What will we cover?
We will demonstrate how to read CSV data from a GitHub. How to 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 chart.
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 represents and a list over their sales in 2020.
Step 2: Use GroupBy to get sales of each 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.
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 in the top of the post.
Want to learn more?
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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!
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
Rune