CSV GroupBy Processing to Excel with Charts using pandas (Python)

How to create Excel charts from a CSV file in Python

You will learn how to read CSV data to Excel using Python. It will be a bit more, you will read the CSV data from GitHub, then group the data by unique values in a column and sum it. Then how to group and sum data on a monthly basis. Finally, how to export this into a multiple-sheet Excel document with the chart.

In this tutorial, you will learn:

• Reading CSV data to Excel: Understand how to read CSV data and export it to an Excel file using Python.
• Grouping and Summing Data: Learn how to group data by unique values in a column and calculate the sum of corresponding values.
• Monthly Grouping and Summing: Explore how to group and sum data on a monthly basis using date-related information.
• Exporting to Multiple-Sheet Excel: Discover how to export the processed data into a multi-sheet Excel document, allowing for better organization and presentation.
• Adding Charts: Learn how to incorporate charts into the Excel document to visualize the data effectively.

By the end of the tutorial, you will be proficient in reading CSV data, performing grouping and summing operations, and exporting the data to Excel. You will also be able to create charts to enhance data visualization and presentation within the Excel file.

Step 1: Get and inspect the data

We can use pandas to read the CSV data (see more about CSV files here).

import pandas as pd
url = 'https://raw.githubusercontent.com/LearnPythonWithRune/LearnPython/main/files/SalesData.csv'
data = pd.read_csv(url, delimiter=';', parse_dates=True, index_col='Date')
print(data.head())

This will read our data directly from GitHub and show the first few lines.

            Sales rep        Item  Price  Quantity  Sale
Date                                                    
2020-05-31        Mia     Markers      4         1     4
2020-02-01        Mia  Desk chair    199         2   398
2020-09-21     Oliver       Table   1099         2  2198
2020-07-15  Charlotte    Desk pad      9         2    18
2020-05-27       Emma        Book     12         1    12

This data shows different sales representatives and a list of their sales in 2020.

Step 2: Use GroupBy to get sales of each to represent and monthly sales

It is easy to group data by columns. The below code will first group all the Sales reps and sum their sales. Second, it will group the data in months and sum it up.

repr_sales = data.groupby("Sales rep").sum()['Sale']
print(repr_sales)
monthly_sale = data.groupby(pd.Grouper(freq='M')).sum()['Sale']
monthly_sale.index = monthly_sale.index.month_name()
print(monthly_sale)

This gives.

Sales rep
Charlotte     74599
Emma          65867
Ethan         40970
Liam          66989
Mia           88199
Noah          78575
Oliver        89355
Sophia       103480
William       80400
Name: Sale, dtype: int64

Date
January      69990
February     51847
March        67500
April        58401
May          40319
June         59397
July         64251
August       51571
September    55666
October      50093
November     57458
December     61941
Name: Sale, dtype: int64

Step 3: Create a multiple-sheet Excel document with charts

Now for the export magic.

workbook = pd.ExcelWriter("SalesReport.xlsx")
repr_sales.to_excel(workbook, sheet_name='Sales per rep')
monthly_sale.to_excel(workbook, sheet_name='Monthly')
chart1 = workbook.book.add_chart({'type': 'column'})
# Configure the first series.
chart1.add_series({
    'name':       'Sales per rep',
    'categories': '=\'Sales per rep\'!$A$2:$A$10',
    'values':     '=\'Sales per rep\'!$B$2:$B$10',
})
workbook.sheets['Sales per rep'].insert_chart('D2', chart1)
chart1 = workbook.book.add_chart({'type': 'column'})
# Configure the first series.
chart1.add_series({
    'name':       'Monthly sales',
    'categories': '=Monthly!$A$2:$A$13',
    'values':     '=Monthly!$B$2:$B$13',
})
workbook.sheets['Monthly'].insert_chart('D2', chart1)
workbook.close()

This will create an Excel document called SalesReport.xlsx in your working directory.

To get a detailed explanation see the video at the top of the post.

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