Fundamentals of Python Programming

Quick review on Python Programming Language

Python is a high-level, interpreted programming language known for its simplicity and readability. It supports multiple programming paradigms, including procedural, object-oriented, and functional programming.

Key Concepts:

• Interpreted: Python code is executed line by line, making it easy to test and debug.

• Dynamic Typing: Variables in Python are dynamically typed, meaning their types are inferred at runtime.

• Indentation: Python uses indentation to define blocks of code, enhancing readability.

Data Types, Variables, and Operators

Data Types:

• Numeric Types: Integers (int), Floating-Point Numbers (float), Complex Numbers (complex).

• Sequence Types: Lists (list), Tuples (tuple), Strings (str).

• Mapping Type: Dictionary (dict).

• Boolean Type: Boolean (bool).

Variables:

• Variables are used to store data values. They are created when a value is assigned to them.

• Variable names must start with a letter or underscore (_), followed by letters, digits, or underscores.

Operators:

• Arithmetic Operators: Addition (+), Subtraction (-), Multiplication (*), Division (/), Modulus (%), Exponentiation (**).

• Comparison Operators: Equal to (==), Not equal to (!=), Greater than (>), Less than (<), Greater than or equal to (>=), Less than or equal to (<=).

• Logical Operators: AND (and), OR (or), NOT (not).

Control Flow: Conditional Statements and Loops

Conditional Statements:

• if Statement: Executes a block of code if a specified condition is true.

• else Statement: Executes a block of code if the preceding if condition is false.

• elif Statement: Adds additional conditions to evaluate if the preceding conditions are false.

Loops:

• for Loop: Iterates over a sequence (e.g., list, tuple, string) or other iterable objects.

• while Loop: Executes a block of code repeatedly as long as a specified condition is true.

• Loop Control Statements: break (terminates the loop), continue (skips the current iteration), pass (placeholder, does nothing).

Functions and Modules

Functions:

• Functions are reusable blocks of code that perform a specific task.

• They improve code readability, organization, and modularity.

• A function can accept parameters (inputs), perform operations, and return a result.

Modules:

• Modules are files containing Python code, which can include functions, classes, and variables.

• They allow code reuse and organization by separating related functionalities into different files.

• Modules can be imported into other Python scripts using the import statement.

Introduction to NumPy and Pandas Libraries for Data Manipulation

NumPy:

• NumPy (Numerical Python) is a powerful library for numerical computing in Python.

• It provides support for multidimensional arrays (ndarrays) and various mathematical functions for array operations.

• NumPy arrays are efficient and allow for fast vectorized operations, making them suitable for handling large datasets.

Pandas:

• Pandas is a popular library for data manipulation and analysis built on top of NumPy.

• It introduces two main data structures: Series (one-dimensional labeled arrays) and DataFrame (two-dimensional labeled data structures).

• Pandas provides functionalities for data cleaning, filtering, aggregation, and visualization, making it essential for data preprocessing and exploration.