Heatmaps for correlation in Data Analysis Python - Mini Project: Build & Apply

Heatmaps for correlation

📖 Scenario: You work as a data analyst for a retail company. You have collected sales data for different products and want to understand how the sales of these products relate to each other. Visualizing these relationships can help the team make better decisions.

🎯 Goal: Build a heatmap to visualize the correlation between sales of different products using Python.

📋 What You'll Learn

Create a pandas DataFrame with sales data for 4 products over 5 days.

Create a variable to store the correlation matrix of the DataFrame.

Use seaborn to create a heatmap of the correlation matrix.

Display the heatmap with clear labels.

💡 Why This Matters

🌍 Real World

Correlation heatmaps help businesses understand how different products' sales relate, guiding marketing and inventory decisions.

💼 Career

Data analysts and scientists use correlation heatmaps to quickly spot patterns and relationships in data, which is essential for data-driven decision making.

1

Create sales data DataFrame

Create a pandas DataFrame called sales_data with these exact columns and values:
'Product_A': [10, 15, 14, 20, 18], 'Product_B': [8, 12, 11, 19, 17], 'Product_C': [5, 7, 6, 9, 8], 'Product_D': [12, 18, 16, 22, 20].

Data Analysis Python

import pandas as pd
# Create the sales_data DataFrame with the given product sales
# Your code here

Hint

Use pd.DataFrame and pass a dictionary with product names as keys and lists of sales as values.

2

Calculate correlation matrix

Create a variable called correlation_matrix that stores the correlation matrix of sales_data using the corr() method.

Data Analysis Python

import pandas as pd
sales_data = pd.DataFrame({
    'Product_A': [10, 15, 14, 20, 18],
    'Product_B': [8, 12, 11, 19, 17],
    'Product_C': [5, 7, 6, 9, 8],
    'Product_D': [12, 18, 16, 22, 20]
})
# Calculate the correlation matrix of sales_data
# Your code here

Hint

Use sales_data.corr() to get the correlation matrix.

3

Create heatmap of correlation matrix

Import seaborn as sns and matplotlib.pyplot as plt. Use sns.heatmap() to create a heatmap of correlation_matrix with annotations enabled (annot=True).

Data Analysis Python

import pandas as pd
sales_data = pd.DataFrame({
    'Product_A': [10, 15, 14, 20, 18],
    'Product_B': [8, 12, 11, 19, 17],
    'Product_C': [5, 7, 6, 9, 8],
    'Product_D': [12, 18, 16, 22, 20]
})
correlation_matrix = sales_data.corr()
import seaborn as sns
import matplotlib.pyplot as plt
# Create a heatmap of correlation_matrix with annotations
# Your code here

Hint

Use sns.heatmap(correlation_matrix, annot=True) to create the heatmap.

4

Display the heatmap

Use plt.show() to display the heatmap plot.

Data Analysis Python

import pandas as pd
sales_data = pd.DataFrame({
    'Product_A': [10, 15, 14, 20, 18],
    'Product_B': [8, 12, 11, 19, 17],
    'Product_C': [5, 7, 6, 9, 8],
    'Product_D': [12, 18, 16, 22, 20]
})
correlation_matrix = sales_data.corr()
import seaborn as sns
import matplotlib.pyplot as plt
sns.heatmap(correlation_matrix, annot=True)
plt.title('Correlation Heatmap of Product Sales')
# Display the heatmap plot
# Your code here

Hint

Use plt.show() to display the plot window.