以热图样式显示 Pandas DataFrame
Python编程语言中的Pandas库因其创建各种数据结构的能力而被广泛使用，并且它还提供了许多要对数字和时间序列数据执行的操作。通过以热图样式显示熊猫数据框，用户可以获得数字数据的可视化。它概述了完整的数据帧，这使得理解数据帧中的关键点变得非常容易。
热图是一种矩阵类型的二维图形，它以单元格的形式提供数字数据的可视化。热图的每个单元格都是彩色的，颜色的深浅表示值与数据框的某种关系。以下是一些以热图样式显示 Panda 数据框的方法。
以这个数据框为例：
方法一：使用 Pandas 库
在这种方法中，Pandas 库将用于生成数据框和热图。热图的单元格将显示与数据框对应的值。下面是实现。
# Python program to generate a heatmap
# which displays the value in each cell
# corresponding to the given dataframe
# import required libraries
import pandas as pd
# defining index for the dataframe
idx = ['1', '2', '3', '4']
# defining columns for the dataframe
cols = list('ABCD')
# entering values in the index and columns
# and converting them into a panda dataframe
df = pd.DataFrame([[10, 20, 30, 40], [50, 30, 8, 15],
[25, 14, 41, 8], [7, 14, 21, 28]],
columns = cols, index = idx)
# displaying dataframe as an heatmap
# with diverging colourmap as virdis
df.style.background_gradient(cmap ='viridis')\
.set_properties(**{'font-size': '20px'})
输出 ： 
方法2：使用matplotlib库
在此方法中，Panda 数据框将显示为热图，其中热图的单元格将根据数据框中的值进行颜色编码。除了作为图形图例的热图之外，还会出现一个颜色条。下面是实现。
# Python program to generate a heatmap
# which represents panda dataframe
# in colour coding schemes
# import required libraries
import matplotlib.pyplot as plt
import pandas as pd
# Defining index for the dataframe
idx = ['1', '2', '3', '4']
# Defining columns for the dataframe
cols = list('ABCD')
# Entering values in the index and columns
# and converting them into a panda dataframe
df = pd.DataFrame([[10, 20, 30, 40], [50, 30, 8, 15],
[25, 14, 41, 8], [7, 14, 21, 28]],
columns = cols, index = idx)
# Displaying dataframe as an heatmap
# with diverging colourmap as RdYlBu
plt.imshow(df, cmap ="RdYlBu")
# Displaying a color bar to understand
# which color represents which range of data
plt.colorbar()
# Assigning labels of x-axis
# according to dataframe
plt.xticks(range(len(df)), df.columns)
# Assigning labels of y-axis
# according to dataframe
plt.yticks(range(len(df)), df.index)
# Displaying the figure
plt.show()
输出 ： 
方法 3：使用 Seaborn 库
在这种方法中，将从 Panda 数据框生成热图，其中热图的单元格将包含与数据框对应的值，并将进行颜色编码。除了作为图形图例的热图之外，还将出现一个颜色条。下面是实现。
# Python program to generate heatmap which
# represents panda dataframe in color-coding schemes
# along with values mentioned in each cell
# import required libraries
import pandas as pd
import seaborn as sns % matplotlib inline
# Defining figure size
# for the output plot
fig, ax = plt.subplots(figsize = (12, 7))
# Defining index for the dataframe
idx = ['1', '2', '3', '4']
# Defining columns for the dataframe
cols = list('ABCD')
# Entering values in the index and columns
# and converting them into a panda dataframe
df = pd.DataFrame([[10, 20, 30, 40], [50, 30, 8, 15],
[25, 14, 41, 8], [7, 14, 21, 28]],
columns = cols, index = idx)
# Displaying dataframe as an heatmap
# with diverging colourmap as RdYlGn
sns.heatmap(df, cmap ='RdYlGn', linewidths = 0.30, annot = True)
输出 ： 
If the uppermost and the lowermost row of output figure does not appear with proper height then add below two lines after the last line of the above code.
方法四：使用 Panda 库生成相关矩阵
相关矩阵是一种特殊的热图，它显示了数据框的一些见解。此热图的单元格显示相关系数，这是数据帧变量之间的线性历史关系。在这种方法中，仅使用 Pandas 库来生成相关矩阵。下面是实现。
bottom, top = ax.get_ylim()
ax.set_ylim(bottom + 0.5, top - 0.5)
输出 ： 
方法5：使用Seaborn库生成相关矩阵
相关矩阵也可以使用 Seaborn 库生成。生成的热图的单元格将包含相关系数，但与 Pandas 库生成的热图不同，这些值是四舍五入的。下面是实现。
# Python program to generate heatmap
# which represents correlation between
# columns of panda dataframe
# import required libraries
import pandas as pd
# Defining index for the dataframe
idx = ['1', '2', '3', '4']
# Defining columns for the dataframe
cols = list('ABCD')
# Entering values in the index and columns
# and converting them into a panda dataframe
df = pd.DataFrame([[10, 20, 30, 40], [50, 30, 8, 15],
[25, 14, 41, 8], [7, 14, 21, 28]],
columns = cols, index = idx)
# generating pairwise correlation
corr = df.corr()
# Displaying dataframe as an heatmap
# with diverging colourmap as coolwarm
corr.style.background_gradient(cmap ='coolwarm')
输出 ： 
如果输出图形的最上一行和最下一行没有以适当的高度出现，则在上述代码的最后一行之后添加以下两行。
# Python program to generate a heatmap
# which represents the correlation between
# columns of panda dataframe
# import required libraries
import pandas as pd
import seaborn as sn
# Defining figure size
# for the output plot
fig, ax = plt.subplots(figsize = (12, 7))
# Defining index for the dataframe
idx = ['1', '2', '3', '4']
# Defining columns for the dataframe
cols = list('ABCD')
# Entering values in the index and columns
# and converting them into a panda dataframe
df = pd.DataFrame([[10, 20, 30, 40], [50, 30, 8, 15],
[25, 14, 41, 8], [7, 14, 21, 28]],
columns = cols, index = idx)
df = pd.DataFrame(df, columns =['A', 'B', 'C', 'D'])
corr = df.corr()
sn.heatmap(corr, annot = True)