Combine Matplotlib with Seaborn Plots
Description:
This code combines a Seaborn bar plot for revenue with a Matplotlib line plot to show units sold, allowing for a more detailed comparison.
Code Explanation:
-
First, we import the necessary libraries:
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pandas for data manipulation
-
seaborn for statistical plotting
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matplotlib for additional customizations
-
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We create a sample DataFrame containing product names, revenue, and units sold.
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A Seaborn bar plot is used to plot the revenue for each product (
sns.barplot()). -
We add a line plot on top of the bar plot using Matplotlib to show the units sold for each product. The line is drawn using
plt.plot()with markers. -
Titles, axis labels, and a legend are added for clarity and understanding.
-
Finally,
plt.tight_layout()adjusts the layout, andplt.show()displays the final combined plot.
Program:
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
# Sample data
data = {
'Product': ['A', 'B', 'C', 'D', 'E'],
'Revenue': [1000, 1500, 1200, 1800, 2200],
'Units Sold': [10, 12, 9, 14, 16]
}
df = pd.DataFrame(data)
# Creating a seaborn barplot
plt.figure(figsize=(10, 6))
sns.barplot(x='Product', y='Revenue', data=df, color='lightblue')
# Adding a line plot (Matplotlib)
plt.plot(df['Product'], df['Units Sold'], color='red', marker='o', label='Units Sold', linewidth=2)
# Formatting the plot
plt.title('Revenue and Units Sold by Product')
plt.xlabel('Product')
plt.ylabel('Values')
plt.legend()
# Show plot
plt.tight_layout()
plt.show()
Output:
