10433518 17252530\n", | |
| "124 261879 15364167 3123383\n", | |
| "125 239671 0 827696\n", | |
| "126 0 176378 50591\n", | |
| "127 304588 940257 3676340\n", | |
| "128 309946 671737 1587421\n", | |
| "129 0 0 3750\n", | |
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| "131 247338 591250 399393\n", | |
| "132 349487 2070306 8407016\n", | |
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| "134 0 0 119292\n", | |
| "135 415189 0 1868758\n", | |
| "136 265214 3181250 1092663\n", | |
| "137 278601 765920 875760\n", | |
| "138 274975 384728 1272284\n", | |
| "\n", | |
| "[139 rows x 3 columns]" | |
| ] | |
| }, | |
| "execution_count": 6, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df = pd.DataFrame(features)\n", | |
| "df" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 126, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import numpy as np\n", | |
| "features=np.array(features)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 131, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "sal = [x for count, x in enumerate(features[:,0]) if x > 2000000]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 132, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "ename": "IndexError", | |
| "evalue": "list index out of range", | |
| "output_type": "error", | |
| "traceback": [ | |
| "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", | |
| "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", | |
| "\u001b[0;32m<ipython-input-132-4e4bcd9c691f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mitem\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmy_dataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mitem\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'salary'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0msal\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0mitem\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'salary'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;31mIndexError\u001b[0m: list index out of range" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "for key, item in my_dataset.items():\n", | |
| " if item['salary'] == sal[0]:\n", | |
| " print item['salary'], key" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 133, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "ename": "KeyError", | |
| "evalue": "'TOTAL'", | |
| "output_type": "error", | |
| "traceback": [ | |
| "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", | |
| "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", | |
| "\u001b[0;32m<ipython-input-133-eaba9dcb8a27>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"TOTAL\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", | |
| "\u001b[0;31mKeyError\u001b[0m: 'TOTAL'" | |
| ] | |
| } | |
| ], | |
| "source": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 119, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "display_name": "Python 2", | |
| "language": "python", | |
| "name": "python2" | |
| }, | |
| "language_info": { | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 2 | |
| }, | |
| "file_extension": ".py", | |
| "mimetype": "text/x-python", | |
| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython2", | |
| "version": "2.7.10" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 0 | |
| } |
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