{ "metadata": { "name": "", "signature": "sha256:ea8ccb8121c099d7558d09fc639aaec7964eb7d634834ff7191b6ae1bb24283e" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#Number munging: vectors, Pandas, probabilities" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Render our plots inline\n", "%matplotlib inline\n", "\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "pd.set_option('display.mpl_style', 'default') # Make the graphs a bit prettier\n", "plt.rcParams['figure.figsize'] = (15, 5)\n", "\n", "\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "#this presumes you've the two data sets locally" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can find the dataset and documentation at http://harvardx.harvard.edu/dataset" ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "#Our ritual: Exploratory data analysis\n", "\n", "\n", "> Exploratory data analysis (EDA) seeks to reveal structure, or simple descriptions, in data. We look at numbers and graphs and try to find patterns. \n", " - Persi Diaconis, \"Theories of Data Analysis: From Magical Thinking Through Classical statistics\"\n", "\n", "> . . . proceeding via a \u2018dustbowl\u2019 empiricism is dangerous at worst and foolish at best . . . . The purely empirical approach is particularly dangerous in an age when computers and packaged programs are readily available, since there is temptation to substitute immediate empirical analysis for more analytic thought and theory building.\n", " - Einhorn, \u201cAlchemy in the Behavioral Sciences,\u201d 1972\n", "\n", ">. . . we can view the techniques of EDA as a ritual designed to reveal patters in a data set. Thus, we may believe that naturally occurring data sets contain structure, that EDA is a useful vehicle for revealing the structure. . . . If we make no attempt to check whether the structure could have arisen by chance, and tend to accept the findinds as gospel, then the ritual comes close to magical thinking. ... a controlled form of magical thinking--in the guise of 'working hypothesis'--is a basic ingredient of scientific progress. \n", " - Persi Diaconis, \"Theories of Data Analysis: From Magical Thinking Through Classical statistics\"\n", "\n", "#From data to databases to data mining\n", "- move from accessing and manipulating data to performing ever more complicated *queries* on our data\n", "\n", "\n", "#Pandas first-line python tool for EDA\n", "- rich data structures\n", "- powerful ways to slice, dice, reformate, fix, and eliminate data\n", " - taste of what can do\n", "- rich queries like databases\n", " \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Pandas: charismatic megafauna" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import pandas as pd" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Series" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use example of multiple average house prices by year by CPI." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we wanted to understanding the fluctuations in house prices over time, we can use a new datatype called series." ] }, { "cell_type": "code", "collapsed": false, "input": [ "CPI={\"2010\": 218.056, \"2011\": 224.939, \"2012\": 229.594, \"2013\": 232.957} #http://www.bls.gov/cpi/home.htm" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The series method converts this dict to " ] }, { "cell_type": "code", "collapsed": false, "input": [ "CPI_series=pd.Series(CPI)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "CPI_series" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ "2010 218.056\n", "2011 224.939\n", "2012 229.594\n", "2013 232.957\n", "dtype: float64" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "House_sale_mean={\"2010\": 100000, \"2011\": 100000, \"2012\": 100000, \"2013\": 100000}" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "House_sale_series=pd.Series(House_sale_mean)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "House_sale_series" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 9, "text": [ "2010 100000\n", "2011 100000\n", "2012 100000\n", "2013 100000\n", "dtype: int64" ] } ], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The CPI provides \"a measure of the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services.\" A *higher* number means it costs more to buy the same goods. It was set to 100 in 1982-4.\n", "\n", "We can thus use it to measure the effects of inflation on the value of houses in our toy example." ] }, { "cell_type": "code", "collapsed": false, "input": [ "(House_sale_series/CPI_series)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 10, "text": [ "2010 458.597791\n", "2011 444.564971\n", "2012 435.551452\n", "2013 429.263770\n", "dtype: float64" ] } ], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "What's going here: pandas has taken the two series and divided the values that share the same index." ] }, { "cell_type": "code", "collapsed": false, "input": [ "#If we multiply each one by 100, we'll get the value of our houses in 1982-4 dollars.\n", "(House_sale_series/CPI_series)*100" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 11, "text": [ "2010 45859.779139\n", "2011 44456.497095\n", "2012 43555.145169\n", "2013 42926.376971\n", "dtype: float64" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "inflation_adjusted=(House_sale_series/CPI_series)*100" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "#can perform calculations on individual \n", "inflation_adjusted['2013']/inflation_adjusted['2010']" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 13, "text": [ "0.93603540567572574" ] } ], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "#plotting is simple as pie\n", "inflation_adjusted.plot()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 14, "text": [ "" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "/usr/lib/pymodules/python2.7/matplotlib/font_manager.py:1236: UserWarning: findfont: Font family ['monospace'] not found. Falling back to Bitstream Vera Sans\n", " (prop.get_family(), self.defaultFamily[fontext]))\n", "/usr/lib/pymodules/python2.7/matplotlib/font_manager.py:1246: UserWarning: findfont: Could not match :family=Bitstream Vera Sans:style=normal:variant=normal:weight=normal:stretch=normal:size=medium. Returning /usr/share/matplotlib/mpl-data/fonts/ttf/cmb10.ttf\n", " UserWarning)\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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+RAAAMMYNa88eAIxWOS6bclz9n94ZCoVU196jyvpObT/Roe/tqFdKUkL44Dc7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\nPXsAACDmNHT2aHe9N9zzl2g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"text": [ "" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "(House_sale_series/CPI_series).plot(title=\"Sorry, kids. Blame X, where X is current politician we don't like.\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 15, "text": [ "" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "/usr/lib/pymodules/python2.7/matplotlib/font_manager.py:1246: UserWarning: findfont: Could not match :family=Bitstream Vera Sans:style=normal:variant=normal:weight=normal:stretch=normal:size=x-large. Returning /usr/share/matplotlib/mpl-data/fonts/ttf/cmb10.ttf\n", " UserWarning)\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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pLRCrUWG4ryMsTDnLk4iIiIiIqDkderbUQ2mOBwe4Y83MENzT1wV7zhQifu1x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\nfe/rS5cuFdV4uC7ua7fbrfmYsU/ckSNHNDo6qr6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"text": [ "" ] } ], "prompt_number": 15 }, { "cell_type": "markdown", "metadata": {}, "source": [ "What's *super* deceptive about this plot? " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#dataframes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The library Pandas provides us with a powerful overlay that lets us use matrices but always keep their row and column names, or as a spreadsheet on speed. It allows us to work directly with the datatype \"Dataframes\" that keeps track of values and their names for us. And it allows us to perform many operations on slices of the dataframe without having to run for loops and the like. This is more convenient and involves faster processing." ] }, { "cell_type": "code", "collapsed": false, "input": [ "#Let's start with yet another way to read csv files, this time from pandas\n", "df=pd.read_csv('./HMXPC13_DI_v2_5-14-14.csv', sep=\",\")" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "#take a look--it's a biggie\n", "df" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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0 HarvardX/CB22x/2013_Spring MHxPC130442623 1 0 0 0 United States NaN NaN NaN 0 2012-12-19 2013-11-17 NaN 9NaNNaN 0NaN 1
1 HarvardX/CS50x/2012 MHxPC130442623 1 1 0 0 United States NaN NaN NaN 0 2012-10-15 NaN NaN 9NaN 1 0NaN 1
2 HarvardX/CB22x/2013_Spring MHxPC130275857 1 0 0 0 United States NaN NaN NaN 0 2013-02-08 2013-11-17 NaN 16NaNNaN 0NaN 1
3 HarvardX/CS50x/2012 MHxPC130275857 1 0 0 0 United States NaN NaN NaN 0 2012-09-17 NaN NaN 16NaNNaN 0NaN 1
4 HarvardX/ER22x/2013_Spring MHxPC130275857 1 0 0 0 United States NaN NaN NaN 0 2012-12-19 NaN NaN 16NaNNaN 0NaN 1
5 HarvardX/PH207x/2012_Fall MHxPC130275857 1 1 1 0 United States NaN NaN NaN 0 2012-09-17 2013-05-23 502 16 50 12 0NaNNaN
6 HarvardX/PH278x/2013_Spring MHxPC130275857 1 0 0 0 United States NaN NaN NaN 0 2013-02-08 NaN NaN 16NaNNaN 0NaN 1
7 HarvardX/CB22x/2013_Spring MHxPC130539455 1 1 0 0 France NaN NaN NaN 0 2013-01-01 2013-05-14 42 6NaN 3 0NaNNaN
8 HarvardX/CB22x/2013_Spring MHxPC130088379 1 1 0 0 United States NaN NaN NaN 0 2013-02-18 2013-03-17 70 3NaN 3 0NaNNaN
9 HarvardX/CS50x/2012 MHxPC130088379 1 1 0 0 United States NaN NaN NaN 0 2012-10-20 NaN NaN 12NaN 3 0NaN 1
10 HarvardX/ER22x/2013_Spring MHxPC130088379 1 1 0 0 United States NaN NaN NaN 0 2013-02-23 2013-06-14 17 2NaN 2 0NaNNaN
11 HarvardX/ER22x/2013_Spring MHxPC130198098 1 1 0 0 United States NaN NaN NaN 0 2013-06-17 2013-06-17 32 1NaN 3 0NaNNaN
12 HarvardX/CB22x/2013_Spring MHxPC130024894 1 1 0 0 United States NaN NaN NaN 0.07 2013-01-24 2013-08-03 175 9NaN 7 0NaNNaN
13 HarvardX/CS50x/2012 MHxPC130024894 1 1 0 0 United States NaN NaN NaN 0 2013-06-27 NaN NaN 2NaN 2 0NaN 1
14 HarvardX/ER22x/2013_Spring MHxPC130024894 1 1 0 0 United States NaN NaN NaN 0 2012-12-19 2013-08-17 78 5NaN 4 0NaNNaN
15 HarvardX/PH207x/2012_Fall MHxPC130024894 1 1 0 0 United States NaN NaN NaN 0 2012-07-26 2013-01-16 75 14 5 2 0NaNNaN
16 HarvardX/PH278x/2013_Spring MHxPC130024894 1 1 0 0 United States NaN NaN NaN 0 2013-07-30 2013-08-27 11 2 2 1 0NaNNaN
17 HarvardX/CS50x/2012 MHxPC130080986 1 1 0 0 United States NaN NaN NaN 0 2012-10-15 NaN NaN 11NaN 1 0NaN 1
18 HarvardX/PH207x/2012_Fall MHxPC130080986 1 1 0 0 United States NaN NaN NaN 0 2012-10-25 2012-12-04 56 11 1 2 1NaNNaN
19 HarvardX/CS50x/2012 MHxPC130063375 1 1 0 0 Unknown/Other NaN NaN NaN 0 2012-10-19 NaN NaNNaNNaN 1 0NaN 1
20 HarvardX/CS50x/2012 MHxPC130094371 1 1 0 0 United States NaN NaN NaN 0 2013-03-03 2013-03-03 7 1NaN 2 0NaNNaN
21 HarvardX/CS50x/2012 MHxPC130229084 1 1 0 0 Mexico NaN NaN NaN 0 2012-10-15 NaN NaNNaNNaN 1 0NaN 1
22 HarvardX/CS50x/2012 MHxPC130300925 1 1 0 0 United States NaN NaN NaN 0 2012-10-24 NaN NaN 2NaN 1 0NaN 1
23 HarvardX/ER22x/2013_Spring MHxPC130300925 1 1 0 0 United States NaN NaN NaN 0 2012-12-20 2013-05-18 15 2NaN 2 0NaNNaN
24 HarvardX/CS50x/2012 MHxPC130417650 1 1 0 0 Australia NaN NaN NaN 0 2012-10-29 2013-03-04 1 1NaN 2 0NaNNaN
25 HarvardX/CS50x/2012 MHxPC130506580 1 0 0 0 United States NaN NaN NaN 0 2012-09-04 NaN NaNNaNNaNNaN 0NaNNaN
26 HarvardX/CS50x/2012 MHxPC130298257 1 0 0 0 United States NaN NaN NaN 0 2012-09-05 NaN NaNNaNNaN 3 0NaN 1
27 HarvardX/CS50x/2012 MHxPC130500569 1 1 0 0 United States NaN NaN NaN 0 2012-10-22 2013-03-30 6 1NaN 5 0NaNNaN
28 HarvardX/CS50x/2012 MHxPC130466479 1 1 0 0 Unknown/Other NaN NaN NaN 0 2013-01-07 NaN NaNNaNNaN 1 0NaN 1
29 HarvardX/CB22x/2013_Spring MHxPC130340959 1 1 0 0 United States NaN NaN NaN 0.05 2013-02-11 2013-04-06 285 8NaN 4 0NaNNaN
...............................................................
641108 MITx/6.002x/2013_Spring MHxPC130140735 1 1 0 0 United States Bachelor's 1991 m NaN 2013-09-07 2013-09-07 59 1 5 3 0NaNNaN
641109 MITx/6.00x/2013_Spring MHxPC130493130 1 0 0 0 United Kingdom Master's 1977 m NaN 2013-09-07 NaN NaNNaNNaN 2 0NaN 1
641110 MITx/6.00x/2013_Spring MHxPC130400592 1 1 0 0 Other Europe Secondary 1992 m NaN 2013-09-07 2013-09-07 395 1 51 4 0NaNNaN
641111 MITx/6.00x/2013_Spring MHxPC130109892 1 1 0 0 India Secondary 1995 m NaN 2013-09-07 2013-09-07 49 1 14 2 0NaNNaN
641112 MITx/14.73x/2013_Spring MHxPC130183007 1 0 0 0 India Master's 1985 m NaN 2013-09-07 NaN NaNNaNNaNNaN 0NaNNaN
641113 MITx/8.MReV/2013_Summer MHxPC130261281 1 1 0 0 India Secondary 1994 m 0 2013-09-07 2013-09-07 8 1NaN 1 0NaNNaN
641114 MITx/6.00x/2013_Spring MHxPC130481990 1 1 0 0 India Bachelor's 1989 m NaN 2013-09-07 2013-09-07 22 1 5 1 0NaNNaN
641115 MITx/6.00x/2013_Spring MHxPC130528581 1 0 0 0 United States Bachelor's 1990 f NaN 2013-09-07 2013-09-07 2 1NaN 3 0NaNNaN
641116 MITx/14.73x/2013_Spring MHxPC130555418 1 0 0 0 Unknown/Other Bachelor's 1988 m NaN 2013-09-07 NaN NaNNaNNaNNaN 0NaNNaN
641117 MITx/6.002x/2013_Spring MHxPC130408810 1 0 0 0 India Secondary 1993 m NaN 2013-09-07 2013-09-07 2 1NaN 3 0NaNNaN
641118 MITx/6.00x/2013_Spring MHxPC130040184 1 0 0 0 United States Secondary 1991 m NaN 2013-09-07 NaN NaNNaNNaNNaN 0NaNNaN
641119 MITx/6.002x/2013_Spring MHxPC130566049 1 0 0 0 Other Europe Master's 1982 m NaN 2013-09-07 2013-09-07 2 1NaN 2 0NaNNaN
641120 MITx/8.MReV/2013_Summer MHxPC130374105 1 1 0 0 India Bachelor's 1992 m 0 2013-09-07 2013-09-07 49 1NaN 1 0NaNNaN
641121 MITx/6.00x/2013_Spring MHxPC130282999 1 0 0 0 Other Europe Master's 1979 m NaN 2013-09-07 NaN NaNNaNNaN 7 0NaN 1
641122 MITx/8.MReV/2013_Summer MHxPC130556398 1 0 0 0 India Bachelor's 1985 m 0 2013-09-07 2013-09-07 1 1NaNNaN 0NaNNaN
641123 MITx/6.00x/2013_Spring MHxPC130573334 1 0 0 0 Spain Bachelor's 1989 m NaN 2013-09-07 2013-09-07 1 1NaNNaN 0NaNNaN
641124 MITx/6.00x/2013_Spring MHxPC130505931 1 1 0 0 India Secondary 1995 m NaN 2013-09-07 2013-09-07 59 1NaN 2 0NaNNaN
641125 MITx/6.002x/2013_Spring MHxPC130280976 1 0 0 0 United States Bachelor's NaN m NaN 2013-09-07 2013-09-07 2 1NaNNaN 0NaNNaN
641126 MITx/6.00x/2013_Spring MHxPC130137331 1 1 0 0 United States Secondary 1992 m NaN 2013-09-07 2013-09-07 251 1 77 4 0NaNNaN
641127 MITx/6.002x/2013_Spring MHxPC130271624 1 0 0 0 India Bachelor's 1989 m NaN 2013-09-07 2013-09-07 1 1NaNNaN 0NaNNaN
641128 MITx/14.73x/2013_Spring MHxPC130256541 1 1 0 0 United States Master's 1982 m NaN 2013-09-07 2013-09-07 51 1 1 1 0NaNNaN
641129 MITx/6.00x/2013_Spring MHxPC130021638 1 0 0 0 Unknown/Other Bachelor's 1988 m NaN 2013-09-07 NaN NaNNaNNaNNaN 0NaNNaN
641130 MITx/14.73x/2013_Spring MHxPC130591057 1 0 0 0 Canada Bachelor's NaN f NaN 2013-09-07 2013-09-07 6 1NaNNaN 0NaNNaN
641131 MITx/8.02x/2013_Spring MHxPC130226305 1 0 0 0 Unknown/Other Bachelor's 1988 m NaN 2013-09-07 2013-09-07 11 1NaN 2 0NaNNaN
641132 MITx/6.002x/2013_Spring MHxPC130030805 1 1 0 0 Pakistan Master's 1989 m NaN 2013-09-07 2013-09-07 29 1NaN 1 0NaNNaN
641133 MITx/6.00x/2013_Spring MHxPC130184108 1 1 0 0 Canada Bachelor's 1991 m NaN 2013-09-07 2013-09-07 97 1 4 2 0NaNNaN
641134 MITx/6.00x/2013_Spring MHxPC130359782 1 0 0 0 Other Europe Bachelor's 1991 f NaN 2013-09-07 2013-09-07 1 1NaNNaN 0NaNNaN
641135 MITx/6.002x/2013_Spring MHxPC130098513 1 0 0 0 United States Doctorate 1979 m NaN 2013-09-07 2013-09-07 1 1NaNNaN 0NaNNaN
641136 MITx/6.00x/2013_Spring MHxPC130098513 1 1 0 0 United States Doctorate 1979 m NaN 2013-09-07 2013-09-07 74 1 14 1 0NaNNaN
641137 MITx/8.02x/2013_Spring MHxPC130098513 1 0 0 0 United States Doctorate 1979 m NaN 2013-09-07 NaN NaN 1NaNNaN 0NaN 1
\n", "

641138 rows \u00d7 20 columns

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" ] }, { "cell_type": "code", "collapsed": false, "input": [ "df[\"course_id\"]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 18, "text": [ "0 HarvardX/CB22x/2013_Spring\n", "1 HarvardX/CS50x/2012\n", "2 HarvardX/CB22x/2013_Spring\n", "3 HarvardX/CS50x/2012\n", "4 HarvardX/ER22x/2013_Spring\n", "5 HarvardX/PH207x/2012_Fall\n", "6 HarvardX/PH278x/2013_Spring\n", "7 HarvardX/CB22x/2013_Spring\n", "8 HarvardX/CB22x/2013_Spring\n", "9 HarvardX/CS50x/2012\n", "10 HarvardX/ER22x/2013_Spring\n", "11 HarvardX/ER22x/2013_Spring\n", "12 HarvardX/CB22x/2013_Spring\n", "13 HarvardX/CS50x/2012\n", "14 HarvardX/ER22x/2013_Spring\n", "...\n", "641123 MITx/6.00x/2013_Spring\n", "641124 MITx/6.00x/2013_Spring\n", "641125 MITx/6.002x/2013_Spring\n", "641126 MITx/6.00x/2013_Spring\n", "641127 MITx/6.002x/2013_Spring\n", "641128 MITx/14.73x/2013_Spring\n", "641129 MITx/6.00x/2013_Spring\n", "641130 MITx/14.73x/2013_Spring\n", "641131 MITx/8.02x/2013_Spring\n", "641132 MITx/6.002x/2013_Spring\n", "641133 MITx/6.00x/2013_Spring\n", "641134 MITx/6.00x/2013_Spring\n", "641135 MITx/6.002x/2013_Spring\n", "641136 MITx/6.00x/2013_Spring\n", "641137 MITx/8.02x/2013_Spring\n", "Name: course_id, Length: 641138, dtype: object" ] } ], "prompt_number": 18 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can select some subset of rows as you might expect:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "df[\"course_id\"][3340:3350]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 19, "text": [ "3340 HarvardX/CS50x/2012\n", "3341 HarvardX/ER22x/2013_Spring\n", "3342 HarvardX/PH278x/2013_Spring\n", "3343 HarvardX/CS50x/2012\n", "3344 HarvardX/CS50x/2012\n", "3345 HarvardX/ER22x/2013_Spring\n", "3346 HarvardX/CS50x/2012\n", "3347 HarvardX/CB22x/2013_Spring\n", "3348 HarvardX/CS50x/2012\n", "3349 HarvardX/CS50x/2012\n", "Name: course_id, dtype: object" ] } ], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ "df[3340:3350]" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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NaN NaN NaN 0 \n", "3347 0 0 United States NaN NaN NaN 0.04 \n", "3348 0 0 United Kingdom NaN NaN NaN 0 \n", "3349 0 0 Russian Federation NaN NaN NaN 0.0 \n", "\n", " start_time_DI last_event_DI nevents ndays_act nplay_video nchapters \\\n", "3340 2012-08-17 NaN NaN NaN NaN 2 \n", "3341 2013-09-05 2013-09-05 16 1 NaN 2 \n", "3342 2012-12-25 NaN NaN 1 NaN NaN \n", "3343 2012-11-30 NaN NaN NaN NaN 5 \n", "3344 2013-07-12 2013-07-12 8 1 NaN 1 \n", "3345 2012-12-23 NaN NaN NaN NaN NaN \n", "3346 2012-08-17 NaN NaN NaN NaN NaN \n", "3347 2013-01-23 2013-04-04 333 8 NaN 4 \n", "3348 2012-08-18 NaN NaN NaN NaN 2 \n", "3349 2012-09-03 NaN NaN NaN NaN NaN \n", "\n", " nforum_posts roles incomplete_flag \n", "3340 0 NaN 1 \n", "3341 0 NaN NaN \n", "3342 0 NaN 1 \n", "3343 0 NaN 1 \n", "3344 0 NaN NaN \n", "3345 0 NaN NaN \n", "3346 0 NaN NaN \n", "3347 0 NaN NaN \n", "3348 0 NaN 1 \n", "3349 0 NaN NaN " ] } ], "prompt_number": 20 }, { "cell_type": "code", "collapsed": false, "input": [ "df[666]" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "KeyError", "evalue": "666", "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[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m666\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 1682\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 1683\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1684\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 1685\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1686\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/usr/local/lib/python2.7/dist-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 1689\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 1690\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-> 1691\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 1692\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1693\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/usr/local/lib/python2.7/dist-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/usr/local/lib/python2.7/dist-packages/pandas/core/internals.pyc\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m 2535\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2536\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-> 2537\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 2538\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2539\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/usr/local/lib/python2.7/dist-packages/pandas/core/index.pyc\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 1154\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 1155\u001b[0m \"\"\"\n\u001b[0;32m-> 1156\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 1157\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1158\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/usr/local/lib/python2.7/dist-packages/pandas/index.so\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:3353)\u001b[0;34m()\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/index.so\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:3233)\u001b[0;34m()\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/hashtable.so\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:11148)\u001b[0;34m()\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/hashtable.so\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:11101)\u001b[0;34m()\u001b[0m\n", "\u001b[0;31mKeyError\u001b[0m: 666" ] } ], "prompt_number": 21 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Huh? I have row 666! pandas doesn't let you select a single row directly. Instead, use the .ix method." ] }, { "cell_type": "code", "collapsed": false, "input": [ "df.ix[666]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 22, "text": [ "course_id HarvardX/CS50x/2012\n", "userid_DI MHxPC130297337\n", "registered 1\n", "viewed 0\n", "explored 0\n", "certified 0\n", "final_cc_cname_DI United Kingdom\n", "LoE_DI NaN\n", "YoB NaN\n", "gender NaN\n", "grade 0\n", "start_time_DI 2012-08-17\n", "last_event_DI NaN\n", "nevents NaN\n", "ndays_act NaN\n", "nplay_video NaN\n", "nchapters NaN\n", "nforum_posts 0\n", "roles NaN\n", "incomplete_flag NaN\n", "Name: 666, dtype: object" ] } ], "prompt_number": 22 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Why? A good question. Now try passing a list:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "df.ix[[666]]" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
666 HarvardX/CS50x/2012 MHxPC130297337 1 0 0 0 United Kingdom NaNNaN NaN 0 2012-08-17 NaNNaNNaNNaNNaN 0NaNNaN
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" ], "metadata": {}, "output_type": "pyout", "prompt_number": 23, "text": [ " course_id userid_DI registered viewed explored \\\n", "666 HarvardX/CS50x/2012 MHxPC130297337 1 0 0 \n", "\n", " certified final_cc_cname_DI LoE_DI YoB gender grade start_time_DI \\\n", "666 0 United Kingdom NaN NaN NaN 0 2012-08-17 \n", "\n", " last_event_DI nevents ndays_act nplay_video nchapters nforum_posts \\\n", "666 NaN NaN NaN NaN NaN 0 \n", "\n", " roles incomplete_flag \n", "666 NaN NaN " ] } ], "prompt_number": 23 }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "What types of data did pandas import from the csv?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "df.dtypes" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 24, "text": [ "course_id object\n", "userid_DI object\n", "registered int64\n", "viewed int64\n", "explored int64\n", "certified int64\n", "final_cc_cname_DI object\n", "LoE_DI object\n", "YoB float64\n", "gender object\n", "grade object\n", "start_time_DI object\n", "last_event_DI object\n", "nevents float64\n", "ndays_act float64\n", "nplay_video float64\n", "nchapters float64\n", "nforum_posts int64\n", "roles float64\n", "incomplete_flag float64\n", "dtype: object" ] } ], "prompt_number": 24 }, { "cell_type": "markdown", "metadata": {}, "source": [ "In inputing CSV, Pandas parses each column and attempts to discern what sort of data is within. It's good but not infallible.\n", "- Pandas is particularly good with dates: you simple tell it which columns to parse as dates.\n", "Let's refine our reading of the CSV to parse the dates." ] }, { "cell_type": "code", "collapsed": false, "input": [ "df=pd.read_csv('HMXPC13_DI_v2_5-14-14.csv', sep=\",\" , parse_dates=['start_time_DI', 'last_event_DI'])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 25 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We pass a list of the columns to consider as dates." ] }, { "cell_type": "code", "collapsed": false, "input": [ "df.dtypes" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 26, "text": [ "course_id object\n", "userid_DI object\n", "registered int64\n", "viewed int64\n", "explored int64\n", "certified int64\n", "final_cc_cname_DI object\n", "LoE_DI object\n", "YoB float64\n", "gender object\n", "grade object\n", "start_time_DI datetime64[ns]\n", "last_event_DI datetime64[ns]\n", "nevents float64\n", "ndays_act float64\n", "nplay_video float64\n", "nchapters float64\n", "nforum_posts int64\n", "roles float64\n", "incomplete_flag float64\n", "dtype: object" ] } ], "prompt_number": 26 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We don't need all those fields. We can cull them by name using the method drop." ] }, { "cell_type": "code", "collapsed": false, "input": [ "df=df.drop(['userid_DI', 'roles', 'incomplete_flag', 'nforum_posts'], axis=1) # axis=1 means the column names, not the rows" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 27 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can pick out columns using their names--just like with dicts:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "df['final_cc_cname_DI'][100:110]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 28, "text": [ "100 United States\n", "101 United States\n", "102 United States\n", "103 United States\n", "104 Russian Federation\n", "105 Russian Federation\n", "106 United States\n", "107 United States\n", "108 United States\n", "109 United States\n", "Name: final_cc_cname_DI, dtype: object" ] } ], "prompt_number": 28 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can easily produce new dataframes that retain the relevant indices." ] }, { "cell_type": "code", "collapsed": false, "input": [ "df[['gender', 'ndays_act','nplay_video']][1781:1787] \n", "#note the double [[]]--you're providing a list [x,y. . . ] of the columns you want" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
genderndays_actnplay_video
1781 NaNNaNNaN
1782 NaN 1NaN
1783 NaN 2NaN
1784 NaN 3NaN
1785 NaN 1NaN
1786 NaN 1NaN
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" ], "metadata": {}, "output_type": "pyout", "prompt_number": 29, "text": [ " gender ndays_act nplay_video\n", "1781 NaN NaN NaN\n", "1782 NaN 1 NaN\n", "1783 NaN 2 NaN\n", "1784 NaN 3 NaN\n", "1785 NaN 1 NaN\n", "1786 NaN 1 NaN" ] } ], "prompt_number": 29 }, { "cell_type": "markdown", "metadata": {}, "source": [ "\"NaN\" is not a English grandmother. It is an unreported or empty value. \n", "\n", "Often we might treat it as a zero, but we need to be careful. \n", "\n", "Would it matter in the case of nplay_video?\n", "\n", "How about gender?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "#Vectors, matrices, and functions, oh my!\n", "\n", "You've been writing functions and methods for the past couple of weeks. You'll recall functions like $f(x)$ and $g(x)$ from your previous math training.\n", "\n", "Functions can be defined on vector and matrix qualities.\n", "\n", "For our example, just consider the vector from the intersection of the x and y axes to a point (a,b), which we might write $\\bar{v}=[a,b]$.\n", "\n", "\n", "Some take a vector and return a non-vectoral number, called a scalar. \n", "For example, if we have the point (2,2) on the x and y axes, the length of the vector [2,2] from the origin to that point can be found with a standard distance function\n", "\n", "$d([x,y])=|\\sqrt{x^2+y^2}|$\n", "\n", "$d([2,2])=|\\sqrt{2^2+2^2}|=|\\sqrt{8}|$\n", "\n", "Or, more easily, we might compute the mean of the elements of a column in a vector.\n", "\n", "\n", "Some functions take a vector and return the same kind of vector.\n", "\n", "$f([x,y])=[2x,3y]$\n", "\n", "$f([1,2])=[2,6]$\n", "\n", "For our purposes\n", "\n", "\n", "1. function drawing upon all the parts of a vector or matrix (a mean)\n", "2. function applied to each element individually\n", "3. function applied to a particular slice of the data, or a systematic slice of the data\n", "\n", "\n", "It's pretty easy for us to think of vectors in 2 or 3 dimensions. In much data analysis, we'll use many more dimensions. Consider just one of our rows in our dataframe today:\n" ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 29 }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Pandas makes it supremely easy to plot time series\n", "- we had it parse the dates in two columns above\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "df['start_time_DI'] # This produces a series of start times" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 30, "text": [ "0 2012-12-19\n", "1 2012-10-15\n", "2 2013-02-08\n", "3 2012-09-17\n", "4 2012-12-19\n", "5 2012-09-17\n", "6 2013-02-08\n", "7 2013-01-01\n", "8 2013-02-18\n", "9 2012-10-20\n", "10 2013-02-23\n", "11 2013-06-17\n", "12 2013-01-24\n", "13 2013-06-27\n", "14 2012-12-19\n", "...\n", "641123 2013-09-07\n", "641124 2013-09-07\n", "641125 2013-09-07\n", "641126 2013-09-07\n", "641127 2013-09-07\n", "641128 2013-09-07\n", "641129 2013-09-07\n", "641130 2013-09-07\n", "641131 2013-09-07\n", "641132 2013-09-07\n", "641133 2013-09-07\n", "641134 2013-09-07\n", "641135 2013-09-07\n", "641136 2013-09-07\n", "641137 2013-09-07\n", "Name: start_time_DI, Length: 641138, dtype: datetime64[ns]" ] } ], "prompt_number": 30 }, { "cell_type": "markdown", "metadata": {}, "source": [ "What if we wanted to figure out how many people started each day? We could run a for loop and compute it ourslves.\n", "\n", "But panda has got this cover. \n", "\n", "We use the .value_counts() method. It sums the number of times a each given value in the data occurs in a series." ] }, { "cell_type": "code", "collapsed": false, "input": [ "startdates=df['start_time_DI'].value_counts()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 31 }, { "cell_type": "code", "collapsed": false, "input": [ "startdates" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 32, "text": [ "2012-08-17 10165\n", "2013-01-23 8368\n", "2012-10-15 6766\n", "2012-08-16 6369\n", "2012-12-20 5858\n", "2013-02-14 5810\n", "2012-12-21 5809\n", "2012-08-18 5531\n", "2012-08-13 5247\n", "2013-03-03 5053\n", "2012-10-16 4639\n", "2012-07-24 4635\n", "2013-02-15 4436\n", "2013-01-22 4263\n", "2012-08-20 4107\n", "...\n", "2013-07-15 396\n", "2013-07-18 390\n", "2013-07-10 386\n", "2013-07-20 378\n", "2013-07-09 374\n", "2013-07-08 365\n", "2013-07-04 357\n", "2013-07-12 334\n", "2013-07-05 307\n", "2013-07-14 279\n", "2013-07-13 275\n", "2013-07-06 274\n", "2013-07-07 273\n", "2012-07-23 5\n", "2013-09-08 1\n", "Length: 413" ] } ], "prompt_number": 32 }, { "cell_type": "code", "collapsed": false, "input": [ "startdates.plot()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 33, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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dcytsY2n0mbiWgLfmFQOsxDVe087EZVqtsliJs4Yde+sVTYNXXPy34fMIkN1c\niVMLL2qZMdykeXviBAiCMdwkZuFFmsV2Snf8O7Lj85oah/F2F8bbXLZN4lRNx1xSse35iaSiIer3\nlAw2qbjsWxJZiXMIubid0s8kjgzG83/xtuQRXTOQopzi9tJVU4nLG14UsXjhd66dkmfiiIgoj22T\nOEXToepAfAVXHxvZe5vKtVOWVuIqTadM8ZdwQzXvTJxe2k7JJK7p7NhbX1yJd/tgk0a0UzblTFze\nvs+wz7Oi30MrfiyqDlGAa9aY2PF5TY3DeLsL420uWydxADCXtHYyWCVJ2ajElVv2XX46pcc1v4Sd\nruxgE56JI2TPxC7+25A8QslrhJsY0ynNbadsBjmvDTTgtXaFiKzpiPq9rmmnJCKi2tg+iZtdQRLX\njDNxqeJl32rlShx/CTdW8/bEFVbiIqzEWcKOvfWKWlSJc3k7pazp8OZd8PCaUJls2p64TF9syOIV\nIrKqI+p3z0VAOz6vqXEYb3dhvM1l2yQue7V2pcNNGiV3Jq5MJa7cYJOgJLrml7DTpVUNkrfwTJzV\nu6TIHpS8NjyAg01SSuEk19VYiQtK1l6Ak1WjdZ9n4oiIKJ8pSdzBgwexd+9eHDhwoO77KpFz7ZTL\n/8XV6D1xUb8XsqoXDF+pdiaOlbjGsupMXMTnZSXOAnbsrS85E+cRXX0mLi5rCPs8udvSKjkTl98y\nbSRx1rZTtgSsrQY2kx2f19Q4jLe7MN7mWnYSF4/H8YUvfAEDAwPYv38/9uzZg3379mFoaKjm+6pZ\nDZW4gFfMnHnJS+L0ykkcK3HOUDydMszplJRRvCfS7YNNFtJqQRLnEQUoNp04nC+77BvI7Pi0uJ2y\nxe/l7w8iIirgXe4XPvroo5iYmMDrr7+OtrY2AEBrayuOHz+OWCxW0329vb0Vv7+iGecAVjLYpNFn\n4gJeET6PWHB+QqlwJs54I2C80X/06ChSqo6P7FzbsMfnRs3qtU4VLfv2ewRoemYsude2HcqOY8fe\n+tJKnGDpomirFSdx3lWzJ07LS+KsXQ+jaMb5a7fsGbXj85oah/F2F8bbXMtK4oaGhjAzMwMAmJ2d\nhSAYv+xEUcTU1BSSySTEzIS2SvdNT09X/RmKqqMjKGHOplP/koqGgCTC5xEKBhdUXfadeSMwOJ0s\n2S9Hq4cx2GQxWRMEIbdmgEmcuxVX4nxub6dMqwhJhYNNVsOeOFnV89opPRhfSFv2WNKZwSbTCdmy\nx0BERPaZGmC7AAAgAElEQVSzrHeczz77LN71rncBANLpdC6J03UdiqIgnV78hVfuPk3ToCjVK2yK\npqMj5F1RJa7Re+L8eZW4rEorBgKSmLuSenk+hZH5VMMem1s1q9daVrWC4RVAZs0AWyqbyo699eUq\ncW4ebLIgl2mnXBVn4vSCSpyVVTBjOqV7VgzY8XlNjcN4uwvjba66K3EvvfQS3vrWt+aSsnA4nKuq\n6bqOaDQKQRCq3gcA0Wi06s8ZuXIF3T09mIzLuaBny7C13s5a7tdXuz05E0DA2wvJI+DFl15Bt1/H\n7t27oWg6zg2cQf/4yYLPT6hAUmkBAFyYmIesLb7Ra8Tjc+PtrEb/vPOXhtEqacAta3Mf19OBXBJn\nl78Pp9/Ossvj2b17NxRVx+WRIfT3D2L37t2QPCLODl5A//yALR5fs28vpDUcffUl+ETjtlcU8PrR\n41g4py77+x87dqzhjz+thHKVuEvnzuDivBfARkv+PpNpGSODZ5BAtyU/v9m3mxFf3rbPbcbbXbcZ\n7/pvh0IhVCLoen2nzB955BHIsowrV67g5MmT+MQnPoGnnnoKDz/8MB566CE88MADkGUZTzzxxJL3\n7dy5s+zPOHDgAH40046usA+Hh+fwvz5wbT0PsSl+/Ttv4Is/sxX/98Hz+MO7NmJbl/GX/GcHBnHn\n5jbcfVV7wefLqob7/+F1fP9jN+P9/3QUXo+Af37gekT9XisevuPpuo6TY3FctyZs+vf+m/6L2NoR\nxH3Xdefu+6MnB/DBG3twW2+L6T+PVo9/evUyAOA/3roOAPAvr11BUtHw629Zb+XDsoSq6fjFbxzB\nk79+S65b43P7z+IXr+3C2za2WvzoKss+7h993Pj99OKFWfzw1AT+689eZcnj+cVvHMGf/uxV+IdX\nRvA3919tyWMgIiJrHD58GPfee2/Zj9XdTvmRj3wE99xzD1RVzf1i7urqwt69e9HX14edO3fi9ttv\nr+m+ahQN6AhJK1ox0EhJOTvYpPBMnFyhnVLKXNUdnkuhOyJhXdSPK/PWnbNwuivzaXx2/9mGfO+0\nqqP47FvE58G8Tc9vUvMUrxhx82CThbSKkOTJ/Z4AzGmnbLS0qhWcawxJIuIWtTLqug5F1dHid8+K\nASIiqs2yykCbNm3CZz/72dztbNkv34MPPljTfZWomo7OkBdzKQW6rhe8EahVf39/2cdmhpRqJHF+\nb+mZuHKDTQDjgPy5qQTWRv0IekVcnk9he1flMinVJz/ek3EZC2m14t6+lUgXTacEjDUD3BXXXI18\nfi+XounwejjYBCg9DweYM52y0XGXiy7SBH0ey/bEKZoOUQBCksc1Kwbs+LymxmG83YXxNpdtR+nJ\n2uKSWDv+8spW4oqvtFdLGgJeEeenElgX9WFdix9X5liJa5TJuDHJbSWDcSoxhh6UVuJiafN/Fq0u\n5Spxbk3iiidTAqujEierhRNGg17RsiqYohmvNQHJusdARET2ZNskTtV0eAQBLQHvstvUGpXtq5oO\nVTemlxnTKQtXDHg9FZI4ScS5qSTWRf1YG/XhMidUmio/3lOZJG62AUmcXK4S5/Ngge2UTWXHq3ll\nkziXTqdcSGsNqcQ1Ou5pTSu4SBOSrKvEZZeOByUPkhY9hmaz4/OaGofxdhfG21y2TeKyZ8ta/Ctb\nM9AI2fUCgiAYZ+KU2itxg9MJrG3xYV3Uj8s8E9cwjazEpRQd/rKVOHe8yaLKileMSGLhRR43KV70\nDRhJnGz3SpyiF6wQCVi47DtbFfR7jL+3lSbARETkHLZN4tRMRas14Fl2NSU7qtNsSUXLvYkvW4mr\nciZuYkHGuqgf61p8uDzHSpyZ8uOdrcQ1Yll8QlYRLHpzGvEziWu2Rj2/V6J4sJHPK0B26WCTuKwi\nVPQ88Zh0Jq6RZK2w0h7wGnvitPoGOZsiWxUUBMF4HDY8WmA2Oz6vqXEYb3dhvM1l2yROya/E2axN\nLaVoCEj5SVxtg00CmcPy66I+rIn4MLEg88pqg0zGZayJ+BrSThmXVYSl4kqcl4NNqGwlzq1n4hbS\nKsJSYRInrYIzccVnXj2iAJ9XRMqCBErOWzoesHjpOBER2Yt9kzjVeDMUDXgxn1reG/FG9d4mM+2U\nACB5hZJKnFSxEici4vMg4vdC8ohoD3kxtsCWSrPkx3syrmBLR6Ah7ZTxtIagVFqJ44qB5rJjb72s\ncrBJVlxWEfaZP9ik0XGXVa2gnRKwbs2AnLfuIOD1IKk4/zXGjs9rahzG210Yb3PZN4nLXNFuDXgb\nUk1ZiaSi5apqfo9Y0C6lLFGJWxv15W6vi3JCZaNMxWVsaQ9idpkXAKop1yYW9nHFAC22gWe5e0+c\nVvI88XvFgjPEdpRWdfiKzrwGJdGSwSJyXlUwyAmVRESUx7ZJnKpn2yk9y1743cgzcdkkrnjZd/Uz\ncSLWtfhzt5s9oVLVdHzmRwO4MJ1o2s9spmy8U4qGlKphQ6vf9EqcoulQNB3+oiv1EZ8HMVbimsqO\nvfWyphVV4kTbD/JolHKDTVoDXsys8DnZqLifuBKDruslKwYA4zyzFZU4Y8WA8ViCXusGrDSTHZ/X\n1DiMt7sw3uaybRJntCWJiPqNhd92ksprp/R5xJqnUwYlD9YVVeKaOdxkIa3i8PA8Pv3kWVycSTbt\n5zbbVFxGR1BCW9D8Kq6x+8pTsnw+kqnE6RYMPyD7KD4T53NzO2XmuZKvLeDFTMJer+eAEbdP/XAA\n4wtypp2ytBJnRRWs+EycVasOiIjIfmybxKkmtFM27EycvFiJK26XqjbY5P3Xd+ODN/bkbve2+XFp\ntnlJXCytoifiw6/dtg6ffnJgWZPO9p+exHRm8qPdZOM9GZfRGZIy6ynMfdNjtFKWPm18XhEQ4Npx\n8lawY2998XRKY7CJ86sn5ZSrxLUHvZheYRJXLu6yquHo5fllf88r82komo7JuJxppyyqxHlr3xX3\nl89dQP/gzLIfS778yq6xK875/5bs+LymxmG83YXxNpdtkzjjbBkaUk1ZqZSaV4nzlq4YqDTYpD0k\noT0o5W73tQZwqYkVsfmUgqjfg/fs6MSmtgAOXZyt+3v865FRvDG20IBHZ56puIyOkLch5ykTculQ\nkyzuiiPj4tPiy6qbB5sslBls0hb0YiZR/0WgY1di+Oqh4aof/9y/n1v2+cPhOeN1eDIuVxxsUksl\nTtd1HLo4hzMT8WU9jmL5kzIDLmmnJCKi2tg6iZM8ovFGfJlXbht2Jk4uPhNX22CTYhta/RiNpZs2\ncns+pSLi8wIA7rmqHQfPTtf19Yqm48p8KreDzW6y8c5V4gLmt+LGy4xNz4r4PFjgubimsWNvfXE7\ntZsHm8TTWplKnLSsM3FPn5nCayNGpa1c3M9NJpCQNRwZWV41bjjTETEVlyFrpYNNam1lHJpNYSap\nmNauLqs6fLlKnDtWDNjxeU2Nw3i7C+NtLlsncR5RQFummmKns0YlZ+JqHGxSzOcR0RX2YaRJ5+Lm\nUypa/Mabqjs3t+H1kfm61jeMzqeg6sCUDc+05DMqcRJCkrGjy8w30XFZQ1Aq/7Thwm9SVKODIMvn\n8sEmxWfiIn4PErJWV4uppus4dHEWY7HKk3zPTSWwqS2AFy/U310AAMNzKbQGvJhckJFWylXiPDVV\nwY5diWFrR8C0Nvn8qqAx2ISvL0REZLB1EufNLFmVPMKyJoM1ck9coMJgk+LBBkvpa/U3raUyllIQ\nySRxYZ8HuzZE0X++9jc9w3OLV6vtqPhMnCAY003nTTwXV269QFbY50Esbe8E10ns2FtvtFOznRLI\n7okrfK6IgoCWgKeuNufT43FE/F6kVR0LabVs3M9OJvDLO9fixYuz0JZxwW9oNoWb1kUwkanEFQ82\nCdS4J+7o5Rh+7uouXJ5PmdJhkf9YApLHFSsG7Pi8psZhvN2F8TaXbZM4TQeyF0NbA8s7R9EolVYM\nqJoOXQfqyOHQ1xbApdnmJHHzKRVRvzd3+56rOvDs2amav354NoWeiGTbJC5rMq6gI2ScPWwx+Vxc\nXNYQqlCJi/rtd36Tmqu4nVrKLLdeTmKxmum6kXCVu+DRHpTqGm7y4oVZ3LGpFWsivrLVuLSqYWQu\nhTs3tSLi8+LN8frPow3PpnDT2kjmTFzpYJOQ5CnZEzc4lSgYDqXrOo5eieG23ig6QxKumLA+Rsmb\nTumWdkoiIqqNbZM4ryjkxri3BZe3W6ihe+KkxcEm2dagbBWuePx8NRvbArg406x2SmOwSdZb+1pw\nciyOeI0tgEN5b3TsRtN17H3qJwCMSmFnJolrDXhNXfhdbmx61tqID1fmuby9WezYW5+/1wsABEGA\nJLqvGpdUNEiiULYrwZhQWftryIsXZ3HHRiOJG42lS+J+aSaJtS1++Lwi7tjUWndLZVrRMJWQcd2a\ncGY6ZWk7ZbBMJe4vfnwBjx4by90ejaWhaTo2tPgzQ6tW/rouq1puUFbQ645l33Z8XlPjMN7uwnib\ny7ZJXP7V7EZMGVyJwjNxAlKZJK6eoSZZfW1NbKdMq4jkVeJ8XhEdodpHfg/PpXDj2ogtK3EDEwl8\n40IQz5ydwlRiMYlrCXhNXfidkNWKZ+LWtTR37x/ZT7nXAKOlcnW/+f7UE2cwVEfHQLmhJln17Iq7\nPJfCbFLBNT0hI4krc5Hk7GQCWzuCAIC3b2qte+ruyHwKayI+rIn4jMEmaulgk2DRYBNN1zE0m8K+\nN8Zz7fRHL8dw49oIBEEw7XW9sJ2SZ+KIiGiRbZM4qSiJW86C2KadictcZa9nqElWds1AMwa3zKXU\ngkocALQFpJpbVYdnU7h+bQSzSQWqzYY1jC2ksbk9gP/54jCSspb7c7aa3OIYl7WKZ+LWt/gwMsdK\nXLPYsbe+3IoRySOu+krchelkXVXmhSpnR9uCUs2v50dG5rFrQxSiIKAnKmE0li6J+9mpBK7qNJK4\n7V0hjMyl6lqKPTybwoYWP6J+D5KKhlhaLYlhsOg82nhMRtTnwfauEJ4emIKq6Xh+cAY3rosAMDos\nzGiTl9XF3ylhnwcxF0y/tePzmhqH8XYXxttctk3i8q9mtwUlW1fiitsp69ES8MLnETEVb/yfL5ZS\nES16Y9Ua9GK6hr/bbMvRhhY/Iv7ltbc20sSCjBvXRvDQ3Ztw47pIrqU1GvCYWomLy5XbKde1+E05\nB0OrV8VKnM0uetRD1XTMJpW6LqSVW/Sd1VZHO+XgdDJXZVsTMVayFDuXV4nzigI2tQdxbjJR82Md\nnk2ht9UPQRDQEZRwZT5dMtjEaGVcTKAuziTR1+bHh27swaPHxvCnBwaRVDTcu60DQOass1ntlJnW\nzqs6QjgzEbfVpGYiIrKObZM4rwmVuGbsiZM8ItJKphKn15/EAcYv/ItNGG5inInzFtxXa2vT5UzL\nkUcU0Bny2q6lcjyWxsLECG7rbcGXf35b7n6jFdfE6ZRpteJgk86QhFharasKQMtnx976cpU43ypv\np5xNKtAz/61VufUCWe11nHEenEpgSy6JMwab5Mdd13Wcm0rgqsznAMD2riBO17Fse2g2hQ2tAQDI\nDSQpGWziK6zEXZpNYmNbADeviyDs88DvFfFnP3dVLnHta/Xj0mxtHRYvXZrFwYHyA6bSee2UnWEJ\nQcmTmxJ8cTqJv39xqOY/JwDT9tc1kh2f19Q4jLe7MN7mWhVJXFvAXpWfhLJ4LsrvFXN7yIwdUfUn\ncRubdC5uPqXmVgxk1To0ZijTcgQAHSH7TaiciMto8Za+UW7xm7vwOyFrCFZ4cyoKAtZG/Rxu4mLl\nXgMksXCX5GqTrZrV8xpcbr1AVrXplClFy+2u1HUd56eT2NKeSeKipWfixhdkSKKA9swZWMBoqTxT\nTyVuLoUNrcZrW2dYwsSCXLpioGioyKWZJPraAhAEAX/13u146O5NBefoWgPGxbJa/s4ODkzjxQrn\n+IonZV7bE8IbowsAgGfPTeP5wZka/5TA5IKM33jsJCYW+PpEROQEqyOJC9rrTFz+G/n8FQPLORMH\nGJW4C9ONT+JiRdMpASNBnq2htangjY4Nk7jxhTR277qh5P5WkwebLMgqwr7KT5t10eYtb3c7u/XW\nZ8+Jlh9sspqTOOP5M1tXO6VW8XnSVmVlzHePj+Gvn78EAJhJKNB0HR0hIyFqD3qxIKu47W1vz33+\ngYEp7NoQLfge27tCGKijEjc8m8xdoOoMSdCBsisG8oeKXJxJoS9TvZM8YslEYkEQap5QeXJsAZcr\nnKVV1MLK7nVrIjiRSeJeujSHibhcsKe0msHpBDQdePbsdE2fbxW7Pa+psRhvd2G8zbUqkji7TadM\nylquEmcMNtGg6/qyk7jtXcZZh0ZKKxo0Hbk20KxahwwY50aMNy0dQQmTy0iqG2k8JqMr7Cu53+x/\nO9UqcQCwnhMqXUvVdHg9pc9/n0dc1e2UU3EZIUms63kUr7AjDjAqcZVecw4Pz+PIyDxUTcfgdAJb\n2oO5BEkUBPSEF3fFJRUN3zsxjv/j5jUF32NTewCX51IFO9wqSSka5tMqusJGJS871bbcioHiStzG\ntkDV793b6sfwEm3y0wkZk3EZlyucpZU1raAqeN2aME6OLWAqLmNkLoW10drXmgxOJbC1I4CDNk/i\niIioNvZN4jyFlbjlvBFvVO9tXFYRzCRD2avuql75TdxStnUGMTidbOgbvfm00UpZfMW41nbK4eJ2\nyoXmVuISslox0dV0HVNxGaePvFzysZaAx9R2ymp74gBjuMkI2ymbwm699XKFiziSR0DawsEmCVnF\nb33vVK7tu14zCQWb24OYSdb+nF9IqwhXeJ60Zl7PixegpxQNpyfiaAl4cHoijsGpJLZ0FCZKPREf\nDv70NQDAk6cmcF1PGJvbgwWf4/OI6GsL5IabVKtUjS+k0R2WIGZeFxeTuHIrBozvM5dUkFa1XIWw\nku6ID+NLvE6eHFvATesiUDU910aaT1YL9w5u7Qjiynwaz56bxq4NUfS2+jFS4zCl89NJvPfabkwn\nFFxsQufHctnteU2NxXi7C+NtLvsmcWUqcXaYyqXrembZ9+IbFJ9HRFrRKr6JW0pQ8mB91IfBBv5i\nLTfUBKh9sEl+O2VHSMJkHct6zfDEyQn85XMXyn5sJqEg5PPAW+Zfc3tQwmzSvGEjcVmrONgEMNYM\nsBLnTpWm01q9J24slsaZiQReujS3rK+fSsjY3BGob7BJlRUDXlFAyFc6NfbEaAxb2oN428ZWHB6e\nx/npBDYVJWhroz7Mysbf53eOjWHPLWvL/oxsd8Nzg9O4/x9fx8e/8wb++bUrJZ83Op9Gd14FP5vE\nFbdTZpO6tKphaDaVOw9XTWdIwuQSbecnRxdwbU84s2Oy9OJP/ooBwPi729EVwrdeH8XtfS11Vf4H\nM6sY7t7ahgNnyw9SISKi1cO2SZwn7xekzyPC5xGwkK7vjXgjem/TmcEF+b9YjXNxGlRNL3jc9djR\nHcLp8ca1VMZSKiJl3lTVUolLyCpiKSXXctRhwXTKg2encX46WTYZm1iQ0R2Wysbb7xWxoyuEY1di\npjyOeJU3pwCwLuqv2BpF5rJbb33FSpzFg00mFmSIAnDgzPLeuE8nFGxpD9Z1Lnl4NoUWf+XnSXtQ\nKnndeW0khp0boti1IYrDw/MVK3H+7l58+dkLuKY7hB3dobLff3tXCM+em8ZXXhjC39y/Aw/dsxl7\nX7tSUo0cW5CxJlKaxEli6a/GoCQiKWvGeoHMBa1qakni3hiLG0lc1F/2LK3RTln4b+q6NWFMJxS8\npa8Fa6P+mnZTqpqOSzNJbGoL4F3bOvCMjVsq7fa8psZivN2F8TaXbZO44l9ctbb9NVoir5UyK7vw\ne7ln4gDg6u4w3hxfMOMhljVfZtE3YExvjKWqL+8emUthXYu/oOWomUncxekkphMKruoMYqDM1Dmj\nJar0PFzWbb1RvDI0v+LHIasaNE0vuUqfb03Uh/GYbLtl6NR4lSpxPosHm0zGZdze14LXRuaXNeRn\nOiGjt9WPtKrX1JL5/OAMhmdTuGtre8XPaQt4SyZUvjY8j53rI7hxbQRnJuK4MJMsaZVcE/HheyfG\nAQCfvmdzxe+/oyuEE6ML+M07NuDq7jB2dIWMDoKi9sbxWBo9+Ulc5kKVz1sax5DkQVxWc5Mpl9KV\nmXRZiarpODMRxzXdIaxvKT8QyWinLPx9c9O6CK5fE0Z7UDIq/zVcNLo8n0J7SELI58G2ziDmkgpi\nJraZExFR89k2iSue8NYa8NY1HQ1oTO9tQikdbOHzGoMLlGWeiQOAq7tDeLOBlbj5lIJooLSd0iMK\niPqrT3DMXy8AGINNZhLNa289eHYKd29tw3U9Ybw5Vproji/I6ApLFeN964YWvDpUuZXssWNjNbVb\nJmQNIV/pucJ8Po+ItqA3N3yBGsduvfXFrW9ZVrdTTsZlbGoL4LbeFjxXx0j6rOm4go6QhJaAZ8mW\nyum4jL/9ySV86p2bSoYo5WsPFk6onE8puDSbxDU9YQQlD3Z0hdAW8JasKbh5fQR3d6XxmXs2F4z0\nL7atK4i/vm877rmqI3dfd0QqeV6OFiVxIUmE3yuWrcQFJBHfPT6Ol4bmakvilqjEnZtKYE3Eh4jf\na7RTlknGZFWHr+jf1K0boviLXzB2YVaq4BUbnEpic7vxmAVBqGsgSrPZ7XlNjcV4uwvjbS7bJnHF\nC3PbAqXtN1bIn0yZlV0zUOlKfC22dARxeT5d19mtc5MJ/Oq338DTZ6ZKhgQUm0+piFZoA1yqymlM\nplxM4nxeEQFJxFyq8UutdV3HM2en8a5tHbi6O4xTZRLdiYU0uiNSma82bOsKYi6llk2sJuMy/veh\nYfzTq5eXfCxxufpQk6z1LbW9sSJnqXwmToRsYWV2YkFGZ9iHe7d14Kkzk0u+VhSbTshoD3qN1+Al\nLqT9w6uX8e5tHbhuTbjq5xVPxX39cgzXrwnnErNdG6K5pCNfd9iH3Z3ykvs4RUHA9WsiBff1hEsH\njYzF0ujJe+0QBAG//fbeXEUu34dvWgO/V8RdW9pwy7pIyceLtQa9WEirFauXr4/M49oe4+9pfYW2\nyOLplNnHmL1vXYsfo7H0kpX/85lJn1lrIn5c4YUmIqJVzbZJXLlKXLlEI55W8cSpibLfoxG9t4my\nSZyIVGawyXKWfQPGgfWtHQGcmah9Se1PLs5iY5sf339jHH964HzVz50vsyMua6k9fMNzKaxvLXxD\n1RFsTkvlqfE4PKKAbZ3BitXK8QUZXSFfxXiLgoBdG6Jlq3EnRmO4rieMAwPTud1SRuI4hV/79hu4\nML0Yj3i6NPblXNsTxreP1lbdo+WzW2+9rGkV2imNs1RWmYzL6ApJuK03Co8g4KEfDpQsza5E0XTE\nZQ0tAW9N6zqOXYnh3m0dVT8HANZEJAznXeh4bXget6xf3Pd2/3Vd+OQdvWW/drlx7474Si7kjBVV\n4gDgZ3d0lo3ju7d34ONvWY9f2bUOkTJDooqJgoD2YPnzw5fnU/jW0THcd10XAGBdhYFIxdMpiwW8\nIqJ+z5Jn7wanktjckZfElVmcbhd2e15TYzHe7sJ4m8u2SVzxL9HWYPl2yjMTcXzlhUuYbtIZrYSs\nIuAtaqc0oRIH1H8u7tWhOdx/XTf+/Oe34eWhuapX2GNpteIbj7aAt+r48OGidkrAOO/RjJbBU2ML\n2Lk+CkEQsKHVj4W0WhLr8YXCq+nl3LohileGS8/FnRhdwNs2teDjt6/Hf3/uAv73T4fwu4+fxrde\nH8Pm9gAODCwOAEjUWIn72K3r0B2W8EdPni177kTRjPOT5CyqVnrxCTD2hV2csW6k+2RcRmdYguQR\n8d9+cTtu623Bb3//TQzPLl0tnknIaAl4IArCkhd7FtIqxhdkbCpTQSt2y3pjeEnWayPz2JmXxEX8\nXqxvWXp4SD16whLGFxZfszRdzwxFqnyedqW6wqUtlaqm48+fuYAP39SD7V3GYJbusA+zKQWponUI\nSyVxQLaKVz2W56cTBUNi1kTs205JRES1WTVJXFuFStxoLA1NB545VzptqyFn4spU4qTMwu+VDDYB\ngGu6QzhZ5sxXOQtpFeemErhxbQQhnwdRnwfjscqJWKXBJsDSC7+H5wrbKQFjmuYbNT7WlTg/vXiW\nQxQE7OgOlbRUZhd9V4v3rRtacGRkviTRfWN0AdevieA92zvwzq3taAtK+MjOtfi7912Nj+xci2fP\nTefO/hlj05d+ynhEAX9w10Z0hLz40enSiYCPvHYF3zy8dPsmVWe33npFK/+GOzvu3ioTC3Ju6qJH\nFPDhm9fgIzvX4svPnl/yrN5UQkFH0PjaSq/BWWcn49jaEaipG2FrZxDzKRWj82lMLKQxmzQGF9Vi\nuXHvifgwlvcaOZ1QEPZ54K9ydm+lOssMU/n+G+MISCI+cGNP7j6PaCwyv1J0Lq5SdTefcZ6uckKW\nUjSMxdLozeumWBv1YTRmz5Zvuz2vqbEYb3dhvM21apK4Sq08Y7E0rukO4elljs+uV0JRy56Jk02o\nxN24LoJjVxZqGhjy2vA8rusJ596A9Lb5cWm28tX+qu2UVXbFzaeMxbbtwcIq3o1rIzhxpRlJXKKg\nDeia7lBBtTK76LsrVL0S1xmWEJTEgl1MCVnF+ekkdnSFIAgC9tyyFh++eQ3eurEVHlHAVZ1BeEUh\nlzQmZK2mShxgJJw3r4tipEy14/iVmKVv6qkxFE0rm8Bs7Qzi0kxy2cu2V0LVdMwmjcEk+f7DdV1o\nC3iXPAs6HZfRnknilmqnPD2RyFWWlpJrcR6ew2sjRiuluMz1LLXqDvswlleJG4ulsSbauCocAHSG\nfJgoqsS9eGEWH7ihu+TPu67FV3Iurtx0ymLrltgVd2psAVs6ggW/m9bauJ2SiIhqY0oSd/DgQezd\nuxcHDhyo+75Kit8MVTpbMBaT8Z4dnZhJKAXnl4AGnokraqcM+zyYTynLXvad1R32IewTcaGG1qtX\nhr20Mu4AACAASURBVOdwa29L7nZfawCXqnydUYmr0E5ZZbBJtpWyeCLjtT1hnJ6IN/SNqa7ruDBt\n7DbKurrb+LlZs5lF3z6vuGS8t3WGMDC5+LVvjhuVg0pX4gVBwN1b2/FspsobT5cm8NWsa/EVnPsB\njDfVpyfiOFdmVQLVx2699YqmlwxkAoxzS+tb/Dg/ZW5L5eBUAr/67RP4xssjJRWcrJmEcfGm+HVJ\nEAT8X3dtxL6TE1UTs+mEkruAU66l/cvPnMcrmbOmZybi2FFjEgcYLc6vDs9nVgtEl/6CjOXGvSci\nYTxWmMQ1spUSyLRT5lXi0oqGN8fjJUNXgPI7JmW1/L+pfOuj5c/TZR29EsNNawt/3pqID1di6aZN\nGK6H3Z7X1FiMt7sw3uaqO4lLp9N4/PHH8dWvfhVPP/00BgYGsH//fuzZswf79u3D0NBQzfdVU9yW\n1NsawFCZqsZoLI21UR/uuaodTw80foFpUtYQKHoj39cWwMWZpLHsewVJHGBUuI5err6YWtd1vDo0\nj9t6F9/49LUFcGmm8i/y6u2UXkwnyrdiDs+lsKHMYtuwz4PeVj8G6hjEUq+xmIyg5EFL3mqETe2F\nf87R2NLn4bK2dYVyw0sA4zxcuTdT+e6+qh0/PjcNVdOxkFkxUKsNZcaGX5xJoiMoIa3qFf/OaXVS\nqjz/d3SHCi4+mOHMRBxrIj4kVQ0Pfv80zk6Wfv/J+GIrZbG2oIQdRZXtYtMJGe2h/HbKxX+zsZSC\nH5+bxvcze9vOTMRrrsQBiy3Or43EsHP90tMeVyq7rmAhbQwcque1Y7k6Q1JBJe7U+AI2tQdKVicA\nxtnJ4os7coUW3cKvC+B8lQt4Ry/HcFPRNM2I3wuPIGC+CROGq0krWsk5QCIiqk3dSdzTTz+N559/\nHh/5yEfwjW98A6+//jra2toAAK2trTh+/DiOHDlS033VFL8Z6o5IiKXV3C/grLFYGj1hH961rR3P\nF+1AatyeuMK/tk3tAVyYTq74TBxgLHJ9fYkk7tiVGFRNL6hQ9bVWb6eMpRREKq0YqDI6vNxQk6zr\n10Rw/Er1x7oS56cTJWPG10R8mErISGd+8Q/NpnJnPZaK9/bOIM7kvUk6MRpbchT6xrYAOoISjl6O\n1TzYJKsn4sPkglxw7ujU2AKu6QlhS0cQg1Osxq2E3XrrjUpc+ZfURpyLG5pN4ca1EXzybb148M5e\n/PH+cyUVmYl4umISBwDXdIdxaqzy48qvxLUVtVO+PDSPG9ZG8MbYAi5MJzCxIGNjDfvTsjrDEjpD\nEkQBdQ0xWW7cBUEwWioz1bjxWBprIg1upyyqxB0ZiVVcT3D31nb85MJswf48WdWq7sMDgKsyy7vL\nDapJq0bl74a1pT9zTdRn+ZqBf3z1csn5YLs9r6mxGG93YbzNVXcSd9ttt+H9738/wuEwfD4fHn30\nUYiZNy6iKGJqagpzc3NL3jc9Xb1q5i1q3xMFwUhU8q44arqO8cyOsM3tQUwu1LdnbTkSslqy7HtL\newDnzUri1kZx9HKsYpvLk6cm8F8PnMfv7O4raHHsawtUTOI0XUcsXV87ZfbnG0NNyr8xu3FtGMdH\nG5nEJUuSOI8oYE3Eh5FMhevSTBJ9ZSqF5WQrcbquQ1Y1nBqL4/qe6kkcYIwWf2pgCvG0ilAd7ZSS\nR0RHqHCK56nxOK7pDmNrRxDnTG6vI2spauVKXKOSuA2Z5+ZdW9rx4ZvX4M8Oni/4nIkFGV1ldp5l\nVVrbkVVwJi5YmMS9eGEG91zVjnduacffvTiErR3BujsRbuttwa4N0ZJ27UbpjixOqByLyehucBJX\nvPDbqIqVbx1tD0l4x5Y2/OCksTInu/ttqb9Tjyjgzs1teG6w9Hfq6fE4elv9ZSt/xoRKa4ebvHhx\ntmoHCRERVVZ3EtfT04O3v/3tOHnyJNra2nDXXXflPqbrOhRFQTqdrnqfpmlQlOr7hrxlWkiybYtZ\nMwkFQcmDoOSBRxTQ12ZUxLKatSdubdSPmYSM+ZS64iRuTdSHgFcsO5L8laE57H19FH/13u1428bW\ngo91hSXE01pJpRIwrqZH/d6KbwbKDTb55L+9iQ9+8yheOD+DvrbKlbgTowt1Lw+ulTEWu3Ri3YYW\nf6619lJeJW6peHeGJHhFAeMLMg5dnMPWjmCuVayae65qx4sXZjEZl0sS+KWsb/EXnIvLVuK2dgRw\njpW4FakW77mkgv/ywzNNfDTIXMQp/7GtHZWHm6iavuSy5nKGZwsvYNx3bRcuzSYxn7fWwlgvUDlR\nuabHWGtS6aLRdEJBR14lLvs6IasaXhmax1s3tuLnr+nEkZFYXa2UWR/duRb/6a0b6vqalbyu50+o\nHG1GJS7TTqnrOlKKhjOTcdywtvKFow/e0IPH35jI7R1d6jxc1ju3tOG5ok4UoHwrZZbVw02GZ1O4\nPJfKXZDL4pkZd2G83YXxNtfSG0vLSCaTOHjwIL7whS/gK1/5CiTJeCOs6zqiUeOqarbSVu4+AIhG\nqx9kHxm6BNy2HsBi+bWvbRsuzSRztzt37ERPRMrd3tzRh8HpJCZOvwZg8R9L9uNm3E7IKi6ePYP+\niVO5j7/4kxfQ7g1gcCqBHV2hFf+8tZ4F/Fv/6/i9+95W8PHDwibcd20XLhx/BRfKfH1vaxcuzZT+\n+X/w/CtoFxeTleKf9+qhF6GooVyV8akf92NoJoRvPnAj5lMqLhx/BeNvln+8EZ8H3zv4E/T4dVP+\nfs9NJvCDF1/HrjYF56e78P7re0o+X4hN4oXXJ7B781txaTaJicGT6L+s1fT9t3WF8Pjzr+LIrBf3\n79pa0+M78eohbPD58eIFDXdsaq3rz7O+xYf+IyeRvqDg1rfegZH5NK6ceg1zSRHn5jtW/PfF2+Vv\njyYFHL0cgqrpePEnLzTl5ys918LrESt+fENrJ85PJTH25uGCj//lvkMYWPDi7x+4FR5RqOnn6Tow\nMhfBhlZ/wcd3dIXw6DMvY3tExe7duzG5IMM/fxn9CwNlv19nSAJUGfue+Qnuf9edBR+/8847cXk+\nhfOnjmL+nI4777wTaVXHs8/142JCRG9rJzpDEk4ePoR1gQCu6QnV/fcX8nma+u+jO+zDq6fOonXi\nFMZiLeiJ+Br680I+D3RNxYHnXkDn9puxpT2IVw+9WPHzN7YH0ONN4n89+RJ+/T23Qary7yn/tqYD\nU/FWDM+mMHjs5dzHj16JYTvG0d9/vuTr17TtwNBsyrLn63jbDuze0oYXBqfx/PP9eMc77PP6wdu8\nzdu8bZfboVDlC6SCvozxVF/5ylfQ29uLZDKJlpYWvPTSS3j44Yfx0EMP4YEHHoAsy3jiiSeWvG/n\nzp1lv/+BAwfwprgee25ZW3D/c4PTODAwjYd/xnjz/eNz0/jxuWl8/t3G7W8fHcVkXMYn39ab+8Nn\n/yLM8rn9Z/EL13Thjk2FlbA/f/Y8jozE8N5ru/CRnWsrfHVt9p+exOHhefzRPZtz9+m6jl/51gn8\n2c9ehU3t5fcpfemZ87itN4qf2d5ZcP93jo5iYkHGJ+/orfgzf+Oxk/jDuzZhR3cIh4fn8M+Hr+Cv\n7tux5GP9y+cuYHtXCPdf113Tn20p/9/LI/j266P48i9sw+f3n8W3P3pjSfXrBycncHo8jt/d3Yf/\n8I+v49FfuQkBr1hTvP/hlRHMJBX8+NwM/mXP9TVX1voHZ/AnBwbxJ+/ZWlIFreY7R0cxkfk3+frI\nPL7+ygj+5v6rkVQ0fOibR/G9j9284uqtW1WL92vD83joyQE8suf6hk8gzPrhqQmcGovjD+7aWPbj\nlZ4rn35yAOcmE3jf9d345RpfO0bn0/j9fafxL798Q8H933h5BKIo4GO3rgMA/NGTA/jADT14S19L\nuW8DAPiTp89h9+Y2vGtbR8H956cT+OP9Z/HND1+fa3d84F+O4a/v24FHDl/BhlZ/7jV6IdNq3Iy2\nyJW8rj91xnhtvb2vFd8+Oor/+b6rG/6YP/6dN/D5d2/Bv50YR6vfi197y/qqn98/OIP9pyfx++/Y\niP/83VP49kdvrOnn/D8vXEJ3WMrFRNF0fOibR/FPH76+YDhU1osXZvHEqQn86c9eVf8fygSffnIA\n7722C1954RL+7n1XoyvzPG3E722yL8bbXRjv+h0+fBj33ntv2Y/V3U75zDPPoL+/H//6r/+K733v\ne9i0aRO6urqwd+9e9PX1YefOnbj99ttruq+acm9sN7YVjtEfi6XRk9cOs6U9iPMNblFLKqXTKQFj\nuMlkXK65/aWabZ1BnC2aUnZhJgkBQtXBAcaZwdLzBYNT5dsS892wJoJjmSElZyYS2N5dW2vUzeui\nODIyv+TnvTI0h+8eH1vyDMbp8Tjuu64Lf3pgMLPbrTTJ2tBqtCiOxtJoD0oI1LGsd1tXCD96cxJv\n39RaV2vk7RtbEPV76hpsAhg7nLK74k6OL+CabqOVKuAV0RPxVV0LQcuXPbs1sdC8CaCKppdtA8/a\ntaEFP704W/I1p8YW8Oe/sA3ff2McJ2o8Y3ppNll2auy1a8J4I+97TMSrn4kDjOEm5c7F/fTiLO7Y\n2FqQ5LQFJPynx05haDaFe/OSvrDP07RzbSvRE/bhwnQSXzs0jN++o7cpj7krLOEfXrmMo5djBQu+\nK9neFcKZyXhmR1ztj++uLW0Fw71Oji1gXYu/bAIHWNtOGU+rODW2gF3ro1jf4sdIlRUJRERUXvlX\n9yruuece3HPPPQX33XDDDSWf9+CDD9Z0X8UHViYZWt/ix2gsDVnVIHlEjMXSBVPNNncYA0ayGpHt\nG3viyiRxbUaStNIVA4Bx9u/KfAppRYMv87NeujSH2/taqr7p6GsL4NmzpYfbz00l8b4bqr95uGFt\nGP3nZ/DBG3twZiJec7XplvUR/P1Ph6DpesHy2n989TJUTcd/vHUdjl6ex188ewG397Vg75FR3LAm\njN97x0a0Fr250HUdZybi+PTdmyAKQtm9gIBxJm54Lomh2SR6897I1hLv7Z0haDrwnu0dS35uPp9H\nxJd+flvJoJWlGGsGjH1MBwemC6qhxnCTpRNsKq9avOdSFiVxVZ7/b9vYgr/pv4i5pJJ7Yz0wEcfa\nqA9bOoL4xO0b8I+vXsZf/ML2JX/W8Gyq4N9+1rU9YXx5PJ5bdzK5UHnFQNbV3SF845XSpd8/vTCH\nj+4qrAx+6p0bEfV7Cy6eNdtKXtd7Ij4MTCbw7u0duL7MxMZG6AxJeG0khv9x346S17xyeiISZFXH\naCy95KLvfDeujWAsls5d3Hz50hze0lu5AtuT2RVX/NrdDIdH5nFNTxghnyeTxKVxk1E85lV6l2G8\n3YXxNpcpy74boVwy5POI6An7clftRmPpguliXSHjl99MA/dvGefGSv/asm/uzWiN83lEbGjxF+z+\neemikcRV09caKBmIomg6hmcLF2aXc8PaCI5dMQYcnK5jaW9X2IfWgLegcjg4lcAPTk5gYDKO3993\nGl965gL++N4t+MN3bsK/7Lke61v8+M/fPYWTY4X7qS7PpxGURLSHJHzybRvwqXduqvAzJSyk1Mzk\ntfqSqp6IhN+8oxc3VjjsX82OrtCS476LrY0aE+DeGF1AWtULxotfvyaMrx36/9k788Aoy2v/f95Z\nM5PMTPZksu8JAYJsYVWUVVxQrFq19na7dtW2t73eLr9e6+1mrd2s9Vpb11u3KqKICBhAlLAECBAg\nZN/3ZSbbJJNktt8fIUNCdsjO8/lLySTzJGfe933Oc77ne6r49f4Sdl5oEHPjxpFLlbjJqzSMlMRp\nlHKWhOlJL71ULTlba2H+xWTihhhvik3WUVVH+o7W6IvBQ4GvVklpk5VOu5Muh3PI+ZC9JARoKTJb\n+43CaLLaKGvuHGCKEeunndIE7mrx91SSHOjJv48gaRxP7kkJ4ve3xhOkG93fTZIk4vy05Na3j6kS\nJ5dJpEYY3NXek5Wtw8poPVVyQvVqTlUNraTonqAZblnVbSwO7emLN4pKnEAgEFwR0zaJG0qWGOHt\nQflFyWD9Ze5ikiQR5XOpGtfbIDie9MyJG8SuWadCrZCNSyUO6DdHrL3bQaGpgwUhw5vBRPh4UN9u\n6+dQWdHcSaBXz9qGo+c1ErkNHbR22geVag3FwpBLkkqXy8XfjlXxhYXB/GpTLGtjfXh0TYR7M6iU\ny3hoWSifXxDEu+fq+/2c/IZLyaMkSUOuWSZJhOjVZFS09nPOHE28JUnizrkBk3byrFHK8VLJ+b9T\nNdyS5NevknrH3AB+d0s8qRF6zte189V3cth2tm5S1jUbGC7erZ12/LRKGiazEucYecTImpie4fG9\nnKu5lMSp5DLWxPiwr9A84ntdXoXuS3KgJzn1HbxyspolocNX76HnMxrp7UFOn3lxJypaWRiiG/Oh\nxWRwNfd1pVzGn7ck4DsKV9rxItpXM6b7KUC8v4YL9e1jlueviDBwpKwFc4eN2rZukkcYoXJHsj8f\nXGgY9GtOl4svv3OBx9OKx30UQUGj1X2vD9Wr+iVxE/HcFkxfRLyvLUS8x5fp94S+yFDJUIS32l1t\nqrPYBlhER/lq+kkqx5vOQUYMQE9iEentMaaT0+GI8dNQfLG6daKilblBXiP2filkEnF+GvL7zKMq\nMVuJGaVcb16QF++dryfGb2zznq4L0XGmuqcP51h5K43t3dw2xx+ZJLF1XiCp4QOlmSsiDAPm4eU3\ndpAwyl68UIOavIYOwsdYiZsKQvRqztW2s+EyCadMkojw8WBDvB8/uSmKx9dHk17aMvgPEYyJlk47\nsX6afjO6JprRzIlMDdeT32ilqcOGw+kiu6693yDmDfG+7CswD2n538tQlTjo6Yt7+2wdx8pb+M81\ng5usXM6iUB2ZVa3u/z9a1sKKyOEr/4KJI85PS059+5iT6CVhOnLr2/m0uInrQnQj3sdvivPlQl37\ngCHxAHkNHWgUMuL8tXzn/Twe+7iI987XX3V1zuF0UWy2EncxiTPqRCVOIBAIroRpm8QNlQyFe/fM\n17J02XE4XQOkQlE+Hu4K1nhrb10uFx02x5DJ1MpIA0bd2E5ch6K3XwogvbSZ1VGj61FLCuiR4fQy\nlp6recFefFbSPOZ5TwuMXmTXWcgob+GPh8p5eFX4iJvZ3splXyOW/IaOUb9375DjvpW46aq1NurV\nrIoyuIcmD0WsX0/Mr2Rm2LXIcPFu6XQQ66txD3aeDEaTxKkVMlLD9bySWUNWTRveGkW/qlBigBaZ\nBBcukxr3pcvuxGy1ETyEPG9+sBetnXZ+vj4GnXp0bc+LQ3V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Z5a0sizBwndGLMyPIdmcL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\n9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"text": [ "" ] } ], "prompt_number": 33 }, { "cell_type": "markdown", "metadata": {}, "source": [ "##How much video did each person watch?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "df['nplay_video'].dropna().plot()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 34, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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I1y6ytY18bSNfeEFBCAAAAAAFioIQvkCvu11kaxv52kW2tpGvbeQLLygIAQAAAKBAURDC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\nFbpzS33MQo39m1Q7QisT2r3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"text": [ "" ] } ], "prompt_number": 34 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Who are the frickin' overachievers? We'll investigate in just a sec." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "How about: how many people enrolled per country?\n", " \n", " work with your BFF for 2 minutes to figure out\n", " 1. how to pick out the field including country\n", " 2. how to count them" ] }, { "cell_type": "code", "collapsed": false, "input": [ "country=df['final_cc_cname_DI'].value_counts()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 35 }, { "cell_type": "code", "collapsed": false, "input": [ "country" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 36, "text": [ "United States 184240\n", "India 88696\n", "Unknown/Other 82029\n", "Other Europe 40377\n", "Other Africa 23897\n", "United Kingdom 22131\n", "Brazil 17856\n", "Other Middle East/Central Asia 17325\n", "Other South Asia 12992\n", "Canada 12738\n", "Pakistan 10824\n", "Russian Federation 10432\n", "Spain 10003\n", "Other South America 9916\n", "Egypt 9286\n", "Germany 8074\n", "Nigeria 7483\n", "Other East Asia 6446\n", "Australia 6419\n", "Mexico 5638\n", "Philippines 5374\n", "Poland 5226\n", "China 5170\n", "Greece 5162\n", "Colombia 4803\n", "France 4700\n", "Other North & Central Amer., Caribbean 4434\n", "Ukraine 4100\n", "Morocco 3966\n", "Indonesia 3410\n", "Bangladesh 3182\n", "Japan 2270\n", "Portugal 2193\n", "Other Oceania 346\n", "dtype: int64" ] } ], "prompt_number": 36 }, { "cell_type": "code", "collapsed": false, "input": [ "country[:15].plot(kind='bar')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 37, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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EzAa2/vKbPXt2iaVgeXl5LlbjvCNHjmjUqFEKDg7W119/rT//+c9ul+SYI0eO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\ndf1Lq0w0depU/fLLLzpx4oT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gpJPLpwYPHixJ6tatm2bOnKmYmBg3SyuXunXratKkSerd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\nf3ArpiA1NTUoLy+Xt73V19ezsPNzr732msc2N5f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"text": [ "" ] } ], "prompt_number": 37 }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Selecting data: boolean indexing\n", "\n", "Often we want to pick out all the rows that have certain attributes from the entire dataframe: those belonging to French people, or all the self-identified women, or the overachievers. \n", "\n", "To do this create a vector where each element indicates whether each row satisfies the condition. It looks like a long list of trues and falses, so it's called a *boolean* vector." ] }, { "cell_type": "code", "collapsed": false, "input": [ "#pick out all the people who watched over 10000[!] minutes of video\n", "df['nplay_video']>10000" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 38, "text": [ "0 False\n", "1 False\n", "2 False\n", "3 False\n", "4 False\n", "5 False\n", "6 False\n", "7 False\n", "8 False\n", "9 False\n", "10 False\n", "11 False\n", "12 False\n", "13 False\n", "14 False\n", "...\n", "641123 False\n", "641124 False\n", "641125 False\n", "641126 False\n", "641127 False\n", "641128 False\n", "641129 False\n", "641130 False\n", "641131 False\n", "641132 False\n", "641133 False\n", "641134 False\n", "641135 False\n", "641136 False\n", "641137 False\n", "Name: nplay_video, Length: 641138, dtype: bool" ] } ], "prompt_number": 38 }, { "cell_type": "code", "collapsed": false, "input": [ "#use that long vector of trues and falses to pick out just those rows\n", "df[df['nplay_video']>10000]" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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473105 MITx/6.002x/2012_Fall 1 1 1 1 Spain Bachelor's 1993 m 0.712012-10-082013-01-03 16649 48 11458 15
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4111 HarvardX/CS50x/2012 1 1 1 1 France NaN NaN NaN 12012-09-262013-05-11 115 25 NaN 12
6318 HarvardX/CS50x/2012 1 1 1 1 France NaN NaN NaN 12012-07-262013-05-10 434 47 NaN 12
8138 HarvardX/CS50x/2012 1 1 1 1 France NaN NaN NaN 12012-07-282013-05-10 47 26 NaN 12
8343 HarvardX/PH207x/2012_Fall 1 1 1 1 France NaN NaN NaN 0.92012-07-252013-07-15 5497 53 1165 16
10974 HarvardX/CS50x/2012 1 1 1 1 France NaN NaN NaN 12012-07-292013-05-24 311 23 NaN 12
13590 HarvardX/CS50x/2012 1 1 1 1 France NaN NaN NaN 12012-07-242013-05-12 683 50 NaN 12
17323 HarvardX/PH278x/2013_Spring 1 1 1 1 France NaN NaN NaN 0.72012-12-252013-08-07 1416 29 48 9
53864 HarvardX/CS50x/2012 1 1 1 1 France Bachelor's 1992 m 12012-08-172013-08-09 342 73 NaN 12
73021 HarvardX/CS50x/2012 1 1 1 1 France Master's 1986 m 12012-08-302013-05-12 543 41 NaN 12
84967 HarvardX/ER22x/2013_Spring 1 1 1 1 France Bachelor's 1990 f 0.842012-12-212013-07-31 1198 16 NaN 30
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 55, "text": [ " course_id registered viewed explored certified \\\n", "4111 HarvardX/CS50x/2012 1 1 1 1 \n", "6318 HarvardX/CS50x/2012 1 1 1 1 \n", "8138 HarvardX/CS50x/2012 1 1 1 1 \n", "8343 HarvardX/PH207x/2012_Fall 1 1 1 1 \n", "10974 HarvardX/CS50x/2012 1 1 1 1 \n", "13590 HarvardX/CS50x/2012 1 1 1 1 \n", "17323 HarvardX/PH278x/2013_Spring 1 1 1 1 \n", "53864 HarvardX/CS50x/2012 1 1 1 1 \n", "73021 HarvardX/CS50x/2012 1 1 1 1 \n", "84967 HarvardX/ER22x/2013_Spring 1 1 1 1 \n", "\n", " final_cc_cname_DI LoE_DI YoB gender grade start_time_DI \\\n", "4111 France NaN NaN NaN 1 2012-09-26 \n", "6318 France NaN NaN NaN 1 2012-07-26 \n", "8138 France NaN NaN NaN 1 2012-07-28 \n", "8343 France NaN NaN NaN 0.9 2012-07-25 \n", "10974 France NaN NaN NaN 1 2012-07-29 \n", "13590 France NaN NaN NaN 1 2012-07-24 \n", "17323 France NaN NaN NaN 0.7 2012-12-25 \n", "53864 France Bachelor's 1992 m 1 2012-08-17 \n", "73021 France Master's 1986 m 1 2012-08-30 \n", "84967 France Bachelor's 1990 f 0.84 2012-12-21 \n", "\n", " last_event_DI nevents ndays_act nplay_video nchapters \n", "4111 2013-05-11 115 25 NaN 12 \n", "6318 2013-05-10 434 47 NaN 12 \n", "8138 2013-05-10 47 26 NaN 12 \n", "8343 2013-07-15 5497 53 1165 16 \n", "10974 2013-05-24 311 23 NaN 12 \n", "13590 2013-05-12 683 50 NaN 12 \n", "17323 2013-08-07 1416 29 48 9 \n", "53864 2013-08-09 342 73 NaN 12 \n", "73021 2013-05-12 543 41 NaN 12 \n", "84967 2013-07-31 1198 16 NaN 30 " ] } ], "prompt_number": 55 }, { "cell_type": "code", "collapsed": false, "input": [ "len(france) #how many?" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 56, "text": [ "4700" ] } ], "prompt_number": 56 }, { "cell_type": "markdown", "metadata": {}, "source": [ "What proportion of the French users finish the course by getting certified?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "len(france_certified) " ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 57, "text": [ "204" ] } ], "prompt_number": 57 }, { "cell_type": "code", "collapsed": false, "input": [ "len(france_certified)/len(france)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 58, "text": [ "0" ] } ], "prompt_number": 58 }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Zut! \n", "\u00c7a ne marche pas!" ] }, { "cell_type": "code", "collapsed": false, "input": [ "float(len(france_certified))/len(france)\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 59, "text": [ "0.04340425531914894" ] } ], "prompt_number": 59 }, { "cell_type": "markdown", "metadata": {}, "source": [ "So about 4.3% of self-reported French enrollees earn a certificate.\n", "\n", "#We could think about this as a *probability.*\n", "\n", "We could write this:\n", "\n", "P(that a French enrollee earns a certificate)=.043\n", "\n", "Or,\n", "\n", "P(Some person completes a certificate given that the person is French)\n", "\n", "If A means \"some person completes a certificate\" and\n", "B and means \"someone is French\", then we write\n", "\n", "$P(A|B)=.043$\n", "\n", "The vertical bar (|) means \"given that.\"\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Can we figure out the probabilities for each country?\n", "\n", "Can we do it without computing each individually as we did for France?\n", "\n", "Oui, for pandas will let us do vector operations directly! \n", "\n", "For France, we divided the total number of explorers over the total number of enrollees.\n", "\n", "How can we do that division for every country all at once?\n", "\n", "Let's compute the number of explorers and the number of enrollees for each country." ] }, { "cell_type": "code", "collapsed": false, "input": [ "explored_by_country=explored['final_cc_cname_DI'].value_counts()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 60 }, { "cell_type": "code", "collapsed": false, "input": [ "explored_by_country" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 61, "text": [ "United States 10132\n", "India 7097\n", "Other Europe 3973\n", "United Kingdom 1759\n", "Other Africa 1621\n", "Spain 1415\n", "Russian Federation 1135\n", "Brazil 975\n", "Unknown/Other 965\n", "Other Middle East/Central Asia 875\n", "Canada 874\n", "Other South Asia 854\n", "Germany 823\n", "Poland 702\n", "Other South America 666\n", "Greece 518\n", "Ukraine 469\n", "Australia 443\n", "Nigeria 438\n", "Pakistan 437\n", "France 436\n", "Colombia 418\n", "Egypt 414\n", "Mexico 375\n", "Other North & Central Amer., Caribbean 313\n", "Other East Asia 304\n", "Portugal 225\n", "Indonesia 223\n", "Philippines 219\n", "China 176\n", "Bangladesh 143\n", "Morocco 132\n", "Japan 112\n", "Other Oceania 25\n", "dtype: int64" ] } ], "prompt_number": 61 }, { "cell_type": "code", "collapsed": false, "input": [ "enrolled_by_country=df['final_cc_cname_DI'].value_counts()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 62 }, { "cell_type": "code", "collapsed": false, "input": [ "enrolled_by_country" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 63, "text": [ "United States 184240\n", "India 88696\n", "Unknown/Other 82029\n", "Other Europe 40377\n", "Other Africa 23897\n", "United Kingdom 22131\n", "Brazil 17856\n", "Other Middle East/Central Asia 17325\n", "Other South Asia 12992\n", "Canada 12738\n", "Pakistan 10824\n", "Russian Federation 10432\n", "Spain 10003\n", "Other South America 9916\n", "Egypt 9286\n", "Germany 8074\n", "Nigeria 7483\n", "Other East Asia 6446\n", "Australia 6419\n", "Mexico 5638\n", "Philippines 5374\n", "Poland 5226\n", "China 5170\n", "Greece 5162\n", "Colombia 4803\n", "France 4700\n", "Other North & Central Amer., Caribbean 4434\n", "Ukraine 4100\n", "Morocco 3966\n", "Indonesia 3410\n", "Bangladesh 3182\n", "Japan 2270\n", "Portugal 2193\n", "Other Oceania 346\n", "dtype: int64" ] } ], "prompt_number": 63 }, { "cell_type": "markdown", "metadata": {}, "source": [ "And now to divide each country's exploration number by its enrollment number. We could do it with a for loop over all the countries. But we can *vectorize*." ] }, { "cell_type": "code", "collapsed": false, "input": [ "explored_by_country/enrolled_by_country" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 64, "text": [ "Australia 0.069014\n", "Bangladesh 0.044940\n", "Brazil 0.054603\n", "Canada 0.068614\n", "China 0.034043\n", "Colombia 0.087029\n", "Egypt 0.044583\n", "France 0.092766\n", "Germany 0.101932\n", "Greece 0.100349\n", "India 0.080015\n", "Indonesia 0.065396\n", "Japan 0.049339\n", "Mexico 0.066513\n", "Morocco 0.033283\n", "Nigeria 0.058533\n", "Other Africa 0.067833\n", "Other East Asia 0.047161\n", "Other Europe 0.098398\n", "Other Middle East/Central Asia 0.050505\n", "Other North & Central Amer., Caribbean 0.070591\n", "Other Oceania 0.072254\n", "Other South America 0.067164\n", "Other South Asia 0.065733\n", "Pakistan 0.040373\n", "Philippines 0.040752\n", "Poland 0.134328\n", "Portugal 0.102599\n", "Russian Federation 0.108800\n", "Spain 0.141458\n", "Ukraine 0.114390\n", "United Kingdom 0.079481\n", "United States 0.054993\n", "Unknown/Other 0.011764\n", "dtype: float64" ] } ], "prompt_number": 64 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Cool beans!\n", "\n", "Think about what that division means:\n", "for each country, pick out the value in the explored vector for that country:\n", " \n", "$A = \\begin{pmatrix} exploration_{US} \\\\ exploration_{china} \\\\ exploration_{france} \\end{pmatrix}$\n", "\n", "and then pick out the value in the enrolled vector for that country:\n", "\n", "$B = \\begin{pmatrix} enrollment_{US} \\\\ enrollment_{china} \\\\ enrollment_{france} \\end{pmatrix}$\n", "\n", "\n", "Before we talked about different kinds of functions on vectors. Here we want corresponding terms to be divided. \n", "\n", "$\\begin{pmatrix} exploration_{US}/enrollment_{US} \\\\ exploration_{china}/enrollment_{china} \\\\ exploration_{france}/enrollment_{france} \\\\ \\cdots \\end{pmatrix}$\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "diligence_in_exploration=(explored_by_country/enrolled_by_country)\n", "#Can we plot it? Yes!" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 65 }, { "cell_type": "code", "collapsed": false, "input": [ "diligence_in_exploration.plot(kind='bar', title='Diligence: Exploration per enrollment')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 66, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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AAMBFPN1Rc+JYU6floIbwx7cjBzU4I4fb49uRgxqckYMawh/fjhxeqMEOXtgP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\nHBzdThkfH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"text": [ "" ] } ], "prompt_number": 66 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some nations appear to have a much higher rate of diligence in exploring chapters.\n", "\n", "\n", "And there appears to be a *positive correlation* of diligence and being between 'Po' and 'Uk' in the alphabet." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Crucial Principle of Data Analysis\n", "###Correlation equals causation\n", "" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.display import Image\n", "Image(\"https://sslimgs.xkcd.com/comics/correlation.png\")\n", " " ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "png": 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9qHnIlbW+nhWE2V03ob3+M+43bsKpU5jQV/SYV0IV65dv+okA43WMtDfr37+1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\nrTHLMy0NhYXPrKsqytkEGI/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"prompt_number": 67, "text": [ "" ] } ], "prompt_number": 67 }, { "cell_type": "code", "collapsed": false, "input": [ "certified=df[df['certified']==1]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 68 }, { "cell_type": "code", "collapsed": false, "input": [ "len(certified)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 69, "text": [ "17687" ] } ], "prompt_number": 69 }, { "cell_type": "code", "collapsed": false, "input": [ "certified_by_country=certified['final_cc_cname_DI'].value_counts()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 70 }, { "cell_type": "code", "collapsed": false, "input": [ "diligence=(certified_by_country/enrolled_by_country)\n", "diligence.plot(kind='bar', title='Diligence redux: certification per enrollment')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 71, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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zNVzxa3v2V2vZ3h7CHb+laG/Rj9+1a1c15axDH7ds2aK1a9cqIyNDixYt0qRJ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\nrw4ePEhQA4B74Hp+A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"text": [ "" ] } ], "prompt_number": 71 }, { "cell_type": "markdown", "metadata": {}, "source": [ "what if we compared completion by gender? or by age?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "certified_male=certified[certified['gender']=='m']\n", "certified_male_by_country=certified_male['final_cc_cname_DI'].value_counts()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 72 }, { "cell_type": "code", "collapsed": false, "input": [ "enrolled_male=df[df['gender']=='m']" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 73 }, { "cell_type": "code", "collapsed": false, "input": [ "enrolled_male_by_country=enrolled_male['final_cc_cname_DI'].value_counts()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 74 }, { "cell_type": "code", "collapsed": false, "input": [ "diligence_male=(certified_male_by_country/enrolled_male_by_country)\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 75 }, { "cell_type": "code", "collapsed": false, "input": [ "diligence_male.plot(kind='bar', title='Diligence: Certification per enrollment(dudes)')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 76, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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7clCDHdhGoXGsRia+pztqAAAAAOBGzFEDAACA1Zij1jTMUXMn5qgBAAAAgEt4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goKlF43NzfHl19+CVNTU6rnq6qqIgPjf//9NzVZeK4FUQoKCnDhwgWyU923b194enpyIhdeX18P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\nCIoj3uAAAAAASUVORK5CYII=\n", "text": [ "" ] } ], "prompt_number": 76 }, { "cell_type": "code", "collapsed": false, "input": [ "certified_female=certified[certified['gender']=='f']\n", "certified_female_by_country=certified_female['final_cc_cname_DI'].value_counts()\n", "enrolled_female=df[df['gender']=='f']\n", "enrolled_female_by_country=enrolled_female['final_cc_cname_DI'].value_counts()\n", "diligence_female=(certified_female_by_country/enrolled_female_by_country)\n", "diligence_female.plot(kind='bar', title='Diligence: Certification per enrollment(lasses)')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 77, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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6kpOTNWLECC1YsECzZs1So0aNKu2keeX4bymCFt+LHNQQ/fhe5KAGf+QIenwv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\nu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femalemale
Australia 0.040091 0.026940
Brazil 0.021733 0.024028
China 0.022312 0.009840
Colombia 0.058757 0.032022
Egypt 0.019417 0.010848
France 0.062500 0.039514
Germany 0.076320 0.052922
Greece 0.065415 0.055712
India 0.045643 0.033067
Indonesia 0.041284 0.022642
Japan 0.008811 0.013754
Mexico 0.040107 0.021894
Morocco 0.015075 0.006401
Nigeria 0.035941 0.016881
Other Africa 0.041943 0.021750
Other East Asia 0.022238 0.019379
Other Europe 0.049466 0.041956
Other Middle East/Central Asia 0.027838 0.015398
Other North & Central Amer., Caribbean 0.046016 0.032689
Other Oceania 0.023810 NaN
Other South America 0.045977 0.023626
Other South Asia 0.035273 0.030398
Pakistan 0.022563 0.012190
Philippines 0.010365 0.019212
Poland 0.055306 0.084135
Portugal 0.084211 0.047189
Russian Federation 0.031308 0.060886
Spain 0.090592 0.077693
Ukraine 0.033624 0.055294
United Kingdom 0.044515 0.031626
United States 0.024095 0.019549
Unknown/Other 0.000907 0.000643
\n", "