带pcolor的matplotlib中的热图？

Question

我想做一个这样的热图（如FlowingData所示）：

源数据是datasets.flowingdata.com/ppg2008.csv" rel="noreferrer">here，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中制作热图非常简单：

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

我甚至发现了一个colormap参数，看起来很对：heatmap = plt.pcolor(data, cmap=matplotlib.cm.Blues)

但除此之外，我还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（原点在左上角而不是左下角）。

试图操作heatmap.axes（例如heatmap.axes.set_xticklabels = column_labels）都失败了。我错过了什么？

Answer 1

主要问题是，首先需要设置x和y记号的位置。另外，使用matplotlib更面向对象的接口也很有帮助。即，直接与axes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()

希望能有所帮助。

Answer 2

现在已经晚了，但这里是我的flowingdata NBA热图的python实现。

更新时间：2014年1月4日

# -*- 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：

有一个ipython笔记本上面有这么多代码here。我从“溢出”中学到了很多，所以希望有人会觉得这很有用。

Answer 3

python seaborn模块基于matplotlib，生成了一个非常好的热图。

下面是seaborn的一个实现，它是为ipython/jupyter笔记本设计的。

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蓝色地图，但是我个人觉得默认的颜色非常漂亮。我使用matplotlib旋转x轴标签，因为找不到seaborn语法。正如grexor所指出的，有必要通过反复试验来指定尺寸（如图所示），我发现这有点令人沮丧。

正如Paul H所指出的，可以很容易地将这些值添加到热图中（annot=True），但在本例中，我认为这并没有改善这个数字。一些代码片段是从joelotz的优秀答案中提取的。