Data Analysis Using Python > Data Visualization > HeatMap

HeatMap

A heatmap is a type of data visualization that shows how values vary across a matrix using colors instead of numbers. It uses colored grid where each cell’s color represents the value in that cell — darker or lighter colors often mean higher or lower values.

import seaborn as sns

sns.heatmap(df.isnull(), yticklabels=False, cbar=False, cmap='cividis')

 

Explanation:

• df- Dataframe consisting the data
• isnull() - Creates a Boolean DataFrame where each cell is:
  • True → if the value is missing (NaN)
  • False → if the value is present
• heatmap(...)- Plots a heatmap (colored grid) showing which cells are missing.
• Arguments :
  • yticklabels=False → hides the row labels for clarity.
  • cbar=False → removes the color bar from the side.
  • cmap='cividis' → uses the “cividis” color palette for the heatmap (a visually accessible gradient).

Output:

• Yellow cells or light color = missing values
• Dark cells = non-missing values

So, this chart quickly detect patterns of missing data

Example:

import pandas as pd

import seaborn as sns

import matplotlib.pyplot as plt

data = {'A':[1, 2, None, 4], 'B':[5, None, 7, 8], 'C':[9, 10, 11, None]}

data

Output:

{'A': [1, 2, None, 4], 'B': [5, None, 7, 8], 'C': [9, 10, 11, None]}

df = pd.DataFrame(data)

Output:

	A	B	C
0	1.0	5.0	9.0
1	2.0	NaN	10.0
2	NaN	7.0	11.0
3	4.0	8.0	NaN

 

sns.heatmap(df.isnull(), yticklabels=False, cbar=False, cmap='cividis')

plt.show()

No More

Next

Counter Bar Plot

Feedback

Submit