Python for Data Science: Numpy and Pandas Libraries for Data

Learn how to declare and manipulate variables in Python, using the assignment operator, valid variable names and avoiding reserved words, basic data types, with arithmetic, comparison, logical, and casting operations.

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.

Explore Python control flow with if statements, for and while loops, using range, break, and continue; practice with enumerate, zip, and unzipping to iterate over colors and letters.

Master data structures in python by working with dictionaries and sets: create, update, retrieve, and delete items; use methods like get and items, and perform union, intersection, and difference operations.

Explore Python error handling basics, using try and except blocks to manage runtime errors, such as name errors, and print statements, to maintain smooth program execution.

Master defining and using Python functions with def, passing parameters, returning values, and managing local and global scope, through examples like rectangle area, even checks, and lambda expressions.

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.

Learn object oriented programming in Python, focusing on constructors and __init__ to initialize class instances. Create classes like user data and rectangle, and compute age averages and areas with methods.

Learn to implement Python dunder methods to control object construction, printing, and length with __init__, __str__, and __len__, illustrated by a user class.

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

Explore encapsulation in object oriented programming by hiding internal object data and restricting access through getter and setter methods, demonstrated with a Python class in a Jupyter notebook.

Learn how to implement multiple inheritance in Python by combining fruit and citrus fruit classes, inheriting attributes like season, color, and name.

Learn how a subclass overrides a method from its superclass and how to call the superclass method with super, demonstrating key concepts of method overriding in Python.

Explore advanced object oriented programming with class and static methods, using decorators to improve readability and understand how class state and instance relate to these methods.

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.

Explore building a color choices game milestone, selecting colors such as white, red, yellow, green, and blue, and modeling player and computer turns with win conditions.

Learn helpful notes and steps to build a choices game: define player and computer inputs, use random for moves, loop until win or tie, and review the project walkthrough.

Designs a Python color-choices game solution with importing needed modules, random computer draws, and player input, tracking computer and player scores, and guiding win conditions and replay prompts.

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.

Explore helpful notes for building a hangman game in Python. Import random modules, pick a word, track guesses with a result list, manage six tries, and determine win or loss.

Begin coding a Hangman game project in a Python notebook, import the random module, load the word list, select a random word, and initialize the game with six tries.

Complete the Hangman game project by using the random module to select words, building a result list of underscores, updating with correct letters as players guess, and displaying hangman_live visuals.

Learn Numpy fundamentals: turn data into numeric arrays, use fast C-backed computations, and employ broadcasting in Python to speed data science and machine learning workflows.

Learn to work with numpy arrays, inspect shape and size, and understand array dimensions. Convert arrays to pandas data frame and reshape to four by six as needed.

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.

Learn Numpy calculations and operators, including array creation, element-wise operations, division and floor division, power, modulo, exponential, and log, plus shape and broadcasting concepts.

Explore how numpy aggregation functions operate on arrays to compute sums, means, minimum, maximum, variance, and standard deviation, and derive square roots with clear examples.

Learn to reshape and transpose numpy arrays to align shapes for operations, avoid value errors from broadcasting, and perform safe multiplications with proper reshaping and transposing.

Learn to compare NumPy arrays with element-wise operators such as greater than, less than, equal, and not equal; ensure matching shapes and compute dot products with NumPy's dot function.

Learn to process images in Python with numpy by loading an image into an array, examining its size and shape, and accessing the RGB pixel values.

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

Download and install PostgreSQL on Windows using the graphical installer; use the default port 5432, set the server password, and optionally launch Stack Builder.

Create a new Airlines database from the server, rename it Airlines, select template zero, and save; then view the public schema in schemes, noting no tables exist yet.

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 frame operations for airline data, including selecting columns, iloc and tail to view rows, and using info and rank to inspect the data.

Visualize data with pandas by loading a csv into a dataframe and visualizing monthly sales with bar charts, showing August, then July and June as top months.

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 avoid sampling error by using multiple, larger samples from the population; analyze monthly ticket sales from airline bookings and visualize that July leads, followed by August and June.

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.

Install the lxml library to scrape a webpage with pandas, load the page data into a data frame, and extract columns like popularity and website, then compute descriptive statistics.

Visualize scraped data using numpy and pandas by creating dataframes, blending data from an ADM file, and plotting an exploded pie chart of unique visitors per month by website.

Learn how to save scraped data to a database using Python by cleaning column names, creating a database, and writing a data frame to a table.

Explore exploratory data analysis (EDA) using Python to visualize and understand the Google Play Store Apps dataset with pandas, numpy, matplotlib, and seaborn, focusing on data cleaning, processing, and insights.

Clean and preprocess the Google Play Store dataset by handling missing values, removing duplicates, addressing outliers, and applying normalization, standardization, and one-hot, label, and ordinal encoding with pandas.

Explore data visualization techniques for exploratory data analysis using matplotlib and seaborn to illustrate distributions, relationships, and patterns with the Google Play Store dataset.

Explore statistical analysis and hypothesis testing with the Google Play Store Apps dataset, using descriptive statistics, correlation and covariance, and a t-test to compare free versus paid apps.

Explore data storytelling and presentation techniques using the Google Play Store Apps dataset. Structure your story with a clear objective, engaging narrative, visual appeal, and actionable insights.

Conclude the journey exploring the Kaggle Google Play Store Apps dataset with data cleaning, EDA, visualizations, and hypothesis testing. Emphasize documenting and sharing your work on Kaggle and GitHub.