Quick Summary

🟠 Using Python for Automation is a futuristic approach worth investing in. It is easier to build and better for your users.
🟠 Python is a programming language that makes automation tasks seamless for you due to its simplicity, agility, and easy accessibility of its libraries and modules.

Table of Contents

Introduction to Python for Automation

For every manual task, there is automation, and for each automation, you have a choice to use multiple tools and technologies.

But which technology tops the chart?

Which one helps you with agile development?

After all, no one wants to spend hours despite heavily investing in automation.

The past 12 months’ statistics on Google Trends show Python tops the chart when considering technology for automation. A 30-year-old programming language is still developers’ favorite, and the recent use case of Python for automation, i.e. ChatGPT is the talk of the town.

So, let’s discuss how Python would play a crucial role in making automation intelligent and improving users’ lives.

Why is Python Best for Automation?

Python is a preferred programming language by developers when it comes to intelligent automation and hyper-automation because it is scalable, faster, offers 24/7 support, and has cross-platform accessibility. Well, these are just the initial points of discussion; in the succeeding blog, we will dwell deeper.

As you have read in the introduction about the popularity of Python for automation projects, let’s discuss why every product owner, like you, prefers Python to achieve scalable and innovative automation.

Indeed, Python tops the chart due to several convincing reasons, such as

Why is Python Best for Automation

Understandable Syntax

Unlike other programming languages, Python replicates the English language, making it the only programming language having the most easily understandable syntax. Besides, Python requires less coding than other programming languages, making it the most preferred open-source programming language by both – novice and experienced Python professionals.

It’s Easy to Create Python Scripts

Using an intuitive and appealing GUI toolkit; Python developers can create automation scripts without much fuss and quickly. These scripts help you automate your repetitive tasks with minimum effort.

It Facilitates Agile Development

Python, an open-source and easy-to-use programming language, contributes significantly to automating tasks and implementing scripts necessary for business process automation. Speedy automation leads to faster time to market. So, kill the project development waiting period using python for automation.

It Enables Cross-Platform Development

Since Python is a stand-alone programming language, running automation scripts in a Python environment runs seamlessly on any platform (Windows, Linux, and Mac).

Liberty to Leverage Third-Party Libraries

Automation tasks are always complex, but when you use Python for automation, the entire automation game becomes child’s play. Besides, the language’s accessibility to various third-party libraries makes it possible to achieve automation at scale. The range of libraries includes data manipulation, web scrapping, and API automation.

Compatibility With All Types of Automated Tasks

Be it data, desktop, network, testing, cloud, web, or system automation tasks, Python remains compatible with all the tasks. You name the automation, and Python does it for you.

Ready to automate your workflows with Python?
Contact us today to learn how our Python development services can help you streamline your processes and boost your productivity.

Top Five Processes You Can Automate with Python

Python is an age-old yet the most picked-up programming language by developers. Due to its versatility and multi-paradigm, this excellent programming language is explored in almost all industries and niches.

As explained in the above point, Python is capable of automating almost all your business processes, no matter how complex they are or how intensive they are. As it supports ML models and helps induce AI libraries in robotic and business process automation, Python is a choice for many to automate time-consuming, error-prone, and labor-intensive tasks.

Here are the top five tasks that you can automate with Python,

Email Automation

Sending emails is one of the tedious tasks to perform, but what if it is automated, and you could spend more time in productive work? ‘smtplib library’ is one of the greatest Python libraries that can be used to automate mail-sending tasks through the Simple Mail Transfer Protocol.

Here is an example of an email automation script using Python:

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import smtplib

def send_email(from_addr, to_addr, subject, body, password):
 message = f"From: {from_addr}\nTo: {to_addr}\nSubject: {subject}\n\n{body}"
 server = smtplib.SMTP('smtp.gmail.com', 587)
 server.login(from_addr, password)
 server.sendmail(from_addr, to_addr, message)

# Example usage:
send_email('[email protected]', '[email protected]', 'Test email', 'This is a test email sent using Python.', 'your_email_password')

File Operations

Repetitive tasks are vulnerable to human errors. Naming a few tasks like changing name conventions, making copies of older files, or copying ample files across folders would consume a lot of time; plus, any minor error would make your entire effort worthless.

If you use Python language for automation, it will lend you a helping hand with all your file management requirements. It makes tasks like naming copies, deleting, compressing, or moving files across your device easier, error-free, and manageable.

Here is an example of a file operations script using Python:

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# Read contents of a file
with open('example.txt', 'r') as file:
 contents = file.read()

# Write contents to a file
with open('example.txt', 'w') as file:
 file.write('This is a new line.')

# Append contents to a file
with open('example.txt', 'a') as file:
 file.write('\nThis is another line.')

Data Compilation

Fetching data manually out of any report, PDF, or document would be an arduous task. Besides, there are chances of errors as well. And it wouldn’t be a feasible solution to spend an entire day or two on tasks that hardly require minutes.

Using Python modules, you can fetch and compile data of any sort. Besides, It allows you to write Python scripts that assist in fetching data of whatever size and type from the documents of your choice. Moreover, you can use Python to download the extracted wealth of information in your preferred file format.

Here is an example of a data compilation script using Python:

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import pandas as pd

# Read CSV files and concatenate them
df1 = pd.read_csv('file1.csv')
df2 = pd.read_csv('file2.csv')
df_combined = pd.concat([df1, df2])

# Group data and calculate statistics
grouped = df_combined.groupby('category')
stats = grouped.mean()

# Save results to a new CSV file

Web Scraping

Web scraping is similar to data compilation automation. The only difference is web scraping is the method of extracting information from an online source because reading through online web pages and summarizing the required data would take ages for you to gain the desired outcome.

All you have to do is write a Python script for web scraping, and you are empowered to fetch a wealth of information from all over the internet. Additionally, it allows you to organize the extracted data in your preferred format.

Here is an example of a web scraping script using Python:

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import requests
from bs4 import BeautifulSoup

# Make a GET request to a webpage
response = requests.get('https://www.example.com')
html = response.text

# Parse the HTML using BeautifulSoup
soup = BeautifulSoup(html, 'html.parser')

# Find all links on the page
links = []
for link in soup.find_all('a'):

# Find all images on the page
images = []
for img in soup.find_all('img'):

Generate Reports

We know how challenging it is to generate reports manually. Python comes to your rescue. Python not only does data compilation or web scraping, but is also capable of generating reports. You can code in Python to run your commands, set your parameters, and generate reports at regular time intervals if you use the appropriate Python scripts for automation.

If you want to email the concerned party and if the contact list is too long, you can send automated emails using Python.

Here is an example for generating a report script using Python:

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import csv

def generate_report(input_file, output_file):
    # Open input file and create output file
    with open(input_file, 'r') as input_csv, open(output_file, 'w') as output_script:
        # Create a CSV reader object
        reader = csv.reader(input_csv)
        # Write header to output file
        output_script.write('# Report Script\n\n')
        # Iterate over rows in CSV file
        for row in reader:
            # Format row data as string
            row_str = ', '.join(row)
            # Write row data to output file
    print('Report script generated successfully.')

# Example usage
generate_report('input_data.csv', 'report_script.py')

Powerful Python Scripts for Automation

This section contains some of the best automation scripts in Python that streamline your mundane tasks and lets you boost productivity.

Fetch Text Out of PDF

Uses the “PyPDF2” package to fetch texts out of PDF. The extracted data out of a PDF file would be useful in generating reports, invoices, and data mining too. Run “pip install PyPDF2” for installing the package.

Now let’s see what opportunities it unlocks.

For example, if you want information out of a single page from a multipage PDF, you can automate the entire process by running the following script:

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# import module PyPDF2
import PyPDF2
# put 'example.pdf' in working directory
# and open it in read binary mode
pdfFileObj = open('example.pdf', 'rb')
# call and store PdfFileReader
# object in pdfReader
pdfReader = PyPDF2.PdfFileReader(pdfFileObj)
# to print the total number of pages in pdf
# print(pdfReader.numPages)
# get specific page of pdf by passing
# number since it stores pages in list
# to access first page pass 0
pageObj = pdfReader.getPage(0)
# extract the page object
# by extractText() function
texts = pageObj.extractText()
# print the extracted texts

Text Filtering

Text matching and filtering is an arduous task, but when it comes to using Python scripts, it becomes easier to filter than ever. Let’s understand this with an example. In the list of sales-confirmation messages, if you want to fetch out the credit card numbers, then the script below would help you find out:

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# Filter Text
# Import re module
import re
# Take any string data
string = """a string we are using to filter specific items.
perhaps we would like to match credit card numbers
mistakenly entered into the user input. 4444 3232 1010 8989
and perhaps another? 9191 0232 9999 1111"""

# Define the searching pattern
pattern = '(([0-9](\s+)?){4}){4}'

# match the pattern with input value
found = re.search(pattern, string)
# Print message based on the return value
if found:
 print("Found a credit card number!")
 print("No credit card numbers present in input")

Convert CSV to Excel

You may mostly encounter CSV files to download. Now, the downloading process is simple, but using Python would help you to skip to the good part. It allows you to convert the CSV data into Excel without letting you do the manual work.

Use “pip install openpxl” to download the “openpyxl” file.

Run the below script and convert the CSV file into an Excel spreadsheet using the below-mentioned Python script for conversion.

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# -*- coding: utf-8 -*-

import openpyxl
import sys

print("This programme writes the data in any Comma-separated value file (such as: .csv or .data) to a Excel file.")
print("The input and output files must be in the same directory of the python file for the programme to work.\n")

csv_name = input("Name of the CSV file for input (with the extension): ")
sep = input("Separator of the CSV file: ")
excel_name = input("Name of the excel file for output (with the extension): ")
sheet_name = input("Name of the excel sheet for output: ")

#opening the files
 wb = openpyxl.load_workbook(excel_name)
 sheet = wb.get_sheet_by_name(sheet_name)

 file = open(csv_name,"r",encoding = "utf-8")
 print("File Error!")

#rows and columns
row = 1
column = 1

#for each line in the file
for line in file:
 #remove the \n from the line and make it a list with the separator
 line = line[:-1]
 line = line.split(sep)

 #for each data in the line
 for data in line:
 #write the data to the cell
 sheet.cell(row,column).value = data
 #after each data column number increases by 1
 column += 1

 #to write the next line column number is set to 1 and row number is increased by 1
 column = 1
 row += 1

#saving the excel file and closing the csv file

Extract Content from Wiki

Wikipedia is a pool of ample information, and the information can be in transaction emails. It also has various other uses, such as monitoring changes and creating reports. Try the Wikipedia package for Python to swiftly collect information from Wikipedia.

Install the Wikipedia package using “pip install Wikipedia.”

If you know the particular page to fetch information from, then you can run the following Python script:

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import wikipedia
page_content = wikipedia.page("parsec").content
# outputs the text content of the "Parsec" page on wikipedia

And if you are looking for the specific page that matches the particular text, then run below Python code:

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import wikipedia
search_results = wikipedia.search("arc second")
# outputs an array of pages matching the search term

Top Seven Python Modules for Automation

Along with the Python scripts, there are several modules and tools used by Python for automation testing. You may encounter several challenges when building software, but you may overcome them with Python for automation and testing.

Top Python Modules for Automation

1. Paramiko

Python Paramiko is a library that helps to connect and communicate with remote servers through an encrypted medium. With the use of Paramiko, you can automate and streamline tasks, like transferring files, accessing remote shells, and executing commands. By using SSH clients, developers can perform ad-hoc commands. And all the commands are implemented in remote servers.

2. Pandas

Python Pandas is an open-source library that helps in data analysis and manipulation. Pandas contain data structures that work amazingly well with structured data, such as time series, spreadsheets, and tables. You can use Pandas for several purposes, such as:

  • Gaining insights through data visualization and statistical analysis
  • Adding values and managing duplicates
  • Selecting and sorting data
  • Using CSV, SQL databases, and Excel for reading and writing data

3. Selenium

One of the popular Python modules for automation is Selenium. It is an open-source tool that helps test web apps. Using Python Selenium’s scripting interface, you can even control a web browser. By doing this, you can streamline and automate all your monotonous and repetitive tasks.

Use Selenium to interact with mostly all browsers; to name a few would include Safari, Firefox, Chrome, and Internet Explorer. Talking about the features, Selenium allows you to take:

  • Screenshots
  • Fetch data out of web pages
  • Test and evaluate web apps

Selenium leverages client-server architecture through which Python code engages with a driver of the browsers that actually has control over the browser. It also allows you to code that helps automate complex manual tasks. Lastly, you can perform automation testing using Python Selenium.


If you want to interchange data between web apps and web services, then you must try JSON lightweight data interchange format. JSON is part of Python’s standard library that mainly focuses on the serialization and deserialization of data.

JSON module has two methods: “json.dumps()” and “json.loads(),” wherein the former encodes a Python object into a JSON string and the latter decodes the JSON string into a Python object.

5. Requests

Python Requests is a third-party library that ensures simplification in enabling HTTP requests. With the help of Python Requests, developers would be able to interact with APIs and several other web services by allowing them to create requests and manage responses. Due to this, even HTTP requests to web servers, uploading data, downloading files, and handling cookies become easily possible.

6. Automagica

Automagica is a robotic process automation open-source intelligent platform. However, being a comprehensive Python Module, it gives us another opportunistic area to leverage – Python for RPA. It allows you to automate several cross-platform functionalities, including

  • Manipulating PDF files
  • Automating a browser
  • Email company
  • File & folder management

The best part is, Automagica can be used to expedite the software development process.

7. PyBuilder

PyBuilder is an automation tool that streamlines repetitive tasks aligned with the development, testing, and deployment of Python projects. Thus, using Python for automation makes the development and configuration of projects better and swifter. This open-source automation tool could be personalized and bolsters plugins to add extensive features to your project. Out of several features, a few are as follows:

  • Resolution & management dependency
  • Python automation packages and automation scripts in python distribution
  • Unit, integrated, and acceptance tests
  • Bolsters several Python versions & environments
  • Enables integration with coverage, tox, and PyLint

5 Use Cases of Python Automation

Python for Automation is extensively used in several industries and apps. To name a few, are as follows:

Use Cases of Python Automation

🟠 Test Automation

One of the uses of Python automation is to test the software to check for its performance and functionality. There are several testing libraries that run tests and check for any loopholes. Running a test would help identify bugs and errors. Pytest and unit test allows developers to write and run codes as continuous integration (CI) pipeline.

🟠 Web Scraping

Another use case when Python would be a great help with automation would be when fetching data out from websites. Here, you can leverage Beautiful Soup and Scrapy Python automation library for swift data extraction and save them in your preferred format. Besides, you can even leverage web scraping for generating leads, market research, and competitor analysis.

🟠 System Administration

The third use case when it comes to automating with Python is related to system administration. The task includes keeping track of system resources, handling all the network devices, and backing up systems automatically.

🟠 Data Analysis & Visualization

Python would also contribute to data analysis and visualization. All thanks to accessibility to a large group of libraries, which includes NumPy, Pandas, and Matplotlib. Due to these libraries, the task of processing, analyzing, and visualizing becomes easier and swifter.

🟠 Robotics & IoT

As discussed earlier, Python has been popular amongst cognitive technologies, robotics, and the Internet of Things. For interacting with sensors and microcontrollers, Python is ready with PyUSB and PySerial. The use of Python includes collecting and analyzing sensor data, handling robots, etc.


Python is a potential programming language that efficiently executes automation tasks – thanks to Python scripts, modules, and libraries. Python’s excellence in executing tasks for different niches, such as web scraping, data visualization, robotics, system administration, and data analysis has been the talk of the town. And being simple and easy to understand has become a cherry on the cake for Python developers to leverage the language and perform automation tasks.

There are tons of benefits to leveraging Python for automation tasks, and all our developers are well-versed with each upgraded version. If you are planning to build an automation project, do not miss and hire python developers. The decision rests on you, but the discussion would make things simpler and beneficial for you.

Frequently Asked Questions (FAQs)

There are several automation tools for Python, each with its own benefits and pitfalls. However, to kick start your automation project using Python, our developers use the following automation tools:

  • Fabric
  • Selenium
  • PyAutoGUI
  • Robot framework
  • Ansible

Automating using Python is a step-wise process since it includes:

  • Automation task identification
  • Selection of library
  • Writing & testing the code
  • Scheduling the task

Python, a versatile programming language has a wide variety of uses. From web development to data science to scripting and desktop applications and scientific computing, this programming language makes your development process faster and more efficient.

Put Your Productivity On Autopilot With Python!

Automate your processes with Python and gain an advantage over your competitors! Contact us today to learn how we can help you automate your workflows and make your life easier.


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