How To Do Conditional Formatting In Pandas DataFrame?

This post turns into the very first speedy read tip of the new forthcoming area on my blog. My objective of presenting fast peruses is on give a simple, straightforward, straightforward, and clear comprehension of some, obviously, information science-related points.

Styling DataFrames

Some of the time it's expected to introduce information in text tables, and feature a few qualities, control the accuracy of floats or essentially show information in the configuration that is generally helpful for you. Perusing this post, you will discover far to style your DataFrames. How about we start!

Stacking a minuscule dataset

import pandas as pd
clicks = pd.read_csv('clicks.csv')
clicks.head()

def highlight_max(s):
    '''
    highlight max number of variable
    '''
    is_max = s == s.max()
    return ['background-color: yellow' if v else '' for v in is_max]

def color_negative_red(val):
    """
    Takes a scalar and returns a string with
    the css property `'color: red'` for negative
    strings, blue for 2019 or 2020 values,
    black otherwise.
    """
    color = 'red' if val < 0 else 'black'
    if val < 0:
        color = 'red'
    elif val == 2019 or val == 2020:
        color = 'blue'
    else:
        color = 'black'
    
    return 'color: %s' % color

Summary

Up until this point, you have perceived how to style your text tables utilizing capabilities recorded underneath. To dive deeper into the Style object, you can investigate the documentation.

style.applymap()
style.format()
style.apply()
style.highlight_null()
and two user functions