Mega Python - Pandas, Numpy, ML, APIs, GraphQL, AWS, PySpark

download the sample Python notebook and upload it to the Jupyter notebook environment, then open it and run commands to explore Python structures.

Explore Python string formatting techniques using f-strings and format to create universal templates with placeholders, dynamic variables, and reassignment, illustrated through greetings.

Explore modulus and floor division to compute remainders and truncated quotients in Python, using examples like 11 mod 5 and 11 // 5 to distinguish remainder from truncation.

Explore numpy array attributes, focusing on shape as a tuple of dimensions. Determine how many rows and columns a 2d array has, and distinguish single versus multi dimensional arrays.

Explore NumPy array attributes such as ndim, size, dtype, and nbytes, demonstrating how to determine dimensions, count elements, inspect data types, and measure storage for one- and two-dimensional arrays.

Discover how to create arrays from space values using range, linspace, and logspace in NumPy, with start, end, and step, noting end exclusion for range and end inclusion for linspace.

Create NumPy arrays from functions using custom and lambda functions, defining shape and dtype for 1d and 2d arrays, including a 3x4 example.

Learn indexing and slicing of a 2d array, using row and column indices (zero-based), with examples like retrieving 52 and comparing double-bracket versus single-bracket notation, plus slicing ranges.

Explore numpy views and copies to understand how slicing creates a view that reflects changes in the original array, and how copying with copy() isolates modifications.

Learn how to reshape a 1d numpy array to 2d with numpy.reshape, using a 4x2 example, and flatten or ravel to 1d. Reshape and ravel produce views, flatten copies.

Learn how to join NumPy arrays using concatenate along different axes, and use stack, hstack, vstack, and dstack to combine arrays into new shapes.

Explore how to use numpy's array_split, split, and vsplit to divide 1d and 2d arrays into multiple equal or uneven sections, including along rows and columns.

Learn how NumPy's sort method arranges elements in ascending or descending order, returning a copy to keep the original array intact and applying to numbers, strings, booleans, and 2D arrays.

Learn to iterate through 1D, 2D, and 3D numpy arrays using Python for loops, printing each scalar element and building from single loops to nested loops.

Create an empty data frame by calling the pandas data frame constructor with no arguments, yielding zero columns and indices for later data insertion.

Learn to convert a list of dictionaries into a pandas DataFrame using pd.DataFrame, with dictionary keys becoming columns and optional index customization.

Resample a date time column in pandas to compute daily, monthly, and yearly means, using the on parameter or a time index, with stock and UFO data examples.

Use pandas query to filter and calculate mean values without intermediate variables, perform group-by symbol means, chain operations, and merge to reveal missing rows.

Build fast, production-ready rest APIs with Python using FastAPI. Create CRUD operations, database connections, JWT security, and OpenAPI documentation.

FastAPI's built-in interactive API documentation at /docs, with automatic swagger/openapi, endpoint listings, path parameters, data validation, and a live try-out to explore and test your APIs.

Distinguish path parameters from query parameters and define defaults, types, and optional values for API endpoints. Use skip and limit to control article lists and demonstrate input validation.

Define a SQLAlchemy model for the postgres database by creating article class mapped to the articles table, with id as a primary key, and title string(100) and description string(255) columns.

Learn how http status codes signal API responses, from 200 and 201 to internal server error and bad request, and use FastAPI’s status class to assign these codes in endpoints.