Data Visualization with matplotlib: Build Line Chart for Retail Sales Analysis in 7 easy steps

In this blog on data visualization, we will walk you through the process of reading and visualizing data using Python’s library Matplotlib for visualization. By the end of this tutorial, you’ll have a clear understanding of how to load data, create a comparison plot, and annotate the graph with important information.

Step 1: Importing Required Libraries

import pandas as pd
import matplotlib.pyplot as plt

Pandas are used to load and manipulate the datasets.
Matplotlib is used to create plots and visualizations.

Data Visualization with matplotlib

Step 2: Load the Datasets

df1 = pd.read_csv('retail_sales_2023_monthwise.csv')
df2 = pd.read_csv('retail_sales_2024_monthwise.csv')

We use pd.read_csv() to load the two datasets from CSV files into DataFrames. Each file contains monthwise retail sales data for 2023 and 2024. Here’s a preview of the data:

Data Visualization with matplotlib

Step 3: Create the Plot:

plt.figure(figsize=(8,6))
plt.title('Monthwise Sales Analysis for 2023 & 2024', color='blue', fontsize=25)

• plt.figure() initializes the plot and sets the size to 8×6 inches
• plt.title() adds a title to the plot with a specific color and font size.

Data Visualization with matplotlib

Step 4: Plot the Sales Data

plt.plot(df1['Month'], df1['sales'], marker='o', color='red', label='2023')
plt.plot(df2['Month'], df2['sales'], marker='s', color='green', label='2024')

• plt.plot() Create line plots for the sales data for 2023 and 2024.
• We use different markers (o and s) to distinguish between the two years.
• The label parameter assigns labels for each year, which will be displayed in the legend.

Data Visualization with matplotlib

Step 5: Customize Axis Labels and Ticks

plt.xlabel('Months', fontsize=15, color='blue')
plt.ylabel('Sales', fontsize=15, color='blue')
plt.xticks(rotation=45)
plt.grid()

• plt.xlabel() and plt.ylabel() add labels to the x-axis and y-axis, respectively.
• plt.xticks(rotation=45) rotates the month names for better readability.
• plt.grid() adds grid lines to the plot for easier comparison.

Data Visualization with matplotlib

Step 6: Add Annotations to the Plot

for month, sale in zip(df1['Month'], df1['sales']):
    plt.text(month, sale, round(sale / 100000, 2), ha='right', va='top')

for month, sale in zip(df2['Month'], df2['sales']):
    plt.text(month, sale, round(sale / 100000, 2), ha='right', va='top')

plt.text() adds annotations to each data point, showing the sales in lakhs (one lakh = 100,000).
ha (horizontal alignment) and va (vertical alignment) position the text relative to the data points.

Data Visualization with matplotlib

Step 7: Add Legend and Display the Plot

plt.legend()
plt.show()

plt.legend() displays the legend, indicating which line corresponds to which year.
plt.show() renders the plot.

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Data Visualization with matplotlib

Output and Insights

The plot generated by this code compares monthwise retail sales for 2023 and 2024. Some key takeaways include:

• Identifying Trends: You can quickly see if sales are increasing or decreasing in specific months.
• Comparing Performance: It’s easy to compare performance between the two years for the same month.
• Spotting Outliers: Any significant drop or surge in sales will be visually prominent.

Conclusion

In this tutorial, we demonstrated how to load datasets, create line plots, and add annotations using Python. The ability to visualize data trends over time is essential for data-driven decision-making. With a simple yet effective plot, you can gain insights that can help drive business strategies.

Try experimenting with different datasets or visual elements to further enhance the plot. For example, you could:

• Use bar plots for individual monthly comparisons.
• Add trendlines to identify overall growth patterns.
• Incorporate interactive charts with libraries like Plotly.

The Code:

import pandas as pd
import matplotlib.pyplot as plt

df1 = pd.read_csv('retail_sales_2023_monthwise.csv')
df2 = pd.read_csv('retail_sales_2024_monthwise.csv')

plt.figure(figsize=(8,6))
plt.title('Monthwise Sales Analysis for 2023 & 2024', color='blue', fontsize=25)

plt.plot(df1['Month'], df1['sales'], marker='o', color='red', label='2023')
plt.plot(df2['Month'], df2['sales'], marker='s', color='green', label='2024')

plt.xlabel('Months', fontsize=15, color='blue')
plt.ylabel('Sales', fontsize=15, color='blue')
plt.xticks(rotation=45)
plt.grid()

for month, sale in zip(df1['Month'], df1['sales']):
    plt.text(month, sale, round(sale / 100000, 2), ha='right', va='top')

for month, sale in zip(df2['Month'], df2['sales']):
    plt.text(month, sale, round(sale / 100000, 2), ha='right', va='top')

plt.legend()
plt.show()

Data Visualization with matplotlib

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Data Visualization with matplotlib