Understand MVC\/MVT architecture and REST APIs<\/li>\n<\/ul>\nData (NumPy\/Pandas\/Matplotlib)<\/h2>\n
Python is a powerhouse for data manipulation, analysis, and visualization thanks to libraries like NumPy, Pandas, and Matplotlib. These tools are essential in data science, machine learning, automation, and business intelligence workflows.<\/p>\n
NumPy \u2013 Numerical Python<\/h3>\n
NumPy provides fast, efficient handling of large arrays and matrices, along with a wide variety of mathematical functions.<\/p>\n
Key Features:<\/strong><\/p>\n\n- Multi-dimensional arrays (ndarray)<\/li>\n
- Vectorized operations (faster than native Python loops)<\/li>\n
- Linear algebra, statistics, and random number generation<\/li>\n
- Basis for most scientific computing libraries in Python<\/li>\n<\/ul>\n
Example:<\/strong><\/p>\nimport numpy as np<\/em><\/p>\na = np.array([1, 2, 3])<\/em>
\nb = np.array([4, 5, 6])<\/em><\/p>\nprint(a + b) # Output: [5 7 9]<\/em><\/p>\nPandas \u2013 Data Analysis Made Easy<\/h3>\n
Pandas builds on NumPy and adds powerful DataFrame and Series structures that make data manipulation easy and intuitive.<\/p>\n
Key Features:<\/strong><\/p>\n\n- DataFrames for tabular data (like Excel sheets)<\/li>\n
- Read\/write CSV, Excel, JSON, SQL, etc.<\/li>\n
- Data cleaning, filtering, grouping, merging<\/li>\n
- Time series support<\/li>\n<\/ul>\n
Example:<\/strong><\/p>\nimport pandas as pd<\/em><\/p>\ndata = {‘Name’: [‘Alice’, ‘Bob’], ‘Age’: [25, 30]}<\/em>
\ndf = pd.DataFrame(data)<\/em><\/p>\nprint(df[df[‘Age’] > 26])<\/em><\/p>\nMatplotlib \u2013 Data Visualization<\/h3>\n
Matplotlib is a flexible library for creating static, animated, and interactive plots in Python.<\/p>\n
Key Features:<\/strong><\/p>\n\n- Line charts, bar charts, scatter plots, histograms, etc.<\/li>\n
- Fully customizable plots<\/li>\n
- Can be combined with Pandas and NumPy<\/li>\n<\/ul>\n
Example:<\/strong><\/p>\nimport matplotlib.pyplot as plt<\/em><\/p>\nx = [1, 2, 3]<\/em>
\ny = [10, 20, 30]<\/em><\/p>\nplt.plot(x, y)<\/em>
\nplt.title(“Simple Line Plot”)<\/em>
\nplt.xlabel(“X Axis”)<\/em>
\nplt.ylabel(“Y Axis”)<\/em>
\nplt.show()<\/em><\/p>\nCombined Example: Pandas + Matplotlib<\/strong><\/p>\nimport pandas as pd<\/em>
\nimport matplotlib.pyplot as plt<\/em><\/p>\ndf = pd.read_csv(“sales.csv”)<\/em>
\ndf[‘Revenue’].plot(kind=’bar’)<\/em>
\nplt.title(“Monthly Revenue”)<\/em>
\nplt.show()<\/em><\/p>\nLearning Outcomes<\/strong><\/p>\n\n- Work with large datasets using NumPy arrays and vectorized operations<\/li>\n
- Analyze and clean real-world datasets with Pandas<\/li>\n
- Visualize trends and patterns with Matplotlib<\/li>\n
- Perform exploratory data analysis (EDA)<\/li>\n
- Build the foundation for machine learning, automation, or dashboard projects<\/li>\n<\/ul>\n
Automation (Selenium)<\/h2>\n
Selenium is a powerful Python tool for automating web browsers. It simulates user actions like clicking buttons, filling forms, or scraping data from dynamic websites\u2014making it perfect for testing, data collection, and workflow automation.<\/p>\n
What is Selenium?
\nSelenium is a browser automation framework that allows Python scripts to control web browsers (Chrome, Firefox, etc.) just like a human user would.<\/p>\n
Key Features:<\/strong><\/p>\n\n- Open web pages and interact with elements (buttons, forms, links)<\/li>\n
- Fill out and submit forms automatically<\/li>\n
- Scrape content from dynamic websites (JavaScript-heavy)<\/li>\n
- Automate repetitive web-based tasks (e.g., logins, downloads)<\/li>\n
- Capture screenshots or generate reports<\/li>\n
- Supports headless operation (run browser in background)<\/li>\n<\/ul>\n
Setup:<\/strong><\/p>\n1. Install Selenium:<\/h3>\n
pip install selenium<\/em><\/p>\n2. Download a WebDriver (e.g., ChromeDriver):<\/h3>\n\n- Match the version with your browser.<\/li>\n
- Place the driver in your system PATH or reference it in code.<\/li>\n<\/ul>\n
Basic Example:<\/strong><\/p>\nfrom selenium import webdriver<\/em>
\nfrom selenium.webdriver.common.by import By<\/em>
\nfrom selenium.webdriver.common.keys import Keys<\/em><\/p>\n# Set up the driver<\/em>
\ndriver = webdriver.Chrome()<\/em><\/p>\n# Open a website<\/em>
\ndriver.get(“https:\/\/www.google.com”)<\/em><\/p>\n# Find the search box and search<\/em>
\nsearch_box = driver.find_element(By.NAME, “q”)<\/em>
\nsearch_box.send_keys(“Python Selenium”)<\/em>
\nsearch_box.send_keys(Keys.RETURN)<\/em><\/p>\n# Wait and close<\/em>
\ndriver.implicitly_wait(5)<\/em>
\ndriver.quit()<\/em><\/p>\nUseful Selenium Functions<\/strong><\/p>\n\n\n\nAction<\/strong><\/th>\n| Method<\/strong><\/th>\n<\/tr>\n<\/thead>\n | |