Display the Pandas DataFrame in Heatmap style

# 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'})

# 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()

# 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)

# 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')

# 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)

bottom, top = ax.get_ylim()
ax.set_ylim(bottom + 0.5, top - 0.5)