Python for Automation: Enhance Your Productivity

Software development can be a complex process and involves various moving elements. Developers don't have enough time during the day to manually review every task; this is where automation comes in handy. Python is a popular programming language used for building various projects because of its simplicity and ease of use. Python for automation is simple to learn, and given its open-source community, several libraries, modules, tools, and algorithms are freely available for use. There are also many pre-built features, like data manipulation, data handling, regular expressions, etc.

Python can be used along with third-party libraries, automating activities such as email management, system administration, and web scraping. In this guide, we will cover everything regarding using Python for automation.

Why use Python for Automation?

Python doesn't have a steep learning curve, and its syntax is easy to grasp. Many developers consider using Python for automation because of its readable code, and Python is an almost universal programming language that can solve various tasks. It can be used for data analytics, web scraping, and test automation for desktop and web apps.

Python can write with just one line of code, whereas Java and other frameworks use multiple lines. It can enhance team productivity and has a massive community backing that can help save time. Python script automation makes developers' lives easier from development to deployment, production, and continuous integrations in test environments.

The best framework for Python automation projects is Pytest currently. It can handle integration and unit testing and seamlessly integrates with other frameworks like Flash and Django. Python frameworks have ready-to-use libraries and packages. Developers can take advantage of their distributed functions without experiencing any negative consequences.

The command line is another excellent test automation workflow. Python's rich command line support simplifies test automation and can use calls to drive manual testing where needed. Many Python development companies are taking advantage of this.

Python Scripts for Automation

Python scripts for automation can be used to store, process, and share data. They offer the ability to create lists and dictionaries and can improve automation responsiveness. Python features a vast set of libraries and its intuitiveness. Its intuitiveness when it comes to designing the latest applications is one among many reasons why developers choose it.

Python APIs and automation scripts make it convenient to pull live traffic data from third parties in real-time. Developers can use these scripts to extract huge volumes of data from web pages, consolidate information, and compile files.

Every automation script has a different purpose. Below are some of the most popular Python automation scripts you can use to get started:

1. Python Proofreading Script

You can use this proofreading script to spot spelling errors and fix grammatical mistakes in writing. It uses the Lmproof module and is excellent for editors as well:

# pip install lmproof

Import am proof

def proofread(text):

   proofread = lmproof.load("en")

   correction = proofread.proofread(text)

   print("Original: {}".format(text))

   print("Correction: {}".format(correction))

  proofread("Your Text")

2. Automatically Convert PDF to CSV Files

To convert PDF to CSV files for use in different projects, you can use the following script:

import tabula 

filename = input("Enter File Path: ")

df = tabula.read_pdf(filename, encoding='utf-8', spreadsheet=True, pages='1') 

df.to_csv('output.csv')

It uses the Tabula library to execute the code and requires the installation of pip. The read_pdf() takes in the file, and the to_csv() function converts the desired output into CSV format.

3. Photo Compression

Do you want to compress high-quality images without losing quality or going through heavy pixelation? If so, then a Python photo compression script is just what you need. The Python Imaging Library (PIL) is used to edit images, add filters, smooth and sharper, and can even be used for edge detection.

Try out this script:

Import PIL

from PIL import Image

from Tkinter. File dialog import * 

fl=askopenfilenames()

img = Image.open(fl[0])

img.save("output.jpg", "JPEG", optimize = True, quality = 10)

4. Automatic Text to Speech Converter

The Google Text-to-Speech API can be used with Python to convert text to speech. The API is constantly updated and understands various languages, dialects, pitches, and voices.

Here is the script:

from pygame import mixer

from gets import TTS

def main():

   tts = gTTS('Like This Article)  

tts.save('output.mp3')  

mixer.init()  

mixer. music.load('output.mp3')  

mixer. music.play()

if __name__ == "__main__":

main()

5. URL Shortener

You can shorten URLs automatically using a Python script. You can use this code snippet for it:

from __future__ import with_statement

import contextlib

Try:

                    From urllib. Parse import urlencode

Except for ImportError:

         From urllib import urlencode

try:

         From urllib.request import urlopen

Except ImportError:

         from urllib2 import urlopen

import sys

def make_tiny(url):

         request_url = ('http://tinyurl.com/app-index.php?' +

         urlencode({'url':url}))

         with contextlib.closing(urlopen(request_url)) as response:

                 return response.read().decode('utf-8')

def main():

         for tinyurl in map(make_tiny, sys.argv[1:]):

                 print(tinyurl)

if __name__ == '__main__':

         main()

Use Case of Python Automation

Sending HTTP Requests

The GET And POST Python scripts can send HTTP requests and data to and from servers.

Do Maths Equations

Python can be used to do quick math equations using the SymPy library. Developers can perform advanced mathematical functions using the expressions in the code.

Calculate Exchange Rates

The Forex-Python module can be used to calculate exchange rates. The module will output real-time currency exchange values.

Data Science Applications

Python libraries can be used to extract valuable information and process data. They can help data scientists generate data visualizations and graphs. Matplotlib and Seaborn are two popular data visualization libraries, and many research-based companies are using Python automation scripts to streamline the data analysis process.

Artificial Intelligence and Game Development

One of the best real-world use cases of Python for automation is in the area of Artificial Intelligence. From image recognition, advanced data processing, Machine Learning models, and complex computations, AI libraries in Python and automation scripts can make a big difference.

Game development is another prominent industry where Python automation scripts are being used. They are meant for interactive game development and real-world Python gaming projects, including World of Tanks, Frets on Fire, and Battlefield 2. PySoy and PyGame libraries are used with Python automation scripts to design different game levels and handle multiple in-game requests. They can also be used to build powerful interfaces and install 3D game engines.

Python Automation for Projects

Some of the most popular Python automation projects you can do are:

• Machine Learning Prediction Modeling – You can build an app with Python and design machine learning models to make predictions across various industries. Artificial intelligence has recently been powering drones, and the entire code is written in Python. TensorFlow, Scitkit-learn, and Pandas are popular Python libraries for machine learning projects. 

• Share Market Tracker – You can track stocks and shares in markets by using Python and make forecasts about profitable investments. LSTM (Long Short-Term Memory) is a recurrent neural network architecture used with Python for making accurate stock market predictions. It can analyze and provide insights on the latest market trends and scan large volumes of data. 

• File Management – Python automation scripts can be used for various file management operations. You can use them on various projects requiring automatically creating, deleting, renaming, or modifying files. 

• Data Mining – Data mining converts raw data into structured text using statistical modeling and analysis. Python libraries like Numpy and Pandas can extract data from multiple sources on the internet. Regression and classification ML techniques can refine results and deliver companies meaningful information and insights for better decision-making. 

• Schedule Automated Reminders & Emails – Python automation can send automated emails and schedule client reminders. It enhances the productivity of development teams, and the smtplib package can be used for such tasks.

Conclusion:

Every project is unique, and you can use Python automation scripts to streamline different aspects of custom application development.

You can contact us if you need help getting started with Python automation projects. We help you hire Python developers for different industry verticals, and our developers are experienced with various frameworks.