{ "metadata": { "name": "", "signature": "sha256:c23fe55a5cb0859c9a2553bc7229cd39c6922503474091837f0bacaf572dc8e2" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import pandas as pd\n", "loansData = pd.read_csv('http://spark-public.s3.amazonaws.com/dataanalysis/loansData.csv')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "loansData['Interest.Rate'][0:5]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ "81174 8.90%\n", "99592 12.12%\n", "80059 21.98%\n", "15825 9.99%\n", "33182 11.71%\n", "Name: Interest.Rate, dtype: object" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "/Users/nitin/dl/mangodata.csv" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "mangoData = pd.read_csv('/Users/nitin/dl/mangodata.csv')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "mangoData" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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datemedscheduledactual
0 4/14/13 lisinopril 8:00 8:01
1 4/15/13 lisinopril 8:00 8:10
2 4/16/13 lisinopril 8:00 8:02
3 4/17/13 lisinopril 8:00 8:01
4 4/18/13 lisinopril 8:00 7:54
5 4/19/13 lisinopril 8:00 8:03
6 4/20/13 lisinopril 8:00 7:51
7 4/21/13 lisinopril 8:00 8:01
8 4/22/13 lisinopril 8:00 8:13
9 4/23/13 lisinopril 8:00 8:14
10 4/23/13 atorvastatin 17:00 17:14
11 4/24/13 lisinopril 8:00 7:51
12 4/24/13 atorvastatin 17:00 18:03
13 4/25/13 lisinopril 8:00 8:10
14 4/25/13 atorvastatin 17:00 NaN
15 4/26/13 lisinopril 8:00 8:19
16 4/26/13 atorvastatin 17:00 17:28
17 4/27/13 lisinopril 8:00 8:05
18 4/27/13 atorvastatin 17:00 18:01
19 4/28/13 lisinopril 8:00 8:17
20 4/28/13 atorvastatin 17:00 18:03
21 4/29/13 lisinopril 8:00 8:30
22 4/29/13 atorvastatin 17:00 NaN
23 4/30/13 lisinopril 8:00 NaN
24 4/30/13 atorvastatin 17:00 17:24
25 5/1/13 lisinopril 8:00 8:29
26 5/1/13 atorvastatin 17:00 17:46
27 5/2/13 lisinopril 8:00 8:13
28 5/2/13 atorvastatin 17:00 17:28
29 5/3/13 lisinopril 8:00 8:08
...............
585 5/22/14 atorvastatin 8:00 9:58
586 5/23/14 atorvastatin 8:00 NaN
587 5/24/14 atorvastatin 8:00 10:14
588 5/25/14 atorvastatin 8:00 8:05
589 5/26/14 atorvastatin 8:00 NaN
590 5/27/14 atorvastatin 8:00 NaN
591 5/28/14 atorvastatin 8:00 NaN
592 5/29/14 atorvastatin 8:00 NaN
593 5/30/14 atorvastatin 8:00 NaN
594 5/31/14 atorvastatin 8:00 NaN
595 6/1/14 atorvastatin 8:00 NaN
596 6/2/14 atorvastatin 8:00 NaN
597 6/3/14 atorvastatin 8:00 7:57
598 6/4/14 atorvastatin 8:00 9:24
599 6/5/14 atorvastatin 8:00 NaN
600 6/6/14 atorvastatin 8:00 NaN
601 6/7/14 atorvastatin 8:00 9:21
602 6/8/14 atorvastatin 8:00 NaN
603 6/9/14 atorvastatin 8:00 NaN
604 6/10/14 atorvastatin 8:00 9:06
605 6/11/14 atorvastatin 8:00 8:40
606 6/12/14 atorvastatin 8:00 10:07
607 6/13/14 atorvastatin 8:00 9:59
608 6/14/14 atorvastatin 8:00 8:02
609 6/15/14 atorvastatin 8:00 10:55
610 6/16/14 atorvastatin 8:00 9:00
611 6/17/14 atorvastatin 8:00 NaN
612 6/18/14 atorvastatin 8:00 NaN
613 6/19/14 atorvastatin 8:00 NaN
614 6/20/14 atorvastatin 8:00 NaN
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615 rows \u00d7 4 columns

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" ], "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ " date med scheduled actual\n", "0 4/14/13 lisinopril 8:00 8:01\n", "1 4/15/13 lisinopril 8:00 8:10\n", "2 4/16/13 lisinopril 8:00 8:02\n", "3 4/17/13 lisinopril 8:00 8:01\n", "4 4/18/13 lisinopril 8:00 7:54\n", "5 4/19/13 lisinopril 8:00 8:03\n", "6 4/20/13 lisinopril 8:00 7:51\n", "7 4/21/13 lisinopril 8:00 8:01\n", "8 4/22/13 lisinopril 8:00 8:13\n", "9 4/23/13 lisinopril 8:00 8:14\n", "10 4/23/13 atorvastatin 17:00 17:14\n", "11 4/24/13 lisinopril 8:00 7:51\n", "12 4/24/13 atorvastatin 17:00 18:03\n", "13 4/25/13 lisinopril 8:00 8:10\n", "14 4/25/13 atorvastatin 17:00 NaN\n", "15 4/26/13 lisinopril 8:00 8:19\n", "16 4/26/13 atorvastatin 17:00 17:28\n", "17 4/27/13 lisinopril 8:00 8:05\n", "18 4/27/13 atorvastatin 17:00 18:01\n", "19 4/28/13 lisinopril 8:00 8:17\n", "20 4/28/13 atorvastatin 17:00 18:03\n", "21 4/29/13 lisinopril 8:00 8:30\n", "22 4/29/13 atorvastatin 17:00 NaN\n", "23 4/30/13 lisinopril 8:00 NaN\n", "24 4/30/13 atorvastatin 17:00 17:24\n", "25 5/1/13 lisinopril 8:00 8:29\n", "26 5/1/13 atorvastatin 17:00 17:46\n", "27 5/2/13 lisinopril 8:00 8:13\n", "28 5/2/13 atorvastatin 17:00 17:28\n", "29 5/3/13 lisinopril 8:00 8:08\n", ".. ... ... ... ...\n", "585 5/22/14 atorvastatin 8:00 9:58\n", "586 5/23/14 atorvastatin 8:00 NaN\n", "587 5/24/14 atorvastatin 8:00 10:14\n", "588 5/25/14 atorvastatin 8:00 8:05\n", "589 5/26/14 atorvastatin 8:00 NaN\n", "590 5/27/14 atorvastatin 8:00 NaN\n", "591 5/28/14 atorvastatin 8:00 NaN\n", "592 5/29/14 atorvastatin 8:00 NaN\n", "593 5/30/14 atorvastatin 8:00 NaN\n", "594 5/31/14 atorvastatin 8:00 NaN\n", "595 6/1/14 atorvastatin 8:00 NaN\n", "596 6/2/14 atorvastatin 8:00 NaN\n", "597 6/3/14 atorvastatin 8:00 7:57\n", "598 6/4/14 atorvastatin 8:00 9:24\n", "599 6/5/14 atorvastatin 8:00 NaN\n", "600 6/6/14 atorvastatin 8:00 NaN\n", "601 6/7/14 atorvastatin 8:00 9:21\n", "602 6/8/14 atorvastatin 8:00 NaN\n", "603 6/9/14 atorvastatin 8:00 NaN\n", "604 6/10/14 atorvastatin 8:00 9:06\n", "605 6/11/14 atorvastatin 8:00 8:40\n", "606 6/12/14 atorvastatin 8:00 10:07\n", "607 6/13/14 atorvastatin 8:00 9:59\n", "608 6/14/14 atorvastatin 8:00 8:02\n", "609 6/15/14 atorvastatin 8:00 10:55\n", "610 6/16/14 atorvastatin 8:00 9:00\n", "611 6/17/14 atorvastatin 8:00 NaN\n", "612 6/18/14 atorvastatin 8:00 NaN\n", "613 6/19/14 atorvastatin 8:00 NaN\n", "614 6/20/14 atorvastatin 8:00 NaN\n", "\n", "[615 rows x 4 columns]" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "mangoData2 = pd.read_csv('/Users/nitin/dl/mangodata2.csv')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "mangoData2" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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datedowmedscheduledactual
0 4/14/2013 sunday lisinopril 8:00 8:01
1 4/15/2013 monday lisinopril 8:00 8:10
2 4/16/2013 tuesday lisinopril 8:00 8:02
3 4/17/2013 wednesday lisinopril 8:00 8:01
4 4/18/2013 thursday lisinopril 8:00 7:54
5 4/19/2013 friday lisinopril 8:00 8:03
6 4/20/2013 saturday lisinopril 8:00 7:51
7 4/21/2013 sunday lisinopril 8:00 8:01
8 4/22/2013 monday lisinopril 8:00 8:13
9 4/23/2013 tuesday lisinopril 8:00 8:14
10 4/24/2013 wednesday lisinopril 8:00 7:51
11 4/25/2013 thursday lisinopril 8:00 8:10
12 4/26/2013 friday lisinopril 8:00 8:19
13 4/27/2013 saturday lisinopril 8:00 8:05
14 4/28/2013 sunday lisinopril 8:00 8:17
15 4/29/2013 monday lisinopril 8:00 8:30
16 4/30/2013 tuesday lisinopril 8:00 NaN
17 5/1/2013 wednesday lisinopril 8:00 8:29
18 5/2/2013 thursday lisinopril 8:00 8:13
19 5/3/2013 friday lisinopril 8:00 8:08
20 5/4/2013 saturday lisinopril 8:00 8:22
21 5/5/2013 sunday lisinopril 8:00 8:17
22 5/6/2013 monday lisinopril 8:00 8:12
23 5/7/2013 tuesday lisinopril 8:00 8:15
24 5/8/2013 wednesday lisinopril 8:00 8:13
25 5/9/2013 thursday lisinopril 8:00 8:02
26 5/10/2013 friday lisinopril 8:00 8:02
27 5/11/2013 saturday lisinopril 8:00 8:11
28 5/12/2013 sunday lisinopril 8:00 8:28
29 5/13/2013 monday lisinopril 8:00 NaN
..................
585 5/22/2014 friday atorvastatin 8:00 9:58
586 5/23/2014 saturday atorvastatin 8:00 NaN
587 5/24/2014 sunday atorvastatin 8:00 10:14
588 5/25/2014 monday atorvastatin 8:00 8:05
589 5/26/2014 tuesday atorvastatin 8:00 NaN
590 5/27/2014 wednesday atorvastatin 8:00 NaN
591 5/28/2014 thursday atorvastatin 8:00 NaN
592 5/29/2014 friday atorvastatin 8:00 NaN
593 5/30/2014 saturday atorvastatin 8:00 NaN
594 5/31/2014 sunday atorvastatin 8:00 NaN
595 6/1/2014 monday atorvastatin 8:00 NaN
596 6/2/2014 tuesday atorvastatin 8:00 NaN
597 6/3/2014 wednesday atorvastatin 8:00 7:57
598 6/4/2014 thursday atorvastatin 8:00 9:24
599 6/5/2014 friday atorvastatin 8:00 NaN
600 6/6/2014 saturday atorvastatin 8:00 NaN
601 6/7/2014 sunday atorvastatin 8:00 9:21
602 6/8/2014 monday atorvastatin 8:00 NaN
603 6/9/2014 tuesday atorvastatin 8:00 NaN
604 6/10/2014 wednesday atorvastatin 8:00 9:06
605 6/11/2014 thursday atorvastatin 8:00 8:40
606 6/12/2014 friday atorvastatin 8:00 10:07
607 6/13/2014 saturday atorvastatin 8:00 9:59
608 6/14/2014 sunday atorvastatin 8:00 8:02
609 6/15/2014 monday atorvastatin 8:00 10:55
610 6/16/2014 tuesday atorvastatin 8:00 9:00
611 6/17/2014 wednesday atorvastatin 8:00 NaN
612 6/18/2014 thursday atorvastatin 8:00 NaN
613 6/19/2014 friday atorvastatin 8:00 NaN
614 6/20/2014 saturday atorvastatin 8:00 NaN
\n", "

615 rows \u00d7 5 columns

\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ " date dow med scheduled actual\n", "0 4/14/2013 sunday lisinopril 8:00 8:01\n", "1 4/15/2013 monday lisinopril 8:00 8:10\n", "2 4/16/2013 tuesday lisinopril 8:00 8:02\n", "3 4/17/2013 wednesday lisinopril 8:00 8:01\n", "4 4/18/2013 thursday lisinopril 8:00 7:54\n", "5 4/19/2013 friday lisinopril 8:00 8:03\n", "6 4/20/2013 saturday lisinopril 8:00 7:51\n", "7 4/21/2013 sunday lisinopril 8:00 8:01\n", "8 4/22/2013 monday lisinopril 8:00 8:13\n", "9 4/23/2013 tuesday lisinopril 8:00 8:14\n", "10 4/24/2013 wednesday lisinopril 8:00 7:51\n", "11 4/25/2013 thursday lisinopril 8:00 8:10\n", "12 4/26/2013 friday lisinopril 8:00 8:19\n", "13 4/27/2013 saturday lisinopril 8:00 8:05\n", "14 4/28/2013 sunday lisinopril 8:00 8:17\n", "15 4/29/2013 monday lisinopril 8:00 8:30\n", "16 4/30/2013 tuesday lisinopril 8:00 NaN\n", "17 5/1/2013 wednesday lisinopril 8:00 8:29\n", "18 5/2/2013 thursday lisinopril 8:00 8:13\n", "19 5/3/2013 friday lisinopril 8:00 8:08\n", "20 5/4/2013 saturday lisinopril 8:00 8:22\n", "21 5/5/2013 sunday lisinopril 8:00 8:17\n", "22 5/6/2013 monday lisinopril 8:00 8:12\n", "23 5/7/2013 tuesday lisinopril 8:00 8:15\n", "24 5/8/2013 wednesday lisinopril 8:00 8:13\n", "25 5/9/2013 thursday lisinopril 8:00 8:02\n", "26 5/10/2013 friday lisinopril 8:00 8:02\n", "27 5/11/2013 saturday lisinopril 8:00 8:11\n", "28 5/12/2013 sunday lisinopril 8:00 8:28\n", "29 5/13/2013 monday lisinopril 8:00 NaN\n", ".. ... ... ... ... ...\n", "585 5/22/2014 friday atorvastatin 8:00 9:58\n", "586 5/23/2014 saturday atorvastatin 8:00 NaN\n", "587 5/24/2014 sunday atorvastatin 8:00 10:14\n", "588 5/25/2014 monday atorvastatin 8:00 8:05\n", "589 5/26/2014 tuesday atorvastatin 8:00 NaN\n", "590 5/27/2014 wednesday atorvastatin 8:00 NaN\n", "591 5/28/2014 thursday atorvastatin 8:00 NaN\n", "592 5/29/2014 friday atorvastatin 8:00 NaN\n", "593 5/30/2014 saturday atorvastatin 8:00 NaN\n", "594 5/31/2014 sunday atorvastatin 8:00 NaN\n", "595 6/1/2014 monday atorvastatin 8:00 NaN\n", "596 6/2/2014 tuesday atorvastatin 8:00 NaN\n", "597 6/3/2014 wednesday atorvastatin 8:00 7:57\n", "598 6/4/2014 thursday atorvastatin 8:00 9:24\n", "599 6/5/2014 friday atorvastatin 8:00 NaN\n", "600 6/6/2014 saturday atorvastatin 8:00 NaN\n", "601 6/7/2014 sunday atorvastatin 8:00 9:21\n", "602 6/8/2014 monday atorvastatin 8:00 NaN\n", "603 6/9/2014 tuesday atorvastatin 8:00 NaN\n", "604 6/10/2014 wednesday atorvastatin 8:00 9:06\n", "605 6/11/2014 thursday atorvastatin 8:00 8:40\n", "606 6/12/2014 friday atorvastatin 8:00 10:07\n", "607 6/13/2014 saturday atorvastatin 8:00 9:59\n", "608 6/14/2014 sunday atorvastatin 8:00 8:02\n", "609 6/15/2014 monday atorvastatin 8:00 10:55\n", "610 6/16/2014 tuesday atorvastatin 8:00 9:00\n", "611 6/17/2014 wednesday atorvastatin 8:00 NaN\n", "612 6/18/2014 thursday atorvastatin 8:00 NaN\n", "613 6/19/2014 friday atorvastatin 8:00 NaN\n", "614 6/20/2014 saturday atorvastatin 8:00 NaN\n", "\n", "[615 rows x 5 columns]" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "# plot missed by dow \n", "# plot delay by dow\n", "# plot delay by num of days into program\n", "# plot on time %\n", "\n", "lisin = mangoData2['med'== 'lisinopril']" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "KeyError", "evalue": "False", "output_type": "pyerr", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m# plot on time %\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mlisin\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmangoData2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'med'\u001b[0m\u001b[0;34m==\u001b[0m \u001b[0;34m'lisinopril'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m//anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 1676\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1677\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1678\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1679\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1680\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m//anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 1683\u001b[0m \u001b[0;31m# get column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1684\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1685\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1686\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1687\u001b[0m \u001b[0;31m# duplicate columns & possible reduce dimensionaility\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m//anaconda/lib/python2.7/site-packages/pandas/core/generic.pyc\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m 1050\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1051\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1052\u001b[0;31m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1053\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1054\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m//anaconda/lib/python2.7/site-packages/pandas/core/internals.pyc\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, item, fastpath)\u001b[0m\n\u001b[1;32m 2563\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2564\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2565\u001b[0;31m \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2566\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2567\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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[0m\n", "\u001b[0;32m//anaconda/lib/python2.7/site-packages/pandas/core/index.pyc\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 1179\u001b[0m \u001b[0mloc\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0munique\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpossibly\u001b[0m \u001b[0mslice\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mmask\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1180\u001b[0m \"\"\"\n\u001b[0;32m-> 1181\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_values_from_object\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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 1182\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1183\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseries\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m//anaconda/lib/python2.7/site-packages/pandas/index.so\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:3656)\u001b[0;34m()\u001b[0m\n", "\u001b[0;32m//anaconda/lib/python2.7/site-packages/pandas/index.so\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:3534)\u001b[0;34m()\u001b[0m\n", "\u001b[0;32m//anaconda/lib/python2.7/site-packages/pandas/hashtable.so\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:11911)\u001b[0;34m()\u001b[0m\n", "\u001b[0;32m//anaconda/lib/python2.7/site-packages/pandas/hashtable.so\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:11864)\u001b[0;34m()\u001b[0m\n", "\u001b[0;31mKeyError\u001b[0m: False" ] } ], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "lisin = mangoData2[0:5]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "lisin\n" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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datedowmedscheduledactual
0 4/14/2013 sunday lisinopril 8:00 8:01
1 4/15/2013 monday lisinopril 8:00 8:10
2 4/16/2013 tuesday lisinopril 8:00 8:02
3 4/17/2013 wednesday lisinopril 8:00 8:01
4 4/18/2013 thursday lisinopril 8:00 7:54
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 10, "text": [ " date dow med scheduled actual\n", "0 4/14/2013 sunday lisinopril 8:00 8:01\n", "1 4/15/2013 monday lisinopril 8:00 8:10\n", "2 4/16/2013 tuesday lisinopril 8:00 8:02\n", "3 4/17/2013 wednesday lisinopril 8:00 8:01\n", "4 4/18/2013 thursday lisinopril 8:00 7:54" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "lisin = mangoData2[mangoData2['med'] == 'lisinopril']" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "ator = mangoData2[mangoData2['med'] == 'atorvastatin']" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "import datetime as dt\n", "import dt.time as tm\n", "def mk_hr_min(stime):\n", " (hr,min) = [ int(x) for x in stime.split(':') ]\n", " \n", "def tdiff(s1, s2):\n", " h1,m1 = mk_hr_min(s1)\n", " h2,m2 = mk_hr_min(s2)\n", " t1 = tm(h1,m1,0)\n", " t2 = tm(h2,m2,0)\n", " td = t2 - t1\n", " return 60*td.hours + td.minutes\n", " \n", " " ], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }