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    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')

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

                Sales rep        Item  Price  Quantity  Sale
    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']
    monthly_sale = data.groupby(pd.Grouper(freq='M')).sum()['Sale']
    monthly_sale.index = monthly_sale.index.month_name()

    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
    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.
        'name':       'Sales per rep',
        'categories': '=\'Sales per rep\'!$A$2:$A$10',
        'values':     '=\'Sales per rep\'!$B$2:$B$10',
    workbook.sheets['Sales per rep'].insert_chart('D2', chart1)
    chart1 = workbook.book.add_chart({'type': 'column'})
    # Configure the first series.
        'name':       'Monthly sales',
        'categories': '=Monthly!$A$2:$A$13',
        'values':     '=Monthly!$B$2:$B$13',
    workbook.sheets['Monthly'].insert_chart('D2', chart1)

    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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    3 thoughts on “CSV GroupBy Processing to Excel with Charts using pandas (Python)”

    1. in windows 10 i get error when trying the example above:
      AttributeError: ‘OpenpyxlWriter’ object has no attribute ‘add_chart’

      using pytho3.10 64bit

      Nevertheless, I like your style of presenting python, and the hard work put into all the tutorials. A big thank you! I have a long way to go, but I am enjoying it so far!

    2. further to my earlier message where i noted this doesn’t work in win doze environ. I am glad to advise that it does work fine under macos big sur (arm64), also running python3.10 (but natively under arm64)

      • Hi,
        There can be two things.
        Most likely it just needs to install the XlsxWriter.
        This is done as follows:

        pip install xlsxwriter

        This should be run in a terminal in the environment you use.

        If this does not solve the problem, try to update the pandas package

        pip install –upgrade pandas

        This will update it to the newest version.

        Hope it helps


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