{ "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": "3.4.1" }, "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "[![Py4Life](https://raw.githubusercontent.com/Py4Life/TAU2015/gh-pages/img/Py4Life-logo-small.png)](http://py4life.github.io/TAU2015/)\n", "\n", "## Lecture 7 - 6.5.2015\n", "### Last update: 5.5.2015" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Previously\n", "\n", "- BioPython\n", "- Sequence analysis\n", "- Sequence data in Python" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Today\n", "\n", "#### Topics \n", "\n", "- Data analysis\n", "- Summary statistics\n", "- Data visualization\n", "\n", "#### Packages\n", "\n", "- [NumPy](http://www.numpy.org/) is the fundamental package for scientific computing with Python. It contains arrays, math functions, linear algebra, random number capabilities and much more.\n", "- [Matplotlib](http://matplotlib.org/) is a plotting library which produces publication quality figures\n", "- [Pandas](pandas.pydata.org) provides high-performance, easy-to-use data structures and data analysis tools.\n", "- [Seaborn](http://stanford.edu/~mwaskom/software/seaborn/) is a visualization library based on _matplotlib_ that provides a high-level interface for plotting attractive statistical figures." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Analyzing Patient Data\n", "\n", "We are studying inflammation in patients who have been given a new treatment for arthritis, and need to analyze the first dozen data sets. The data sets are stored in comma-separated values (CSV) format: each row holds information for a single patient, and the columns represent successive days. The first few rows of our data file look like this:\n", "\n", "> 0,0,1,3,1,2,4,7,8,3,3,3,10,5,7,4,7,7,12,18,6,13,11,11,7,7,4,6,8,8,4,4,5,7,3,4,2,3,0,0\n", "0,1,2,1,2,1,3,2,2,6,10,11,5,9,4,4,7,16,8,6,18,4,12,5,12,7,11,5,11,3,3,5,4,4,5,5,1,1,0,1\n", "0,1,1,3,3,2,6,2,5,9,5,7,4,5,4,15,5,11,9,10,19,14,12,17,7,12,11,7,4,2,10,5,4,2,2,3,2,2,1,1\n", "0,0,2,0,4,2,2,1,6,7,10,7,9,13,8,8,15,10,10,7,17,4,4,7,6,15,6,4,9,11,3,5,6,3,3,4,2,3,2,1\n", "0,1,1,3,3,1,3,5,2,4,4,7,6,5,3,10,8,10,6,17,9,14,9,7,13,9,12,6,7,7,9,6,3,2,2,4,2,0,1,1\n", "\n", "#### Objectives\n", "\n", "- Load __NumPy__, the basic scientific Python library \n", "- Read tabular data from a file into Python.\n", "- Select individual values and subsections from data.\n", "- Perform operations on arrays of data.\n", "- Display simple plots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading Data\n", "While a lot of powerful tools are built into Python, even more tools exist in the libraries and packages that are written in Python.\n", "\n", "In order to load our inflammation data, we need to import a library called __NumPy__. We could load the data using the __csv__ module, but you should use NumPy when you want to do things with numbers, especially if you have matrices or tables.\n", "\n", "We can load _NumPy_ using import. We also load __urllib__ to read the data from the web:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "import urllib" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "First thing, we need to copy the file from the web to our local disk. This is done using the `urllib.request.urlretrieve` function:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "url = r\"https://raw.githubusercontent.com/swcarpentry/python-novice-inflammation/gh-pages/data/inflammation-01.csv\"\n", "fname = \"inflammation-01.csv\"\n", "urllib.request.urlretrieve(url, fname)\n", "!cat $fname" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0,0,1,3,1,2,4,7,8,3,3,3,10,5,7,4,7,7,12,18,6,13,11,11,7,7,4,6,8,8,4,4,5,7,3,4,2,3,0,0\n", "0,1,2,1,2,1,3,2,2,6,10,11,5,9,4,4,7,16,8,6,18,4,12,5,12,7,11,5,11,3,3,5,4,4,5,5,1,1,0,1\n", "0,1,1,3,3,2,6,2,5,9,5,7,4,5,4,15,5,11,9,10,19,14,12,17,7,12,11,7,4,2,10,5,4,2,2,3,2,2,1,1\n", "0,0,2,0,4,2,2,1,6,7,10,7,9,13,8,8,15,10,10,7,17,4,4,7,6,15,6,4,9,11,3,5,6,3,3,4,2,3,2,1\n", "0,1,1,3,3,1,3,5,2,4,4,7,6,5,3,10,8,10,6,17,9,14,9,7,13,9,12,6,7,7,9,6,3,2,2,4,2,0,1,1\n", "0,0,1,2,2,4,2,1,6,4,7,6,6,9,9,15,4,16,18,12,12,5,18,9,5,3,10,3,12,7,8,4,7,3,5,4,4,3,2,1\n", "0,0,2,2,4,2,2,5,5,8,6,5,11,9,4,13,5,12,10,6,9,17,15,8,9,3,13,7,8,2,8,8,4,2,3,5,4,1,1,1\n", "0,0,1,2,3,1,2,3,5,3,7,8,8,5,10,9,15,11,18,19,20,8,5,13,15,10,6,10,6,7,4,9,3,5,2,5,3,2,2,1\n", "0,0,0,3,1,5,6,5,5,8,2,4,11,12,10,11,9,10,17,11,6,16,12,6,8,14,6,13,10,11,4,6,4,7,6,3,2,1,0,0\n", "0,1,1,2,1,3,5,3,5,8,6,8,12,5,13,6,13,8,16,8,18,15,16,14,12,7,3,8,9,11,2,5,4,5,1,4,1,2,0,0\n", "0,1,0,0,4,3,3,5,5,4,5,8,7,10,13,3,7,13,15,18,8,15,15,16,11,14,12,4,10,10,4,3,4,5,5,3,3,2,2,1\n", "0,1,0,0,3,4,2,7,8,5,2,8,11,5,5,8,14,11,6,11,9,16,18,6,12,5,4,3,5,7,8,3,5,4,5,5,4,0,1,1\n", "0,0,2,1,4,3,6,4,6,7,9,9,3,11,6,12,4,17,13,15,13,12,8,7,4,7,12,9,5,6,5,4,7,3,5,4,2,3,0,1\n", "0,0,0,0,1,3,1,6,6,5,5,6,3,6,13,3,10,13,9,16,15,9,11,4,6,4,11,11,12,3,5,8,7,4,6,4,1,3,0,0\n", "0,1,2,1,1,1,4,1,5,2,3,3,10,7,13,5,7,17,6,9,12,13,10,4,12,4,6,7,6,10,8,2,5,1,3,4,2,0,2,0\n", "0,1,1,0,1,2,4,3,6,4,7,5,5,7,5,10,7,8,18,17,9,8,12,11,11,11,14,6,11,2,10,9,5,6,5,3,4,2,2,0\n", "0,0,0,0,2,3,6,5,7,4,3,2,10,7,9,11,12,5,12,9,13,19,14,17,5,13,8,11,5,10,9,8,7,5,3,1,4,0,2,1\n", "0,0,0,1,2,1,4,3,6,7,4,2,12,6,12,4,14,7,8,14,13,19,6,9,12,6,4,13,6,7,2,3,6,5,4,2,3,0,1,0\n", "0,0,2,1,2,5,4,2,7,8,4,7,11,9,8,11,15,17,11,12,7,12,7,6,7,4,13,5,7,6,6,9,2,1,1,2,2,0,1,0\n", "0,1,2,0,1,4,3,2,2,7,3,3,12,13,11,13,6,5,9,16,9,19,16,11,8,9,14,12,11,9,6,6,6,1,1,2,4,3,1,1\n", "0,1,1,3,1,4,4,1,8,2,2,3,12,12,10,15,13,6,5,5,18,19,9,6,11,12,7,6,3,6,3,2,4,3,1,5,4,2,2,0\n", "0,0,2,3,2,3,2,6,3,8,7,4,6,6,9,5,12,12,8,5,12,10,16,7,14,12,5,4,6,9,8,5,6,6,1,4,3,0,2,0\n", "0,0,0,3,4,5,1,7,7,8,2,5,12,4,10,14,5,5,17,13,16,15,13,6,12,9,10,3,3,7,4,4,8,2,6,5,1,0,1,0\n", "0,1,1,1,1,3,3,2,6,3,9,7,8,8,4,13,7,14,11,15,14,13,5,13,7,14,9,10,5,11,5,3,5,1,1,4,4,1,2,0\n", "0,1,1,1,2,3,5,3,6,3,7,10,3,8,12,4,12,9,15,5,17,16,5,10,10,15,7,5,3,11,5,5,6,1,1,1,1,0,2,1\n", "0,0,2,1,3,3,2,7,4,4,3,8,12,9,12,9,5,16,8,17,7,11,14,7,13,11,7,12,12,7,8,5,7,2,2,4,1,1,1,0\n", "0,0,1,2,4,2,2,3,5,7,10,5,5,12,3,13,4,13,7,15,9,12,18,14,16,12,3,11,3,2,7,4,8,2,2,1,3,0,1,1\n", "0,0,1,1,1,5,1,5,2,2,4,10,4,8,14,6,15,6,12,15,15,13,7,17,4,5,11,4,8,7,9,4,5,3,2,5,4,3,2,1\n", "0,0,2,2,3,4,6,3,7,6,4,5,8,4,7,7,6,11,12,19,20,18,9,5,4,7,14,8,4,3,7,7,8,3,5,4,1,3,1,0\n", "0,0,0,1,4,4,6,3,8,6,4,10,12,3,3,6,8,7,17,16,14,15,17,4,14,13,4,4,12,11,6,9,5,5,2,5,2,1,0,1\n", "0,1,1,0,3,2,4,6,8,6,2,3,11,3,14,14,12,8,8,16,13,7,6,9,15,7,6,4,10,8,10,4,2,6,5,5,2,3,2,1\n", "0,0,2,3,3,4,5,3,6,7,10,5,10,13,14,3,8,10,9,9,19,15,15,6,8,8,11,5,5,7,3,6,6,4,5,2,2,3,0,0\n", "0,1,2,2,2,3,6,6,6,7,6,3,11,12,13,15,15,10,14,11,11,8,6,12,10,5,12,7,7,11,5,8,5,2,5,5,2,0,2,1\n", "0,0,2,1,3,5,6,7,5,8,9,3,12,10,12,4,12,9,13,10,10,6,10,11,4,15,13,7,3,4,2,9,7,2,4,2,1,2,1,1\n", "0,0,1,2,4,1,5,5,2,3,4,8,8,12,5,15,9,17,7,19,14,18,12,17,14,4,13,13,8,11,5,6,6,2,3,5,2,1,1,1\n", "0,0,0,3,1,3,6,4,3,4,8,3,4,8,3,11,5,7,10,5,15,9,16,17,16,3,8,9,8,3,3,9,5,1,6,5,4,2,2,0\n", "0,1,2,2,2,5,5,1,4,6,3,6,5,9,6,7,4,7,16,7,16,13,9,16,12,6,7,9,10,3,6,4,5,4,6,3,4,3,2,1\n", "0,1,1,2,3,1,5,1,2,2,5,7,6,6,5,10,6,7,17,13,15,16,17,14,4,4,10,10,10,11,9,9,5,4,4,2,1,0,1,0\n", "0,1,0,3,2,4,1,1,5,9,10,7,12,10,9,15,12,13,13,6,19,9,10,6,13,5,13,6,7,2,5,5,2,1,1,1,1,3,0,1\n", "0,1,1,3,1,1,5,5,3,7,2,2,3,12,4,6,8,15,16,16,15,4,14,5,13,10,7,10,6,3,2,3,6,3,3,5,4,3,2,1\n", "0,0,0,2,2,1,3,4,5,5,6,5,5,12,13,5,7,5,11,15,18,7,9,10,14,12,11,9,10,3,2,9,6,2,2,5,3,0,0,1\n", "0,0,1,3,3,1,2,1,8,9,2,8,10,3,8,6,10,13,11,17,19,6,4,11,6,12,7,5,5,4,4,8,2,6,6,4,2,2,0,0\n", "0,1,1,3,4,5,2,1,3,7,9,6,10,5,8,15,11,12,15,6,12,16,6,4,14,3,12,9,6,11,5,8,5,5,6,1,2,1,2,0\n", "0,0,1,3,1,4,3,6,7,8,5,7,11,3,6,11,6,10,6,19,18,14,6,10,7,9,8,5,8,3,10,2,5,1,5,4,2,1,0,1\n", "0,1,1,3,3,4,4,6,3,4,9,9,7,6,8,15,12,15,6,11,6,18,5,14,15,12,9,8,3,6,10,6,8,7,2,5,4,3,1,1\n", "0,1,2,2,4,3,1,4,8,9,5,10,10,3,4,6,7,11,16,6,14,9,11,10,10,7,10,8,8,4,5,8,4,4,5,2,4,1,1,0\n", "0,0,2,3,4,5,4,6,2,9,7,4,9,10,8,11,16,12,15,17,19,10,18,13,15,11,8,4,7,11,6,7,6,5,1,3,1,0,0,0\n", "0,1,1,3,1,4,6,2,8,2,10,3,11,9,13,15,5,15,6,10,10,5,14,15,12,7,4,5,11,4,6,9,5,6,1,1,2,1,2,1\n", "0,0,1,3,2,5,1,2,7,6,6,3,12,9,4,14,4,6,12,9,12,7,11,7,16,8,13,6,7,6,10,7,6,3,1,5,4,3,0,0\n", "0,0,1,2,3,4,5,7,5,4,10,5,12,12,5,4,7,9,18,16,16,10,15,15,10,4,3,7,5,9,4,6,2,4,1,4,2,2,2,1\n", "0,1,2,1,1,3,5,3,6,3,10,10,11,10,13,10,13,6,6,14,5,4,5,5,9,4,12,7,7,4,7,9,3,3,6,3,4,1,2,0\n", "0,1,2,2,3,5,2,4,5,6,8,3,5,4,3,15,15,12,16,7,20,15,12,8,9,6,12,5,8,3,8,5,4,1,3,2,1,3,1,0\n", "0,0,0,2,4,4,5,3,3,3,10,4,4,4,14,11,15,13,10,14,11,17,9,11,11,7,10,12,10,10,10,8,7,5,2,2,4,1,2,1\n", "0,0,2,1,1,4,4,7,2,9,4,10,12,7,6,6,11,12,9,15,15,6,6,13,5,12,9,6,4,7,7,6,5,4,1,4,2,2,2,1\n", "0,1,2,1,1,4,5,4,4,5,9,7,10,3,13,13,8,9,17,16,16,15,12,13,5,12,10,9,11,9,4,5,5,2,2,5,1,0,0,1\n", "0,0,1,3,2,3,6,4,5,7,2,4,11,11,3,8,8,16,5,13,16,5,8,8,6,9,10,10,9,3,3,5,3,5,4,5,3,3,0,1\n", "0,1,1,2,2,5,1,7,4,2,5,5,4,6,6,4,16,11,14,16,14,14,8,17,4,14,13,7,6,3,7,7,5,6,3,4,2,2,1,1\n", "0,1,1,1,4,1,6,4,6,3,6,5,6,4,14,13,13,9,12,19,9,10,15,10,9,10,10,7,5,6,8,6,6,4,3,5,2,1,1,1\n", "0,0,0,1,4,5,6,3,8,7,9,10,8,6,5,12,15,5,10,5,8,13,18,17,14,9,13,4,10,11,10,8,8,6,5,5,2,0,2,0\n", "0,0,1,0,3,2,5,4,8,2,9,3,3,10,12,9,14,11,13,8,6,18,11,9,13,11,8,5,5,2,8,5,3,5,4,1,3,1,1,0\n" ] } ], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We saved the file to the local filesystem. \n", "We can have a look at the file contents from the IPython notebook by calling a system command `cat` or `type`.\n", "To do this, we need to put an `!` at the beginning of the line and prepend any variable name with `$`.\n", "\n", "Once it's done, we can ask _NumPy_ to read the data file:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "data = np.loadtxt(fname, delimiter=',')\n", "print(data)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[ 0. 0. 1. ..., 3. 0. 0.]\n", " [ 0. 1. 2. ..., 1. 0. 1.]\n", " [ 0. 1. 1. ..., 2. 1. 1.]\n", " ..., \n", " [ 0. 1. 1. ..., 1. 1. 1.]\n", " [ 0. 0. 0. ..., 0. 2. 0.]\n", " [ 0. 0. 1. ..., 1. 1. 0.]]\n" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The expression `np.loadtxt(...)` is a function call that asks Python to run the function `loadtxt` that belongs to the `numpy` library. This dotted notation is used everywhere in Python to refer to the parts of things as `thing.component` (see: _namespaces_).\n", "\n", "`numpy.loadtxt` has two parameters: the name of the file we want to read, and the delimiter that separates values on a line. These both need to be character strings (or strings for short), so we put them in quotes.\n", "\n", "We saved the output of `loadtxt` in the variable `data`. When we `print(data)`, only a few rows and columns are shown (with `...` to omit elements when displaying big arrays). To save space, Python displays numbers as `1.` instead of `1.0` when there's nothing interesting after the decimal point." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Manipulating Data\n", "\n", "Now that our data is in memory, we can start doing things with it. First, let's ask what type of thing data refers to:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print(type(data))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The output tells us that data currently refers to an N-dimensional array created by the NumPy library. We can see what its shape is like this:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print(data.shape)\n", "n_patients,n_days = data.shape" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(60, 40)\n" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This tells us that `data` has 60 rows and 40 columns, which are 60 patients and 40 days. `data.shape` is a member of `data`, i.e., a value that is stored as part of a larger value. We use the same dotted notation for the members of values that we use for the functions in libraries because they have the same part-and-whole relationship.\n", "\n", "If we want to get a single value from the matrix, we must provide an index in square brackets, just as we do with a `list`, but with as many indices as the number of dimensions in `shape` (two in this case):" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print(\"first value in data\",data[0,0])\n", "print(\"middle value in data:\", data[30, 20])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "first value in data 0.0\n", "middle value in data: 13.0\n" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The expression `data[30, 20]` may not surprise you, but `data[0, 0]` might. Programming languages like Fortran and MATLAB start counting at 1, because that's what human beings have done for thousands of years. Languages in the C family (including C++, Java, Perl, and Python) count from 0 because that's simpler for computers to do. Just like with `list` and `str`, if we have an M\u00d7N array in Python, its indices go from 0 to M-1 on the first axis and 0 to N-1 on the second. It takes a bit of getting used to, but one way to remember the rule is that the index is how many steps we have to take from the start to get the item we want.\n", "\n", "> #### In the Corner\n", "> What may also surprise you is that when Python displays an array, it shows the element with index [0, 0] in the upper left corner rather than the lower left. This is consistent with the way mathematicians draw matrices, but different from the Cartesian coordinates. The indices are (row, column) instead of (column, row) for the same reason, which can be confusing when plotting data.\n", "\n", "An index like `[30, 20]` selects a single element of an array, but we can select whole sections as well. For example, we can select the first ten days (columns) of values for the first four (rows) patients like this:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print(data[0:4, 0:10])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[ 0. 0. 1. 3. 1. 2. 4. 7. 8. 3.]\n", " [ 0. 1. 2. 1. 2. 1. 3. 2. 2. 6.]\n", " [ 0. 1. 1. 3. 3. 2. 6. 2. 5. 9.]\n", " [ 0. 0. 2. 0. 4. 2. 2. 1. 6. 7.]]\n" ] } ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The slice `0:4` means, \"Start at index 0 and go up to, but not including, index 4.\" Again, the up-to-but-not-including takes a bit of getting used to, but the rule is that the difference between the upper and lower bounds is the number of values in the slice.\n", "\n", "We don't have to start slices at 0:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print(data[5:10, 0:10])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[ 0. 0. 1. 2. 2. 4. 2. 1. 6. 4.]\n", " [ 0. 0. 2. 2. 4. 2. 2. 5. 5. 8.]\n", " [ 0. 0. 1. 2. 3. 1. 2. 3. 5. 3.]\n", " [ 0. 0. 0. 3. 1. 5. 6. 5. 5. 8.]\n", " [ 0. 1. 1. 2. 1. 3. 5. 3. 5. 8.]]\n" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We also don't have to include the upper and lower bound on the slice. If we don't include the lower bound, Python uses 0 by default; if we don't include the upper, the slice runs to the end of the axis, and if we don't include either (i.e., if we just use ':' on its own), the slice includes everything:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "small = data[:3, 36:]\n", "print('small is:')\n", "print(small)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "small is:\n", "[[ 2. 3. 0. 0.]\n", " [ 1. 1. 0. 1.]\n", " [ 2. 2. 1. 1.]]\n" ] } ], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Arrays also know how to perform common mathematical operations on their values. The simplest operations with data are arithmetic: add, subtract, multiply, and divide. When you do such operations on arrays, the operation is done on each individual element of the array. Thus:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "doubledata = data * 2.0" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "will create a new array `doubledata` whose elements have the value of two times the value of the corresponding elements in `data`." ] }, { "cell_type": "code", "collapsed": false, "input": [ "print('original:')\n", "print(data[:3, 36:])\n", "print('doubledata:')\n", "print(doubledata[:3, 36:])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "original:\n", "[[ 2. 3. 0. 0.]\n", " [ 1. 1. 0. 1.]\n", " [ 2. 2. 1. 1.]]\n", "doubledata:\n", "[[ 4. 6. 0. 0.]\n", " [ 2. 2. 0. 2.]\n", " [ 4. 4. 2. 2.]]\n" ] } ], "prompt_number": 11 }, { "cell_type": "markdown", "metadata": {}, "source": [ "If, instead of taking an array and doing arithmetic with a single value (as above) you did the arithmetic operation with another array of the same size and shape, the operation will be done on corresponding elements of the two arrays. Thus:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "tripledata = doubledata + data" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": {}, "source": [ "will give you an array where `tripledata[0,0]` will equal `doubledata[0,0]` plus `data[0,0]`, and so on for all other elements of the arrays." ] }, { "cell_type": "code", "collapsed": false, "input": [ "print('tripledata:')\n", "print(tripledata[:3, 36:])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "tripledata:\n", "[[ 6. 9. 0. 0.]\n", " [ 3. 3. 0. 3.]\n", " [ 6. 6. 3. 3.]]\n" ] } ], "prompt_number": 13 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise A - square root\n", "\n", "Calculate the square root of the data using `numpy`. \n", "Print the result for the first 5 columns of the first 3 rows." ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": null }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Descriptive statistics\n", "\n", "Often, we want to do more than add, subtract, multiply, and divide values of data. Arrays also know how to do more complex operations on their values. If we want to find the average inflammation for all patients on all days, for example, we can just ask the array for its mean value" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print(data.mean())" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "6.14875\n" ] } ], "prompt_number": 14 }, { "cell_type": "markdown", "metadata": {}, "source": [ "`mean` is a method of the array, i.e., a function that belongs to it in the same way that the member shape does. If variables are nouns, methods are verbs: they are what the thing in question knows how to do. This is why `data.shape` doesn't need to be called (it's just a thing) but `data.mean()` does (it's an action). It is also why we need empty parentheses for `ata.mean()`: even when we're not passing in any parameters, parentheses are how we tell Python to go and do something for us.\n", "\n", "NumPy arrays have lots of useful methods:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print('maximum inflammation:', data.max())\n", "print('minimum inflammation:', data.min())\n", "print('standard deviation:', data.std())" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "maximum inflammation: 20.0\n", "minimum inflammation: 0.0\n", "standard deviation: 4.61383319712\n" ] } ], "prompt_number": 15 }, { "cell_type": "markdown", "metadata": {}, "source": [ "When analyzing data, though, we often want to look at partial statistics, such as the maximum value per patient or the average value per day. One way to do this is to select the data we want to create a new temporary array, then ask it to do the calculation:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "patient_0 = data[0, :] # 0 on the first axis, everything on the second\n", "print('maximum inflammation for patient 0:', patient_0.max())" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "maximum inflammation for patient 0: 18.0\n" ] } ], "prompt_number": 16 }, { "cell_type": "markdown", "metadata": {}, "source": [ "What if we need the maximum inflammation for all patients, or the average for each day? As the diagram below shows, we want to perform the operation across an axis:\n", "![axis example](http://software-carpentry.org/v5/novice/python/img/python-operations-across-axes.svg)\n", "To support this, most array methods allow us to specify the axis we want to work on. If we ask for the average across axis 0, we get:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print(data.mean(axis=0))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[ 0. 0.45 1.11666667 1.75 2.43333333 3.15\n", " 3.8 3.88333333 5.23333333 5.51666667 5.95 5.9\n", " 8.35 7.73333333 8.36666667 9.5 9.58333333\n", " 10.63333333 11.56666667 12.35 13.25 11.96666667\n", " 11.03333333 10.16666667 10. 8.66666667 9.15 7.25\n", " 7.33333333 6.58333333 6.06666667 5.95 5.11666667 3.6\n", " 3.3 3.56666667 2.48333333 1.5 1.13333333\n", " 0.56666667]\n" ] } ], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": {}, "source": [ "As a quick check, we can ask this array what its shape is:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print(data.mean(axis=0).shape)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(40,)\n" ] } ], "prompt_number": 18 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The expression `(40,)` tells us we have an N\u00d71 vector, so this is the average inflammation per day for all patients. If we average across axis 1, we get:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print(data.mean(axis=1))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[ 5.45 5.425 6.1 5.9 5.55 6.225 5.975 6.65 6.625 6.525\n", " 6.775 5.8 6.225 5.75 5.225 6.3 6.55 5.7 5.85 6.55\n", " 5.775 5.825 6.175 6.1 5.8 6.425 6.05 6.025 6.175 6.55\n", " 6.175 6.35 6.725 6.125 7.075 5.725 5.925 6.15 6.075 5.75\n", " 5.975 5.725 6.3 5.9 6.75 5.925 7.225 6.15 5.95 6.275 5.7\n", " 6.1 6.825 5.975 6.725 5.7 6.25 6.4 7.05 5.9 ]\n" ] } ], "prompt_number": 19 }, { "cell_type": "markdown", "metadata": {}, "source": [ "which is the average inflammation per patient across all days." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise B - sum\n", "\n", "On which day did each patient had the most inflammation?\n", "Use `.data.argmax` to find out." ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": null }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting\n", "\n", "The mathematician [Richard Hamming](http://en.wikipedia.org/wiki/Richard_Hamming) once said, **\"The purpose of computing is insight, not numbers\"**, and the best way to develop insight is often to visualize data. Visualization deserves an entire lecture (or course) of its own, but we can explore a few features of Python's `matplotlib` here. While there is no \"official\" plotting library, this package is the _de facto_ standard. First, let's tell the IPython Notebook that we want our plots displayed inline, rather than in a separate viewing window. This command will also import many other useful scientific python functions and modules, including NumPy:" ] }, { "cell_type": "code", "collapsed": true, "input": [ "%matplotlib?" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 31 }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "from matplotlib.pyplot import *" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 34 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `%` at the start of the line signals that this is a command for the _notebook_, rather than a statement in Python. \n", "\n", "Next, use two of `matplotlib`'s functions to create and display a heat map of our data:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "imshow(data, aspect=0.66); # aspect control the width of one x unit vs. one y unit" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 35, "text": [ "" 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TE9OS6IYkaYlpw+LrYzqf0Pnk/dhqiYw8OjLEugvvVIxTbyDkfA4gsP04ZZUP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ORcBWFD50dEoPRSiGZklLumjIRBuisauW2uYc/ITjYAc/pVYZLpOYKALhA6asU2DAyBiTxpip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\nB2zFlB1TtizrFfPdisnVluR1RniWo+ueZO5TLzTdQmL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"text": [ "" ] } ], "prompt_number": 35 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Blue regions in this heat map are low values, while red shows high values. As we can see, inflammation rises and falls over a 40-day period (The `;` at the end of the line supresses the automatic printing of function's return value, which is an `AxesImage` in this case).\n", "\n", "Let's take a look at the average inflammation over time:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "avg_inflammation = data.mean(axis=0)\n", "plot(avg_inflammation);" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 36 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here, we have put the average per day across all patients in the variable `avg_inflammation`, then used `plot` to create and display a line graph of those values. The result is roughly a linear rise and fall, which is suspicious: based on other studies, we expect a sharper rise and slower fall. Let's have a look at two other statistics:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "plot(avg_inflammation, label='avg')\n", "plot(data.max(axis=0), label='max')\n", "plot(data.min(axis=0), label='min')\n", "title('inflammation per day')\n", "xlabel('day')\n", "ylabel('inflammation')\n", "legend();" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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CEqHVyTZK0oAcBnISyDq+rGyJ5RfKTJphwxRgY+B+4P3kMwGnJQ27azooGB2g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\n3BCpLjAKGO64qXXRinybR/B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"text": [ "" ] } ], "prompt_number": 37 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The maximum value rises and falls perfectly smoothly, while the minimum seems to be a step function. Neither result seems particularly likely, so either there's a mistake in our calculations or something is wrong with our data." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`matplotlib` has many more plotting commands. For example, we can calculate the _stadard deviation of the mean_ (`sem`) which is defined as the standard deviation of the sample divided by the square root of the number of the samples:\n", "$$\n", "\\sqrt{\\frac{\\sum_i{(x_{i=1}^{n} - \\mu)^2}}{N}}\n", "$$\n", "This can be calculated using the method `std` and the function `sqrt`. \n", "\n", "Note that when calculating the standard deviation of a sample, the degrees of freedom is $n-1$ - this is represented by the `ddof=1` argument to `std`." ] }, { "cell_type": "code", "collapsed": false, "input": [ "sem_inflammation = data.std(axis=0, ddof=1) / np.sqrt(n_patients)\n", "errorbar(x=range(n_days), y=avg_inflammation, yerr=sem_inflammation, ecolor='k')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 39, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 39 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Challenges\n", "\n", "- Why do all of our plots stop just short of the upper end of our graph? \n", "- Why are the vertical lines in our plot of the minimum inflammation per day not perfectly vertical?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise C\n", "\n", "In exercise B we calculated the day in which each patient had the most inflammation.\n", "Let's plot it now - make a scatter plot using the `scatter` command, with patient number on x-axis and day of max inflammation on y-axis.\n", "Don't forget to add axis labels with `xlabel` and `ylabel`!" ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": null }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Statistical Analysis of Life History Traits" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Many times we want to display data with some smoothing or fitting of a regression line over it.\n", "To do this, we will use the _pandas_ and _seaborn_ library.\n", "_pandas_ is a very strong library for manipulation large datasets. _seaborn_ adds on top of _pandas_ a set of sophisticated statistical visualizations.\n", "\n", "Unfortunately, _seaborn_ doesn't ship with _pyzo_ so we need to install it manually. The best way is to use _conda_ or _pip_ from the _pyzo_ folder (make sure you write the proper path, on my PC it's `D:\\pyzo2014a`):\n", "```\n", "D:\\pyzo2014a\\Scripts\\conda install seaborn\n", "```\n", "or\n", "```\n", "D:\\pyzo2014a\\Scripts\\pip install seaborn\n", "```\n", "On linux `pip` is in the `bin` rather than the `Scripts` folder and must be called using `python3` from the same folder." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import zipfile\n", "import pandas as pd\n", "import seaborn as sns\n", "sns.set_style(\"ticks\") # control the plotting style\n", "sns.set_context(\"talk\") # set to talk because this is a lecture! hit shift-tab after the \"(\" to see other options." ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 40 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will analyze animal life-history data from [AnAge](http://genomics.senescence.info/download.html#anage). \n", "We will get the data from the download page, but it's compressed with zip so we need to unzip it and then we can read the data using _pandas_ `read_table` function:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "urllib.request.urlretrieve('http://genomics.senescence.info/species/dataset.zip', 'anage_dataset.zip')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 31, "text": [ "('anage_dataset.zip', )" ] } ], "prompt_number": 31 }, { "cell_type": "code", "collapsed": false, "input": [ "with zipfile.ZipFile('anage_dataset.zip') as z:\n", " f = z.open('anage_data.txt')\n", " data = pd.read_table(f)\n", "print(type(data))\n", "print(data.shape)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n", "(4212, 31)\n" ] } ], "prompt_number": 51 }, { "cell_type": "markdown", "metadata": {}, "source": [ "_pandas_ holds data in `DataFrame` (similar to _R_).\n", "`DataFrame` have a single row per observation (in contrast to the previous exercise in which each table cell was one observation), and each column has a single variable. Variables can be numbers or strings." ] }, { "cell_type": "code", "collapsed": false, "input": [ "data.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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HAGRIDKingdomPhylumClassOrderFamilyGenusSpeciesCommon nameFemale maturity (days)...SourceSpecimen originSample sizeData qualityIMR (per yr)MRDT (yrs)Metabolic rate (W)Body mass (g)Temperature (K)References
0 4 Animalia Arthropoda Insecta Diptera Drosophilidae Drosophila melanogaster Fruit fly 7... NaN captivity large acceptable 0.05 0.04NaNNaNNaN 2,20,32,47,53,68,69,240,241,242,243,274,602,98...
1 5 Animalia Arthropoda Insecta Hymenoptera Apidae Apis mellifera Honey beeNaN... 812 unknown medium acceptable NaN NaNNaNNaNNaN 63,407,408,741,805,806,808,812,815,828,830,831...
2 7 Animalia Arthropoda Insecta Hymenoptera Formicidae Lasius niger Black garden antNaN... 411 unknown medium acceptable NaN NaNNaNNaNNaN 411,813,814
3 8 Animalia Arthropoda Insecta Lepidoptera Nymphalidae Bicyclus anynana Squinting bush brown 15... 811 wild medium acceptable NaN NaNNaNNaNNaN 418,809,811
4 9 Animalia Arthropoda Malacostraca Decapoda Nephropidae Homarus americanus American lobsterNaN... 2 wild medium acceptable NaN NaNNaNNaNNaN 2,13,594
\n", "

5 rows \u00d7 31 columns

\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 52, "text": [ " HAGRID Kingdom Phylum Class Order Family \\\n", "0 4 Animalia Arthropoda Insecta Diptera Drosophilidae \n", "1 5 Animalia Arthropoda Insecta Hymenoptera Apidae \n", "2 7 Animalia Arthropoda Insecta Hymenoptera Formicidae \n", "3 8 Animalia Arthropoda Insecta Lepidoptera Nymphalidae \n", "4 9 Animalia Arthropoda Malacostraca Decapoda Nephropidae \n", "\n", " Genus Species Common name Female maturity (days) \\\n", "0 Drosophila melanogaster Fruit fly 7 \n", "1 Apis mellifera Honey bee NaN \n", "2 Lasius niger Black garden ant NaN \n", "3 Bicyclus anynana Squinting bush brown 15 \n", "4 Homarus americanus American lobster NaN \n", "\n", " ... Source Specimen origin \\\n", "0 ... NaN captivity \n", "1 ... 812 unknown \n", "2 ... 411 unknown \n", "3 ... 811 wild \n", "4 ... 2 wild \n", "\n", " Sample size Data quality IMR (per yr) MRDT (yrs) Metabolic rate (W) \\\n", "0 large acceptable 0.05 0.04 NaN \n", "1 medium acceptable NaN NaN NaN \n", "2 medium acceptable NaN NaN NaN \n", "3 medium acceptable NaN NaN NaN \n", "4 medium acceptable NaN NaN NaN \n", "\n", " Body mass (g) Temperature (K) \\\n", "0 NaN NaN \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 NaN NaN \n", "\n", " References \n", "0 2,20,32,47,53,68,69,240,241,242,243,274,602,98... \n", "1 63,407,408,741,805,806,808,812,815,828,830,831... \n", "2 411,813,814 \n", "3 418,809,811 \n", "4 2,13,594 \n", "\n", "[5 rows x 31 columns]" ] } ], "prompt_number": 52 }, { "cell_type": "markdown", "metadata": {}, "source": [ "`DataFrame` has many of the features of `numpy.ndarray` - it also has a `shape` and various statistical methods (`max`, `mean` etc.).\n", "However, `DataFrame` allows richer indexing.\n", "For example, let's browse our data for species that have body mass greater than 300 kg.\n", "First we will a new column that tells us if a row is a large animal row or not:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "large_index = data['Body mass (g)'] > 300*1000 # 300 kg\n", "print(large_index)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0 False\n", "1 False\n", "2 False\n", "3 False\n", "4 False\n", "5 False\n", "6 False\n", "7 False\n", "8 False\n", "9 False\n", "10 False\n", "11 False\n", "12 False\n", "13 False\n", "14 False\n", "...\n", "4197 False\n", "4198 False\n", "4199 False\n", "4200 False\n", "4201 False\n", "4202 False\n", "4203 False\n", "4204 False\n", "4205 False\n", "4206 False\n", "4207 False\n", "4208 False\n", "4209 False\n", "4210 False\n", "4211 False\n", "Name: Body mass (g), Length: 4212, dtype: bool\n" ] } ], "prompt_number": 53 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we slice or data with this boolean index. \n", "The `iterrows` method let's us iterate over the rows of the data.\n", "For each row we get both the row as a `Series` object (similar to `dict` for our use)\n", "and the row number as an `int`." ] }, { "cell_type": "code", "collapsed": false, "input": [ "large_data = data[large_index]\n", "for i,row in large_data.iterrows(): \n", " print(row['Common name'], row['Body mass (g)']/1000, 'kg')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Domestic cattle 347.0 kg\n", "Dromedary 407.0 kg\n", "Moose 325.0 kg\n", "Asian elephant 3672.0 kg\n", "West Indian manatee 450.0 kg\n" ] } ], "prompt_number": 54 }, { "cell_type": "markdown", "metadata": {}, "source": [ "So... a [Dromedary](http://en.wikipedia.org/wiki/Dromedary) is a Camel.\n", "\n", "Let's continue with small and medium animals. For starters, let's plot a scatter of Body mass vs. Metabolic rate.\n", "Because we work with _pandas_, we can do that with the `plot` method of `DataFrame`, specifying the columns for `x` and `y` and a plotting style (without the style we would get a line plot which makes no sense here)." ] }, { "cell_type": "code", "collapsed": false, "input": [ "data = data[data['Body mass (g)'] < 3e5] \n", "data.plot(x='Body mass (g)', y='Metabolic rate (W)', style='o')\n", "ylabel('Metabolic rate (W)')\n", "sns.despine(offset=10)\n", "ylim(0, 20);" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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c375L7dvfzni+ffvbgbeGwhmtTWhunKhbFk3XiJpKjaip0q3XTtfVFxEY8+V2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\n+nYJ5dpVlSoMMxFj25y9vwt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"text": [ "" ] } ], "prompt_number": 55 }, { "cell_type": "markdown", "metadata": {}, "source": [ "`sns.despine` removes the upper and right axes and the additional `offset` keyword creates a space between the plot and the axis.\n", "\n", "From this plot it seems that 1) there is a correlation between body mass and metabolic rate, and 2) there are many small animals (less than 30 kg) and not many medium animals (between 50 and 300 kg).\n", "\n", "Before we continue, I prefer to have mass in kg, let's add a new column:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "data['Body mass (kg)'] = data['Body mass (g)']/1000" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 56 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, let's check how many records do we have for each Class (as in the taxonomic unit): " ] }, { "cell_type": "code", "collapsed": false, "input": [ "class_counts = data.Class.value_counts()\n", "print(class_counts)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Mammalia 417\n", "Aves 171\n", "Amphibia 18\n", "Reptilia 16\n", "dtype: int64\n" ] } ], "prompt_number": 57 }, { "cell_type": "code", "collapsed": false, "input": [ "class_counts.plot(kind='bar')\n", "ylabel('# species')\n", "sns.despine()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 58 }, { "cell_type": "markdown", "metadata": {}, "source": [ "So we have lots of mammals and birds, and a few reptiles and amphibians. This is important as amphibian and reptiles could have a different replationship between mass and metabolism.\n", "\n", "Let's do a simple linear regression plot; but let's do it in separate for each Class. This is done using _seaborn_'s `lmplot` function:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "sns.lmplot(x='Body mass (kg)', y='Metabolic rate (W)', data=data, hue='Class', ci=False, size=6, legend_out=False);" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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wMuAGrMwdXwm8p2na65jPhjoB/YBy35Bt5MiRPP/88xgMBr755hvGjRtHkyZN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\nZAGoqPBSVemjqspHeakHnyeQNM/NgNmbqS/ANDVktj/TGKxWC6lOGyn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"text": [ "" ] } ], "prompt_number": 59 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that `hue` means _color_, but it also causes _seaborn_ to fit a different linear model to each of the Classes. \n", "As for the last 3 paramteres:\n", "- `ci` controls the confidence intervals. I chose `False`, but setting it to `True` will show them.\n", "- `size` controls the size of the plot\n", "- `legend_out` decides if the legend is inside the plot or outside. We have enough space for it in the left corner.\n", "\n", "We can see that mammals and birds have a clear correlation between size and metabolism and that it extends over a nice range of mass, so let's stick to mammals; next up we will see which Orders of mammals we have." ] }, { "cell_type": "code", "collapsed": false, "input": [ "mammalia = data[data.Class=='Mammalia']\n", "order_counts = mammalia.Order.value_counts()\n", "ax=order_counts.plot(kind='bar')\n", "ylabel('# species')\n", "sns.despine();" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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l/pxJFrtl5nXAfbrKzgG2ectZkiRJzZhro6ElSZI0h5gsSpIkqZbJoiRJkmqZ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\nfSPd7mVeryGfjdoJxPuZrVd6CLclzblnEBknJD2FTINrXWy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"text": [ "" ] } ], "prompt_number": 60 }, { "cell_type": "markdown", "metadata": {}, "source": [ "You see we have alot of rodents and carnivores, but also a good number of bats (_Chiroptera_) and primates.\n", "\n", "Let's continue only with orders that have at least 20 species - this also includes some cool marsupials like Kangaroo, Koala and [Taz](http://upload.wikimedia.org/wikipedia/en/c/c4/Taz-Looney_Tunes.svg) (Diprotodontia and Dasyuromorphia)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "orders = order_counts[order_counts >= 20]\n", "print(orders)\n", "abund_mammalia = mammalia[mammalia.Order.isin(orders.index)]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Rodentia 155\n", "Carnivora 52\n", "Chiroptera 33\n", "Primates 26\n", "Diprotodontia 22\n", "Dasyuromorphia 20\n", "dtype: int64\n" ] } ], "prompt_number": 61 }, { "cell_type": "code", "collapsed": false, "input": [ "sns.lmplot(x='Body mass (kg)', y='Metabolic rate (W)', data=abund_mammalia, hue=\"Order\", \n", " ci=False, size=8, legend_out=False, line_kws={'lw':2}, scatter_kws={'s':50, 'alpha':0.5});" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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d6f5Ye9ztXLE0K/9lChXt7v+PLEpQFOUR4NyuXbvw9a2c2ZWa4NChQzz//PPs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\nY7QMdeCw2rlz8we4ZuVlp36iECJhhmHQMjTCrqNtvNHtpmd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fMaWLcFcrq/weNi0rQV25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TsWSS++0AINTb5FM5l856kcJ7P38OS8cAOQzRd58uAYt1x7dnuQCSFqjwQcsaweH9zDFx7/BjP5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\nDJ/gkjuAL2qt92it0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"text": [ "" ] } ], "prompt_number": 62 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Because there is alot of data here I made the lines thinner - this can be done by giving _matplotlib_ keywords as a dictionary to the argument `line_kws` - and I made the markers bigger but with alpha (transperancy) 0.5 using the `scatter_kws` argument.\n", "\n", "Still ,there's too much data, and part of the problem is that some Orders are large - primates - and some are small - rodents.\n", "Let's plot a separate plot for each Order. We do this using the `col` and `row` arguments of `lmplot`, but in general this can be done for any plot using [_seaborn_'s `FacetGrid` function](http://stanford.edu/~mwaskom/software/seaborn/tutorial/axis_grids.html)." ] }, { "cell_type": "code", "collapsed": false, "input": [ "sns.lmplot(x='Body mass (kg)', y='Metabolic rate (W)', data=abund_mammalia, hue=\"Order\", \n", " col=\"Order\", col_wrap=3, ci=None, scatter_kws={'s':40}, sharex=False, sharey=False);" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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TJzOYtmAN6+yRgHiP9g2ZcGtf6sdEupSZ6wYDjYCNzrQY5H2LWG+MedJaO6OU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flwLAXUumJyEaEUkHSmKkibf/tIPW1g5CCUkMn8/H4lMmc/rZMzyKTEQkPfn8ATo6mvpaSlZu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\nIpKmjDGZwI+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"text": [ "" ] } ], "prompt_number": 63 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We used the `sharex=False` and `sharey=False` arguments so that each Order will have a different axis range and so the data is will spread nicely.\n", "Last but not least, let's have a closer look at the corelation between mass and metabolism in primates. \n", "We will do a joint plot which will give us the pearson correlation and the distribution of each parameter." ] }, { "cell_type": "code", "collapsed": false, "input": [ "primates = mammalia[mammalia.Order == 'Primates']\n", "print(sorted(primates[\"Common name\"]))\n", "sns.jointplot(x='Body mass (kg)', y='Metabolic rate (W)', data=primates, kind='reg', size=8);" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "['Blue monkey', 'Brown lemur', 'Calabar angwantibo', \"Demidoff's galago\", 'Fat-tailed dwarf lemur', \"Geoffroy's tamarin\", 'Greater galago', 'Guereza', 'Hamadryas baboon', 'Human', 'Mantled howler monkey', 'Northern night monkey', 'Patas monkey', 'Philippine tarsier', 'Potto', 'Pygmy marmoset', 'Senegal galago', 'Slender loris', 'Slow loris', 'Small-eared galago', 'South African galago', 'South American squirrel monkey', 'Spectral tarsier', \"Verreaux's sifaka\", 'Western needle-clawed galago', 'White-tufted-ear marmoset']\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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BUfgREZExL5F0NLYGSCad5vfIQVP4ERGRMS0UidPiD+L1evF4taJLDp7Cj4iI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\n9avmUldXl/BOP29oA+Baq2HL2rT7jD8lM2demin7mRp++mL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"text": [ "" ] } ], "prompt_number": 65 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## More\n", "\n", "- Slides: [Statistical inference with Python](https://docs.google.com/presentation/d/1imQAEmNg4GB3bCAblauMOOLlAC95-XvkTSKB1_dB3Tg/pub?slide=id.p) by Allen Downey\n", "- Book: [Think Stats](greenteapress.com/thinkstats2/html/index.html) by Allen Downey - statistics with Python. Free Ebook.\n", "- Blog post: [A modern guide to getting started with Data Science and Python](http://twiecki.github.io/blog/2014/11/18/python-for-data-science/)\n", "- Slides: [Losing your loops](https://speakerdeck.com/jakevdp/losing-your-loops-fast-numerical-computing-with-numpy-pycon-2015): Fast numerical computing with NumPy\n", "- Notebooks: [Long Matplotlib tutorial](http://nbviewer.ipython.org/github/jrjohansson/scientific-python-lectures/blob/master/Lecture-4-Matplotlib.ipynb), [IPython-Matplotlib gallery](https://github.com/rasbt/matplotlib-gallery)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fin\n", "This notebook is part of the _Python Programming for Life Sciences Graduate Students_ course given in Tel-Aviv University, Spring 2015.\n", "\n", "The notebook was written using [Python](http://pytho.org/) 3.4.1 and [IPython](http://ipython.org/) 2.1.0 (download from [PyZo](http://www.pyzo.org/downloads.html)).\n", "\n", "The code is available at https://github.com//Py4Life/TAU2015/blob/master/lecture7.ipynb.\n", "\n", "The notebook can be viewed online at http://nbviewer.ipython.org/github//Py4Life/TAU2015/blob/master/lecture7.ipynb.\n", "\n", "The notebook is also available as a PDF at https://github.com/Py4Life/TAU2015/blob/master/lecture7.pdf?raw=true.\n", "\n", "This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License.\n", "\n", "![Python logo](https://www.python.org/static/community_logos/python-logo.png)" ] } ], "metadata": {} } ] }