pandas.ipynb

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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cd ~/swc/pandas-data/"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/home/zonca/swc/pandas-data\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pwd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "u'/home/zonca/swc/pandas-data'"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Introduction"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's start with a hypothetical problem we want to solve. We are interested in understanding the relationship between the weather and the number of mosquitos occuring in a particular year so that we can plan mosquito control measures accordingly. Since we want to apply these mosquito control measures at a number of different sites we need to understand both the relationship at a particular site and whether or not it is consistent across sites. The data we have to address this problem comes from the local government and are stored in tables in comma-separated values (CSV) files. Each file holds the data for a single location, each row holds the information for a single year at that location, and the columns hold the data on both mosquito numbers and the average temperature and rainfall from the beginning of mosquito breeding season. The first few rows of our first file look like:\n",
      "\n",
      "~~~\n",
      "year,temperature,rainfall,mosquitos\n",
      "2001,87,222,198\n",
      "2002,72,103,105\n",
      "2003,77,176,166\n",
      "~~~"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Objectives"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* Conduct variable assignment, looping, and conditionals in Python\n",
      "* Use an external Python library\n",
      "* Read tabular data from a file\n",
      "* Subset and perform analysis on data\n",
      "* Display simple graphs"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Loading Data"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In order to load the data, we need to import a library called Pandas that knows\n",
      "how to operate on tables of data."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can now use Pandas to read our data file."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pandas.read_csv('A1_mosquito_data.csv', sep=',')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>year</th>\n",
        "      <th>temperature</th>\n",
        "      <th>rainfall</th>\n",
        "      <th>mosquitos</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 2001</td>\n",
        "      <td> 80</td>\n",
        "      <td> 157</td>\n",
        "      <td> 150</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 2002</td>\n",
        "      <td> 85</td>\n",
        "      <td> 252</td>\n",
        "      <td> 217</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 2003</td>\n",
        "      <td> 86</td>\n",
        "      <td> 154</td>\n",
        "      <td> 153</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 2004</td>\n",
        "      <td> 87</td>\n",
        "      <td> 159</td>\n",
        "      <td> 158</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 2005</td>\n",
        "      <td> 74</td>\n",
        "      <td> 292</td>\n",
        "      <td> 243</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td> 2006</td>\n",
        "      <td> 75</td>\n",
        "      <td> 283</td>\n",
        "      <td> 237</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td> 2007</td>\n",
        "      <td> 80</td>\n",
        "      <td> 214</td>\n",
        "      <td> 190</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td> 2008</td>\n",
        "      <td> 85</td>\n",
        "      <td> 197</td>\n",
        "      <td> 181</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td> 2009</td>\n",
        "      <td> 74</td>\n",
        "      <td> 231</td>\n",
        "      <td> 200</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td> 2010</td>\n",
        "      <td> 74</td>\n",
        "      <td> 207</td>\n",
        "      <td> 184</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "   year  temperature  rainfall  mosquitos\n",
        "0  2001           80       157        150\n",
        "1  2002           85       252        217\n",
        "2  2003           86       154        153\n",
        "3  2004           87       159        158\n",
        "4  2005           74       292        243\n",
        "5  2006           75       283        237\n",
        "6  2007           80       214        190\n",
        "7  2008           85       197        181\n",
        "8  2009           74       231        200\n",
        "9  2010           74       207        184"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The `read_csv()` function belongs to the `pandas` library. In order to run it we need to tell Python that it is part of `pandas` and we do this using the dot notation, which is used everywhere in Python to refer to parts of larger things.\n",
      "\n",
      "When we are finished typing and press Shift+Enter, the notebook runs our command and shows us its output. In this case, the output is the data we just loaded."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The first column on the left is the `index` column, a way for `pandas` to label each row, by default it just uses integers.\n",
      "However in this case we have a better way of indexing the data, we can use the `year` column. We can use the `index_col` keyword argument to `read_csv()` to specify the index column:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pandas.read_csv('A1_mosquito_data.csv', sep=',', index_col='year')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>temperature</th>\n",
        "      <th>rainfall</th>\n",
        "      <th>mosquitos</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>year</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2001</th>\n",
        "      <td> 80</td>\n",
        "      <td> 157</td>\n",
        "      <td> 150</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2002</th>\n",
        "      <td> 85</td>\n",
        "      <td> 252</td>\n",
        "      <td> 217</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2003</th>\n",
        "      <td> 86</td>\n",
        "      <td> 154</td>\n",
        "      <td> 153</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004</th>\n",
        "      <td> 87</td>\n",
        "      <td> 159</td>\n",
        "      <td> 158</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2005</th>\n",
        "      <td> 74</td>\n",
        "      <td> 292</td>\n",
        "      <td> 243</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2006</th>\n",
        "      <td> 75</td>\n",
        "      <td> 283</td>\n",
        "      <td> 237</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2007</th>\n",
        "      <td> 80</td>\n",
        "      <td> 214</td>\n",
        "      <td> 190</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2008</th>\n",
        "      <td> 85</td>\n",
        "      <td> 197</td>\n",
        "      <td> 181</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2009</th>\n",
        "      <td> 74</td>\n",
        "      <td> 231</td>\n",
        "      <td> 200</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010</th>\n",
        "      <td> 74</td>\n",
        "      <td> 207</td>\n",
        "      <td> 184</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "      temperature  rainfall  mosquitos\n",
        "year                                  \n",
        "2001           80       157        150\n",
        "2002           85       252        217\n",
        "2003           86       154        153\n",
        "2004           87       159        158\n",
        "2005           74       292        243\n",
        "2006           75       283        237\n",
        "2007           80       214        190\n",
        "2008           85       197        181\n",
        "2009           74       231        200\n",
        "2010           74       207        184"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Our call to `pandas.read_csv()` read data into memory, but didn't save it anywhere. To do that, we need to assign the array to a variable."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = pandas.read_csv('A1_mosquito_data.csv', sep=',', index_col='year')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This statement doesn't produce any output because assignment doesn't display anything. If we want to check that our data has been loaded, we can print the variable's value:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(data)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "      temperature  rainfall  mosquitos\n",
        "year                                  \n",
        "2001           80       157        150\n",
        "2002           85       252        217\n",
        "2003           86       154        153\n",
        "2004           87       159        158\n",
        "2005           74       292        243\n",
        "2006           75       283        237\n",
        "2007           80       214        190\n",
        "2008           85       197        181\n",
        "2009           74       231        200\n",
        "2010           74       207        184\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "`print data` tells Python to display the text. Alternatively we could just include `data` as the last value in a code cell:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>temperature</th>\n",
        "      <th>rainfall</th>\n",
        "      <th>mosquitos</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>year</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2001</th>\n",
        "      <td> 80</td>\n",
        "      <td> 157</td>\n",
        "      <td> 150</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2002</th>\n",
        "      <td> 85</td>\n",
        "      <td> 252</td>\n",
        "      <td> 217</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2003</th>\n",
        "      <td> 86</td>\n",
        "      <td> 154</td>\n",
        "      <td> 153</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004</th>\n",
        "      <td> 87</td>\n",
        "      <td> 159</td>\n",
        "      <td> 158</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2005</th>\n",
        "      <td> 74</td>\n",
        "      <td> 292</td>\n",
        "      <td> 243</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2006</th>\n",
        "      <td> 75</td>\n",
        "      <td> 283</td>\n",
        "      <td> 237</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2007</th>\n",
        "      <td> 80</td>\n",
        "      <td> 214</td>\n",
        "      <td> 190</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2008</th>\n",
        "      <td> 85</td>\n",
        "      <td> 197</td>\n",
        "      <td> 181</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2009</th>\n",
        "      <td> 74</td>\n",
        "      <td> 231</td>\n",
        "      <td> 200</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010</th>\n",
        "      <td> 74</td>\n",
        "      <td> 207</td>\n",
        "      <td> 184</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "      temperature  rainfall  mosquitos\n",
        "year                                  \n",
        "2001           80       157        150\n",
        "2002           85       252        217\n",
        "2003           86       154        153\n",
        "2004           87       159        158\n",
        "2005           74       292        243\n",
        "2006           75       283        237\n",
        "2007           80       214        190\n",
        "2008           85       197        181\n",
        "2009           74       231        200\n",
        "2010           74       207        184"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This tells the IPython Notebook to display the `data` object, which is why we see a pretty formated table."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Manipulating data"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Once we have imported the data we can start doing things with it. First, let's ask what type of thing `data` refers to:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(type(data))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The data is stored in a data structure called a DataFrame. There are other kinds of data structures that are also commonly used in scientific computing including Numpy arrays, and Numpy matrices, which can be used for doing linear algebra."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can select an individual column of data using its name:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(data['temperature'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "year\n",
        "2001    80\n",
        "2002    85\n",
        "2003    86\n",
        "2004    87\n",
        "2005    74\n",
        "2006    75\n",
        "2007    80\n",
        "2008    85\n",
        "2009    74\n",
        "2010    74\n",
        "Name: temperature, dtype: int64\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Or we can select several columns of data at once:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(data[['rainfall', 'temperature']])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "      rainfall  temperature\n",
        "year                       \n",
        "2001       157           80\n",
        "2002       252           85\n",
        "2003       154           86\n",
        "2004       159           87\n",
        "2005       292           74\n",
        "2006       283           75\n",
        "2007       214           80\n",
        "2008       197           85\n",
        "2009       231           74\n",
        "2010       207           74\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "Int64Index([2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010], dtype='int64')"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can also select subsets of rows using slicing. Say we just want the first two rows of data:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(data[:2])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "      temperature  rainfall  mosquitos\n",
        "year                                  \n",
        "2001           80       157        150\n",
        "2002           85       252        217\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "However most of the time we want to index using the `index`!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.ix[2004:2008]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>temperature</th>\n",
        "      <th>rainfall</th>\n",
        "      <th>mosquitos</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>year</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2004</th>\n",
        "      <td> 87</td>\n",
        "      <td> 159</td>\n",
        "      <td> 158</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2005</th>\n",
        "      <td> 74</td>\n",
        "      <td> 292</td>\n",
        "      <td> 243</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2006</th>\n",
        "      <td> 75</td>\n",
        "      <td> 283</td>\n",
        "      <td> 237</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2007</th>\n",
        "      <td> 80</td>\n",
        "      <td> 214</td>\n",
        "      <td> 190</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2008</th>\n",
        "      <td> 85</td>\n",
        "      <td> 197</td>\n",
        "      <td> 181</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "      temperature  rainfall  mosquitos\n",
        "year                                  \n",
        "2004           87       159        158\n",
        "2005           74       292        243\n",
        "2006           75       283        237\n",
        "2007           80       214        190\n",
        "2008           85       197        181"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.ix[2004:2008][\"temperature\"]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "year\n",
        "2004    87\n",
        "2005    74\n",
        "2006    75\n",
        "2007    80\n",
        "2008    85\n",
        "Name: temperature, dtype: int64"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.ix[2004]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "temperature     87\n",
        "rainfall       159\n",
        "mosquitos      158\n",
        "Name: 2004, dtype: int64"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can also subset the data based on the value of other rows:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data['temperature'][data['mosquitos'] > 200]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "year\n",
        "2002    85\n",
        "2005    74\n",
        "2006    75\n",
        "Name: temperature, dtype: int64"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Challenge"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Print the mosquitos number when temperature is more than 75 degrees between 2005 and 2008"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data['mosquitos'][data['temperature'] > 75].ix[2005:2008]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "year\n",
        "2007    190\n",
        "2008    181\n",
        "Name: mosquitos, dtype: int64"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data['mosquitos'].ix[2005:2008][data['temperature'] > 75]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "year\n",
        "2007    190\n",
        "2008    181\n",
        "Name: mosquitos, dtype: int64"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Data frames also know how to perform common mathematical operations on their values. If we want to find the average value for each variable, we can just ask the data frame for its mean values"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(data.mean())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "temperature     80.0\n",
        "rainfall       214.6\n",
        "mosquitos      191.3\n",
        "dtype: float64\n"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Data frames have lots of useful methods:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(data.max())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "temperature     87\n",
        "rainfall       292\n",
        "mosquitos      243\n",
        "dtype: int64\n"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(data['temperature'].min())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "74\n"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(data['mosquitos'][1:3].std())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "45.2548339959\n"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Summary on how to use parentheses"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "abs(-1) # call function\n",
      "data.max() # call method on an object\n",
      "\n",
      "data[:3] # slicing by row\n",
      "data[\"temperature\"] # get a column of a data frame \"slice a column\"\n",
      "dataslice = data.ix[2004:2008] # this is a special case, we are slicing ix"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 24
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Challenge"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Import the data from `A2_mosquito_data.csv`, create a new variable that holds a data frame with only the weather data, and print the means and standard deviations for the weather variables."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Plotting"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from matplotlib import pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now, let's make a simple plot showing how the number of mosquitos varies over time. We'll use the site you've been doing exercises with since it has a longer time-series."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = pandas.read_csv('A2_mosquito_data.csv', index_col=\"year\")\n",
      "data['mosquitos'].plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x7f76348b1190>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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wc6cZgvmcc7y359HqKW/YdYgi/Hmk3Krq7YV16/r73RDd6mlrM1G/n92TNRs3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\nRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f7634896a90>"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Pandas can automatically plot a complete table:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.plot(figsize=(13,8), marker='s')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x7f7634811210>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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FBUTCBAUFCssUdDiUHRQcsB0Q7XpC5Uh5KCggiPTH4/Wgz9OHoryipK+lRLOx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       "text": [
        "<matplotlib.figure.Figure at 0x7f763479a510>"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Fitting a line through the data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import statsmodels.api as sm\n",
      "\n",
      "regr_results = sm.OLS.from_formula('mosquitos ~ temperature', data).fit()\n",
      "regr_results.summary()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<table class=\"simpletable\">\n",
        "<caption>OLS Regression Results</caption>\n",
        "<tr>\n",
        "  <th>Dep. Variable:</th>        <td>mosquitos</td>    <th>  R-squared:         </th> <td>   0.004</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>  -0.017</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>  0.1735</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Date:</th>             <td>Sat, 21 Feb 2015</td> <th>  Prob (F-statistic):</th>  <td> 0.679</td> \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Time:</th>                 <td>21:01:26</td>     <th>  Log-Likelihood:    </th> <td> -259.30</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>No. Observations:</th>      <td>    51</td>      <th>  AIC:               </th> <td>   522.6</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Df Residuals:</th>          <td>    49</td>      <th>  BIC:               </th> <td>   526.5</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
        "</tr>\n",
        "</table>\n",
        "<table class=\"simpletable\">\n",
        "<tr>\n",
        "       <td></td>          <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Intercept</th>   <td>  154.4695</td> <td>   74.077</td> <td>    2.085</td> <td> 0.042</td> <td>    5.605   303.334</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>temperature</th> <td>    0.3827</td> <td>    0.919</td> <td>    0.417</td> <td> 0.679</td> <td>   -1.464     2.229</td>\n",
        "</tr>\n",
        "</table>\n",
        "<table class=\"simpletable\">\n",
        "<tr>\n",
        "  <th>Omnibus:</th>       <td> 8.529</td> <th>  Durbin-Watson:     </th> <td>   1.854</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Prob(Omnibus):</th> <td> 0.014</td> <th>  Jarque-Bera (JB):  </th> <td>   2.660</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Skew:</th>          <td>-0.072</td> <th>  Prob(JB):          </th> <td>   0.265</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Kurtosis:</th>      <td> 1.891</td> <th>  Cond. No.          </th> <td>1.07e+03</td>\n",
        "</tr>\n",
        "</table>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "<class 'statsmodels.iolib.summary.Summary'>\n",
        "\"\"\"\n",
        "                            OLS Regression Results                            \n",
        "==============================================================================\n",
        "Dep. Variable:              mosquitos   R-squared:                       0.004\n",
        "Model:                            OLS   Adj. R-squared:                 -0.017\n",
        "Method:                 Least Squares   F-statistic:                    0.1735\n",
        "Date:                Sat, 21 Feb 2015   Prob (F-statistic):              0.679\n",
        "Time:                        21:01:26   Log-Likelihood:                -259.30\n",
        "No. Observations:                  51   AIC:                             522.6\n",
        "Df Residuals:                      49   BIC:                             526.5\n",
        "Df Model:                           1                                         \n",
        "===============================================================================\n",
        "                  coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
        "-------------------------------------------------------------------------------\n",
        "Intercept     154.4695     74.077      2.085      0.042         5.605   303.334\n",
        "temperature     0.3827      0.919      0.417      0.679        -1.464     2.229\n",
        "==============================================================================\n",
        "Omnibus:                        8.529   Durbin-Watson:                   1.854\n",
        "Prob(Omnibus):                  0.014   Jarque-Bera (JB):                2.660\n",
        "Skew:                          -0.072   Prob(JB):                        0.265\n",
        "Kurtosis:                       1.891   Cond. No.                     1.07e+03\n",
        "==============================================================================\n",
        "\n",
        "Warnings:\n",
        "[1] The condition number is large, 1.07e+03. This might indicate that there are\n",
        "strong multicollinearity or other numerical problems.\n",
        "\"\"\""
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "line_fit = regr_results.params['Intercept'] + regr_results.params['temperature'] * data['temperature']\n",
      "plt.plot(data['temperature'], data['mosquitos'], '.', label=\"data\")\n",
      "plt.plot(data['temperature'], line_fit, 'red', label=\"fit\")\n",
      "plt.xlabel('temperature')\n",
      "plt.ylabel('mosquitos')\n",
      "plt.legend()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "<matplotlib.legend.Legend at 0x7f76208a0c90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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4NSKulfQNYHBEzKhpBhtMp/N5DrApIr5T21w1lrSjxa+B90XE3yT9b+CXwPvx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\nJwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f762085ecd0>"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Challenge"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Perform the line fit and the plot for `rainfall` instead."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    }
   ],
   "metadata": {}
  }
 ]
}