Module 1: Pandas Functions for Working with Data Frames

- Introduction to Pandas
- Basic data structures: Series and DataFrame

- DataFrame Operations
- Loading and saving data (CSV, Excel, etc.)
- Basic operations (selection, filtering, indexing)

- Data modification
- Adding, removing columns and rows
- Modifying values in DataFrame

- Data aggregation and grouping
- GroupBy
- Aggregation, summing, averaging, etc.

- Combining and merging DataFrames
- merge, join, concat

- Working with missing data
- Identification of missing data
- Methods of completing missing data

Module 2: Optimizing program runtime

- Introduction to Optimization
- The importance of optimization in programming

- Code optimization
- Efficient data structures
- Avoiding repetitive calculations
- Loop optimization

- Optimization Pandas
- Vectorization of operations
- Avoiding apply and lambda
- Working with large data sets

- Simplifying code by creating functions
- Creating and using functions
- Code refactoring

Module 3: Working with the numpy library

- Introduction to NumPy
- Importing a library
- Basic data structures: ndarray

- Table operations
- Creating and modifying tables
- Indexing and slicing tables

- Mathematical and statistical functions
- Basic mathematical operations
- Statistical and aggregation functions

- Linear Algebra
- Matrix multiplication
- Determinant, inverse matrix

- Working with multidimensional data
- 2D, 3D and higher dimensional boards
- Shape transformations of boards

- Integration with other libraries

Module 4: Creating Graphs in Excelu Using Pythona

- Introduction to openpyxl and xlsxwriter

- Creating charts in Excelu
- Creating simple graphs (line, bar, etc.)
- Chart formatting

- Generating charts as images (PNG)
- Using matplotlib to generate graphs
- Saving charts as PNG files

- Advanced charts in Excelu

- Report automation
- Creating automated reports with charts
- Connecting Pandas to openpyxl/xlsxwriter