Python可视化：Seaborn库热力图使用进阶

前言

在日常工作中，经常可以见到各种各种精美的热力图，热力图的应用非常广泛，下面一起来学习下Python的Seaborn库中热力图（heatmap）如何来进行使用。

本次运行的环境为：

windows 64位系统
python 3.5
jupyter notebook

1 构造数据

import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
% matplotlib inline

region = [\'Albania\', \'Algeria\', \'Angola\', \'Argentina\', \'Armenia\', \'Azerbaijan\',
       \'Bahamas\', \'Bangladesh\', \'Belize\', \'Bhutan\', \'Bolivia\',
       \'Bosnia and Herzegovina\', \'Brazil\', \'Burkina Faso\', \'Burundi\',
       \'Cambodia\', \'Cameroon\', \'Cape Verde\', \'Chile\', \'China\', \'Colombia\',
       \'Costa Rica\', \'Cote d Ivoire\', \'Cuba\', \'Cyprus\',
       "Democratic People\'s Republic of Korea",
       \'Democratic Republic of the Congo\', \'Dominican Republic\', \'Ecuador\',
       \'Egypt\', \'El Salvador\', \'Equatorial Guinea\', \'Ethiopia\', \'Fiji\',
       \'Gambia\', \'Georgia\', \'Ghana\', \'Guatemala\', \'Guyana\', \'Honduras\']

kind = [\'Afforestation & reforestation\', \'Biofuels\', \'Biogas\',
        \'Biomass\', \'Cement\', \'Energy efficiency\', \'Fuel switch\',
       \'HFC reduction/avoidance\', \'Hydro power\',
        \'Leak reduction\', \'Material use\', \'Methane avoidance\',             
       \'N2O decomposition\', \'Other renewable energies\',
       \'PFC reduction and substitution\',\'PV\',
       \'SF6 replacement\', \'Transportation\', \'Waste gas/heat utilization\',
      \'Wind power\']

print(len(region))
print(len(kind))

40
20

np.random.seed(100)
arr_region = np.random.choice(region, size=(10000,))
list_region = list(arr_region)

arr_kind = np.random.choice(kind, size=(10000,))
list_kind = list(arr_kind)

values = np.random.randint(50, 1000, 10000)
list_values = list(values)

df = pd.DataFrame({\'region\':list_region,
                  \'kind\': list_kind,
                  \'values\':list_values})
df.head()

pt = df.pivot_table(index=\'kind\', columns=\'region\', values=\'values\', aggfunc=np.sum)
pt.head()

f, ax = plt.subplots(figsize = (10, 4))
cmap = sns.cubehelix_palette(start = 1, rot = 3, gamma=0.8, as_cmap = True)
sns.heatmap(pt, cmap = cmap, linewidths = 0.05, ax = ax)
ax.set_title(\'Amounts per kind and region\')
ax.set_xlabel(\'region\')
ax.set_ylabel(\'kind\')

f.savefig(\'sns_heatmap_normal.jpg\', bbox_inches=\'tight\')
# ax.set_xticklabels(ax.get_xticklabels(), rotation=-90)

2 Seaborn的heatmap各个参数介绍

seaborn.heatmap

seaborn.heatmap(data, vmin=None, vmax=None, cmap=None, center=None, robust=False, annot=None, fmt=’.2g’, annot_kws=None, linewidths=0, linecolor=’white’, cbar=True, cbar_kws=None, cbar_ax=None, square=False, ax=None, xticklabels=True, yticklabels=True, mask=None, **kwargs)

data：矩阵数据集，可以使numpy的数组（array），如果是pandas的dataframe，则df的index/column信息会分别对应到heatmap的columns和rows
linewidths,热力图矩阵之间的间隔大小
vmax,vmin, 图例中最大值和最小值的显示值，没有该参数时默认不显示

2.1 cmap

cmap：matplotlib的colormap名称或颜色对象；如果没有提供，默认为cubehelix map (数据集为连续数据集时) 或 RdBu_r (数据集为离散数据集时)

f, (ax1,ax2) = plt.subplots(figsize = (10, 8),nrows=2)

# cubehelix map颜色
cmap = sns.cubehelix_palette(start = 1.5, rot = 3, gamma=0.8, as_cmap = True)
sns.heatmap(pt, linewidths = 0.05, ax = ax1, vmax=15000, vmin=0, cmap=cmap)
ax1.set_title(\'cubehelix map\')
ax1.set_xlabel(\'\')
ax1.set_xticklabels([]) #设置x轴图例为空值
ax1.set_ylabel(\'kind\')

# matplotlib colormap
sns.heatmap(pt, linewidths = 0.05, ax = ax2, vmax=15000, vmin=0, cmap=\'rainbow\') 
# rainbow为 matplotlib 的colormap名称
ax2.set_title(\'matplotlib colormap\')
ax2.set_xlabel(\'region\')
ax2.set_ylabel(\'kind\')

f.savefig(\'sns_heatmap_cmap.jpg\', bbox_inches=\'tight\')

2.2 center

center:将数据设置为图例中的均值数据，即图例中心的数据值；通过设置center值，可以调整生成的图像颜色的整体深浅；设置center数据时，如果有数据溢出，则手动设置的vmax、vmin会自动改变

f, (ax1,ax2) = plt.subplots(figsize = (10, 8),nrows=2)

cmap = sns.cubehelix_palette(start = 1.5, rot = 3, gamma=0.8, as_cmap = True)

sns.heatmap(pt, linewidths = 0.05, ax = ax1, vmax=15000, vmin=0, cmap=cmap, center=None )
# center为None时，由于最小值为0，最大值为15000，相当于center值为vamx和vmin的均值，即7500
ax1.set_title(\'center=None\')
ax1.set_xlabel(\'\')
ax1.set_xticklabels([]) #设置x轴图例为空值
ax1.set_ylabel(\'kind\')

sns.heatmap(pt, linewidths = 0.05, ax = ax2, vmax=15000, vmin=0, cmap=cmap, center=3000 ) 
# 由于均值为2000，当center设置为3000时，大部分数据会比7500大，所以center=3000时，生成的图片颜色要深
# 设置center数据时，如果有数据溢出，则手动设置的vmax或vmin会自动改变
ax2.set_title(\'center=3000\')
ax2.set_xlabel(\'region\')
ax2.set_ylabel(\'kind\')

f.savefig(\'sns_heatmap_center.jpg\', bbox_inches=\'tight\')

2.3 robust

f, (ax1,ax2) = plt.subplots(figsize = (10, 8),nrows=2)

cmap = sns.cubehelix_palette(start = 1.5, rot = 3, gamma=0.8, as_cmap = True)

sns.heatmap(pt, linewidths = 0.05, ax = ax1, cmap=cmap, center=None, robust=False )
# robust默认为False
ax1.set_title(\'robust=False\')
ax1.set_xlabel(\'\')
ax1.set_xticklabels([]) #设置x轴图例为空值
ax1.set_ylabel(\'kind\')

sns.heatmap(pt, linewidths = 0.05, ax = ax2, cmap=cmap, center=None, robust=True ) 
# If True and vmin or vmax are absent, the colormap range is computed with robust quantiles instead of the extreme values.
ax2.set_title(\'robust=True\')
ax2.set_xlabel(\'region\')
ax2.set_ylabel(\'kind\')

f.savefig(\'sns_heatmap_robust.jpg\', bbox_inches=\'tight\')

2.4 mask

f, (ax1,ax2) = plt.subplots(figsize = (10, 8),nrows=2)

cmap = sns.cubehelix_palette(start = 1.5, rot = 3, gamma=0.8, as_cmap = True)

p1 = sns.heatmap(pt, linewidths = 0.05,ax=ax1, vmax=15000, vmin=0, cmap=cmap, center=None, robust=False, mask=None )
# robust默认为False
ax1.set_title(\'mask=None\')
ax1.set_xlabel(\'\')
ax1.set_xticklabels([]) #设置x轴图例为空值
ax1.set_ylabel(\'kind\')

p2 = sns.heatmap(pt, linewidths = 0.05, ax=ax2, vmax=15000, vmin=0, cmap=cmap, center=None, robust=False, annot=False,mask=pt<10000 ) 
# mask: boolean array or DataFrame

ax2.set_title(\'mask: boolean DataFrame\')
ax2.set_xlabel(\'region\')
ax2.set_ylabel(\'kind\')

f.savefig(\'sns_heatmap_mask.jpg\', bbox_inches=\'tight\')

2.5 xticklabels, yticklabels

xticklabels: 如果是True，则绘制dataframe的列名。如果是False，则不绘制列名。如果是列表，则绘制列表中的内容作为xticklabels。如果是整数n，则绘制列名，但每个n绘制一个label。默认为True。
yticklabels: 如果是True，则绘制dataframe的行名。如果是False，则不绘制行名。如果是列表，则绘制列表中的内容作为yticklabels。如果是整数n，则绘制列名，但每个n绘制一个label。默认为True。默认为True。

f, (ax1,ax2) = plt.subplots(figsize = (10, 8),nrows=2)

cmap = sns.cubehelix_palette(start = 1.5, rot = 3, gamma=0.8, as_cmap = True)

p1 = sns.heatmap(pt, linewidths = 0.05,ax=ax1, vmax=15000, vmin=0, cmap=cmap, center=None, robust=False, mask=None, xticklabels=False )
# robust默认为False
ax1.set_title(\'xticklabels=None\')
ax1.set_xlabel(\'\')
# ax1.set_xticklabels([]) #设置x轴图例为空值
ax1.set_ylabel(\'kind\')

p2 = sns.heatmap(pt, linewidths = 0.05, ax=ax2, vmax=15000, vmin=0, cmap=cmap, center=None, robust=False, annot=False,mask=None,xticklabels=3, yticklabels=list(range(20)) ) 
# mask: boolean array or DataFrame

ax2.set_title(\'xticklabels=3, yticklabels is a list\')
ax2.set_xlabel(\'region\')
ax2.set_ylabel(\'kind\')

f.savefig(\'sns_heatmap_xyticklabels.jpg\', bbox_inches=\'tight\')

2.6 annot

annotate的缩写，annot默认为False，当annot为True时，在heatmap中每个方格写入数据
annot_kws，当annot为True时，可设置各个参数，包括大小，颜色，加粗，斜体字等

np.random.seed(0)
x = np.random.randn(10, 10)
f, (ax1, ax2) = plt.subplots(figsize=(8,8),nrows=2)

sns.heatmap(x, annot=True, ax=ax1)
sns.heatmap(x, annot=True, ax=ax2, annot_kws={\'size\':9,\'weight\':\'bold\', \'color\':\'blue\'})
# Keyword arguments for ax.text when annot is True.
# http://stackoverflow.com/questions/35024475/seaborn-heatmap-key-words

f.savefig(\'sns_heatmap_annot.jpg\')

**关于annot_kws的设置，还有很多值得研究的地方，ax.text有很多属性，有兴趣的可以去研究下；

ax.text可参考官方文档：http://matplotlib.org/api/text_api.html#matplotlib.text.Text

2.7 fmt

fmt，格式设置

np.random.seed(0)
x = np.random.randn(10, 10)
f, (ax1, ax2) = plt.subplots(figsize=(8,8),nrows=2)

sns.heatmap(x, annot=True, ax=ax1)
sns.heatmap(x, annot=True, fmt=\'.1f\', ax=ax2)

f.savefig(\'sns_heatmap_fmt.jpg\')

3 案例应用：突出显示某些数据

3.1 method 1：利用mask来实现

f,ax=plt.subplots(figsize=(10,5))

x = np.random.randn(10, 10)
sns.heatmap(x, annot=True, ax=ax)
sns.heatmap(x, mask=x < 1, cbar=False, ax=ax,
            annot=True, annot_kws={"weight": "bold"})

f.savefig(\'sns_heatmap_eg1.jpg\')

3.2 method 2：利用ax.texts来实现

f,ax=plt.subplots(figsize=(10,5))

flights = sns.load_dataset("flights")
flights = flights.pivot("month", "year", "passengers")
pic = sns.heatmap(flights, annot=True, fmt="d", ax=ax)

for text in pic.texts:
    text.set_size(8)
    if text.get_text() == \'118\':
        text.set_size(12)
        text.set_weight(\'bold\')
        text.set_style(\'italic\')

f.savefig(\'sns_heatmap_eg2.jpg\')

你可能会发现本文中seaborn的heatmap中还有些参数没有进行介绍，介于篇幅，这里就不在啰嗦了，建议各位小伙伴自己可以研究下其他参数如何使用。

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