Python Pandas Data Crash Course 2025 Learn by Doing.

Explore variables and Python's dynamic typing, learning to store and reassign values across integers, strings, booleans, and floats. Discover built-in data types—lists, tuples, and dictionaries—and how mutability shapes your code.

Explore Python strings by learning creation with quotes and performing operations like concatenation, repetition, and indexing. Discover methods such as lower, upper, split, and join to manipulate text.

Learn to concatenate strings with the plus operator, compute length with the length function, and access characters using zero-based indexing in Python.

Master Python string methods like lower, upper, and strip to transform text and clean whitespace, and explore built-in data types such as integers, floats, strings, lists, tuples, and dictionaries.

Explore essential Python string methods, including split with a delimiter and replace, to master text processing and manipulate strings effectively.

Learn how to format strings in Python using placeholders and string interpolation, employing the format method and f-strings (Python 3.6+) to embed variables like name and age into dynamic messages.

Explore Python tuples as immutable, zero-indexed sequences that support indexing, slicing, and iteration, with conversion to lists or sets, note they are faster than lists and usable as dictionary keys.

Explore Python sets, created with curly braces or the set() function, and perform add, remove, union, intersection, difference, and symmetric difference to manage unique elements.

Explore Python dictionaries, an unordered collection of key-value pairs, learn to access, update, add, and remove items, and use built-in methods like get, items, keys, values, and update.

Learn to use Python for loops to iterate over lists, tuples, strings, arrays, and other sequences with range, enumerate, and zip, printing items and their positions.

Master advanced for loops by unpacking tuples and dictionaries, using range with start, stop, and step, and applying break, continue, and else.

Explore while loops in Python, compare with for loops, and master recursion, break, and max iterations, plus list and dictionary comprehensions to avoid infinite loops.

Master practical techniques for while loops in Python, including pre-loop variable setup, descriptive loop counters, and guarding against infinite loops with break conditions.

Master Python error handling with try and except blocks, catch name errors, handle undefined variables, and respond to unexpected issues in your code.

Master function arguments in Python by handling positional and keyword arguments and using default parameters for fallback values.

Explore recursion in Python by learning how a function can call itself, define base and recursive cases, and solve problems like countdowns and factorials.

Explore function decorators and higher order functions in Python to enhance function behavior without changing source code.

Explore lambda functions, also known as anonymous one-line functions in Python, and apply them with map, filter, and reduce for concise one-time operations.

Learn how function caching and memoization boost performance by storing results of expensive calls. See examples with Fibonacci and factorial, using decorators from the functools module to reuse cached results.

Learn how to create and import Python modules, define functions such as even or odd, and perform file input/output with various open modes, reading lines, and proper closing.

Learn object-oriented programming in Python by defining a class, distinguishing class attributes from instance attributes, and implementing methods with self, including the __init__ constructor, to create and use objects.

Explore object oriented programming in Python by creating classes with constructors, initializing instances with name and age, and defining methods to calculate the average age and rectangle areas.

Learn to implement dunder methods in Python, using __init__ to initialize name and age, and __str__ and __len__ to print objects and measure length.

Learn how inheritance enables a subclass to extend a superclass, override attributes, and reuse methods, illustrated with a user data model and a phone number subclass.

Learn encapsulation in object-oriented programming by hiding internal data and exposing it with getter and setter methods, demonstrated with a Python class in a Jupyter notebook.

Learn how to implement multiple inheritance by combining fruit-related classes, sharing attributes such as season, color, and name, and create a citrus fruit object to display its properties.

Explore how a subclass overrides a method of its parent class in Python, using self to access attributes and demonstrating override behavior.

Explore decorators in Python by comparing regular, class method, and static method definitions, showing how self and cls affect access to class state and code readability.

Explore built-in decorators and the property decorator to manage fruit class attributes, implement getters and setters, and keep user code stable while maintaining name and color logic (lemon, yellow, orange).

Collect data from diverse sources; clean and organize it by removing duplicates, correcting timestamps and column names, and filling missing values. Use exploratory data analysis to inform decisions.

Learn to download and install PostgreSQL on Windows using the graphical installer, configure the server password, and complete the setup with Stack Builder.

Learn database creation via the admin interface, rename the new database to airlines using template zero, then review the definitions tab and public schema; note there are no tables yet.

learn how to restore the airlines database using a graphical user interface by right-clicking the airlines database, selecting restore from the file menu, and confirming the restoration.

Install Jupyter Lab and pandas, launch the lab server, create a Python 3 notebook, and begin coding in the first cell.

Export a portion of the airlines database to a csv via an sql query, clean city names using json data, and load and inspect the csv with pandas for analysis.

Learn to load a PostgreSQL database in Python by installing psycopg2, connecting with a cursor, and fetching records from ticket_flights, first 20, then 10, then all.

Learn to query an airlines database with Python pandas, using a connection and cursor to execute SQL, fetch results into dataframes, and apply natural joins to avoid duplicate columns.

Learn to work with a pandas data frame: select columns, view top rows, slice with iloc, query rank and info, and access the pandas docs via a web browser.

Load data with Pandas from a CSV into a dataframe and compute the mean of sales. Visualize monthly sales with bar charts to show August leads, then July and June.

Learn how to use pandas to read a csv, inspect data types, and compute statistics for numeric columns, focusing on sales to obtain mean, std, min, max, quartiles, and median.

learn to avoid sampling error by using larger samples from the population, analyze bookings data by month, and identify july as the peak sales month for tickets.

Learn to scrape websites with Python using requests and BeautifulSoup, parse HTML, and extract text for analysis, using Wikipedia as a demo while considering robots.txt and privacy.

Install lxml and use pandas to scrape an HTML table from a webpage, convert it to a data frame, and describe key columns like popularity and unique visitors per month.

Visualize the scraped data by importing, blending, and storing it in data frames, then chart websites and their unique visitors per month using a pie chart with explode effects.

Learn to save scraped website data as a table in a database using Python and pandas, clean column names, and store data in popular_language table using a database engine.

Design and run an A/B test in Python using pandas, calculate conversion rates, build contingency tables, and apply chi-square tests to make data-driven marketing decisions.

Design and analyze an A/B test with segmentation in Python to compare conversion rates across age groups, using pandas dataframes and chi-square tests to interpret results.

Calculate lift and perform significance testing on marketing data in Python to evaluate conversion rates between A/B groups using a chi-square test and a contingency table.

Perform exploratory data analysis on Kaggle playground dataset using Pandas, NumPy, and visualizations with matplotlib and seaborn, then preprocess by cleaning data and identifying categorical and float and integer features.

Learn to preprocess data with a pipeline of iterative imputer, standard scaler, and one hot encoder, then transform and visualize training and test sets to reveal feature distributions.

Split the data with train_test_split (test size 0.15) and compare four models—decision tree, random forest, SVC, and logistic regression—using cross-validation to select the best, with random forest highlighted as top.

Learn how to tune hyperparameters and fit an XGBoost model using cross-validation and early stopping to find optimal boosting rounds while preventing overfitting.

Apply the well-tuned XGBoost model to predict probabilities on the test set using predict_proba, build a submission dataframe, and save results as submission.csv for submission.

Explore predictive modeling with Python and scikit-learn, using pandas and the Titanic dataset to build a logistic regression model that predicts survival, handles missing values, encodes categoricals, and scales features.

Conduct data exploration and preprocessing of the Titanic dataset with pandas, matplotlib, and seaborn; handle missing values, remove outliers, one-hot encode sex, and standardize age and fare for modeling.

Explore model selection and evaluation for the Titanic dataset, comparing logistic regression, decision trees, random forests, and support vector machines using pandas and scikit-learn, with accuracy, precision, recall, and F1.

Train logistic regression, decision tree, and random forest on the Titanic dataset, using train–test splits and grid or random search with cross‑validation to find best hyperparameters.

Deploy trained predictive models to production, save and load with joblib, and generate predictions on new Titanic data while analyzing feature importance and addressing bias, monitoring, and explainability.

Explore five chat prompts that shape data analysis by specifying datasets and desired outcomes, visualizing data, building predictive models, segmenting data, and identifying correlations.

Discover five targeted chat prompts to sharpen data analysis, visualize insights with charts and graphs, build predictive models, segment data, and uncover correlations for smarter decisions.