🐍 Data Analysis Using Python
0.5.dev0+git.20260720.c8f33de - July 20, 2026 16:01 UTC

🐍 Data Analysis Using Python

Section 7 of the Data Analytics hub — 33 of 33 lessons.

Doing the whole workflow in Python: NumPy, pandas, and plotting for real analytical tasks.

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🐍 Python Fundamentals

From first program and Jupyter to variables, types, functions, and clean code.

• 001 · Introduction to Python and Programming Fundamentals — why analysts learn Python, and the programming ideas underneath it

• 002 · Python Fundamentals — the core building blocks of Python — values, expressions, statements, and flow

• 003 · Jupyter Notebook and Coding Environments — where analysts write and run Python — notebooks, IDEs, and the interactive workflow

• 004 · Object-Oriented Programming (OOP) in Python — the objects-and-methods model that shapes Python, including pandas and numpy

• 005 · Variables in Python — named containers for values — assigning, reassigning, and using them

• 006 · Naming Conventions and Restrictions in Python — the rules for valid names and the conventions for good ones (PEP 8)

• 007 · Data Types and Type Conversion in Python — the kinds of values Python holds, and converting between them safely

• 008 · Functions in Python — named, reusable blocks of code — defining, calling, arguments, and returns

• 009 · Code Reusability, Modularity, and Clean Code in Python — writing Python that is reusable, modular, and clean — the engineering virtues in code

• 010 · Comments, Algorithms, and Docstrings in Python — documenting code — comments, docstrings, and thinking in algorithms

🔀 Control Flow

Booleans, branching, and iteration with while, for, and range.

• 011 · Boolean Data, Comparators, and Logical Operators in Python — True/False values and the comparisons and logic that produce them

• 012 · Branching and Conditional Statements in Python — making code decide — if, elif, and else direct which code runs

• 013 · While Loops and Iteration in Python — repeating code while a condition holds — the while loop and iteration basics

• 014 · For Loops in Python — repeating code over the items of a collection — the for loop

• 015 · range() Function and Loop Control in Python — generating number sequences and controlling loops with break and continue

📚 Strings & Data Structures

Strings, lists, tuples, comprehensions, dictionaries, and sets in practice.

• 016 · Strings in Python — working with text — the string type, its operations, and its immutability

• 017 · String Indexing and Slicing in Python — reaching into strings by position — single characters and substrings

• 018 · String Formatting with .format() in Python — building strings from values cleanly — the .format() method and f-strings

• 019 · Data Types vs Data Structures & Introduction to Lists — the difference between a value’s type and a structure that holds many, and the list

• 020 · Modifying Lists in Python — changing lists in place — adding, removing, and updating elements

• 021 · Tuples in Python — ordered, immutable collections — like lists that cannot change

• 022 · Advanced Use of Loops, Lists, Tuples & List Comprehension — combining loops and structures, and the concise list comprehension idiom

• 023 · Dictionaries in Python — key-value collections — looking values up by a meaningful key

• 024 · Advanced Dictionary Usage in Python — iterating, nesting, and safely accessing dictionaries for real data

• 025 · Sets in Python — unordered collections of unique values — membership and set operations

🐼 NumPy & pandas

Vectorized arrays and DataFrames: masking, groupby, and combining data.

• 026 · Libraries, Packages, and Modules in Python — reusing others’ code — importing modules, packages, and the library ecosystem

• 027 · Introduction to NumPy and Vectorization — the numerical library and vectorization — fast array operations without loops

• 028 · NumPy Arrays (ndarray) and Core Concepts — the ndarray, NumPy’s core structure, and how it differs from a list

• 029 · Introduction to Pandas (Data Analysis Library) — the DataFrame library that brings spreadsheet/SQL-style tables to Python

• 030 · Pandas DataFrame & Series — the two core pandas structures — the table (DataFrame) and the column (Series)

• 031 · Boolean Masking in Pandas — filtering rows by a condition — the pandas equivalent of WHERE and spreadsheet filters

• 032 · Grouping and Aggregation in Pandas (groupby, agg) — grouping rows and computing per-group aggregates — pandas’ GROUP BY

• 033 · Combining Data in Pandas (concat and merge) — bringing DataFrames together — stacking with concat, joining on keys with merge

🔤 Every lesson, A–Z index

🔠 Open the full alphabetical index

Advanced Dictionary Usage in Python Advanced Use of Loops, Lists, Tuples & List Comprehension Boolean Data, Comparators, and Logical Operators in Python Boolean Masking in Pandas Branching and Conditional Statements in Python Code Reusability, Modularity, and Clean Code in Python Combining Data in Pandas (concat and merge) Comments, Algorithms, and Docstrings in Python Data Types and Type Conversion in Python Data Types vs Data Structures & Introduction to Lists Dictionaries in Python For Loops in Python Functions in Python Grouping and Aggregation in Pandas (groupby, agg) Introduction to NumPy and Vectorization Introduction to Pandas (Data Analysis Library) Introduction to Python and Programming Fundamentals

Jupyter Notebook and Coding Environments Libraries, Packages, and Modules in Python Modifying Lists in Python Naming Conventions and Restrictions in Python NumPy Arrays (ndarray) and Core Concepts Object-Oriented Programming (OOP) in Python Pandas DataFrame & Series Python Fundamentals range() Function and Loop Control in Python Sets in Python String Formatting with .format() in Python String Indexing and Slicing in Python Strings in Python Tuples in Python Variables in Python While Loops and Iteration in Python

Tags: purpose: reference topic: data analytics