Python Energy Learning

This repository documents Python learning journey with a focus on:

• Data analysis
• Energy and power-system applications
• Forecasting
• Machine learning
• Numerical computing

Current Progress

Python Fundamentals

• Variables, input/output, conditionals, loops
• Lists, strings, tuples, sets, dictionaries
• Functions and return values
• Exception handling
• File handling
• Modules and packages
• Object-oriented programming
• Inheritance, composition, abstraction, properties, class methods, static methods

Statistics and Forecasting Basics

• Mean, median, range
• Variance and standard deviation
• Z-scores
• Moving averages
• Forecast metrics:
  • MAE
  • MSE
  • RMSE
  • MAPE
  • Bias

NumPy

Current NumPy topics covered:

• Creating ndarray objects
• shape, ndim, size, dtype
• 1D, 2D, and higher-dimensional array concepts
• Indexing and slicing
• Vectorized arithmetic
• Boolean masks and filtering
• np.where()
• Aggregations:
  • sum
  • mean
  • min
  • max
  • argmax
  • argmin
• Variance, standard deviation, median, quantiles
• np.diff() and change detection
• Cumulative sum and cumulative product
• Normalization and z-score standardization
• Reshaping and flattening
• reshape()
• ravel()
• squeeze()
• expand_dims()
• Transpose
• Copies vs views
• Broadcasting
• Stacking:
  • stack
  • vstack
  • hstack
  • column_stack
• Concatenation
• Splitting:
  • split
  • array_split
  • hsplit
  • vsplit
• Unique values and frequency counts
• Array creation:
  • zeros
  • ones
  • full
  • eye
  • arange
  • linspace
• Random number generation
• Uniform random values
• Normal distributions
• Random seeds and reproducibility
• Integer and floating-point dtypes
• int32, int64, float32, float64
• Memory inspection using itemsize and nbytes
• Missing and invalid values:
  • np.nan
  • np.isnan
  • np.isinf
  • np.isfinite
  • nanmean
  • nanmin
  • nanmax
  • nansum