intermediate_scientific_python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Intermediate Scientific Python\n",
    "\n",
    "This lesson assumes some programming background, but not necessarily with Python. It moves at a faster pace than a novice lesson, however. \n",
    "\n",
    "We will learn how to do the following:\n",
    "\n",
    "1. Reading and manipulating tabular data\n",
    "2. Visualization and plotting\n",
    "3. Modularization and documentation\n",
    "4. Defensive programming\n",
    "5. Writing command line scripts with Python\n",
    "\n",
    "This lesson is based heavily on the [Software Carpentry](http://software-carpentry.org/) [``python-intermediate-mosquitoes``](https://github.com/swcarpentry/python-intermediate-mosquitoes) lesson, and uses its datasets. It also includes some mixed-in components from the [``python-novice-inflammation``](https://github.com/swcarpentry/python-novice-inflammation) lesson."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting started"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "year,temperature,rainfall,mosquitos\r\n",
      "2001,80,157,150\r\n",
      "2002,85,252,217\r\n",
      "2003,86,154,153\r\n",
      "2004,87,159,158\r\n"
     ]
    }
   ],
   "source": [
    "%cat A1_mosquito_data.csv | head -n 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we have five files to work with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A1_mosquito_data.csv  A3_mosquito_data.csv  B2_mosquito_data.csv\r\n",
      "A2_mosquito_data.csv  B1_mosquito_data.csv\r\n"
     ]
    }
   ],
   "source": [
    "%ls *.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: commands preceded with a `%` are known as \"magics\". These are specfic to the notebook environment. They are *not* Python commands."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since this is tabular data, our tool of choice is [``pandas``](http://pandas.pydata.org/), a library that provides special data structures for doing fast numerical operations on tabular data. Internally, ``pandas`` uses [``numpy``](http://www.numpy.org/) to do the heavy lifting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing a library in this way allows us to use the components defined inside of it. First, we'll read in a single dataset using the ``pandas.read_csv`` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>"
      ],
      "text/plain": [
       "   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"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('A1_mosquito_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This reads the CSV from disk and deserializes it into a [``pandas.DataFrame``](http://pandas.pydata.org/pandas-docs/stable/dsintro.html#dataframe). But unless we attach a name to the `DataFrame` the function returns, we cannot keep working with the object in memory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('A1_mosquito_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>"
      ],
      "text/plain": [
       "   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"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can refer to the `DataFrame` directly, without having to read it from disk every time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A bit about names. If I attach the name ``weight_kg`` to the value ``55``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weight_kg = 55"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I can then use this value to calculate the same weight in ``weight_lb``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weight_lb = 2.2 * weight_kg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "121.00000000000001"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weight_lb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If I change my `weight_kg` to 66, what is the current value of `weight_lb`?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weight_kg = 66"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "121.00000000000001"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weight_lb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There's no change. This is because the name `weight_lb` is attached to a value, in this case a floating point number. The value has no concept of how it came to be."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "weight_lb = 2.2 * weight_kg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "145.20000000000002"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weight_lb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Back to our `DataFrame`. A `DataFrame` is an **object**. We can see what type of object it is with the Python builtin, ``type``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It turns out that in Python, **everything** is an object. We'll see what this means as we go, but the most important aspect of this is that in Python we have **names**, and we assign these to **objects**. Any name can point to any object, and more than one name can point to a single object."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Anyway, a ``DataFrame`` allows us to get at individual components of our tabular data. We can get single columns like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2001\n",
       "1    2002\n",
       "2    2003\n",
       "3    2004\n",
       "4    2005\n",
       "5    2006\n",
       "6    2007\n",
       "7    2008\n",
       "8    2009\n",
       "9    2010\n",
       "Name: year, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['year']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or multiple columns with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rainfall</th>\n",
       "      <th>temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>157</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>252</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>154</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>159</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>292</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>283</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>214</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>197</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>231</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>207</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rainfall  temperature\n",
       "0       157           80\n",
       "1       252           85\n",
       "2       154           86\n",
       "3       159           87\n",
       "4       292           74\n",
       "5       283           75\n",
       "6       214           80\n",
       "7       197           85\n",
       "8       231           74\n",
       "9       207           74"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['rainfall', 'temperature']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Slicing can be used to get back subsets of rows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  temperature  rainfall  mosquitos\n",
       "0  2001           80       157        150\n",
       "1  2002           85       252        217"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[0:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python indices are 0-based, meaning counting goes as 0, 1, 2, 3...; this means that the first row is row 0, the second row is row 1, etc. It's best to refer to row 0 as the \"zeroth row\" to avoid confusion.\n",
    "\n",
    "This slice should be read as \"get the 0th element up to and not including the 2nd element\". The \"not including\" is important, and the cause of much initial frustration. It does take some getting used to."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What if we want a single row?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "1",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m/usr/lib/python3.5/site-packages/pandas/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   1875\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1876\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1877\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:4027)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:3891)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12408)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12359)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 1",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-18-c805864c0d75>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m/usr/lib/python3.5/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1990\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1991\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1992\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1993\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1994\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/lib/python3.5/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_getitem_column\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1997\u001b[0m         \u001b[1;31m# get column\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1998\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1999\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2000\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2001\u001b[0m         \u001b[1;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/lib/python3.5/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_get_item_cache\u001b[1;34m(self, item)\u001b[0m\n\u001b[0;32m   1343\u001b[0m         \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1344\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1345\u001b[1;33m             \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1346\u001b[0m             \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1347\u001b[0m             \u001b[0mcache\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/lib/python3.5/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mget\u001b[1;34m(self, item, fastpath)\u001b[0m\n\u001b[0;32m   3223\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3224\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misnull\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3225\u001b[1;33m                 \u001b[0mloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3226\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3227\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0misnull\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/lib/python3.5/site-packages/pandas/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   1876\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1877\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1878\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1879\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1880\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:4027)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:3891)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12408)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12359)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 1"
     ]
    }
   ],
   "source": [
    "data[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a DataFrame, this is ambiguous, since a single value is interpreted as a column name. We can only get at rows by slicing at the top level:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>1</th>\n",
       "      <td>2002</td>\n",
       "      <td>85</td>\n",
       "      <td>252</td>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  temperature  rainfall  mosquitos\n",
       "1  2002           85       252        217"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[1:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or we could use `.iloc`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year           2002\n",
       "temperature      85\n",
       "rainfall        252\n",
       "mosquitos       217\n",
       "Name: 1, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Getting a single row in this way returns a `Series`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data.iloc[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A `Series` is a 1-D column of values, having all the same datatype. Since each of the datatypes of our columns were integers, we got a `Series` with dtype `int64` this time. If we had columns with, e.g. strings, then we'd get back dtype `object`, which is a catchall for ``pandas``."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also get the data in our ``Series`` as a raw ``numpy`` array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data.iloc[1].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas is a relatively young library, but it's built on top of the venerable ``numpy`` array, which makes it possible to do fast numerical work in Python. A `Series` is basically a 1-D ``numpy`` array with the ability to select by labeled indices:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Subsetting data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More usefully than simple slicing, we can use boolean indexing to subselect our data. Say we want only data for years beyond 2005?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>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>"
      ],
      "text/plain": [
       "   year  temperature  rainfall  mosquitos\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"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['year'] > 2005]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There's no magic here; we get a boolean index directly from a comparison:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "4    False\n",
       "5     True\n",
       "6     True\n",
       "7     True\n",
       "8     True\n",
       "9     True\n",
       "Name: year, dtype: bool"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gt_2005 = data['year'] > 2005\n",
    "gt_2005"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And using this `Series` of bools will then give only the rows for which the `Series` had `True`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>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>"
      ],
      "text/plain": [
       "   year  temperature  rainfall  mosquitos\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"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[gt_2005]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the same behavior as ``numpy`` arrays: using most binary operators, such as ``+``, ``*``, ``>``, ``&``, work element-wise. With a single value on one side (such as ``2005``), we get the result of the operation for each element."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A ``DataFrame`` is an *object*, and objects have **methods**. These are functions that are *part of* the object itself, often doing operations on the object's data. One of these is ``DataFrame.mean``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year           2005.5\n",
       "temperature      80.0\n",
       "rainfall        214.6\n",
       "mosquitos       191.3\n",
       "dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get back the mean value of each column as a single ``Series``. There's more like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year           2010\n",
       "temperature      87\n",
       "rainfall        292\n",
       "mosquitos       243\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There's also ``DataFrame.describe``, which gives common descriptive statistics of the whole `DataFrame`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>count</th>\n",
       "      <td>10.00000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2005.50000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>214.600000</td>\n",
       "      <td>191.30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.02765</td>\n",
       "      <td>5.456902</td>\n",
       "      <td>50.317216</td>\n",
       "      <td>33.23335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2001.00000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>150.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2003.25000</td>\n",
       "      <td>74.250000</td>\n",
       "      <td>168.500000</td>\n",
       "      <td>163.75000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2005.50000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>210.500000</td>\n",
       "      <td>187.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2007.75000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>246.750000</td>\n",
       "      <td>212.75000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2010.00000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>292.000000</td>\n",
       "      <td>243.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             year  temperature    rainfall  mosquitos\n",
       "count    10.00000    10.000000   10.000000   10.00000\n",
       "mean   2005.50000    80.000000  214.600000  191.30000\n",
       "std       3.02765     5.456902   50.317216   33.23335\n",
       "min    2001.00000    74.000000  154.000000  150.00000\n",
       "25%    2003.25000    74.250000  168.500000  163.75000\n",
       "50%    2005.50000    80.000000  210.500000  187.00000\n",
       "75%    2007.75000    85.000000  246.750000  212.75000\n",
       "max    2010.00000    87.000000  292.000000  243.00000"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is, itself, a ``DataFrame``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    10.000000\n",
       "mean     80.000000\n",
       "std       5.456902\n",
       "min      74.000000\n",
       "25%      74.250000\n",
       "50%      80.000000\n",
       "75%      85.000000\n",
       "max      87.000000\n",
       "Name: temperature, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()['temperature']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Documentation is a key part of Python's design. In the notebook, you can get a quick look at the docs for a given Python function or method with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data.describe?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or more generally (built-in Python behavior):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method describe in module pandas.core.generic:\n",
      "\n",
      "describe(percentiles=None, include=None, exclude=None) method of pandas.core.frame.DataFrame instance\n",
      "    Generate various summary statistics, excluding NaN values.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    percentiles : array-like, optional\n",
      "        The percentiles to include in the output. Should all\n",
      "        be in the interval [0, 1]. By default `percentiles` is\n",
      "        [.25, .5, .75], returning the 25th, 50th, and 75th percentiles.\n",
      "    include, exclude : list-like, 'all', or None (default)\n",
      "        Specify the form of the returned result. Either:\n",
      "    \n",
      "        - None to both (default). The result will include only\n",
      "          numeric-typed columns or, if none are, only categorical columns.\n",
      "        - A list of dtypes or strings to be included/excluded.\n",
      "          To select all numeric types use numpy numpy.number. To select\n",
      "          categorical objects use type object. See also the select_dtypes\n",
      "          documentation. eg. df.describe(include=['O'])\n",
      "        - If include is the string 'all', the output column-set will\n",
      "          match the input one.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    summary: NDFrame of summary statistics\n",
      "    \n",
      "    Notes\n",
      "    -----\n",
      "    The output DataFrame index depends on the requested dtypes:\n",
      "    \n",
      "    For numeric dtypes, it will include: count, mean, std, min,\n",
      "    max, and lower, 50, and upper percentiles.\n",
      "    \n",
      "    For object dtypes (e.g. timestamps or strings), the index\n",
      "    will include the count, unique, most common, and frequency of the\n",
      "    most common. Timestamps also include the first and last items.\n",
      "    \n",
      "    For mixed dtypes, the index will be the union of the corresponding\n",
      "    output types. Non-applicable entries will be filled with NaN.\n",
      "    Note that mixed-dtype outputs can only be returned from mixed-dtype\n",
      "    inputs and appropriate use of the include/exclude arguments.\n",
      "    \n",
      "    If multiple values have the highest count, then the\n",
      "    `count` and `most common` pair will be arbitrarily chosen from\n",
      "    among those with the highest count.\n",
      "    \n",
      "    The include, exclude arguments are ignored for Series.\n",
      "    \n",
      "    See Also\n",
      "    --------\n",
      "    DataFrame.select_dtypes\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(data.describe)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------------\n",
    "### Challenge: obtain the standard deviation of mosquito count for years in which the rainfall was greater than 200:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One way we could do this is to first grab the ``\"mosquitos\"`` column, then use a fancy index obtained from comparing the ``\"rainfall\"`` column to ``200``. We can then call the ``std`` method of the resulting ``Series``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24.587937421969063"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['mosquitos'][data['rainfall'] > 200].std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that this is a key part of the power of ``pandas`` objects: operations for subsetting and calculating descriptive statistics can often be stacked to great effect.\n",
    "\n",
    "-----------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What if we know our temperatures are in fahrenheit, but we want them in celsius? We can convert them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    26.666667\n",
       "1    29.444444\n",
       "2    30.000000\n",
       "3    30.555556\n",
       "4    23.333333\n",
       "5    23.888889\n",
       "6    26.666667\n",
       "7    29.444444\n",
       "8    23.333333\n",
       "9    23.333333\n",
       "Name: temperature, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(data['temperature'] - 32) * 5 / 9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This gives us back a new ``Series`` object. If we want to change the values in our existing ``DataFrame`` to be these, we can just set the column to this new ``Series``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data['temperature'] = (data['temperature'] - 32) * 5 / 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    26.666667\n",
       "1    29.444444\n",
       "2    30.000000\n",
       "3    30.555556\n",
       "4    23.333333\n",
       "5    23.888889\n",
       "6    26.666667\n",
       "7    29.444444\n",
       "8    23.333333\n",
       "9    23.333333\n",
       "Name: temperature, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['temperature']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, it's also possible to add new columns. We could add one giving us, e.g. the ratio of rainfall to mosquitos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data['rainfall / mosquitos'] = data['rainfall'] / data['mosquitos']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "      <th>rainfall / mosquitos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2001</td>\n",
       "      <td>26.666667</td>\n",
       "      <td>157</td>\n",
       "      <td>150</td>\n",
       "      <td>1.046667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2002</td>\n",
       "      <td>29.444444</td>\n",
       "      <td>252</td>\n",
       "      <td>217</td>\n",
       "      <td>1.161290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2003</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>154</td>\n",
       "      <td>153</td>\n",
       "      <td>1.006536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2004</td>\n",
       "      <td>30.555556</td>\n",
       "      <td>159</td>\n",
       "      <td>158</td>\n",
       "      <td>1.006329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2005</td>\n",
       "      <td>23.333333</td>\n",
       "      <td>292</td>\n",
       "      <td>243</td>\n",
       "      <td>1.201646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2006</td>\n",
       "      <td>23.888889</td>\n",
       "      <td>283</td>\n",
       "      <td>237</td>\n",
       "      <td>1.194093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2007</td>\n",
       "      <td>26.666667</td>\n",
       "      <td>214</td>\n",
       "      <td>190</td>\n",
       "      <td>1.126316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2008</td>\n",
       "      <td>29.444444</td>\n",
       "      <td>197</td>\n",
       "      <td>181</td>\n",
       "      <td>1.088398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2009</td>\n",
       "      <td>23.333333</td>\n",
       "      <td>231</td>\n",
       "      <td>200</td>\n",
       "      <td>1.155000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2010</td>\n",
       "      <td>23.333333</td>\n",
       "      <td>207</td>\n",
       "      <td>184</td>\n",
       "      <td>1.125000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  temperature  rainfall  mosquitos  rainfall / mosquitos\n",
       "0  2001    26.666667       157        150              1.046667\n",
       "1  2002    29.444444       252        217              1.161290\n",
       "2  2003    30.000000       154        153              1.006536\n",
       "3  2004    30.555556       159        158              1.006329\n",
       "4  2005    23.333333       292        243              1.201646\n",
       "5  2006    23.888889       283        237              1.194093\n",
       "6  2007    26.666667       214        190              1.126316\n",
       "7  2008    29.444444       197        181              1.088398\n",
       "8  2009    23.333333       231        200              1.155000\n",
       "9  2010    23.333333       207        184              1.125000"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's probably a bit silly to have such a column. So, let's get rid of it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del data['rainfall / mosquitos']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>26.666667</td>\n",
       "      <td>157</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2002</td>\n",
       "      <td>29.444444</td>\n",
       "      <td>252</td>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2003</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>154</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2004</td>\n",
       "      <td>30.555556</td>\n",
       "      <td>159</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2005</td>\n",
       "      <td>23.333333</td>\n",
       "      <td>292</td>\n",
       "      <td>243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2006</td>\n",
       "      <td>23.888889</td>\n",
       "      <td>283</td>\n",
       "      <td>237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2007</td>\n",
       "      <td>26.666667</td>\n",
       "      <td>214</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2008</td>\n",
       "      <td>29.444444</td>\n",
       "      <td>197</td>\n",
       "      <td>181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2009</td>\n",
       "      <td>23.333333</td>\n",
       "      <td>231</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2010</td>\n",
       "      <td>23.333333</td>\n",
       "      <td>207</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  temperature  rainfall  mosquitos\n",
       "0  2001    26.666667       157        150\n",
       "1  2002    29.444444       252        217\n",
       "2  2003    30.000000       154        153\n",
       "3  2004    30.555556       159        158\n",
       "4  2005    23.333333       292        243\n",
       "5  2006    23.888889       283        237\n",
       "6  2007    26.666667       214        190\n",
       "7  2008    29.444444       197        181\n",
       "8  2009    23.333333       231        200\n",
       "9  2010    23.333333       207        184"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Don't like the order of your columns? We can make a ``DataFrame`` with a different column order by selecting them out in the order we want:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rainfall</th>\n",
       "      <th>year</th>\n",
       "      <th>mosquitos</th>\n",
       "      <th>temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>157</td>\n",
       "      <td>2001</td>\n",
       "      <td>150</td>\n",
       "      <td>26.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>252</td>\n",
       "      <td>2002</td>\n",
       "      <td>217</td>\n",
       "      <td>29.444444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>154</td>\n",
       "      <td>2003</td>\n",
       "      <td>153</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>159</td>\n",
       "      <td>2004</td>\n",
       "      <td>158</td>\n",
       "      <td>30.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>292</td>\n",
       "      <td>2005</td>\n",
       "      <td>243</td>\n",
       "      <td>23.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>283</td>\n",
       "      <td>2006</td>\n",
       "      <td>237</td>\n",
       "      <td>23.888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>214</td>\n",
       "      <td>2007</td>\n",
       "      <td>190</td>\n",
       "      <td>26.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>197</td>\n",
       "      <td>2008</td>\n",
       "      <td>181</td>\n",
       "      <td>29.444444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>231</td>\n",
       "      <td>2009</td>\n",
       "      <td>200</td>\n",
       "      <td>23.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>207</td>\n",
       "      <td>2010</td>\n",
       "      <td>184</td>\n",
       "      <td>23.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rainfall  year  mosquitos  temperature\n",
       "0       157  2001        150    26.666667\n",
       "1       252  2002        217    29.444444\n",
       "2       154  2003        153    30.000000\n",
       "3       159  2004        158    30.555556\n",
       "4       292  2005        243    23.333333\n",
       "5       283  2006        237    23.888889\n",
       "6       214  2007        190    26.666667\n",
       "7       197  2008        181    29.444444\n",
       "8       231  2009        200    23.333333\n",
       "9       207  2010        184    23.333333"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['rainfall', 'year', 'mosquitos', 'temperature']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can even have duplicates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rainfall</th>\n",
       "      <th>year</th>\n",
       "      <th>mosquitos</th>\n",
       "      <th>temperature</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>157</td>\n",
       "      <td>2001</td>\n",
       "      <td>150</td>\n",
       "      <td>26.666667</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>252</td>\n",
       "      <td>2002</td>\n",
       "      <td>217</td>\n",
       "      <td>29.444444</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>154</td>\n",
       "      <td>2003</td>\n",
       "      <td>153</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>159</td>\n",
       "      <td>2004</td>\n",
       "      <td>158</td>\n",
       "      <td>30.555556</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>292</td>\n",
       "      <td>2005</td>\n",
       "      <td>243</td>\n",
       "      <td>23.333333</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>283</td>\n",
       "      <td>2006</td>\n",
       "      <td>237</td>\n",
       "      <td>23.888889</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>214</td>\n",
       "      <td>2007</td>\n",
       "      <td>190</td>\n",
       "      <td>26.666667</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>197</td>\n",
       "      <td>2008</td>\n",
       "      <td>181</td>\n",
       "      <td>29.444444</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>231</td>\n",
       "      <td>2009</td>\n",
       "      <td>200</td>\n",
       "      <td>23.333333</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>207</td>\n",
       "      <td>2010</td>\n",
       "      <td>184</td>\n",
       "      <td>23.333333</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rainfall  year  mosquitos  temperature  year\n",
       "0       157  2001        150    26.666667  2001\n",
       "1       252  2002        217    29.444444  2002\n",
       "2       154  2003        153    30.000000  2003\n",
       "3       159  2004        158    30.555556  2004\n",
       "4       292  2005        243    23.333333  2005\n",
       "5       283  2006        237    23.888889  2006\n",
       "6       214  2007        190    26.666667  2007\n",
       "7       197  2008        181    29.444444  2008\n",
       "8       231  2009        200    23.333333  2009\n",
       "9       207  2010        184    23.333333  2010"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['rainfall', 'year', 'mosquitos', 'temperature', 'year']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember: this returns a copy. It does not change our existing ``DataFrame``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>26.666667</td>\n",
       "      <td>157</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2002</td>\n",
       "      <td>29.444444</td>\n",
       "      <td>252</td>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2003</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>154</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2004</td>\n",
       "      <td>30.555556</td>\n",
       "      <td>159</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2005</td>\n",
       "      <td>23.333333</td>\n",
       "      <td>292</td>\n",
       "      <td>243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2006</td>\n",
       "      <td>23.888889</td>\n",
       "      <td>283</td>\n",
       "      <td>237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2007</td>\n",
       "      <td>26.666667</td>\n",
       "      <td>214</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2008</td>\n",
       "      <td>29.444444</td>\n",
       "      <td>197</td>\n",
       "      <td>181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2009</td>\n",
       "      <td>23.333333</td>\n",
       "      <td>231</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2010</td>\n",
       "      <td>23.333333</td>\n",
       "      <td>207</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  temperature  rainfall  mosquitos\n",
       "0  2001    26.666667       157        150\n",
       "1  2002    29.444444       252        217\n",
       "2  2003    30.000000       154        153\n",
       "3  2004    30.555556       159        158\n",
       "4  2005    23.333333       292        243\n",
       "5  2006    23.888889       283        237\n",
       "6  2007    26.666667       214        190\n",
       "7  2008    29.444444       197        181\n",
       "8  2009    23.333333       231        200\n",
       "9  2010    23.333333       207        184"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting data from a DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just as ``pandas`` is the de-facto library for working with tabular data, ``matplotlib`` is the de-facto library for producing high-quality plots from numerical data. A saying often goes that matplotlib makes easy things easy and hard things possible when it comes to making plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to tell the notebook to render plots in the notebook itself:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we use ``matplotlib`` explicitly, ``pandas`` objects feature convenience methods for common plotting operations. These use ``matplotlib`` internally, so everything we learn later about ``matplotlib`` objects applies to these. For example, we can plot both ``\"temperature\"`` and ``\"mosquitos\"`` as a function of ``\"year\"``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f339711ce80>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAENCAYAAAAfTp5aAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f339712c208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(x='year', y=['temperature', 'mosquitos'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's load a larger dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('A2_mosquito_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset has 51 rows, instead of just 10:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "51"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f33970c72e8>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAENCAYAAAAMmd6uAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n3166IDJup82nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f33970c1f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(x='year', y=['temperature', 'mosquitos'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are [other convenience methods](http://pandas.pydata.org/pandas-docs/stable/visualization.html) for different ways of plotting the data. For example, we can get a kernel-density estimate:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3397030160>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZAAAAD/CAYAAADfaoD3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3396ff97f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['mosquitos'].plot.kde()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These convenience methods are great, but for more complex plots we can, and should, use ``matplotlib`` directly. We can make a multi-paneled figure giving both \"temperature\" and \"rainfall\" for each \"year.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f3390d64ef0>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAARsAAAF8CAYAAAD/3B/DAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f33915cf860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(4, 6))\n",
    "\n",
    "ax1 = fig.add_subplot(2, 1, 1)\n",
    "\n",
    "ax1.plot(data['year'], data['temperature'], 'ro-')\n",
    "\n",
    "ax1.set_xlabel('Year')\n",
    "ax1.set_ylabel('Temperature')\n",
    "\n",
    "ax2 = fig.add_subplot(2, 1, 2)\n",
    "\n",
    "ax2.plot(data['year'], data['rainfall'], 'bs-')\n",
    "\n",
    "ax2.set_xlabel('Year')\n",
    "ax2.set_ylabel('Rainfall')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----------\n",
    "### Challenge: plot the relationship between the number of mosquitos and temperature and the number of mosquitos and rainfall."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can do this in a similar way as we did above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f3390d15978>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAARgAAAF8CAYAAAAU66TAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nD7yQqJyz545eAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3390d340b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(4, 6))\n",
    "\n",
    "ax1 = fig.add_subplot(2, 1, 1)\n",
    "\n",
    "ax1.plot(data['temperature'], data['mosquitos'], 'ro')\n",
    "\n",
    "ax1.set_xlabel('Temperature')\n",
    "ax1.set_ylabel('Mosquitos')\n",
    "\n",
    "ax2 = fig.add_subplot(2, 1, 2)\n",
    "\n",
    "ax2.plot(data['rainfall'], data['mosquitos'], 'bs')\n",
    "\n",
    "ax2.set_xlabel('Rainfall')\n",
    "ax2.set_ylabel('Mosquitos')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the *linestyle* code on the right can be used to turn off interpolation lines, since we want to just plot the points, and don't care about their order.\n",
    "\n",
    "-------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this one dataset we see what looks like a linear relationship between mosquitos and rainfall, but not really any relationship between mosquitos and temperature. We'd like to quantify this further by applying a statistical model to the data, but we also want to do this same treatment to **all** our datasets. We need to learn a few more things, including **loops**, before continuing further."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Repetition with loops"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, an aside with strings. Most programming languages generally deal in a few basic data types, including integers, floating-point numbers, and characters. A string is a sequence of characters, and we can manipulate them easily with python. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "word = 'bird'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have a string ``'lead'`` that we've assigned the name ``word``. We can examine the string's type, and indeed any object in python, with the ``type`` builtin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(word)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also access its characters (it's a sequence!) with indexing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'b'"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'i'"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And slicing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ird'"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word[1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If I want to output each letter of ``word`` on a new line, I could do:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b\n",
      "i\n",
      "r\n",
      "d\n"
     ]
    }
   ],
   "source": [
    "print(word[0])\n",
    "print(word[1])\n",
    "print(word[2])\n",
    "print(word[3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But this doesn't scale to words of arbitrary length. Instead, let's let the computer do the boring work of iterating with a ``for`` loop:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "l\n",
      "e\n",
      "a\n",
      "d\n"
     ]
    }
   ],
   "source": [
    "for letter in word:\n",
    "    print letter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This same bit of code will work for *any* ``word``, including ridiculously long ones. Like this gem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "supercallifragilisticexpialidocious\n"
     ]
    }
   ],
   "source": [
    "word = 'supercallifragilisticexpialidocious'\n",
    "print(word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s\n",
      "u\n",
      "p\n",
      "e\n",
      "r\n",
      "c\n",
      "a\n",
      "l\n",
      "l\n",
      "i\n",
      "f\n",
      "r\n",
      "a\n",
      "g\n",
      "i\n",
      "l\n",
      "i\n",
      "s\n",
      "t\n",
      "i\n",
      "c\n",
      "e\n",
      "x\n",
      "p\n",
      "i\n",
      "a\n",
      "l\n",
      "i\n",
      "d\n",
      "o\n",
      "c\n",
      "i\n",
      "o\n",
      "u\n",
      "s\n"
     ]
    }
   ],
   "source": [
    "for letter in word:\n",
    "    print(letter)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can do more with loops than iterate through the elements of a sequence. A common use-case is building a sum. For example, getting the number of letters in ``word``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35\n"
     ]
    }
   ],
   "source": [
    "counter = 0\n",
    "for letter in word:\n",
    "    counter = counter + 1\n",
    "\n",
    "print(counter)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this very particular use, there's a python built-in, which is guaranteed to be more efficient. Use it instead:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(word)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It works on any sequence! For ``pandas.DataFrame``s it gives the length of the number of rows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "51"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lists: ordered collections of other objects (even other lists)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loops work best when they have something to iterate over. In python, the ``list`` is a built-in data structure that serves as a container for a sequence of other objects. We can make an empty list with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "stuff = list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n"
     ]
    }
   ],
   "source": [
    "print(stuff)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we can append things to the end of the list, in this case a bunch of random strings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "stuff.append('marklar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "stuff.append('chewbacca')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "stuff.append('chickenfingers')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['marklar', 'chewbacca', 'chickenfingers']"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stuff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just like with the ``word`` example, we can iterate through the elements of the list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "marklar\n",
      "chewbacca\n",
      "chickenfingers\n"
     ]
    }
   ],
   "source": [
    "for item in stuff:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And slicing works too! Slicing with negative numbers counts backwards from the end of the list. The slice ``stuff[-2:]`` could be read as \"get the 2nd-to-last element of ``stuff``, through the last element\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chewbacca\n",
      "chickenfingers\n"
     ]
    }
   ],
   "source": [
    "for item in stuff[-2:]:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So indexing with ``-1`` gives the last element:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'chickenfingers'"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stuff[-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And consistently, but uselessly, slicing from and to the same index yeilds nothing. Remember, for a slice ``n:m``, it reads as \"get all elements starting with element ``n`` and up to but not including element ``m``.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stuff[0:0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lists are mutable: that is, their elements can be changed. For example, we can replace ``'chewbacca'`` with ``'hansolo'``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'chewbacca'"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stuff[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "stuff[1] = 'hansolo'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'hansolo'"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stuff[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "...or replace ``'hansolo'`` with a ``DataFrame``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "stuff[1] = data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>1960</td>\n",
       "      <td>82</td>\n",
       "      <td>200</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1961</td>\n",
       "      <td>70</td>\n",
       "      <td>227</td>\n",
       "      <td>194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1962</td>\n",
       "      <td>89</td>\n",
       "      <td>231</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1963</td>\n",
       "      <td>74</td>\n",
       "      <td>114</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1964</td>\n",
       "      <td>78</td>\n",
       "      <td>147</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1965</td>\n",
       "      <td>85</td>\n",
       "      <td>151</td>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1966</td>\n",
       "      <td>86</td>\n",
       "      <td>172</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1967</td>\n",
       "      <td>75</td>\n",
       "      <td>106</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1968</td>\n",
       "      <td>70</td>\n",
       "      <td>276</td>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1969</td>\n",
       "      <td>86</td>\n",
       "      <td>165</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1970</td>\n",
       "      <td>83</td>\n",
       "      <td>222</td>\n",
       "      <td>198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1971</td>\n",
       "      <td>78</td>\n",
       "      <td>297</td>\n",
       "      <td>247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1972</td>\n",
       "      <td>87</td>\n",
       "      <td>288</td>\n",
       "      <td>248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1973</td>\n",
       "      <td>76</td>\n",
       "      <td>286</td>\n",
       "      <td>239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1974</td>\n",
       "      <td>86</td>\n",
       "      <td>231</td>\n",
       "      <td>202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1975</td>\n",
       "      <td>90</td>\n",
       "      <td>284</td>\n",
       "      <td>243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1976</td>\n",
       "      <td>76</td>\n",
       "      <td>190</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1977</td>\n",
       "      <td>87</td>\n",
       "      <td>257</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1978</td>\n",
       "      <td>88</td>\n",
       "      <td>128</td>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1979</td>\n",
       "      <td>87</td>\n",
       "      <td>218</td>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1980</td>\n",
       "      <td>81</td>\n",
       "      <td>206</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1981</td>\n",
       "      <td>74</td>\n",
       "      <td>175</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1982</td>\n",
       "      <td>85</td>\n",
       "      <td>202</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1983</td>\n",
       "      <td>71</td>\n",
       "      <td>130</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1984</td>\n",
       "      <td>80</td>\n",
       "      <td>225</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1985</td>\n",
       "      <td>72</td>\n",
       "      <td>196</td>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1986</td>\n",
       "      <td>76</td>\n",
       "      <td>261</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1987</td>\n",
       "      <td>85</td>\n",
       "      <td>111</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1988</td>\n",
       "      <td>83</td>\n",
       "      <td>247</td>\n",
       "      <td>210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1989</td>\n",
       "      <td>86</td>\n",
       "      <td>137</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1990</td>\n",
       "      <td>82</td>\n",
       "      <td>159</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1991</td>\n",
       "      <td>77</td>\n",
       "      <td>172</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1992</td>\n",
       "      <td>74</td>\n",
       "      <td>280</td>\n",
       "      <td>231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1993</td>\n",
       "      <td>70</td>\n",
       "      <td>291</td>\n",
       "      <td>238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1994</td>\n",
       "      <td>77</td>\n",
       "      <td>126</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1995</td>\n",
       "      <td>89</td>\n",
       "      <td>191</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1996</td>\n",
       "      <td>83</td>\n",
       "      <td>298</td>\n",
       "      <td>248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1997</td>\n",
       "      <td>80</td>\n",
       "      <td>282</td>\n",
       "      <td>237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1998</td>\n",
       "      <td>86</td>\n",
       "      <td>219</td>\n",
       "      <td>195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1999</td>\n",
       "      <td>72</td>\n",
       "      <td>143</td>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2000</td>\n",
       "      <td>79</td>\n",
       "      <td>262</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2001</td>\n",
       "      <td>85</td>\n",
       "      <td>189</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2002</td>\n",
       "      <td>86</td>\n",
       "      <td>205</td>\n",
       "      <td>186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2003</td>\n",
       "      <td>72</td>\n",
       "      <td>195</td>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2004</td>\n",
       "      <td>78</td>\n",
       "      <td>148</td>\n",
       "      <td>146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2005</td>\n",
       "      <td>71</td>\n",
       "      <td>262</td>\n",
       "      <td>219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2006</td>\n",
       "      <td>88</td>\n",
       "      <td>255</td>\n",
       "      <td>226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2007</td>\n",
       "      <td>79</td>\n",
       "      <td>262</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2008</td>\n",
       "      <td>73</td>\n",
       "      <td>198</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2009</td>\n",
       "      <td>86</td>\n",
       "      <td>215</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>2010</td>\n",
       "      <td>87</td>\n",
       "      <td>127</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year  temperature  rainfall  mosquitos\n",
       "0   1960           82       200        180\n",
       "1   1961           70       227        194\n",
       "2   1962           89       231        207\n",
       "3   1963           74       114        121\n",
       "4   1964           78       147        140\n",
       "5   1965           85       151        148\n",
       "6   1966           86       172        162\n",
       "7   1967           75       106        112\n",
       "8   1968           70       276        230\n",
       "9   1969           86       165        162\n",
       "10  1970           83       222        198\n",
       "11  1971           78       297        247\n",
       "12  1972           87       288        248\n",
       "13  1973           76       286        239\n",
       "14  1974           86       231        202\n",
       "15  1975           90       284        243\n",
       "16  1976           76       190        175\n",
       "17  1977           87       257        225\n",
       "18  1978           88       128        133\n",
       "19  1979           87       218        199\n",
       "20  1980           81       206        184\n",
       "21  1981           74       175        160\n",
       "22  1982           85       202        187\n",
       "23  1983           71       130        126\n",
       "24  1984           80       225        200\n",
       "25  1985           72       196        173\n",
       "26  1986           76       261        222\n",
       "27  1987           85       111        121\n",
       "28  1988           83       247        210\n",
       "29  1989           86       137        142\n",
       "30  1990           82       159        152\n",
       "31  1991           77       172        160\n",
       "32  1992           74       280        231\n",
       "33  1993           70       291        238\n",
       "34  1994           77       126        125\n",
       "35  1995           89       191        178\n",
       "36  1996           83       298        248\n",
       "37  1997           80       282        237\n",
       "38  1998           86       219        195\n",
       "39  1999           72       143        134\n",
       "40  2000           79       262        221\n",
       "41  2001           85       189        175\n",
       "42  2002           86       205        186\n",
       "43  2003           72       195        173\n",
       "44  2004           78       148        146\n",
       "45  2005           71       262        219\n",
       "46  2006           88       255        226\n",
       "47  2007           79       262        221\n",
       "48  2008           73       198        176\n",
       "49  2009           86       215        187\n",
       "50  2010           87       127        129"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stuff[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['marklar',     year  temperature  rainfall  mosquitos\n",
      "0   1960           82       200        180\n",
      "1   1961           70       227        194\n",
      "2   1962           89       231        207\n",
      "3   1963           74       114        121\n",
      "4   1964           78       147        140\n",
      "5   1965           85       151        148\n",
      "6   1966           86       172        162\n",
      "7   1967           75       106        112\n",
      "8   1968           70       276        230\n",
      "9   1969           86       165        162\n",
      "10  1970           83       222        198\n",
      "11  1971           78       297        247\n",
      "12  1972           87       288        248\n",
      "13  1973           76       286        239\n",
      "14  1974           86       231        202\n",
      "15  1975           90       284        243\n",
      "16  1976           76       190        175\n",
      "17  1977           87       257        225\n",
      "18  1978           88       128        133\n",
      "19  1979           87       218        199\n",
      "20  1980           81       206        184\n",
      "21  1981           74       175        160\n",
      "22  1982           85       202        187\n",
      "23  1983           71       130        126\n",
      "24  1984           80       225        200\n",
      "25  1985           72       196        173\n",
      "26  1986           76       261        222\n",
      "27  1987           85       111        121\n",
      "28  1988           83       247        210\n",
      "29  1989           86       137        142\n",
      "30  1990           82       159        152\n",
      "31  1991           77       172        160\n",
      "32  1992           74       280        231\n",
      "33  1993           70       291        238\n",
      "34  1994           77       126        125\n",
      "35  1995           89       191        178\n",
      "36  1996           83       298        248\n",
      "37  1997           80       282        237\n",
      "38  1998           86       219        195\n",
      "39  1999           72       143        134\n",
      "40  2000           79       262        221\n",
      "41  2001           85       189        175\n",
      "42  2002           86       205        186\n",
      "43  2003           72       195        173\n",
      "44  2004           78       148        146\n",
      "45  2005           71       262        219\n",
      "46  2006           88       255        226\n",
      "47  2007           79       262        221\n",
      "48  2008           73       198        176\n",
      "49  2009           86       215        187\n",
      "50  2010           87       127        129, 'chickenfingers']\n"
     ]
    }
   ],
   "source": [
    "print(stuff)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also add a list as an element to itself:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "stuff.append(stuff)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So now ``stuff[-1]`` points to the same list the name ``stuff`` does:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stuff[-1] is stuff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's important to recognize that these are exactly the same list. Modifications to ``stuff[-1]`` will be seen in ``stuff``. These are just names, and in this case they refer to the same object in memory.\n",
    "\n",
    "Lists can contain lists can contain lists can contain lists...and of course any number of other python objects. Even functions can be included as elements:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "stuff.append(len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['marklar',     year  temperature  rainfall  mosquitos\n",
       " 0   1960           82       200        180\n",
       " 1   1961           70       227        194\n",
       " 2   1962           89       231        207\n",
       " 3   1963           74       114        121\n",
       " 4   1964           78       147        140\n",
       " 5   1965           85       151        148\n",
       " 6   1966           86       172        162\n",
       " 7   1967           75       106        112\n",
       " 8   1968           70       276        230\n",
       " 9   1969           86       165        162\n",
       " 10  1970           83       222        198\n",
       " 11  1971           78       297        247\n",
       " 12  1972           87       288        248\n",
       " 13  1973           76       286        239\n",
       " 14  1974           86       231        202\n",
       " 15  1975           90       284        243\n",
       " 16  1976           76       190        175\n",
       " 17  1977           87       257        225\n",
       " 18  1978           88       128        133\n",
       " 19  1979           87       218        199\n",
       " 20  1980           81       206        184\n",
       " 21  1981           74       175        160\n",
       " 22  1982           85       202        187\n",
       " 23  1983           71       130        126\n",
       " 24  1984           80       225        200\n",
       " 25  1985           72       196        173\n",
       " 26  1986           76       261        222\n",
       " 27  1987           85       111        121\n",
       " 28  1988           83       247        210\n",
       " 29  1989           86       137        142\n",
       " 30  1990           82       159        152\n",
       " 31  1991           77       172        160\n",
       " 32  1992           74       280        231\n",
       " 33  1993           70       291        238\n",
       " 34  1994           77       126        125\n",
       " 35  1995           89       191        178\n",
       " 36  1996           83       298        248\n",
       " 37  1997           80       282        237\n",
       " 38  1998           86       219        195\n",
       " 39  1999           72       143        134\n",
       " 40  2000           79       262        221\n",
       " 41  2001           85       189        175\n",
       " 42  2002           86       205        186\n",
       " 43  2003           72       195        173\n",
       " 44  2004           78       148        146\n",
       " 45  2005           71       262        219\n",
       " 46  2006           88       255        226\n",
       " 47  2007           79       262        221\n",
       " 48  2008           73       198        176\n",
       " 49  2009           86       215        187\n",
       " 50  2010           87       127        129, 'chickenfingers', [...], <function len>]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stuff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lists also have their own methods, which usually alter the list itself:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "stuff.reverse()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<function len>,\n",
       " [...],\n",
       " 'chickenfingers',\n",
       "     year  temperature  rainfall  mosquitos\n",
       " 0   1960           82       200        180\n",
       " 1   1961           70       227        194\n",
       " 2   1962           89       231        207\n",
       " 3   1963           74       114        121\n",
       " 4   1964           78       147        140\n",
       " 5   1965           85       151        148\n",
       " 6   1966           86       172        162\n",
       " 7   1967           75       106        112\n",
       " 8   1968           70       276        230\n",
       " 9   1969           86       165        162\n",
       " 10  1970           83       222        198\n",
       " 11  1971           78       297        247\n",
       " 12  1972           87       288        248\n",
       " 13  1973           76       286        239\n",
       " 14  1974           86       231        202\n",
       " 15  1975           90       284        243\n",
       " 16  1976           76       190        175\n",
       " 17  1977           87       257        225\n",
       " 18  1978           88       128        133\n",
       " 19  1979           87       218        199\n",
       " 20  1980           81       206        184\n",
       " 21  1981           74       175        160\n",
       " 22  1982           85       202        187\n",
       " 23  1983           71       130        126\n",
       " 24  1984           80       225        200\n",
       " 25  1985           72       196        173\n",
       " 26  1986           76       261        222\n",
       " 27  1987           85       111        121\n",
       " 28  1988           83       247        210\n",
       " 29  1989           86       137        142\n",
       " 30  1990           82       159        152\n",
       " 31  1991           77       172        160\n",
       " 32  1992           74       280        231\n",
       " 33  1993           70       291        238\n",
       " 34  1994           77       126        125\n",
       " 35  1995           89       191        178\n",
       " 36  1996           83       298        248\n",
       " 37  1997           80       282        237\n",
       " 38  1998           86       219        195\n",
       " 39  1999           72       143        134\n",
       " 40  2000           79       262        221\n",
       " 41  2001           85       189        175\n",
       " 42  2002           86       205        186\n",
       " 43  2003           72       195        173\n",
       " 44  2004           78       148        146\n",
       " 45  2005           71       262        219\n",
       " 46  2006           88       255        226\n",
       " 47  2007           79       262        221\n",
       " 48  2008           73       198        176\n",
       " 49  2009           86       215        187\n",
       " 50  2010           87       127        129,\n",
       " 'marklar']"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stuff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "``list.pop`` removes the element at the given index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>1960</td>\n",
       "      <td>82</td>\n",
       "      <td>200</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1961</td>\n",
       "      <td>70</td>\n",
       "      <td>227</td>\n",
       "      <td>194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1962</td>\n",
       "      <td>89</td>\n",
       "      <td>231</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1963</td>\n",
       "      <td>74</td>\n",
       "      <td>114</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1964</td>\n",
       "      <td>78</td>\n",
       "      <td>147</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1965</td>\n",
       "      <td>85</td>\n",
       "      <td>151</td>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1966</td>\n",
       "      <td>86</td>\n",
       "      <td>172</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1967</td>\n",
       "      <td>75</td>\n",
       "      <td>106</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1968</td>\n",
       "      <td>70</td>\n",
       "      <td>276</td>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1969</td>\n",
       "      <td>86</td>\n",
       "      <td>165</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1970</td>\n",
       "      <td>83</td>\n",
       "      <td>222</td>\n",
       "      <td>198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1971</td>\n",
       "      <td>78</td>\n",
       "      <td>297</td>\n",
       "      <td>247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1972</td>\n",
       "      <td>87</td>\n",
       "      <td>288</td>\n",
       "      <td>248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1973</td>\n",
       "      <td>76</td>\n",
       "      <td>286</td>\n",
       "      <td>239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1974</td>\n",
       "      <td>86</td>\n",
       "      <td>231</td>\n",
       "      <td>202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1975</td>\n",
       "      <td>90</td>\n",
       "      <td>284</td>\n",
       "      <td>243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1976</td>\n",
       "      <td>76</td>\n",
       "      <td>190</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1977</td>\n",
       "      <td>87</td>\n",
       "      <td>257</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1978</td>\n",
       "      <td>88</td>\n",
       "      <td>128</td>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1979</td>\n",
       "      <td>87</td>\n",
       "      <td>218</td>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1980</td>\n",
       "      <td>81</td>\n",
       "      <td>206</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1981</td>\n",
       "      <td>74</td>\n",
       "      <td>175</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1982</td>\n",
       "      <td>85</td>\n",
       "      <td>202</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1983</td>\n",
       "      <td>71</td>\n",
       "      <td>130</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1984</td>\n",
       "      <td>80</td>\n",
       "      <td>225</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1985</td>\n",
       "      <td>72</td>\n",
       "      <td>196</td>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1986</td>\n",
       "      <td>76</td>\n",
       "      <td>261</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1987</td>\n",
       "      <td>85</td>\n",
       "      <td>111</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1988</td>\n",
       "      <td>83</td>\n",
       "      <td>247</td>\n",
       "      <td>210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1989</td>\n",
       "      <td>86</td>\n",
       "      <td>137</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1990</td>\n",
       "      <td>82</td>\n",
       "      <td>159</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1991</td>\n",
       "      <td>77</td>\n",
       "      <td>172</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1992</td>\n",
       "      <td>74</td>\n",
       "      <td>280</td>\n",
       "      <td>231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1993</td>\n",
       "      <td>70</td>\n",
       "      <td>291</td>\n",
       "      <td>238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1994</td>\n",
       "      <td>77</td>\n",
       "      <td>126</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1995</td>\n",
       "      <td>89</td>\n",
       "      <td>191</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1996</td>\n",
       "      <td>83</td>\n",
       "      <td>298</td>\n",
       "      <td>248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1997</td>\n",
       "      <td>80</td>\n",
       "      <td>282</td>\n",
       "      <td>237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1998</td>\n",
       "      <td>86</td>\n",
       "      <td>219</td>\n",
       "      <td>195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1999</td>\n",
       "      <td>72</td>\n",
       "      <td>143</td>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2000</td>\n",
       "      <td>79</td>\n",
       "      <td>262</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2001</td>\n",
       "      <td>85</td>\n",
       "      <td>189</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2002</td>\n",
       "      <td>86</td>\n",
       "      <td>205</td>\n",
       "      <td>186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2003</td>\n",
       "      <td>72</td>\n",
       "      <td>195</td>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2004</td>\n",
       "      <td>78</td>\n",
       "      <td>148</td>\n",
       "      <td>146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2005</td>\n",
       "      <td>71</td>\n",
       "      <td>262</td>\n",
       "      <td>219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2006</td>\n",
       "      <td>88</td>\n",
       "      <td>255</td>\n",
       "      <td>226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2007</td>\n",
       "      <td>79</td>\n",
       "      <td>262</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2008</td>\n",
       "      <td>73</td>\n",
       "      <td>198</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2009</td>\n",
       "      <td>86</td>\n",
       "      <td>215</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>2010</td>\n",
       "      <td>87</td>\n",
       "      <td>127</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year  temperature  rainfall  mosquitos\n",
       "0   1960           82       200        180\n",
       "1   1961           70       227        194\n",
       "2   1962           89       231        207\n",
       "3   1963           74       114        121\n",
       "4   1964           78       147        140\n",
       "5   1965           85       151        148\n",
       "6   1966           86       172        162\n",
       "7   1967           75       106        112\n",
       "8   1968           70       276        230\n",
       "9   1969           86       165        162\n",
       "10  1970           83       222        198\n",
       "11  1971           78       297        247\n",
       "12  1972           87       288        248\n",
       "13  1973           76       286        239\n",
       "14  1974           86       231        202\n",
       "15  1975           90       284        243\n",
       "16  1976           76       190        175\n",
       "17  1977           87       257        225\n",
       "18  1978           88       128        133\n",
       "19  1979           87       218        199\n",
       "20  1980           81       206        184\n",
       "21  1981           74       175        160\n",
       "22  1982           85       202        187\n",
       "23  1983           71       130        126\n",
       "24  1984           80       225        200\n",
       "25  1985           72       196        173\n",
       "26  1986           76       261        222\n",
       "27  1987           85       111        121\n",
       "28  1988           83       247        210\n",
       "29  1989           86       137        142\n",
       "30  1990           82       159        152\n",
       "31  1991           77       172        160\n",
       "32  1992           74       280        231\n",
       "33  1993           70       291        238\n",
       "34  1994           77       126        125\n",
       "35  1995           89       191        178\n",
       "36  1996           83       298        248\n",
       "37  1997           80       282        237\n",
       "38  1998           86       219        195\n",
       "39  1999           72       143        134\n",
       "40  2000           79       262        221\n",
       "41  2001           85       189        175\n",
       "42  2002           86       205        186\n",
       "43  2003           72       195        173\n",
       "44  2004           78       148        146\n",
       "45  2005           71       262        219\n",
       "46  2006           88       255        226\n",
       "47  2007           79       262        221\n",
       "48  2008           73       198        176\n",
       "49  2009           86       215        187\n",
       "50  2010           87       127        129"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stuff.pop(-2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "...while ``list.remove`` removes the first instance of the given element in the list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "stuff.remove('chickenfingers')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So now our list looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<function len>, [...], 'marklar']"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stuff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot all the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we know how to do loops and use lists in Python, we'll use these to make a plot for each dataset. First, we'll import a module called ``glob``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import glob"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use this to grab all the filenames matching a \"globbing\" pattern:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A1_mosquito_data.csv',\n",
       " 'A3_mosquito_data.csv',\n",
       " 'B2_mosquito_data.csv',\n",
       " 'A2_mosquito_data.csv',\n",
       " 'B1_mosquito_data.csv']"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glob.glob(\"*.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"Globbing\" is the name of the shell-completion patterns common for shells like ``sh`` and ``bash``."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Challenge: write a for-loop that produces a plot of mosquito population vs. temperature and mosquito population vs. rainfall for each dataset we have."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can grab the block of code we wrote previously, and loop through it for each file obtained by globbing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A1_mosquito_data.csv\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAARgAAAF8CAYAAAAU66TAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDjBmVowDjJkV8/8BKKdP8eg83NkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3390c561d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A3_mosquito_data.csv\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAARgAAAF8CAYAAAAU66TAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nHxsLr7aCKASqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3390c56fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "B2_mosquito_data.csv\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAARgAAAF8CAYAAAAU66TAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3390aacf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A2_mosquito_data.csv\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAARgAAAF8CAYAAAAU66TAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nD7yQqJyz545eAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3390ad8978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "B1_mosquito_data.csv\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAARgAAAF8CAYAAAAU66TAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKMCISGoUYEQkNQowIpKa/w9ORNdQHLLvnAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3390a14dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import glob\n",
    "\n",
    "files = glob.glob('*.csv')\n",
    "\n",
    "for file in files:\n",
    "    \n",
    "    print(file)\n",
    "    data = pd.read_csv(file)\n",
    "    \n",
    "    fig = plt.figure(figsize=(4, 6))\n",
    "\n",
    "    ax1 = fig.add_subplot(2, 1, 1)\n",
    "\n",
    "    ax1.plot(data['temperature'], data['mosquitos'], 'ro')\n",
    "\n",
    "    ax1.set_xlabel('Temperature')\n",
    "    ax1.set_ylabel('Mosquitos')\n",
    "\n",
    "    ax2 = fig.add_subplot(2, 1, 2)\n",
    "\n",
    "    ax2.plot(data['rainfall'], data['mosquitos'], 'bs')\n",
    "\n",
    "    ax2.set_xlabel('Rainfall')\n",
    "    ax2.set_ylabel('Mosquitos')\n",
    "    \n",
    "    # tell matplotlib to render the plot *now*\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like there's a similar pattern for each area we have data for. We'd like to quantify more closely the relationship between mosquito population and each of these variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Applying a statistical model to the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are many statistics packages in Python, but one of the most well-developed and widely-used is [``statsmodels``](http://statsmodels.sourceforge.net/):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll use our second dataset for the initial stab at fitting a statistical model to our data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('A2_mosquito_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And let's convert the temperatures to Celsius, for good measure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data['temperature'] = (data['temperature'] - 32) * 5 / 9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can build an ordinary least-squares fit to the data using ``statsmodels.OLS``. Remember we can check the docs with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sm.OLS?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to fit the relationship between mosquito population and, for a start, rainfall:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "regr_results = sm.OLS(data['mosquitos'], data['rainfall']).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>   0.996</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.996</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>1.386e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 09 May 2016</td> <th>  Prob (F-statistic):</th> <td>8.69e-63</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:02:14</td>     <th>  Log-Likelihood:    </th> <td> -196.25</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    51</td>      <th>  AIC:               </th> <td>   394.5</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    50</td>      <th>  BIC:               </th> <td>   396.4</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</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>rainfall</th> <td>    0.8811</td> <td>    0.007</td> <td>  117.744</td> <td> 0.000</td> <td>    0.866     0.896</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 4.422</td> <th>  Durbin-Watson:     </th> <td>   1.878</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.110</td> <th>  Jarque-Bera (JB):  </th> <td>   1.959</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.085</td> <th>  Prob(JB):          </th> <td>   0.376</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.055</td> <th>  Cond. No.          </th> <td>    1.00</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:              mosquitos   R-squared:                       0.996\n",
       "Model:                            OLS   Adj. R-squared:                  0.996\n",
       "Method:                 Least Squares   F-statistic:                 1.386e+04\n",
       "Date:                Mon, 09 May 2016   Prob (F-statistic):           8.69e-63\n",
       "Time:                        23:02:14   Log-Likelihood:                -196.25\n",
       "No. Observations:                  51   AIC:                             394.5\n",
       "Df Residuals:                      50   BIC:                             396.4\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
       "------------------------------------------------------------------------------\n",
       "rainfall       0.8811      0.007    117.744      0.000         0.866     0.896\n",
       "==============================================================================\n",
       "Omnibus:                        4.422   Durbin-Watson:                   1.878\n",
       "Prob(Omnibus):                  0.110   Jarque-Bera (JB):                1.959\n",
       "Skew:                          -0.085   Prob(JB):                        0.376\n",
       "Kurtosis:                       2.055   Cond. No.                         1.00\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr_results.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, this gives us a lot of information. But is this what we want? \n",
    "\n",
    "We actually want to quantify which variable correlates more with the mosquito population. One way we can build a multivariate model with statsmodels is to use a **formula**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>   0.997</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.997</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   7889.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 09 May 2016</td> <th>  Prob (F-statistic):</th> <td>3.68e-61</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:02:15</td>     <th>  Log-Likelihood:    </th> <td> -111.54</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    51</td>      <th>  AIC:               </th> <td>   229.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    48</td>      <th>  BIC:               </th> <td>   234.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</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>   17.5457</td> <td>    2.767</td> <td>    6.341</td> <td> 0.000</td> <td>   11.983    23.109</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>temperature</th> <td>    0.8719</td> <td>    0.092</td> <td>    9.457</td> <td> 0.000</td> <td>    0.687     1.057</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>rainfall</th>    <td>    0.6967</td> <td>    0.006</td> <td>  125.385</td> <td> 0.000</td> <td>    0.686     0.708</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 1.651</td> <th>  Durbin-Watson:     </th> <td>   1.872</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.438</td> <th>  Jarque-Bera (JB):  </th> <td>   0.906</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.278</td> <th>  Prob(JB):          </th> <td>   0.636</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.343</td> <th>  Cond. No.          </th> <td>1.92e+03</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:              mosquitos   R-squared:                       0.997\n",
       "Model:                            OLS   Adj. R-squared:                  0.997\n",
       "Method:                 Least Squares   F-statistic:                     7889.\n",
       "Date:                Mon, 09 May 2016   Prob (F-statistic):           3.68e-61\n",
       "Time:                        23:02:15   Log-Likelihood:                -111.54\n",
       "No. Observations:                  51   AIC:                             229.1\n",
       "Df Residuals:                      48   BIC:                             234.9\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===============================================================================\n",
       "                  coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
       "-------------------------------------------------------------------------------\n",
       "Intercept      17.5457      2.767      6.341      0.000        11.983    23.109\n",
       "temperature     0.8719      0.092      9.457      0.000         0.687     1.057\n",
       "rainfall        0.6967      0.006    125.385      0.000         0.686     0.708\n",
       "==============================================================================\n",
       "Omnibus:                        1.651   Durbin-Watson:                   1.872\n",
       "Prob(Omnibus):                  0.438   Jarque-Bera (JB):                0.906\n",
       "Skew:                          -0.278   Prob(JB):                        0.636\n",
       "Kurtosis:                       3.343   Cond. No.                     1.92e+03\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.92e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr_results = sm.OLS.from_formula('mosquitos ~ temperature + rainfall', data).fit()\n",
    "regr_results.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each of these results can be accessed as **attributes** of the ``Results`` object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept      17.545739\n",
      "temperature     0.871943\n",
      "rainfall        0.696717\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(regr_results.params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.996966873691\n"
     ]
    }
   ],
   "source": [
    "print(regr_results.rsquared)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our model \"explains\" most of the data. Nice! Let's plot the predicted population of mosquitos from our fitted model against the measured population to see how well we did:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f338d98c908>"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAENCAYAAAAVPvJNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f338d979cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "figure = plt.figure()\n",
    "\n",
    "ax = figure.add_subplot(1,1,1)\n",
    "\n",
    "parameters = regr_results.params\n",
    "predicted = parameters['Intercept'] + parameters['temperature'] * data['temperature'] + parameters['rainfall'] * data['rainfall']\n",
    "ax.plot(predicted, data['mosquitos'], 'ro')\n",
    "\n",
    "ax.set_xlabel('predicted mosquito population')\n",
    "ax.set_ylabel('measured mosquito population')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The more linear this plot is, the better our model fits the data. Also, it turns out that, as you might have guessed from the plots we made previously, rainfall is the better predictor than average temperature for how many mosquitoes we expect to see."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept        6.341405\n",
       "temperature      9.456614\n",
       "rainfall       125.385116\n",
       "dtype: float64"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr_results.tvalues"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----------------\n",
    "### Challenge: for each dataset, print the t-value for temperature and rainfall, and plot the statsmodel results along with plots for the relationships between mosquitos and the two variables separately."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can take our block of code we worked on previously, and make a three-panel plot instead of just a two-panel one:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A1_mosquito_data.csv\n",
      "Intercept       3.589733\n",
      "temperature     3.456159\n",
      "rainfall       56.825601\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAIkCAYAAAAEdbwuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f338d9ac668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A3_mosquito_data.csv\n",
      "Intercept       9.658621\n",
      "temperature    10.881139\n",
      "rainfall       70.798256\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAIkCAYAAAD1fuejAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZTg5mJlZhpODmZll/D+ms0tShyKycgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3390885320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "B2_mosquito_data.csv\n",
      "Intercept        8.778676\n",
      "temperature     11.647932\n",
      "rainfall       125.016860\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAIkCAYAAAAEdbwuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZrkcIMzMLJcDhJmZ5fp/sdYkJFMAqSQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f338d7e5d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A2_mosquito_data.csv\n",
      "Intercept        6.341405\n",
      "temperature      9.456614\n",
      "rainfall       125.385116\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAIkCAYAAAAEdbwuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmZnlcoAwM7NcDhBmZpbr/wEOph0NH/OvUAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f338d7acb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "B1_mosquito_data.csv\n",
      "Intercept      11.016743\n",
      "temperature    13.396049\n",
      "rainfall       43.072783\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAIkCAYAAAD1fuejAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3390978e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "filenames = glob.glob('*.csv')\n",
    "\n",
    "for filename in filenames:\n",
    "    print(filename)\n",
    "    data = pd.read_csv(filename)\n",
    "    \n",
    "    # convert temperatures to celsius from fahrenheit\n",
    "    data['temperature'] = (data['temperature'] - 32) * 5/9\n",
    "    \n",
    "    # perform fit\n",
    "    regr_results = sm.OLS.from_formula('mosquitos ~ temperature + rainfall', data).fit()\n",
    "    print(regr_results.tvalues)\n",
    "    \n",
    "    fig = plt.figure(figsize=(6, 9))\n",
    "\n",
    "    # plot predicted vs. measured mosquito populations from fitted model\n",
    "    parameters = regr_results.params\n",
    "    predicted = parameters['Intercept'] + parameters['temperature'] * data['temperature'] + parameters['rainfall'] * data['rainfall']\n",
    "    \n",
    "    ax0 = fig.add_subplot(3, 1, 1)\n",
    "    ax0.plot(predicted, data['mosquitos'], 'gd')\n",
    "    \n",
    "    ax0.set_xlabel('predicted mosquito population')\n",
    "    ax0.set_ylabel('measured mosquito population')\n",
    "   \n",
    "    # plot population vs. temperature\n",
    "    ax1 = fig.add_subplot(3, 1, 2)\n",
    "\n",
    "    ax1.plot(data['temperature'], data['mosquitos'], 'ro')\n",
    "    ax1.set_xlabel('Temperature')\n",
    "    ax1.set_ylabel('Mosquitos')\n",
    "\n",
    "    # plot population vs. rainfall\n",
    "    ax2 = fig.add_subplot(3, 1, 3)\n",
    "\n",
    "    ax2.plot(data['rainfall'], data['mosquitos'], 'bs')\n",
    "\n",
    "    ax2.set_xlabel('Rainfall')\n",
    "    ax2.set_ylabel('Mosquitos')\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For all areas, it looks as if rainfall is indeed the stronger predictor of mosquito population than temperature. But what if this is just the beginning of the number of datasets we have to analyze? Are we going to just copy this block of code around each time we want to analyze new data?\n",
    "\n",
    "No. That would be awful."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Writing functions: we need to break things up"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have code blocks we've been copying and pasting around a lot, and this is fine for a while. But once we've built some useful things, we want to re-use them, and we don't want to maintain many versions of the same thing lying around."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def square(x):\n",
    "    x_squared = x ** 2\n",
    "    return x_squared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "square(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "square(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Functions are also **composable**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "square(square(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can improve on this a bit by adding **documentation**. In Python, we can write a **docstring** for our function directly in its definition:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def square(x):\n",
    "    \"\"\"Return the square of the value, `x`.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    x : float\n",
    "        Number to obtain the square of.\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    square : float\n",
    "        The square of `x`.\n",
    "    \n",
    "    \"\"\"\n",
    "    x_squared = x ** 2\n",
    "    return x_squared"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And this docstring is now what we see when we use the builtin help system:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function square in module __main__:\n",
      "\n",
      "square(x)\n",
      "    Return the square of the value, `x`.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    x : float\n",
      "        Number to obtain the square of.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    square : float\n",
      "        The square of `x`.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(square)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or alternatively, in the notebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "square?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------------\n",
    "### Challenge: write your fahrenheit to celsius conversion as a function that takes temperatures in fahrenheit and converts them to temperatures in celsius"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is one way we could write it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def fahrenheit_to_celsius(temp):\n",
    "    \"\"\"Convert temperature in fahrenheit to celsius.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    temp : float or array_like\n",
    "        Temperature(s) in fahrenheit.\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    float or array_like\n",
    "        Temperature(s) in celsius.\n",
    "    \n",
    "    \"\"\"\n",
    "    return (temp - 32) * 5 / 9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that this same function will work for ``pandas.Series`` objects, ``numpy`` arrays, and single numbers. Writing functions in this way takes advantage of the fact that these objects are roughly [**duck-typed**](https://en.wikipedia.org/wiki/Duck_typing): they all behave as if they were single numbers, so doing arithmetic operations with them yields the expected result.\n",
    "\n",
    "------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----------------\n",
    "### Challenge: write a function that prints the t-value for temperature and rainfall, and plots the statsmodel results along with plots for the relationships between mosquitos and the two variables separately."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's do our model building and plotting in a single function, returning a useful panel plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def analyze(data):\n",
    "    \"\"\"Return panel plot of mosquito population vs. temperature, rainfall.\n",
    "    \n",
    "    Also prints t-values for temperature and rainfall.\n",
    "    \n",
    "    Panel plot gives: \n",
    "        1. comparison of modeled values of mosquito population vs. actual values\n",
    "        2. mosquito population vs. average temperature\n",
    "        3. mosquito population vs. total rainfall\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    data : DataFrame\n",
    "        DataFrame giving columns for average temperature, \n",
    "        total rainfall, and mosquito population during mosquito\n",
    "        breeding season for each year.\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    Figure\n",
    "        :mod:`matplotlib.figure.Figure` object giving panel plot.\n",
    "    \n",
    "    \"\"\"\n",
    "    # perform fit\n",
    "    regr_results = sm.OLS.from_formula('mosquitos ~ temperature + rainfall', data).fit()\n",
    "    print(regr_results.tvalues)\n",
    "    \n",
    "    fig = plt.figure(figsize=(6, 9))\n",
    "\n",
    "    # plot prediction from fitted model against measured mosquito population\n",
    "    parameters = regr_results.params\n",
    "    predicted = parameters['Intercept'] + parameters['temperature'] * data['temperature'] + parameters['rainfall'] * data['rainfall']\n",
    "    \n",
    "    ax0 = fig.add_subplot(3, 1, 1)\n",
    "    ax0.plot(predicted, data['mosquitos'], 'go')\n",
    "    \n",
    "    ax0.set_xlabel('predicted mosquito population')\n",
    "    ax0.set_ylabel('measured mosquito population')\n",
    "   \n",
    "    # plot population vs. temperature\n",
    "    ax1 = fig.add_subplot(3, 1, 2)\n",
    "\n",
    "    ax1.plot(data['temperature'], data['mosquitos'], 'ro')\n",
    "    ax1.set_xlabel('Temperature')\n",
    "    ax1.set_ylabel('Mosquitos')\n",
    "\n",
    "    # plot population vs. rainfall\n",
    "    ax2 = fig.add_subplot(3, 1, 3)\n",
    "\n",
    "    ax2.plot(data['rainfall'], data['mosquitos'], 'bs')\n",
    "\n",
    "    ax2.set_xlabel('Rainfall')\n",
    "    ax2.set_ylabel('Mosquitos')\n",
    "\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that since this is Python, and everything is an object, we can return our ``Figure`` object. This is generally a useful thing to do for plotting functions, since in different contexts you may want to modify the resulting ``Figure`` before rendering any plots of it.\n",
    "\n",
    "--------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now re-write our big loop from before, and it should look **a lot** simpler."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A1_mosquito_data.csv\n",
      "Intercept       3.589733\n",
      "temperature     3.456159\n",
      "rainfall       56.825601\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAIkCAYAAAAEdbwuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMzMrcoIwM7MiJwgzMytygjAzs6L/A9gYWda9wCHmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f338d979b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A3_mosquito_data.csv\n",
      "Intercept       9.658621\n",
      "temperature    10.881139\n",
      "rainfall       70.798256\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAIkCAYAAAD1fuejAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n5Tg4mJlZjoODmZnlODiYmVnO/wN955uoy/pVAQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f338d3fa160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "B2_mosquito_data.csv\n",
      "Intercept        8.778676\n",
      "temperature     11.647932\n",
      "rainfall       125.016860\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAIkCAYAAAAEdbwuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f338d619390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A2_mosquito_data.csv\n",
      "Intercept        6.341405\n",
      "temperature      9.456614\n",
      "rainfall       125.385116\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAIkCAYAAAAEdbwuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XmcXHWZ7/HPkwQEEzEQlSVC0jY3CIwsIrKZkE4AERgU\ndRxiNyRGJ3MvmxgcFyAk3HaQzcAIiIKyGqI4yoArxEAWyHLRDIgIREk3SGDYs3QgQMhz//j9qru6\nu6r7dHWfqlPV3/frVa+uOlV16unTXfWr81uex9wdERGRroZUOgAREckmNRAiIlKQGggRESlIDYSI\niBSkBkJERApSAyEiIgUNS2vHZrYtcAtQB7wNzAZWA7cSGqbngSnAVuBGYBzwFjDd3VenFZeIiCST\nWgNB+PB/2d1PNrNRwHJCA3GFu99hZnOBfwa2AV5y91PMbDwwFzghxbhERCQBS2uhnJkdCbzi7o+Y\n2QhC4+DuPjrevyPwDkKDcK27L43bn3H396cSlIiIJJbaGIS7L46Nw77APcA1QJuZXWVm9wJXApuB\nUcDLeU/dmlZMIiKSXKqD1GZ2ATAPOJfQIOwOXObuk4C/AOcRGod35z1NuT9ERDIgzUHqKcBHgIPd\n/a247SGgLT7kFWAHYCHwWWC5mR0LLC2yPzUcIiIlcHcr5XlpnkEcC4wF7jaz+2K30unA7Wb2e+Dj\nwCWEmU67mdmDwNfjpSB3z/xl9uzZFY+hVuKshhgVp+LM+qU/UjuDcPepRe46qsC2KWnFISIipdFC\nORERKUgNxACbOHFipUNIpBrirIYYQXEONMWZHamtgxhoZubVEquISFaYGV7iIHWvYxAxZcZHgXfm\ntrn7PaW8mIiIVI8kg9R3AgY8F287YeGbiIjUsCQNxHbu3pB6JCIiNaSltYVZc2exdsNaRu8wmuaZ\nzdSNrSv5cZXQ6xiEmd0EXOjuLWWJqHgcGoMQkarQ0trC0WcczZP7PwnbAm/CHn/YgwN3PZD1rG9v\nCIBuj6t/uJ4FVy8YsEaiP2MQSRqIZ4CdCSkxnJBwb7dSXqw/1ECISLVoOquJee+aFz70c94k5ImY\nTHtDsO/O+3LXLnd1e1zjxkZ+/N0fD0gsqQ5SuzKrioj0ydoNa0Ma0nzb0rGwYFt4cv8n2bhoI+zR\n/XHPbng2/SAT6HUdhJkdYGYPmdlaM1tsZh8sR2AiItWmpbWFprOa+Mtf/xKyzK3Lu/NNwnSfnG3B\n3rSwnc6P222HsnfSFJSki+k+4Ex3/7OZfRi4xN2PLkt0neNQF5OIZFahcQfuBQ4lLBLIXR8Zn/Am\nfHLtJ/nzS3/O7BhEkpXU5u5/BnD3VcDQhEFta2Y/MbOVZrbMzI7Ou+84M1uWd/s7ZvZgfOwRff0l\nREQqbdbcWR0f9BB+ToKd79+ZE//nRHbffveO1WSxIbhizhUsuHoBjRsbaWhpoHFj44A2Dv2VZJrr\nBjP7FKH9O4zk9RryS46+B1gGjDOz4cBFwGsAZjYJ+IC7H2xmYwnrLvbv028hIlJhxcYd9tl3H+78\n0Z3t01mf3fAsu+2wG81Xd0xnHagB6YGWpIGYAVwGfAv4a7ydRCuwKl7fDAyP1y8iVJf7Qrx9FPAL\nAHdvtWCku+f33omIVETSdQqjdxgdupW6zEjKjSfUja3LbENQTNEuJjPLzV7aAWgGPkOo1ZCoi8m7\nlxy93MxyPXB35z20a8nRDXT00omIVExuXGHe1nksemoR8x6ax34n7seS+5d0e2zzzGbqH67vGHSO\n3Ui59Q7VqKcziJnx8gM6upUsXp+UZOex5OingbOBBwjj+p8GRuQ97BU6lxwdCbxUaH9z5sxpvz5x\n4sRBkU1RRCqjpbWFSZ+fRCutoe/kaOC90PZmG8d/9Xj+9JM/dTqTqBtbx4KrFxTtRiqXRYsWsWjR\nogHZV5JZTB9397vzbh/p7ot73XEoOToF+Iy7v2VmewO3Ay8C2wP7AD+Nl9Pc/TPxMde5+/gC+9Ms\nJhEpi4Izku4DDiF8hR3gxWxpSmWhnJlNJowPTDGz2/Lu+hywZ4J955ccNcIK7A/FfY8B5rv7jHj7\nRDNbBbxB8jEOEZFUFJyR1ECYajORTC1mS1NPXUwvAY8DrwNPxG1OGIvoVQ8lR3H3p4DD825/Ock+\nRUTKoehK6FwnRoYWs6WpaAPh7g8DD5vZHe6+IbfdzM4AHi5HcCIilVBsRhJGx+Dz1dU7+JxUkoVy\n55nZy2a23sw2E7qORESqVi4lRsO0BprOaqKltXOy6kIzkkYsHMGhow7N3GK2NCUZpF4JjCeshfgO\n8FV3P6sMsXWNQ4PUItKjJGsWCg1AF0pv0W1hW4bqNPRF2um+l7r7eDP7vrv/bzO7390/VlKk/aAG\nQkR6kvSDv1gq7mqZldRXaediWhbXM2wysx8B7yjlhURE0lRo5tGT+z/JrLmzOj1u7Ya1nRuH+NjB\nMCupr5LUg/i6mW0Tbx4PfDPdkERE+q7YzKOuH/y9pcSQDj2tg/g2hRPzfRQ4N7WIRESivtRrTvrB\n3zyzmRVnrOjWFTUYZiX1VdExCDPraR3DzalFVITGIEQGl0JjCtvdsx0f3/fjXDHnipIHn3OPrYUB\n6CTSHqSe0HWbu3fPVJUyNRAig0tPdZ3rd9AHf1JpNxDz49UhwL7AJnc/pJQX6w81ECKDS8O0BhbV\nLep+x33AEbU762igpZKLKcfdp+S90FDgpoRBbQvcAtQBbwOz4+tdBKwHngGmxYffCIwD3gKmu/vq\npL+AiNSO/DGH1tWtMJrCq5k166gskhQMaufub5vZdgkfnl9RbhSwHNgKTHb3tWZ2KTCVMBD+kruf\nYmbjgbnACX2JS0SqX6cxhFHACBj2u2FsOWwLPEr4mvkscBCadVQmvTYQZvYc4UPcCMWCvp9w3610\nVJR7g1AD4iJ3Xxu3bSIkzj0IuBbA3ZfmdWmJyCCQO2tY8McFvDD0hVCMeFvgvbBlzBZYDJxIR9rt\nX8Muf9+F5ts06yhtSbqYdi1lx7maEbGi3PXAZe5+tZkNA75CSBs+ATiGzhXltpbyeiJSfTqdNRxD\n97oLjwMn0Tnt9vEwdMHQQT/4XA69rqQ2s8PM7BEzW2tmy8xs/6Q7jyuw5wHnuvsVZrYXsBLYBfio\nu79M94pyGokWGSSK1l14KN7ehoKrnjfZpnKFOKglGYO4Cvh8rC+9H3AD8JHenhQryn0EODhWlDPg\nZ8Dp7r4076ELgc8Cy83sWGBp970FKjkqUlt6rLvwJgxrG8aWN7d0G6geOVRl64spd8nRxe5+ZN7t\nJe7ebW1EgefdDBxIKDxkwAeAHYE/0FHb+iZgPmG2055AG9CUN06Rvz9NcxWpMcXWOux8z84cddhR\nnDD+BE65+BS2HLulfQxi2O+GsfA/FjLhY71+DAnpr4O4Ffg74Zv9h+PlBwDufk8pL1oKNRAitSfJ\n6ucl9y9h6temsu7tdYwcOpKbL71ZjUMfpN1A3FjkLnf36aW8aCnUQIjUJq1+TleqDUR8gQOAeuBh\nd/9bKS/UX2ogRET6LtV6EGb2FcI6hQnAj83sX0t5IRERqS5JupiWA0e4+9a4huE+dx9flug6x6Ez\nCJEq0pdU3ZKeVHMxAVvcfSuAu2+J+ZhERIrqljbjTVhxxoqCGVglu5KUHP29md1hZmeZ2W3AH9MO\nSkSqW9Lyn5JtvTYQ7n4hcDVhTeNP3f3M1KMSkYpqaW2h6awmGqY10HRWEy2tLX16vuo+14YkyfrG\nAWcDY4FHzGyVu/897cBEpDIGontIdZ9rQ5IuphuB6wizmH4C/DTViESkogaie6h5ZjP1D9eHRgI6\n6j7PVAbWapKkgdjq7r9091fd/S5CVnYRqVED0T1UN7aOBVcvoHFjIw0tDTRubNQAdRVKMovpcTOb\nQUeqjadjtxOq/CZSO3LTUv/y17/AGkKlllxOvBK6h+rG1qkkaJVLsg7iviJ3ubtP6uF5hUqOvg1c\nBmwBFrj7+XFtRa8lR7UOQiQ9hXIicS9wKPDO7vmRpHqkXZO6oZQdU7jk6NvARHd/3swWmNlBwP6o\n5KjIgCh1cVrBugyTOrKqNl+tRW6DUZ9qUvdRK91Ljj7m7s/Hbb8lDHwfjEqOipQs1yj87dm/8egz\nj9I2ua3Ps4+K1WXYZ9991E00iCUZpC6Juy+ORYb2Be4Bvkfn0qIbCD2cO6GSoyIlyXUNzXvXPFa+\nvDI0DiXMPmqflppP01IHvURnEGb2SUJBn1XuXmxMotDzLgA+TVhH8RzhjCFnJ+AF+lByVBXlRDrr\n1DXklDz7qHlmMyvOWNGtLkPz1ZqWWm3KXVHuUkI1uAcIH/CPuPsFve44lBydAnwmlhwdAjwCTAZe\nJJQm/1Lc597ufk4sOdrk7k0F9qdBapEuGqY1sKhuUbixCDicbovTGjc2JuomUl2G2pR2waD73f1j\nebcXufvEBEF1LTnqwEXA5YTZSvPdfa6ZbYNKjoqUpOmsJuZtnQePErqIXgKOBt4bbm9/z/Y8+rNH\n9UE/iKWdzdXMbEhM923A9kl27O5Ti9x1QJfHvUU40xCRBPJnKg1rG8awNcPY8omOms38kpA5bTgc\ns+8xahykZEkaiJuBZWa2DPgI8F/phiQixXTLk7QQ+ASdp6f+I7AU6neo54o5V1QqVKkBSRqI/yKM\nF+xP6ApSoj6RCum2XmEIBQemd357Zy1sk34rOs3VzD5gZh8nTFGtI0xL3ZnwnUVEKqBbniSj4PTU\now46So2D9FtPZxAfAj5FaBRyYwQOfD/toESksG5ptA8gpMSYhKanyoBLMotpvLsvLVM8PcWhWUwy\n6BXKmbT78t05sO5ANr69UdNTpZtUZjGZ2dXufgZwqZl1+mR298NLeTGRwabU3Eg97eu9I97L2/e9\nzS677UL9bvU036AGQdJR9AzCzHaOSfXGdL3P3Z9KPbLu8egMQqpKoW/79Q/Xc8M3buC626/rU6NR\nbF8aiJbepL1QrtuqaXf/v6W8WH+ogZBq03RWE/PeNa/zoPKLMOIPIzpyJiX8oC+4rz6skpbBqz8N\nRJJkfc/Hy4vAboQM8SLSi4KV2R6lpIR6A1HlTaSvktSD+EH+bTO7Pr1wRGpHtxlHECqilPBBX3Bf\nyrYqKev1DMLMxuVdDgS6jUn08vyTzezb8fpHzWxJvNwUE/hhZt8xswfNbKWZHVHSbyKSMc0zmxmx\ncETHOoU3CefiJaTVbp7ZTP3D9Z32Vf9wPc0zNZ1V0tPXkqNvAN9z97t63XHI23Q38DHgSnc/18zu\nJ5YUNbNbgZ8TFuCd6e4nmdlY4E5337/A/jQGIZlVbLbSoZ89lJUvrwwriIyQF/kxoIE+DzYr26qU\nIpMlR93dY/ruUwn1piHUot4pnjnsQMjeehTwi/icVgtGuvu6Ul5XpNy65UfKq+S25257snKvlZ27\nhraHsfeNpW5cXfigT1jOs25snQakpax6bSDM7BlgV8LJ8XuB9YR03e7uPZ4Xxwyw+V/7rwEWAM8Q\nKsetAP6JwpXm1EBI5hQ6UyhUzzk38FywEE9rPQtu0/RUyb4kyfqWA19z9xYz2wuYVaigT2/M7J3A\npUC9u79gZucD5xMah/yKciMJWe27UUU5qaRiZwrv2e49ITFNvjjwXDe2jgVXL+jcNZTwjEGkFOWu\nKLfE3Sfk3V7q7uMTv4DZVGAv4FuEsiYfdPc3zGw6oetpAXCau3/GzPYGriu0f41BSDkVO1MotBZh\n7H1jaW1o1RoFyaS0Cwa1mtklwDLgMEId6T5z99fiWcO9ZraZMP4wzd1fNbMTzWwVYRB8Rin7F0mq\nt/QXS+5fwvFfPT6sV0hwprDLHrsw9OGhqucsNSfJGcQ2wDRC3sgngWvd/fX0Q+sWh84gpN96S1nR\n0trCfifuR9s/tvXpTCF3hqEZRpI1aafa+CCwB7ARuAC43N3LXhNCDYQMhG4pK9YBf4RRb4ziXUPf\nxYa2Dbwy8hWY3P25hz52KC+2vah8SFJV0k61cS0hzcYZQDNwcSkvJJIFnVJWrANWAuPh5eNeprWh\nlVeGvhJWOxdYzFb/vtAYNG5spKGlgcaNjWocpKYlGYPA3f/bzIa5+zIzey3toETS0illxUN0LFgj\n/jyOMG3iPjotZhuxcATNP2nWWgQZVJKcQWwxsx8CfzSzT6QdkEiaOqWscArmRWIz8GHCtIyFMOKX\nI/j15b/WmYIMOkkaiCZgCTCXsEbhlFQjEklRbl1C48ZGdt60c8GuJN4F/Al2fnNnGg9o5E93/YkJ\nH5tQYG8itS3JIPVIYA5QD6wCLnP3tvRD6xaHBqllQBWa0cR9wIfjameNL0gNSHsW088Jb5sHgCOB\nQ9x9Sikv1h9qICQNuTURT77wJP/z9P90lPHUNFWpEWk3EIvcfWLe7YXuXmASYLrUQIiI9F3a01zN\nzHaJV0YBI0p5IRERqS5Jprl+E1hkZq8Tkuqdnm5IIiKSBb12MbU/MJw9vFKpfh51MdW+3nIk9fVx\nIpL+GMRsQgK99hforQ5El+efDOzv7t80szHArYSureeBKYS6EDcSMru+Raw4V2A/aiBqWG85kvr6\nOBEJ0h6DOAHY3d13y10SBmVmdg9wA2FJEoSCQVe4+8eAp4B/JlSce8ndDyF0Z83t6y8h1a+96M5r\nwD3Ar+HJJ59k/GfG09La0v1xBYrziMjAStJAPE1YOtQn8ev+scBp0J4V9kB3vyM+pJmQ1CC/5OhS\nQtZYGWTWblgbGoflwETgJODTsHbbtRw5/cj2RqJTLqWcWJxHRAZWkgaiBXjOzNaYWYuZrUm6c3ff\nSsfZwyigzcy+a2b3AlcSkhqMonPJ0a1J9y+1Y/QOo+GPhCyq+bmRJsHft/y9/QyhPZdSvjdhtx0S\n93qKSEJJZjEdA7wf2NTP19oI7E5IF/60mX0dOI/uJUeLDjSo5Gjtap7ZzM8//XM2b7u58x3bAkM6\nzhAK1nhWcR6RduUuOXobMKPU9Bq5kqPufq6ZLQNOcPdXzOxfgLHAGmAfdz/HzI4FmgrVvNYgde37\n5Bc/yV273NWtIA9LofGAjvKduVlMKs4j0ru0ZzE9BrwP+Cvh2727++F9CC6/gfgwcGm8ax0wHXgd\nuAXYk1CGtMnd1xbYjxqIGtfS2sLEf53I0x95uiM30r2w+/a7s/iGxWoEREqQdgMxpus2d3+qlBfr\nDzUQg0NLawtnN5/NysdXwptw6L6HcsWcK9Q4iJQo1QYiK9RAVBctZhPJBjUQkilazCaSHWkvlBPp\nEy1mE6kNRae5mtlyikw57csgtQw+azesDatb8mkxm0jV6WkdxMnx50XAXcAK4GPAfmkHJdnW2/hC\n+2K2LtNVtZhNpLqUUjDobnf/eNqBFYhDYxAZkGR8QWMQItmR9hiEm9lxZraDmX0C2K6UF5Jsa2lt\noemsJhqmNdB0VlOnBHn5kowv1I2tY8HVC2jc2EhDSwONGxvVOIhUoSSpNk4FLiEscHsc+EKqEUnZ\ndfrGPwp4E1acsaLgh3rS8YW6sXXtK59FpDr1egbh7n8n1HC4CriAkLxPakhfZh0pWZ7I4NFrA2Fm\nlwBNwL8ARwDfSTsoKa++pNBuntlM/cP1HY1ELlneTCXLE6k1ScYgxrt7I7DR3a8HDu7LC5jZyWb2\n7S7bjouJ+3K3v2NmD5rZSjM7oi/7l2R6GmPoy1mBxhdEBo8kYxBvmdl2hMHqxCPh8bF3E6bGXpm3\nfThh6uxr8fYk4APufrCZjQXuBPZP+jrSu57GGADaNrSx3fLt2HzM5kQptDW+IDI4JGkgLiPU+Rod\nf/5Hkh27u8f03acS6k3nXEQoPZob7M6vKNcaS5WOdPd1yX4F6U2xMYazm8/m0ecfDfftCCyF7dq2\n45gDj+HKq6/UWYHIINdjAxHPAlYDRxI+5Fvd/aWkO3f3rWbWvnjBzA4DRhLOLHINRNeKchviY9RA\nDJBiM49WPr6S5yc+HxqObYHJsPnNzbxr47vUOIhIzw1EPAu41t0nA3/ozwvFmtSXAJ8GRuTd9Qqd\nK8qNBAo2QqooV5piK5v9TVd9Z5EaU+6Kcr8FDFiV2+bu5yZ+gVgwiFAU6GfAi8D2wD7AT+PlNHf/\njJntDVzn7uML7EcrqUtUbGXzP7znH7hz9J3dGo7GjY0aYxCpEf1ZSZ1kDOInpey4K3d/HPgQtBch\nmu/uM+LtE81sFfAGMGMgXk865GYedSrTGQeg/3zGn1XfWUQKSnIGMaHrNndfklpExePQGUQCfS3U\no/rOIrUt7ZKj8+PVIcC+wCZ3P6SUF+sPNRDddW0MZnxuBtMvnq4keSLSrmwV5cxsKHCTu59Syov1\nhxqIzgqNK4z45Qja/rFNYwoi0q5sFeXc/W2UzTUTCq1taBvZpllJIjJgkuRies7Mno0/XwAeK0Nc\n0ouC+ZOGokR6IjJgep3F5O67liMQSSY37vCXv/4F1gAHEVaOAOwLIxaOoG1ym2YliUi/JRmkPgl4\nH/A6oSbEpe4+twyxdY1j0I9BFBp34F7gUOCdoTG44Rs3cN3t12lWkogA6c9iWgYcB/wAmAo84O4H\nlfJi/VGrDURfpqU2ndXEvHfN6zYIvfM9O3PUYUepMRCRbtJeKPc2sBnY4u6bzey1Ul5Iums/Ixj7\nJDwFvA13nngnv/7er5nwsW7LT4rmVNpn3300S0lEBlySWUx/I+RhusvMvgasTTekwWPW3FmhcVgF\nHA5MhrZ/bOP4rx5fsCa0qrmJSDkl6WIyYHd3f9rM/gF43d2fLEt0neOouS6mhmkNLHpqUWgcEqxd\nKJZTSQvhRKSYtLuYJgBNcZEchAJA43p4fNfgTgb2d/dvmtknCPUg1gPPANPiw26M+3wLmO7uq5Pu\nv5qN3mF06MBLuHahWE4lNQ4ikoYkZxD/j1CH+rPAH4E3k8xi6lpRzt3PNbMngEnuvtbMLgWeABz4\nkLt/xczGA1939xMK7C8TZxA9DSqXkgdpvxP30+pnEUlN2rOY7nX3SWZ2tbufYWYL3P3ohIENIVaU\niw3EGe5+dbxvNtBGmMl/rbsvjdufcff3F9hXxRuInrp4gJK6f5bcv4Tjv3p8t7UL6jYSkYGQdgPx\ne+Bi4BTgeuCH7v7BPgQ3FdgrV0PCzIYBXyF0L00AbgO+4u5/ifc/7e57FNhPxRuIYtNMGzc2AhS9\nr7czAWVUFZG0pD0GcQphfOByoBmYU8oLAZjZXoQGYRHwUXffZGZdK8pVvh+piGLTTJ/d8CyOF72v\nN3Vj69SdJCKZkyTVxnNmtj9wGHAu/cvF9DPg9Fx3UrSQML6x3MyOBZYWfCaVLzlarHRn+zTTnu4T\nESmDcpccvQR4P6Fs6A+Avd19ZuIX6Cg5ej3wMGFNhRHOFG4C5hPKke5JGJNocvduay2y0MWUxhiE\niEiaUk+14e6Hm9l97t5gZksL1YxOWxYaCOh5vEBjCSKSNWk3EIuBjwO/ASYDSwZzAyEiUk3SLhh0\nGbAc+If489pSXijLWlpbaDqriYZpDTSd1VQwzYWIyGCTqOSome1AmMnU4u4vpx5V4RhSOYNQ+goR\nqWVpdzEdC/wr8I7cNnc/rpQX64+0Goie1jZo6qmIVLu010FcBswAajLNd09rG0REBrMkDcQz7r48\n9UgqpNe1DSIig1SSLqbzgOOBx3Pb3H16ynEVikNjECIifZT2GMTjhBXUm3Lb3P3uUl6sP9Kc5qr1\nCyJSq9JuIH7t7seXFNkA0joIEZG+S3uQeoiZLSDUgtgKkMvMKiIitStJA/GT1KMQEZHM6XUltbvf\n3PXSlxcws5PN7Nvx+mQz+6OZrTSzb8Vtw8zs1rjtfjNLXM40iwYqi2LaqiHOaogRFOdAU5zZkSTV\nRkksuAe4gY4aD9cAx7n7IcAhZnYQoeLcS3HbN4Fey5lmWbX801RDnNUQIyjOgaY4syO1BiKOKB8L\nnAYQzwzWuvvz8SG/JVSUOwr4RXzOUuCAtGISEZHkUmsgANx9Kx1nD6OA/DxOG4CRwE5dtm9NMyYR\nEUkmUbK+fr1AR8Ggm4Gr3P2YuP1rhLUVR8Tty+P2p9x9TIH9aI6riEgJ0pzmOlBWA6PNbBfgReAE\n4EvAGyQoOVrqLygiIqUpWwPh7m5mZwO/A94C5rv7ajNrAW4xsweJJUfLFZOIiBSXeheTiIhUp1QH\nqUtVDWsnusT4CTP7bzNbZGY/jvFVPMauceZtO87MluXd/o6ZPRhjPaL8UXY7nmPMbEk8bj83s22z\neDzN7KMxziVmdpOZDYnbK3I843H6SXzdZWZ2tJlNytr7p0icmXsPFYoz777MvIeKHM+BeQ+5e2Yu\ngAH3EGpPXBS3PQ7sHK8vAA4CpgNXxG3jgV9VOMYngNHx+qXAFysZY7E44/bhwEPAsnh7EnBHvD4W\neLjScQK/Ak6K1+cCp2TxeAL3A+Pi9VuBT1XyeAJTgWvi9VGEcb/HsvT+6SHOxzP4HsqP8z3A6ng9\na++hQsdzQN5DmTqD8BB5ptdOdI0xusrd18brmwjTdyu6vqNInAAXERYs5uTH2UpY4ziyHDHG1+z6\nN98GONDd74gPaSZ8sGXxeG4BdopnDjsQxtAqeTxbge/H628AI4Bns/T+iVrpHufVWXsP0TnOzYSG\nATL2HqLw8RyQ91CmGgiojrUTXWLE3a+Op2//BnwOuInusZd9fUfXOM3sMMLxy0/XXuwYl02Bv3mb\nmX3XzO4FriS8OTN3PAkfEguAR4E9gRVU8Hi6+2J3f8TM9iWc7XyvSCyVfv90jfOyLL6HCsR5uZkd\nSsbeQwXivIbwHrqqv++hck5zLcUrdD7QOwEvxO3vztte0ZF2M9sLuA1YBHzU3TeZWdZi3Aa4BPg0\n4RtGTtc4RwIvlTG0rjYCuwOXu/vTZvZ14DzCP3aWjuc7CV0h9e7+gpmdD5xP9zjLejzN7ALC3/hs\n4DnCGUNOZt4/+XG6+6Ksvoe6HM8HgIVk8D3UJc4HCe+Zy/r7HsrcGUQX7WsnzGwoYe3EAuBewtoJ\nrIe1E2X0M8I/+jnuniustJBsxVgP7AjcDswH9jWz64Df0xHn3sCr7t5WqSDj8XuI0F0D4c33Jtk7\nnjnr48/6SDEgAAAgAElEQVRcEfP8OMt6PM1sCvAR4GB3XwT8lQy+f7rGaWZGBt9DBY7nnmTwPdQ1\nzoF8D2X6DMI9+2snzKyOMDB1YfxHd8Lp8c1ZiRHA3R8HPgRhlhDhWM6It080s1WE/ssZlYuy3RnA\n7eFwso4wuPY62Tqer8WzhnvNbHOMaZq7v1rB43ks4X/x7rz/xS+TvfdP1zg/QPjgzdp7qNvxdPcs\nvocK/d1PZwDeQ1oHISIiBWW9i0lERCpEDYSIiBSkBkJERApSAyEiIgWpgRARkYLUQIiISEFqIKSi\nzGy2mc0ws/3juoJij/tyzHmUZJ9TrUsG20oxs4+b2Zfi9cS/Q4rx3Ghmx/Rw/yfN7APx+n+WLzLJ\nIjUQkoq4YCcxd3/Y3b/Vw0POpm8LOzOxwMfd73b3H8abff0dKuFTwP8CcPfPVjgWqbCs/7NKRlmo\nNf7PwDbA+4DvufsPzOw+YE14iH0J+C6wN/BO4Dx3v9fMGoGZhDQVDvzUzI4E/re7TzGzM4FGQort\n2+JjdgFuAU6OZxpHAdsDV7r7fDObDFxMyOX0OvBwgXhPJiQoqyek5/4Y8H7g39z9d2Y2g5A62Qmp\nnL9mZhMIOaxeB14lpE0eBdwYf/c1wBh3n2Rmz7n7rvH15gPXAnXABwl5kXYhrGT9PHA1sC+wLSEb\n8G1d4n2ekH11L0IajyZgKHAdsBvhvTvb3RfGldG/IqTy3hSP3d654xn31x5bvD08Htt3xctlwP8Q\nVuUeaGYPAQ+5+65mdjjw7XhcXiak4t4fuDD+DXcDfu/u30RqSznyletSexfCB+n98fo7gb8BOwP3\nAVPi9unAv8frIwm5gXYi1CjYLm7/DSE1wZGED6y9gOWExmEb4Lb4uDXx9qS8be8A/hL3+QTwvrj9\nWvJqYOTFuyBe/zQdufwPA/6L8K15BTAkbv8Z4dv0j4BT47aTgHHAPODYuG0acG+8/mze680nJMqb\nSkf9iDWEBuGLhIYt/9i9p0u8bwP/K16/HDiHkLb57LjtvcDaeJxagKPj9jOBq3LHM29/z8afNwLH\nAP8A/HPcdkjesbkxb1+55zwO7JK3/yvj/v8S/ybbAK9U+n9Sl4G/qItJ+uMBCHmJCCmv3x+3/zH+\n/DBwQkw5fAfhQ68eeMzdN8fHrOyyz/2ABzx4y90/n3efxX1+JO7zd3ScEWxy9xfi41YUiTcX1ybC\nhx6EAkDbxtdd5iGlN8CyuN/zgP3M7HbgE4QzlAMIjRh5P3Px5fT03jqQkDAv/9iN6fKYl9z9r/H6\nA4RcOwfkPe9F4EXC2QyE4kW5n133VSie9cAxZvY94F8JZyfdfg8zGwW87u7/k7f/+nj94fg3egtY\nZ2bb9vA7SxVSAyH9cSC0d1fsTci+m+8J4GZ3nwScSPhW3gLsY2bviOMUh3d5zuq8/b7bzBbmjWdY\n3Ofv4j6PJnz7/zMw0szeEx9XrORjT/nv/wYcnPdaE4BVwFmEKlyfIxRmORV4knDmAaEyV87QGPdQ\nQvdRVx5/h7/lnm9mIwhnJV2P3Sgz2yNe/xjwSJfn7Qa8w91zaaU/nPfYP9NROAYzG01HQ5JzDnCf\nu59GqCGQH2O+V4B3x4YCwpnDqgK/W5/GnKQ6aAxC+mNYHHPYEWh2941mlv8B8wPguvhtfyhwqbu/\nZGYXE77l/50uefPd/WEzu89Cvd8hwFx3dzNbTmhsTjazifF1twF+5O6vm9lpwD1m9iyhv79P4uv+\nBlhhZq8DS9z9Pgt1NO4wszcIFeROITRKP7RQ3ObZvN1838x+RijOsonuVhAylE4FbjSzJYQP1m+4\n+8Yuj90AXGJmY4FngK8RjvNNMb2zEbqqcr5oZpfE1z6J0EBsNbMfEY59rqpc7u/zX8C1ZvYFwpnY\nrhYKSv0/4NKYmZR47E8HfmNmbYR6Ev9CGO/I/1tnYlKADKzUsrnG081bCIN0bwOzCd+SbiW88Z8H\nphC+1d1I+Bb1FjDd3bt+m5KMiYO+e7n7uZWOpZIsFLq5Np7RDOR+Ow0q9/LYFkJt7LcGMgaRNM8g\npgAvx298owh9tasJp+t3mNlcOmbBvOTup5jZeEKB7RNSjEukGvTlm1uu60pkQKV5BnEkYWbDI7Gf\ndTXhjHV0vH9HwiyUuYRvYEvj9mfc/f3F9isiIuWR2iC1p1hIW0RE0pfqLCYLhbTnAecSGoTdCYW0\nJxHmUGeuGL2IiASpjUFY50Lab8VtXQtp70BHIe3l1kMh7S6zY0REJCF3L2mMKs0ziPxC2vfFbqVc\nIe3fAx8npDC4BdgtFtL+erwUVOlVhf25zJ49u+IxDMbYFX/lL4q/spf+SO0Mwt2nFrnrqALbpqQV\nh4iIlEYrqUVEpCA1EGUyceLESodQsmqOHRR/pSn+6pXaOoiBZmZeLbGKiGSFmeEZHKQWEZEqpgZC\nREQKUjZXkSr1VEsLN82axda1axkyejTTmpsZU1dX6bD6pRZ/p2qmMQipOvoQCcfgqqOP5sInn2Q4\nIbf47Pp6zlywoGqPRS3+TlnQnzGIii/i6MNiDxdpXbPGz6mv9zZwB28DP6e+3lvXrKl0aGU1p7Gx\n/Rh43rGY09hY6dBKVou/UxbEz86SPnc1BiFV5aZZs9q/YQIMBy588klumjWrkmGV3da1a9uPQc5w\nYOuzzxZ6eFWoxd+p2qmBkKqiD5FgyOjR3UrWbQKG7LZbJcIZELX4O1U7NRBSVfQhEkxrbmZ2fX37\nscj1109rbq5kWP1SDb/TUy0tXNjUxOyGBi5sauKplpZKh5SqspYcdfcF8b7jgPPd/fB4+zuEIvFb\ngZnu/kCB/XlasUr10EBmh/bB+mefZchuu9XEYH2Wf6dq/d/rzyB1mg3EVOCj7n66mb0HWObu48xs\nOPAA8Jq7H25mk4Az3f2kWKD9Tnffv8D+1EAIkO0PEaldFzY18dV58zp1cW4CLm9sZPaPf1ypsHrV\nnwYizXUQrcCqeH0ztB/XiwjV5b4Qbx8F/ALA3VstGOnu61KMTQZYOaeejqmry/QbUmrTYBz/SjPd\n92KAWHL0euByMzsUGAncTUcD0bXk6Ib4mLI2EJpbX7qCp94rVmT+1FukL3LjX13PIGp5/CvVldSx\n5OingbMJ3UoL4+0ReQ97hc4lR0cCLxXa35w5c9qvT5w4ccCyLOoDrn+KTT29fNasqv6mry8Nkm9a\nczOzV6zoPgaRoUF0gEWLFrFo0aKB2VmpCyh6uxCKAN0FbBNv7w08AtwLLAfWA9cBk4Gf5z1maZH9\n9X/FSBFaoNM/F0yc2OnY5S4XNDRUOrSSaUGeFNK6Zo3PaWz0CxoafE5jY1X8P9CPhXJpnkHklxy1\nGOSHAMxsDDDf3WfE2yea2SrgDWBGijEVNBj7FgdSLZ561+pZkfTPYBv/qkTJUdz9KeDwvNtfTiuO\nJGrxA66cquXUuy/0pUFE2VyB2vyAK6cxdXWcuWABl+dNPT2zyvvr9aVBRNlc22luveSr1kVRIl1l\ncqHcQNNCudJoJk7p9KVBaoEaiAFQix+k+hYsImog+qlWP0irNTWAiAyc/jQQyuZK7dYY0EwcEekP\nzWKidj9INRNHBota7CLOAjUQ1O4HqabvSrUp5YNeqXJSVOoS7HJfSDHVRi2nVajG1AAyOJX6PlSq\nnJ6R0VQbVaMWF3rlDLbUAFK9Sk1vUqtdxFmQWgNRqKJcfL2LCIn6ngGmxYffCIwD3gKmu/vqtOIq\nRh+k/aM+YOmvUj/oa7WLOAvSPIOYArzs7ieb2ShCBtetwGR3X2tmlwJTAQdecvdTzGw8MBc4IcW4\nZIBVQx/wA0uWMHfqVIa/+iqbdtyRmTffzBETJlQ6LMlT6ge9xtpSVGrfVG8X4EjgQ/H6COBZ4Iy8\n+2cD5wC3AePztj9TZH8D3DMnAyXrfcD3L17sU4cN69S3PXXYML9/8eJKhyZ5+jMWqLG24sjiGIR3\nryh3mbtfbWbDgK8AnwMmAMfQuaLc1rRiSoO6VrLfBzx36lRu2bKlU9/2NVu2cOrUqRzR0lLJ0CRP\nf8YC1UWcjrJVlHP3RWa2F+GMYRHwUXffZGZdK8pVx9JuqqNrpRyy3gc8/NVXCzZgw9ep7HnW6IM+\nW9IcpJ4CfAQ42N3fikWDfgac7u5L8x66EPgssNzMjgWWdt9bkFbJ0VKpqEyQ9T7gTTvuyKb167s1\nYJtGjqxUSCKpGciSo6nlYjKzm4EDCfWlDfgAsCPwh3jbgZuA+YTZTnsCbUCTu68tsD9PK9ZSzW5o\n4MICf4jZDQ1ceO+95Q+ogrKc+fSBJUu4fvJkrondTJuA04cN418WLtRAtdS8/uRiqkhFuQKmpBVH\nmrLetVJOWe4aOGLCBFi4kFOnTmX4unVsGjlSs5hEElA2136o1SywIlI7lO67grLctSIiogZCREQK\nyuQYhIikS2twJG06gxCpQhr/kqRUUU5kkKnVKoiSLepiEqlCWU9vUg3URdc7NRAiVUhrcPpHaXKS\nUReTSBWa1tzM7Pp6NsXbuTGIaRlJb5J16qJLRmcQIlWolqsgloO66JJRAyFSpbKc3gSy3cevLrpk\n0kzWV6jk6NvAZcAWYIG7nx/rQ/RaclTTXEWqR9an4WY9voGUyZXUZjaVUPPh9LySo28DE939eTNb\nAHwD2J9Qee4rseTo1929W8lRNRBSbln+Bpx1FzY18dV587p9Q7+8sTEzZz2DJU1OWVZSm9k2hGpv\nBwF/dPe3e3lKK7AqXn+DUHb0MXd/Pm77LaGi3MHAtQDuvtTM5ieOXiQlmuXSP6X28ZezUc56F10W\nJGogzOxbhLKgOwGHAM8Dp/T0nAIlR78H7Jf3kA3A7nGfVVtyVGpTNRSDyvIZTil9/GqUsyfpGcSR\n7j7ezH7q7seY2fIkT8ovOQo8RzhjyNkJeAFIXHI0axXlpHZlfZZL1j9MS6kyWA2NcjUYyIpySRuI\nbcxsDOFbP8DQ3p5QoOToEGC0me0CvAicAHyJ0P3U55KjImnK+iyXrH+YljINN+uNcrXo+uX5wgsv\nLHlfSRuIeYTa0VPM7EfAfyZ4zrHAWODuWI/agS8DvyPMVprv7qvNrAW4xcweJJYc7duvIDLwsl5n\nu6o+TBNOLsl6ozwouXuiC/BewgD1u5I+ZyAvIdT0tK5Z43MaG/2CiRN9TmOjt65Zk+rrSfa1/080\nNGTuf2JOY6O3hY/e9ksb+JzGxkqH5u7h2J1TX98eYxv4OfX1PR7DUp4jvYufnaV97iZ6EHwe+Ctw\nJ/AY8MlSX7DkQFNsIPSPKdUm6/+zpTZgWW6Uq1V/GohE6yDioPRkd3/NzEYAv3X38QN+OtNzDJ4k\n1lJUw5xtka6yPI9/dkMDFxYYKJ3d0MCF995b/oAGsXKsg3jT3V8DcPc2M6upFWtV1Z8rEmV5Hr/G\nE2pD0myuj5rZlWZ2opl9G3gmzaDKLffPnE//zCKlU7bZ2pC0i2kI8AXgQOBvwLXu/kbKsXWNIbUu\npsGUl0WkXLLcBTaYpNbFFBuGYYSke6cCtwIG3AycXMoLZpFSJ4v0rF+rtpVDrWr1eAZhZl8mrILe\nhbASGsJ6hhXu3ph+eJ1iSe0MQkSKK+UM+6mWFuZOnMhFTz/d/pxz99iDmYsW6YtXmaWezdXMznL3\n75byAgNFDYRIZZQyy+/fPvlJ5tx1V7fnzDnxRC67884Uo5Wu0uxi+pK7/xDY1cwuyr/P3c8t5QVF\nal2Wk+iVopRZfs+vWFHwOc+vXDnA0Umaepvm+vf48/G0AxGpBVlPoleKUqastsXHdH1OWxoBSmp6\nnObq7nfHqy0FLomY2clxaixm9lEzWxIvN8VBcMzsO2b2oJmtNLMjSvpNRDKgWBK9m2bNqmRY/VLK\nlNUxhx3GrPjY3HNmAWMOPTTVWGVgJV0o93/izyHAvoS/9yE9PSEm6Lsb+BhwZdw8l1hS1MxuBU40\nsw3AB9z9YDMbS0jnsX9ffgmRrKjFRZelzPI7+4or+PdVq7j4739nCKHIS9vuu3PeFVeULW7pv0QN\nhLtPyV03s6HATQme4zF996mEetMQalHvFM8cdiCccR4F/CI+p9WCke6+ri+/iEgWlLqCOOvjFn1d\ntT2mro7zFi9uXwcxbLfdOC9jv5P0LnHJ0Rx3f9vMtkv42K1d0nJcAywgrMTeCqwA/onOFeU2ACMB\nNRBSdUpJE16L4xaQ7VQgkkzSaa7PEdY/GKFY0Pfd/YJEL2A2FdgL+BbwKHCIu79gZucT6lRvBR5x\n9/nx8X8GDnX3ti770TRXqQp9XUGsZJGVkfWztoGSerI+d9+1lJ0XsT7+fJbQ9bQQOA2Yb2Z7A692\nbRxyBnPJ0cHyz1wL+vrNuRbHLbKuVs/aoAIlR82saH5ed5+UZB8xVfj5wL1mtpkw/jDN3V+NSQBX\nEcqPzii2j8FacrQa/pnVgJWuGjKf1trfN+slW/tjIEuOJi3WcyMwjdBV9CVCTqa9gL1KLUTR1wsp\nV5TLslqsHiYdsn78sh5fKS6YOLHT+yl3uaChodKhDTj6UTAoabrvD7j7Te7+hIeV1bvF60+U3jQN\nXg8sWcJn6uo4deRIPlNXxwNLlvT4+Kx3QdTi3P9yap9G2tjI7IYGLm9szNTZYS3+fZXiP5mks5je\nilNWlwOHAdumF1Jte2DJEq6fPJlbtmwJ3UXr13P65MmwcCFHTJhQ8DlZ74LIegNWDbI846cW/76l\nzDYbjJKeQXwBOAVYRhhQ/lJqEdW4uVOnck1sHCC80a7ZsoW5U6cWfU7Wi6/o21htq8W/b9bP2rIi\n6TTXdwLvBzYSFr7Nc/eyVpWrlWmup44cyS3r1xfe/uqrRZ+X5eIrKrhU2/T3rW7lSPf9c+DHwCeA\nVuBod28o5QVLVSsNxGfq6riltbVbd9GpY8fy85bEKa4yJ8sNmPSf/r7VqxwNxL3uPsnMbnf3z5nZ\nUncfX8oLlqpWGojcGESum2kTcPqwYfxLD2MQIiKlSn2hHLC9mZ0KPG1mo4F3lPJiQmgEFi7k1KlT\nGb5uHZtGjmTmzTercRCRzEl6BjEBmAqcB5wFLHL3e1KOrWsMNXEGISJSTql3McUX+RRQD6xy9/tK\nebH+UAMhItJ3/WkgEk1zNbNLgUZCYr2zzOz/lvJiIiJSPZKugzjc3f/J3a9w95MAdZiLSI/6mjFA\nsifpILWZ2RAP9R2GANsnfQEzOxnY392/aWZjCHmchgDPA1MIZyU3EjK7vkWsONeXX0JEsqWUjAGS\nPUkHqf8F+CJhJfVHgN+4+8W9PKdTyVF3P9fMfgX8yN3vMLO5wH8D2wAfcvevmNl44OvufkKB/WkM\nogRZzsKZ5dikf2p1vU81Sm2aq5ndkHfzSUIq7t/TUUK0KPfOJUfNbBvgQHe/Iz6kmTBddi5wbXzO\nUjOb3+ffQgrKcprwLMcm/Tf81VcL5m8avk6FIqtJb2MQxwNHAE8DtwCfIXyY/zTJzt19K6ESHcAo\noM3MvhvrS1wJbI7b80uObk0cvfQoy1k4sxyb9N+mHXcsmL9p08iRlQhHStTbGMSuwCTgZOBi4HfA\nfHf/UwmvtRHYHbjc3Z82s68T1lW8DLw773FF+5EGc0W5UmQ5C2eWY5P+m3nzzZxeIGPAzJtvrnRo\nNa9sFeXiGcDvgd/HLqJjgUvMbIy779OXF3L3TWb2EKGSHMArwA6EkqOfBZbHLqmlxfYxWCvKlSrL\nacKzHJv0nzIGVM5AVpTrSzbXTwKfB94L/Ke7X57oBcymEirPnWtmHwYujXetA6YDrxO6r/YkNB5N\n7r62wH40SN1HWc7CmeXYRGpJaiupzexEwlTUvYA7Cd1LFZmCqgaiNFnOwpnl2ERqRZoNxFbgr8Cq\nuKn9we7++VJesFRqIERE+i7NbK5lrfkgMlhpTYhkUeJkfZWmMwipVRqPkTSlnqxPRNKjNSGSVUlz\nMYlISqphTYi6wAYnNRAiFZb1NSFKizJ4qYtJqs5TLS1c2NTE7IYGLmxq4qkqT/42rbmZ2fX17akp\ncmMQ05qbKxlWO3WBDV46g5CqUovfZsfU1XHmggVcnrcm5MwMdeFUQxeYpEMNhFSVYt9mL581i9k/\n/nElQ+uXMXV1mY0/611gkh51MUlV0bfZ8st6F5ikJ/UziPyKcnnbjgPOd/fD4+3vEMqYbgVmuvsD\naccl1UnfZsuv1C4wzXyqAe6eygUw4B7gNeCivO3DgYeAZfH2JOCOeH0s8HCR/blI65o1fk59vbeB\nO3gb+Dn19d66Zk2lQ5M8+jtlR/zsLOlzPLUuphjYscBpXe66CLgm7/ZRwC/ic1oJ1UpVVUQKGlNX\nx0k33MCpY8dy6siRnDp2LCfdcIO+mWaMZj7VhlTHILxzRTnM7DBgJKFWdU7XinIb4mNEunmqpYU7\npk/nltZWblm3jltaW7lj+vSqn+paazRWVBvKNkgdCw5dApzT5XVfoXNFuZHAS+WKS6qLvplWh9xY\nUT6NFVWfck5zrQd2BG4Htgf2MbPrCPWtTwPmm9newKvu3lZoByo5KvpmWh2mNTcze8WK7gkINfMp\ndQNZcjT1bK75FeXyto0hFB/KzWL6D2A88AYww90fKbAfTztWyb4Lm5r46rx53WYxXd7YmNl1BIOV\nCkJlQ2oFg7KklhoITf8rnVJji/SNGogqog+4/tM3U+lKX7qKUwNRRdRFIjKw9KWrZyoYVEU0yCoy\nsDSzLT1qIMpM0/9EBpa+dKVHDUSZKfGZSHGl1PrQl670aAyiAjTIKtJdqWMJGoPomQapRaTq9WcC\nh750FdefBkIFg/pJ0+tKp2Mn+fozlpDlgkvVTA1EP9Ri+cty0bGTrlTrI3s0SN0Pml5XOh076UoT\nOLJHZxD9oOl1pdOxk65KrVwn6SlryVEz+wShYNB64BlgWnzYjcA44C1guruvTjuugVANp8RZ7eev\nhmMnHcr1f6SxhIwptRRdbxcKlBwFngBGx+uXAl8EpgNXxG3jgV8V2V8JxfbSlfWyilmOL8uxSWf6\nW1U3+lFyNNVprmY2BDgVGOfu55rZGe5+dbxvNtAGHARc6+5L4/Zn3P39BfblacZaqixPr8t63qcs\nHzvpkPX/I+lZZqe5uvtWM/O821eb2TDgK8DngAnAMXQuObo1zZgGWpZPibPez5/lYycdsv5/JOkp\n6yC1me0F3AYsAj7q7pvMrGvJ0aKnCaoo1zfq55eBoP+j6lK1FeXM7E/A6bnupHj/l4C93f0cMzsW\naHL3pgL7yWQXU5YpBYEMBP0fVbdMp9rINRDA9cDDwB8IA9gO3ATMB24B9iSMSTS5+9oC+1EDUQL1\n88tA0P9R9cp0AzFQ1ECIiPRdZgeppTplde2EiJSXziCkE/U3i9QWlRyVAaMcSSKSowZCOtGcdxHJ\nUQMhnah8o4jkqIGQTpRyWURyNEgt3WjOu0jt0DoIEREpSLOYRERkwKmBEBGRglJvIMzsZDP7drw+\n2cz+aGYrzexbcdswM7s1brvfzMalHZOIiPQutQbCgnuAG+hI4X0NcJy7HwIcYmYHEQoKvRS3fROY\nm1ZMlTRQ6XcroZpjB8VfaYq/eqXWQMQR5WOB0wDimcFad38+PuS3hIJBRwG/iM9ZChyQVkyVVM3/\nZNUcOyj+SlP81SvVLiZ330rH2cMoOleO2wCMBHaiiivKiYjUqnIOUr9CaBBydgJeiNsTVZQTEZHy\nKWfBoPOAPwOTgReB+4AvEbqZElWUSzVQEZEalfl6EO7uZnY28DvgLWC+u682sxbgFjN7kFhRrsjz\nS/oFRUSkNFWzklpERMpLC+VERKSgTDYQZratmf0kLp5bZmZHm9mkrovssqhI7J8ws/82s0Vm9mMz\ny2yp10Lx5913nJktq2R8vSly/MeY2ZK4EPPnZrZtpeMspkj8B8f4l5jZTWaWyfctgJmNMLM7zGyx\nmT1gZh8utEA2i4rEXk3v3a7xH5h3X2nvXXfP3AWYClwTr48CVgOPATvHbb8HDqp0nH2I/XFgdNx2\nGTC90nEmjP89wOp4fTjwELCs0jGWcPx/BZwUt80FTql0nH2MfykwLm67FfhUpePsIf4LgC/H6xPj\nsa+W927X2H9ZZe/d/PgbgF/G6yW/d7PaGrYCq+L1N4ARwGPeeZHdeOCP5Q+tV610j/0id18bt7UB\nO1YgrqRa6Yh/M7QXmLuIsBL+CxWIqS9a6X78D3T3O+K2ZiCzZxAUjv85YKd45rAD4X8oqxYAT8br\n7yGsd3q2St67hWK/qoreu/nxjyLED/1472aygXD3xQBmti9wPfA9YL+8h6wH3l+B0HpVIPbL3P3q\neGr6FeBzhKm9mVQg/svN7FDCGpa7yXgDUSD+a4BTzewqYF/g78BZlYuwZ4WOPyHmBcAzhIWkyysW\nYC/cfTmAmf2G8C22mc6fM1l+73aN/fPufkcVvXe7xd/v926lT4t6OV16iHCqtxdwT959XwNOr3SM\nSWKPt/cifGP6DjC80vH18dhvAywhfKMaCyyvdHx9jH848BqwR7zv64RGu+JxJoz/nUAL8L543yzg\n4krH2EPso4Gh8foehIWwd+fdn9n3boHYnwXGVct7t0D8zwGL+/PezeRgl5lNAT4CHOzui4C/AqPN\nbBczGwqcQPhGlTldYzczA34GnO3u57h715LPmVLg2O9JOK2+HZgP7GNm11Uuwp51jT8e74fo6JZ5\nBXizUvH1psDxz63/WR9/ri30vAy5CjgmXt8MvAS838x2zfp7l+6xtwH/SZW8d+ke/yhCxoqS37uZ\nXAdhZjcDBxL+uYyQfuMiwul2bpFdJrO+Foj9A4QP2D/Q8bvc5O63VCzIHhQ69u4+Kd43hnDsD69g\niD0q8r9zDmGAEWAdYaBxQ+E9VFaR+H8InE7Hh9Y0d3+1YkH2wMw+CFwHvE3oWroAGEp1vHe7xn4D\n8B9Uz3u3a/yz4peMkt+7mWwgRESk8jLZxSQiIpWnBkJERApSAyEiIgWpgRARkYLUQIiISEFqIERE\npLjOVbgAABl2SURBVKBMptoQ6Q8z+z5h9fouhDQDj8W7PuHub1QsMMDM3g181t1/VMk4RJLQOgip\nWbHc7ZHuPr0Cr21e4M1lZmMJC5YOK3UfIuWiMwgZNMzsfOAoYHvgSnefb2b3EVK5jCO8H5YRUl0Y\n8EngJOCfCTmp3gdc6+7fN7NxwHfjvl4i1Fffj5CcbjPwLTPbFfgysIWQWfNzhIwAe5vZ1+Jzn3P3\n68xsL+D77t5gZn8FFgKvmNm/Az8Ado0xnOnuD6d5nERyNAYhg4KZTQL2cfeJhIycs8wsl7p5cdz+\nCvBkTC2ympARE2AHdz8aOAz4qpntTMi0+mV3P5JQZ/0b8bE7Ase6+xKgDjjK3ScQ0k0cCHwT+Iu7\nX1ogzNzZwjBCSodzCckFF7v7ZOCLhFQKImWhMwgZLD4MfMTM7iWcHWwlZLyEjtoEm+gYr3gNeEe8\n/gCAu79mZn8mpKveD7g25GJkW0KDAvCIu2+N19cDV5nZ6/E5Q3uIr+uXtVxNiA8DH49J/IxQH0Kk\nLNRAyGDxBPA7dz8r5vefQ6gWBqGx6MmBAGY2HNiHjiqBJ7v7C2Y2kTAY3i6enZzj7nua2TuAlfEu\npyND6xt0FGQ6oMhrPw7c4O6/MLPRwOd7+0VFBooaCBkU3P2XZjYxjjlsA/zI3d8ws/xB4GLXt4nP\n2xFodveNZnYaMD+msH4Z+D/A3nmv92qsw/wg8BSh1OaZwBRgRzM7i5CG+Ydmtg/hvZh7zfzX/vf4\nmDMIDdn5/TwUIomlNospFoa/hdAP+zYwm/DN61bC6fTzhDfLVuBGwiDhW4RUzKsL7VOk3OJMqL3i\neIDIoJLmGcQU4GV3P9nMRhHKJK4GrvBQxm8uHbNDXnL3U8xsPKGo/AkpxiUiIgmkeQZxJPCKuz9i\nZiMIjYO7++h4/46EQcC5hKmDS+P2Z9w9kzVrRUQGk9Smubr74tg47AvcQyge32ZmV8WZJFfSURbv\n5byn9jZgKCIiZZDqSmozuwD4NHA28CDwIvBBd3/azL5OKKY9GrjK3ZfH5zzl7mMK7EsrSkVESuDu\n1vujukvtDKIPxeMXAp+NzzkWWFpsn+6uywBdZs+eXfEYauWiY6njmeVLf6Q5SH0sMBa428JqIicU\nXr89Li5aB0wHXgduidMB24CmFGMSEZGEUmsg3H1qkbuOKrBtSlpxiIhIaZSLaZCaOHFipUOoGTqW\nA0vHMzuqJt23Mh+LiPSdmeFZG6QWEZHqpgZCREQKUrI+EZEKmzHjYlav3txt+7hx23Hddd8o8Izk\nz+8PNRAiIhW2evVmFi+eU+CeQts6mzHjYm6//XHWr7+ppOf3RF1MIiJVbPXqzaxfPzaVfauBEBGR\ngtRAiIhIQRqDEBEpg7QGktOUWgNRqKKcuy+I9x0HnO/uh8fb3wEmEFJ9z3T3B9KKS0SkEnoaiA6N\nRPf7kjce3Z//7ne3Mm7cB1m8uE9hdlKuinLvAZYB42Lh94uA1wDMbBLwAXc/2MzGAncC+6cYl4hI\npiSZylpMaEQKnZl8kOuu+wbXX//NkvedZgPRCqyK1zcDw+P1iwjFg74Qbx8F/ALA3VstGOnu61KM\nTUQkEx56qJWJE+cAydc95OtP49KbNLO5LgaIFeWuBy43s0OBkcDddDQQXSvKbYiPUQMhIjVv/fqx\neV1Pc9q393fx3EBIdZC6S0W5BwjFgT4NjMh72CvAu/NujwReSjMuEZG0df2Af+ihVkIDsB3Q+wd8\nfxbPDZQ0B6nzK8q9ZWZ7AzsCtwPbA/uY2XXAT4HTgPnxMa+6e1uhfc6ZM6f9+sSJE5UWWEQyq9gH\n/LvfPQ2Ylre4bWBnMS1atIhFixYNyL7KWlHO3T8EYGZjgPnuPiPePtHMVgFvADOK7TC/gRARqUYH\nHDAWoMjZQf91/fJ84YUXlryvSlSUw92fAg7Pu/3ltOIQEZHSaKGciEiZ9X/dQ3mogRARKbMks5Cy\n0Iio5KiIyADJn7n0xBOP8vrrYfnX9tsPYa+99gDKO00V+ldyVGcQIiJ90NP6hGIzlw44YA6LFnXf\nnnVqIERE+iAL6xPKRem+RUSkIJ1BiEjmZSHtxGCkBkJEMq9Yt85DD01j9eqO7WowBpYaCBGpWp0T\n3UGlxwGyMDV1IKmBEBHpg54agVo7e1EDISI144knHm2vrZBvID+8a60R6ElZS47G17sIWA88A0yL\nD78RGAe8BUx399VpxSUitev114fHLqeTyc+Sev/9m7n99mnssstmHn/8J5UKr+qUq+ToKGA5oeb0\nZHdfa2aXAlMBB15y91PMbDwwFzghxbhEpMp07dZ56KHWmC67WN/+/2/v7oPsqus7jr8/4cFEqRuh\nncKgsoKTiLSyCoRRedhGoGAd6lhQYkECw8QReVCcKVgZu1gGgwKCgFhmVB6qUWxxGGaKPLoxQHQi\nTPoAZdda1ygSMAoLeaIh+faP31n27t2zu+fevec+7ec1k8m9v3vOub975uz93vN7+s4Hbnn12c6d\nMDoK479JrYhmpRx9mZQk6IqIeDor20JKDnQYcBNARKyRtKrEOplZB6pu1hkf9rqdysAxNDQvCwRb\nco+zdWt+ueVrZsrRL0fEDZJ2Bz4NfBg4BjiBiSlHd5VVJzPrDlP1A/T3D7BxI8Buua/v2pVfbvma\nlnI0IgYlLQa+AwwCSyJii6TqlKNTrsjnjHJmZtNrZEa50lZzzVKOLgP+Jks5KuDfgU9GxJqK7c4B\nDo6Iz0g6ETg9Ik7POZ5XczWzaY01Pa1e/SQpu/FEu+12Gq+8Mrc6qWezmmuZAeJW4J3AJkDAgaSc\n1D/LngepF2kVabTTW4HNpADxdM7xHCDMrJDXvvYktm07clL5ggU/ZevWe1pQo9Zpy+W+p0s5mmNZ\nWfUws7lnyZIjc5fmWLJkcplNzRPlzKzrdNuSF63ijHJmZl2sLZuYzMzyeOnuzuEAYWZNNZcysnU6\nZ5QzM7NcvoMwMzf7WC4HCDNzs4/lchOTmZnl8h2EmTWV5yh0DgcIM2sq92l0jmZnlNsJfBl4Bbg/\nIi7Nlv92RjmzFhoaeoK8X/Wp3OaqZmeU2wn0R8Szku6XdBhwKM4oZ1aK4qOT/oj8DumzS6qZdYLC\nAULSHqRkPocBj0XEzhl2GWFyRrn/johns7J7SAmDjsAZ5cwKqXU4atHRSYsXvzlLtMOkcpu7CgUI\nSZeTsr7tDRwJPAucMd0+ORnlvga8o2KTF4E3Zcd0RjmzAqb7ws8LHuvXjzSjWtalit5BHBsRR0v6\nXkScIGltkZ0qM8oBz5DuGMbsDTwHOKOcWQPkB4/q59btGplRrmiA2EPSAaRf/TBVwtcKWUa5w4Ej\nsoxy84D9Je0L/I7Uz3AOqfnpFGBtllFuzVTHrAwQZmY2WfWP58suu6zuYxUNEN8GHgSWSfoG8C8F\n9jkR6AXuzdKNBnAh8EPSaKVVETEs6ZfAbZLWkWWUq+0jmM1FK4Hx5qTxpqSVQO3DSD03wfIUChAR\ncb2k7wJvBj4VES8V2GeqjHJ9VdvtwBnlzGq0ncov9NHRsUcDVdvNp6dnOX19vQwNbWDbttTFNzS0\nhf7+tK3XW7KpFO2k/ihwGfAksEjSJRFxV6k1M7NJxn7pr18/UhEUpnMJfX0DDA4O0N8/8Gofxego\nFaOWBsqoqnWBok1M5wOHRsRWSXuRhqg6QJg12dgv/fRlP/n1np4R+voGJpS5mcjqVTRA/F9EbAWI\niM2SnPvTrA319fUyODjQ6mpYlygaIJ6QdC3wEPBu4DflVcnMzNpB0QBxHnAWcALwP7jR0qylPOrI\nmmHaAJHNXdidtOjex4DbAQG3AqeVXjuzDlFGRrZGH9NBxWo10x3E+aRZ0PsCT2VlAfykzEqZdZr8\nWcwrWb/+KYaHJ5YX/YJvdJY3D2W1Wk0bICLiOuA6SRdExFebVCezLrGd0dFbckYbDbSgLma1mzbl\nqKRzsof7Sbqi8l8T6mbWldavH2HFipWtrobZjGZqYvp19v9T025lZoWNjvbm9i2YtZuZmpjuzR7+\nsgl1MTOzNlJ0mOsnsv/nAYcAW0h5IWYk6TTSLOzPSloCXJW99L+k9KK7JF1NWgp8F3BRRDxS9AOY\ntYO8EULFl8MofszxcrPyFV2s79XF9CTtBtwy0z7ZCq73AkcB12bF15DlnJZ0O3CypBeBAyPiCEm9\npCU8Dq3hM5g1XZEhqCtWrOSOO5YzOtpbtdV8KldinYpHHVmr1ZyTOiJ2SprxJ0xERJbf4WPAoqz4\nFWDvbH7F60nLex8H3JntM6JkYUS8UGvdzJqlyBDUm2++hOHhgYYOVTVrpqKruT5Dmv8gUrKgrxfZ\nL2s+qly36UbgftJSHbtI8ylOZWLK0ReBhYADhJWqjMltecdyM5F1qqJNTPvN9o0kvRb4EnBQRDwn\n6VLgUlJwqEw5uhDYlHcMpxy1Rmr0RLQ8biayZmt6ylFJD031WkQsrfE9x7rtfktqenoQOBdYJelg\n4PmI2Jy3o1OOWitV3nGkDG4D2SvzqSeLm1kZWpFy9FfAamAtcDRwLHB5LW+U5ZK4FHhI0nZS/8Py\niHhe0smSHiflp15Ry3HNarFixUruvnuYbdt2sXlzZVa2mb/km3HHYdZOigaIAyPirOzxkKRlETFU\nZMeIuLXi8bdJ+a2rt7mwYD3MZmV4eDsbN34z55WBCc/G7hYq03SmgDJZZZIe9y1YNykaIHZkI5LW\nkvJB7FlelcxaL/9uofp54iQ91q2KBoizgJXA1cAvgHOm39ys04zQ07Ocvr5eFi2a76UwzCgeIH4P\nXAa8RJrXsKW0Gpm1RC99fbx6J9DfP9DS2pi1g6IB4nbgn4GTgJHs+V+UVCezNjUfWA70Zv0OvYD7\nHax7FQ0Qb4iIH2Sd01dIOqnUWpmVZNGi+QwNnf1qx/OYBQu2sGjRYTPsfQmpH2KAvr4B9ztY1ysa\nIBZI+hiwQdL+wGtKrJNZaYpOXBubAV05iglSIFm8eMB3DTYnKCJm3kg6BjgT+BxwATAYEfeVXLfq\nOkSRupqZ2ThJRITq2rfol66kDwIHAY9HxI/qebPZcIAwM6vdbALEtClHK97gS8DfkhbYu0DSF+p5\nMzMz6xxFm5gejoijKp4PRkR/mRXLqYPvIMzMajSbO4iindSSNC9bvnsesKCGylVmlDuANER2HvAs\nsIx0V/It0sJ9O8gSCtXyIax91LKEdjOW2zaz+hUNELcAj0p6FDgc+MFMO0yRUe5G4CvZkNlrgI8A\newCbIuIMSUeTss59oKZPYW2jlgXt7r77MTZuPKSqdAOPPvrbSYHDQcOs+aYNEJIqVzX7BWml1QcY\nzxA3peqMcpL2AN4ZEWPB5R9Jw2WvAW7K9lkjaVXNn8I60rZtryMvcOzYkZeFbfJ2Zlaume4g/oqU\n2W0VaaG+22o5eFVGuX2AzZK+CvwZ8Gvgwqy8MqPcxBlMZmbWEjMFiP2ApcBppMX6fgisioj/qOO9\nXgLeBFwVERskXUyaV1GdUW7KnmhnlDMzm17TMspFxC5Sk9IDWRPRicCVkg6IiLfX8kYRsUXSelKi\nIIA/AK8nZZQ7BVibNUmtmeoYzihnZja9pmeUy/JJ/zXwUeANQF7GlSLOA+5I/de8AJwNbANuk7SO\nFDxOr/PY1gbGlqjIL59owYJ5jI5OKjazNjHtPAhJJ5OGoi4G7iI1L7VkCKrnQXSfvGGuQ0MbgJdY\nvHji6CaPYjKrT2lLbUjaBfwceDwrenXjiPhoPW9YLwcIM7PalTlRzjkfDPCkNrO5aKZO6tXNqoi1\nt1omwJlZdyi0WJ+Zmc09RZfasC7hpiIzK8oBYo5xU5GZFeUA0cXy7hbWrx8hTYr33YKZTc8Boos1\n8m6hlglwZtYdHCCsEPdPmM09HsVkZma5Sr+DqMwoV1H2fuDSiHhP9vxq4BjSUt8XRcQjZdernZU9\n0qinZ4S+voFJxzYzq1RagJgioxySXgdcAWzNni8FDoyIIyT1ktZ8OrSsenWCskca9fX1MjjYmGOZ\nWfcqLUBUZ5SreOkKUurRs7LnxwF3ZvuMKFkYES+UVbe5YMWKlQwNPUFPz/IJ5QsWzGPRohkTApqZ\nldvEVJVRDknvBhaS7izGAkR1RrkXs20cIGZheHg7Gzd+f1J5X9+AO5zNrJCmjWLKEg5dCXwI2Kvi\npT8wMaPcQmBTs+plZmb5mjnM9SBSsqE7gAXA2yXdDHwPOBdYJelg4PmI2Jx3AKccNTObXtNSjjZS\nRDwF/DmApANIyYdWZM9PlvQ48DKwYqpjzJWUo56UZmb1anrK0dmIiFtzyn4FvKfi+YVl16OTFO0j\nmG44rJnZbHkmdUmasWrqdMNhfRdiZrPlAFGSVq+a6pFKZjZbXmrDzMxyOUCYmVkuNzE12FjfQ8q7\nMFDxynycg8HMOokDRIM1s+/BHdFmViYHiCYZW0G1kV/e7og2szI5QDTZ8PB2+vsHgMYOeTUzazQH\niAaZ2Pcw2ehob07TU/VzM7P24VFMDTLW9zA62tvqqpiZNYTvIBpuYsdxT88IAKOjb2tJbczM6tXU\nlKOSTiIlDBoFfgMszzb7Fimp0A7g7IgYbmQd8pa9GBraALzE4sWHTCiffb/AxH3HUnuuXu2+BjPr\nLM1OOXotsDQinpb0JeBMIIBNEXGGpKOBa4APNLIu0w093bixujxvOzOzuafZKUevj4ins8dbSMmB\nDgNuyvZZI2lVWXVqFc9XMLNO1NSUoxFxg6TdgU8DHwaOAU5gYsrRXWXWqSzTBQEPZTWzTtTUTmpJ\ni4HvAIPAkojYIqk65Wjk7QvtnVHOQcDM2kFHZpTLfB/4ZESsqSh7EDgFWJs1Sa3J3ZO5k1HOzKxe\nHZVRboyktwC9wGVZB3YAtwC3ArdJWgdsBk5v9HvnNf+Mj2IayNnWzMwUMWWLTluRFJ1SVzOzdiGJ\niFA9+3omtZmZ5eqKmdTNyP9sZjbXdEWAaHX+ZzOzbuQmJjMzy+UAYWZmuRwgzMwslwOEmZnl6opO\nai+GZ2bWeJ4oZ2bWxTxRzszMGq70ACHpNElfzB6/T9Jjkn4q6fKsbHdJt2dlD0taNP0RzcysGUoL\nEEruA77J+BLeNwLvj4gjgSMlHUZKKLQpK/ssKaOclaxRywGbz2Wj+Xy2j9ICRNZhcCJwLkB2Z/B0\nRDybbXIPKWHQccCd2T5rgL6y6mTj/EfYOD6XjeXz2T5KbWKKiF2M3z3sw8TMcS+SUo7uTRdklDMz\n6zbN7KT+AykgjNkbeC4rL5RRzszMmqf0Ya6SzgQWA58D/gt4H/A74EfAOaRmpoMj4jNZRrnTI2JS\n0qDK3NZmZlZcvcNcmzZRLiJC0qeAHwI7gFURMSzplxTIKFfvBzQzs/p0zEQ5MzNrLk+UMzOzXG0Z\nIDy5rnGqzuVHsnP2UPbv8Kz8aknrstfe29oatx9Je0r6bnZ+HpV0vKSlvi7rM8X59LVZJ0l7SfqB\npNWSHpH0rkZ9b7bVYn2SBNwLHAVcmxXfCBwbEc9Kuj+bXHcoaXLdGZKOJk2u+0BLKt2mpjiX7wLO\ni4h1FdstBQ6MiCMk9QJ3kc6vjVsG/D4iTpO0D7AW2An0+7qsS975vBNfm/W6CBiMiOsk9QNfAA6i\nAddnW91BeHJd41Sfy8zbgM9L+rGkL0qax8RzOUKKLQurjzfHjQBfzx6/DOwF/NbXZd1GmHw+D8bX\nZr3uB1Zlj/+YNMesIddnWwUI8OS6Rqo6lwAPA+dHxDHAnwCfYPK5HDvHlomI1RHxn5IOAe4Dvoav\ny7rlnM+r8LVZt4hYGxHPSfo34HbSdIKGXJ9t1cSUw5PrGuvqLGgA/CvwIdKclMpzuRDY1OyKtTtJ\nnyedr08Bz5B+kY3xdVmjyvMZEYOS5vnarI+k/YGNEfF+SW8G1gPrKjap+/psuzuIKsPA/pL2lbQb\nqb3sfuAh4BSAbHLdmtZVsTNI2gPYIGnsAllKuogeZPxcHgw8HxGbW1PL9iRpGXA4cEREDAI/x9dl\n3arPp6Q98bU5G9cDJ2SPt5OC6Bsl7Tfb67Ot7yBmO7nOxkXEDkkXAQ9KepH0JffNiHhF0smSHie1\nB69oaUXb04lAL3Bv1vkfwIX4uqxX3vn0tVm/vwdulvR3pO/0jwO7kfoeZnV9eqKcmZnlavcmJjMz\naxEHCDMzy+UAYWZmuRwgzMwslwOEmZnlcoAwM7NcDhA250k6QNJotoroj7IVMX8s6S1TbH/x2Gqj\nU7z+l5KezBZIy3v9TElXZO+7tlGfw6zR2nqinFkTPRERS8eeSPoH4ALg09UbRsSVMxzrSOCfIuKx\nAu/riUjWthwgzJLqlLb7kJZ/uBJ4L7AHaUnliyV9i7R65n7Aqdn2+wPfAAaBs4CXJT1CWnb5QuAV\n0qJpp2LWIRwgzJK3S3qIFCj+FNgTeCdwUUQcla0X9AxwcdV+r4mI4yXtCzwSETdIugV4JiJ+Jul4\n4LiI2CrpHlJODrOO4ABhllQ3MX0H+CDweklfJ60FlPf38jOAiNgoaUHO6y8A10vaBryRtEaOWUdw\ngDBLqpuYhkjNRj0RcU62jPLHc/ar7EOYcAxJbwA+ExFvlfQa4CcF3tesbThAmCXVncVbgbcC75D0\nMPA48ICk83K2zT1GRDyf5QVeB/yKtHz1+cDd07yvWdvwaq5mZpbL8yDMzCyXA4SZmeVygDAzs1wO\nEGZmlssBwszMcjlAmJlZLgcIMzPL5QBhZma5/h9CR1YKuQF1cwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f338d853f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "B1_mosquito_data.csv\n",
      "Intercept      11.016743\n",
      "temperature    13.396049\n",
      "rainfall       43.072783\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAIkCAYAAAD1fuejAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f338d833ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "filenames = glob.glob('*.csv')\n",
    "\n",
    "for filename in filenames:\n",
    "    print(filename)\n",
    "    data = pd.read_csv(filename)\n",
    "    \n",
    "    # convert temperatures to celsius\n",
    "    data['temperature'] = fahrenheit_to_celsius(data['temperature'])\n",
    "    \n",
    "    # get t-values; return plots\n",
    "    analyze(data)\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our for loop went from being long and complicated to simple and easy-to-understand. This is part of the power of functions. Because the human brain can only keep about $7 \\pm 2$ concepts in working memory at any given time, functions allow us to abstract and build more complicated code without making it more difficult to understand. Instead of having to worry about how filter_data does its thing, we need only know its inputs and its outputs to wield it effectively. It can be re-used without copying and pasting, and we can maintain it and improve it easily since it can be defined in a single place.\n",
    "\n",
    "Since we may want to use these functions later in other notebooks/code, we can put their definitions in a file and import it. Opening a text editor and copying the function definitions (and the imports they need) to a file datastuff.py, that looks like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "import statsmodels.api as sm\r\n",
      "import matplotlib.pyplot as plt\r\n",
      "\r\n",
      "\r\n",
      "def fahrenheit_to_celsius(temp):\r\n",
      "    \"\"\"Convert temperature in fahrenheit to celsius.\r\n",
      "    \r\n",
      "    Parameters\r\n",
      "    ----------\r\n",
      "    temp : float or array_like\r\n",
      "        Temperature(s) in fahrenheit.\r\n",
      "        \r\n",
      "    Returns\r\n",
      "    -------\r\n",
      "    float or array_like\r\n",
      "        Temperatures in celsius.\r\n",
      "\r\n",
      "    bubbles\r\n",
      "    \r\n",
      "    \"\"\"\r\n",
      "    try:\r\n",
      "        newtemps = (temp - 32) * 5/9\r\n",
      "    except TypeError:\r\n",
      "        newtemps = []\r\n",
      "        for value in temp:\r\n",
      "            newtemps.append(fahrenheit_to_celsius(value))\r\n",
      "\r\n",
      "    return newtemps\r\n",
      "\r\n",
      "\r\n",
      "def analyze(data):\r\n",
      "    \"\"\"Return panel plot of mosquito population vs. temperature, rainfall.\r\n",
      "    \r\n",
      "    Also prints t-values for temperature and rainfall.\r\n",
      "    \r\n",
      "    Panel plot gives: \r\n",
      "        1. comparison of modeled values of mosquito population vs. actual values\r\n",
      "        2. mosquito population vs. average temperature\r\n",
      "        3. mosquito population vs. total rainfall\r\n",
      "    \r\n",
      "    Parameters\r\n",
      "    ----------\r\n",
      "    data : DataFrame\r\n",
      "        DataFrame giving columns for average temperature, \r\n",
      "        total rainfall, and mosquito population during mosquito\r\n",
      "        breeding season for each year.\r\n",
      "    \r\n",
      "    Returns\r\n",
      "    -------\r\n",
      "    Figure\r\n",
      "        :mod:`matplotlib.figure.Figure` object giving panel plot.\r\n",
      "    \r\n",
      "    \"\"\"\r\n",
      "    # perform fit\r\n",
      "    regr_results = sm.OLS.from_formula('mosquitos ~ temperature + rainfall', data).fit()\r\n",
      "    print(regr_results.tvalues)\r\n",
      "    \r\n",
      "    fig = plt.figure(figsize=(6, 9))\r\n",
      "\r\n",
      "    # plot prediction from fitted model against measured mosquito population\r\n",
      "    parameters = regr_results.params\r\n",
      "    predicted = parameters['Intercept'] + parameters['temperature'] * data['temperature'] + parameters['rainfall'] * data['rainfall']\r\n",
      "    \r\n",
      "    ax0 = fig.add_subplot(3, 1, 1)\r\n",
      "    ax0.plot(predicted, data['mosquitos'], 'go')\r\n",
      "    \r\n",
      "    ax0.set_xlabel('predicted mosquito population')\r\n",
      "    ax0.set_ylabel('measured mosquito population')\r\n",
      "   \r\n",
      "    # plot population vs. temperature\r\n",
      "    ax1 = fig.add_subplot(3, 1, 2)\r\n",
      "\r\n",
      "    ax1.plot(data['temperature'], data['mosquitos'], 'ro')\r\n",
      "    ax1.set_xlabel('Temperature')\r\n",
      "    ax1.set_ylabel('Mosquitos')\r\n",
      "\r\n",
      "    # plot population vs. rainfall\r\n",
      "    ax2 = fig.add_subplot(3, 1, 3)\r\n",
      "\r\n",
      "    ax2.plot(data['rainfall'], data['mosquitos'], 'bs')\r\n",
      "\r\n",
      "    ax2.set_xlabel('Rainfall')\r\n",
      "    ax2.set_ylabel('Mosquitos')\r\n",
      "\r\n",
      "    return fig\r\n"
     ]
    }
   ],
   "source": [
    "%cat datastuff.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Congratulations: you just made your first Python module! We can import it directly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import datastuff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we can see our documentation for these functions and use them as you'd expect:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "datastuff.fahrenheit_to_celsius?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept      11.016743\n",
      "temperature    13.396049\n",
      "rainfall       43.072783\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAIkCAYAAAD1fuejAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f338d957b00>"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAIkCAYAAAD1fuejAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f338d957b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "datastuff.analyze(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: if you make modifications to your module, the will not show up in this session, even if you rerun ``import datastuff``. You will either need to restart your Python session, or explicitly tell the Python session to rebuild the module object by re-reading the file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import importlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'datastuff' from '/home/alter/Documents/SWC/Events/2016_UCSF/swc_ucsf_intermediate_data/python/datastuff.py'>"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importlib.reload(datastuff)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The reason this is the case is because it's more efficient for the Python interpreter to build a module object once from a module file, and then with each import encountered use that same object instead of building a whole new one. It's faster, and requires less memory, for this to be the case."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## An aside: combining DataFrames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although we didn't need to do it for these examples, ``pandas`` is generally more useful when we can pack as much of our data as is reasonable into a single ``DataFrame``. Since all our datasets have the same form, we can probably gain by combining them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import glob"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the ``pandas.concat`` function to make a single ``DataFrame`` from all our others:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "filenames = glob.glob('*.csv')\n",
    "datas = []\n",
    "keys = []\n",
    "for filename in filenames:\n",
    "    keys.append(filename.split('_')[0])\n",
    "    datas.append(pd.read_csv(filename))\n",
    "    \n",
    "df = pd.concat(datas, keys=keys, names=['location'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have a \"multi-index\" ``DataFrame``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>temperature</th>\n",
       "      <th>rainfall</th>\n",
       "      <th>mosquitos</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">A1</th>\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",
       "    <tr>\n",
       "      <th rowspan=\"20\" valign=\"top\">A3</th>\n",
       "      <th>0</th>\n",
       "      <td>1960</td>\n",
       "      <td>76</td>\n",
       "      <td>191</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1961</td>\n",
       "      <td>73</td>\n",
       "      <td>249</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1962</td>\n",
       "      <td>81</td>\n",
       "      <td>112</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1963</td>\n",
       "      <td>78</td>\n",
       "      <td>113</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1964</td>\n",
       "      <td>81</td>\n",
       "      <td>159</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1965</td>\n",
       "      <td>87</td>\n",
       "      <td>222</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1966</td>\n",
       "      <td>72</td>\n",
       "      <td>103</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1967</td>\n",
       "      <td>77</td>\n",
       "      <td>176</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1968</td>\n",
       "      <td>89</td>\n",
       "      <td>236</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1969</td>\n",
       "      <td>88</td>\n",
       "      <td>283</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1970</td>\n",
       "      <td>89</td>\n",
       "      <td>151</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1971</td>\n",
       "      <td>71</td>\n",
       "      <td>121</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1972</td>\n",
       "      <td>88</td>\n",
       "      <td>267</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1973</td>\n",
       "      <td>85</td>\n",
       "      <td>211</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1974</td>\n",
       "      <td>75</td>\n",
       "      <td>101</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1975</td>\n",
       "      <td>72</td>\n",
       "      <td>173</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1976</td>\n",
       "      <td>74</td>\n",
       "      <td>254</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1977</td>\n",
       "      <td>85</td>\n",
       "      <td>155</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1978</td>\n",
       "      <td>89</td>\n",
       "      <td>158</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1979</td>\n",
       "      <td>86</td>\n",
       "      <td>117</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"30\" valign=\"top\">B1</th>\n",
       "      <th>21</th>\n",
       "      <td>1981</td>\n",
       "      <td>77</td>\n",
       "      <td>115</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1982</td>\n",
       "      <td>71</td>\n",
       "      <td>267</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1983</td>\n",
       "      <td>89</td>\n",
       "      <td>164</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1984</td>\n",
       "      <td>75</td>\n",
       "      <td>175</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1985</td>\n",
       "      <td>70</td>\n",
       "      <td>210</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1986</td>\n",
       "      <td>72</td>\n",
       "      <td>214</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1987</td>\n",
       "      <td>79</td>\n",
       "      <td>123</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1988</td>\n",
       "      <td>74</td>\n",
       "      <td>195</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1989</td>\n",
       "      <td>84</td>\n",
       "      <td>129</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1990</td>\n",
       "      <td>80</td>\n",
       "      <td>276</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1991</td>\n",
       "      <td>73</td>\n",
       "      <td>147</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1992</td>\n",
       "      <td>73</td>\n",
       "      <td>140</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1993</td>\n",
       "      <td>78</td>\n",
       "      <td>285</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1994</td>\n",
       "      <td>71</td>\n",
       "      <td>280</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1995</td>\n",
       "      <td>80</td>\n",
       "      <td>180</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1996</td>\n",
       "      <td>90</td>\n",
       "      <td>280</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1997</td>\n",
       "      <td>86</td>\n",
       "      <td>166</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1998</td>\n",
       "      <td>82</td>\n",
       "      <td>285</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1999</td>\n",
       "      <td>75</td>\n",
       "      <td>215</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2000</td>\n",
       "      <td>86</td>\n",
       "      <td>212</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2001</td>\n",
       "      <td>72</td>\n",
       "      <td>138</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2002</td>\n",
       "      <td>84</td>\n",
       "      <td>130</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2003</td>\n",
       "      <td>74</td>\n",
       "      <td>166</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2004</td>\n",
       "      <td>87</td>\n",
       "      <td>280</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2005</td>\n",
       "      <td>85</td>\n",
       "      <td>130</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2006</td>\n",
       "      <td>83</td>\n",
       "      <td>102</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2007</td>\n",
       "      <td>85</td>\n",
       "      <td>163</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2008</td>\n",
       "      <td>83</td>\n",
       "      <td>190</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2009</td>\n",
       "      <td>79</td>\n",
       "      <td>151</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>2010</td>\n",
       "      <td>88</td>\n",
       "      <td>295</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>214 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             year  temperature  rainfall  mosquitos\n",
       "location                                           \n",
       "A1       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\n",
       "A3       0   1960           76       191         93\n",
       "         1   1961           73       249        109\n",
       "         2   1962           81       112         73\n",
       "         3   1963           78       113         72\n",
       "         4   1964           81       159         89\n",
       "         5   1965           87       222        109\n",
       "         6   1966           72       103         68\n",
       "         7   1967           77       176         92\n",
       "         8   1968           89       236        114\n",
       "         9   1969           88       283        128\n",
       "         10  1970           89       151         89\n",
       "         11  1971           71       121         72\n",
       "         12  1972           88       267        124\n",
       "         13  1973           85       211        102\n",
       "         14  1974           75       101         67\n",
       "         15  1975           72       173         85\n",
       "         16  1976           74       254        117\n",
       "         17  1977           85       155         90\n",
       "         18  1978           89       158         92\n",
       "         19  1979           86       117         75\n",
       "...           ...          ...       ...        ...\n",
       "B1       21  1981           77       115         77\n",
       "         22  1982           71       267        106\n",
       "         23  1983           89       164         99\n",
       "         24  1984           75       175         90\n",
       "         25  1985           70       210         95\n",
       "         26  1986           72       214         99\n",
       "         27  1987           79       123         81\n",
       "         28  1988           74       195         96\n",
       "         29  1989           84       129         90\n",
       "         30  1990           80       276        111\n",
       "         31  1991           73       147         82\n",
       "         32  1992           73       140         80\n",
       "         33  1993           78       285        115\n",
       "         34  1994           71       280        107\n",
       "         35  1995           80       180         95\n",
       "         36  1996           90       280        119\n",
       "         37  1997           86       166        100\n",
       "         38  1998           82       285        117\n",
       "         39  1999           75       215        101\n",
       "         40  2000           86       212        101\n",
       "         41  2001           72       138         83\n",
       "         42  2002           84       130         85\n",
       "         43  2003           74       166         87\n",
       "         44  2004           87       280        121\n",
       "         45  2005           85       130         88\n",
       "         46  2006           83       102         77\n",
       "         47  2007           85       163         94\n",
       "         48  2008           83       190         99\n",
       "         49  2009           79       151         88\n",
       "         50  2010           88       295        122\n",
       "\n",
       "[214 rows x 4 columns]"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The outer index gives the area name (\"location\") the rows came from. These allow us to select rows on the basis of area:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year           2002\n",
       "temperature      85\n",
       "rainfall        252\n",
       "mosquitos       217\n",
       "Name: (A1, 1), dtype: int64"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[('A1', 1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More powerfully, this allows us to get aggregates over all areas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81.040000000000006"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['year'] > 2005]['temperature'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or get descriptive statistics directly as a function of location:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A1</th>\n",
       "      <td>2005.5</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>214.600000</td>\n",
       "      <td>191.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A3</th>\n",
       "      <td>1985.0</td>\n",
       "      <td>80.333333</td>\n",
       "      <td>193.274510</td>\n",
       "      <td>97.647059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B2</th>\n",
       "      <td>1985.0</td>\n",
       "      <td>79.764706</td>\n",
       "      <td>196.823529</td>\n",
       "      <td>196.058824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2</th>\n",
       "      <td>1985.0</td>\n",
       "      <td>80.392157</td>\n",
       "      <td>207.039216</td>\n",
       "      <td>185.235294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B1</th>\n",
       "      <td>1985.0</td>\n",
       "      <td>79.627451</td>\n",
       "      <td>204.588235</td>\n",
       "      <td>99.509804</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            year  temperature    rainfall   mosquitos\n",
       "location                                             \n",
       "A1        2005.5    80.000000  214.600000  191.300000\n",
       "A3        1985.0    80.333333  193.274510   97.647059\n",
       "B2        1985.0    79.764706  196.823529  196.058824\n",
       "A2        1985.0    80.392157  207.039216  185.235294\n",
       "B1        1985.0    79.627451  204.588235   99.509804"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean(level='location')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------------\n",
    "### Challenge: obtain the minimum rainfall for each location given that the  temperature was between 75F and 90F."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is best read backwards. First, let's filter our rows so we only get those for the temperature range we want:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>temperature</th>\n",
       "      <th>rainfall</th>\n",
       "      <th>mosquitos</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">A1</th>\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>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 rowspan=\"24\" valign=\"top\">A3</th>\n",
       "      <th>0</th>\n",
       "      <td>1960</td>\n",
       "      <td>76</td>\n",
       "      <td>191</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1962</td>\n",
       "      <td>81</td>\n",
       "      <td>112</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1963</td>\n",
       "      <td>78</td>\n",
       "      <td>113</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1964</td>\n",
       "      <td>81</td>\n",
       "      <td>159</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1965</td>\n",
       "      <td>87</td>\n",
       "      <td>222</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1967</td>\n",
       "      <td>77</td>\n",
       "      <td>176</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1968</td>\n",
       "      <td>89</td>\n",
       "      <td>236</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1969</td>\n",
       "      <td>88</td>\n",
       "      <td>283</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1970</td>\n",
       "      <td>89</td>\n",
       "      <td>151</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1972</td>\n",
       "      <td>88</td>\n",
       "      <td>267</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1973</td>\n",
       "      <td>85</td>\n",
       "      <td>211</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1977</td>\n",
       "      <td>85</td>\n",
       "      <td>155</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1978</td>\n",
       "      <td>89</td>\n",
       "      <td>158</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1979</td>\n",
       "      <td>86</td>\n",
       "      <td>117</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1981</td>\n",
       "      <td>80</td>\n",
       "      <td>153</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1983</td>\n",
       "      <td>80</td>\n",
       "      <td>145</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1987</td>\n",
       "      <td>83</td>\n",
       "      <td>261</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1988</td>\n",
       "      <td>89</td>\n",
       "      <td>280</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1989</td>\n",
       "      <td>81</td>\n",
       "      <td>271</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1990</td>\n",
       "      <td>77</td>\n",
       "      <td>188</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1991</td>\n",
       "      <td>80</td>\n",
       "      <td>218</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1993</td>\n",
       "      <td>83</td>\n",
       "      <td>211</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1994</td>\n",
       "      <td>77</td>\n",
       "      <td>224</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1996</td>\n",
       "      <td>86</td>\n",
       "      <td>247</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"30\" valign=\"top\">B1</th>\n",
       "      <th>5</th>\n",
       "      <td>1965</td>\n",
       "      <td>76</td>\n",
       "      <td>272</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1966</td>\n",
       "      <td>77</td>\n",
       "      <td>140</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1967</td>\n",
       "      <td>87</td>\n",
       "      <td>207</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1968</td>\n",
       "      <td>86</td>\n",
       "      <td>266</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1969</td>\n",
       "      <td>79</td>\n",
       "      <td>159</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1971</td>\n",
       "      <td>79</td>\n",
       "      <td>252</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1973</td>\n",
       "      <td>82</td>\n",
       "      <td>281</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1974</td>\n",
       "      <td>80</td>\n",
       "      <td>295</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1975</td>\n",
       "      <td>86</td>\n",
       "      <td>166</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1976</td>\n",
       "      <td>87</td>\n",
       "      <td>298</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1977</td>\n",
       "      <td>79</td>\n",
       "      <td>184</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1979</td>\n",
       "      <td>86</td>\n",
       "      <td>157</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1981</td>\n",
       "      <td>77</td>\n",
       "      <td>115</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1983</td>\n",
       "      <td>89</td>\n",
       "      <td>164</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1987</td>\n",
       "      <td>79</td>\n",
       "      <td>123</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1989</td>\n",
       "      <td>84</td>\n",
       "      <td>129</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1990</td>\n",
       "      <td>80</td>\n",
       "      <td>276</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1993</td>\n",
       "      <td>78</td>\n",
       "      <td>285</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1995</td>\n",
       "      <td>80</td>\n",
       "      <td>180</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1997</td>\n",
       "      <td>86</td>\n",
       "      <td>166</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1998</td>\n",
       "      <td>82</td>\n",
       "      <td>285</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2000</td>\n",
       "      <td>86</td>\n",
       "      <td>212</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2002</td>\n",
       "      <td>84</td>\n",
       "      <td>130</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2004</td>\n",
       "      <td>87</td>\n",
       "      <td>280</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2005</td>\n",
       "      <td>85</td>\n",
       "      <td>130</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2006</td>\n",
       "      <td>83</td>\n",
       "      <td>102</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2007</td>\n",
       "      <td>85</td>\n",
       "      <td>163</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2008</td>\n",
       "      <td>83</td>\n",
       "      <td>190</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2009</td>\n",
       "      <td>79</td>\n",
       "      <td>151</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>2010</td>\n",
       "      <td>88</td>\n",
       "      <td>295</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>151 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             year  temperature  rainfall  mosquitos\n",
       "location                                           \n",
       "A1       0   2001           80       157        150\n",
       "         1   2002           85       252        217\n",
       "         2   2003           86       154        153\n",
       "         3   2004           87       159        158\n",
       "         6   2007           80       214        190\n",
       "         7   2008           85       197        181\n",
       "A3       0   1960           76       191         93\n",
       "         2   1962           81       112         73\n",
       "         3   1963           78       113         72\n",
       "         4   1964           81       159         89\n",
       "         5   1965           87       222        109\n",
       "         7   1967           77       176         92\n",
       "         8   1968           89       236        114\n",
       "         9   1969           88       283        128\n",
       "         10  1970           89       151         89\n",
       "         12  1972           88       267        124\n",
       "         13  1973           85       211        102\n",
       "         17  1977           85       155         90\n",
       "         18  1978           89       158         92\n",
       "         19  1979           86       117         75\n",
       "         21  1981           80       153         87\n",
       "         23  1983           80       145         82\n",
       "         27  1987           83       261        117\n",
       "         28  1988           89       280        127\n",
       "         29  1989           81       271        121\n",
       "         30  1990           77       188         94\n",
       "         31  1991           80       218        105\n",
       "         33  1993           83       211        105\n",
       "         34  1994           77       224        108\n",
       "         36  1996           86       247        115\n",
       "...           ...          ...       ...        ...\n",
       "B1       5   1965           76       272        113\n",
       "         6   1966           77       140         87\n",
       "         7   1967           87       207        106\n",
       "         8   1968           86       266        115\n",
       "         9   1969           79       159         92\n",
       "         11  1971           79       252        108\n",
       "         13  1973           82       281        116\n",
       "         14  1974           80       295        120\n",
       "         15  1975           86       166         99\n",
       "         16  1976           87       298        123\n",
       "         17  1977           79       184         95\n",
       "         19  1979           86       157         95\n",
       "         21  1981           77       115         77\n",
       "         23  1983           89       164         99\n",
       "         27  1987           79       123         81\n",
       "         29  1989           84       129         90\n",
       "         30  1990           80       276        111\n",
       "         33  1993           78       285        115\n",
       "         35  1995           80       180         95\n",
       "         37  1997           86       166        100\n",
       "         38  1998           82       285        117\n",
       "         40  2000           86       212        101\n",
       "         42  2002           84       130         85\n",
       "         44  2004           87       280        121\n",
       "         45  2005           85       130         88\n",
       "         46  2006           83       102         77\n",
       "         47  2007           85       163         94\n",
       "         48  2008           83       190         99\n",
       "         49  2009           79       151         88\n",
       "         50  2010           88       295        122\n",
       "\n",
       "[151 rows x 4 columns]"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['temperature'] > 75) & (df['temperature'] < 90)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we can grab the minimum rainfall for these:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['temperature'] > 75) & (df['temperature'] < 90)]['rainfall'].min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But we wanted minimum rainfall **for each location**; we can tell ``Series.min`` to split the data by location:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location\n",
       "A1    154\n",
       "A3    112\n",
       "B2    100\n",
       "A2    111\n",
       "B1    102\n",
       "Name: rainfall, dtype: int64"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['temperature'] > 75) & (df['temperature'] < 90)]['rainfall'].min(level='location')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We don't have time during this lesson to get into ``groupby``s, but they are such a central part of ``pandas`` that we'd be remiss if we excluded them. A ``groupby`` allows us to split our rows according to values in one or more column. For example, we could ask for the minimum rainfall measured across all areas for each year:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "1960    129\n",
       "1961    135\n",
       "1962    112\n",
       "1963    113\n",
       "1964    147\n",
       "1965    151\n",
       "1966    103\n",
       "1967    106\n",
       "1968    138\n",
       "1969    147\n",
       "1970    151\n",
       "1971    121\n",
       "1972    153\n",
       "1973    100\n",
       "1974    101\n",
       "1975    134\n",
       "1976    116\n",
       "1977    155\n",
       "1978    128\n",
       "1979    117\n",
       "1980    138\n",
       "1981    115\n",
       "1982    202\n",
       "1983    130\n",
       "1984    151\n",
       "1985    152\n",
       "1986    214\n",
       "1987    111\n",
       "1988    195\n",
       "1989    129\n",
       "1990    159\n",
       "1991    147\n",
       "1992    140\n",
       "1993    211\n",
       "1994    126\n",
       "1995    180\n",
       "1996    187\n",
       "1997    123\n",
       "1998    127\n",
       "1999    123\n",
       "2000    212\n",
       "2001    112\n",
       "2002    130\n",
       "2003    132\n",
       "2004    119\n",
       "2005    130\n",
       "2006    102\n",
       "2007    163\n",
       "2008    186\n",
       "2009    151\n",
       "2010    127\n",
       "Name: rainfall, dtype: int64"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('year')['rainfall'].min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This should be read as \"group the data by year, then get the minimum rainfall for each group.\" If we want to do something more complex for each group of rows, we can use a ``for`` loop:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1960 129\n",
      "1961 135\n",
      "1962 112\n",
      "1963 113\n",
      "1964 147\n",
      "1965 151\n",
      "1966 103\n",
      "1967 106\n",
      "1968 138\n",
      "1969 147\n",
      "1970 151\n",
      "1971 121\n",
      "1972 153\n",
      "1973 100\n",
      "1974 101\n",
      "1975 134\n",
      "1976 116\n",
      "1977 155\n",
      "1978 128\n",
      "1979 117\n",
      "1980 138\n",
      "1981 115\n",
      "1982 202\n",
      "1983 130\n",
      "1984 151\n",
      "1985 152\n",
      "1986 214\n",
      "1987 111\n",
      "1988 195\n",
      "1989 129\n",
      "1990 159\n",
      "1991 147\n",
      "1992 140\n",
      "1993 211\n",
      "1994 126\n",
      "1995 180\n",
      "1996 187\n",
      "1997 123\n",
      "1998 127\n",
      "1999 123\n",
      "2000 212\n",
      "2001 112\n",
      "2002 130\n",
      "2003 132\n",
      "2004 119\n",
      "2005 130\n",
      "2006 102\n",
      "2007 163\n",
      "2008 186\n",
      "2009 151\n",
      "2010 127\n"
     ]
    }
   ],
   "source": [
    "for name, group in df.groupby('year'):\n",
    "    print(name, group['rainfall'].min())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To learn more about ``groupby``s, check out the ``pandas`` docs on the \"split-apply-combine\" approach to working with datasets with ``groupby``: http://pandas.pydata.org/pandas-docs/stable/groupby.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conditionals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We've already used boolean operations to filter rows on ``DataFrames``, but more broadly we must be able to write code that makes decisions based on the inputs it is given."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use conditionals. To demonstrate how these work, we'll use absurd toy examples. Say we have some number:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num = 37"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can ask whether that number is greater than 100 with a conditional like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "not greater\n"
     ]
    }
   ],
   "source": [
    "if num > 100:\n",
    "    print('greater')\n",
    "else:\n",
    "    print('not greater')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could add some more sophisticatioin to the conditional by checking for equality:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "not greater\n"
     ]
    }
   ],
   "source": [
    "if num > 100:\n",
    "    print('greater')\n",
    "elif num == 100:\n",
    "    print('equal!')\n",
    "else:\n",
    "    print('not greater')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "...and we could test it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "equal!\n"
     ]
    }
   ],
   "source": [
    "if num > 100:\n",
    "    print('greater')\n",
    "elif num == 100:\n",
    "    print('equal!')\n",
    "else:\n",
    "    print('not greater')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conditionals can include boolean operators such as ``and``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "one part is not true\n"
     ]
    }
   ],
   "source": [
    "if (1 > 0) and (-1 > 0):\n",
    "    print('both parts are true')\n",
    "else:\n",
    "    print('one part is not true')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A \"truth table\" for ``and``, given two boolean values ``s1`` and ``s2``, each either ``True`` or ``False`` gives:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| s1    | s2    | s1 and s2 |\n",
    "| ----- | ----- | --------- |\n",
    "| True  | True  | True      |\n",
    "| True  | False | False     |\n",
    "| False | True  | False     |\n",
    "| False | False | False     |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There's also ``or``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "at least one part is true\n"
     ]
    }
   ],
   "source": [
    "if (1 > 0) or (-1 > 0):\n",
    "    print('at least one part is true')\n",
    "else:\n",
    "    print('no part is true')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| s1    | s2    | s1 or s2 |\n",
    "| ----- | ----- | --------- |\n",
    "| True  | True  | True      |\n",
    "| True  | False | True      |\n",
    "| False | True  | True      |\n",
    "| False | False | False     |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And ``not``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "both parts are true\n"
     ]
    }
   ],
   "source": [
    "if (1 > 0) and not (-1 > 0):\n",
    "    print('both parts are true')\n",
    "else:\n",
    "    print('one part is not true')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| s1    | not s1 |\n",
    "| ----- | ------ |\n",
    "| True  | False  |\n",
    "| False | True   |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------\n",
    "### Challenge: rewrite your Fahrenheit to Celsius converter with an if-else statement such that it can also take a list of values in addition to arrays/single numbers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are perhaps other ways to do this, but if we want to specifically handle the case of **lists** of temperatures, we can use the builtin ``isinstance`` to check that we are dealing with a list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def fahrenheit_to_celsius(temp):\n",
    "    \"\"\"Convert temperature in fahrenheit to celsius.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    temp : float or array_like\n",
    "        Temperature(s) in fahrenheit.\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    float or array_like\n",
    "        Temperatures in celsius.\n",
    "\n",
    "    bubbles\n",
    "    \n",
    "    \"\"\"\n",
    "    if isinstance(temp, list):\n",
    "        newtemps = []\n",
    "        for value in temp:\n",
    "            newtemps.append(fahrenheit_to_celsius(value))\n",
    "    else:\n",
    "        newtemps = (temp - 32) * 5/9\n",
    "\n",
    "    return newtemps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function takes advantage of recursion for the case of a list, calling itself on each value in the list and converting those values accordingly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Errors and exceptions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Encountering errors is part of coding. If you are coding, you will hit errors. The important thing to remember is that errors that are loud are the best kind, because they usually give hints as to what the problem is. In Python, errors are known as **exceptions**, and there are a few common varieties. Familiarity with these varieties helps to more quickly identify the root of the problem, which means we can more quickly fix it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to run a function inside a module called ``errors_01.py``; it's inside our ``scripts`` directory:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "argv_list.py  errors_01.py  errors_02.py  \u001b[0m\u001b[01;32mplot_rand_mp.py\u001b[0m*\r\n"
     ]
    }
   ],
   "source": [
    "%ls scripts/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try to import it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "No module named 'errors_01'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-181-56eb787402eb>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0merrors_01\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mImportError\u001b[0m: No module named 'errors_01'"
     ]
    }
   ],
   "source": [
    "import errors_01"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We got an ``ImportError``. This exception tells us that Python cannot find a module we are trying to import. To troubleshoot why, we should first look at ``sys.path``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['',\n",
       " '/home/alter/.local/lib/python3.5/site-packages/scandir-1.1-py3.5-linux-x86_64.egg',\n",
       " '/home/alter/Library/datreant/datreant.core/src',\n",
       " '/home/alter/Library/datreant/datreant.data/src',\n",
       " '/home/alter/Library/asciitree',\n",
       " '/usr/lib/python35.zip',\n",
       " '/usr/lib/python3.5',\n",
       " '/usr/lib/python3.5/plat-linux',\n",
       " '/usr/lib/python3.5/lib-dynload',\n",
       " '/home/alter/.local/lib/python3.5/site-packages',\n",
       " '/usr/lib/python3.5/site-packages',\n",
       " '/usr/lib/python3.5/site-packages/gtk-2.0',\n",
       " '/home/alter/.local/lib/python3.5/site-packages/IPython/extensions',\n",
       " '/home/alter/.ipython']"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sys\n",
    "sys.path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Python interpreter looks at each location in this list, in order, when we try to import a module by name. Since our ``scripts`` directory is not in this list, ``errors_01`` is not found. We can add the directory to the list, though, to make ``errors_01`` findable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sys.path.append('scripts/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['',\n",
       " '/home/alter/.local/lib/python3.5/site-packages/scandir-1.1-py3.5-linux-x86_64.egg',\n",
       " '/home/alter/Library/datreant/datreant.core/src',\n",
       " '/home/alter/Library/datreant/datreant.data/src',\n",
       " '/home/alter/Library/asciitree',\n",
       " '/usr/lib/python35.zip',\n",
       " '/usr/lib/python3.5',\n",
       " '/usr/lib/python3.5/plat-linux',\n",
       " '/usr/lib/python3.5/lib-dynload',\n",
       " '/home/alter/.local/lib/python3.5/site-packages',\n",
       " '/usr/lib/python3.5/site-packages',\n",
       " '/usr/lib/python3.5/site-packages/gtk-2.0',\n",
       " '/home/alter/.local/lib/python3.5/site-packages/IPython/extensions',\n",
       " '/home/alter/.ipython',\n",
       " 'scripts/']"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sys.path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that this change will only be present in this session. It is not permanent. This is a perfectly fine method, though, for using a module you've built yourself. Let's import ``errors_01`` now:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "Missing parentheses in call to 'print' (errors_01.py, line 7)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"scripts/errors_01.py\"\u001b[1;36m, line \u001b[1;32m7\u001b[0m\n\u001b[1;33m    print ice_creams[3]\u001b[0m\n\u001b[1;37m                   ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m Missing parentheses in call to 'print'\n"
     ]
    }
   ],
   "source": [
    "import errors_01"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get a different exception now: a ``SyntaxError``. This the Python interpreter's way of telling us that we are using poor grammar, and it doesn't understand what we are telling it. In this case, it tells us exactly what's wrong:\n",
    "\n",
    "    SyntaxError: Missing parentheses in call to 'print'\n",
    "    \n",
    "If we make the suggested fix in, say, ``errors_02.py`` (we copied the file here for demo purposes, but normally we would fix it and [version control it with git](https://git-scm.com/)):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "def favorite_ice_cream():\r\n",
      "    ice_creams = [\r\n",
      "        \"chocolate\",\r\n",
      "        \"vanilla\",\r\n",
      "        \"strawberry\"\r\n",
      "    ]\r\n",
      "    print(ice_creams[3])\r\n"
     ]
    }
   ],
   "source": [
    "%cat scripts/errors_02.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import errors_02"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This one imports just fine. Let's run ``errors_02.favorite_ice_cream``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-188-8c34ad3792fd>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0merrors_02\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfavorite_ice_cream\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m/home/alter/Documents/SWC/Events/2016_UCSF/swc_ucsf_intermediate_data/python/scripts/errors_02.py\u001b[0m in \u001b[0;36mfavorite_ice_cream\u001b[1;34m()\u001b[0m\n\u001b[0;32m      5\u001b[0m         \u001b[1;34m\"strawberry\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m     ]\n\u001b[1;32m----> 7\u001b[1;33m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mice_creams\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "errors_02.favorite_ice_cream()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This time we get an ``IndexError``. Let's dissect this. The first line shows where the error originates in our code. In this case it's the only line in the cell. The next set of lines shows where the error originates at the source: that is, inside the definition of ``errors_02.favorite_ice_cream`` in line 7 of the module ``errors_02``. The last line gives us a hint as to the nature of the ``IndexError``: we appear to be using an index for an element of a list that doesn't exist."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are [other types of exceptions](https://docs.python.org/3/library/exceptions.html), but these are some of the most common. You need not know all of them, but having some knowledge of the basic varieties is valuable for understanding what's going wroing, and then how to fix it!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aside: catching exceptions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition to being useful tools for debugging, exceptions are **catchable**. That is, you can write code in such a way that it is able to recover from failure or exceptional cases. As an example, let's rewrite our Fahrenheit to Celsius converter one last time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def fahrenheit_to_celsius(temp):\n",
    "    \"\"\"Convert temperature in fahrenheit to celsius.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    temp : float or array_like\n",
    "        Temperature(s) in fahrenheit.\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    float or array_like\n",
    "        Temperatures in celsius.\n",
    "\n",
    "    bubbles\n",
    "    \n",
    "    \"\"\"\n",
    "    try:\n",
    "        newtemps = (temp - 32) * 5/9\n",
    "    except TypeError:\n",
    "        newtemps = []\n",
    "        for value in temp:\n",
    "            newtemps.append(fahrenheit_to_celsius(value))\n",
    "\n",
    "    return newtemps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function can take lists just like the one with the ``if-else`` and ``isinstance`` check, but it turns the problem on its head. It uses a ``try-except`` block instead, first trying to use ``temp`` as a single number or array, and when that fails with a ``TypeError`` (as it would if ``temp`` is a list), it then handles ``temp`` as if it were a list.\n",
    "\n",
    "This is known as \"ask forgiveness\" style: we do the most common thing, and if that fails, we then do something else. This is in contrast to \"ask permission\" style, as exhibited in the ``if-else`` implementation: we first checked if we had a funny case, and if not, did the common thing.\n",
    "\n",
    "Which you choose is often a matter of style, though there are performance benefits to \"ask forgiveness\" when the odd cases truly are rare; when the other cases a function must deal with are almost equally common, it is better to use ``if-else`` statements to direct flow."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defensive programming: being skeptical of your code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Say we write a function that does something a bit obtuse (heh): Given the coordinates of the lower-left and upper-right corners of a rectangle, it normalizes the rectangle so that its lower-left coordinate is at the origin (0, 0), and its longest side is 1.0 units long:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def normalize_rectangle(rect):\n",
    "    \"\"\"Normalizes a rectangle so that it is at the origin, \n",
    "    and 1.0 units long on its longest axis.\n",
    "    \n",
    "    :Arguments:\n",
    "        *rect*\n",
    "            a tuple of size four, giving (x0, y0, x1, y1), the (x,y)\n",
    "            positions of the lower-left and upper-right corner\n",
    "            \n",
    "    :Returns:\n",
    "        *newrect*\n",
    "            a tuple of size four, giving the new coordinates of our rectangle's\n",
    "            vertices\n",
    "    \n",
    "    \"\"\"\n",
    "    x0, y0, x1, y1 = rect\n",
    "    \n",
    "    assert x0 < x1, 'Invalid x coordinates'\n",
    "    assert y0 < y1, 'Invalid y coordinates'\n",
    "    \n",
    "    dx = x1 - x0\n",
    "    dy = y1 - y0\n",
    "    \n",
    "    if dx > dy:\n",
    "        scaled = float(dx) / dy\n",
    "        upper_x, upper_y = 1.0, scaled\n",
    "    else:\n",
    "        scaled = float(dx) / dy\n",
    "        upper_x, upper_y = scaled, 1.0\n",
    "        \n",
    "    assert 0 < upper_x <= 1.0, 'Calculated upper X coordinate invalid'\n",
    "    assert 0 < upper_y <= 1.0, 'Calculated upper Y coordinate invalid'\n",
    "        \n",
    "    return (0, 0, upper_x, upper_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that we added a nice documentation string at the top that explicitly states the functions inputs (arguments) and outputs (returns), so when we use it we don't need to mind its details anymore. Also, at the beginning of the function we have ``assert`` statements that check the validity of the inputs, while at the end of the function we have ``assert`` statements that check the validity of the outputs. \n",
    "\n",
    "These statements codify what our documentation says about what the inputs should mean and what the output should look like, guarding against cases where the function is given inputs it wasn't designed to handle."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's test the function!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 0, 0.5, 1.0)"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalize_rectangle((3, 4, 4, 6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks good. First point is at the origin; longest side is 1.0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 0, 0.5, 1.0)"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalize_rectangle((0.0, 1.0, 2.0, 5.0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nice!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "Calculated upper Y coordinate invalid",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-193-a41732464e3f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnormalize_rectangle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m5.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-190-a7edcf11432e>\u001b[0m in \u001b[0;36mnormalize_rectangle\u001b[1;34m(rect)\u001b[0m\n\u001b[0;32m     30\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     31\u001b[0m     \u001b[1;32massert\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mupper_x\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Calculated upper X coordinate invalid'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 32\u001b[1;33m     \u001b[1;32massert\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mupper_y\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Calculated upper Y coordinate invalid'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     33\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     34\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mupper_x\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mupper_y\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAssertionError\u001b[0m: Calculated upper Y coordinate invalid"
     ]
    }
   ],
   "source": [
    "normalize_rectangle((0.0, 0.0, 5.0, 1.0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Oh...what happened here? The inputs should be fine, but an assertion caught something strange in the output. Looking closely at the code, we spot a mistake in line 26:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def normalize_rectangle(rect):\n",
    "    \"\"\"Normalizes a rectangle so that it is at the origin, \n",
    "    and 1.0 units long on its longest axis.\n",
    "    \n",
    "    :Arguments:\n",
    "        *rect*\n",
    "            a tuple of size four, giving (x0, y0, x1, y1), the (x,y)\n",
    "            positions of the lower-left and upper-right corner\n",
    "            \n",
    "    :Returns:\n",
    "        *newrect*\n",
    "            a tuple of size four, giving the new coordinates of our rectangle's\n",
    "            vertices\n",
    "    \n",
    "    \"\"\"\n",
    "    x0, y0, x1, y1 = rect\n",
    "    \n",
    "    assert x0 < x1, 'Invalid x coordinates'\n",
    "    assert y0 < y1, 'Invalid y coordinates'\n",
    "    \n",
    "    dx = x1 - x0\n",
    "    dy = y1 - y0\n",
    "    \n",
    "    if dx > dy:\n",
    "        scaled = float(dy) / dx\n",
    "        upper_x, upper_y = 1.0, scaled\n",
    "    else:\n",
    "        scaled = float(dx) / dy\n",
    "        upper_x, upper_y = scaled, 1.0\n",
    "        \n",
    "    assert 0 < upper_x <= 1.0, 'Calculated upper X coordinate invalid'\n",
    "    assert 0 < upper_y <= 1.0, 'Calculated upper Y coordinate invalid'\n",
    "        \n",
    "    return (0, 0, upper_x, upper_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now we get:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 0, 1.0, 0.2)"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalize_rectangle((0.0, 0.0, 5.0, 1.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 0, 0.6666666666666666, 1.0)"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalize_rectangle((2, 3, 4, 6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That looks better."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What if we were to try feeding in only three values instead of four?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "not enough values to unpack (expected 4, got 3)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-197-c5d8e9954719>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnormalize_rectangle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-194-d4ce9e9a4cfa>\u001b[0m in \u001b[0;36mnormalize_rectangle\u001b[1;34m(rect)\u001b[0m\n\u001b[0;32m     14\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     15\u001b[0m     \"\"\"\n\u001b[1;32m---> 16\u001b[1;33m     \u001b[0mx0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrect\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     17\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m     \u001b[1;32massert\u001b[0m \u001b[0mx0\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mx1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Invalid x coordinates'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: not enough values to unpack (expected 4, got 3)"
     ]
    }
   ],
   "source": [
    "normalize_rectangle((2, 4, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's a rather unhelpful error message. We can also add an assertion that checks that the input is long enough, and gives a message indicating what the problem is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def normalize_rectangle(rect):\n",
    "    \"\"\"Normalizes a rectangle so that it is at the origin, \n",
    "    and 1.0 units long on its longest axis.\n",
    "    \n",
    "    :Arguments:\n",
    "        *rect*\n",
    "            a tuple of size four, giving (x0, y0, x1, y1), the (x,y)\n",
    "            positions of the lower-left and upper-right corner\n",
    "            \n",
    "    :Returns:\n",
    "        *newrect*\n",
    "            a tuple of size four, giving the new coordinates of our rectangle's\n",
    "            vertices\n",
    "    \n",
    "    \"\"\"\n",
    "    assert len(rect) == 4, \"Rectangle must have 4 elements\"\n",
    "\n",
    "    x0, y0, x1, y1 = rect\n",
    "    \n",
    "    assert x0 < x1, 'Invalid x coordinates'\n",
    "    assert y0 < y1, 'Invalid y coordinates'\n",
    "    \n",
    "    dx = x1 - x0\n",
    "    dy = y1 - y0\n",
    "    \n",
    "    if dx > dy:\n",
    "        scaled = float(dy) / dx\n",
    "        upper_x, upper_y = 1.0, scaled\n",
    "    else:\n",
    "        scaled = float(dx) / dy\n",
    "        upper_x, upper_y = scaled, 1.0\n",
    "        \n",
    "    assert 0 < upper_x <= 1.0, 'Calculated upper X coordinate invalid'\n",
    "    assert 0 < upper_y <= 1.0, 'Calculated upper Y coordinate invalid'\n",
    "        \n",
    "    return (0, 0, upper_x, upper_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "Rectangle must have 4 elements",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-199-c5d8e9954719>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnormalize_rectangle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-198-e9732fa1861f>\u001b[0m in \u001b[0;36mnormalize_rectangle\u001b[1;34m(rect)\u001b[0m\n\u001b[0;32m     14\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     15\u001b[0m     \"\"\"\n\u001b[1;32m---> 16\u001b[1;33m     \u001b[1;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrect\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"Rectangle must have 4 elements\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     17\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m     \u001b[0mx0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrect\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAssertionError\u001b[0m: Rectangle must have 4 elements"
     ]
    }
   ],
   "source": [
    "normalize_rectangle((2, 4, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now when we use this function six months later, we won't have to scratch our heads too long when this sort of thing happens! *Defensive programming* in this way makes code more robust, easier to debug, and easier to maintain. This is especially important for scientific code, since incorrect results can occur silently and end up getting published, perhaps only getting uncovered years later. Much embarrassment and misinformation can be avoided by being skeptical of our code, using tools such as ``assert`` statements to make sure it keeps doing what we think it should."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Writing Python scripts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Running Python code from a notebook is great, but we often need to run Python code from the command line, perhaps on files as input data. We can turn our module ``datastuff.py`` into a simple script by adding some meat at the bottom:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "if __name__ == '__main__':\n",
    "\n",
    "    import pandas as pd\n",
    "    import sys\n",
    "\n",
    "    for dataset in sys.argv[1:]:\n",
    "        print(dataset)\n",
    "\n",
    "        data = pd.read_csv(dataset)\n",
    "        data['temperature'] = fahrenheit_to_celsius(\n",
    "                data['temperature'])\n",
    "\n",
    "        fig = analyze_mosquitos(data)\n",
    "\n",
    "        fig.savefig(dataset + \"_plot.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ``if __name__ == '__main__'`` statement at the top says that this code should only be executed if the module is being run as a script. It's a common check that the current namespace is `__main__`, as it would only be if the code were being executed as a script.\n",
    "\n",
    "Adding this allows us to run the script on any number of input files as:\n",
    "\n",
    "    python datastuff.py A1_mosquito_data.csv A2_mosquito_data.csv\n",
    "    \n",
    "or even:\n",
    "\n",
    "    python datastuff.py *.csv\n",
    "    \n",
    "It will loop through each filename given as arguments, convert the temperature column to Celsius, then generate and save a PDF of the analysis figures for that dataset.\n",
    "\n",
    "``sys.argv`` is a list having the name of the script as its zeroth element, then each argument to the script in order from the first to the last. We do ``sys.argv[1:]`` to slice out only those arguments so we can iterate through them. This is a quick-and-dirty way to parse arguments, but if you're interested in writing more sophisticated command-line utilities, definitely use [``argparse``](https://docs.python.org/3/library/argparse.html)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}