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Essential Numpy and Pandas

Chapter 1: Introduction to Data Handling in Python

What are Numpy and Pandas?

Importance for AI and Data Science

Setting Up Your Environment

Running Your First Code Snippets

Introduction to Jupyter Notebooks

Hands-on practical: Setup and Verification

Quiz for Chapter 1

Chapter 2: Getting Started with NumPy Arrays

Understanding NumPy N-dimensional Arrays

Creating Arrays from Python Lists

Built-in Array Creation Functions

Understanding Array Data Types

Basic Array Attributes

Hands-on practical: Creating and Inspecting Arrays

Quiz for Chapter 2

Chapter 3: NumPy Array Indexing and Slicing

Accessing Single Elements

Slicing 1D Arrays

Slicing 2D Arrays

Boolean Indexing

Modifying Array Subsets

Hands-on practical: Selecting Data from Arrays

Quiz for Chapter 3

Chapter 4: Fundamental NumPy Operations

Basic Arithmetic Operations

Introduction to Universal Functions (ufuncs)

Mathematical and Statistical Functions

Logical Operations on Arrays

Introduction to Broadcasting

Hands-on practical: Performing Calculations on Arrays

Quiz for Chapter 4

Chapter 5: Introduction to Pandas

What is Pandas?

Pandas Data Structure: Series

Creating Series

Pandas Data Structure: DataFrame

Creating DataFrames

Inspecting DataFrames

Hands-on practical: Creating and Examining Series/DataFrames

Quiz for Chapter 5

Chapter 6: Loading and Saving Data with Pandas

Reading Data from CSV Files

Reading Data from Excel Files

Reading Data from Other Formats

Writing Data to CSV Files

Writing Data to Excel Files

Hands-on practical: Importing and Exporting Datasets

Quiz for Chapter 6

Chapter 7: Data Selection and Indexing in Pandas

Selecting Columns

Selecting Rows Using Labels (.loc)

Selecting Rows Using Integer Position (.iloc)

Mixing Label and Position Based Indexing

Conditional Selection (Boolean Indexing)

Setting DataFrame Index

Resetting DataFrame Index

Hands-on practical: Accessing Specific Data Subsets

Quiz for Chapter 7

Chapter 8: Basic Data Manipulation with Pandas

Detecting Missing Data

Handling Missing Data: Dropping

Handling Missing Data: Filling

Dropping Columns and Rows

Adding New Columns

Modifying Existing Columns

Renaming Columns

Hands-on practical: Cleaning and Modifying DataFrames

Quiz for Chapter 8

Chapter 9: Grouping and Aggregating Data

The Split-Apply-Combine Concept

Grouping Data with groupby()

Applying Aggregation Functions

Applying Multiple Aggregations

Grouping by Multiple Columns

Iterating Through Groups

Hands-on practical: Summarizing Data with GroupBy

Quiz for Chapter 9

Chapter 10: Combining DataFrames

Introduction to Combining Data

Concatenating DataFrames (pd.concat)

Database-Style Merging (pd.merge)

Understanding Merge Types (Joins)

Merging on Index

Index-Based Joining (.join)

Hands-on practical: Combining Datasets

Quiz for Chapter 10

Quiz
Beta

Chapter: Introduction to Data Handling in Python

Test your understanding and practice the concepts from this chapter

Quiz Instructions

This quiz contains 10 questions to help you practice.
You need to score at least 70% to pass.
Attempts: Unlimited.
Your highest score will be kept.
Please attempt this quiz without assistance; however, feel free to refer to the chapter notes or use a code interpreter if needed.
This assessment is for your learning and understanding, It is not formally accredited (yet).

Question Format

The questions are designed to be engaging, focusing on understanding, application, and interpretation rather than rote memorization. Expect scenario-based problems that test your ability to apply what you've learned.

Attempts

Best scores and quiz attempts will appear.

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