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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
- Reading and writing data (CSV, Excel, etc.)
- Basic operations (selection, filtering, indexing)
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Data modification
- Adding, removing columns and rows
- Modifying values in a DataFrame
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Data aggregation and grouping
- GroupBy
- Aggregation, summation, averages, etc.
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Merging and joining DataFrames
- merge, join, concat
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Handling missing data
- Identifying missing data
- Methods for imputing missing data
Module 2: Optimising programme 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 repeated calculations
- Loop optimisation
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Pandas optimisation
- Vectorising operations
- Avoiding apply and lambda
- Working with large datasets
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Simplifying code through function creation
- Creating and using functions
- Refactoring code
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
- Indexing and slicing arrays
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Mathematical and statistical functions
- Basic mathematical operations
- Statistical and aggregate functions
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Linear algebra
- Matrix multiplication
- Determinants, inverse matrices
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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 simple 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 Excel charts
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Report automation
- Creating automated reports with charts
- Integrating Pandas with openpyxl/xlsxwriter