{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Data analysis\n", "\n", "In this notebook, we will go over some of the fundamentals of using Python for data analysis, focusing on what we need for iGEM competition. We assume you are familiar with the content [in our Intro. to Python notebook](http://nbviewer.ipython.org/github/thmosqueiro/modeligem/blob/master/notebooks/Python_Intro.ipynb). Following the same spirit of our suplementary material, this will be extremely driven by application and directed towards systems biology modeling.\n", "\n", "
\n", "\n", "This is intended as a suplementary material for iGEM 2015 competition. You can contact our team (Team USP-Brasil) if you would like a live demonstration!!\n", "\n", "This is our Facebook page: https://www.facebook.com/brasilusp?fref=ts\n", "\n", "\n", "
\n", "\n", "**This notebook is a free software, you are free to use it the way you'd like to. Visit our [Wiki](https://github.com/thmosqueiro/modeligem/wiki) or find all source codes [here](https://github.com/thmosqueiro/modeligem/wiki).**\n", "\n", "
" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import scipy as sp\n", "import scipy.stats as stats\n", "import pandas as pd\n", "\n", "%matplotlib inline\n", "import pylab as pl" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Mosquitos dataset from Mozilla Software Carpentry\n", "\n", "Let's start dealing with Pandas library, and showcase some of their powerful tools to handle data.\n", "\n", "For this example, we will use a dataset available at [Mozilla Software Carpentry](https://raw.githubusercontent.com/swcarpentry/bc/master/intermediate/python/A1_mosquito_data.csv). Since it is available online, you don't even need to download it to be able to use it with python: we will use pandas to read it from the url." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "Mdata = pd.read_csv('https://raw.githubusercontent.com/swcarpentry/bc/master/intermediate/python/A2_mosquito_data.csv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you'd have a local file instead, simply use its path as argument ti pd.read_csv.\n", "\n", "Let's quickly visualize this dataset:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shape of this dataset: (51, 4)\n", "Column names: Index([u'year', u'temperature', u'rainfall', u'mosquitos'], dtype='object')\n", "\n", "First rows in the dataset:\n" ] }, { "data": { "text/html": [ "
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yeartemperaturerainfallmosquitos
0 1960 82 200 180
1 1961 70 227 194
2 1962 89 231 207
3 1963 74 114 121
4 1964 78 147 140
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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" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print 'Shape of this dataset: ', Mdata.shape\n", "print 'Column names: ', data1.columns\n", "\n", "print '\\nFirst rows in the dataset:'\n", "Mdata.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since there are too many rows (51), we are using head() method to print only the first five rows. Notice that it is already using a very nice format to display the data.\n", "\n", "Don't mind the lower-case u's appearing next to each column name: it means they are unicode strings (which means you can even use special characters!).\n", "\n", "
\n", "\n", "Let's try to understand this data before moving on. Take this question in consideration: is the weather somehow related to the number of mosquitos occuring in a particular year? We could use the answer of this question to target efficient control measures of mosquitoes population. We will briefly investigate this data using pandas and some other tools.\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Let's manipulate our dataset\n", "\n", "Let's make a series of manipulations on our dataset to make sure we know how to handle any possible question we may want to answer." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1- Let's create another dataset with only mosquitoes and rainfall" ] }, { "cell_type": "code", "execution_count": 32, "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", "
rainfallmosquitos
0 200 180
1 227 194
2 231 207
3 114 121
4 147 140
\n", "
" ], "text/plain": [ " rainfall mosquitos\n", "0 200 180\n", "1 227 194\n", "2 231 207\n", "3 114 121\n", "4 147 140" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Mdata_MvsR = Mdata[['rainfall', 'mosquitos']]\n", "\n", "Mdata_MvsR.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The underlying message here is that you can access columns by using their name. For instance, ``Mdata['rainfall']`` will give you only the data in the rainfall column. If you need more than one column at the same time, just use ``[['ColumnName1', 'ColumnName2', ...]]``.\n", "\n", "\n", "2- What is the year of our last record?" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Last year on our records: 2010\n" ] } ], "source": [ "print 'Last year on our records: ', Mdata['year'].max()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Following the same line of question 1, we first get all years by using ``Mdata['year']`` and, then, apply the max() method. This returns the largest value on year column.\n", "\n", "3- Let's get the last five years?" ] }, { "cell_type": "code", "execution_count": 34, "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", "
yeartemperaturerainfallmosquitos
46 2006 88 255 226
47 2007 79 262 221
48 2008 73 198 176
49 2009 86 215 187
50 2010 87 127 129
\n", "
" ], "text/plain": [ " year temperature rainfall mosquitos\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": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## First we get all elements in the last five years of our data set:\n", "IDX = Mdata['year'] > Mdata['year'].max() - 5\n", "\n", "Mdata[ IDX ]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "4- What are the mean values for each variable?" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "year 1985.000000\n", "temperature 80.392157\n", "rainfall 207.039216\n", "mosquitos 185.235294\n", "dtype: float64" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Mdata.mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### How about plotting our data?" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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A1wG857Ad1xJiTM3y5ZRXkZwMNG8e6NYELzU1pE7szxlpgSI6msTVYmMD3RLG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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Mdata.plot(x='year', y=['mosquitos','temperature','rainfall'], \n", " kind='line', ylim=(50,400), figsize=(9,5), \n", " fontsize=12, style=['r-', 'g-', 'b--'])" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Mdata.plot(x='mosquitos', y='temperature', kind='scatter', \n", " legend=False, figsize=(8,4), fontsize=12)\n", "Mdata.plot(x='mosquitos', y='rainfall', kind='scatter', \n", " legend=False, figsize=(8,4), fontsize=12)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is no obvious sasonal effect on the number of mosquitoes with respect to year, and there seems to be no obvious pattern with temperature as well. However, rainfall vs mosquitoes show an amazing linear relationship!!\n", "\n", "**Tip:** pandas offer a lot of easy-to-use plot options, make sure to check it [here](http://pandas.pydata.org/pandas-docs/dev/generated/pandas.DataFrame.plot.html)!!\n", "\n", "
\n", "\n", "The most natural next step would be to fit a linear function." ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "---\n", "Fitting a*x + b...\n", "Fit done.\n", "---\n", "\n", "Adjusted parameters:\n", "a: 0.695884966872\n", "b: 41.1598163687\n", "\n", "Goodness of fitting (R-squared): 0.991315935461\n" ] } ], "source": [ "print '---\\nFitting a*x + b...'\n", "\n", "slope, intercept, r, p, stderr = stats.linregress(\n", " Mdata_MvsR['rainfall'], Mdata_MvsR['mosquitos'])\n", "\n", "print 'Fit done.\\n---\\n'\n", "\n", "print 'Adjusted parameters:'\n", "print 'a: ', slope\n", "print 'b: ', intercept\n", "\n", "print '\\nGoodness of fitting (R-squared): ', r**2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's now plot our fitted curve and the data together." ] }, { "cell_type": "code", "execution_count": 106, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pl.figure( figsize=(6,4) )\n", "\n", "pl.plot(Mdata_MvsR['rainfall'], Mdata_MvsR['mosquitos'], \n", " 'o', color=(.3,.3,1.)) # using RGB for color\n", "\n", "X = np.arange(100,300,0.1)\n", "Y = slope*X + intercept\n", "pl.plot(X, Y, 'g-', linewidth=5., alpha=0.5) ## alpha option makes \n", " ## the line somewhat transparent\n", "\n", "pl.xlabel('Rainfall')\n", "pl.ylabel('Mosquitoes count')\n", "\n", "pl.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.8" } }, "nbformat": 4, "nbformat_minor": 0 }