Python for Data Science: Numpy and Pandas Libraries for Data

Explore Python string functions through practical examples, including length, indexing, counting, and finding. Learn to transform text with lower, upper, capitalize, swapcase, title, join, replace, and strip operations.

Create and import Python modules to reuse code, and define simple functions such as even or odd. Open, read, write, and close files using appropriate modes, handling non-existent files.

This lecture introduces object oriented programming in python by defining a simple class with class and instance attributes. It covers the self parameter and a basic class example.

Explore inheritance in Python by creating a superclass and extending it with subclasses, and practice overriding to update name, age, and related data.

Learn to use Python's built-in decorators, especially the property decorator, to manage name and color in a fruit class and convert fruit_type from an attribute to a method.

This lecture walks through building a hangman game in Python, detailing the word list, hangman figures, gameplay flow, and coding steps, with hands-on demonstrations.

Learn to create NumPy nd arrays with zeros, ones, and arange, control shape and elements, and generate random arrays with NumPy random functions, using seed for reproducible results.

Learn numpy unique and practical array slicing and indexing. Practice selecting elements from multi-dimensional arrays, including the last element of every matrix in a 3x4x5x6 array.

Install Jupyter Lab and Pandas, set up the orbital lab environment, launch a Python 3 notebook, and begin coding in the first cell.

Restore databases by right-clicking the airlines database and selecting restore, then choosing the sql file extension from the file menu and clicking restore; verify the airline's database table after completion.

learn to export a SQL query to csv from an airlines database, load it with Python pandas, extract English city names, clean them, and inspect data with head for analysis.

Connect a PostgreSQL database with Python by installing the PostgreSQL package, run a query, and use fetchmany and fetchall to retrieve varying records from the ticket_flights table.

Learn to query an airlines database from Python using pandas to fetch tickets and aircraft data, load into data frames, and use natural joins to avoid duplicate seat number columns.

Explore Pandas data analysis by reading a csv, inspecting dtypes, and computing statistics with describe, focusing on the sales column to reveal count, mean, std, min, max, and quartiles.

Learn to scrape website data with Python using requests and BeautifulSoup, extract and clean HTML content, and load results into pandas and PostgreSQL, while respecting robots.txt.