使用seaborn包繪制熱圖

# 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
# 計算相關(guān)性矩陣
corr_matrix=df.corr()
corr_matrix.head()

# plot it
# 設(shè)置cmap參數(shù)更改熱圖顏色
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
# 設(shè)置annot=True參數(shù)添加文本注釋
sns.heatmap(df, annot=True, annot_kws={"size": 7})

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# plot a heatmap with custom grid lines
# 設(shè)置linewidths和linecolor參數(shù)更改熱圖邊框線的寬度和顏色
sns.heatmap(df, linewidths=2, linecolor='yellow')

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# plot a heatmap
# 設(shè)置yticklabels=False參數(shù)去掉y軸標簽
sns.heatmap(df, yticklabels=False)

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# plot a heatmap
# 設(shè)置cbar=False參數(shù)去掉圖例
sns.heatmap(df, cbar=False) 

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# color bar range between 0 and 0.5
# 設(shè)置vmin和vmax參數(shù)更改圖例范圍
sns.heatmap(df, cmap="YlGnBu", vmin=0, vmax=0.5)

image.png

# Normalize it by row:
# 對數(shù)據(jù)按行進行歸一化
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')

image.png

# Now if we normalize it by column:
# 對數(shù)據(jù)按列進行歸一化
df_norm_col=(df-df.mean())/df.std()

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

image.png

對熱圖添加聚類樹

# 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

# 進行數(shù)據(jù)歸一化
# Standardize or Normalize every column in the figure
# Standardize:
sns.clustermap(df, standard_scale=1)
plt.show()

image.png

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

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# 設(shè)置不同的距離計算方法
# 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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# 設(shè)置不同的聚類方法
# 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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# 更改不同的熱圖顏色
# 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

# 添加行注釋信息
# 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

原文鏈接：https://www.python-graph-gallery.com/heatmap/