- Data Structures:
- Series and DataFrames
- Indexing and selecting data
- Creating Series and DataFrames
- Data Cleaning:
- Handling missing data (NaN)
- Removing duplicates
- Data type conversions
- Renaming columns
- Data normalization and standardization
- Data Exploration:
- Basic statistics (mean, median, etc.)
- Grouping and aggregation
- Pivot tables
- Value counts and frequencies
- Sorting and ranking data
- Data Filtering and Selection:
- Conditional selection
- Boolean indexing
- Querying DataFrames
- Slicing and subsetting data
- Data Transformation:
- Merging and joining DataFrames
- Reshaping data (pivoting, melting, stacking)
- Combining and splitting columns
- Applying functions to data
- Time Series Data:
- Working with dates and times
- Resampling and time-based operations
- Time series plotting
- Visualization:
- Plotting with Matplotlib and Seaborn
- Creating bar charts, line charts, and scatter plots
- Customizing plots and adding labels
- File I/O:
- Reading and writing data from/to various file formats (CSV, Excel, SQL, etc.)
- Performance Optimization:
- Vectorized operations
- Using the apply function efficiently
- Working with large datasets
- Advanced Topics:
- Hierarchical indexing
- Handling multi-level DataFrames
- Categorical data
- Cross-tabulation and contingency tables
- Time series analysis and forecasting
- Real-World Projects:
- Working on real datasets and solving data analysis problems
- Building data pipelines
- Data cleaning and preprocessing for machine learning
- Pandas Best Practices:
- Writing efficient and readable code
- Using the appropriate methods and functions
- Avoiding common pitfalls and mistakes
