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    <p>This is late, but here is my python implementation of the flowingdata NBA heatmap.</p> <p><strong>updated:1/4/2014</strong>: thanks everyone</p> <pre><code># -*- coding: utf-8 -*- # &lt;nbformat&gt;3.0&lt;/nbformat&gt; # ------------------------------------------------------------------------ # Filename : heatmap.py # Date : 2013-04-19 # Updated : 2014-01-04 # Author : @LotzJoe &gt;&gt; Joe Lotz # Description: My attempt at reproducing the FlowingData graphic in Python # Source : http://flowingdata.com/2010/01/21/how-to-make-a-heatmap-a-quick-and-easy-solution/ # # Other Links: # http://stackoverflow.com/questions/14391959/heatmap-in-matplotlib-with-pcolor # # ------------------------------------------------------------------------ import matplotlib.pyplot as plt import pandas as pd from urllib2 import urlopen import numpy as np %pylab inline page = urlopen("http://datasets.flowingdata.com/ppg2008.csv") nba = pd.read_csv(page, index_col=0) # Normalize data columns nba_norm = (nba - nba.mean()) / (nba.max() - nba.min()) # Sort data according to Points, lowest to highest # This was just a design choice made by Yau # inplace=False (default) -&gt;thanks SO user d1337 nba_sort = nba_norm.sort('PTS', ascending=True) nba_sort['PTS'].head(10) # Plot it out fig, ax = plt.subplots() heatmap = ax.pcolor(nba_sort, cmap=plt.cm.Blues, alpha=0.8) # Format fig = plt.gcf() fig.set_size_inches(8, 11) # turn off the frame ax.set_frame_on(False) # put the major ticks at the middle of each cell ax.set_yticks(np.arange(nba_sort.shape[0]) + 0.5, minor=False) ax.set_xticks(np.arange(nba_sort.shape[1]) + 0.5, minor=False) # want a more natural, table-like display ax.invert_yaxis() ax.xaxis.tick_top() # Set the labels # label source:https://en.wikipedia.org/wiki/Basketball_statistics labels = [ 'Games', 'Minutes', 'Points', 'Field goals made', 'Field goal attempts', 'Field goal percentage', 'Free throws made', 'Free throws attempts', 'Free throws percentage', 'Three-pointers made', 'Three-point attempt', 'Three-point percentage', 'Offensive rebounds', 'Defensive rebounds', 'Total rebounds', 'Assists', 'Steals', 'Blocks', 'Turnover', 'Personal foul'] # note I could have used nba_sort.columns but made "labels" instead ax.set_xticklabels(labels, minor=False) ax.set_yticklabels(nba_sort.index, minor=False) # rotate the plt.xticks(rotation=90) ax.grid(False) # Turn off all the ticks ax = plt.gca() for t in ax.xaxis.get_major_ticks(): t.tick1On = False t.tick2On = False for t in ax.yaxis.get_major_ticks(): t.tick1On = False t.tick2On = False </code></pre> <p>The output looks like this: <img src="https://i.stack.imgur.com/7B4Dk.png" alt="flowingdata-like nba heatmap"></p> <p>There's an ipython notebook with all this code <a href="http://nbviewer.ipython.org/5427209">here</a>. I've learned a lot from 'overflow so hopefully someone will find this useful.</p>
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