mirror of
https://github.com/donnemartin/data-science-ipython-notebooks.git
synced 2024-03-22 13:30:56 +08:00
1795 lines
1.0 MiB
Python
1795 lines
1.0 MiB
Python
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<!--BOOK_INFORMATION-->\n",
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"<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
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"*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
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"\n",
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"*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*\n",
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"\n",
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"*No changes were made to the contents of this notebook from the original.*"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<!--NAVIGATION-->\n",
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"< [Geographic Data with Basemap](04.13-Geographic-Data-With-Basemap.ipynb) | [Contents](Index.ipynb) | [Further Resources](04.15-Further-Resources.ipynb) >"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Visualization with Seaborn"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Matplotlib has proven to be an incredibly useful and popular visualization tool, but even avid users will admit it often leaves much to be desired.\n",
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"There are several valid complaints about Matplotlib that often come up:\n",
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"\n",
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"- Prior to version 2.0, Matplotlib's defaults are not exactly the best choices. It was based off of MATLAB circa 1999, and this often shows.\n",
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"- Matplotlib's API is relatively low level. Doing sophisticated statistical visualization is possible, but often requires a *lot* of boilerplate code.\n",
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"- Matplotlib predated Pandas by more than a decade, and thus is not designed for use with Pandas ``DataFrame``s. In order to visualize data from a Pandas ``DataFrame``, you must extract each ``Series`` and often concatenate them together into the right format. It would be nicer to have a plotting library that can intelligently use the ``DataFrame`` labels in a plot.\n",
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"\n",
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"An answer to these problems is [Seaborn](http://seaborn.pydata.org/). Seaborn provides an API on top of Matplotlib that offers sane choices for plot style and color defaults, defines simple high-level functions for common statistical plot types, and integrates with the functionality provided by Pandas ``DataFrame``s.\n",
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"\n",
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"To be fair, the Matplotlib team is addressing this: it has recently added the ``plt.style`` tools discussed in [Customizing Matplotlib: Configurations and Style Sheets](04.11-Settings-and-Stylesheets.ipynb), and is starting to handle Pandas data more seamlessly.\n",
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"The 2.0 release of the library will include a new default stylesheet that will improve on the current status quo.\n",
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"But for all the reasons just discussed, Seaborn remains an extremely useful addon."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Seaborn Versus Matplotlib\n",
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"\n",
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"Here is an example of a simple random-walk plot in Matplotlib, using its classic plot formatting and colors.\n",
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"We start with the typical imports:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"plt.style.use('classic')\n",
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"%matplotlib inline\n",
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"import numpy as np\n",
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"import pandas as pd"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Now we create some random walk data:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# Create some data\n",
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"rng = np.random.RandomState(0)\n",
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"x = np.linspace(0, 10, 500)\n",
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"y = np.cumsum(rng.randn(500, 6), 0)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"And do a simple plot:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEACAYAAACwB81wAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nfT3/6CBvzpmCbMPvBUd
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"text/plain": [
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"<matplotlib.figure.Figure at 0x1023b2cc0>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"# Plot the data with Matplotlib defaults\n",
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"plt.plot(x, y)\n",
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"plt.legend('ABCDEF', ncol=2, loc='upper left');"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Although the result contains all the information we'd like it to convey, it does so in a way that is not all that aesthetically pleasing, and even looks a bit old-fashioned in the context of 21st-century data visualization.\n",
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"\n",
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"Now let's take a look at how it works with Seaborn.\n",
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"As we will see, Seaborn has many of its own high-level plotting routines, but it can also overwrite Matplotlib's default parameters and in turn get even simple Matplotlib scripts to produce vastly superior output.\n",
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"We can set the style by calling Seaborn's ``set()`` method.\n",
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"By convention, Seaborn is imported as ``sns``:"
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]
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},
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{
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"cell_type": "code",
|
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"execution_count": 4,
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"metadata": {
|
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"collapsed": false
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},
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"outputs": [],
|
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"source": [
|
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"import seaborn as sns\n",
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"sns.set()"
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]
|
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|
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},
|
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|
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{
|
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|
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"cell_type": "markdown",
|
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|
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"metadata": {},
|
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|
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"source": [
|
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|
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"Now let's rerun the same two lines as before:"
|
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]
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},
|
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{
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"cell_type": "code",
|
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|
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"execution_count": 5,
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"metadata": {
|
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"collapsed": false
|
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|
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},
|
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|
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"outputs": [
|
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|
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{
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"data": {
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|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAe8AAAFXCAYAAACLEMbVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nV/9zpJBXT4ePQ/u6+OT
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x11a0d73c8>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# same plotting code as above!\n",
|
||
|
|
"plt.plot(x, y)\n",
|
||
|
|
"plt.legend('ABCDEF', ncol=2, loc='upper left');"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Ah, much better!"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Exploring Seaborn Plots\n",
|
||
|
|
"\n",
|
||
|
|
"The main idea of Seaborn is that it provides high-level commands to create a variety of plot types useful for statistical data exploration, and even some statistical model fitting.\n",
|
||
|
|
"\n",
|
||
|
|
"Let's take a look at a few of the datasets and plot types available in Seaborn. Note that all of the following *could* be done using raw Matplotlib commands (this is, in fact, what Seaborn does under the hood) but the Seaborn API is much more convenient."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"### Histograms, KDE, and densities\n",
|
||
|
|
"\n",
|
||
|
|
"Often in statistical data visualization, all you want is to plot histograms and joint distributions of variables.\n",
|
||
|
|
"We have seen that this is relatively straightforward in Matplotlib:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 6,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAfAAAAFXCAYAAABdtRywAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nl8fjUWNjo9ra2saNqay
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x11a191080>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"data = np.random.multivariate_normal([0, 0], [[5, 2], [2, 2]], size=2000)\n",
|
||
|
|
"data = pd.DataFrame(data, columns=['x', 'y'])\n",
|
||
|
|
"\n",
|
||
|
|
"for col in 'xy':\n",
|
||
|
|
" plt.hist(data[col], normed=True, alpha=0.5)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Rather than a histogram, we can get a smooth estimate of the distribution using a kernel density estimation, which Seaborn does with ``sns.kdeplot``:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 7,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAfAAAAFXCAYAAABdtRywAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngAFOlEITAV5mLU1wZGZ
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x11a195a90>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"for col in 'xy':\n",
|
||
|
|
" sns.kdeplot(data[col], shade=True)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Histograms and KDE can be combined using ``distplot``:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 8,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAfAAAAFmCAYAAACSk8i4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nVj2b3qb0kzx4CE41czU
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x11acced30>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"sns.distplot(data['x'])\n",
|
||
|
|
"sns.distplot(data['y']);"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"If we pass the full two-dimensional dataset to ``kdeplot``, we will get a two-dimensional visualization of the data:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 9,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAeIAAAFSCAYAAAAuI9zWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nv+qfQ3OTJImfflrE999
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x11aebdbe0>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"sns.kdeplot(data);"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"We can see the joint distribution and the marginal distributions together using ``sns.jointplot``.\n",
|
||
|
|
"For this plot, we'll set the style to a white background:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 10,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAaQAAAGmCAYAAAAzhPA+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n2h+J0f5ITghHUlwYdPx
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x11ad139b0>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"with sns.axes_style('white'):\n",
|
||
|
|
" sns.jointplot(\"x\", \"y\", data, kind='kde');"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"There are other parameters that can be passed to ``jointplot``—for example, we can use a hexagonally based histogram instead:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 11,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAaQAAAGmCAYAAAAzhPA+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkK655ho4XoCFnAshFhe
|
||
|
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"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x11543ac18>"
|
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|
]
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},
|
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"metadata": {},
|
||
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"output_type": "display_data"
|
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}
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],
|
||
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"source": [
|
||
|
|
"with sns.axes_style('white'):\n",
|
||
|
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" sns.jointplot(\"x\", \"y\", data, kind='hex')"
|
||
|
|
]
|
||
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},
|
||
|
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{
|
||
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"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"### Pair plots\n",
|
||
|
|
"\n",
|
||
|
|
"When you generalize joint plots to datasets of larger dimensions, you end up with *pair plots*. This is very useful for exploring correlations between multidimensional data, when you'd like to plot all pairs of values against each other.\n",
|
||
|
|
"\n",
|
||
|
|
"We'll demo this with the well-known Iris dataset, which lists measurements of petals and sepals of three iris species:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 12,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/html": [
|
||
|
|
"<div>\n",
|
||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
||
|
|
" <thead>\n",
|
||
|
|
" <tr style=\"text-align: right;\">\n",
|
||
|
|
" <th></th>\n",
|
||
|
|
" <th>sepal_length</th>\n",
|
||
|
|
" <th>sepal_width</th>\n",
|
||
|
|
" <th>petal_length</th>\n",
|
||
|
|
" <th>petal_width</th>\n",
|
||
|
|
" <th>species</th>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </thead>\n",
|
||
|
|
" <tbody>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>0</th>\n",
|
||
|
|
" <td>5.1</td>\n",
|
||
|
|
" <td>3.5</td>\n",
|
||
|
|
" <td>1.4</td>\n",
|
||
|
|
" <td>0.2</td>\n",
|
||
|
|
" <td>setosa</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>1</th>\n",
|
||
|
|
" <td>4.9</td>\n",
|
||
|
|
" <td>3.0</td>\n",
|
||
|
|
" <td>1.4</td>\n",
|
||
|
|
" <td>0.2</td>\n",
|
||
|
|
" <td>setosa</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>2</th>\n",
|
||
|
|
" <td>4.7</td>\n",
|
||
|
|
" <td>3.2</td>\n",
|
||
|
|
" <td>1.3</td>\n",
|
||
|
|
" <td>0.2</td>\n",
|
||
|
|
" <td>setosa</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>3</th>\n",
|
||
|
|
" <td>4.6</td>\n",
|
||
|
|
" <td>3.1</td>\n",
|
||
|
|
" <td>1.5</td>\n",
|
||
|
|
" <td>0.2</td>\n",
|
||
|
|
" <td>setosa</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>4</th>\n",
|
||
|
|
" <td>5.0</td>\n",
|
||
|
|
" <td>3.6</td>\n",
|
||
|
|
" <td>1.4</td>\n",
|
||
|
|
" <td>0.2</td>\n",
|
||
|
|
" <td>setosa</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </tbody>\n",
|
||
|
|
"</table>\n",
|
||
|
|
"</div>"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
" sepal_length sepal_width petal_length petal_width species\n",
|
||
|
|
"0 5.1 3.5 1.4 0.2 setosa\n",
|
||
|
|
"1 4.9 3.0 1.4 0.2 setosa\n",
|
||
|
|
"2 4.7 3.2 1.3 0.2 setosa\n",
|
||
|
|
"3 4.6 3.1 1.5 0.2 setosa\n",
|
||
|
|
"4 5.0 3.6 1.4 0.2 setosa"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 12,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"iris = sns.load_dataset(\"iris\")\n",
|
||
|
|
"iris.head()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Visualizing the multidimensional relationships among the samples is as easy as calling ``sns.pairplot``:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 13,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
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|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x11ae9d160>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
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}
|
||
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|
],
|
||
|
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"source": [
|
||
|
|
"sns.pairplot(iris, hue='species', size=2.5);"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"### Faceted histograms\n",
|
||
|
|
"\n",
|
||
|
|
"Sometimes the best way to view data is via histograms of subsets. Seaborn's ``FacetGrid`` makes this extremely simple.\n",
|
||
|
|
"We'll take a look at some data that shows the amount that restaurant staff receive in tips based on various indicator data:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 14,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/html": [
|
||
|
|
"<div>\n",
|
||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
||
|
|
" <thead>\n",
|
||
|
|
" <tr style=\"text-align: right;\">\n",
|
||
|
|
" <th></th>\n",
|
||
|
|
" <th>total_bill</th>\n",
|
||
|
|
" <th>tip</th>\n",
|
||
|
|
" <th>sex</th>\n",
|
||
|
|
" <th>smoker</th>\n",
|
||
|
|
" <th>day</th>\n",
|
||
|
|
" <th>time</th>\n",
|
||
|
|
" <th>size</th>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </thead>\n",
|
||
|
|
" <tbody>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>0</th>\n",
|
||
|
|
" <td>16.99</td>\n",
|
||
|
|
" <td>1.01</td>\n",
|
||
|
|
" <td>Female</td>\n",
|
||
|
|
" <td>No</td>\n",
|
||
|
|
" <td>Sun</td>\n",
|
||
|
|
" <td>Dinner</td>\n",
|
||
|
|
" <td>2</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>1</th>\n",
|
||
|
|
" <td>10.34</td>\n",
|
||
|
|
" <td>1.66</td>\n",
|
||
|
|
" <td>Male</td>\n",
|
||
|
|
" <td>No</td>\n",
|
||
|
|
" <td>Sun</td>\n",
|
||
|
|
" <td>Dinner</td>\n",
|
||
|
|
" <td>3</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>2</th>\n",
|
||
|
|
" <td>21.01</td>\n",
|
||
|
|
" <td>3.50</td>\n",
|
||
|
|
" <td>Male</td>\n",
|
||
|
|
" <td>No</td>\n",
|
||
|
|
" <td>Sun</td>\n",
|
||
|
|
" <td>Dinner</td>\n",
|
||
|
|
" <td>3</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>3</th>\n",
|
||
|
|
" <td>23.68</td>\n",
|
||
|
|
" <td>3.31</td>\n",
|
||
|
|
" <td>Male</td>\n",
|
||
|
|
" <td>No</td>\n",
|
||
|
|
" <td>Sun</td>\n",
|
||
|
|
" <td>Dinner</td>\n",
|
||
|
|
" <td>2</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>4</th>\n",
|
||
|
|
" <td>24.59</td>\n",
|
||
|
|
" <td>3.61</td>\n",
|
||
|
|
" <td>Female</td>\n",
|
||
|
|
" <td>No</td>\n",
|
||
|
|
" <td>Sun</td>\n",
|
||
|
|
" <td>Dinner</td>\n",
|
||
|
|
" <td>4</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </tbody>\n",
|
||
|
|
"</table>\n",
|
||
|
|
"</div>"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
" total_bill tip sex smoker day time size\n",
|
||
|
|
"0 16.99 1.01 Female No Sun Dinner 2\n",
|
||
|
|
"1 10.34 1.66 Male No Sun Dinner 3\n",
|
||
|
|
"2 21.01 3.50 Male No Sun Dinner 3\n",
|
||
|
|
"3 23.68 3.31 Male No Sun Dinner 2\n",
|
||
|
|
"4 24.59 3.61 Female No Sun Dinner 4"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 14,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"tips = sns.load_dataset('tips')\n",
|
||
|
|
"tips.head()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 15,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAasAAAGnCAYAAAAJ03gWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3X1UVPW+P/D3zKg3nkRmGMywk4pi3lN4T6KkGBpYrCyT\nB6V8QA92w/qJGvkQonnWMcJOHpWunHuFe+u6FOvEMpGDViqoodcDlN2TdkMtTYkxYWCQeBSZ2b8/\nXJAgsIdh73EP836t1YoZ5vuZD9v5rjd77y97qwRBEEBERKRg6nvdABERkRiGFRERKR7DioiIFI9h\nRUREisewIiIixWNYERGR4jGsZFBfX49ly5YBACorK7F06dJ70kdoaCiuXbsmS22DwYDQ0FBZalP/\npZS5ERsbi6effhqRkZGYNWsWoqOj8cUXX7R/f+nSpTAajfekN+ragHvdQH9048YNnD9/HgDg4+OD\njIyMe9KHSqVy6PrU/yhlbgBAamoqAgMDAQDffvstXnrpJXz44Yfw8/O7p31R1xhWMnj77bdRWVmJ\n5cuXIykpCbGxsTh27BjWrVsHFxcXnDlzBnV1dUhOTkZubi4uXLiAsLAwvPHGG7BYLHj33XdRUlIC\ni8WCyMhILF68uEP9/Px8pKendwiLkSNHYtu2bR1e193fe4eGhiIrKwsPPPAASkpKsGPHDuzZswex\nsbEICAjAmTNnUFNTgw0bNuCJJ57AtWvXsG7dOphMJri4uCAlJQVubm5obm7GqlWrcPHiRXh6euIv\nf/kLPD09pd+g1G8oZW4AHefHI488gpkzZ2Lfvn1444032udIcXExTp48idraWvz000+YOnUqNm7c\niJKSEmRkZOC+++7DpUuXMHbsWGzduhUDBgzAgQMHsHv3bgiCgN/+9rfYuHEjBg0ahMcffxyPPPII\nqqursW/fPmg0Gvk2dH8kkOTKy8uF0NDQu75OSkoSEhISBEEQhJycHCEwMFAwmUxCfX298Nhjjwl1\ndXXCRx99JLzzzjuCIAjCzZs3hYULFwpfffWVTX08+eSTgsFguOv50NDQ9ueLi4uF2NhYQRAEYeHC\nhUJqaqogCIJw7NgxISoqShAEQYiPjxc+/PBDQRAE4cSJE8Jrr70mlJeXCw8//LBw7tw5QRAEYfny\n5cLevXtt6pOch1LmxsKFC4WSkpIOz+3du1eIj48XBOHXubN//37hySefFBobG4WmpiZh2rRpwsWL\nF4Xi4mLhd7/7nVBRUSFYLBZhzpw5wvHjx4Xvv/9emD9/vnDz5k1BEARh69atwn/8x38IgiAIY8eO\nFb788kub+iVB4J6VnYWEhAAAHnjgAfj7+8PLywsAMGTIEPzyyy84ffo0Lly4gL///e8AgKamJly8\neBETJkxor9H22+OdRo0a1eVvj10RerjC1hNPPAEAGDNmDGprawEAJSUl7bWnTZuGadOmwWAwYOjQ\noXjkkUfaX19TU2PV+xN15V7PDZVKhX/6p3+66/nf/e53cHFxAQA8+OCD7fPC398fPj4+AAA/Pz/c\nuHEDBoMBV69exQsvvABBENDa2orf/va37bUCAgKs3h7UEcPKzgYOHNj+dVeHASwWC9asWYMZM2YA\nAGpqauDm5tbhNTNmzGj/vi1UKlV7YLW2tnb4XttkvfM1d/YMAJcuXcJ9993Xof87X09ki3s9Ny5c\nuIDRo0ff9fygQYM6PG77nN/5fNthR7PZjGeeeQbr168HcDtQzWZz+2s61yLrcTWgDAYMGND+AbVW\n2wR4/PHH8fHHH6O1tRUNDQ2YP38+vvnmG5t76SpAtFotfvjhBwBAQUGBaI2JEyfi008/BQD8z//8\nDzZu3NhtbaKeKGlu3Ons2bM4cuQI5s6d26c6kyZNQn5+PkwmEwRBwB/+8Afs2rULAOdLX3HPSgY6\nnQ73338/Fi9ejNTUVKvGtP1m9uKLL+Lq1auIjIyE2WzGnDlzMHHiRJt7ee6559r3elQqFb7++msk\nJCQgJSUF6enpmDp16l09dPbmm29i/fr12Lt3L1xcXPD222/3+Hqi7ihpbmzYsAGurq4AAFdXV6Sl\npWHYsGEd3rO7Xrrz8MMPY9myZVi8eDEEQcC4ceMQHx9v1VjqmUoQifuWlhYsWLAAt27dgtlsRnh4\nOBISEpCeno7s7GzodDoAQGJiYvsxZyIiIimJhhVw+7iri4sLzGYz5s2bhw0bNqCwsBBubm6Ii4uz\nR59EROTErDpn1bYSpqWlpcMJeR6DJSIie7AqrCwWCyIiIhAcHIzg4OD25ZdZWVmYPXs21q9fj7q6\nOlkbJSIi52XVYcA2bdf1evPNN6HVauHl5QWVSoXt27fDaDRafcKUiIioN3q1dN3d3R2TJk3CyZMn\nodVq21e3xMTE4Ny5c6LjediQqGecI0RdE126bjKZMHDgQHh4eKC5uRmnT59GfHw8jEYj9Ho9AODo\n0aPw9/cXfTOVSgWjUfrDhXq9hyx15aztaHXlrO1oddtqy4FzxHHrylnb0eq21ZaSaFgZjUYkJSXB\nYrHAYrFg5syZmDZtGtauXYvS0lKo1Wr4+vpi06ZNkjZGRETURjSsxo4di5ycnLuef/fdd2VpiIiI\nqDNebomIiBSPYUVERIrHsCIiIsVjWBERkeIxrIiISPEYVkREpHgMKyIiUjyGFRERKR7DioiIFI9h\nRUREisewIiIixWNYERGR4jGsiIhI8RhWRESkeKK3CCEisgez2YwrVy63P66pcYfJVC86bsSIUdBo\nNHK2RgrAsCIiRbhy5TJWbvkbXD19rB7TWFuJ99Y8Dz+/MTJ2RkogGlYtLS1YsGABbt26BbPZjPDw\ncCQkJKC2thaJiYkwGAwYPnw40tLS4OEhz62+icg5uHr6wN3L9163QQokes5q0KBB2L17Nw4cOIAD\nBw6gsLAQZ8+eRWZmJiZPnozDhw8jKCgIGRkZ9uiXiIickFULLFxcXADc3stqbW0FABQUFCAyMhIA\nEBkZifz8fJlaJCIiZ2dVWFksFkRERCA4OBjBwcEICAhAdXU1vL29AQB6vR4mk0nWRomIyHlZtcBC\nrVbjwIEDqK+vx7Jly/D9999DpVJ1eE3nx93R6+U5ryVXXTlrO1pdOWs7Wl05OeK2kKJ2TY27TeO0\nWvdev7/St0V/qCu1Xq0GdHd3x6RJk3Dy5EnodDpUVVXB29sbRqMRWq3WqhpGY51NjfZEr/eQpa6c\ntR2trpy1Ha1uW225OOK2kKK2NcvUuxvXm/d3hG3h6HXbaktJ9DCgyWRCXd3tH6a5uRmnT5+Gn58f\nQkNDsX//fgBATk4OwsLCJG2MiIiojeieldFoRFJSEiwWCywWC2bOnIlp06Zh/PjxeO211/DJJ5/A\n19cXaWlp9uiXiIickGhYjR07Fjk5OXc9P2TIEOzatUuOnoiIiDrgtQGJiEjxGFZERKR4DCsiIlI8\nhhURESkew4qIiBSPYUVERIrHsCIiIsVjWBERkeIxrIiISPEYVkREpHgMKyIiUjyGFRERKR7DioiI\nFI9hRUREisewIiIixRO9n9X169exdu1aVFdXQ61WIyYmBrGxsUhPT0d2djZ0Oh0AIDExESEhIbI3\nTEREzkc0rDQaDdatW4dx48ahoaEBUVFRmDJlCgAgLi4OcXFxsjdJRETOTTSs9Ho99Ho9AMDNzQ1+\nfn6orKwEAAiCIG93RERE6OU5q/Lycpw/fx4BAQEAgKysLMyePRvr169HXV2dLA0SERFZHVYNDQ1Y\nsWIFkpOT4ebmhvnz56OgoAC5ubnw9vbG5s2b5eyTiIicmEqw4lhea2srli5dipCQECxevPiu7xsM\nBrzyyivIy8uTpUki6v8uXryIpe/kw93L1+ox9TUGZCTNgL+/v4ydkRKInrMCgOTkZIwePbpDUBmN\nxvZzWUePHrX6w2I0Sn+4UK/3kKWunLUdra6ctR2tblttuTjitpCitslUb/O43ry/I2wLR6/bVltK\nomF15swZ5OXlwd/fHxE
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x11c3a3fd0>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"tips['tip_pct'] = 100 * tips['tip'] / tips['total_bill']\n",
|
||
|
|
"\n",
|
||
|
|
"grid = sns.FacetGrid(tips, row=\"sex\", col=\"time\", margin_titles=True)\n",
|
||
|
|
"grid.map(plt.hist, \"tip_pct\", bins=np.linspace(0, 40, 15));"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"### Factor plots\n",
|
||
|
|
"\n",
|
||
|
|
"Factor plots can be useful for this kind of visualization as well. This allows you to view the distribution of a parameter within bins defined by any other parameter:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 16,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAVgAAAEWCAYAAAAjPo9cAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n0Wgwb948q17I09MTYWF
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x11c6f0518>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"with sns.axes_style(style='ticks'):\n",
|
||
|
|
" g = sns.factorplot(\"day\", \"total_bill\", \"sex\", data=tips, kind=\"box\")\n",
|
||
|
|
" g.set_axis_labels(\"Day\", \"Total Bill\");"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"### Joint distributions\n",
|
||
|
|
"\n",
|
||
|
|
"Similar to the pairplot we saw earlier, we can use ``sns.jointplot`` to show the joint distribution between different datasets, along with the associated marginal distributions:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 17,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAaIAAAGmCAYAAAA+moB5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\naGhoCLt27cK//du/jXn
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x11d25ef98>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"with sns.axes_style('white'):\n",
|
||
|
|
" sns.jointplot(\"total_bill\", \"tip\", data=tips, kind='hex')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"The joint plot can even do some automatic kernel density estimation and regression:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 18,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAaQAAAGmCAYAAAAzhPA+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nE0lIaUZVNTbuquYvb50
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x11c3b0550>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"sns.jointplot(\"total_bill\", \"tip\", data=tips, kind='reg');"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"### Bar plots\n",
|
||
|
|
"\n",
|
||
|
|
"Time series can be plotted using ``sns.factorplot``. In the following example, we'll use the Planets data that we first saw in [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb):"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 19,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/html": [
|
||
|
|
"<div>\n",
|
||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
||
|
|
" <thead>\n",
|
||
|
|
" <tr style=\"text-align: right;\">\n",
|
||
|
|
" <th></th>\n",
|
||
|
|
" <th>method</th>\n",
|
||
|
|
" <th>number</th>\n",
|
||
|
|
" <th>orbital_period</th>\n",
|
||
|
|
" <th>mass</th>\n",
|
||
|
|
" <th>distance</th>\n",
|
||
|
|
" <th>year</th>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </thead>\n",
|
||
|
|
" <tbody>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>0</th>\n",
|
||
|
|
" <td>Radial Velocity</td>\n",
|
||
|
|
" <td>1</td>\n",
|
||
|
|
" <td>269.300</td>\n",
|
||
|
|
" <td>7.10</td>\n",
|
||
|
|
" <td>77.40</td>\n",
|
||
|
|
" <td>2006</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>1</th>\n",
|
||
|
|
" <td>Radial Velocity</td>\n",
|
||
|
|
" <td>1</td>\n",
|
||
|
|
" <td>874.774</td>\n",
|
||
|
|
" <td>2.21</td>\n",
|
||
|
|
" <td>56.95</td>\n",
|
||
|
|
" <td>2008</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>2</th>\n",
|
||
|
|
" <td>Radial Velocity</td>\n",
|
||
|
|
" <td>1</td>\n",
|
||
|
|
" <td>763.000</td>\n",
|
||
|
|
" <td>2.60</td>\n",
|
||
|
|
" <td>19.84</td>\n",
|
||
|
|
" <td>2011</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>3</th>\n",
|
||
|
|
" <td>Radial Velocity</td>\n",
|
||
|
|
" <td>1</td>\n",
|
||
|
|
" <td>326.030</td>\n",
|
||
|
|
" <td>19.40</td>\n",
|
||
|
|
" <td>110.62</td>\n",
|
||
|
|
" <td>2007</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>4</th>\n",
|
||
|
|
" <td>Radial Velocity</td>\n",
|
||
|
|
" <td>1</td>\n",
|
||
|
|
" <td>516.220</td>\n",
|
||
|
|
" <td>10.50</td>\n",
|
||
|
|
" <td>119.47</td>\n",
|
||
|
|
" <td>2009</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </tbody>\n",
|
||
|
|
"</table>\n",
|
||
|
|
"</div>"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
" method number orbital_period mass distance year\n",
|
||
|
|
"0 Radial Velocity 1 269.300 7.10 77.40 2006\n",
|
||
|
|
"1 Radial Velocity 1 874.774 2.21 56.95 2008\n",
|
||
|
|
"2 Radial Velocity 1 763.000 2.60 19.84 2011\n",
|
||
|
|
"3 Radial Velocity 1 326.030 19.40 110.62 2007\n",
|
||
|
|
"4 Radial Velocity 1 516.220 10.50 119.47 2009"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 19,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"planets = sns.load_dataset('planets')\n",
|
||
|
|
"planets.head()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 20,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAkEAAAEWCAYAAABhZ0N/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAGh9JREFUeJzt3X1QVNcd//HPuouNQZEislK2gxbDaCbVjHVSzdhWwar4\nMIgPHeNjxarTJhIN1FY0MVNN6zRNZX61TWBqYlsTnfgUo6IxrkmstiXGmQRNJKM7tiK6q4gYBauw\n3t8f/rK/oKZZZB+A8379xR53z34vx+t8PPfce2yWZVkCAAAwTIdoFwAAABANhCAAAGAkQhAAADAS\nIQgAABiJEAQAAIxECAIAAEYKawjyer2aOXOmxowZo3Hjxumvf/2rJOny5cvKzc3VyJEjNWfOHF25\nciXwmeLiYo0YMUJZWVk6ePBgOMsDAAAGs4XzOUEXLlxQdXW1+vbtq7q6Ok2YMEF/+tOftHXrVsXH\nx2vu3LkqKSnRZ599poKCAp08eVIFBQXavHmzvF6vZs+erb1798pms4WrRAAAYKiwzgR1795dffv2\nlSTFxsYqLS1NPp9PbrdbOTk5kqScnBzt27dPkrR//36NHj1aDodDLpdLqampKi8vD2eJAADAUBFb\nE3TmzBlVVFSof//+unjxohITEyXdCko1NTWSJJ/Pp+Tk5MBnnE6nfD7fl/bZ2NioM2fOqLGxMbzF\nAwCAdiciIaiurk55eXkqLCxUbGzsHZe37vVyl9frVWZmprxebyjKBAAABgl7CGpsbFReXp6ys7M1\nfPhwSVK3bt1UXV0t6da6oYSEBEm3Zn7OnTsX+KzX65XT6Qx3iQAAwEBhD0GFhYXq3bu3Zs2aFWjL\nyMjQ1q1bJUnbtm1TZmZmoL20tFQ3btxQZWWlTp8+rX79+oW7RAAAYCBHODs/cuSIduzYofT0dI0f\nP142m02LFi3S3LlztXDhQm3ZskUpKSkqKiqSJPXu3VtZWVkaM2aMHA6Hli9fzp1hAAAgLMJ6i3y4\nnTlzRpmZmXK73XK5XNEuBwAAtCE8MRoAABiJEAQAAIxECAIAAEYiBAEAACMRggAAgJEIQQAAwEiE\nIAAAYCRCEAAAMBIhCAAAGIkQBAAAjEQIAgAARgrrBqoAAODL+f1+eTyekPSVlpYmu90ekr5MQQgC\nACBKPB6P5qx8WbHxSS3qp672vNYuy1V6enqIKjMDIQgAgCiKjU9Sl8TkaJdhJNYEAQAAIxGCAACA\nkQhBAADASIQgAABgJEIQAAAwEiEIAAAYiRAEAACMRAgCAABGIgQBAAAjEYIAAICRCEEAAMBIhCAA\nAGAkQhAAADASIQgAABiJEAQAAIxECAIAAEYiBAEAACMRggAAgJEIQQAAwEiEIAAAYCRCEAAAMBIh\nCAAAGIkQBAAAjEQIAgAARiIEAQAAIxGCAACAkQhBAADASIQgAABgJEIQAAAwEiEIAAAYiRAEAACM\nRAgCAABGIgQBAAAjEYIAAICRwhqCCgsL9eijj2rcuHGBtjVr1uj73/++cnJylJOTowMHDgT+rLi4\nWCNGjFBWVpYOHjwYztIAAIDhHOHsfMKECZoxY4YWL17cpH327NmaPXt2kzaPx6Pdu3ertLRUXq9X\ns2fP1t69e2Wz2cJZIgAAMFRYZ4IGDhyouLi4O9oty7qjze12a/To0XI4HHK5XEpNTVV5eXk4ywMA\nAAaLypqg9evXKzs7W0uXLtWVK1ckST6fT8nJyYH3OJ1O+Xy+aJQHAAAMEPEQNHXqVLndbm3fvl2J\niYlatWpVpEsAAACIfAhKSEgIrPP50Y9+FLjk5XQ6de7cucD7vF6vnE5npMsDAACGCHsIun39z4UL\nFwI/v/3220pPT5ckZWRkqLS0VDdu3FBlZaVOnz6tfv36hbs8AABgqLDeHZafn6+ysjLV1tZq6NCh\nWrBggcrKynT8+HF16NBBKSkp+tWvfiVJ6t27t7KysjRmzBg5HA4tX76cO8MAAEDYhDUEvfDCC3e0\nTZw48UvfP3/+fM2fPz+cJQEAAEjiidEAAMBQhCAAAGAkQhAAADASIQgAABiJEAQAAIwU1rvDAABA\ndPj9fnk8npD0lZaWJrvdHpK+WhNCEAAA7ZDH49GclS8rNj6pRf3U1Z7X2mW5gYcbtyeEIAAA2qnY\n+CR1SUz+6jcaijVBAADASIQgAABgJEIQAAAwEiEIAAAYiRAEAACMRAgCAABGIgQBAAAjEYIAAICR\nCEEAAMBIhCAAAGAkQhAAADASIQgAABiJEAQAAIxECAIAAEYiBAEAACMRggAAgJEIQQAAwEiEIAAA\nYCRCEAAAMBIhCAAAGIkQBAAAjEQIAgAARiIEAQAAIzmiXQAAAGg7/H6/PB5PyPpLS0uT3W4PWX/N\nQQgCAABB83g8mrPyZcXGJ7W4r7ra81q7LFfp6ekhqKz5CEEAAKBZYuOT1CUxOdpltBhrggAAgJEI\nQQAAwEiEIAAAYCRCEAAAMBIhCAAAGIkQBAAAjEQIAgAARgoqBK1YseKOtl/84hchLwYAACBS/ufD\nEpcuXarKykodO3ZMJ06cCLQ3NjbqypUrYS8OAAAgXP5nCPrpT3+qqqoqPffcc3riiScC7Xa7XWlp\naWEvDgAAIFz+ZwhyuVxyuVx68803dfXqVV25ckWWZUmS6uvrFR8fH5EiAQAAQi2ovcOKi4tVXFzc\nJPTYbDa53e6wFQYAABBOQYWgTZs2ad++fUpISAh3PQAAABER1N1hycnJ6tq1a7hrAQAAiJigZoJ6\n9uypqVOn6rvf/a46duwYaP/iYum7KSws1Lvvvqtu3bppx44dkqTLly9r0aJFqqqqksvlUlFRkbp0\n6SLp1mW3LVu2yG63a+nSpRoyZMi9HhcAAMD/FFQIcjqdcjqdze58woQJmjFjhhYvXhxoKykp0eDB\ngzV37lyVlJSouLhYBQUFOnnypHbv3q3S0lJ5vV7Nnj1be/fulc1ma/b3AgAQKn6/Xx6PJ2T9paWl\nyW63h6w/3LugQtBXzfh8mYEDB6qqqqpJm9vt1vr16yVJOTk5mjFjhgoKCrR//36NHj1aDodDLpdL\nqampKi8vV//+/e/puwEACAWPx6M5K19WbHxSi/uqqz2vtctylZ6eHoLK0FJBhaA+ffrcMSOTlJSk\n9957r9lfWFNTo8TERElS9+7dVVNTI0ny+Xx6+OGHA+9zOp3y+XzN7h8AgFCLjU9Sl8TkaJeBEAsq\nBFVUVAR+bmho0L59+/Thhx+GpAAudwEAgGgIKgR9UUxMjLKysvTSSy/d0xd269ZN1dXVSkxM1IUL\nFwK33TudTp07dy7wPq/Xe0/rkAAAZgrl2h3W7ZghqBD0xhtvBH62LEsnTpxQTExMUF/w+ROmP5eR\nkaGtW7dq3rx52rZtmzIzMwPtBQUF+vGPfyyfz6fTp0+rX79+wR4HAMBwoVq7w7odcwQVgsrKypq8\n/vrXv67Vq1d/5efy8/NVVlam2tpaDR06VAsWLNC8efP05JNPasuWLUpJSVFRUZEkqXfv3srKytKY\nMWPkcDi0fPlyLpUBAJqFtTtojqBC0G9+8xs1NDTo1KlT8vv9euCBB+RwfPVHX3jhhbu2r1u37q7t\n8+fP1/z584MpCQAAoEWCCkHHjh1TXl6e4uPjdfPmTVVXV+uPf/wjt68DAIA2K6gQtHLlSq1evToQ\nej788EOtWLFCmzdvDmtxAAAA4RLU3mH19fVNZn0efvhhXb9+PWxFAQAAhFtQIahr167at29f4PW+\nffsUHx8ftqIAAADCLajLYStWrND8+fO1dOnSQNvGjRvDVhQAAEC4BTUTdODAAXXq1EnvvPOO/vKX\nvyghIUHvv/9+uGsDAAAIm6BC0Ouvv64NGzbo/vvvV58+fbR169bAJqgAAABtUVAhqKGhockTooN9\nWjQAAEBrFdSaoOHDh2vWrFnKysqSJO3duzew3QUAAEBbFFQI+vnPf649e/bo8OHDcjgcmjlzpoYP\nHx7u2gAAAMIm6F3kR40
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x11ade0dd8>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"with sns.axes_style('white'):\n",
|
||
|
|
" g = sns.factorplot(\"year\", data=planets, aspect=2,\n",
|
||
|
|
" kind=\"count\", color='steelblue')\n",
|
||
|
|
" g.set_xticklabels(step=5)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"We can learn more by looking at the *method* of discovery of each of these planets:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 21,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAABT4AAAEWCAYAAABPHojpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n1eAzPj5e69atU0JCglJ
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x11bd8d668>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"with sns.axes_style('white'):\n",
|
||
|
|
" g = sns.factorplot(\"year\", data=planets, aspect=4.0, kind='count',\n",
|
||
|
|
" hue='method', order=range(2001, 2015))\n",
|
||
|
|
" g.set_ylabels('Number of Planets Discovered')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"For more information on plotting with Seaborn, see the [Seaborn documentation](http://seaborn.pydata.org/), a [tutorial](http://seaborn.pydata.org/\n",
|
||
|
|
"tutorial.htm), and the [Seaborn gallery](http://seaborn.pydata.org/examples/index.html)."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Example: Exploring Marathon Finishing Times\n",
|
||
|
|
"\n",
|
||
|
|
"Here we'll look at using Seaborn to help visualize and understand finishing results from a marathon.\n",
|
||
|
|
"I've scraped the data from sources on the Web, aggregated it and removed any identifying information, and put it on GitHub where it can be downloaded\n",
|
||
|
|
"(if you are interested in using Python for web scraping, I would recommend [*Web Scraping with Python*](http://shop.oreilly.com/product/0636920034391.do) by Ryan Mitchell).\n",
|
||
|
|
"We will start by downloading the data from\n",
|
||
|
|
"the Web, and loading it into Pandas:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
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"execution_count": 22,
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"metadata": {
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"collapsed": false
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},
|
||
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"outputs": [],
|
||
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"source": [
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||
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"# !curl -O https://raw.githubusercontent.com/jakevdp/marathon-data/master/marathon-data.csv"
|
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|
]
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},
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" <tr style=\"text-align: right;\">\n",
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" <th>gender</th>\n",
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" <th>split</th>\n",
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" <th>final</th>\n",
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" <td>M</td>\n",
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" <td>01:05:38</td>\n",
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" <td>02:08:51</td>\n",
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" <td>M</td>\n",
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" <td>01:06:49</td>\n",
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" <td>02:10:42</td>\n",
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||
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" </tr>\n",
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||
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" <tr>\n",
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" <th>3</th>\n",
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" <td>38</td>\n",
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" <td>M</td>\n",
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" <td>31</td>\n",
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" <td>M</td>\n",
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" <td>01:06:32</td>\n",
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||
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" <td>02:13:59</td>\n",
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||
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" </tr>\n",
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" </tbody>\n",
|
||
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"</table>\n",
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"</div>"
|
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],
|
||
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"text/plain": [
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||
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" age gender split final\n",
|
||
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"0 33 M 01:05:38 02:08:51\n",
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||
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"1 32 M 01:06:26 02:09:28\n",
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"2 31 M 01:06:49 02:10:42\n",
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"3 38 M 01:06:16 02:13:45\n",
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"execution_count": 23,
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"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"data = pd.read_csv('marathon-data.csv')\n",
|
||
|
|
"data.head()"
|
||
|
|
]
|
||
|
|
},
|
||
|
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{
|
||
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|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"By default, Pandas loaded the time columns as Python strings (type ``object``); we can see this by looking at the ``dtypes`` attribute of the DataFrame:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
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"cell_type": "code",
|
||
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"execution_count": 24,
|
||
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"metadata": {
|
||
|
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"collapsed": false
|
||
|
|
},
|
||
|
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"outputs": [
|
||
|
|
{
|
||
|
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"data": {
|
||
|
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"text/plain": [
|
||
|
|
"age int64\n",
|
||
|
|
"gender object\n",
|
||
|
|
"split object\n",
|
||
|
|
"final object\n",
|
||
|
|
"dtype: object"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 24,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
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"source": [
|
||
|
|
"data.dtypes"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Let's fix this by providing a converter for the times:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 25,
|
||
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|
"metadata": {
|
||
|
|
"collapsed": false
|
||
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},
|
||
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"outputs": [
|
||
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{
|
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"data": {
|
||
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"text/html": [
|
||
|
|
"<div>\n",
|
||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
||
|
|
" <thead>\n",
|
||
|
|
" <tr style=\"text-align: right;\">\n",
|
||
|
|
" <th></th>\n",
|
||
|
|
" <th>age</th>\n",
|
||
|
|
" <th>gender</th>\n",
|
||
|
|
" <th>split</th>\n",
|
||
|
|
" <th>final</th>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </thead>\n",
|
||
|
|
" <tbody>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>0</th>\n",
|
||
|
|
" <td>33</td>\n",
|
||
|
|
" <td>M</td>\n",
|
||
|
|
" <td>01:05:38</td>\n",
|
||
|
|
" <td>02:08:51</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>1</th>\n",
|
||
|
|
" <td>32</td>\n",
|
||
|
|
" <td>M</td>\n",
|
||
|
|
" <td>01:06:26</td>\n",
|
||
|
|
" <td>02:09:28</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>2</th>\n",
|
||
|
|
" <td>31</td>\n",
|
||
|
|
" <td>M</td>\n",
|
||
|
|
" <td>01:06:49</td>\n",
|
||
|
|
" <td>02:10:42</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>3</th>\n",
|
||
|
|
" <td>38</td>\n",
|
||
|
|
" <td>M</td>\n",
|
||
|
|
" <td>01:06:16</td>\n",
|
||
|
|
" <td>02:13:45</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>4</th>\n",
|
||
|
|
" <td>31</td>\n",
|
||
|
|
" <td>M</td>\n",
|
||
|
|
" <td>01:06:32</td>\n",
|
||
|
|
" <td>02:13:59</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </tbody>\n",
|
||
|
|
"</table>\n",
|
||
|
|
"</div>"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
" age gender split final\n",
|
||
|
|
"0 33 M 01:05:38 02:08:51\n",
|
||
|
|
"1 32 M 01:06:26 02:09:28\n",
|
||
|
|
"2 31 M 01:06:49 02:10:42\n",
|
||
|
|
"3 38 M 01:06:16 02:13:45\n",
|
||
|
|
"4 31 M 01:06:32 02:13:59"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 25,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"def convert_time(s):\n",
|
||
|
|
" h, m, s = map(int, s.split(':'))\n",
|
||
|
|
" return pd.datetools.timedelta(hours=h, minutes=m, seconds=s)\n",
|
||
|
|
"\n",
|
||
|
|
"data = pd.read_csv('marathon-data.csv',\n",
|
||
|
|
" converters={'split':convert_time, 'final':convert_time})\n",
|
||
|
|
"data.head()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 26,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"age int64\n",
|
||
|
|
"gender object\n",
|
||
|
|
"split timedelta64[ns]\n",
|
||
|
|
"final timedelta64[ns]\n",
|
||
|
|
"dtype: object"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 26,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"data.dtypes"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"That looks much better. For the purpose of our Seaborn plotting utilities, let's next add columns that give the times in seconds:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 27,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/html": [
|
||
|
|
"<div>\n",
|
||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
||
|
|
" <thead>\n",
|
||
|
|
" <tr style=\"text-align: right;\">\n",
|
||
|
|
" <th></th>\n",
|
||
|
|
" <th>age</th>\n",
|
||
|
|
" <th>gender</th>\n",
|
||
|
|
" <th>split</th>\n",
|
||
|
|
" <th>final</th>\n",
|
||
|
|
" <th>split_sec</th>\n",
|
||
|
|
" <th>final_sec</th>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </thead>\n",
|
||
|
|
" <tbody>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>0</th>\n",
|
||
|
|
" <td>33</td>\n",
|
||
|
|
" <td>M</td>\n",
|
||
|
|
" <td>01:05:38</td>\n",
|
||
|
|
" <td>02:08:51</td>\n",
|
||
|
|
" <td>3938.0</td>\n",
|
||
|
|
" <td>7731.0</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>1</th>\n",
|
||
|
|
" <td>32</td>\n",
|
||
|
|
" <td>M</td>\n",
|
||
|
|
" <td>01:06:26</td>\n",
|
||
|
|
" <td>02:09:28</td>\n",
|
||
|
|
" <td>3986.0</td>\n",
|
||
|
|
" <td>7768.0</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>2</th>\n",
|
||
|
|
" <td>31</td>\n",
|
||
|
|
" <td>M</td>\n",
|
||
|
|
" <td>01:06:49</td>\n",
|
||
|
|
" <td>02:10:42</td>\n",
|
||
|
|
" <td>4009.0</td>\n",
|
||
|
|
" <td>7842.0</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>3</th>\n",
|
||
|
|
" <td>38</td>\n",
|
||
|
|
" <td>M</td>\n",
|
||
|
|
" <td>01:06:16</td>\n",
|
||
|
|
" <td>02:13:45</td>\n",
|
||
|
|
" <td>3976.0</td>\n",
|
||
|
|
" <td>8025.0</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>4</th>\n",
|
||
|
|
" <td>31</td>\n",
|
||
|
|
" <td>M</td>\n",
|
||
|
|
" <td>01:06:32</td>\n",
|
||
|
|
" <td>02:13:59</td>\n",
|
||
|
|
" <td>3992.0</td>\n",
|
||
|
|
" <td>8039.0</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </tbody>\n",
|
||
|
|
"</table>\n",
|
||
|
|
"</div>"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
" age gender split final split_sec final_sec\n",
|
||
|
|
"0 33 M 01:05:38 02:08:51 3938.0 7731.0\n",
|
||
|
|
"1 32 M 01:06:26 02:09:28 3986.0 7768.0\n",
|
||
|
|
"2 31 M 01:06:49 02:10:42 4009.0 7842.0\n",
|
||
|
|
"3 38 M 01:06:16 02:13:45 3976.0 8025.0\n",
|
||
|
|
"4 31 M 01:06:32 02:13:59 3992.0 8039.0"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 27,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"data['split_sec'] = data['split'].astype(int) / 1E9\n",
|
||
|
|
"data['final_sec'] = data['final'].astype(int) / 1E9\n",
|
||
|
|
"data.head()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"To get an idea of what the data looks like, we can plot a ``jointplot`` over the data:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 28,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAbUAAAGmCAYAAAD76BroAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmDED9fX1eOWVVzBnzhw
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x11b646ac8>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"with sns.axes_style('white'):\n",
|
||
|
|
" g = sns.jointplot(\"split_sec\", \"final_sec\", data, kind='hex')\n",
|
||
|
|
" g.ax_joint.plot(np.linspace(4000, 16000),\n",
|
||
|
|
" np.linspace(8000, 32000), ':k')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"The dotted line shows where someone's time would lie if they ran the marathon at a perfectly steady pace. The fact that the distribution lies above this indicates (as you might expect) that most people slow down over the course of the marathon.\n",
|
||
|
|
"If you have run competitively, you'll know that those who do the opposite—run faster during the second half of the race—are said to have \"negative-split\" the race.\n",
|
||
|
|
"\n",
|
||
|
|
"Let's create another column in the data, the split fraction, which measures the degree to which each runner negative-splits or positive-splits the race:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 29,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/html": [
|
||
|
|
"<div>\n",
|
||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
||
|
|
" <thead>\n",
|
||
|
|
" <tr style=\"text-align: right;\">\n",
|
||
|
|
" <th></th>\n",
|
||
|
|
" <th>age</th>\n",
|
||
|
|
" <th>gender</th>\n",
|
||
|
|
" <th>split</th>\n",
|
||
|
|
" <th>final</th>\n",
|
||
|
|
" <th>split_sec</th>\n",
|
||
|
|
" <th>final_sec</th>\n",
|
||
|
|
" <th>split_frac</th>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </thead>\n",
|
||
|
|
" <tbody>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>0</th>\n",
|
||
|
|
" <td>33</td>\n",
|
||
|
|
" <td>M</td>\n",
|
||
|
|
" <td>01:05:38</td>\n",
|
||
|
|
" <td>02:08:51</td>\n",
|
||
|
|
" <td>3938.0</td>\n",
|
||
|
|
" <td>7731.0</td>\n",
|
||
|
|
" <td>-0.018756</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>1</th>\n",
|
||
|
|
" <td>32</td>\n",
|
||
|
|
" <td>M</td>\n",
|
||
|
|
" <td>01:06:26</td>\n",
|
||
|
|
" <td>02:09:28</td>\n",
|
||
|
|
" <td>3986.0</td>\n",
|
||
|
|
" <td>7768.0</td>\n",
|
||
|
|
" <td>-0.026262</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>2</th>\n",
|
||
|
|
" <td>31</td>\n",
|
||
|
|
" <td>M</td>\n",
|
||
|
|
" <td>01:06:49</td>\n",
|
||
|
|
" <td>02:10:42</td>\n",
|
||
|
|
" <td>4009.0</td>\n",
|
||
|
|
" <td>7842.0</td>\n",
|
||
|
|
" <td>-0.022443</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>3</th>\n",
|
||
|
|
" <td>38</td>\n",
|
||
|
|
" <td>M</td>\n",
|
||
|
|
" <td>01:06:16</td>\n",
|
||
|
|
" <td>02:13:45</td>\n",
|
||
|
|
" <td>3976.0</td>\n",
|
||
|
|
" <td>8025.0</td>\n",
|
||
|
|
" <td>0.009097</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>4</th>\n",
|
||
|
|
" <td>31</td>\n",
|
||
|
|
" <td>M</td>\n",
|
||
|
|
" <td>01:06:32</td>\n",
|
||
|
|
" <td>02:13:59</td>\n",
|
||
|
|
" <td>3992.0</td>\n",
|
||
|
|
" <td>8039.0</td>\n",
|
||
|
|
" <td>0.006842</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </tbody>\n",
|
||
|
|
"</table>\n",
|
||
|
|
"</div>"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
" age gender split final split_sec final_sec split_frac\n",
|
||
|
|
"0 33 M 01:05:38 02:08:51 3938.0 7731.0 -0.018756\n",
|
||
|
|
"1 32 M 01:06:26 02:09:28 3986.0 7768.0 -0.026262\n",
|
||
|
|
"2 31 M 01:06:49 02:10:42 4009.0 7842.0 -0.022443\n",
|
||
|
|
"3 38 M 01:06:16 02:13:45 3976.0 8025.0 0.009097\n",
|
||
|
|
"4 31 M 01:06:32 02:13:59 3992.0 8039.0 0.006842"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 29,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"data['split_frac'] = 1 - 2 * data['split_sec'] / data['final_sec']\n",
|
||
|
|
"data.head()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Where this split difference is less than zero, the person negative-split the race by that fraction.\n",
|
||
|
|
"Let's do a distribution plot of this split fraction:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 30,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAfUAAAFmCAYAAAB0ugP8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCoXaB+0Phdr1f/5vWKl
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x11bb754a8>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"sns.distplot(data['split_frac'], kde=False);\n",
|
||
|
|
"plt.axvline(0, color=\"k\", linestyle=\"--\");"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 31,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"251"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 31,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"sum(data.split_frac < 0)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Out of nearly 40,000 participants, there were only 250 people who negative-split their marathon.\n",
|
||
|
|
"\n",
|
||
|
|
"Let's see whether there is any correlation between this split fraction and other variables. We'll do this using a ``pairgrid``, which draws plots of all these correlations:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 32,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAwcAAALGCAYAAAAQm+3qAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nz0/wMypIHQMAIVFCcqL
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x11aeb5908>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"g = sns.PairGrid(data, vars=['age', 'split_sec', 'final_sec', 'split_frac'],\n",
|
||
|
|
" hue='gender', palette='RdBu_r')\n",
|
||
|
|
"g.map(plt.scatter, alpha=0.8)\n",
|
||
|
|
"g.add_legend();"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"It looks like the split fraction does not correlate particularly with age, but does correlate with the final time: faster runners tend to have closer to even splits on their marathon time.\n",
|
||
|
|
"(We see here that Seaborn is no panacea for Matplotlib's ills when it comes to plot styles: in particular, the x-axis labels overlap. Because the output is a simple Matplotlib plot, however, the methods in [Customizing Ticks](04.10-Customizing-Ticks.ipynb) can be used to adjust such things if desired.)\n",
|
||
|
|
"\n",
|
||
|
|
"The difference between men and women here is interesting. Let's look at the histogram of split fractions for these two groups:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 33,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAeIAAAFmCAYAAACxyJltAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhRlxltuXiBzHICbyuIG
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x11bdb3a58>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"sns.kdeplot(data.split_frac[data.gender=='M'], label='men', shade=True)\n",
|
||
|
|
"sns.kdeplot(data.split_frac[data.gender=='W'], label='women', shade=True)\n",
|
||
|
|
"plt.xlabel('split_frac');"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"The interesting thing here is that there are many more men than women who are running close to an even split!\n",
|
||
|
|
"This almost looks like some kind of bimodal distribution among the men and women. Let's see if we can suss-out what's going on by looking at the distributions as a function of age.\n",
|
||
|
|
"\n",
|
||
|
|
"A nice way to compare distributions is to use a *violin plot*"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 34,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAfsAAAFmCAYAAABqczNPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAqdqeefO2NKTTL+xhT
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x11d2aeeb8>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"sns.violinplot(\"gender\", \"split_frac\", data=data,\n",
|
||
|
|
" palette=[\"lightblue\", \"lightpink\"]);"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"This is yet another way to compare the distributions between men and women.\n",
|
||
|
|
"\n",
|
||
|
|
"Let's look a little deeper, and compare these violin plots as a function of age. We'll start by creating a new column in the array that specifies the decade of age that each person is in:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 35,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/html": [
|
||
|
|
"<div>\n",
|
||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
||
|
|
" <thead>\n",
|
||
|
|
" <tr style=\"text-align: right;\">\n",
|
||
|
|
" <th></th>\n",
|
||
|
|
" <th>age</th>\n",
|
||
|
|
" <th>gender</th>\n",
|
||
|
|
" <th>split</th>\n",
|
||
|
|
" <th>final</th>\n",
|
||
|
|
" <th>split_sec</th>\n",
|
||
|
|
" <th>final_sec</th>\n",
|
||
|
|
" <th>split_frac</th>\n",
|
||
|
|
" <th>age_dec</th>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </thead>\n",
|
||
|
|
" <tbody>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>0</th>\n",
|
||
|
|
" <td>33</td>\n",
|
||
|
|
" <td>M</td>\n",
|
||
|
|
" <td>01:05:38</td>\n",
|
||
|
|
" <td>02:08:51</td>\n",
|
||
|
|
" <td>3938.0</td>\n",
|
||
|
|
" <td>7731.0</td>\n",
|
||
|
|
" <td>-0.018756</td>\n",
|
||
|
|
" <td>30</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>1</th>\n",
|
||
|
|
" <td>32</td>\n",
|
||
|
|
" <td>M</td>\n",
|
||
|
|
" <td>01:06:26</td>\n",
|
||
|
|
" <td>02:09:28</td>\n",
|
||
|
|
" <td>3986.0</td>\n",
|
||
|
|
" <td>7768.0</td>\n",
|
||
|
|
" <td>-0.026262</td>\n",
|
||
|
|
" <td>30</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>2</th>\n",
|
||
|
|
" <td>31</td>\n",
|
||
|
|
" <td>M</td>\n",
|
||
|
|
" <td>01:06:49</td>\n",
|
||
|
|
" <td>02:10:42</td>\n",
|
||
|
|
" <td>4009.0</td>\n",
|
||
|
|
" <td>7842.0</td>\n",
|
||
|
|
" <td>-0.022443</td>\n",
|
||
|
|
" <td>30</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>3</th>\n",
|
||
|
|
" <td>38</td>\n",
|
||
|
|
" <td>M</td>\n",
|
||
|
|
" <td>01:06:16</td>\n",
|
||
|
|
" <td>02:13:45</td>\n",
|
||
|
|
" <td>3976.0</td>\n",
|
||
|
|
" <td>8025.0</td>\n",
|
||
|
|
" <td>0.009097</td>\n",
|
||
|
|
" <td>30</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>4</th>\n",
|
||
|
|
" <td>31</td>\n",
|
||
|
|
" <td>M</td>\n",
|
||
|
|
" <td>01:06:32</td>\n",
|
||
|
|
" <td>02:13:59</td>\n",
|
||
|
|
" <td>3992.0</td>\n",
|
||
|
|
" <td>8039.0</td>\n",
|
||
|
|
" <td>0.006842</td>\n",
|
||
|
|
" <td>30</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </tbody>\n",
|
||
|
|
"</table>\n",
|
||
|
|
"</div>"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
" age gender split final split_sec final_sec split_frac age_dec\n",
|
||
|
|
"0 33 M 01:05:38 02:08:51 3938.0 7731.0 -0.018756 30\n",
|
||
|
|
"1 32 M 01:06:26 02:09:28 3986.0 7768.0 -0.026262 30\n",
|
||
|
|
"2 31 M 01:06:49 02:10:42 4009.0 7842.0 -0.022443 30\n",
|
||
|
|
"3 38 M 01:06:16 02:13:45 3976.0 8025.0 0.009097 30\n",
|
||
|
|
"4 31 M 01:06:32 02:13:59 3992.0 8039.0 0.006842 30"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 35,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"data['age_dec'] = data.age.map(lambda age: 10 * (age // 10))\n",
|
||
|
|
"data.head()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 36,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAfsAAAFhCAYAAAB3dgP3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xd0W9eVL/7vRSVIsPdeRYoUSRWqF9rqsmTLckkkl3hs\nz++tJOuXeMbJyqS85cwktmfezKS8OG3GEzt2HCuyZBWrWLYoWRLVSFGkKFLsvVeAIAmAJEDgvj9A\nQOxEuZcgr/ZnLa8lguDFuQZw9z3n7LMPw7IsC0IIIYQIlsjdDSCEEEIIvyjYE0IIIQJHwZ4QQggR\nOAr2hBBCiMBRsCeEEEIEjoI9IYQQInASdzeALz09g+5uAiGEEDJvgoO9Z/wd9ewJIYQQgaNgTwgh\nhAgcBXtCCCFE4CjYE0IIIQJHwZ4QQggROAr2hBBCiMBRsCeEEEIEjoI9IYQQInAU7AkhhJB58K//\n+jOUl993y2tTsCeEEEIETrDlcgkhhBAuqFS9+Jd/+d8QiUTw8fFFXFw8BgcHUVtbDQD4h3/4PpYs\nScFLLx1EUlIyGhvr8cgj2/B3f/f3KCjIx3/91+/g6+sHrdZSxr2/X4N/+7efY2hoCJ6envjJT/4F\ntbXV+OMffwupVIpvf/s1pKdncHoO1LMnhBBCZvHRRx/g2WcP4je/+SPi4xNw8+Z1mEwm/O537+Jf\n/uVt/OY3vwQAdHS04/vf/yH++78/wOnTJwEAf/rTf+EXv3gHv/jFb8CyrO14u3btxW9+80fs2vUY\nPv74QwCAXC7H73//P5wHeoB69oQQQsisWlqa8dxzLwIAli3LwOHDf8HIyAhee+1bYFkWAwP9AICQ\nkFB4eSkBAAqFAgCg0+ng7+8PAEhJSQUANDU1oKysBKdOfQqTyYTIyCgAQExMLG/nQMGeEEIImUVc\nXDwqKsoQGhqGiooyxMTEYt26jfjmN/9/6PU6HD9+dMa/9fDwQG9vDwICAlFXZxn2j4mJxfr1G7Fm\nzXpUV1eitbUVAMAw/A22U7AnhBBCZvHCCy/h5z9/A6dOHYdUKsUjj2yDStWL7373m9DpdHjppVcA\nAAzDTPnb1177Hn70o+/Dx8cHUqkMAPDii6/g//yfn+PDD9+HyWTCj370BtRqFa/nwLDWSQSBof3s\nCSGEcOHWresID49EXFw8PvjgTwgNDcNjjz3u7mZNMdt+9tSzJ4QQQmYRFBSCt9/+Z8hkcvj7++O5\n577h7iY5jHr2hBBCiADM1rOnpXeEEEKIwFGwJ4QQQgSOgj0hhBAicBTsCSGEEIGjYE8IIYS40ZYt\na/Dmmz+1/WwymfD44zvwwx++ztlr0NI7QgghZMxfD38AnU7L2fG8vJR48fmXZ32Oh4cCDQ11MBgM\nkMlkKCjIR0hIKGdtACjYE0IIITY6nRYrn3yOs+Pd/exvdj1v/fpNuHXrOh55ZBsuXvwSO3bsxr17\ndzlrh9uH8XNzc7Fnzx7s3r0b77777rTPyc/Px4EDB/D444/jG99YfMUMCCGEkJkwDIMdO3YhJ+dL\nGAwG1NXVIC0tndPXcGvP3mw2480338QHH3yAkJAQPPvss9i+fTsSExNtzxkcHMTPf/5zvP/++wgN\nDYVarXZjix9Ow8NDkMnkEIncfm9ICCGClJCQhM7ODly8+CU2bNgMruvdufXqXVJSgtjYWERGRkIq\nlWLfvn24dOnShOecOXMGu3btQmioZf4iICDAHU19aBmNRrz77m/x5Zdn3d0UQggRtM2bs/GHP/wG\nO3bs5vzYbg32XV1dCA8Pt/0cGhqK7u7uCc9pbGxEf38/vvGNb+CZZ57BqVOn5ruZD7WhIT1MJhMq\nK8vc3RRCCBEkay9+3779eOWV/4WEhMQ5/sJxCz5Bz2Qyoby8HB9++CH0ej0OHTqElStXIjY21t1N\nI4QQQlxm3Ro3ODgEzzxzkJfXcGuwDw0NRXt7u+3nrq4uhISETHmOv78/5HI55HI5Vq9ejcrKyjmD\nvb+/JyQSMS/tfphIJKO2f8+2yQIhhAiBr48Pik8f4fR4c107794tmvLYrl2PYteuRzlrh1uDfUZG\nBpqbm9HW1obg4GCcO3cOv/rVryY8Z/v27XjrrbdgMplgMBhQUlKCV155Zc5j9/Xp+Wr2Q6W//8F6\nU9pJkBAidIcOvsT5Mefr2rlg97MXi8V444038Oqrr4JlWTz77LNITEzEkSNHwDAMDh48iMTERGze\nvBn79++HSCTC17/+dSQlJbmz2Q8Vge6ATAghDxXaz57MSqPpw5///F8AgNdf/7GbW0MIIWQmtJ89\nIYQQ8hCjYE8IIYQIHAV7QgghROAo2BNCCCFu8tvf/grHjj1Y6ve9730X//7vb9t+/t3v/i+OHj3s\n8uss+KI6hBBCyHw58vH70Ol0nB3Py8sLh154dcbfZ2Qsx+XLF/G1rx0Cy7Lo79dgaOjB69+/X4LX\nXvuey+2gYE8IIYSM0el0+IdN2zg73m9ufDXr79PTl+Oddyz1ZRoa6pCQkAiVSgWtVgu5XI6mpkYk\nJy91uR0U7AkhhBA3CQoKgkQiQXd3F0pLS5Cenone3h7cv18CLy8vJCYmQSJxPVRTsCezstZsJoQQ\nwo+MjEyUlt7D/fslOHToxbHAfw9eXkpkZCzn5DUoQY8QQghxo/T05SgtLUF9vWUYf9myDNy/X4qy\nMktPnwsU7MmsqGdPCCH8Sk/PxM2b1+Dj4wOGYeDj4wOtdhD375ciI4OCPSGEELLoJSYmYWCgf0Iv\nPjExCd7e3vDx8eXkNWjOnsyKevaEkIeJl5fXnBn0jh5vLiKRCF98cWXCYz/5yT9z1gaAgj0hhBBi\nM9ua+MWMhvEJIYQQgaNgTwghhAgcBXtCCCFE4CjYE0IIIQJHwZ4QQggROAr2hBBCiMBRsCeEEEIE\njoI9IYQQInAU7AkhhBCBo2BPCCGECBwFe0IIIUTgKNgTQgghAkfBnhBCCBE4CvaEEEKIwFGwJ4QQ\nQgSOgj0hhBAicBTsCSGEEIGjYE8IIYQIHAV7MiuWZd3dBEIIIS6iYE8IIYQIHAV7Mivq2RNCyOJH\nwZ4QQggROAr2ZFbUsyeEkMWPgj0hhBAicBTsCSGEEIGjYE8IIYQIHAV7QgghROAo2BNCCCECR8Ge\nEEIIETgK9uShNjSkx/Hjf0N7e6u7m0IIIbyhYE8eaqWl99Dc3Igvvzzr7qYQQghvKNiTWTEM4+4m\n8MpoNAAA+vs1bm4JIYTwh4I9IQLW29uDs2dPYmhI7+6mEELciII9mZXQe/ZC99VXX6KmphJFRQXu\nbgohxI0o2BMC4d7UqNUqAMDw8JCbW0IIcScK9oQQQojAUbAnsxJqj/fhQbsWEkIo2BPyUKCbNkIe\nbhTsyaxoP/vFjd4+QgiwAIJ9bm4u9uzZg927d+Pdd9+d8XklJSVYtmwZLly4MI+tI3r9w7Fki25q\nCCFC5tZgbzab8eabb+K9997D2bNnce7cOdTV1U37vF/+8pfYvHmzG1pJCCGELG5uDfYlJSWIjY1F\nZGQkpFIp9u3bh0uXLk153kcffYTdu3cjICDADa18uNFULyGELH5uDfZdXV0IDw+3/RwaGoru7u4p\nz7l48SKef/75+W4eAQBQtCeEkMXO7XP2c/nXf/1X/OAHP7D9THOr84uyuAkhZPGTuPPFQ0ND0d7e\nbvu5q6sLISEhE55z//59vP7662BZFn19fcjNzYVEIsH27dtnPba/vyckEjEv7X6YjI5qbf8ODvZ2\nY0v44ekpA2C5qRHi+Vnv1RQKmSDPjxBiH7cG+4yMDDQ3N6OtrQ3BwcE4d+4cfvWrX014zvg5/B//\n+MfYunXrnIEeAPr65i+LvKioAGKxGMuXr5q315wv4/8/9vQMurEl/NDpRmz/FuL5WUfChoeNgjw/\nQsgDs93QuzXYi8VivPHGG3j11VfBsiyeffZZJCYm4siRI2AYBgcPHnRn8+x29epFABBksBc6azAU\n6nQFzXoRQgA3B3sAyM7
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x11bd81c18>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"men = (data.gender == 'M')\n",
|
||
|
|
"women = (data.gender == 'W')\n",
|
||
|
|
"\n",
|
||
|
|
"with sns.axes_style(style=None):\n",
|
||
|
|
" sns.violinplot(\"age_dec\", \"split_frac\", hue=\"gender\", data=data,\n",
|
||
|
|
" split=True, inner=\"quartile\",\n",
|
||
|
|
" palette=[\"lightblue\", \"lightpink\"]);"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Looking at this, we can see where the distributions of men and women differ: the split distributions of men in their 20s to 50s show a pronounced over-density toward lower splits when compared to women of the same age (or of any age, for that matter).\n",
|
||
|
|
"\n",
|
||
|
|
"Also surprisingly, the 80-year-old women seem to outperform *everyone* in terms of their split time. This is probably due to the fact that we're estimating the distribution from small numbers, as there are only a handful of runners in that range:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 38,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"7"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 38,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"(data.age > 80).sum()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Back to the men with negative splits: who are these runners? Does this split fraction correlate with finishing quickly? We can plot this very easily. We'll use ``regplot``, which will automatically fit a linear regression to the data:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 37,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
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"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x11e0dfba8>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
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"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"g = sns.lmplot('final_sec', 'split_frac', col='gender', data=data,\n",
|
||
|
|
" markers=\".\", scatter_kws=dict(color='c'))\n",
|
||
|
|
"g.map(plt.axhline, y=0.1, color=\"k\", ls=\":\");"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Apparently the people with fast splits are the elite runners who are finishing within ~15,000 seconds, or about 4 hours. People slower than that are much less likely to have a fast second split."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"<!--NAVIGATION-->\n",
|
||
|
|
"< [Geographic Data with Basemap](04.13-Geographic-Data-With-Basemap.ipynb) | [Contents](Index.ipynb) | [Further Resources](04.15-Further-Resources.ipynb) >"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
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"anaconda-cloud": {},
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
|
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
|
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"version": 3
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},
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"file_extension": ".py",
|
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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||
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|
"version": "3.4.3"
|
||
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}
|
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},
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"nbformat": 4,
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"nbformat_minor": 0
|
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}
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