{ "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": [ "
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yeartemperaturerainfallmosquitos
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" ], "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": [ "
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yeartemperaturerainfallmosquitos
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" ], "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": [ "
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" ], "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": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
yeartemperaturerainfallmosquitos
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" ], "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\u001b[0m in \u001b[0;36m\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": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
yeartemperaturerainfallmosquitos
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" ], "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": [ "
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yeartemperaturerainfallmosquitos
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" ], "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": [ "
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yeartemperaturerainfallmosquitos
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" ], "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": [ "
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yeartemperaturerainfallmosquitos
count10.0000010.00000010.00000010.00000
mean2005.5000080.000000214.600000191.30000
std3.027655.45690250.31721633.23335
min2001.0000074.000000154.000000150.00000
25%2003.2500074.250000168.500000163.75000
50%2005.5000080.000000210.500000187.00000
75%2007.7500085.000000246.750000212.75000
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" ], "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": [ "
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yeartemperaturerainfallmosquitosrainfall / mosquitos
0200126.6666671571501.046667
1200229.4444442522171.161290
2200330.0000001541531.006536
3200430.5555561591581.006329
4200523.3333332922431.201646
5200623.8888892832371.194093
6200726.6666672141901.126316
7200829.4444441971811.088398
8200923.3333332312001.155000
9201023.3333332071841.125000
\n", "
" ], "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": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
yeartemperaturerainfallmosquitos
0200126.666667157150
1200229.444444252217
2200330.000000154153
3200430.555556159158
4200523.333333292243
5200623.888889283237
6200726.666667214190
7200829.444444197181
8200923.333333231200
9201023.333333207184
\n", "
" ], "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": [ "
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rainfallyearmosquitostemperature
0157200115026.666667
1252200221729.444444
2154200315330.000000
3159200415830.555556
4292200524323.333333
5283200623723.888889
6214200719026.666667
7197200818129.444444
8231200920023.333333
9207201018423.333333
\n", "
" ], "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": [ "
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rainfallyearmosquitostemperatureyear
0157200115026.6666672001
1252200221729.4444442002
2154200315330.0000002003
3159200415830.5555562004
4292200524323.3333332005
5283200623723.8888892006
6214200719026.6666672007
7197200818129.4444442008
8231200920023.3333332009
9207201018423.3333332010
\n", "
" ], "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": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
yeartemperaturerainfallmosquitos
0200126.666667157150
1200229.444444252217
2200330.000000154153
3200430.555556159158
4200523.333333292243
5200623.888889283237
6200726.666667214190
7200829.444444197181
8200923.333333231200
9201023.333333207184
\n", "
" ], "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": [ "" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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P8fHxBAQEuNYjGGPo2LEjVapUoXTp0mnL+vn5ERj4zxyAIpI29n3HHXdcsVyZ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\n", "text/plain": [ "" ] }, "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": [ "" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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dcFcdWiU7IdAIAib4SqWY3oRKhA/I+7bBXKEjIiZuWx2tqGyu9NmSltXS8fbw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\nduwQPifl5cLvDgelGzZ0Fv8LL5QWek+am4Mj2vc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"text/plain": [ "" ] }, "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": [ "" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "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": [ "" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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WZfPNb/qvH8SMGj/e+Te3iEUQM6qqSpG0VloeqK6poe6YY1QkLcLpQapraqi7/HIVlfuv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\nomB4PZLBwep6NlWhVTGbzZsP8sgjfQC88EL2L33lGjJF6MhsFAyLTSP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"text/plain": [ "" ] }, "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": [ "" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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5fJHPh9gCTIvQJsMwakhkDkZVpxfZNTGgbEpUdhiGER822TEhNDU1xW1CZGS5\nbpD9+lVCajJ5TfrDMOJBRNCkBXkNwzDMwRiGERk2m9owMs6sWQtoa9veo3z06L25884rQx1bLuZg\nDCPjtLVtp7W1OWBP97JZsxbwwANr6Oi4p+C4uWXf27pIhmEAzhF1dDRW9ZrmYAzDiAxzMIZhRIbF\nYAwjQwQFaVevbgcWsHthh9phDsYwMkSxgO6QITMYO7Z7+ejRewdcYW8Kg7+VUFPJTFVd6vedBlyl\nqsf799fj1uLpAi5T1Seisssw6pGxYxtpaWnu9RjncHoOUbe2ln/fKFswU4C3VPUsEfkQbkXA0X5p\ni/m4dXVyC34dpKpH+2VDfwUcEaFdhmEEUJgTk0MkmcPU7cAdfns7u9e3mY9Tt8sxEXgQQFXbcUsO\nd1tDxzCMyli9up2mpmZmzVpQ0/tGKdfQCuAlM38CXCcin8QtwPUIcK4/tFAy811/zOaobDOMrJEL\n7rqAbnPenr2BK+noaKS1tZm1az9PU1Nzj/ODsnqrQaRB3gLJzCdw6nWT6b6859vAkLz3DbjF0nvQ\n3Ny8a7upqcmmyRuGp3i27gycw3EB3W3bBvWZ1dvS0kJLS0tV7IoyyJsvmblDRA7Brfz3ADAQOFRE\n7gTuB3KrAB4CvKOqW4Kume9gDMMIQyOljgoV/njPnVt+DKamkpmqejiAiIwAlqjqLP/+DBFZhVuL\neFaENhmGUUPikMxEVdcDx+e9vzgqOwzDiA9LtDOMDDNkSHu3BLu1a/vR0VG7+5uDMYwUkxs9Wrv2\neYYMmbGrfODAfowZcyCjRx/cbXTIHd/c4zrBWb2VY5q8hpESis0z6qnfAied1Nxn5m5YKtHktRaM\nYaSE4KHowvfJwuQaDMOIDHMwhmFEhnWRDKOKlCKwXQ+YgzGMKlIsZX/16hm0tTVH4Gj29lovjd1K\noxoVKhVzMIZRA3KTDSsJyjqn0fP8wqHoJGEOxjBSQlKdSG+YgzGMmrFgly5LPlmOz9RUMtPfbz7Q\nAbyMm0sOcDcwGtgBzFTVtqjsMoz42E5Hx3ZaW9u7la5YsZ3HHz+LNWt+EY9ZEVIrycxhwEqc5u7J\nqvqKiFwLTAcUeFNVzxaRccANwOkR2mUYkZGLk7gM28a8PTm9272Be7qds3MnbNw4o0YW1pYoHUw7\nsMpvv4cTmZqvqq/4sk6cuNSRwO0AqrpcRJZEaJNhREquq9NzuHq7dzqdgedt3RpcnnZqKZn5A1W9\nVUT6A5dIz4+dAAARxElEQVQCX8CtJHAq3SUzu6KyyTBqRVBMpampmdbWNYHHd3XtEbVJsVAzyUxV\nbRGRMcDPgRbgGFXtFJFCycyiMxpNMtMwoqeakpmRzab2kplTgM95yUwB/gRcoKrL8447DzhEVS8X\nkUnANFWdFnA9m01tpJpZsxbwk5+swqnGdmePPc7i/feTGeStZDZ1lA5mIfBxnIC3AAfhNHn/6N8r\nLtq1BDfa9FFgC87BvBJwPXMwRurZZ59Ps23bsT3KBw58mq1bfxeDRX2TSLmG3iQzA5gSlR2GkSSO\nOebYwKkExxzTsywLWKKdYdSQ4un+yZg7VG1M0c4wjF5JZBfJMLKGSTGUjjkYwwhJ8dUTg8oMMEU7\nwzAixFowRuaxrk18mIMxMo91beLDukiGYUSGtWAMIyT1lsNSDczBGEZILF5TOuZgjMyzdu3zBLU8\nXLkRJbWWzNwJ/AB4H1iqqld5fRiTzDQi5AMEB3Rn1tiO+qPWkpk7gSZV3SQiS0XkSOAITDLTqJDe\nhqLHjDmQjRt7njNmzIE1sKy+Ce1gRGQATm3uSOBZVd3Zxynt9JTM/IuqbvJlv8Mp2h2NSWYaIejN\nidhQdDIJ5WBE5Ls4Wct9gWOBTcDZvZ0TIJn5I+BjeYe8C3zEX9MkM40+6W3VRCOZhG3BnKSq40Tk\nflU9VURWhjkpXzITeA3XYsmxL/A6YJKZRkV0V+83KqWakplhHcwAERmBa3UA9KlQ7CUzjwKO9pKZ\n/YDhIrIf8AYuznIervt0JrDSS2YuL3bNfAdj1CsLcMt/5NOOW6QiGMtfKY3CH++5c+eWfa2wDmYx\nsAyYIiJ3Af8Z4pxJQCPwiNfjVeBi4GHcaNESVW0TkXXAIhF5Bi+ZWVoVjPpiO8FxlRmBR69e3Y77\nGNrcozgI5WBU9RYR+QVwIG6FgL+HOKeYZObYguN2YJKZRsX0I+d49thjDTt3HgxAR8fBtLbmnEpz\nHIbVNWGDvF8C5gIvAKNF5EpV/VWklhlGAaNH783q1Wvo6AjaeyA5BzJ48Aw6OpprZ5hRlLBdpAuB\nI1R1q4gMxg0xm4Mxasqdd15JW1szra099w0Z0s7Ysc0ArF3bWcQJGbUmrIP5h6puBVDVLSJi4rhG\nohg7tpGWlmbAraAYlFhn1J6wDuZ5EbkJeAw4Dng5OpMMozg2IpQuwjqYrwPn4taR/l8sWmbERJhR\nIHNCyaHXZUt87kp/3KTFc3LFwEJVPSt687rZYsuWpJRyJStN6jIZRLlsyYW4LNz9gDW+TIGnyrmZ\nUZ90T/HfnSi3evUa2tpceZDTsPlF6adXB6OqPwR+KCIXqerNNbLJyDS7E+U6OsgbEWqOxxwjUnrV\n5BWR8/zm/iIyP/9VA9uMOmL16nZmzVoQtxlGlemri/Q3/3dNr0cZRoV0dDQGxluMdNNXF+kRv7mu\nBrYYhpExwg5Tf83/7Qcchpu6emyYE0XkLFwW8LdE5BjgOr/rJZw8ZpeIXI+TcugCLlPVJ8JWwEg+\n+cPGq1e3h86yteHm9BN2suOuyYgisgdwT1/n+BnUjwCfAm7yxTfgNXdF5F7gDBF5FzhIVY8WkUbc\nFIQjSqiDkUCKDTHvt992YEaAhsveFMow2FB0+ilZk1dVd4pInz8hqqpe3+UcnKA3OLHvfX1+zQdx\n8gwTgQf9Oe3iaFDVzaXaZiSHYkPMJ53UzH77YcPPdULY2dSv4fJfBCc2dUeY83z3Jz877jZgKW6q\nQRcun+bzdJfMfBdoAMzBJJBqJL9Z16d+CNtF2r/SG4nIPsC1wChVfV1ErgKuwjmXfMnMBuDNoGuY\nZGb8VCP5zbo+yabmkpki8lixfao6ocR75kJ8r+K6TsuA84ElInII8I6qbgk60SQz42PWrAX8+tdt\nvPHGVro7k72BK7sd19a23SvJFT/OSC5xSGauB1pxaxuNA04CvlvKjbyWzFXAYyKyHRd/maGq74jI\nGSKyCqfPO6uU6xq1oa1tOxs3/ixgT3OP4yy+YuQI62AOUtVz/fZaEZmiqmvDnKiqC/O2F+P0fQuP\nuTikHUYiWcCTT66hoWEGW7YEJ8vlBKEszlJfhHUwO/yI0EqcHsye0ZlkpI/t7NjxC5/f0hx4RL4g\nlFE/hHUw5+KmwV4PvIhbbsQwgHb22GM7O/ta59OoS3qd7JjHWzjR74nAE/S2CI1RZzQyeLB1e4xg\nwrZg7gXuAz6NW+XqXmB8RDYZCWT06L1Zu3Ym27Z1X9l34MBO4AN56f/5OS7tDBniukcWe6lPelW0\n23WQyGOqOkFEHlDVL4jIclUdVwP78m0wRbuE0tTUXHTk6KSTsNhLyolS0S7HQBE5B9ggIsOBvcq5\nmZFNemvdjB59ZExWGUkgbAvmRGA68B3gIqBFVR+N2LZCG6wFYxgxUEkLJpSD8Tf5LDAKWKWqfyjn\nZpVgDsYw4qESBxNqFElErgWm4iYoXiQi/17OzQzDqC/CdpFWqOqn8t63qGpTlIYF2GAtGMOIgVoE\neUVE+nn5hX7AwHJuZlSXvqQTbF0hI27COph7gCdF5EngKOChsDcokMwcgcuh6QdsAqbgul1342ZW\n78Ar3oWuQR3T18RCm3hoxE2vDkZE8qfPvoib6fx7divU9XZukGTmbcCNqvqQiNwAfBEYALypqmeL\nyDicrObppVbE6Mnatc/T3ZlsALp48smNNDXtLrcWjREVfbVgPoNTlluCm+i4KOyFCyUzRWQA8HFV\nzbV+5uHyaW4AbvfnLBeRJaVVwSjGtm2DCGqt7NhRmBjX8xjDqAZ9jSLtD1wAfBg32bEJeC1vOZNe\nUdUunNQmwDBgi4jc7AWsbsKpPA+ju2Rm92wtwzBSS1/rInXhukS/9y2QScA1IjJCVQ8t8V5/Bz4C\nXKeqG0TkClziXqFkZtGhIpPMNIzoiUMycx/gX4EvAUOBIGmzXlHVThFZjVOyA3gbt7LAMuBMYKXv\nUi0vdg2TzOxOX+LZAwf2C70GkWHkqJlkpoicgRvpGYNbr+jyCkd4vg484OK/bAZmAtuARSLyDM75\nTKvg+nVFX4HZMWMOZOPGGhljGAH0mmgnIl3AX4FVvmjXwar6pWhN62GLJdqVSGEezNq1G9i2rYuB\nAzsZM+awXeU2imT0RmRzkUTkpGL7VLW1nBuWSz07GEuYM+IkskzeWjsRIxhLmDPSSljJTMMwjJIx\nB2MYRmSEnYtkRITFV4wsYw4mZiy+YmQZczA1prDFsnsN5+JrN/eVUGcYScUcTI0pp8ViXSUjrViQ\n1zCMyDAHYxhGZFgXqUKqNQo0ZEg7Y8c2dzvfMNJO5A4mXzIzr+w04CpVPd6/vx44EacFc5mqPhG1\nXdWiWqNAY8c22gqIRuaIzMEUkcxERAYB84Gt/v0E4CBVPVpEGnGzto+Iyq44mTVrAWvXPs+QITO6\nlQ8c2I/Ro/tUITWM1BGZgymUzMzbNR+nzXuufz8ReNCf0y6OBlXdHJVtcdHWtp2NG/+jR/nYsc02\nUmRkkkiDvAWSmYjIcUADrmWTo1Ay811/jGEYKadmQV4vuXkNMBkYnLfrbbpLZjYAbwZdwyQzDSN6\nai6ZWSVG4eQ2H8At3HaoiNwJ3A+cDywRkUOAd1R1S9AFkiiZaVm2RtaomWRmNVHVNcDhAH4BtiWq\nOsu/P0NEVgHv4dZeSg29xU7KmRZgGFkicgejqgsDytYDx+e9vzhqO0qlGvktxYawhwyZYTkvRl1g\niXZFiHKWs+W8GPWCTRUwDCMyzMEYhhEZ1kUKYNasBXkB2XwsVmIYpWAOJoC2tu10dNwTsKe5pOvY\nELZR75iDKYEhQ9oZPfrg0Mdb+r9R71gMpkTa2rbT1NTMrFkL4jbFMBKPtWAK2B1/6UlHR2Pe0HVz\n4DGGYezGWjAFuPhLY9xmGEYmsBZMIEHB2XYgfPzFMAxzMEUICs42Fyk3DKMYNZXMFJFP4wSnOoCX\ngRn+sLtxolQ7gJmq2lbOvfLnD61du4Ft27oAGDiwkzFjDgNsxUTDqCW1lsy8CZigqq+IyLXAdJwg\n1ZuqeraIjANuAE4v557F5g91dDSzcWOuvOf+MJgot2GUTq0lM29R1Vf8didOXOpI4HZ/znIRWRKV\nTWEonhx3sLV8DKNEIu0iqWqXiGje+1tFpD9wKfAF3EoCp9JdMrMrSpv6wpyIYVSPmgZ5RWQM8HOg\nBThGVTtFpFAyU4POBZPMNIxakFbJTID/AC5Q1eV5ZcuAM4GVvku1PPBMkimZaRhZI5WSmSIyEmgE\n5voAsAL3AAuBRSLyDLAFmFbuPfLjJz1HkZrzjjEMoxaIatEeSaIQEU2LrYaRJUQEVZVyzk1tol21\n1oQ2DCM6UutgotTMNQyjOthkR8MwIsMcjGEYkWEOxjCMyDAHYxhGZKQ2yGuC2oaRfCwPxjCMXqkk\nD8a6SIZhRIY5GMMwIsMcjGEYkRG5gxGRs0Tk+377ZBF5VkSeFpHv+rL+InKvL1shIqN7v2I2qdb0\n+CSS5bpB9utXCZE5GHE8CvyM3RovtwGnqeqxwLEiciRO8e5NX/YtnGRm3ZHlD2mW6wbZr18lROZg\n/JDPJOB8AN8yeUVVN/lDfodTtJsIPOjPWQ6MjcomwzBqS6RdJFXtYnfrZRjdpTHfxWny7kuCJDMN\nw6gekefBiMh0YAxOWOoWVT3Vl38TJ/x9gi9f6cvXq+qIgOtYEoxhxEQa9GDagOEish/wBm5pkvOA\n9wghmVluBQ3DiI+aORi/jMklwMO4BdaWqGqbiKyjSpKZhmEki9RMFTAMI31Yop1hGJGRSAeT5eS8\ngrp90dfhMf86ypdfLyLP+H0nxGtx34jIniLyC2/vkyJyiohMyMpzK1K/TDw7ABEZLCIPiUiriDwh\nIp+o2vdOVRPzAgR4FNgKzPdla4B/9ttLcUvNzgRu9GXjgN/EbXuZdbsGOLrguAnAQ367EfhT3LaH\nqNt04Da/PQwX0P9LFp5bL/VbkIVn5229GrjYbzcBv6nW80tUC0ad5ZlMziusm+dg4GoReVxEvi8i\n/ehet3ZcUnRDre0tkXbgDr/9HjAYeDULz83TTs/6HUI2nh04B5JbE/5DuBy1qjy/RDkYyHZyXkHd\nAFYAF6rqicA/AV+jZ91ydU4sqtqqqs+JyGG4VtqPyNZzK6zfdWTk2QGo6kpVfV1EfgvcC/wPVXp+\nSVe0e5vuD2hf4HVfHmo964RzvXc6AL8EJuNyhPLr1gC8WWvDSkVErsbZfwnwGu4XL0fqn1t+/VS1\nRUT6ZejZDQc2quppInIgsBp4Ju+Qsp9f4lowBexKzhORPXDJeUuBx3DJefS1nnVSEZEBwAYRyT2w\nCbiHmlurGxE5BHhHVbfEY2U4RGQKcBQuJtEC/JUMPbfC+onInmTk2XluAU7129txTvHDIrJ/pc8v\n0S0Y1ewm56nqDhG5DFgmIu/ivpQ/U9X3ReQMEVmF6+/PitXQcEzCBTUfEdm17vjFZOe5BdUvK88O\n4NvAnX76Tn/gq8AeuNhLRc/PEu0Mw4iMpHeRDMNIMeZgDMOIDHMwhmFEhjkYwzAiwxyMYRiRYQ7G\nMIzIMAdjhEJERohIh585/Ac/6/ZxERlZ5PgrcjOMi+z/FxF5QdzKEkH7p4vIfH/fldWqh1FbEp1o\nZySO51V1Qu6NiMwBLgIuLTxQVa/p41rHAj9W1WdD3NeStVKKORijFAp1kYfhUuavwYm3DwBaVPUK\nEbkbN0N3f+Dz/vjhwF1AC3Au8J6IPAGMwmX+vo+bWPd5jExgDsYohUNF5DGco/lnYE/g48Blqvop\nP0fnNeCKgvP2UtVTxAm+P6Gqt4rIPcBrqvpHETkFmKiqW0Xkd8AnalYjI1LMwRilUNhF+jnwWeCD\nInIHbv5N0GfqjwCqulFEBgbs3wzcIiLbgA/j5sEYGcAcjFEKhV2ktbhuzxBVPc9P9f9qwHn5MZRu\n1xCRocDlqvpREdkLeCrEfY2UYA7GKIXCYOtW4KPAx0RkBbAK+L2IfD3g2MBrqOo7Xvv1GWA9TvLg\nQuDXvdzXSAk2m9owjMiwPBjDMCLDHIxhGJFhDsYwjMgwB2MYRmSYgzEMIzLMwRiGERnmYAzDiIz/\nD7yQqJyz545eAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "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": [ "
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\n", "
" ], "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', [...], ]" ] }, "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": [ "[,\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": [ "
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" ], "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": [ "[, [...], '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": 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NDWRXc2vNQD1m7q9OGipgHWKotAF0snHjx3d8MHcjrzXU0wZQqyPbAKytHGCsocHS1dw6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\nDjBmVowDjJkV8/8BKKdP8eg83NkAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "A3_mosquito_data.csv\n" ] }, { "data": { "image/png": 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byYbM+k2/UiiSrCxlda5KJGFmSvLa1Dt5JU2SdGHN/cGSHpM0NLq/maQbJD0o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\nHxsLr7aCKASqAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "B2_mosquito_data.csv\n" ] }, { "data": { "image/png": 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kXQWcRlgd7lfAU8DY6NbllYQe/1fpumJAA2HkZ+GxvDfauRoxM/W9V3dxdvJO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\neI2wssBDwBnAimgVgmU9HSM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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "A2_mosquito_data.csv\n" ] }, { "data": { "image/png": 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5fJHPh9gCTIvQJsMwakhkDkZVpxfZNTGgbEpUdhiGER822TEhNDU1xW1CZGS5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\nD7yQqJyz545eAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "B1_mosquito_data.csv\n" ] }, { "data": { "image/png": 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dRYomy/lCRb0VPZIi1GCU2VFKK4uO0rRkUSPKOrNjbdu+dc9fR5kdRTKXRY0o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\nKMCISGoUYEQkNQowIpKa/w9ORNdQHLLvnAAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "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": [ "\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
OLS Regression Results
Dep. Variable: mosquitos R-squared: 0.996
Model: OLS Adj. R-squared: 0.996
Method: Least Squares F-statistic: 1.386e+04
Date: Mon, 09 May 2016 Prob (F-statistic): 8.69e-63
Time: 23:02:14 Log-Likelihood: -196.25
No. Observations: 51 AIC: 394.5
Df Residuals: 50 BIC: 396.4
Df Model: 1
Covariance Type: nonrobust
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
coef std err t P>|t| [95.0% Conf. Int.]
rainfall 0.8811 0.007 117.744 0.000 0.866 0.896
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
Omnibus: 4.422 Durbin-Watson: 1.878
Prob(Omnibus): 0.110 Jarque-Bera (JB): 1.959
Skew: -0.085 Prob(JB): 0.376
Kurtosis: 2.055 Cond. No. 1.00
" ], "text/plain": [ "\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": [ "\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
OLS Regression Results
Dep. Variable: mosquitos R-squared: 0.997
Model: OLS Adj. R-squared: 0.997
Method: Least Squares F-statistic: 7889.
Date: Mon, 09 May 2016 Prob (F-statistic): 3.68e-61
Time: 23:02:15 Log-Likelihood: -111.54
No. Observations: 51 AIC: 229.1
Df Residuals: 48 BIC: 234.9
Df Model: 2
Covariance Type: nonrobust
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 17.5457 2.767 6.341 0.000 11.983 23.109
temperature 0.8719 0.092 9.457 0.000 0.687 1.057
rainfall 0.6967 0.006 125.385 0.000 0.686 0.708
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
Omnibus: 1.651 Durbin-Watson: 1.872
Prob(Omnibus): 0.438 Jarque-Bera (JB): 0.906
Skew: -0.278 Prob(JB): 0.636
Kurtosis: 3.343 Cond. No. 1.92e+03
" ], "text/plain": [ "\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": [ "" ] }, "execution_count": 137, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "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": 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DGtBvIlvmx1hMSggnkxJb+e/utR3i3Qj8XbZ8GXA2adjmydm2vwWWZb+nJcAR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\n/G6MeLa6HtV2RjlNno51DwP7AedHxFpJ84EzSR9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"text/plain": [ "" ] }, "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": 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JSUSkknIpHDZF8xI8mhBXV9rNSXgVbI3hHw3NWy2DQiK8Fc1FzwkUEUmUXOc5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\nV6UJA+CbwFtJ+pc0X885wONlB1Z1kj5Mcr0uBh4leRob4Xt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"text/plain": [ "" ] }, "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": 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D2hgTuiB9EAIc6BfY+y6wTVVL0xJd7Tiyqg+isTWS4p+fdOmkOvMcYnMalr26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\nZrkcIMzMLJcDhJmZ5fp/sdYkJFMAqSQAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "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": 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M7Ktm9jczW2BmvzGzLtHt5qia3CNmNqD1v0ZpNTeo3JoB52mTptFtQTc4pPH2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\nl7QBeAlYPtU55kvKUU9KM7N6NT3l6GxExC05ZY8Db6/YXlF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"text/plain": [ "" ] }, "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": 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mFbvtWlaO+V6TTMdX23R8tauUY4syfEbZuPuV7j7C3Q8DJgLPADcAZ7j7lwiK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\nP8zOOTmYmbWvV81K/5P+/7FxjzKzSWk118/Q0Hb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"text/plain": [ "" ] }, "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": 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FvRPp9lFvkV3Db3qeL2kPUqmk92s3VtJLs+WjgAd6PW8csGtE9AxH/cqafX/J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\nMzMrcoIwM7MiJwgzMytygjAzs6L/A9gYWda9wCHmAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "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": 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7gaXAgjqK/0zgsuj+WOB39RA/YMBcwrop34q2PVHwP38PsDswFfj3aNt+wP/W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\n5Tg4mJlZjoODmZnlODiYmVnO/wN955uoy/pVAQAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "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": 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ZmJIUED9JPQoREcmcHmdSu/vcwltvTmBmZ5nZN6L7Hzaz35rZEjP7sZkNiW53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\njIgHs5+fIdV+qKwodyfQU1abbIz/J6wfX8v68vVsHaUOMUX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"text/plain": [ "" ] }, "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": 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x+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/gt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"text/plain": [ "" ] }, "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": 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csg2ED5ucnsddF8fX89jcfWbUzPJV4ATgJur02KD78bn7One/38z+CWgH1gMv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\ngJndQ/j22kb3z5k0v3d7xn6yu/+yjt6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"text/plain": [ "" ] }, "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": 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csg2ED5ucnsddF8fX89jcfWbUzPJV4ATgJur02KD78bn7One/38z+CWgH1gMv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\ngJndQ/j22kb3z5k0v3d7xn6yu/+yjt6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iwtnebC6cHKxIngth80W/VmWdcHJE3JUNRH9K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\nQmCC\n", "text/plain": [ "" ] }, "execution_count": 153, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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csg2ED5ucnsddF8fX89jcfWbUzPJV4ATgJur02KD78bn7One/38z+CWgH1gMv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\ngJndQ/j22kb3z5k0v3d7xn6yu/+yjt6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"text/plain": [ "" ] }, "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": [ "" ] }, "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": [ "
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yeartemperaturerainfallmosquitos
location
A10200180157150
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..................
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214 rows × 4 columns

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" ], "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": [ "
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yeartemperaturerainfallmosquitos
location
A12005.580.000000214.600000191.300000
A31985.080.333333193.27451097.647059
B21985.079.764706196.823529196.058824
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" ], "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": [ "
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yeartemperaturerainfallmosquitos
location
A10200180157150
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..................
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151 rows × 4 columns

\n", "
" ], "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\u001b[0m in \u001b[0;36m\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\u001b[0m in \u001b[0;36m\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\u001b[0m in \u001b[0;36m\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\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\u001b[0m in \u001b[0;36m\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\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\u001b[0m in \u001b[0;36m\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\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 }