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Apprendre Challenge 5: Matrix Plots | Seaborn
Data Science Interview Challenge

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Challenge 5: Matrix Plots

Data often comes in a matrix format, where rows and columns represent different variables or categories. To visualize this structured data effectively, matrix plots come to the rescue. Seaborn, known for its comprehensive plotting capabilities, offers specialized tools for creating powerful matrix visualizations.

Matrix plots in Seaborn allow you to:

  • Visualize the relationship between two categorical variables.

  • Display the distribution of data in a heatmap format.

  • Explore hierarchical structures in the data using cluster maps.

Leveraging Seaborn's matrix plots, analysts can navigate intricate data structures, extract patterns, and make data-driven decisions with ease.

Tâche

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Using Seaborn, demonstrate the matrix structure in a dataset:

  1. Plot a heatmap of a correlation matrix.
  2. Annotate the heatmap with the actual correlation values.

Solution

import seaborn as sns
import matplotlib.pyplot as plt

# Sample data
data = sns.load_dataset('flights').pivot(index='month', columns='year', values='passengers')

# 1. Heatmap of a correlation matrix
sns.heatmap(data.corr())
plt.title('Correlation Matrix Heatmap')
plt.show()

# 2. Annotated heatmap
sns.heatmap(data.corr(), annot=True)
plt.title('Annotated Correlation Matrix Heatmap')
plt.show()

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Section 5. Chapitre 5
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import seaborn as sns
import matplotlib.pyplot as plt

# Sample data
data = sns.load_dataset('flights').pivot(index='month', columns='year', values='passengers')

# 1. Heatmap of a correlation matrix
sns.___(data.corr())
plt.title('Correlation Matrix Heatmap')
plt.show()

# 2. Annotated heatmap
sns.___(data.corr(), ___=True)
plt.title('Annotated Correlation Matrix Heatmap')
plt.show()

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