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Making heatmap from pandas DataFrame

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How do you plot a heat map?

Heat maps are a standard way to plot grouped data. The basic idea of a heat map is that the graph is divided into rectangles or squares, each representing one cell on the data table, one row and one data set. The rectangle or square is color coded according to the value of that cell in the table.

What is heat map in pandas?

A heatmap is a matrix kind of 2-dimensional figure which gives a visualisation of numerical data in the form of cells. Each cell of the heatmap is coloured and the shades of colour represent some kind of relationship of the value with the dataframe. Following are some ways to display a Panda dataframe in Heatmap style.


For people looking at this today, I would recommend the Seaborn heatmap() as documented here.

The example above would be done as follows:

import numpy as np 
from pandas import DataFrame
import seaborn as sns
%matplotlib inline

Index= ['aaa', 'bbb', 'ccc', 'ddd', 'eee']
Cols = ['A', 'B', 'C', 'D']
df = DataFrame(abs(np.random.randn(5, 4)), index=Index, columns=Cols)

sns.heatmap(df, annot=True)

Where %matplotlib is an IPython magic function for those unfamiliar.


If you don't need a plot per say, and you're simply interested in adding color to represent the values in a table format, you can use the style.background_gradient() method of the pandas data frame. This method colorizes the HTML table that is displayed when viewing pandas data frames in e.g. the JupyterLab Notebook and the result is similar to using "conditional formatting" in spreadsheet software:

import numpy as np 
import pandas as pd


index= ['aaa', 'bbb', 'ccc', 'ddd', 'eee']
cols = ['A', 'B', 'C', 'D']
df = pd.DataFrame(abs(np.random.randn(5, 4)), index=index, columns=cols)
df.style.background_gradient(cmap='Blues')

enter image description here

For detailed usage, please see the more elaborate answer I provided on the same topic previously and the styling section of the pandas documentation.


You want matplotlib.pcolor:

import numpy as np 
from pandas import DataFrame
import matplotlib.pyplot as plt

index = ['aaa', 'bbb', 'ccc', 'ddd', 'eee']
columns = ['A', 'B', 'C', 'D']
df = DataFrame(abs(np.random.randn(5, 4)), index=index, columns=columns)

plt.pcolor(df)
plt.yticks(np.arange(0.5, len(df.index), 1), df.index)
plt.xticks(np.arange(0.5, len(df.columns), 1), df.columns)
plt.show()

This gives:

Output sample


Useful sns.heatmap api is here. Check out the parameters, there are a good number of them. Example:

import seaborn as sns
%matplotlib inline

idx= ['aaa','bbb','ccc','ddd','eee']
cols = list('ABCD')
df = DataFrame(abs(np.random.randn(5,4)), index=idx, columns=cols)

# _r reverses the normal order of the color map 'RdYlGn'
sns.heatmap(df, cmap='RdYlGn_r', linewidths=0.5, annot=True)

enter image description here