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Course Outline
Module 1: Pandas Functions for Working with DataFrames
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Introduction to Pandas
- Fundamental data structures: Series and DataFrame
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Operations on DataFrames
- Loading and saving data (CSV, Excel, etc.)
- Basic operations (selection, filtering, indexing)
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Data modification
- Adding and removing columns and rows
- Modifying values within a DataFrame
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Data aggregation and grouping
- GroupBy
- Aggregation, summation, averages, and more
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Combining and merging DataFrames
- merge, join, concat
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Working with missing data
- Identifying missing data
- Methods for filling in missing values
Module 2: Program Execution Time Optimisation
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Introduction to optimisation
- The importance of optimisation in programming
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Code optimisation
- Efficient data structures
- Avoiding redundant calculations
- Loop optimisation
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Pandas-specific optimisation
- Vectorisation of operations
- Avoiding the use of apply and lambda
- Working with large datasets
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Simplifying code through function creation
- Creating and using functions
- Code refactoring
Module 3: Working with the NumPy Library
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Introduction to NumPy
- Importing the library
- Fundamental data structures: ndarray
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Array operations
- Creating and modifying arrays
- Indexing and slicing arrays
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Mathematical and statistical functions
- Basic mathematical operations
- Statistical and aggregation functions
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Linear algebra
- Matrix multiplication
- Determinant and inverse matrix
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Working with multidimensional data
- 2D, 3D, and higher-dimensional arrays
- Reshaping arrays
- Integration with other libraries
Module 4: Creating Charts in Excel Using Python
- Introduction to openpyxl and xlsxwriter
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Creating charts in Excel
- Creating basic charts (line, bar, etc.)
- Formatting charts
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Generating charts as images (PNG)
- Using matplotlib to generate charts
- Saving charts as PNG files
- Advanced charts in Excel
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Automating reports
- Creating automated reports with charts
- Integrating Pandas with openpyxl/xlsxwriter
16 Hours