Programming in Python Training Course

Module 1: Pandas Functions for Data Frame Operations

• Introduction to Pandas
  • Core data structures: Series and DataFrame
• DataFrame Operations
  • Ingesting and exporting data (CSV, Excel, etc.)
  • Fundamental operations (selection, filtering, indexing)
• Data Modification
  • Inserting and removing columns and rows
  • Updating values within a DataFrame
• Data Aggregation and Grouping
  • GroupBy methodology
  • Aggregation techniques, including summation and averages
• Merging and Joining DataFrames
  • Utilizing merge, join, and concat functions
• Handling Missing Data
  • Detecting absent values
  • Techniques for populating missing data

Module 2: Code Optimization

• Introduction to Optimization
  • The significance of optimization in software development
• Code Efficiency Strategies
  • Utilizing efficient data structures
  • Eliminating redundant calculations
  • Optimizing loop performance
• Pandas Performance Enhancements
  • Implementing vectorized operations
  • Minimizing reliance on apply and lambda functions
  • Managing large-scale datasets effectively
• Streamlining Code via Functions
  • Developing and implementing reusable functions
  • Refactoring code for clarity and efficiency

Module 3: Working with the NumPy Library

• Introduction to NumPy
  • Loading the library
  • Core data structure: ndarray
• Array Manipulation
  • Constructing and altering arrays
  • Array indexing and slicing techniques
• Mathematical and Statistical Functions
  • Essential mathematical operations
  • Statistical analysis and aggregation functions
• Linear Algebra Applications
  • Matrix multiplication
  • Calculating determinants and matrix inverses
• Multidimensional Data Management
  • Handling 2D, 3D, and higher-dimensional arrays
  • Reshaping array configurations
• Integration with External Libraries

Module 4: Creating Charts in Excel Using Python

• Overview of openpyxl and xlsxwriter libraries
• Generating Excel Charts
  • Constructing basic visualizations (line, bar, etc.)
  • Applying formatting to charts
• Exporting Charts as Images (PNG)
  • Leveraging matplotlib for chart generation
  • Preserving charts as PNG files
• Advanced Excel Charting Techniques
• Report Automation
  • Developing automated reporting workflows with visualizations
  • Integrating Pandas with openpyxl/xlsxwriter for government data processing and for government analysis