用 pcolor 在 matplotlib 中绘制热图？

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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.

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最佳答案

This is late, but here is my python implementation of the flowingdata NBA heatmap.

updated:1/4/2014: thanks everyone

# -*- coding: utf-8 -*-
# <nbformat>3.0</nbformat>


# ------------------------------------------------------------------------
# Filename   : heatmap.py
# Date       : 2013-04-19
# Updated    : 2014-01-04
# Author     : @LotzJoe >> 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) ->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: flowingdata-like nba heatmap

There's an ipython notebook with all this code here. I've learned a lot from 'overflow so hopefully someone will find this useful.

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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.

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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(column_labels, minor=False)
ax.set_yticklabels(row_labels, minor=False)
plt.show()

And here's the output: