Set Pandas dataframe background Color and font color in Python - GeeksforGeeks (2025)

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Using DataFrame.style property Using User-defined Function Using Seaborn Library Similar Reads Pandas DataFrame Practice Exercises Pandas Dataframe Rows Practice Exercise Pandas Dataframe Columns Practice Exercise Pandas Series Practice Exercise Pandas Date and Time Practice Exercise DataFrame String Manipulation Accessing and Manipulating Data in DataFrame DataFrame Visualization and Exporting

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Last Updated : 06 Aug, 2021

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As we know, the basic idea behind styling is to make more impactful for the end-user readability. We can make changes like the color and format of the data visualized in order to communicate insight more efficiently. For the more impactful visualization on the pandas DataFrame, generally, we DataFrame.style property, which returns styler object having a number of useful methods for formatting and visualizing the data frames.

Using DataFrame.style property

• df.style.set_properties: By using this, we can use inbuilt functionality to manipulate data frame styling from font color to background color.

Python3

# Importing the necessary libraries -->import pandas as pdimport numpy as np# Seeding random data from numpynp.random.seed(24)# Making the DataFramedf = pd.DataFrame({'A': np.linspace(1, 10, 10)})df = pd.concat([df, pd.DataFrame(np.random.randn(10, 4), columns=list('BCDE'))], axis=1)# DataFrame without any stylingprint("Original DataFrame:\n")print(df)print("\nModified Stlying DataFrame:")df.style.set_properties(**{'background-color': 'black', 'color': 'green'})

Output:

• df.style.highlight_null : With the help of this, we can highlight the missing or null values inside the data frame.

Python3

# Replacing the locating value by NaN (Not a Number)df.iloc[0, 3] = np.nandf.iloc[2, 3] = np.nandf.iloc[4, 2] = np.nandf.iloc[7, 4] = np.nan# Highlight the NaN values in DataFrameprint("\nModified Stlying DataFrame:")df.style.highlight_null(null_color='red')

Output:

• df.style.highlight_min : For highlighting the minimum value in each column throughout the data frame.

Python3

# Highlight the Min values in each column print("\nModified Stlying DataFrame:")df.style.highlight_min(axis=0)

Output:

• df.style.highlight_max : For highlighting the maximum value in each column throughout the data frame.

Python3

# Highlight the Max values in each column print("\nModified Stlying DataFrame:")df.style.highlight_max(axis=0)

Output:

Using User-defined Function

• We can modify DataFrame using a user-defined function: With the help of this function, we can customizing the font color of positive data values inside the data frame.

Python3

# function for set text color of positive# values in Dataframesdef color_positive_green(val): """ Takes a scalar and returns a string with the css property `'color: green'` for positive strings, black otherwise. """ if val > 0: color = 'green' else: color = 'black' return 'color: %s' % colordf.style.applymap(color_positive_green)

Output:

Using Seaborn Library

• Using color palette for gradient fill in DataFrame: By importing the light palette of colors from the seaborn library, we can map the color gradient for the background of the data frame.

Python3

# Import seaborn libraryimport seaborn as sns# Declaring the cm variable by the # color palette from seaborncm = sns.light_palette("green", as_cmap=True)# Visualizing the DataFrame with set precisionprint("\nModified Stlying DataFrame:")df.style.background_gradient(cmap=cm).set_precision(2)

Output:

• Using color palette with highlight null or missing values: Here, we highlight the NaN values in red color with gradient color palette of seaborn.

Python3

# Highlight the NaN values in DataFrame # using seaborn color paletteprint("\nModified Stlying DataFrame:")df.style.background_gradient(cmap=cm).set_precision(2).highlight_null('red')

Output:

• Assemble Seaborn properties with DataFrame.style property: Customizing the seaborn color palette with highlight properties of a data frame for more impactful data visualization.

Python3

# Highlight the NaN values in DataFrame # using seaborn color palette as well as# min('lighblue') and max('blue') values # in each columnprint("\nModified Stlying DataFrame:")df.style.background_gradient(cmap=cm).set_precision(2).highlight_null('red').highlight_min(axis=0, color='lightblue').highlight_max(axis=0, color='blue')

Output:

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Pandas Exercises and Programs Pandas is an open-source Python Library that is made mainly for working with relational or labelled data both easily and intuitively. This Python library is built on top of the NumPy library, providing various operations and data structures for manipulating numerical data and time series. Pandas is 6 min read Different ways to create Pandas Dataframe It is the most commonly used Pandas object. The pd.DataFrame() function is used to create a DataFrame in Pandas. There are several ways to create a Pandas Dataframe in Python.Example: Creating a DataFrame from a DictionaryPythonimport pandas as pd # initialize data of lists. data = {'Name': ['Tom', 7 min read

Pandas DataFrame Practice Exercises

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Pandas Series Practice Exercise

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DataFrame String Manipulation

Accessing and Manipulating Data in DataFrame

DataFrame Visualization and Exporting

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