使用pcolor在matplotlib中进行热图绘制? [英] Heatmap in matplotlib with pcolor?

问题描述

我想制作一个这样的热图(显示在

I'd like to make a heatmap like this (shown on FlowingData):

源数据位于此处，但是可以使用随机数据和标签，例如

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)

在matplotlib中制作热图很容易:

Making the heatmap is easy enough in matplotlib:

from matplotlib import pyplot as plt
heatmap = plt.pcolor(data)

我什至发现了一个看起来正确的 colormap 参数:heatmap = plt.pcolor(data, cmap=matplotlib.cm.Blues)

And I even found a colormap arguments that look about right: heatmap = plt.pcolor(data, cmap=matplotlib.cm.Blues)

但是，除此之外，我不知道如何显示列和行的标签以及如何以正确的方向显示数据(起源在左上角而不是左下角).

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).

尝试操作heatmap.axes(例如heatmap.axes.set_xticklabels = column_labels)均失败.我在这里想念什么?

Attempts to manipulate heatmap.axes (e.g. heatmap.axes.set_xticklabels = column_labels) have all failed. What am I missing here?

推荐答案

这很晚了，但这是我对flowingdata NBA热图的python实现.

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

更新时间:2014年1月4日:谢谢大家

# -*- 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

输出看起来像这样:

The output looks like this:

在此处有一个带有所有这些代码的ipython笔记本.我从溢出"中学到了很多东西，所以希望有人会发现它有用.

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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