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July 25, 2015 02:25
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "execution_count": 83, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Populating the interactive namespace from numpy and matplotlib\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "%pylab inline" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 84, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import pandas as pd" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 85, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "n = pd.DataFrame.from_csv('north.csv')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 86, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "s = pd.DataFrame.from_csv('south.csv')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 87, | |
| "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>move</th>\n", | |
| " <th>transport</th>\n", | |
| " <th>start</th>\n", | |
| " <th>end</th>\n", | |
| " <th>duration</th>\n", | |
| " <th>time</th>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>useless</th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>2014-09-15</th>\n", | |
| " <td> move</td>\n", | |
| " <td> transport</td>\n", | |
| " <td> 2014-09-15T17:59:30-04:00</td>\n", | |
| " <td> 2014-09-15T18:17:59-04:00</td>\n", | |
| " <td> 1109</td>\n", | |
| " <td> 17:59:30</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2014-09-17</th>\n", | |
| " <td> move</td>\n", | |
| " <td> transport</td>\n", | |
| " <td> 2014-09-17T18:50:14-04:00</td>\n", | |
| " <td> 2014-09-17T19:05:49-04:00</td>\n", | |
| " <td> 935</td>\n", | |
| " <td> 18:50:14</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2014-10-23</th>\n", | |
| " <td> move</td>\n", | |
| " <td> transport</td>\n", | |
| " <td> 2014-10-23T18:42:22-04:00</td>\n", | |
| " <td> 2014-10-23T18:58:24-04:00</td>\n", | |
| " <td> 962</td>\n", | |
| " <td> 18:42:22</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2014-09-18</th>\n", | |
| " <td> move</td>\n", | |
| " <td> transport</td>\n", | |
| " <td> 2014-09-18T18:07:02-04:00</td>\n", | |
| " <td> 2014-09-18T18:30:45-04:00</td>\n", | |
| " <td> 1423</td>\n", | |
| " <td> 18:07:02</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2014-10-30</th>\n", | |
| " <td> move</td>\n", | |
| " <td> transport</td>\n", | |
| " <td> 2014-10-30T18:41:34-04:00</td>\n", | |
| " <td> 2014-10-30T18:58:27-04:00</td>\n", | |
| " <td> 1013</td>\n", | |
| " <td> 18:41:34</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " move transport start \\\n", | |
| "useless \n", | |
| "2014-09-15 move transport 2014-09-15T17:59:30-04:00 \n", | |
| "2014-09-17 move transport 2014-09-17T18:50:14-04:00 \n", | |
| "2014-10-23 move transport 2014-10-23T18:42:22-04:00 \n", | |
| "2014-09-18 move transport 2014-09-18T18:07:02-04:00 \n", | |
| "2014-10-30 move transport 2014-10-30T18:41:34-04:00 \n", | |
| "\n", | |
| " end duration time \n", | |
| "useless \n", | |
| "2014-09-15 2014-09-15T18:17:59-04:00 1109 17:59:30 \n", | |
| "2014-09-17 2014-09-17T19:05:49-04:00 935 18:50:14 \n", | |
| "2014-10-23 2014-10-23T18:58:24-04:00 962 18:42:22 \n", | |
| "2014-09-18 2014-09-18T18:30:45-04:00 1423 18:07:02 \n", | |
| "2014-10-30 2014-10-30T18:58:27-04:00 1013 18:41:34 " | |
| ] | |
| }, | |
| "execution_count": 87, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "n.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 88, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "useless\n", | |
| "2014-09-15 1109\n", | |
| "2014-09-17 935\n", | |
| "2014-10-23 962\n", | |
| "2014-09-18 1423\n", | |
| "2014-10-30 1013\n", | |
| "2014-11-03 1125\n", | |
| "2014-11-05 1026\n", | |
| "2014-11-06 1000\n", | |
| "2014-11-12 1265\n", | |
| "2014-11-13 820\n", | |
| "2014-11-17 840\n", | |
| "2014-11-24 963\n", | |
| "2014-11-25 1248\n", | |
| "2014-12-02 1074\n", | |
| "2014-12-03 979\n", | |
| "...\n", | |
| "2015-05-06 928\n", | |
| "2015-05-11 967\n", | |
| "2015-05-12 1019\n", | |
| "2015-05-13 1287\n", | |
| "2015-05-18 1017\n", | |
| "2015-05-19 887\n", | |
| "2015-05-27 1045\n", | |
| "2015-05-28 1394\n", | |
| "2015-06-01 1031\n", | |
| "2015-06-03 894\n", | |
| "2015-06-09 1226\n", | |
| "2015-06-10 909\n", | |
| "2015-06-15 962\n", | |
| "2014-10-21 900\n", | |
| "2014-10-22 834\n", | |
| "Name: duration, Length: 79" | |
| ] | |
| }, | |
| "execution_count": 88, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "n['duration']" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 89, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.text.Text at 0x10ff13750>" | |
| ] | |
| }, | |
| "execution_count": 89, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
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| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "hist(s.duration, bins=15);title('south')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 90, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.text.Text at 0x111d3a190>" | |
| ] | |
| }, | |
| "execution_count": 90, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x10f7c3250>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "hist(n.duration, bins=15);title('north')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 91, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "n.time = pd.to_datetime(n.time)\n", | |
| "s.time = pd.to_datetime(s.time)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 92, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "n['floattime'] = ( n.time - n.time.min() ) / timedelta64(1,'s')\n", | |
| "s['floattime'] = ( s.time - s.time.min() ) / timedelta64(1,'s')\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 93, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import statsmodels.formula.api as sm" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 94, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "result = sm.ols(formula = 'duration ~ floattime', data = s).fit()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 95, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " OLS Regression Results \n", | |
| "==============================================================================\n", | |
| "Dep. Variable: duration R-squared: 0.031\n", | |
| "Model: OLS Adj. R-squared: 0.019\n", | |
| "Method: Least Squares F-statistic: 2.498\n", | |
| "Date: Fri, 24 Jul 2015 Prob (F-statistic): 0.118\n", | |
| "Time: 22:20:52 Log-Likelihood: -549.53\n", | |
| "No. Observations: 80 AIC: 1103.\n", | |
| "Df Residuals: 78 BIC: 1108.\n", | |
| "Df Model: 1 \n", | |
| "Covariance Type: nonrobust \n", | |
| "==============================================================================\n", | |
| " coef std err t P>|t| [95.0% Conf. Int.]\n", | |
| "------------------------------------------------------------------------------\n", | |
| "Intercept 949.6407 54.512 17.421 0.000 841.115 1058.166\n", | |
| "floattime -0.0282 0.018 -1.581 0.118 -0.064 0.007\n", | |
| "==============================================================================\n", | |
| "Omnibus: 121.941 Durbin-Watson: 2.186\n", | |
| "Prob(Omnibus): 0.000 Jarque-Bera (JB): 3929.112\n", | |
| "Skew: 5.025 Prob(JB): 0.00\n", | |
| "Kurtosis: 35.829 Cond. No. 6.32e+03\n", | |
| "==============================================================================\n", | |
| "\n", | |
| "Warnings:\n", | |
| "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", | |
| "[2] The condition number is large, 6.32e+03. This might indicate that there are\n", | |
| "strong multicollinearity or other numerical problems.\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "print(result.summary())" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 97, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " OLS Regression Results \n", | |
| "==============================================================================\n", | |
| "Dep. Variable: duration R-squared: 0.041\n", | |
| "Model: OLS Adj. R-squared: 0.029\n", | |
| "Method: Least Squares F-statistic: 3.318\n", | |
| "Date: Fri, 24 Jul 2015 Prob (F-statistic): 0.0724\n", | |
| "Time: 22:22:47 Log-Likelihood: -527.09\n", | |
| "No. Observations: 79 AIC: 1058.\n", | |
| "Df Residuals: 77 BIC: 1063.\n", | |
| "Df Model: 1 \n", | |
| "Covariance Type: nonrobust \n", | |
| "==============================================================================\n", | |
| " coef std err t P>|t| [95.0% Conf. Int.]\n", | |
| "------------------------------------------------------------------------------\n", | |
| "Intercept 1265.3228 135.026 9.371 0.000 996.451 1534.194\n", | |
| "floattime -0.0126 0.007 -1.821 0.072 -0.026 0.001\n", | |
| "==============================================================================\n", | |
| "Omnibus: 17.488 Durbin-Watson: 1.851\n", | |
| "Prob(Omnibus): 0.000 Jarque-Bera (JB): 22.601\n", | |
| "Skew: 1.000 Prob(JB): 1.24e-05\n", | |
| "Kurtosis: 4.693 Cond. No. 1.21e+05\n", | |
| "==============================================================================\n", | |
| "\n", | |
| "Warnings:\n", | |
| "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", | |
| "[2] The condition number is large, 1.21e+05. This might indicate that there are\n", | |
| "strong multicollinearity or other numerical problems.\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "result = sm.ols(formula = 'duration ~ floattime', data = n).fit()\n", | |
| "print(result.summary())" | |
| ] | |
| }, | |
| { | |
| "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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