Heatmap in matplotlib with pcolor

Question

I d like to make a heatmap like this  shown on FlowingData     The source data is here  but random data and labels would be fine to use  i e   import numpy column labels   list  ABCD   row labels   list  WXYZ   data   numpy random rand 4 4    Making the heatmap is easy enough in matplotlib   from matplotlib import pyplot as plt heatmap   plt pcolor data    And I even found a colormap arguments that look about right  heatmap   plt pcolor data  cmap matplotlib cm Blues   But beyond that  I can t figure out how to display labels for the columns and rows and display the data in the proper orientation  origin at the top left instead of bottom left    Attempts to manipulate heatmap axes  e g  heatmap axes set xticklabels   column labels  have all failed  What am I missing here

User · Answer

The python seaborn module is based on matplotlib, and produces a very nice heatmap.

Below is an implementation with seaborn, designed for the ipython/jupyter notebook.

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
# import the data directly into a pandas dataframe
nba = pd.read_csv("http://datasets.flowingdata.com/ppg2008.csv", index_col='Name  ')
# remove index title
nba.index.name = ""
# normalize data columns
nba_norm = (nba - nba.mean()) / (nba.max() - nba.min())
# relabel columns
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']
nba_norm.columns = labels
# set appropriate font and dpi
sns.set(font_scale=1.2)
sns.set_style({"savefig.dpi": 100})
# plot it out
ax = sns.heatmap(nba_norm, cmap=plt.cm.Blues, linewidths=.1)
# set the x-axis labels on the top
ax.xaxis.tick_top()
# rotate the x-axis labels
plt.xticks(rotation=90)
# get figure (usually obtained via "fig,ax=plt.subplots()" with matplotlib)
fig = ax.get_figure()
# specify dimensions and save
fig.set_size_inches(15, 20)
fig.savefig("nba.png")

The output looks like this: seaborn nba heatmap I used the matplotlib Blues color map, but personally find the default colors quite beautiful. I used matplotlib to rotate the x-axis labels, as I couldn't find the seaborn syntax. As noted by grexor, it was necessary to specify the dimensions (fig.set_size_inches) by trial and error, which I found a bit frustrating.

As noted by Paul H, you can easily add the values to heat maps (annot=True), but in this case I didn't think it improved the figure. Several code snippets were taken from the excellent answer by joelotz.

User · Answer

This is late  but here is my python implementation of the flowingdata NBA heatmap   updated 1 4 2014  thanks everyone    - - 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   The output looks like this    There s an ipython notebook with all this code here  I ve learned a lot from  overflow so hopefully someone will find this useful

User · Answer

Main issue is that you first need to set the location of your x and y ticks  Also  it helps to use the more object-oriented interface to matplotlib  Namely  interact with the axes object directly   import matplotlib pyplot as plt import numpy as np column labels   list  ABCD   row labels   list  WXYZ   data   np random rand 4 4  fig  ax   plt subplots   heatmap   ax pcolor data     put the major ticks at the middle of each cell  notice  reverse  use of dimension ax set yticks np arange data shape 0   0 5  minor False  ax set xticks np arange data shape 1   0 5  minor False    ax set xticklabels row labels  minor False  ax set yticklabels column labels  minor False  plt show     Hope that helps

User · Answer

Someone edited this question to remove the code I used  so I was forced to add it as an answer  Thanks to all who participated in answering this question  I think most of the other answers are better than this code  I m just leaving this here for reference purposes   With thanks to Paul H  and unutbu  who answered this question   I have some pretty nice-looking output   import matplotlib pyplot as plt import numpy as np column labels   list  ABCD   row labels   list  WXYZ   data   np random rand 4 4  fig  ax   plt subplots   heatmap   ax pcolor data  cmap plt cm Blues     put the major ticks at the middle of each cell ax set xticks np arange data shape 0   0 5  minor False  ax set yticks np arange data shape 1   0 5  minor False     want a more natural  table-like display ax invert yaxis   ax xaxis tick top    ax set xticklabels row labels  minor False  ax set yticklabels column labels  minor False  plt show     And here s the output

[python] Heatmap in matplotlib with pcolor?

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