我想制作这样的热图（显示在FlowingData上）：
源数据在这里，但是可以使用随机数据和标签，即
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：
from matplotlib import pyplot as plt
heatmap = plt.pcolor(data)
我什至发现了一个看起来正确的颜色图参数：heatmap = plt.pcolor(data, cmap=matplotlib.cm.Blues)
但除此之外，我无法弄清楚如何显示列和行的标签并以正确的方向显示数据（原点位于左上角而不是左下角）。
试图操纵heatmap.axes（例如heatmap.axes.set_xticklabels = column_labels）都失败了。我在这里想念什么？
这已经很晚了，但这是我对流动数据 NBA 热图的 python 实现。
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
输出如下所示：
这里有一个包含所有这些代码的 ipython 笔记本。我从“溢出”中学到了很多东西，所以希望有人会觉得这很有用。
python seaborn 模块基于 matplotlib，生成了一个非常漂亮的热图。
下面是为 ipython/jupyter notebook 设计的 seaborn 实现。
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")
输出如下：
我使用了 matplotlib Blues 颜色图，但个人觉得默认颜色相当漂亮。我使用 matplotlib 来旋转 x 轴标签，因为我找不到 seaborn 语法。正如 grexor 所指出的，有必要通过反复试验来指定尺寸（fig.set_size_inches），我觉得这有点令人沮丧。
正如 Paul H 所指出的，您可以轻松地将这些值添加到热图（annot=True），但在这种情况下，我认为它并没有改善这个数字。从 joelotz 的优秀答案中提取了几个代码片段。
主要问题是您首先需要设置 x 和 y 刻度的位置。此外，它有助于使用更面向对象的接口来连接 matplotlib。即，axes直接与对象交互。
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()
希望有帮助。
有人编辑了这个问题以删除我使用的代码，所以我被迫将其添加为答案。感谢所有参与回答这个问题的人！我认为大多数其他答案都比这段代码更好，我只是将其留在这里以供参考。
感谢Paul H和unutbu（谁回答了这个问题），我得到了一些非常漂亮的输出：
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()
这是输出：
