python绘制相关系数热力图
本文讲述如何利用python绘制如上的相关系数热力图
; 一.数据说明和需要安装的库
数据是31个省市有关教育的12个指标，如下所示。， 在文章最后自取：
需要安装如下库：
pip install pandas
pip install matplotlib
pip install seaborn
我感觉在下面这个python package安装比较好
二.准备绘图
首先导入相关库
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
读取数据
data = pd.read_csv('D:\P\数据分析\相关系数热力图\教育指标.csv')
df = pd.DataFrame(data)
计算出相关系数并输出， 这里选择的是皮尔逊相关系数，当然你也可以选择其他相关系数，有关其他相关系数可以参考这篇文章
cor = data.corr(method='pearson')
print(cor)
因为我这里有 中文 ， 所以需要进行下面的设置 。我这里设置为黑体，当然你也可以选择其他字体
rc = {'font.sans-serif': 'SimHei',
'axes.unicode_minus': False}
sns.set(font_scale=0.7,rc=rc)
好了，开始绘图啦：
sns.heatmap(cor,
annot=True,
center=0.5,
fmt='.2f',
linewidth=0.5,
linecolor='blue',
vmin=0, vmax=1,
xticklabels=True, yticklabels=True,
square=True,
cbar=True,
cmap='coolwarm_r',
)
plt.savefig("我是废强热力图.png",dpi=600)
plt.ion()
三.设置配色，画出多幅图
由于这里的配色是在是有太多太多,所以不打算一个个手动更换，因此我们可以使用循环语句
cmap='coolwarm_r'
colors=”Accent, Accent_r, Blues, Blues_r, BrBG, BrBG_r, BuGn, BuGn_r, BuPu, BuPu_r, CMRmap, CMRmap_r, Dark2, Dark2_r, GnBu, GnBu_r, Greens, Greens_r, Greys, Greys_r, OrRd, OrRd_r, Oranges, Oranges_r, PRGn, PRGn_r, Paired, Paired_r, Pastel1, Pastel1_r, Pastel2, Pastel2_r, PiYG, PiYG_r, PuBu, PuBuGn, PuBuGn_r, PuBu_r, PuOr, PuOr_r, PuRd, PuRd_r, Purples, Purples_r, RdBu, RdBu_r, RdGy, RdGy_r, RdPu, RdPu_r,RdYlBu, RdYlBu_r, RdYlGn, RdYlGn_r, Reds, Reds_r, Set1, Set1_r, Set2, Set2_r, Set3, Set3_r, Spectral, Spectral_r, Wistia, Wistia_r, YlGn, YlGnBu, YlGnBu_r, YlGn_r, YlOrBr, YlOrBr_r, YlOrRd, YlOrRd_r, afmhot, afmhot_r, autumn, autumn_r, binary, binary_r, bone, bone_r, brg, brg_r, bwr, bwr_r, cividis, cividis_r, cool, cool_r, coolwarm, coolwarm_r, copper, copper_r, cubehelix, cubehelix_r, flag, flag_r, gist_earth, gist_earth_r, gist_gray, gist_gray_r, gist_heat, gist_heat_r, gist_ncar, gist_ncar_r, gist_rainbow, gist_rainbow_r, gist_stern, gist_stern_r,gist_yarg, gist_yarg_r, gnuplot, gnuplot2, gnuplot2_r, gnuplot_r, gray, gray_r, hot, hot_r,hsv, hsv_r, icefire,icefire_r, inferno,inferno_r, jet, jet_r, magma, magma_r, mako, mako_r, nipy_spectral, nipy_spectral_r, ocean, ocean_r, pink, pink_r, plasma, plasma_r, prism, prism_r, rainbow, rainbow_r, rocket, rocket_r, seismic, seismic_r, spring, spring_r, summer, summer_r, tab10, tab10_r, tab20, tab20_r, tab20b, tab20b_r, tab20c, tab20c_r, terrain, terrain_r, twilight, twilight_r, twilight_shifted, twilight_shifted_r, viridis, viridis_r, vlag, vlag_r, winter, winter_r”
代码循环画图：
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
data = pd.read_csv('D:\P\数据分析\相关系数热力图\教育指标.csv')
df = pd.DataFrame(data)
cor = data.corr(method='pearson')
print(cor)
rc = {'font.sans-serif': 'SimHei',
'axes.unicode_minus': False}
sns.set(font_scale=0.7,rc=rc)
colors="Accent, Accent_r, Blues, Blues_r, BrBG, BrBG_r, BuGn, BuGn_r, BuPu, BuPu_r, CMRmap, CMRmap_r, Dark2, Dark2_r, GnBu, GnBu_r, Greens, Greens_r, Greys, Greys_r, OrRd, OrRd_r, Oranges, Oranges_r, PRGn, PRGn_r, Paired, Paired_r, Pastel1, Pastel1_r, Pastel2, Pastel2_r, PiYG, PiYG_r, PuBu, PuBuGn, PuBuGn_r, PuBu_r, PuOr, PuOr_r, PuRd, PuRd_r, Purples, Purples_r, RdBu, RdBu_r, RdGy, RdGy_r, RdPu, RdPu_r,RdYlBu, RdYlBu_r, RdYlGn, RdYlGn_r, Reds, Reds_r, Set1, Set1_r, Set2, Set2_r, Set3, Set3_r, Spectral, Spectral_r, Wistia, Wistia_r, YlGn, YlGnBu, YlGnBu_r, YlGn_r, YlOrBr, YlOrBr_r, YlOrRd, YlOrRd_r, afmhot, afmhot_r, autumn, autumn_r, binary, binary_r, bone, bone_r, brg, brg_r, bwr, bwr_r, cividis, cividis_r, cool, cool_r, coolwarm, coolwarm_r, copper, copper_r, cubehelix, cubehelix_r, flag, flag_r, gist_earth, gist_earth_r, gist_gray, gist_gray_r, gist_heat, gist_heat_r, gist_ncar, gist_ncar_r, gist_rainbow, gist_rainbow_r, gist_stern, gist_stern_r,gist_yarg, gist_yarg_r, gnuplot, gnuplot2, gnuplot2_r, gnuplot_r, gray, gray_r, hot, hot_r,hsv, hsv_r, icefire,icefire_r, inferno,inferno_r, jet, jet_r, magma, magma_r, mako, mako_r, nipy_spectral, nipy_spectral_r, ocean, ocean_r, pink, pink_r, plasma, plasma_r, prism, prism_r, rainbow, rainbow_r, rocket, rocket_r, seismic, seismic_r, spring, spring_r, summer, summer_r, tab10, tab10_r, tab20, tab20_r, tab20b, tab20b_r, tab20c, tab20c_r, terrain, terrain_r, twilight, twilight_r, twilight_shifted, twilight_shifted_r, viridis, viridis_r, vlag, vlag_r, winter, winter_r"
color=colors.split(',')
for i in color:
i=i.strip()
print(i)
sns.heatmap(cor,
annot=True,
center=0.5,
fmt='.2f',
linewidth=0.5,
linecolor='blue',
vmin=0, vmax=1,
xticklabels=True, yticklabels=True,
square=True,
cbar=True,
cmap=f'{i}',
)
plt.savefig('图片\\'+f"我是废强热力图颜色{i}.png", dpi=600)
plt.ion()
plt.pause(0.5)
plt.close()
全部代码：
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
data = pd.read_csv('D:\P\数据分析\相关系数热力图\教育指标.csv')
df = pd.DataFrame(data)
cor = data.corr(method='pearson')
print(cor)
rc = {'font.sans-serif': 'SimHei',
'axes.unicode_minus': False}
sns.set(font_scale=0.7, rc=rc)
sns.heatmap(cor,
annot=True,
center=0.5,
fmt='.2f',
linewidth=0.5,
linecolor='blue',
vmin=0, vmax=1,
xticklabels=True, yticklabels=True,
square=True,
cbar=True,
cmap='coolwarm_r',
)
plt.savefig("我是废强热力图.png", dpi=600)
plt.ion()
plt.close('all')
colors = "Accent, Accent_r, Blues, Blues_r, BrBG, BrBG_r, BuGn, BuGn_r, BuPu, BuPu_r, CMRmap, CMRmap_r, Dark2, Dark2_r, GnBu, GnBu_r, Greens, Greens_r, Greys, Greys_r, OrRd, OrRd_r, Oranges, Oranges_r, PRGn, PRGn_r, Paired, Paired_r, Pastel1, Pastel1_r, Pastel2, Pastel2_r, PiYG, PiYG_r, PuBu, PuBuGn, PuBuGn_r, PuBu_r, PuOr, PuOr_r, PuRd, PuRd_r, Purples, Purples_r, RdBu, RdBu_r, RdGy, RdGy_r, RdPu, RdPu_r,RdYlBu, RdYlBu_r, RdYlGn, RdYlGn_r, Reds, Reds_r, Set1, Set1_r, Set2, Set2_r, Set3, Set3_r, Spectral, Spectral_r, Wistia, Wistia_r, YlGn, YlGnBu, YlGnBu_r, YlGn_r, YlOrBr, YlOrBr_r, YlOrRd, YlOrRd_r, afmhot, afmhot_r, autumn, autumn_r, binary, binary_r, bone, bone_r, brg, brg_r, bwr, bwr_r, cividis, cividis_r, cool, cool_r, coolwarm, coolwarm_r, copper, copper_r, cubehelix, cubehelix_r, flag, flag_r, gist_earth, gist_earth_r, gist_gray, gist_gray_r, gist_heat, gist_heat_r, gist_ncar, gist_ncar_r, gist_rainbow, gist_rainbow_r, gist_stern, gist_stern_r,gist_yarg, gist_yarg_r, gnuplot, gnuplot2, gnuplot2_r, gnuplot_r, gray, gray_r, hot, hot_r,hsv, hsv_r, icefire,icefire_r, inferno,inferno_r, jet, jet_r, magma, magma_r, mako, mako_r, nipy_spectral, nipy_spectral_r, ocean, ocean_r, pink, pink_r, plasma, plasma_r, prism, prism_r, rainbow, rainbow_r, rocket, rocket_r, seismic, seismic_r, spring, spring_r, summer, summer_r, tab10, tab10_r, tab20, tab20_r, tab20b, tab20b_r, tab20c, tab20c_r, terrain, terrain_r, twilight, twilight_r, twilight_shifted, twilight_shifted_r, viridis, viridis_r, vlag, vlag_r, winter, winter_r"
color = colors.split(',')
for i in color:
i = i.strip()
print(i)
sns.heatmap(cor,
annot=True,
center=0.5,
fmt='.2f',
linewidth=0.5,
linecolor='blue',
vmin=0, vmax=1,
xticklabels=True, yticklabels=True,
square=True,
cbar=True,
cmap=f'{i}',
)
plt.savefig('图片\\' + f"我是废强热力图颜色{i}.png", dpi=600)
plt.ion()
plt.pause(0.5)
plt.close()
最后：数据链接：，直接点击链接，或复制网址，有提取码
链接：https://pan.baidu.com/s/1qcfw5TUh0c4C6igoipmdGA?pwd=5fii
提取码：5fii
参考链接
https://mp.weixin.qq.com/s/shQOmqR0JXkp_pGCfLuCPA
Original: https://blog.csdn.net/qq_54423921/article/details/126921899
Author: hence..
Title: python绘制相关系数热力图
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