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6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

A Comprehensive Guide to Selenium with Python

ayushi9821704
ayushi9821704 20 Aug, 2024
5 min read

Introduction

Suppose you are a developer expected to carry out testing on a large web application. It is impossible to go through each feature and all the interactions one by one which may take days or even weeks. Enter Selenium, the game changing tool which automates web browser interaction and thus is more efficient when it comes to testing. As suggested in the title of the guide we’re about to look at, Selenium and Python are a powerful team when it comes to web automation. Much of its content is centered on issue solving and by the end you should be ready identifying your environment setup, effective scripting of tests as well as common web test issues changing the way you practice web automation and testing.

A Comprehensive Guide to Selenium with Python

Learning Outcomes

  • Master the basics of Selenium and its integration with Python.
  • Set up a Python environment for Selenium and install necessary packages.
  • Write, run, and debug Selenium test scripts for web applications.
  • Understand advanced Selenium features, including handling dynamic content and interacting with web elements.
  • Troubleshoot common issues encountered in web automation with practical solutions.

Table of contents

Why Learn Selenium Python?

Selenium combined with Python offers a powerful toolkit for web automation. Here’s why it’s worth learning:

  • Ease of Use: Python is an ideal language to use when writing test scripts as is made easy therefore easing the automation of tasks.
  • Widely Supported: Selenium supports different browsers as well as different operating systems.
  • Robust Community: A large community and huge documentation guarantee that you can always find the assistance and materials to fix issues and study more.
  • Enhanced Testing Efficiency: Selenium helps automate tests which cuts down on manual work, testing takes lesser time and it is highly accurate.

Pre-requisite to Learn Selenium Python Tutorial

Before diving into Selenium with Python, it’s crucial to have a foundational understanding of both Python programming and web technologies. Here’s what you should know:

  • Basic Python Knowledge: The basic knowledge of Python syntax, functions, and Object-oriented principle will go a long way in writing and interpreting Selenium scripts.
  • HTML/CSS Fundamentals: Due to HTML and CSS knowledge one can interact with the Web elements and search them successfully.
  • Basic Web Concepts: Understanding of how web-pages function, form submission, buttons, links, etc. will help with automating browser functionality.

Getting Started with Selenium and Python

Selenium can be described as a means of automating web browsers where you can create scripts of which can perform functions similar to a human. Python is easy to learn and incredibly easy to read making it very suitable to use while scripting with Selenium. First of all Selenium has to be installed, in addition to a WebDriver for the desired browser.

Installing Selenium

Begin by installing the Selenium package via pip:

pip install selenium

Setting Up WebDriver

Selenium requires a WebDriver for the browser you intend to automate. For Chrome, you’ll use ChromeDriver, while for Firefox, it’s GeckoDriver. Download the appropriate driver and ensure it’s in your system’s PATH or specify its location in your script.

For other browsers, they have their own supported web drivers. Some of them are –

Chrome:https://sites.google.com/a/chromium.org/chromedriver/downloads
Edge:https://developer.microsoft.com/en-us/microsoft-edge/tools/webdriver/
Firefox:https://github.com/mozilla/geckodriver/releases
Safari:https://webkit.org/blog/6900/webdriver-support-in-safari-10/

Writing Your First Selenium Script

Once the installation is complete, one is ready to write his or her first script. Here’s a simple example of a Selenium script in Python that opens a webpage and interacts with it:

from selenium import webdriver

# Initialize the Chrome driver
driver = webdriver.Chrome()

# Open a website
driver.get('https://www.example.com')

# Find an element by its name and send some text
search_box = driver.find_element_by_name('q')
search_box.send_keys('Selenium with Python')

# Submit the form
search_box.submit()

# Close the browser
driver.quit()

Advanced Selenium Features

As you become more familiar with Selenium, you’ll encounter more advanced features:

  • Handling Dynamic Content: Use WebDriverWait to handle elements that take time to load.
  from selenium.webdriver.common.by import By
  from selenium.webdriver.support.ui import WebDriverWait
  from selenium.webdriver.support import expected_conditions as EC

  wait = WebDriverWait(driver, 10)
  element = wait.until(EC.presence_of_element_located((By.ID, 'dynamic-element')))
  • Interacting with Web Elements: Learn how to handle different types of elements like dropdowns, checkboxes, and alerts.
  # Handling a dropdown
  from selenium.webdriver.support.ui import Select
  dropdown = Select(driver.find_element_by_id('dropdown'))
  dropdown.select_by_visible_text('Option 1')

Various Methods One Can Use in Selenium Python

Selenium WebDriver is a powerful tool for automating web-based applications. It provides a set of methods to interact with web elements, control browser behavior, and handle various web-related tasks.

Selenium Methods for Browser Management

MethodDescription
get(url)Navigates to the specified URL.
getTitle()Returns the title of the current page.
getCurrentUrl()Returns the current URL of the page.
getPageSource()Returns the source code of the current page.
close()Closes the current browser window.
quit()Quits the WebDriver instance and closes all browser windows.
getWindowHandle()Returns the handle of the current window.
getWindowHandles()Returns a set of handles of all open windows.

Selenium Methods for Web Elements

Selenium provides a range of methods to interact with web elements. Some of the commonly used methods include:

MethodDescription
find_element_by_idLocates an element by its unique id attribute. Ideal for elements with unique identifiers, ensuring quick and precise access.
find_element_by_nameLocates an element by its name attribute. Useful for form elements or fields with distinct names.
find_element_by_class_nameLocates an element by its CSS class name. Best for targeting elements that share a common class.
find_element_by_tag_nameLocates an element by its HTML tag. Effective for general searches across tags like <input>, <div>, etc.
find_element_by_link_textLocates a hyperlink element by its full visible text. Useful for finding and interacting with specific links.
find_element_by_partial_link_textLocates a hyperlink element by a partial match of its visible text. Helpful for finding links with dynamic or variable text.
find_element_by_xpathLocates an element using XPath expressions. Offers precise control and flexibility for complex queries and hierarchical searches.
find_element_by_css_selectorLocates an element using CSS selectors. Provides powerful selection capabilities based on CSS rules and attributes.

What is Selenium Used For in Python Programming?

Selenium is primarily used for automating web browser interactions and testing web applications. In Python programming, Selenium can be employed for:

  • Web Scraping: Extracting data from web pages.
  • Automated Testing: Running test cases to verify that web applications behave as expected.
  • Form Filling: Automating repetitive data entry tasks.
  • Interaction Simulation: Mimicking user actions like clicking, scrolling, and navigating.

Best Practices to Follow When Using Selenium in Python

To ensure efficient and effective Selenium automation, adhere to these best practices:

  • Use Explicit Waits: Instead of hardcoding delays, use WebDriverWait to wait for specific conditions.
  • Avoid Hardcoding Data: Use configuration files or environment variables to manage test data and settings.
  • Organize Test Cases: Structure your test cases using frameworks like pytest or unittest for better readability and maintenance.
  • Handle Exceptions: Implement error handling to manage unexpected situations and ensure scripts don’t fail abruptly.
  • Keep WebDriver Updated: Ensure your WebDriver version is compatible with your browser version to avoid compatibility issues.

Troubleshooting Common Issues

While working with Selenium, you might run into issues. Here are some common problems and solutions:

  • ElementNotFoundException: Ensure that the element is present on the page and that you’re using the correct selector.
  • TimeoutException: Increase the wait time in WebDriverWait or check if the page is loading correctly.
  • WebDriver Version Mismatch: Ensure that the WebDriver version matches your browser version.

Conclusion

Selenium when used with Python is actually quite a potent package that can greatly speed up and enhance web testing and automation. Mastering even the basic as well as the various features of Selenium will enable the developer to reduce time and automate tests, do in-depth testing. By the knowledge you have gained from this guide, you can now be confident to handle different web automation tasks.

Frequently Asked Questions

Q1. What is Selenium?

A. Selenium is an open-source tool for automating web browsers, allowing you to write scripts that can perform tasks and test web applications automatically.

Q2. How do I install Selenium in Python?

A. Install Selenium using pip with the command pip install selenium.

Q3. What is a WebDriver?

A. A WebDriver is a tool that allows Selenium to control a web browser by interacting with it programmatically. Examples include ChromeDriver for Chrome and GeckoDriver for Firefox.

Q4. How can I handle dynamic web elements in Selenium?

A. Use WebDriverWait to wait for elements to appear or change states before interacting with them.

Q5. What should I do if my WebDriver version does not match my browser version?

A. Download the WebDriver version that matches your browser’s version or update your browser to match the WebDriver version.

automationblockchainEthereumFeaturesGuidepythonseleniumtest
ayushi9821704
ayushi9821704 20 Aug, 2024

My name is Ayushi Trivedi. I am a B. Tech graduate. I have 3 years of experience working as an educator and content editor. I have worked with various python libraries, like numpy, pandas, seaborn, matplotlib, scikit, imblearn, linear regression and many more. I am also an author. My first book named #turning25 has been published and is available on amazon and flipkart. Here, I am technical content editor at Analytics Vidhya. I feel proud and happy to be AVian. I have a great team to work with. I love building the bridge between the technology and the learner.

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