Plotting Hierarchically clustered Heatmaps
Coming to the heat map, it is a graphical representation of data where values are represented using colors. Variation in the intensity of color depicts how data is clustered or varies over space.
The clustermap() function of seaborn plots a hierarchically-clustered heat map of the given matrix dataset. It returns a clustered grid index.
Below are some examples which depict the hierarchically-clustered heat map from a dataset:
In the Flights dataset the data(Number of passengers) is clustered based on month and year:
Example 1:
Python3
# Importing the library import seaborn as sns from sunbird.categorical_encoding import frequency_encoding # Load dataset data = sns.load_dataset('flights') # Categorical encoding frequency_encoding(data, 'month') # Clustering data row-wise and # changing color of the map. sns.clustermap(data, figsize=(7, 7)) |
Output :
The legend to the left of the cluster map indicates information about the cluster map e.g bright color indicates more passengers and dark color indicates fewer passengers.
Example 2:
Python3
# Importing the library import seaborn as sns from sunbird.categorical_encoding import frequency_encoding # Load dataset data = sns.load_dataset('flights') # Categorical encoding frequency_encoding(data, 'month') # Clustering data row-wise and # changing color of the map. sns.clustermap(data, cmap='coolwarm', figsize=(7, 7)) |
Output:
Here we have changed the colors of the cluster map.
Hierarchically-clustered Heatmap in Python with Seaborn Clustermap
Seaborn is an amazing visualization library for statistical graphics plotting in Python. It provides beautiful default styles and color palettes to make statistical plots more attractive. It is built on the top of matplotlib library and also closely integrated into the data structures from pandas.
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