Python for MLOPS

Learn how Python lists act as ordered, changeable containers for numbers, strings, and mixed data types. Master creating, indexing, slicing, and core operations like append, insert, remove, and loop.

Learn how to create and print Python lists, loop through items with a for loop, access items by index, and use list comprehensions to filter and transform data.

Learn how to add data to Python lists using append, insert, and extend, combine lists with plus, and repeat items with asterisk for practical MLOps workflows.

Learn to work with dictionaries in Python by creating and accessing key value pairs, updating and deleting entries, looping through items, and using dictionary comprehension.

Explore tuples and sets in Python, noting tuples are immutable and sets store unique values. Create, access, and unpack tuples; perform unions, intersections, and differences with sets.

Explore techniques to extract data from lists in Python, including index access, negative indexing, slicing, copying, looping through lists, and list comprehensions to filter items.

Extract data from Python dictionaries by key access and the get method; iterate over keys, values, and items, and filter with a dictionary comprehension for string values.

Learn how to define and call functions in Python, use parameters and default values, return single or multiple values, and write modular, readable code.

Explore the basic structure of Python functions, including defining, passing arguments, returning single or multiple values, and using default parameters, with examples like say_hello, greet, square, and sum and differences.

Learn how Python functions receive data with required and default arguments, using built-in int and custom examples like squared and greetings to illustrate positional and keyword calls.

Explore Python's variable and keyword arguments using asterisk args and asterisk kw args to create flexible functions and APIs, mixing fixed parameters with flexible inputs for robust MLOps utilities.

Explore the fundamentals of Python classes in this introduction to object oriented programming, covering class creation, attributes, methods, init, and instantiating objects for real data modeling in MLOps.

Learn how Python constructors initialize objects with __init__, assign attributes like brand and model, and use defaults, with car and temperature examples, and support MLOps workflows.

Learn how to add methods to Python classes using self to access data, making objects like a person, a car, and a counter interactive.

Explore class inheritance in Python by creating child and parent classes, reusing and overriding methods, and using super to extend the parent behavior.

Explore Python modules by importing standard libraries, selecting specific functions, and creating custom modules; alias libraries like NumPy, inspect modules with dir, and reuse code across projects.

Explore a real-world MLOps project structure with a pipeline folder, __init__.py, pre_process and train modules, and use absolute imports to simulate modular, pipeline-ready code.

Learn to write Python scripts that run from the terminal and import safely as modules, using docstrings, sys.argv, and the if __name__ == '__main__' pattern for robust MLOps workflows.

Create and activate virtual environments in Python to isolate dependencies, avoid version conflicts, and reproduce exact setups with a requirements.txt generated by pip freeze for MLOps.

Install pandas in a Python virtual environment, import it as pd, create a data frame from a dictionary, and access rows and columns while computing basic statistics.

Learn to load data into pandas using four sources: csv, excel, python dictionary, and a url, with read_csv, read_excel, and data frame creation for flexible data workflows.

Learn to export a pandas DataFrame to csv, excel, and json, and copy it to the clipboard for quick sharing, enabling seamless data pipelines.

Perform exploratory data analysis with pandas by inspecting data structure, computing descriptive statistics, analyzing categorical variables, and exploring relationships through grouping and correlation.

Explore common data frame operations in pandas, including selecting columns, filtering rows, updating values, creating new columns, sorting data, and dropping or resetting the index.

Learn to manipulate text in pandas dataframes to clean and standardize names and emails in new columns using string methods like lower, upper, title, split, replace, and contains.

Master pandas data transformations by using apply on columns, apply with axis for rows, map for label conversions, and apply map for per-cell operations across a data frame.

Visualize trends, distributions, and relationships in monthly sales and expenses using pandas built-in plotting tools with Metaplot, creating line, bar, pie, scatter, and box plots via Seaborn.

Learn how to create and manipulate numpy arrays, including importing numpy, creating arrays from lists, using arange, zeros, and ones, indexing, slicing, and vectorized operations.

Master numpy array operations, including slicing, stacking, splitting, and copying, to manipulate data and prepare inputs for models. Understand the difference between references and true copies to protect original data.

Turn a Python script into a command line tool with argparse, defining required --name and --age arguments. Parse the args, print a greeting, and see error handling for missing input.

Load the Titanic dataset with pandas and inspect its shape, types, and missing values, then examine statistics and duplicates to guide cleaning and modeling.

Set up a Titanic data project in Python for MLOps by organizing folders, loading the Titanic dataset with pandas and seaborn, saving to data/titanic.csv, and configuring a virtual environment.

Clean the Titanic dataset by removing duplicates, imputing missing ages with the median, and filling embarked with mode, then drop the deck column and save the cleaned data.

Engineer features on the Titanic dataset using Python functions, conditionals, and mappings to create age_group, is_alone, sex_mapped, and embarked_mapped features, preparing data for machine learning.

Analyze Titanic survival patterns with pandas, grouping by sex, age group, passenger class, embarkation, and family size to reveal how gender, class, and age influence outcomes.

Convert a Jupyter notebook into a standalone Python script, refactor with a main function, and add command-line support via argparse for production-ready MLOps workflows.