带 pcolor 的 matplotlib 中的热图? [英] Heatmap in matplotlib with pcolor?

问题描述

我想制作这样的热图(显示在 ，但随机数据和标签也可以使用，即

导入numpycolumn_labels = list('ABCD')row_labels = list('WXYZ')数据 = numpy.random.rand(4,4)

在 matplotlib 中制作热图很容易:

from matplotlib import pyplot as plt热图 = plt.pcolor(数据)

我什至发现了一个 .我从溢出"中学到了很多东西，所以希望有人会发现这很有用.

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?

解决方案

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:

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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