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 columnwise, 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 moduleimport pandas as pd# Creating pandas DataFramedf = 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 DataFrameprint("Original Array : ")print(df)# background color mappingprint("\nDataframe - Gradient color:")df.style.background_gradient() |
Output :
Example 2 :
Create a DataFrame and gradient the specific columns
Python3
# importing pandas moduleimport pandas as pd# Creating pandas DataFramedf = 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 DataFrameprint("Original Array : ")print(df)# background color mappingprint("\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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