Chapter 1: Introduction to Pandas
- Lesson 1: What is Pandas?
- Lesson 2: History and Importance of Pandas
- Lesson 3: Installing Pandas (Windows, macOS, Linux)
- Lesson 4: Importing and Using Pandas
- Lesson 5: Understanding Pandas Series and DataFrame
Chapter 2: Pandas Series Basics
- Lesson 1: Creating a Pandas Series
- Lesson 2: Accessing Elements in a Series
- Lesson 3: Indexing and Slicing a Series
- Lesson 4: Series Operations and Broadcasting
- Lesson 5: Handling Missing Data in Series
Chapter 3: Pandas DataFrame Basics
- Lesson 1: Creating DataFrames from Lists, Dicts, and Arrays
- Lesson 2: Understanding DataFrame Structure
- Lesson 3: Accessing Rows and Columns
- Lesson 4: Modifying DataFrames
- Lesson 5: Adding and Removing Columns
Chapter 4: Data Selection and Filtering
- Lesson 1: Selecting Data Using loc and iloc
- Lesson 2: Filtering Data Using Conditions
- Lesson 3: Using Query Method for Data Filtering
- Lesson 4: Applying Multiple Conditions
- Lesson 5: Boolean Indexing in Pandas
Chapter 5: Handling Missing Data
- Lesson 1: Identifying Missing Values
- Lesson 2: Dropping Missing Data
- Lesson 3: Filling Missing Data
- Lesson 4: Using Interpolation Techniques
- Lesson 5: Handling Duplicates in DataFrames
Chapter 6: Data Cleaning and Transformation
- Lesson 1: String Operations in Pandas
- Lesson 2: Applying Functions with Apply and Map
- Lesson 3: Renaming Columns and Index
- Lesson 4: Changing Data Types
- Lesson 5: Working with Categorical Data
Chapter 7: Sorting and Ranking Data
- Lesson 1: Sorting DataFrames by Values
- Lesson 2: Sorting DataFrames by Index
- Lesson 3: Ranking Data in Pandas
- Lesson 4: Handling Ties in Ranking
- Lesson 5: Sorting with Multiple Conditions
Chapter 8: Grouping and Aggregation
- Lesson 1: Understanding GroupBy in Pandas
- Lesson 2: Aggregation Functions (sum, mean, count, etc.)
- Lesson 3: Custom Aggregation Functions
- Lesson 4: Multi-Level Grouping
- Lesson 5: Applying Multiple Aggregations
Chapter 9: Pivot Tables and Cross Tabulation
- Lesson 1: Introduction to Pivot Tables in Pandas
- Lesson 2: Creating Pivot Tables
- Lesson 3: Using Cross Tabulation
- Lesson 4: Customizing Pivot Tables
- Lesson 5: Handling MultiIndex in Pivot Tables
Chapter 10: Merging, Joining, and Concatenation
- Lesson 1: Concatenating DataFrames
- Lesson 2: Merging DataFrames
- Lesson 3: Inner vs Outer Joins
- Lesson 4: Left, Right, and Full Joins
- Lesson 5: Handling Overlapping Columns in Joins
Chapter 11: Working with Date and Time Data
- Lesson 1: Introduction to DateTime in Pandas
- Lesson 2: Converting Strings to DateTime
- Lesson 3: DateTime Indexing and Resampling
- Lesson 4: DateTime Arithmetic and Operations
- Lesson 5: Handling Time Zones in Pandas
Chapter 12: Input and Output Operations
- Lesson 1: Reading CSV Files
- Lesson 2: Writing CSV Files
- Lesson 3: Reading and Writing Excel Files
- Lesson 4: Handling JSON Data
- Lesson 5: Working with SQL Databases
Chapter 1: Advanced Data Manipulation
- Lesson 1: MultiIndexing in Pandas
- Lesson 2: Stacking and Unstacking DataFrames
- Lesson 3: Melting and Reshaping DataFrames
- Lesson 4: Pivoting DataFrames
- Lesson 5: Working with Large Datasets
Chapter 2: Advanced Indexing Techniques
- Lesson 1: Custom Indexing in Pandas
- Lesson 2: Using MultiIndex for Hierarchical Data
- Lesson 3: Indexing with Intervals
- Lesson 4: Reindexing and Aligning Data
- Lesson 5: Using Index Objects Efficiently
Chapter 3: Performance Optimization in Pandas
- Lesson 1: Vectorization vs Looping in Pandas
- Lesson 2: Using Categorical Data for Efficiency
- Lesson 3: Optimizing Memory Usage in Pandas
- Lesson 4: Applying NumPy Operations on Pandas DataFrames
- Lesson 5: Using Pandas with Dask for Large Datasets
Chapter 4: Time Series Analysis with Pandas
- Lesson 1: Introduction to Time Series Data
- Lesson 2: DateTime Index and Resampling
- Lesson 3: Rolling and Expanding Windows
- Lesson 4: Time Series Forecasting in Pandas
- Lesson 5: Seasonal and Trend Analysis
Chapter 5: Advanced Merging and Joining
- Lesson 1: Merging Data with Different Index Levels
- Lesson 2: Handling Duplicates in Merging
- Lesson 3: Advanced Concatenation Techniques
- Lesson 4: Joining Data on Multiple Keys
- Lesson 5: Understanding Data Alignment Issues
Chapter 6: Advanced Grouping and Aggregation
- Lesson 1: Custom Aggregations with GroupBy
- Lesson 2: Using Transform and Filter with GroupBy
- Lesson 3: Window Functions in Pandas
- Lesson 4: Expanding Windows and Cumulative Aggregation
- Lesson 5: Performance Considerations in GroupBy Operations
Chapter 7: Pandas and Matplotlib for Data Visualization
- Lesson 1: Basic Plotting with Pandas
- Lesson 2: Line, Bar, and Scatter Plots
- Lesson 3: Customizing Pandas Plots
- Lesson 4: Seaborn Integration with Pandas
- Lesson 5: Creating Multi-Faceted Visualizations
Chapter 8: Working with Text Data
- Lesson 1: String Manipulation in Pandas
- Lesson 2: Extracting Data from Text Columns
- Lesson 3: Cleaning and Preprocessing Text Data
- Lesson 4: Tokenization and Text Processing
- Lesson 5: Using Pandas for NLP Applications
Chapter 9: Pandas and Machine Learning
- Lesson 1: Data Preprocessing with Pandas
- Lesson 2: Handling Missing Data for Machine Learning
- Lesson 3: Feature Engineering in Pandas
- Lesson 4: Preparing Data for Scikit-Learn
- Lesson 5: Pandas Integration with ML Pipelines
Chapter 10: Debugging and Error Handling in Pandas
- Lesson 1: Common Pandas Errors and Fixes
- Lesson 2: Debugging DataFrame Issues
- Lesson 3: Handling NaN and Infinite Values
- Lesson 4: Profiling Pandas Code for Performance
- Lesson 5: Best Practices for Writing Robust Pandas Code
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