Make a gradient color mapping on a specified column in Pandas

Let us see how to gradient color mapping on specific columns of a Pandas DataFrame. We can do this using the Styler.background_gradient() function of the Styler class.

Syntax : Styler.background_gradient(cmap=’PuBu’, low=0, high=0, axis=0, subset=None)

Parameters :

cmap : str or colormap (matplotlib colormap)

low, high : float (compress the range by these values.)

axis : int or str (1 or ‘columns’ for colunwise, 0 or ‘index’ for rowwise)

subset : IndexSlice (a valid slice for data to limit the style application to)

Returns :  self

Approach :

• Import Pandas module
• Create DataFrame
• Wisely choose specific column with style.background_gradient() function
• Display DataFrame

Let’s understand with examples:

Example 1 :

Create a DataFrame and gradient all the columns.

Python3

# importing pandas module
import pandas as pd

# Creating pandas DataFrame
df = pd.DataFrame({"A": [1, 2, -3, 4, -5, 6],
                   "B": [3, -5, -6, 7, 3, -2],
                   "C": [-4, 5, 6, -7, 5, 4],
                   "D": [34, 5, 32, -3, -56, -54]})

# Displaying the original DataFrame
print("Original Array : ")
print(df)

# backgroung color mapping
print("\nDataframe - Gradient color:")
df.style.background_gradient()

Output :

Example 2 :

Create a DataFrame and gradient the specific columns

Python3

# importing pandas module
import pandas as pd

# Creating pandas DataFrame
df = pd.DataFrame({"A": [1, 2, -3, 4, -5, 6],
                   "B": [3, -5, -6, 7, 3, -2],
                   "C": [-4, 5, 6, -7, 5, 4],
                   "D": [34, 5, 32, -3, -56, -54]})

# Displaying the original DataFrame
print("Original Array : ")
print(df)

# backgroung color mapping
print("\nDataframe - Gradient color:")

# df.style.background_gradient()
df.style.background_gradient(subset='B')

Output :

If you want to change another column then

Python3

df.style.background_gradient(subset='D')

Output : 

,

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