Introduction to Pandas

• Overview of Pandas library and its role in data analysis
• Installation and setup of Pandas
• Introduction to Series and DataFrame data structures

Data Manipulation with Pandas

• Loading and reading data from various sources (CSV, Excel, databases, etc.)
• Selecting and indexing data
• Filtering, sorting, and transforming data

Data Cleaning and Preprocessing

• Handling missing data in Pandas
• Data imputation and interpolation techniques
• Removing duplicates and handling outliers

Data Exploration and Visualization

• Descriptive statistics and data summarization
• Data aggregation and grouping
• Visualizing data using Pandas and Matplotlib

Data Transformation and Reshaping

• Reshaping data with pivot tables and stacking/unstacking
• Merging and joining data from multiple sources
• Resampling and time series analysis with Pandas

Data Analysis and Statistical Computations

• Statistical analysis using Pandas
• Applying mathematical functions and operations to data
• Correlation and regression analysis

Working with Dates and Time Series Data

• Handling dates and time in Pandas
• Time zone conversion and manipulation
• Resampling and frequency conversion of time series data

Handling Categorical Data

• Encoding and decoding categorical data
• Performing categorical data analysis and visualization
• Working with categorical data in machine learning

Data Input and Output with Pandas

• Writing data to various output formats (CSV, Excel, databases, etc.)
• Handling different file formats in Pandas
• Reading and writing data in chunks for large datasets

Advanced Data Analysis Techniques

• Applying advanced data analysis techniques using Pandas
• Handling hierarchical and multi-indexing data
• Implementing custom functions and operations with Pandas

Time Series Analysis with Pandas

• Working with time series data in Pandas
• Time series decomposition and forecasting
• Seasonality analysis and trend detection

Data Wrangling and Feature Engineering

• Data wrangling techniques with Pandas
• Feature extraction and feature engineering
• Handling outliers and data normalization

Handling Big Data with Pandas

• Strategies for handling big data with Pandas
• Chunking and parallel processing with Pandas
• Optimization techniques for large-scale data processing

Case Studies and Real-World Applications

• Applying Pandas to real-world data analysis projects
• Exploring case studies and examples in different domains
• Best practices and tips for efficient Pandas usage