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

Explore Python basics by declaring variables and strings, using the print function in a Jupyter notebook, and applying variable naming rules and uppercase constants for clear code.

Learn how to use the hash mark to add comments in Python, and see how comments organize modules, variables, and main routines while the interpreter ignores them.

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

Master working with integers and floats in Python, performing arithmetic and exponent operations, using parentheses to control order, and learning underscores for large numbers and multiple assignments.

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.

Learn to find the most common item in a list by removing duplicates with set and using max with a count key to identify the most frequent element.

Learn how Python tuples are immutable, ordered collections with index-based access. Create empty or single-element tuples, and mix data types while using optional parentheses and comma syntax.

Explore numpy, a high‑performance module for array processing in scientific computing. Create arrays with numpy.array, understand that elements share a type, and navigate rank, indexing by tuples, and shape.

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.

Explore numpy data types and how numpy guesses and forces dtypes in arrays, including ints, floats, complex numbers, and booleans, with upcasting rules.

Develop numpy arrays with constant values by using zeros, ones, empty, fill, and full to shape 1d, 2d, and 3d arrays while preserving dimensions.

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.

Explore creating 2d numpy arrays with diagonals using the identity function, shifting diagonals with the k parameter, and customizing diagonal values with diag and full or range-based fills.

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.

Explore numpy array indexing and slicing, including single and multi-dimensional arrays, using linspace to create arrays and indexing from another array or list.

Explore indexing in a multi-dimensional numpy array by constructing a 5 by 5 int array and using index arrays to select specific rows and columns with zero-based indexing.

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.

Compare in place numpy array.sort with the built-in sorted, highlighting return types none and list, and explore index based sorting with argsort to show space and performance trade-offs.

Sort a matrix by rearranging values within its columns while preserving the original shape, using an np matrix created from a string and applying sort to align the columns.

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.

Master iterating multi-dimensional NumPy arrays with nditer to flatten values and ndenumerate to access corresponding indices, enabling filtering and targeted value updates without multiple for loops.

Explore numpy-based mathematical functions to transform arrays, including square roots, exponentials, log base two, trig functions, and controlled rounding.

Compare two numpy arrays element-wise to produce boolean results using operations like ==, !=, >, <, >=, and <=; ensure arrays are the same size for accurate element-wise comparisons.

Explore conditional functions to filter data with logical conditions and vectorized operations, applying on-the-fly rules to arrays A, B, and C using numpy where and related logic.

Explore numpy aggregation functions, including sum, mean, axis handling for 1d and 2d arrays, with initial values and axis-based column or row summaries.

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 create a dataframe from a list of lists, convert a two lists setup into a pandas DataFrame, and set column names with the columns parameter.

Rename pandas DataFrame columns and indices using a stock order type example; convert a list to a DataFrame, set a column name, and map indices to B, S, H.

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

Explore using pandas testing tools to compare dataframe columns and frames with assert_series_equal and assert_frame_equal, controlling data types, names, and exactness for reliable equality checks.

Convert continuous data to categories using pandas by applying cut and qcut on the Titanic age column, creating three bins like child, young adult, and adult.

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.

Learn how to create a cross tabulation report in pandas, comparing sex and PC class using crosstab and pivot_table, including margins for totals and interpreting counts.

Learn to fill missing values in a data frame using linear interpolation. Apply the linear method to estimate gaps in large datasets with missing values.

Learn to create sample dataframes in pandas using dataframe constructors, random data, and utility methods such as make dataframe, make missing dataframe, and make time dataframe for testing.

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.

Explore path parameters in FastAPI by building endpoints like the root and API v1 articles, using article IDs and Python f-string style, with integer validation and built-in 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.

Set up a database connection using SQLAlchemy by creating an engine, declarative base, and a session, then configure a Postgres URL and bind it to the engine.

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.