使用seaborn包绘制热图

# library
import seaborn as sns
import pandas as pd
import numpy as np

# Create a dataset
df = pd.DataFrame(np.random.random((5,5)), columns=["a","b","c","d","e"])
df.head()

# Default heatmap: just a visualization of this square matrix
sns.heatmap(df)

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# Create a dataset
df = pd.DataFrame(np.random.random((100,5)), columns=["a","b","c","d","e"])

# Calculate correlation between each pair of variable
# 计算相关性矩阵
corr_matrix=df.corr()
corr_matrix.head()

# plot it
# 设置cmap参数更改热图颜色
sns.heatmap(corr_matrix, cmap='PuOr')

image.png

# Create a dataset
df = pd.DataFrame(np.random.random((10,10)), columns=["a","b","c","d","e","f","g","h","i","j"])

# plot a heatmap with annotation
# 设置annot=True参数添加文本注释
sns.heatmap(df, annot=True, annot_kws={"size": 7})

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# plot a heatmap with custom grid lines
# 设置linewidths和linecolor参数更改热图边框线的宽度和颜色
sns.heatmap(df, linewidths=2, linecolor='yellow')

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# plot a heatmap
# 设置yticklabels=False参数去掉y轴标签
sns.heatmap(df, yticklabels=False)

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# plot a heatmap
# 设置cbar=False参数去掉图例
sns.heatmap(df, cbar=False) 

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# color bar range between 0 and 0.5
# 设置vmin和vmax参数更改图例范围
sns.heatmap(df, cmap="YlGnBu", vmin=0, vmax=0.5)

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# Normalize it by row:
# 对数据按行进行归一化
df_norm_row = df.apply(lambda x: (x-x.mean())/x.std(), axis = 1)
df_norm_row.head()

# And see the result
sns.heatmap(df_norm_row, cmap='viridis')

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# Now if we normalize it by column:
# 对数据按列进行归一化
df_norm_col=(df-df.mean())/df.std()

sns.heatmap(df_norm_col, cmap='viridis')

image.png

对热图添加聚类树

# Libraries
import seaborn as sns
import pandas as pd
from matplotlib import pyplot as plt

# Data set
url = 'c:/Users/Dell/Downloads/mtcars.csv'
df = pd.read_csv(url,index_col=0)
df.head()

# Default plot
sns.clustermap(df)
# Show the graph
plt.show()

image.png

# 进行数据归一化
# Standardize or Normalize every column in the figure
# Standardize:
sns.clustermap(df, standard_scale=1)
plt.show()

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# Normalize
sns.clustermap(df, z_score=1)
plt.show()

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# 设置不同的距离计算方法
# plot with correlation distance
sns.clustermap(df, metric="correlation", standard_scale=1, cmap="PiYG")
plt.show()

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# plot with euclidean distance
sns.clustermap(df, metric="euclidean", standard_scale=1, cmap="PiYG")
plt.show()

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# 设置不同的聚类方法
# linkage method to use for calculating clusters: single
sns.clustermap(df, metric="euclidean", standard_scale=1, method="single", cmap = "Blues")
plt.show()

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In [38]:

# linkage method to use for calculating clusters: ward
sns.clustermap(df, metric="euclidean", standard_scale=1, method="ward", cmap = "Blues")
plt.show()

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# 更改不同的热图颜色
# Change color palette
sns.clustermap(df, metric="euclidean", standard_scale=1, method="ward", cmap="mako")
plt.show()

image.png

sns.clustermap(df, metric="euclidean", standard_scale=1, method="ward", cmap="viridis")
plt.show()

image.png

# 添加行注释信息
# Prepare a vector of color mapped to the 'cyl' column
my_palette = dict(zip(df.cyl.unique(), ["orange","yellow","brown"]))
row_colors = df.cyl.map(my_palette)
row_colors
Mazda RX4              orange
Mazda RX4 Wag          orange
Datsun 710             yellow
Hornet 4 Drive         orange
Hornet Sportabout       brown
Valiant                orange
Duster 360              brown
Merc 240D              yellow
Merc 230               yellow
Merc 280               orange
Merc 280C              orange
Merc 450SE              brown
Merc 450SL              brown
Merc 450SLC             brown
Cadillac Fleetwood      brown
Lincoln Continental     brown
Chrysler Imperial       brown
Fiat 128               yellow
Honda Civic            yellow
Toyota Corolla         yellow
Toyota Corona          yellow
Dodge Challenger        brown
AMC Javelin             brown
Camaro Z28              brown
Pontiac Firebird        brown
Fiat X1-9              yellow
Porsche 914-2          yellow
Lotus Europa           yellow
Ford Pantera L          brown
Ferrari Dino           orange
Maserati Bora           brown
Volvo 142E             yellow
Name: cyl, dtype: object</pre>

# plot
sns.clustermap(df, metric="correlation", method="single", cmap="Blues", standard_scale=1, row_colors=row_colors)
plt.show()

image.png

原文链接：https://www.python-graph-gallery.com/heatmap/