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
Fancy 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
Sorting Data
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
Handling Missing Data: Dropping
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- pandas.DataFrame.dropna, pandas development team, 2024 - Official documentation for the
dropnamethod, detailing its parameters, behavior, and various ways to remove missing data from DataFrames. - Python for Data Analysis, Wes McKinney, 2022 (O'Reilly Media) - A practical reference for using Pandas, with dedicated sections on handling missing data, including dropping rows/columns and the reasoning behind these choices.
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, Aurélien Géron, 2022 (O'Reilly Media) - This book includes sections on data preprocessing and cleaning, placing missing data handling within a broader machine learning workflow context.
- Data Mining: Concepts and Techniques, Jiawei Han, Micheline Kamber, Jian Pei, 2011 (Morgan Kaufmann) DOI: 10.1016/C2009-0-61819-5 - An academic textbook with a section on data preprocessing that explains the theoretical background of missing data and the various approaches, including elimination methods.