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@mburke05
Created May 25, 2015 23:19
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{"nbformat_minor": 0, "cells": [{"execution_count": 1, "cell_type": "code", "source": "%pylab inline\nimport pandas as pd\nimport statsmodels.api as sm\nimport pylab as pl\nimport numpy as np", "outputs": [{"output_type": "stream", "name": "stdout", "text": "Populating the interactive namespace from numpy and matplotlib\n"}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 2, "cell_type": "code", "source": "df = pd.read_csv(r'C:\\Users\\mburke05\\Desktop\\Flexshopper\\Mercury_OrderDetail_01-05-2015_001.csv', parse_dates = {'parsed_date' : [1, 2]})", "outputs": [{"output_type": "stream", "name": "stderr", "text": "C:\\Users\\mburke05\\Anaconda64\\lib\\site-packages\\pandas\\io\\parsers.py:1159: DtypeWarning: Columns (79,80) have mixed types. Specify dtype option on import or set low_memory=False.\n data = self._reader.read(nrows)\n"}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 3, "cell_type": "code", "source": "# eliminate columns that are either redundant or unncessary to our log regression for flexshopper\ndrop_columns = [3, 4, 5, 41, 42, 43, 44, 46, 47, 51, \n 55, 56, 57, 58, 60, 61, 62, 63, 64, \n 65, 66, 67, 70, 71, 72, 73]\n\n# we add x+1 because of the addn'l column added through parse_dates\ndrop_columns = [x+1 for x in drop_columns] \n\nfor col in df:\n df.drop(df.columns[drop_columns], axis=1)", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 4, "cell_type": "code", "source": "# get dummy variables for our regression\ndummy_stations = pd.get_dummies(df['Station Call Letters'])\n\n# rejoin our dummy variables with the df\ndf = df[:].join(dummy_stations.ix[:])", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 5, "cell_type": "code", "source": "# split out independent variable out (Approval will be our binary dist.)\n\ndummy_indep = pd.get_dummies(df['Decision'])\ndf = df[:].join(dummy_indep.ix[:])", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 6, "cell_type": "code", "source": "# create a time continuous variable using to_datetime\ndf['Earliest Contact Timestamp'] = pd.to_datetime(df['Earliest Contact Timestamp'])\ndf['Decision Timestamp'] = pd.to_datetime(df['Decision Timestamp'])\ndelta_time = pd.Series(data=df['Decision Timestamp'] - df['Earliest Contact Timestamp'], name='time_to_decision')\ndf = df[:].join(delta_time.ix[:])\ndf['Decision Timestamp'][:500]", "outputs": [{"execution_count": 6, "output_type": "execute_result", "data": {"text/plain": "0 NaT\n1 NaT\n2 NaT\n3 NaT\n4 NaT\n5 NaT\n6 NaT\n7 NaT\n8 NaT\n9 NaT\n10 NaT\n11 NaT\n12 NaT\n13 NaT\n14 2015-01-05 00:37:39\n...\n485 NaT\n486 NaT\n487 2015-01-05 20:30:09\n488 NaT\n489 NaT\n490 NaT\n491 NaT\n492 NaT\n493 NaT\n494 NaT\n495 NaT\n496 NaT\n497 NaT\n498 NaT\n499 NaT\nName: Decision Timestamp, Length: 500, dtype: datetime64[ns]"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 7, "cell_type": "code", "source": "# note semantically same as Series.unique()\npd.unique(df['time_to_decision'])", "outputs": [{"execution_count": 7, "output_type": "execute_result", "data": {"text/plain": "array([None, 1119000000000L, 21988000000000L, ..., 5138000000000L,\n 3065000000000L, 3228000000000L], dtype=object)"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 8, "cell_type": "code", "source": "# check to make sure we parse our rows correctly\n# ravel is the numpy method to return a flattened array\npd.unique(df.Decision.ravel()) ", "outputs": [{"execution_count": 8, "output_type": "execute_result", "data": {"text/plain": "array([nan, 'DENIED', 'APPROVAL'], dtype=object)"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 9, "cell_type": "code", "source": "# clear all nan's\n# it looks like decision timestamp is unique, but the decision is arbitrarily \n# used multiple times. so we delete rows first to get only approvals/denials\n# second to get only decision timestamps so we're left with one CID\ndf = df[df.Decision.notnull()]\ndf = df[df['Decision Timestamp'].notnull()]\n\n# optional confirm there aren't any duplicates\n#df.set_index('CID').index.get_duplicates()", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 10, "cell_type": "code", "source": "# make time deltas into ints\ndf['time_to_decision'] = df['time_to_decision'].apply(lambda x: x / np.timedelta64(1,'s')).astype('int64') / (60*60*24)", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 11, "cell_type": "code", "source": "# columns to keep\ndf['Station Call Letters'].unique()", "outputs": [{"execution_count": 11, "output_type": "execute_result", "data": {"text/plain": "array(['COMSPIN', 'WEB', 'FUSE', 'LMN', 'CWPLUS', 'LOGO', 'COM', 'BET',\n 'MTV', 'BOUNCE', 'CHILL', 'DTVA', 'OWN', 'IDL', 'TBS', 'CMT',\n 'DFCL', 'TV1L', 'MTV2', 'BABYFIRST', 'JUST', 'JFAITH', 'OWNL',\n 'NUVOTV', 'QUEENL', 'A&E', 'MTVJAMS', 'MTV3', 'ESCAPE', 'FUSION',\n 'SPIKE', 'USA'], dtype=object)"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 12, "cell_type": "code", "source": "# make an intercept\ndf['intercept'] = 1.0", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 13, "cell_type": "code", "source": "# i only want our logistic columns now\ncols_to_keep = ['APPROVAL', 'time_to_decision', 'COMSPIN', 'FUSE', 'LMN', 'CWPLUS', 'LOGO', 'COM', 'BET',\n 'MTV', 'BOUNCE', 'CHILL', 'DTVA', 'OWN', 'IDL', 'TBS', 'CMT',\n 'DFCL', 'TV1L', 'MTV2', 'BABYFIRST', 'JUST', 'JFAITH', 'OWNL',\n 'NUVOTV', 'QUEENL', 'A&E', 'MTVJAMS', 'MTV3', 'ESCAPE', 'FUSION',\n 'SPIKE', 'USA', 'intercept']\n\ndf = df[cols_to_keep]", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 14, "cell_type": "code", "source": "# the regression\ntrain_cols = df.columns[1:-1]\n\nlogit = sm.Logit(df['APPROVAL'], df[train_cols])\n\n#fit the model\nresult = logit.fit()", "outputs": [{"output_type": "stream", "name": "stdout", "text": "Optimization terminated successfully.\n Current function value: 0.564471\n Iterations 7\n"}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 15, "cell_type": "code", "source": "# results\nprint result.summary()", "outputs": [{"output_type": "stream", "name": "stdout", "text": " Logit Regression Results \n==============================================================================\nDep. Variable: APPROVAL No. Observations: 52026\nModel: Logit Df Residuals: 51994\nMethod: MLE Df Model: 31\nDate: Mon, 25 May 2015 Pseudo R-squ.: -0.3906\nTime: 18:05:34 Log-Likelihood: -29367.\nconverged: True LL-Null: -21118.\n LLR p-value: 1.000\n====================================================================================\n coef std err z P>|z| [95.0% Conf. Int.]\n------------------------------------------------------------------------------------\ntime_to_decision -0.0335 0.002 -15.622 0.000 -0.038 -0.029\nCOMSPIN -1.7134 0.072 -23.945 0.000 -1.854 -1.573\nFUSE -1.8702 0.075 -25.012 0.000 -2.017 -1.724\nLMN -1.8427 0.055 -33.752 0.000 -1.950 -1.736\nCWPLUS -2.1664 0.207 -10.478 0.000 -2.572 -1.761\nLOGO -3.0017 0.591 -5.076 0.000 -4.161 -1.843\nCOM -2.1760 0.351 -6.192 0.000 -2.865 -1.487\nBET -1.7935 0.067 -26.803 0.000 -1.925 -1.662\nMTV -1.8491 0.067 -27.475 0.000 -1.981 -1.717\nBOUNCE -0.7205 0.543 -1.328 0.184 -1.784 0.343\nCHILL -1.1385 0.270 -4.221 0.000 -1.667 -0.610\nDTVA -1.7820 0.056 -31.828 0.000 -1.892 -1.672\nOWN -0.0500 0.959 -0.052 0.958 -1.930 1.830\nIDL -1.5816 0.041 -39.032 0.000 -1.661 -1.502\nTBS -1.5702 0.087 -18.149 0.000 -1.740 -1.401\nCMT -1.1277 0.191 -5.893 0.000 -1.503 -0.753\nDFCL -1.5524 0.077 -20.213 0.000 -1.703 -1.402\nTV1L -2.1724 0.169 -12.868 0.000 -2.503 -1.842\nMTV2 -1.7809 0.112 -15.906 0.000 -2.000 -1.561\nBABYFIRST -1.4512 0.105 -13.850 0.000 -1.657 -1.246\nJUST -1.2303 0.187 -6.594 0.000 -1.596 -0.865\nJFAITH -1.7457 0.242 -7.215 0.000 -2.220 -1.271\nOWNL -1.6639 0.104 -15.942 0.000 -1.869 -1.459\nNUVOTV -1.4749 0.145 -10.145 0.000 -1.760 -1.190\nQUEENL -1.5928 0.316 -5.038 0.000 -2.212 -0.973\nA&E -1.6737 0.206 -8.136 0.000 -2.077 -1.271\nMTVJAMS -1.7986 0.360 -5.001 0.000 -2.504 -1.094\nMTV3 -1.3969 0.298 -4.683 0.000 -1.982 -0.812\nESCAPE -1.4315 0.168 -8.541 0.000 -1.760 -1.103\nFUSION -1.4245 0.249 -5.725 0.000 -1.912 -0.937\nSPIKE -1.4007 0.309 -4.532 0.000 -2.006 -0.795\nUSA -1.2525 0.802 -1.562 0.118 -2.824 0.319\n====================================================================================\n"}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 16, "cell_type": "code", "source": "# odds ratios and 95% CI\nparams = result.params\nconf = result.conf_int()\nconf['OR'] = params\nconf.columns = ['2.5%', '97.5%', 'OR']\nprint np.exp(conf)", "outputs": [{"output_type": "stream", "name": "stdout", "text": " 2.5% 97.5% OR\ntime_to_decision 0.963044 0.971164 0.967095\nCOMSPIN 0.156666 0.207392 0.180253\nFUSE 0.133087 0.178413 0.154092\nLMN 0.142321 0.176283 0.158394\nCWPLUS 0.076414 0.171849 0.114594\nLOGO 0.015598 0.158386 0.049704\nCOM 0.056994 0.226010 0.113496\nBET 0.145928 0.189693 0.166378\nMTV 0.137924 0.179562 0.157372\nBOUNCE 0.167925 1.409379 0.486488\nCHILL 0.188780 0.543458 0.320303\nDTVA 0.150813 0.187825 0.168304\nOWN 0.145210 6.231707 0.951265\nIDL 0.189941 0.222639 0.205641\nTBS 0.175558 0.246439 0.208001\nCMT 0.222511 0.471123 0.323775\nDFCL 0.182156 0.246142 0.211746\nTV1L 0.081811 0.158571 0.113898\nMTV2 0.135290 0.209835 0.168489\nBABYFIRST 0.190797 0.287707 0.234294\nJUST 0.202703 0.421199 0.292195\nJFAITH 0.108609 0.280418 0.174517\nOWNL 0.154354 0.232384 0.189392\nNUVOTV 0.172063 0.304225 0.228792\nQUEENL 0.109432 0.377884 0.203353\nA&E 0.125321 0.280685 0.187552\nMTVJAMS 0.081795 0.334957 0.165523\nMTV3 0.137857 0.443891 0.247373\nESCAPE 0.172053 0.331879 0.238958\nFUSION 0.147756 0.391888 0.240632\nSPIKE 0.134466 0.451634 0.246433\nUSA 0.059367 1.375645 0.285777\n"}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 17, "cell_type": "code", "source": "# instead of generating all possible values of time_to_dec, use \n# evenly spaced range of 10 values from the min to the max\n\ntime_to_decs = np.linspace(df['time_to_decision'].min(), df['time_to_decision'].max(), 10)\nprint time_to_decs", "outputs": [{"output_type": "stream", "name": "stdout", "text": "[ -0.78609954 13.74053112 28.26716178 42.79379244 57.3204231\n 71.84705376 86.37368441 100.90031507 115.42694573 129.95357639]\n"}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 18, "cell_type": "code", "source": "# http://stackoverflow.com/questions/1208118/using-numpy-to-build-an-array-of-all-combinations-of-two-arrays\n\ndef cartesian(arrays, out=None):\n \"\"\"\n Generate a cartesian product of input arrays.\n\n Parameters\n ----------\n arrays : list of array-like\n 1-D arrays to form the cartesian product of.\n out : ndarray\n Array to place the cartesian product in.\n\n Returns\n -------\n out : ndarray\n 2-D array of shape (M, len(arrays)) containing cartesian products\n formed of input arrays.\n\n Examples\n --------\n >>> cartesian(([1, 2, 3], [4, 5], [6, 7]))\n array([[1, 4, 6],\n [1, 4, 7],\n [1, 5, 6],\n [1, 5, 7],\n [2, 4, 6],\n [2, 4, 7],\n [2, 5, 6],\n [2, 5, 7],\n [3, 4, 6],\n [3, 4, 7],\n [3, 5, 6],\n [3, 5, 7]])\n\n \"\"\"\n\n arrays = [np.asarray(x) for x in arrays]\n dtype = arrays[0].dtype\n\n n = np.prod([x.size for x in arrays])\n if out is None:\n out = np.zeros([n, len(arrays)], dtype=dtype)\n\n m = n / arrays[0].size\n out[:,0] = np.repeat(arrays[0], m)\n if arrays[1:]:\n cartesian(arrays[1:], out=out[0:m,1:])\n for j in xrange(1, arrays[0].size):\n out[j*m:(j+1)*m,1:] = out[0:m,1:]\n return out", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 19, "cell_type": "code", "source": "# enumerate all possibilities\ncombos = pd.DataFrame(cartesian([time_to_decs, range(31), [1.]]))", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 20, "cell_type": "code", "source": "# make dummies\ncombos.columns = ['time_to_decision', 'station', 'intercept']\ndummy_stations = pd.get_dummies(combos['station'])\ndummy_stations.columns = ['COMSPIN', 'FUSE', 'LMN', 'CWPLUS', 'LOGO', 'COM', 'BET',\n 'MTV', 'BOUNCE', 'CHILL', 'DTVA', 'OWN', 'IDL', 'TBS', 'CMT',\n 'DFCL', 'TV1L', 'MTV2', 'BABYFIRST', 'JUST', 'JFAITH', 'OWNL',\n 'NUVOTV', 'QUEENL', 'A&E', 'MTVJAMS', 'MTV3', 'ESCAPE', 'FUSION',\n 'SPIKE', 'USA']", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 21, "cell_type": "code", "source": "# keep only what's necessary\ncols_to_keep = ['time_to_decision', 'station', 'intercept']\ncombos = combos[cols_to_keep].join(dummy_stations.ix[:])", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 22, "cell_type": "code", "source": "# make predictions on the enumerated dataset\ncombos['approval_pred'] = result.predict(combos[train_cols])", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 23, "cell_type": "code", "source": "combos.head()", "outputs": [{"execution_count": 23, "output_type": "execute_result", "data": {"text/plain": " time_to_decision station intercept COMSPIN FUSE LMN CWPLUS LOGO \\\n0 -0.7861 0 1 1 0 0 0 0 \n1 -0.7861 1 1 0 1 0 0 0 \n2 -0.7861 2 1 0 0 1 0 0 \n3 -0.7861 3 1 0 0 0 1 0 \n4 -0.7861 4 1 0 0 0 0 1 \n\n COM BET ... NUVOTV QUEENL A&E MTVJAMS MTV3 ESCAPE \\\n0 0 0 ... 0 0 0 0 0 0 \n1 0 0 ... 0 0 0 0 0 0 \n2 0 0 ... 0 0 0 0 0 0 \n3 0 0 ... 0 0 0 0 0 0 \n4 0 0 ... 0 0 0 0 0 0 \n\n FUSION SPIKE USA approval_pred \n0 0 0 0 0.156159 \n1 0 0 0 0.136591 \n2 0 0 0 0.139870 \n3 0 0 0 0.105264 \n4 0 0 0 0.048551 \n\n[5 rows x 35 columns]", "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>time_to_decision</th>\n <th>station</th>\n <th>intercept</th>\n <th>COMSPIN</th>\n <th>FUSE</th>\n <th>LMN</th>\n <th>CWPLUS</th>\n <th>LOGO</th>\n <th>COM</th>\n <th>BET</th>\n <th>...</th>\n <th>NUVOTV</th>\n <th>QUEENL</th>\n <th>A&amp;E</th>\n <th>MTVJAMS</th>\n <th>MTV3</th>\n <th>ESCAPE</th>\n <th>FUSION</th>\n <th>SPIKE</th>\n <th>USA</th>\n <th>approval_pred</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>-0.7861</td>\n <td> 0</td>\n <td> 1</td>\n <td> 1</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td>...</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0.156159</td>\n </tr>\n <tr>\n <th>1</th>\n <td>-0.7861</td>\n <td> 1</td>\n <td> 1</td>\n <td> 0</td>\n <td> 1</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td>...</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0.136591</td>\n </tr>\n <tr>\n <th>2</th>\n <td>-0.7861</td>\n <td> 2</td>\n <td> 1</td>\n <td> 0</td>\n <td> 0</td>\n <td> 1</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td>...</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0.139870</td>\n </tr>\n <tr>\n <th>3</th>\n <td>-0.7861</td>\n <td> 3</td>\n <td> 1</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 1</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td>...</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0.105264</td>\n </tr>\n <tr>\n <th>4</th>\n <td>-0.7861</td>\n <td> 4</td>\n <td> 1</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 1</td>\n <td> 0</td>\n <td> 0</td>\n <td>...</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n <td> 0.048551</td>\n </tr>\n </tbody>\n</table>\n<p>5 rows \u00d7 35 columns</p>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 24, "cell_type": "code", "source": "# our key for obtaining station codes later on\nstations = ['COMSPIN', 'FUSE', 'LMN', 'CWPLUS', 'LOGO', 'COM', 'BET',\n 'MTV', 'BOUNCE', 'CHILL', 'DTVA', 'OWN', 'IDL', 'TBS', 'CMT',\n 'DFCL', 'TV1L', 'MTV2', 'BABYFIRST', 'JUST', 'JFAITH', 'OWNL',\n 'NUVOTV', 'QUEENL', 'A&E', 'MTVJAMS', 'MTV3', 'ESCAPE', 'FUSION',\n 'SPIKE', 'USA']\nstation_key = {i : j for i, j in enumerate(stations)}\n\nstation_key[0]", "outputs": [{"execution_count": 24, "output_type": "execute_result", "data": {"text/plain": "'COMSPIN'"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 31, "cell_type": "code", "source": "# graphing!\n# isolate_and_plot comes from http://blog.yhathq.com/posts/logistic-regression-and-python.html\n# basically it's taking a pivot table using pd.pivot_table to generate graphics\n\ndef isolate_and_plot(variable):\n # isolate gre and class rank\n grouped = pd.pivot_table(combos, values=['approval_pred'], index=['time_to_decision', 'station'],\n aggfunc=np.mean)\n # print grouped\n \n # in case you're curious as to what this looks like\n # print grouped.head()\n # admit_pred\n # gre prestige \n # 220.000000 1 0.282462\n # 2 0.169987\n # 3 0.096544\n # 4 0.079859\n # 284.444444 1 0.311718\n \n # make a plot\n colors = 'rbgyrbgy'\n plt_data = grouped.ix[grouped.index.get_level_values(1)==variable]\n \n #print plt_data\n \n pl.plot(plt_data.index.get_level_values(0), plt_data['approval_pred'], \n color=colors[0])\n pl.axis([0, 120, 0.00, 0.5])\n pl.xlabel('Time to decision (days)')\n pl.ylabel(\"Prob(approval=1)\")\n #pl.legend(station_key[variable], loc='upper center', title='Station')\n pl.title(\"Prob(approval=1): \" + station_key[variable] + \" and time to app.\")\n pl.show()\n", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 32, "cell_type": "code", "source": "for col in combos.station.unique():\n isolate_and_plot(col)", "outputs": [{"output_type": "display_data", "data": {"image/png": 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Av2dhsHjA7RVm5XNwsJFNWoW+91cswcJAMQ140sHCbOg5OFj3SO0V72BhFdT2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\nRU5ErkJggg==\n", 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C5HjguRa2b2ZmJWkqOUTEP+W/32hh2xOBxwvT8/O8XiR9StIc4DLg8Ba2b2ZmJelr4L3P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\nnnTFer3HGv0sr14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JHwBOA3YoK6bhStKEiJjS6TjK0svl64mypV+I8/NjZnFRy+VbkmAaJZKVSL26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\nAABJRU5ErkJggg==\n", 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KzGwgREwENgAuAm5F+kYuVZhZSeqpVroRWIV0V/RbeXFExOYlx1aMwSUHS6QV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\n8QTwuqSV8ja7A6dImh/YMiI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qTg5mrZKuy1id2uRR+TuadI1GvVrHdOApN1tZWZwczNpVugp8NbpPHmOAF6lf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\nTkSuQmCC\n", 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"text/plain": "<matplotlib.figure.Figure at 0x4a7f75c0>"}, "metadata": {}}, {"output_type": "display_data", "data": {"image/png": 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K6craSZwczMxaV0pX1qodfDf/PbI/OzEzs+7S7Inl8fnv3mUFYmZmncO9jszMrIaTg5mZ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\n", 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FJwczs+7rzbGz0zaHRgd9SaO6WsfMzAavnvZWerBPozAzswGlUYP0wQ1eN7qEWMzMbIBo\nVHP4JbA8MKrqMbqL15mZ2SDXac0BmELqqnpn9QJJXy0vJDMza7VGvZXWA16MiOfrLFs5Ip4tO7jC\n/txbycysm0rpyjqQODmYmXVfKV1Zq3bwg/z3sJ7sxMzMBpdmG5Yn5r97lhWImZkNHO51ZGZmNZwc\nzMysRqnJQdIESQ9JeqRee4WkXSTdK2mKpLskbVdmPGZm1pxG1zn0iqRhwPHA9sAs4A5JkyJiWmG1\nayPikrz+RsBfgbXKisnMzJrTbM3h+vx3cje2PQ54NCJmRMQ84AJgl+IKEfF64eko4IVubN/MzErS\nVHKIiO/nv9/rxrbHAE8Xns/M8zqQtKukacAVwEHd2L6ZmZWk0cB7uwEBqN7fiLioi203dXVdRFwM\nXCxpG+BsYN1mXmdmZuVp1ObwWRof4LtKDrOAsYXnY0m1h7oi4kZJwyWtGBEvVi+XdFTh6eSImNzF\n/s3MhhRJ44HxfbKtsobPkDQcmA58ApgN3A5MLDZIS1oTeDwiQtLmwIURsWadbXn4DDOzburNsbOp\n3kqSdgI2AEZW5kXEzxq9JiLmSzoQuAoYBpwWEdMkHZCXnwzsBnxZ0jzgNXwFtpnZgNBlzUHSycCS\nwHbAKcDuwG0R0W/DdrvmYGbWfaWOyippakRsJOm+iNg43z/6yojYuic77AknBzOz7it7VNY38983\nJI0B5gMr92RnZmY2ODTT5nCZpOWBY4G78rxTygvJzMxarVu9lSSNBEZGxMvlhVR3vz6tZGbWTaWe\nVpJ0n6QfSlozIt7q78RgZmb9r5k2h52BBcCfJN0p6RBJq5Ycl5mZtVB3TyutDfwY2CsihpUWVe1+\nfVrJzKyb+uMiuNWBPYAvkGoRP+jJzszMbHDoMjlIug1YAvgTsHtEPF56VGZm1lLNXAS3XkQ81E/x\ndBaDTyuZmXVT2RfBPSvpt/k2nndJ+o2kZXuyMzMzGxyaSQ6nA6+SxlT6AjAXOKPMoMzMrLWaOa10\nb0Rs0tW8Mvm0kplZ95U+tlK+S1tlZ1sDb/RkZ2ZmNjg005X1G8AfC+0Mc4B9ygvJzMxarWFykDQM\n2DsP1b0sQES80i+RmZlZyzRMDhGxQNLWSieunBTMzIaIZk4r3QNcIulCFrU1RERcVF5YZmbWSs0k\nh5HAS6TbhBY5OZiZtaluDbzXKu7KambWfWXfz2FNSZdKekHS85IukfT+nuzMzMwGh2aucziPNOje\nKsB7gQuB88sMyszMWquZK6Tvi4iNq+b5CmkzswGu7Ps5XCHpCBbVFvbI81YAiIiXerJjMzMbuJqp\nOcwAOlspIqL09gfXHMzMuq83x073VjIza1OlnlaStCTwTWBrUg3iRuCkiHirJzs0M7OBr5nTSheS\n7udwDiDgi8CyEbF7+eG9E4NrDmZm3VTqaSVJD0bEBl3NK5OTg5lZ95V9P4e7JW1V2NmWwF092ZmZ\nmQ0OzdQcHgLWAZ4mtTmsCkwH5pN6K23c4OV9E6RrDmZm3Vb2dQ4TerJhMzMbvJruyirp3aQRWgGI\niKfKCqrOvl1zMDPrprIH3ttZ0iPAE8ANwAzgip7szMzMBodmGqR/AWwFPBwRawCfAG4rNSozM2up\nZpLDvIh4AVhM0rCIuB74UMlxmZlZCzWTHOZIGk26MvpcSccBrzW7A0kTJD0k6RFJh9VZvpekeyXd\nJ+kmSaX3fjIzs8aa6cq6NPAWKZHsBSwDnBsRL3a5cWkYqdvr9sAs4A5gYkRMK6yzFfBgRLwiaQJw\nVERsWbUdN0ibmXVTKV1ZlbcaEa/nWQuAM+ut02D744BHI2JGXv8CYBfgneQQEbcU1r8NeF93CmBm\nZn2v0WmlyZIOlbRO9QJJ6+ZTRDd0sf0xpIvnKmbmeZ35KnB5F9s0M7OSNboIbgfSaaQTJH0AmEsa\neG8UcD9wLul0USNNjwcu6ePAV4CPNvsaMzMrR6fJISL+BZwOnJ7bDlbMi16MiAVNbn8WMLbwfCyp\n9tBBboQ+BZgQEXPqbUjSUYWnkyNicpMxmJkNCZLGA+P7ZFvNXCEtaXNgG2AhcFNE3N3UxqXhpAbp\nTwCzgdupbZBeFfg7sHdE3NrJdtwgbWbWTWVfIf0T4CxgBeBdwBmSftzMxiNiPnAgcBXwIPC/ETFN\n0gGSDsir/QRYHjhJ0hRJt/egHGZm1oea6cr6MLBx5c5v+c5w90ZETUN1WVxzMDPrvrLv5zALWLLw\nfCR12g3MzKx9NLrO4Q958hXgAUlX5+efJLUdmJlZm+r0tJKkfVnUFVXV0xFxVunRLYrFp5XMzLqp\n1HtI5x2MIN0NDuChiJjXk531lJODmVn3lXonuNxv9izgyTxrVUn7RERXV0ebmdkg1UxvpbtJ1yZM\nz8/XAS6IiM37Ib5KDK45mJl1U9m9lYZXEgNARDxMc/eeNjOzQaqZg/xdkk4FziE1Ru8F3FlqVGZm\n1lLNnFYaQbrKuTIg3o3AiXnspX7h00pmZt1XWm+lPDbS/RGxXk+D6wuSIuAHwG9JQ3KYmVkXSmtz\nyGMjTZe0Wo8i61ufAm5F2qTVgZiZtbtm2hxWIF0hfTtQuStcRMTO5YVV1yeB/YBrkE4GfkE/ntoy\nMxtKmmlz2LYyWZgd/XmdQ4eqkfRe4ARgXeCrdLzNqJmZZaW0OeTRV78BrAXcB5ze31dGF2LpWEBJ\nwOeB44D/BX5ExGutiM3MbKAqq83hLOCDpMSwI/DrnuygFBFBxIXAB0j3gpiK9MkWR2Vm1jYa1Rym\nRsRGeXo4cEdEbNafwRViaZz9pAnAycB1wMF0cqtRM7OhpKyawztdRmOgdx+NuJJUi3gduB/pcy2O\nyMxsUGtUc1gAvFGYtSTwZp6OiFim5NiKsTSf/aStgVOB+4EDiXi2zNjMzAaqUmoOETEsIkYXHsML\n0/2WGLot4v+ATYGHgXuR9skN2GZm1qSm7ufQaj3OftJmwOnAP4EDiJjRx6GZmQ1YZY/KOnhFTAHG\nAdcDdyJ9G6m9y2xm1gfau+bQcSPrktoiFgO+RsS0vojNzGygcs2hGemeFNsC5wL/QPoh0uItjsrM\nbEAaOskBIGIhEScCHwK2Ae5A6rc72pmZDRZDKzlURDxJuur7N8AVSL8iDRdiZmYM1eQAlSE4zgY2\nBtYgdXv9WIujMjMbEIZOg3TXO9kVOB6YBBxOxKul7s/MrGRukO4LEReThuBYgjQEx44tjsjMrGVc\nc6i/w08ApwA3Ad8j4oV+27eZWR9xzaGvRVwHbAQ8TxoOfA8PwWFmQ4lrDl3vfEvgNOBR4JtEzGpJ\nHGZm3eSaQ5kibgU2B6YA9yDt71qEmbU71xy6F8hGpFrEa8D+RDzW4ojMzDrlmkN/iZgKbAX8DbgN\n6QikgTt8uZlZDzk5dFfEAiJ+A2xJuoDuCaTfIK3a4sjMzPqMk0NPRTxKxERgMyCAKUgXIH24xZGZ\nmfVaqclB0gRJD0l6RNJhdZavJ+kWSW9JOrjMWEoT8RQRh5CG4LgNuBDpRqRdkYa1ODozsx4prUFa\n6cA4HdgemAXcAUyMwn0UJL0LWA3YFZgT6XRNvW0NjAbpZkjDgX8DDgZWBH4HnEnE6y2Ny8yGnIHa\nID0OeDQiZkTEPOACYJfiChHxfETcCcwrMY7+FTGfiAtJDdf7ANsBM5CORnpva4MzM2tOmclhDPB0\n4fnMPG9oSKO+3kzEbqTG66VJYzb9EWnTFkdnZtbQ8BK33afnqyQdVXg6OSIm9+X2S5WuhzgI6Ujg\n68BlSNNJ95O4koiFLY3PzNqCpPHA+D7ZVoltDlsCR0XEhPz8CGBhRBxTZ90jgdfaos2hGdISwBdI\n7RIjgN8C5xDxZkvjMrO2MlDbHO4E1pa0utLBcA/SvRLqaZ8DfzMi3ibiHNKwHN8Cdia1SxyF9O7W\nBmdmVvLwGZI+TeqtMww4LSKOlnQAQEScLGllUi+mZYCFwFxgg4h4rWo77VVzqEdaD/guKYn+Gfgt\nEQ+2NigzG8x6c+z02EoDTere++/AN4G7gf8GrmMwfFBmNqA4ObQjaSTwReD7wAJSkriAiH+1NC4z\nGzScHNpZGh58B1Lj9QdI97n+HyJeamlcZjbgDdQGaesL6XqJq4jYAfgUsDbwGNIJSGu3ODoza1NO\nDoNJxFQi9gM2AOYANyNdjLSNb0BkZn3Jp5UGM2kp0hAd3wNeIbVL/Jk0XImZDXFucxjqpMWAnUiN\n1+8HjgNOIeKVlsZlZi3lNoehLmIhEZOIGA98jnSPiSeQfou0mU85mVl3uebQrqSxpKuvdwOWJN3a\n9DLSNRNvtDI0M+sfPq1kjUnrkE477QR8GLiRlCj+RsSTrQzNzMrj5GDNk5YlXTexE7Aj8AyLahW3\nErGghdGZWR9ycrCeSXfrGwd8hpQs3gdcQUoWVxExp4XRmVkvOTlY30jtFDuSEsW2pLGd0ukneMjj\nO5kNLk4O1vfSNRQfJyWKz5Bu5VpJFDd4jCezgc/JwcqVusJuxKJG7Q2B60jJ4nIinm1hdGbWCScH\n619pWPEJpESxA/AoKVFcBkzxbU/NBgYnB2sdaXHgoyyqVSwDXE5KFNdSdeMmM+s/Tg42cEhrsaj3\n0xbAzVS6ykY80crQzIYaJwcbmKRlgO1Z1Kj9AosatW8mYn4LozNre04ONvClwQE/xKJEsTpwVX7c\nCUx3sjDrW04ONvhIY0jXVGxPGihwDPAAMKXwmOpxoMx6zsnBBj9pNLAxKVFUHusBTwD3UEwavkWq\nWVOcHKw9SUuQ7nq3KYsSxibAy1QnDHjaV3CbdeTkYENHart4PylRFJPGEqQkUUwa0z2QoA1lTg5m\n0sosShSVpLEKcD8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FzVTWTZwczIaKNJbUHFVMGusCawNvB14nJYonq/4u/jxi3rDHblbFycFsOKRO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\nAElFTkSuQmCC\n", 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VynNrJTOzPiqlKetQ4uRgZtZ3pTRlrSrgX/PfI9opxMzMhpdWLyxPzn/3LisQMzMbOtzq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\nAAAAAElFTkSuQmCC\n", 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vRZGadcvJwawsqe1iTWBtYJ38tzI9CpjOgomjkjyeJuKdVoRsVuHkYNYKKXGs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\nmdVwcjAzsxr/H/ImMwt6LeV5AAAAAElFTkSuQmCC\n", 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MIjWKe9iQYcbJwWy4SkljGeCjwJj8WLHwvPIYDTxH4wQyk4g3B7kEViInBzNr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\nrkJggg==\n", "text/plain": "<matplotlib.figure.Figure at 0x29c4a240>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 51, "cell_type": "code", "source": "def on_one_plot():\n # isolate gre and class rank\n grouped = pd.pivot_table(combos, values=['approval_pred'], index=['time_to_decision', 'station'],\n aggfunc=np.mean)\n # print grouped\n \n # in case you're curious as to what this looks like\n # print grouped.head()\n # admit_pred\n # gre prestige \n # 220.000000 1 0.282462\n # 2 0.169987\n # 3 0.096544\n # 4 0.079859\n # 284.444444 1 0.311718\n \n # make a plot\n for col in combos.station.unique():\n print col, station_key[col]\n \n colors = 'rbgyrbgy'\n for col in combos.station.unique():\n if station_key[col] == 'BOUNCE' or 'OWN':\n plt_data = grouped.ix[grouped.index.get_level_values(1)==col]\n pl.plot(plt_data.index.get_level_values(0), plt_data['approval_pred'], color='g')\n \n elif station_key[col] == 'COM' or 'LOGO':\n plt_data = grouped.ix[grouped.index.get_level_values(1)==col]\n pl.plot(plt_data.index.get_level_values(0), plt_data['approval_pred'], color='r')\n \n else: \n plt_data = grouped.ix[grouped.index.get_level_values(1)==col]\n pl.plot(plt_data.index.get_level_values(0), plt_data['approval_pred'], color='k', label='_nolegend_')\n pl.axis([0, 120, 0.00, 0.5])\n pl.xlabel('Time to decision (days)')\n pl.ylabel(\"Prob(approval=1)\")\n #pl.legend(loc='upper right', title='Station')\n pl.title(\"Prob(approval=1), and time to app.\")\n pl.show()", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 52, "cell_type": "code", "source": "on_one_plot()", "outputs": [{"output_type": "stream", "name": "stdout", "text": "0.0 COMSPIN\n1.0 FUSE\n2.0 LMN\n3.0 CWPLUS\n4.0 LOGO\n5.0 COM\n6.0 BET\n7.0 MTV\n8.0 BOUNCE\n9.0 CHILL\n10.0 DTVA\n11.0 OWN\n12.0 IDL\n13.0 TBS\n14.0 CMT\n15.0 DFCL\n16.0 TV1L\n17.0 MTV2\n18.0 BABYFIRST\n19.0 JUST\n20.0 JFAITH\n21.0 OWNL\n22.0 NUVOTV\n23.0 QUEENL\n24.0 A&E\n25.0 MTVJAMS\n26.0 MTV3\n27.0 ESCAPE\n28.0 FUSION\n29.0 SPIKE\n30.0 USA\n"}, {"output_type": "display_data", "data": {"image/png": 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Bu9z91kaOFVrn0BSW2sLA08Dmnviz7T1+JBKJtIUOWefg7pPQqvsB4A3gRnd/08wOMLMD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\nR9KReFz0yPCd6yS1bP8YUCbffcBRZnYMXsDpChiYfK0s/M5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