Python Analyzing Sales Data with Python: Identify Monthly Trends

[Hilton D]

I have a sales dataset containing information about daily sales for a retail store. Each record includes the date and the corresponding sales amount. Here's a sample of the dataset:

sales_data = {
    'date': ['2023-01-01', '2023-01-02', '2023-01-03', '2023-02-01', '2023-02-02', '2023-02-03'],
    'sales_amount': [500, 600, 700, 550, 620, 720]
}

I want to analyze this data to identify monthly sales trends. Specifically, I would like to:

1. Calculate the total sales for each month and store the results in a new data structure.
2. Determine the month with the highest total sales.
3. Plot a bar chart or line chart to visualize the monthly sales trends.

I've only recently started using Python for data analysis, and after trying several articles, I was unable to find the answer. Using Python and well-known data analysis packages like Pandas and Matplotlib, could someone please offer code examples or instructions on how to accomplish these tasks?

 

[phenomenon]

You can achieve these tasks using the Pandas and Matplotlib libraries in Python. First, make sure you have these libraries installed:

pip install pandas matplotlib

Now, you can use the following code to analyze your sales dataset:

import pandas as pd
import matplotlib.pyplot as plt

# Sample data
sales_data = {
    'date': ['2023-01-01', '2023-01-02', '2023-01-03', '2023-02-01', '2023-02-02', '2023-02-03'],
    'sales_amount': [500, 600, 700, 550, 620, 720]
}

# Convert the data to a Pandas DataFrame
df = pd.DataFrame(sales_data)
df['date'] = pd.to_datetime(df['date'])  # Convert 'date' column to datetime format

# Extract month and year from the 'date' column
df['month'] = df['date'].dt.month
df['year'] = df['date'].dt.year

# Group by month and sum the sales_amount for each month
monthly_sales = df.groupby(['year', 'month'])['sales_amount'].sum().reset_index()

# Find the month with the highest total sales
max_sales_month = monthly_sales.loc[monthly_sales['sales_amount'].idxmax()]

# Print the monthly sales data and the month with the highest sales
print("Monthly Sales Data:")
print(monthly_sales)

print("\nMonth with the Highest Sales:")
print(max_sales_month)

# Plotting
plt.figure(figsize=(10, 6))
plt.plot(monthly_sales['date'], monthly_sales['sales_amount'], marker='o')
plt.title('Monthly Sales Trends')
plt.xlabel('Month')
plt.ylabel('Total Sales Amount')
plt.grid(True)
plt.show()

This code converts your data into a Pandas DataFrame, extracts the month and year from the date, calculates the total sales for each month, finds the month with the highest sales, and finally, plots the monthly sales trends.

Note: This assumes that your sales_data dictionary represents a time series dataset with a 'date' column. If your dataset is more complex, you may need to adapt the code accordingly.


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