mirror of
https://github.com/donnemartin/data-science-ipython-notebooks.git
synced 2024-03-22 13:30:56 +08:00
394 lines
122 KiB
Python
394 lines
122 KiB
Python
|
{
|
|||
|
|
"cells": [
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"<!--BOOK_INFORMATION-->\n",
|
|||
|
|
"<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
|
|||
|
|
"*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",
|
|||
|
|
"\n",
|
|||
|
|
"*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",
|
|||
|
|
"\n",
|
|||
|
|
"*No changes were made to the contents of this notebook from the original.*"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"<!--NAVIGATION-->\n",
|
|||
|
|
"< [Density and Contour Plots](04.04-Density-and-Contour-Plots.ipynb) | [Contents](Index.ipynb) | [Customizing Plot Legends](04.06-Customizing-Legends.ipynb) >"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"# Histograms, Binnings, and Density"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"A simple histogram can be a great first step in understanding a dataset.\n",
|
|||
|
|
"Earlier, we saw a preview of Matplotlib's histogram function (see [Comparisons, Masks, and Boolean Logic](02.06-Boolean-Arrays-and-Masks.ipynb)), which creates a basic histogram in one line, once the normal boiler-plate imports are done:"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 1,
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": true
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"%matplotlib inline\n",
|
|||
|
|
"import numpy as np\n",
|
|||
|
|
"import matplotlib.pyplot as plt\n",
|
|||
|
|
"plt.style.use('seaborn-white')\n",
|
|||
|
|
"\n",
|
|||
|
|
"data = np.random.randn(1000)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 2,
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD/CAYAAADhYy38AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n5Z133tGTTz4pSWptbVU
|
|||
|
|
"text/plain": [
|
|||
|
|
"<matplotlib.figure.Figure at 0x1023e5a58>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"plt.hist(data);"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"The ``hist()`` function has many options to tune both the calculation and the display; \n",
|
|||
|
|
"here's an example of a more customized histogram:"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 3,
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD/CAYAAAAKVJb/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nggh3ALAgwh0ALIhwBwA
|
|||
|
|
"text/plain": [
|
|||
|
|
"<matplotlib.figure.Figure at 0x10bfc0588>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"plt.hist(data, bins=30, normed=True, alpha=0.5,\n",
|
|||
|
|
" histtype='stepfilled', color='steelblue',\n",
|
|||
|
|
" edgecolor='none');"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"The ``plt.hist`` docstring has more information on other customization options available.\n",
|
|||
|
|
"I find this combination of ``histtype='stepfilled'`` along with some transparency ``alpha`` to be very useful when comparing histograms of several distributions:"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 4,
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD/CAYAAADhYy38AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSDeMRiP8Pj8ev/txLF+
|
|||
|
|
"text/plain": [
|
|||
|
|
"<matplotlib.figure.Figure at 0x10e666358>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"x1 = np.random.normal(0, 0.8, 1000)\n",
|
|||
|
|
"x2 = np.random.normal(-2, 1, 1000)\n",
|
|||
|
|
"x3 = np.random.normal(3, 2, 1000)\n",
|
|||
|
|
"\n",
|
|||
|
|
"kwargs = dict(histtype='stepfilled', alpha=0.3, normed=True, bins=40)\n",
|
|||
|
|
"\n",
|
|||
|
|
"plt.hist(x1, **kwargs)\n",
|
|||
|
|
"plt.hist(x2, **kwargs)\n",
|
|||
|
|
"plt.hist(x3, **kwargs);"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"If you would like to simply compute the histogram (that is, count the number of points in a given bin) and not display it, the ``np.histogram()`` function is available:"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 5,
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"[ 12 190 468 301 29]\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"counts, bin_edges = np.histogram(data, bins=5)\n",
|
|||
|
|
"print(counts)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"## Two-Dimensional Histograms and Binnings\n",
|
|||
|
|
"\n",
|
|||
|
|
"Just as we create histograms in one dimension by dividing the number-line into bins, we can also create histograms in two-dimensions by dividing points among two-dimensional bins.\n",
|
|||
|
|
"We'll take a brief look at several ways to do this here.\n",
|
|||
|
|
"We'll start by defining some data—an ``x`` and ``y`` array drawn from a multivariate Gaussian distribution:"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 6,
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"mean = [0, 0]\n",
|
|||
|
|
"cov = [[1, 1], [1, 2]]\n",
|
|||
|
|
"x, y = np.random.multivariate_normal(mean, cov, 10000).T"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"### ``plt.hist2d``: Two-dimensional histogram\n",
|
|||
|
|
"\n",
|
|||
|
|
"One straightforward way to plot a two-dimensional histogram is to use Matplotlib's ``plt.hist2d`` function:"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 12,
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAWgAAAD6CAYAAACMPN0pAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nM32ZHRGREXEGTURkUJx
|
|||
|
|
"text/plain": [
|
|||
|
|
"<matplotlib.figure.Figure at 0x1106647f0>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"plt.hist2d(x, y, bins=30, cmap='Blues')\n",
|
|||
|
|
"cb = plt.colorbar()\n",
|
|||
|
|
"cb.set_label('counts in bin')"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"Just as with ``plt.hist``, ``plt.hist2d`` has a number of extra options to fine-tune the plot and the binning, which are nicely outlined in the function docstring.\n",
|
|||
|
|
"Further, just as ``plt.hist`` has a counterpart in ``np.histogram``, ``plt.hist2d`` has a counterpart in ``np.histogram2d``, which can be used as follows:"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 8,
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"counts, xedges, yedges = np.histogram2d(x, y, bins=30)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"For the generalization of this histogram binning in dimensions higher than two, see the ``np.histogramdd`` function."
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"### ``plt.hexbin``: Hexagonal binnings\n",
|
|||
|
|
"\n",
|
|||
|
|
"The two-dimensional histogram creates a tesselation of squares across the axes.\n",
|
|||
|
|
"Another natural shape for such a tesselation is the regular hexagon.\n",
|
|||
|
|
"For this purpose, Matplotlib provides the ``plt.hexbin`` routine, which will represents a two-dimensional dataset binned within a grid of hexagons:"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 9,
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAWgAAAD6CAYAAACMPN0pAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nPnieh6uvvhrnnHMOzjj
|
|||
|
|
"text/plain": [
|
|||
|
|
"<matplotlib.figure.Figure at 0x10e666550>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"plt.hexbin(x, y, gridsize=30, cmap='Blues')\n",
|
|||
|
|
"cb = plt.colorbar(label='count in bin')"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"``plt.hexbin`` has a number of interesting options, including the ability to specify weights for each point, and to change the output in each bin to any NumPy aggregate (mean of weights, standard deviation of weights, etc.)."
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"### Kernel density estimation\n",
|
|||
|
|
"\n",
|
|||
|
|
"Another common method of evaluating densities in multiple dimensions is *kernel density estimation* (KDE).\n",
|
|||
|
|
"This will be discussed more fully in [In-Depth: Kernel Density Estimation](05.13-Kernel-Density-Estimation.ipynb), but for now we'll simply mention that KDE can be thought of as a way to \"smear out\" the points in space and add up the result to obtain a smooth function.\n",
|
|||
|
|
"One extremely quick and simple KDE implementation exists in the ``scipy.stats`` package.\n",
|
|||
|
|
"Here is a quick example of using the KDE on this data:"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 10,
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD/CAYAAADoiI2GAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nz0yhZQObRO2PQZzus9V
|
|||
|
|
"text/plain": [
|
|||
|
|
"<matplotlib.figure.Figure at 0x10e36a198>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"from scipy.stats import gaussian_kde\n",
|
|||
|
|
"\n",
|
|||
|
|
"# fit an array of size [Ndim, Nsamples]\n",
|
|||
|
|
"data = np.vstack([x, y])\n",
|
|||
|
|
"kde = gaussian_kde(data)\n",
|
|||
|
|
"\n",
|
|||
|
|
"# evaluate on a regular grid\n",
|
|||
|
|
"xgrid = np.linspace(-3.5, 3.5, 40)\n",
|
|||
|
|
"ygrid = np.linspace(-6, 6, 40)\n",
|
|||
|
|
"Xgrid, Ygrid = np.meshgrid(xgrid, ygrid)\n",
|
|||
|
|
"Z = kde.evaluate(np.vstack([Xgrid.ravel(), Ygrid.ravel()]))\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Plot the result as an image\n",
|
|||
|
|
"plt.imshow(Z.reshape(Xgrid.shape),\n",
|
|||
|
|
" origin='lower', aspect='auto',\n",
|
|||
|
|
" extent=[-3.5, 3.5, -6, 6],\n",
|
|||
|
|
" cmap='Blues')\n",
|
|||
|
|
"cb = plt.colorbar()\n",
|
|||
|
|
"cb.set_label(\"density\")"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"KDE has a smoothing length that effectively slides the knob between detail and smoothness (one example of the ubiquitous bias–variance trade-off).\n",
|
|||
|
|
"The literature on choosing an appropriate smoothing length is vast: ``gaussian_kde`` uses a rule-of-thumb to attempt to find a nearly optimal smoothing length for the input data.\n",
|
|||
|
|
"\n",
|
|||
|
|
"Other KDE implementations are available within the SciPy ecosystem, each with its own strengths and weaknesses; see, for example, ``sklearn.neighbors.KernelDensity`` and ``statsmodels.nonparametric.kernel_density.KDEMultivariate``.\n",
|
|||
|
|
"For visualizations based on KDE, using Matplotlib tends to be overly verbose.\n",
|
|||
|
|
"The Seaborn library, discussed in [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb), provides a much more terse API for creating KDE-based visualizations."
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"<!--NAVIGATION-->\n",
|
|||
|
|
"< [Density and Contour Plots](04.04-Density-and-Contour-Plots.ipynb) | [Contents](Index.ipynb) | [Customizing Plot Legends](04.06-Customizing-Legends.ipynb) >"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"anaconda-cloud": {},
|
|||
|
|
"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": "3.4.3"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"nbformat": 4,
|
|||
|
|
"nbformat_minor": 0
|
|||
|
|
}
|