Автоматизация отчетов в excel с помощью python

Before you read this article and learn automation in Python….let’s watch a video of Christian Genco (a talented programmer and an entrepreneur) explaining the importance of coding by taking the example of automation.

You might have laughed loudly after watching this video and you surely, you might have understood the importance of automation in real life as well. Let’s come to the topic now…

We all know that Python is ruling all over the world, and we also know that Python is beginner’s friendly and it’s easy to learn in comparison to other languages. One of the best things you can do with Python is Automation. 

Consider a scenario that you’re asked to create an account on a website for 30,000 employees. How would you feel? Surely you will be frustrated doing this task manually and repeatedly. Also, this is going to take too much time which is not a smart decision. 

Now just imagine the life of employees who are into the data entry jobs. Their job is to take the data from tables such as Excel or Google Sheet and insert it somewhere else. They browse different websites and magazine, they collect the data from there, and then they insert it into the database. They also need to do the calculations for the entries. 

Generally, the income is based on the performance in this job. More entries, higher salary (of course everyone wants a higher salary in their job). 

But don’t you think that it’s boring to do the same kind of stuff repeatedly? 

Now the question is….“How can I do it fast?”, “How can I automate my work?”

Instead of doing these kinds of tasks manually, just spend an hour coding and automate these kinds of stuff to make your life easier. You can automate your tedious task by just writing fewer lines of code in Python. 

In this blog, we will create a small project to learn automation in Python. If you’re a beginner then you may prefer to watch some videos to learn the automation in Python and reading this blog might be a boring task for you but here we will go through step by step to explain everything in detail and to make things easier for you.

It will be great if you already know the core concept of Python. We will take an example of an Excel sheet with some entries, and we will learn the automation process. We are going to write a Python program that can process thousands of spreadsheets in under a second. Excited??? Let’s get started…

Intro of The Task

Processing or updating thousands of spreadsheets manually will take too much time. It may take hours, days, or even months. We will write a Python program to automate this task. We will work on a spreadsheet given in the below picture.

In this spreadsheet, we have the record for all kinds of transactions, but let’s say due to an error (human error or system error), the price for the product listed in the third column is wrong. Let’s say we need to decrease the price by 10% (multiply the price by 0.9 and recalculate the value). You can do this task manually by using a mathematical formula in the fourth column but it will take too much time (maybe 1 week or two weeks) if there are thousands of records. 

We will write a python program to automate this process. Also, we will add a chart to it. Our python program will do this task for us in a matter of seconds. 

Let’s Get Into The Coding Zone

To work on this Excel sheet we are going to use a library openpyxl. Create a folder in your directory, give it a name and install the openpyxl package by executing the following command in your terminal.

pip install openpyxl

Now we can import this package to work on our spreadsheet. Before that add the spreadsheet in your project folder. Now create a file app.py in your folder and write down the code given below.

Let’s Understand the Above Code

We are going to explain the code step by step written above to understand the complete process.

Step 1. To work on our spreadsheet import openpyxl package (we have used xl alias to make our code cleaner and shorter).  Also, to add a chart to our spreadsheet, we need to import two classes BarChart and Reference. 

import openpyxl as xl
from openpyxl.chart import BarChart, Reference

Step 2. Now we need to load the Excel workbook python-spreadhsheet.xlsx. Write down the code given below. wb returns the object and with this object, we are accessing Sheet1 from the workbook. 

wb = xl.load_workbook('python-spreadsheet.xlsx')
sheet = wb['Sheet1']

Step 3. To access the entries from rows 2 to 4 in the third column (entry for price column) we need to add a for loop in it. We are saving this entry in a variable cell. 

for row in range(2, sheet.max_row + 1):
    cell = sheet.cell(row, 3)

Step 4. Now we need to calculate the corrected prices. So we are multiplying the values saved in the cell variable with 0.9. Once the calculation is done we need to add all the corrected prices in a new column (column 4). To add a new column we will get a reference to the cell in the given row but in the fourth column. Once the cell is created, we need to set the corrected price values in this cell (fourth column). 

corrected_price = float(cell.value.replace('$','')) * 0.9
corrected_price_cell = sheet.cell(row, 4)
corrected_price_cell.value = corrected_price

Step 5. Half of the work is done. We have calculated the updated price, and we have added that in the fourth column. Now we need to add a chart to the current sheet. To create a chart we need to select a range of values. 

In this project, we will select the values in the fourth column (updated prices) and we will use that in our chart (we just need a bunch of numbers to create a chart, so we have taken the example of the fourth column. This value can be anything as per requirement).

We need to use the reference class to select a range of values. We are going to add five arguments to this constructor. The first argument is the sheet we are working on. The next two arguments min_row = 2, and max_row= sheet.max_row will select the cells from row 2 to row 4. To select the entries from only column fourth we need to pass another two arguments min_col=4 and max_col=4. Store the result in the variable ‘values’.

values = Reference(sheet, min_row=2, max_row=sheet.max_row, min_col=4, max_col=4)

Step 6. Now we are ready to create a chart. We will create an instance ‘chart’ for the class BarChart. Once this is created add the values in this chart. After that add this chart to the sheet into row 2 and column 5 (e2). 

chart = BarChart()
chart.add_data(values)
sheet.add_chart(chart, 'e2')

Step 7. Now we need to save all updated entries and the chart we have created in the above code. We will save this in a new file python-spreadsheet2.xlsx because we don’t’ want to accidentally overwrite the original file in case our program has a bug. 

Run your program and you’re good to go. A newly updated file python-spreadhsheet2.xlsx will be created for you with updated prices and charts. Below is the screenshot for the same.

Step 8. Our program is complete but if you use the above code then it’s not going to automate the process of thousands of spreadsheets. This program is only relying on a specific file that is python-spreadsheet.xlsx. 

To make it work for several spreadsheets we will reorganize this code, and we will move the code inside a function. This function will take the name of the file as an input and it will execute the process. Below is the updated code for the same.

Github Link for the Code with Spreadsheet Attached: Python Automation

Final Thought

That was just one example of using Python to automate repetitive boring tasks. But remember that automation is not just about Excel spreadsheets. There are so many things we can automate. You can search on various sites such as Github and you can automate a lot of things with Python.  

Время на прочтение
6 мин

Количество просмотров 19K

Хотя в среднем для каждодневных задач автоматизация не требуется, бывают случаи, когда она может быть необходима. Создание множества диаграмм, рисунков, таблиц и отчётов может утомить, если вы работаете вручную. Так быть не должно. Специально к старту нового потока курса Fullstack-разработчик на Python делимся с вами кейсом постройки конвейера на Python, с помощью которого Excel и Word легко интегрировать: нужно создать таблицы в Excel, а затем перенести результаты в Word, чтобы практически мгновенно получить отчёт.

Openpyxl

Встречайте Openpyxl — возможно, одну из самых универсальных связок [биндингов] с Python, которая сделает взаимодействие с Excel очень простым. Вооружившись этой библиотекой, вы сможете читать и записывать все нынешние и устаревшие форматы Excel, то есть xlsx и xls. 

Openpyxl позволяет заполнять строки и столбцы, выполнять формулы, создавать 2D и 3D диаграммы, маркировать оси и заголовки, а также предоставляет множество других возможностей, которые могут пригодиться. 

И самое важное — этот пакет позволяет вам перебирать бесконечное количество строк и столбцов в Excel, тем самым избавляя вас от всех этих надоедливых вычислений и построения графиков, которые вам приходилось делать раньше.

Python-docx

Затем идёт Python-docx, этот пакет для Word — то же самое, что Openpyxl для Excel. Если вы ещё не изучили его документацию, вам, вероятно, стоит взглянуть на неё. Python-docx — без преувеличения один из самых простых и понятных мне наборов инструментов, с которыми я работал с тех пор, как начал работать с самим Python. 

Python-docx позволяет автоматизировать создание документов путём автоматической вставки текста, заполнения таблиц и рендеринга изображений в отчёт без каких-либо накладных расходов. Без лишних слов давайте создадим наш собственный автоматизированный конвейер. Запустите Anaconda (или любую другую IDE по вашему выбору) и установите эти пакеты:

pip install openpyxl
pip install python-docx

Автоматизация Microsoft Excel

Сначала загрузим уже созданный лист Excel, вот так:

workbook = xl.load_workbook('Book1.xlsx')
sheet_1 = workbook['Sheet1']

Теперь переберём все строки в нашей таблице, чтобы вычислить и вставить значения мощности, умножив ток на напряжение:

for row in range(2, sheet_1.max_row + 1):
    current = sheet_1.cell(row, 2)
    voltage = sheet_1.cell(row, 3)
    power = float(current.value) * float(voltage.value)
    power_cell = sheet_1.cell(row, 1)
    power_cell.value = power

Как только это будет сделано, мы используем рассчитанные значения мощности, чтобы сгенерировать линейную диаграмму, которая будет вставлена ​​в указанную ячейку, код показан ниже:

values = Reference(sheet_1, min_row = 2, max_row = sheet_1.max_row, min_col = 1, max_col = 1)
chart = LineChart()
chart.y_axis.title = 'Power'
chart.x_axis.title = 'Index'
chart.add_data(values)
sheet_1.add_chart(chart, 'e2') 
workbook.save('Book1.xlsx')

Извлечение диаграммы

Теперь, когда мы сгенерировали нашу диаграмму, нам нужно извлечь её как изображение, чтобы мы могли использовать её в нашем отчёте Word. Сначала укажем точное местоположение файла Excel, а также место, где должно быть сохранено изображение диаграммы:

input_file = "C:/Users/.../Book1.xlsx"
output_image = "C:/Users/.../chart.png"

Затем откройте электронную таблицу, используя следующий метод:

operation = win32com.client.Dispatch("Excel.Application")
operation.Visible = 0
operation.DisplayAlerts = 0
workbook_2 = operation.Workbooks.Open(input_file)
sheet_2 = operation.Sheets(1)

Позднее вы сможете перебирать все объекты диаграммы в электронной таблице (если их несколько) и сохранять их в указанном месте:

for x, chart in enumerate(sheet_2.Shapes):
    chart.Copy()
    image = ImageGrab.grabclipboard()
    image.save(output_image, 'png')
    pass
workbook_2.Close(True)
operation.Quit()

Автоматизация Microsoft Word

Теперь, когда у нас есть сгенерированное изображение диаграммы, мы должны создать шаблон документа, который в принципе является обычным документом Microsoft Word (.docx), сформированным именно так, как мы хотим: отчёт содержит шрифты, размеры шрифтов, структуру и форматирование страниц. 

Теперь всё, что нам нужно сделать, — создать плейсхолдеры для сгенерированного нами контента, то есть табличные значения и изображения, и объявить их с именами переменных, как показано ниже.

Любой сгенерированный контент, включая текст и изображения, может быть объявлен в двойных фигурных скобках {{ variable_name }}. В случае таблиц вам нужно создать таблицу со строкой шаблона со всеми включёнными столбцами, затем нужно добавить одну строку вверху и одну строку ниже со следующей нотацией:

Первая строка:

{%tr for item in variable_name %}

Последняя строка:

{%tr for item in variable_name %}

На рисунке выше — имена переменных:

• table_contents для словаря Python, в котором будут храниться наши табличные данные;

• Index для ключей словаря (первый столбец);

• Power, Current и Voltage для значений словаря (второй, третий и четвёртый столбцы).

Затем импортируем наш шаблонный документ в Python и создаём словарь, в котором будут храниться значения нашей таблицы:

template = DocxTemplate('template.docx')
table_contents = []
for i in range(2, sheet_1.max_row + 1):
    table_contents.append({
        'Index': i-1,
        'Power': sheet_1.cell(i, 1).value,
        'Current': sheet_1.cell(i, 2).value,
        'Voltage': sheet_1.cell(i, 3).value
        })

Далее импортируем ранее созданное в Excel изображение диаграммы и создадим другой словарь для создания экземпляров всех объявленных в документе шаблона переменных-заполнителей:

image = InlineImage(template,'chart.png',Cm(10))
context = {
    'title': 'Automated Report',
    'day': datetime.datetime.now().strftime('%d'),
    'month': datetime.datetime.now().strftime('%b'),
    'year': datetime.datetime.now().strftime('%Y'),
    'table_contents': table_contents,
    'image': image
    }

И, наконец, визуализируем отчёт с нашей таблицей значений и изображением диаграммы:

template.render(context)
template.save('Automated_report.docx')

Результаты

И вот — автоматически сгенерированный отчёт Microsoft Word с числами и созданной в Microsoft Excel диаграммой. Мы получили полностью автоматизированный конвейер, его можно использовать, чтобы создать столько таблиц, диаграмм и документов, сколько вам потребуется.

Исходный код

import openpyxl as xl
from openpyxl.chart import LineChart, Reference

import win32com.client
import PIL
from PIL import ImageGrab, Image
import os
import sys

from docx.shared import Cm
from docxtpl import DocxTemplate, InlineImage
from docx.shared import Cm, Inches, Mm, Emu
import random
import datetime
import matplotlib.pyplot as plt


######## Generate automated excel workbook ########

workbook = xl.load_workbook('Book1.xlsx')
sheet_1 = workbook['Sheet1']
  
for row in range(2, sheet_1.max_row + 1):
    current = sheet_1.cell(row, 2)
    voltage = sheet_1.cell(row, 3)
    power = float(current.value) * float(voltage.value)
    power_cell = sheet_1.cell(row, 1)
    power_cell.value = power
  
values = Reference(sheet_1, min_row = 2, max_row = sheet_1.max_row, min_col = 1, max_col = 1)
chart = LineChart()
chart.y_axis.title = 'Power'
chart.x_axis.title = 'Index'
chart.add_data(values)
sheet_1.add_chart(chart, 'e2')
  
workbook.save('Book1.xlsx')


######## Extract chart image from Excel workbook ########

input_file = "C:/Users/.../Book1.xlsx"
output_image = "C:/Users/.../chart.png"

operation = win32com.client.Dispatch("Excel.Application")
operation.Visible = 0
operation.DisplayAlerts = 0
    
workbook_2 = operation.Workbooks.Open(input_file)
sheet_2 = operation.Sheets(1)
    
for x, chart in enumerate(sheet_2.Shapes):
    chart.Copy()
    image = ImageGrab.grabclipboard()
    image.save(output_image, 'png')
    pass

workbook_2.Close(True)
operation.Quit()


######## Generating automated word document ########

template = DocxTemplate('template.docx')

#Generate list of random values
table_contents = []

for i in range(2, sheet_1.max_row + 1):
    
    table_contents.append({
        'Index': i-1,
        'Power': sheet_1.cell(i, 1).value,
        'Current': sheet_1.cell(i, 2).value,
        'Voltage': sheet_1.cell(i, 3).value
        })

#Import saved figure
image = InlineImage(template,'chart.png',Cm(10))

#Declare template variables
context = {
    'title': 'Automated Report',
    'day': datetime.datetime.now().strftime('%d'),
    'month': datetime.datetime.now().strftime('%b'),
    'year': datetime.datetime.now().strftime('%Y'),
    'table_contents': table_contents,
    'image': image
    }

#Render automated report
template.render(context)
template.save('Automated_report.docx')

Вот мой репозиторий на GitHub с шаблоном документа и исходным кодом для этого туториала. А вот ссылка на курс Fullstack-разработчик на Python, который сделает из вас настоящего универсального солдата от кодинга.

Узнайте, как прокачаться и в других специальностях или освоить их с нуля:

• Профессия Data Scientist

• Профессия Data Analyst

• Курс по Data Engineering

Другие профессии и курсы

Data Analysis forms a crucial part of gaining valuable insights from your customers. Multiple organizations are trying innovative ways to simplify the process of Data Analysis. Some are trying to completely automate this process whereas others are using multiple tools to set up the same. Another innovative technique used by companies to perform Data Analysis is linking their data source with a programming language and having it automate their Data Analysis. One such type of automation is Python Excel Automation.

Python is the most popularly used programming language today and has limitless applications across many fields. Its flexible and dynamic capabilities make it the ideal choice when it comes to deployment and maintenance. MS Excel is one of the popular Microsoft applications that operate in the form of a spreadsheet and is used to save and analyze numerical data. It was one of the first tools used by companies all around the world to analyze data to gain insights. It is also termed a traditional BI (Business Intelligence) tool. By setting up Python Excel Automation, you can transfer data automatically from various sources to multiple destinations seamlessly.

This article provides a step-by-step guide to help you set up Python Excel Automation. It also gives you an overview of Python and Excel to better understand these technologies individually. Read along to find out how you can set up Python Excel Automation for your organization.

Table of Contents

• Prerequisites
• Introduction to Python
• Introduction to MS Excel
• Steps to Set Up Python Excel Automation
  • Step 1: Analyzing the Excel Dataset
  • Step 2: Making Pivot Tables using Pandas
  • Step 3: Designing the Reports using Openpyxl
  • Step 4: Automating the Report with Python
  • Step 5: Scheduling the Python Script
• Conclusion

Prerequisites

• Working knowledge of MS Excel.
• Strong Technical Foundation in Python.
• Knowledge of popular Python Libraries.

Introduction to Python

Python is a high-level programming language that uses an interpreter to process code. It was developed by Guido Von Rossum and was released in 1991. Its seamless code readability and dynamic nature make it an excellent choice when coding. It is used in a wide variety of fields including Machine Learning, Artificial Intelligence, Server-Side Scripting, Data Analytics, and Deep Learning. It was developed by Python Software Foundation and its stable release was on 3 May 2021.

Python is an Interactive, Interpreted, Object-Oriented programming language that incorporates multiple attributes like Exceptions, Modules, Dynamic Typing, Dynamic Binding, Classes, High-level Dynamic Data Types, etc. It can also be used to make system calls to almost all well-known Operating Systems.

Key Features of Python

Python houses a wide range of features that differentiates it from other programming languages. Some of those features are given below:

• It is free and open-source. You can download Python from their official website. It can be downloaded and installed easily. As Python is open-source, users that have a good technical background can make changes to the code as per business use cases and product requirements.
• Python is a beginner-friendly language and hence, most people with basic programming knowledge can easily learn the Python syntax and start coding on it. 
• While using Python, developers do not need to have any information on the System Architecture or manage memory usage manually. The Python Interpreter handles this automatically.
• Python is highly robust and portable. This means that the Python code written on one computer can easily be transferred to another system and can run without any issues.
• Python also supports Object-Oriented Programming that allows users to write readable and reusable code.

With Python Excel Automation, you can use the portable and robust nature of Python in tandem with Excel to improve your Data Analysis efficiently.

Introduction to MS Excel

Microsoft Excel is a spreadsheet program developed by Microsoft for Windows, macOS, Android, and iOS. It was released in 1987 and has been used by multiple calculations, graphic tools, pivot tables, and a macro programming language like Visual Basic for Applications. MS Excel has gained popularity over these years because the data is easy to save and data can be added or removed without any discomfort.

Key Features of MS Excel

MS Excel has many unique features that make it a good choice over other spreadsheet software. Some of those features are given below:

• MS Excel allows you to create headers and footers in the spreadsheet document and allows users to protect their data by providing passwords for their data.
• It supports filtering where you can find the required data in your workbook and replace it with the value you need.
• MS Excel supports Data Sorting, thereby allowing you to sort your data in ascending or descending order.
• It supports multiple variations of formulae, so you can perform all types of calculations easily. It also supports multiple types of Data Visualization and charts.
• You can perform Formula Auditing to form relationships between cells and tables.

With Python Excel Automation, you can use the sorting and robust nature of MS Excel with a powerful programming language like Python to automate reporting seamlessly.

To learn more about MS Excel, click this link.

Now that you have a good understanding of Python and MS Excel, it is now time to understand the steps to set up Python Excel Automation. You can follow the below steps to set Python Excel Automation up:

• Step 1: Analyzing the Excel Dataset
• Step 2: Making Pivot Tables using Pandas
• Step 3: Designing the Reports using Openpyxl
• Step 4: Automating the Report with Python
• Step 5: Scheduling the Python Script

Step 1: Analyzing the Excel Dataset

The first step in Python Excel Automation is to analyze the Dataset. The Dataset used in this tutorial is a Sales Dataset. This Dataset is also available on Kaggle. As this Dataset is in .csv format, you need to change it to .xslx format. The data will be used to create the below report after setting up our Python Excel Automation.

Step 2: Making Pivot Tables using Pandas

The next step in Python Excel Automation is to design Pivot Tables. Before doing that, you need to import the following libraries:

import pandas as pd
import openpyxl
from openpyxl import load_workbook
from openpyxl.styles import Font
from openpyxl.chart import BarChart, Reference
import string

Pandas is used to read the Excel file, create the Pivot table and export it to Excel. You can then use the Openpyxl library in Python to write Excel formulas, make charts and spreadsheets in Python.

To read your Excel file, make sure that the file is in the same place where your Python script is located and run the following code in Excel:

excel_file = pd.read_excel('supermarket_sales.xlsx')
excel_file[['Gender', 'Product line', 'Total']]

To create the pivot table, you need to access the excel_file data frame that you created previously. You can use the “.pivot_table()” to create the table. If you want to create a pivot table to show the total money split up for males and females you can run the below code:

report_table = excel_file.pivot_table(index='Gender',columns='Product line',values='Total',aggfunc='sum').round(0)

Finally, to export the Pivot Table, we will use the “.to_excel()” method as shown below:

report_table.to_excel('report_2021.xlsx',sheet_name='Report',startrow=4)

The Excel folder is now exported in the same location as your Python scripts.

Step 3: Designing the Reports using Openpyxl

The next step in Python Excel Automation is to design the reports. To make the report, you need to use the “load_workbook” method, that is imported from Openpyxl and save it using the “.save()” method. This is shown below:

wb = load_workbook('report_2021.xlsx')
sheet = wb['Report']
# cell references (original spreadsheet)
min_column = wb.active.min_column
max_column = wb.active.max_column
min_row = wb.active.min_row
max_row = wb.active.max_row

Python Excel Automation allows you to create Excel charts using Pivot Tables. To create an Excel chart using a Pivot Table, you need to use the Barchart Module and to identify the position of the data and category values, you can use the Reference Module. Both of these were imported before in Step 1. You can write Excel-based formulas in Python, the same way you write them in Excel. An example of this is shown below:

sheet['B7'] = '=SUM(B5:B6)'
sheet['B7'].style = 'Currency

Step 4: Automating the Report with Python

The next step in Python Excel Automation is to automate your report. You can write all the code into one function so that it will be easy to automate the report. That code is shown below:

import pandas as pd
import openpyxl
from openpyxl import load_workbook
from openpyxl.styles import Font
from openpyxl.chart import BarChart, Reference
import string

def automate_excel(file_name):
   """The file name should have the following structure: sales_month.xlsx"""
   # read excel file
   excel_file = pd.read_excel(file_name)
   # make pivot table
   report_table = excel_file.pivot_table(index='Gender', columns='Product line', values='Total', aggfunc='sum').round(0)
   # splitting the month and extension from the file name
   month_and_extension = file_name.split('_')[1]
   # send the report table to excel file
   report_table.to_excel(f'report_{month_and_extension}', sheet_name='Report', startrow=4)
   # loading workbook and selecting sheet
   wb = load_workbook(f'report_{month_and_extension}')
   sheet = wb['Report']
   # cell references (original spreadsheet)
   min_column = wb.active.min_column
   max_column = wb.active.max_column
   min_row = wb.active.min_row
   max_row = wb.active.max_row
   # adding a chart
   barchart = BarChart()
   data = Reference(sheet, min_col=min_column+1, max_col=max_column, min_row=min_row, max_row=max_row) #including headers
   categories = Reference(sheet, min_col=min_column, max_col=min_column, min_row=min_row+1, max_row=max_row) #not including headers
   barchart.add_data(data, titles_from_data=True)
   barchart.set_categories(categories)
   sheet.add_chart(barchart, "B12") #location chart
   barchart.title = 'Sales by Product line'
   barchart.style = 2 #choose the chart style
   # applying formulas
   # first create alphabet list as references for cells
   alphabet = list(string.ascii_uppercase)
   excel_alphabet = alphabet[0:max_column] #note: Python lists start on 0 -> A=0, B=1, C=2. #note2 the [a:b] takes b-a elements
   # sum in columns B-G
   for i in excel_alphabet:
       if i!='A':
           sheet[f'{i}{max_row+1}'] = f'=SUM({i}{min_row+1}:{i}{max_row})'
           sheet[f'{i}{max_row+1}'].style = 'Currency'
   sheet[f'{excel_alphabet[0]}{max_row+1}'] = 'Total'
   # getting month name
   month_name = month_and_extension.split('.')[0]
   # formatting the report
   sheet['A1'] = 'Sales Report'
   sheet['A2'] = month_name.title()
   sheet['A1'].font = Font('Arial', bold=True, size=20)
   sheet['A2'].font = Font('Arial', bold=True, size=10)
   wb.save(f'report_{month_and_extension}')
   return

Step 5: Scheduling the Python Script

The final step in Python Excel Automation is to run the Python script at different schedules as per data requirements. You just need to use the task scheduler or cron on Windows and Mac respectively.

That’s it! You have successfully set up Python Excel Automation in 5 easy steps!

Conclusion

This article gave you a step-by-step guide on setting up Python Excel Automation. It also gave you an overview of both Python and MS Excel along with their features. Overall, Python Excel Automation is an innovative process that you can use to create visual reports on Python just the same way as you would on Excel seamlessly. Organizations can leverage Python Excel Automation to leverage their business tasks according to their needs.

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Share your experience of setting up Python Excel Automation in the comments section below.