What is Python RPA Library & Top 7 Vendors in 2024

Robotic process automation (RPA) has rapidly gained popularity as companies aim to drive greater efficiency. The RPA market is forecasted to reach $13.74 billion by 2028, as per Grand View Research[1]. With its versatility and ease of use, Python has emerged as a preferred choice for developing software bots, especially for enterprise RPA programs.

According to the TIOBE index of language popularity, Python ranks #1 as of January 2023. The language has seen massive 2.69% year-over-year growth[2]. Python is the tool of choice formany software automation scenarios given its extensive libraries and frameworks for application integration, web scraping, computer vision, natural language processing, and more.

A Python RPA library provides pre-built utilities to accelerate developing automation solutions. Instead of coding capabilities from scratch, developers can leverage Python libraries to quickly build software robots for repetitive tasks. This allows focusing on application logic rather than re-inventing fundamental components.

What Can Python RPA Libraries Do?

Python RPA libraries offer building blocks to assemble software bots capable of:

  • Interacting with GUI-based apps like browsers, desktop software, etc. through user interface (UI) automation
  • Scraping data from websites by programmatically crawling, parsing, and extracting information
  • Connecting to databases and APIs to fetch or update data
  • Reading and writing files like Word docs, PDFs, spreadsheets
  • Making decisions based on business rules and data inputs
  • Performing OCR to convert scanned images into machine-readable text
  • Scheduling and orchestrating tasks across distributed environments
  • Generating analytics to track bot activities and performance

By combining different libraries, RPA developers can build sophisticated bots tailored to in-house needs. This table shows examples of popular Python libraries for various RPA capabilities:

RPA Capability Python Libraries
User Interface Automation Selenium, PyAutoGUI, Pywinauto
Web Scraping Beautiful Soup, Scrapy, lxml
Data Manipulation NumPy, Pandas, Apache Spark
Computer Vision / OCR OpenCV, Tesseract OCR, Google Vision
Connectors Pyodbc, pymongo, pymysql, boto3
Task Scheduling RPA Framework, Robocorp Control Room
Reporting and Analytics Matplotlib, Plotly, Dash

Emergence of Commercial RPA Platforms

While open-source Python libraries provide the raw materials for RPA, commercial RPA platforms make the process more accessible to citizen developers without extensive coding skills. They package various capabilities into an integrated suite with features like:

  • Low-code environment with drag-and-drop bot design
  • Centralized control room for deployment, monitoring, and management
  • Pre-built connectors to enterprise apps like SAP, Oracle, Salesforce etc.
  • AI capabilities like natural language processing, sentiment analysis etc.
  • Built-in analytics and reporting for compliance needs
  • Technical support for implementation and maintenance

Popular commercial RPA platforms like UiPath, Automation Anywhere, Microsoft Power Automate, Blue Prism, etc. incorporate Python and open libraries while making bot development simpler through low-code models. They provide fuller-fledged capabilities for enterprise-grade reliability, security, and scalability.

Organizations should evaluate open source Python libraries versus commercial tools based on their budget, skills, integration needs and other factors.

7 Top Python Libraries for Building RPA Bots

Here are some of the most widely used Python libraries for RPA capabilities:

1. Selenium

Selenium provides a browser automation framework for testing web applications. It supports automating actions like:

  • Navigating to URLs
  • Clicking buttons and links
  • Filling out and submitting forms
  • Asserting page content

Pros:

  • Supports multiple languages including Python
  • Cross-browser compatibility
  • Integrates with testing frameworks like unittest, pytest etc.
  • Large open source community

Cons:

  • Steep learning curve
  • Brittle tests prone to breaking with web page changes
  • Cannot automate desktop or mobile applications
  • No native scheduler or management console

Example: Automating login on a website:

from selenium import webdriver
from selenium.webdriver.common.keys import Keys  

browser = webdriver.Chrome()
browser.get(‘https://www.mysite.com/login‘)

user_field = browser.find_element_by_id(‘username‘) 
user_field.send_keys(‘johndoe‘)  

pass_field = browser.find_element_by_id(‘password‘)
pass_field.send_keys(‘abcd1234‘)  

login_btn = browser.find_element_by_id(‘login‘)
login_btn.click()

2. Beautiful Soup

Beautiful Soup is a popular Python library used for web scraping data from HTML and XML pages. Key features:

  • Parses structured data from documents
  • Navigates and searches parse trees
  • Finds and extracts data like text, links, images etc.

Pros:

  • Simple and intuitive API
  • Integrates well with requests, lxml, etc.
  • Can handle poorly formatted markup
  • Great documentation and examples

Cons:

  • Not built for large scale web crawling
  • Scrapes static pages, not dynamic content
  • Basic string cleaning capabilities

Example: Extracting headlines from a news site:

import requests
from bs4 import BeautifulSoup

page = requests.get("https://www.reuters.com") 
soup = BeautifulSoup(page.content, ‘html.parser‘)

headings = soup.find_all(class_="MediaStoryCard__heading___3Unio") 

for h in headings:
   print(h.text.strip()) 

3. Scrapy

Scrapy is an open source high-performance web crawling and scraping framework in Python. It is used for:

  • Extracting data at scale from websites
  • Writing complex scraping pipelines with middleware
  • Storing scraped data into databases or cloud storage

Pros:

  • Fast and scalable data extraction
  • Powerful parsing capabilities
  • Rich ecosystem of pluggable extensions
  • Integrates well with big data stacks

Cons:

  • Steep learning curve
  • No visual interface, coding required
  • Fragmented documentation

Example: Scraping quotes from a website using Scrapy:

import scrapy


class QuotesSpider(scrapy.Spider):
    name = "quotes"

    def start_requests(self):
        urls = [
            ‘http://quotes.toscrape.com/page/1/‘,
            ‘http://quotes.toscrape.com/page/2/‘,
        ]
        for url in urls:
            yield scrapy.Request(url=url, callback=self.parse)

    def parse(self, response):
        page = response.url.split("/")[-2]
        filename = ‘quotes-%s.html‘ % page
        with open(filename, ‘wb‘) as f:
            f.write(response.body)
        self.log(‘Saved file %s‘ % filename)

4. OpenCV

OpenCV (Open Source Computer Vision Library) provides algorithms for image processing, computer vision, and machine learning. It is used for:

  • Object detection and tracking
  • Image classification
  • Face recognition
  • Optical character recognition (OCR)

Pros:

  • Comprehensive library of computer vision algorithms
  • Supports programming languages like Python, C++, Java etc.
  • Integrates well with machine learning and big data frameworks
  • Vibrant open source community

Cons:

  • Steep learning curve
  • Rapidly evolving API with breaking changes
  • Challenging for production deployment at scale

Example: Detecting and reading text from an image using OpenCV and pytesseract OCR:

import cv2
import pytesseract

img = cv2.imread(‘image.png‘) 

text = pytesseract.image_to_string(img)

print(text)

5. Pandas

Pandas provides easy to use data structures and data analysis tools for Python. It is commonly used for:

  • Importing data from various sources
  • Cleaning and transforming data
  • Merging, joining, and shaping datasets
  • Analyzing and visualizing data

Pros:

  • Fast and efficient for working with large datasets
  • Intuitive data structures like DataFrames
  • Integrates tightly with NumPy, matplotlib and scipy
  • Flexible data retrieval using indexes

Cons:

  • Performance issues with extremely large datasets
  • Not ideal for real-time data processing
  • Too low level for interactive business analysis

Example: Loading, processing, and analyzing CSV data using Pandas:

import pandas as pd

df = pd.read_csv(‘data.csv‘) 

# Remove column
df = df.drop(columns=[‘Address‘])  

# Change data type of column  
df[‘Price‘] = df[‘Price‘].astype(int) 

# Groupby and aggregate
df_aggregated = df.groupby(‘Item‘).agg({‘Price‘: ‘mean‘}) 

print(df_aggregated)

6. PyTorch

PyTorch is an open source machine learning library optimized for deep neural networks. It is commonly used for:

  • Building and training deep learning models
  • Neural network layers, loss functions, and optimizers
  • Multi-GPU and distributed training
  • Deploying models to production

Pros:

  • Seamless GPU acceleration
  • Intuitive Pythonic API for deep learning
  • Lightning-fast performance using C++ and CUDA backends
  • Rich ecosystem of models, datasets and tools

Cons:

  • Documentation can be spotty in parts
  • GPU/TPU resource management is challenging
  • Difficult debugging for dynamic computational graphs

Example: Training an image classifier using PyTorch:

import torch
from torch import nn
from torch.utils.data import DataLoader
from torchvision import datasets, transforms

# Define model  
model = nn.Sequential(
    nn.Conv2d(1, 16, 3, 1, padding=‘same‘), 
    nn.ReLU(),
    nn.Conv2d(16, 32, 3, padding=‘same‘),
    nn.ReLU(),
    nn.Flatten(),
    nn.Linear(32 * 28 * 28, 10)
)

# Load and transform data
transform = transforms.Compose([transforms.ToTensor()])
trainset = datasets.MNIST(‘./data‘, train=True, download=True, transform=transform)
trainloader = DataLoader(trainset, batch_size=64, shuffle=True)

# Train model
model = model.cuda()
criterion = nn.CrossEntropyLoss() 
optimizer = torch.optim.Adam(model.parameters())

for epoch in range(5):  
    for data in trainloader:
        img, label = data
        img = img.cuda()
        label = label.cuda()

        output = model(img)
        loss = criterion(output, label)

        optimizer.zero_grad()
        loss.backward()  
        optimizer.step()

print(‘Finished training‘) 

7. Twilio

Twilio provides a Python library for integrating telephony, messaging, video, and communications capabilities. It enables:

  • Programmatically making and receiving phone calls
  • Sending and receiving SMS
  • Building chatbots on WhatsApp, Facebook Messenger etc.
  • Embedding live video chat into apps

Pros:

  • APIs are simple and intuitive to use
  • Globally scalable cloud infrastructure
  • Flexible pay-as-you-go pricing
  • Supported by detailed documentation

Cons:

  • Can get expensive for high volumes
  • Voice quality issues occasionally reported
  • Some features like email limited to higher pricing tiers

Example: Sending SMS notifications using Twilio:

from twilio.rest import Client

account_sid = ‘ACxxxxxxxxxxxx‘ 
auth_token = ‘xxxxxxxxxxxxxxxx‘
client = Client(account_sid, auth_token)

message = client.messages.create(  
                              messaging_service_sid=‘MGxxxx‘, 
                              body=‘Your order has shipped!‘,      
                              to=‘+15558675309‘ 
                          ) 

print(message.sid)

Key Considerations for Choosing a Python RPA Library

Here are some top factors to evaluate when selecting Python libraries for RPA:

  • Application landscape: Assess whether the library caters to web apps vs. desktop apps, supports mobile devices etc.

  • Developer skills and capacity: Opt for low code libraries like Robot Framework if coding expertise is scarce.

  • Library maturity: Prefer established libraries like Selenium and BeautifulSoup over newer ones.

  • Performance and scalability: Ensure the library can handle the required workload especially for data and ML intensive processes.

  • Integration: Check if the library readily integrates with your broader tech stack.

  • Support and community: Look for libraries with abundant documentation, tutorials, and forums.

  • Licensing: Apache 2, MIT, GPL or proprietary licensing terms.

Evaluate shortlisted libraries through proof of concepts across diverse use cases before full adoption.

The Rise of Python for Enterprise RPA

Python has become a mainstay of enterprise automation initiatives thanks to its versatility. IT leaders prefer Python for RPA given its rich set of libraries, scalability, and easier learning curve compared to traditional languages.

By leveraging Python‘s expansive ecosystem, developers can avoid reinventing the wheel and focus on addressing business needs. Python lowers barriers for rapidly deploying software robots across legacy and modern application landscapes.

As per recent IDC estimates[3], enterprises are looking to double their RPA capacity within two years. With its flexibility and developer-friendly capabilities, Python will continue to fuel next-gen hyperautomation initiatives seeking to emulate human abilities at scale.

References

[1] Research and Markets. Global Robotic Process Automation Market Size, Share & Trends Analysis Report By Type, By Service, By Deployment, By Organization Size, By Application, By End-use, By Region, And Segment Forecasts, 2021-2028. December 2020. https://www.grandviewresearch.com/industry-analysis/robotic-process-automation-rpa-market

[2] TIOBE. TIOBE Index for January 2023. January 2023. https://www.tiobe.com/tiobe-index/

[3] IDC. IDC Survey Spotlights Digital Transformation Investment, Maturity, and Challenges. November 2021. https://www.idc.com/getdoc.jsp?containerId=prUS48514721