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Duration 16 hours
Course Outline
Module 1: Pandas functions for working with DataFrames
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Introduction to Pandas
- Basic data structures: Series and DataFrame
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DataFrame operations
- Loading and saving data (CSV, Excel, etc.)
- Basic operations (selection, filtering, indexing)
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Modifying data
- Adding, removing, and updating columns and rows
- Modifying values within a DataFrame
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Aggregation and grouping data
- GroupBy
- Aggregation, summation, averages, etc.
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Joining and merging DataFrames
- merge, join, concat
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Handling missing data
- Identifying missing values
- Methods for imputing missing data
Module 2: Optimising program execution time
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Introduction to optimisation
- The importance of optimisation in programming
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Code optimisation
- Efficient data structures
- Avoiding repetitive calculations
- Loop optimisation
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Pandas optimisation
- Vectorising operations
- Avoiding apply and lambda functions
- 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
- Basic data structures: ndarray
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Array operations
- Creating and modifying arrays
- Array indexing and slicing
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Mathematical and statistical functions
- Basic mathematical operations
- Statistical and aggregating functions
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Linear algebra
- Matrix multiplication
- Determinants and inverse matrices
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Working with multi-dimensional 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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Report automation
- Creating automated reports with charts
- Integrating Pandas with openpyxl/xlsxwriter