Python Energy Learning

This repository documents my Python learning journey with a focus on:

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

The goal is to build a strong Python foundation and gradually apply it to real energy, grid, forecasting, and power-system problems.

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Current Progress

Python Fundamentals

Topics covered:

• Variables and data types
• Input and output
• Conditionals
• Loops
• Lists
• Strings
• Tuples
• Sets
• Dictionaries
• Functions
• Parameters and return values
• Variable scope
• Exception handling
• try
• except
• else
• finally
• raise
• File handling
• Reading and writing files
• Modules and packages
• Import styles
• __name__
• Object-oriented programming
• Classes and objects
• Constructors with __init__
• Instance attributes and methods
• Class attributes
• Inheritance
• Method overriding
• super()
• Polymorphism
• isinstance()
• Class methods
• Static methods
• Properties and setters
• Encapsulation
• __str__
• Composition
• Abstract base classes
• Abstract methods
• Basic type hints

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Statistics and Forecasting Basics

Topics covered:

• Mean
• Median
• Range
• Variance
• Standard deviation
• Population vs sample statistics
• Z-scores
• Moving averages
• Basic time-series change calculations

Forecast evaluation metrics:

• MAE
• MSE
• RMSE
• MAPE
• Bias

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NumPy

NumPy foundation completed.

Topics covered:

Array Fundamentals

• Creating ndarray objects
• shape
• ndim
• size
• dtype
• 1D, 2D, and higher-dimensional arrays
• Row and column vector concepts
• Indexing
• Slicing
• Reverse slicing

Vectorized Operations

• Vectorized arithmetic
• NumPy ufuncs
• Operator overloading
• Elementwise calculations
• Boolean masks
• Filtering
• Combining conditions with & and |
• np.where()
• np.argwhere()

Aggregations and Statistics

• sum
• mean
• min
• max
• argmax
• argmin
• Range
• Variance
• Standard deviation
• Median
• Quantiles
• Percentiles
• ddof

Change and Cumulative Operations

• np.diff()
• Change detection
• np.sign()
• np.cumsum()
• np.cumprod()

Normalization

• Min-max normalization
• Z-score standardization

Reshaping

• reshape()
• ravel()
• flatten()
• squeeze()
• expand_dims()
• np.newaxis
• Transpose

Copies and Views

• Views
• Copies
• Slicing behavior
• Fancy indexing
• ravel() vs flatten()

Broadcasting

• Broadcasting rules
• Shape compatibility
• Row and column broadcasting

Combining Arrays

• stack
• vstack
• hstack
• column_stack
• concatenate

Splitting Arrays

• split
• array_split
• hsplit
• vsplit

Array Utilities

• unique
• Frequency counts
• take
• delete
• insert
• append
• repeat
• tile
• flip
• sort
• argsort
• np.ix_

Array Creation

• zeros
• ones
• full
• eye
• arange
• linspace

Random Numbers

• Random integer generation
• Uniform random values
• Normal distributions
• Random seeds
• Reproducibility

Data Types and Memory

• int32
• int64
• float32
• float64
• astype()
• Floating-point representation
• itemsize
• nbytes

Missing and Invalid Values

• np.nan
• np.isnan
• np.isinf
• np.isfinite
• nanmean
• nanmin
• nanmax
• nansum

Linear Algebra

• Dot product
• Matrix multiplication
• Matrix-vector multiplication
• Transpose
• Symmetric matrices
• Diagonal extraction
• Trace
• Upper and lower triangular matrices
• Determinant
• Matrix inverse
• Solving linear systems
• Vector norms
• Unit vectors
• Angle between vectors
• Scalar projection
• Vector projection
• Orthogonal components
• np.isclose()
• np.allclose()
• Eigenvalues
• Eigenvectors
• Verifying Av = λv

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Pandas

Current pandas topics covered:

Series

• Creating Series
• Default and custom indexes
• Label-based access
• Position-based access
• .loc
• .iloc
• Boolean filtering
• Series attributes
• .index
• .values
• .dtype
• .shape
• .size

DataFrames

• Creating DataFrames from dictionaries
• Selecting columns
• Selecting rows
• Scalar selection
• Multiple-row and multiple-column selection
• .loc
• .iloc
• Setting indexes
• Resetting indexes
• Index names
• Boolean filtering

Sorting and Inspection

• sort_values()
• Sorting by multiple columns
• sort_index()
• head()
• tail()
• info()
• describe()

Column Transformations

• Creating derived columns
• Boolean columns
• Categorical columns with np.where()
• Dropping rows and columns
• Renaming rows and columns
• value_counts()
• unique()
• nunique()

Missing Data

• isna()
• Missing-value masks
• Missing-value counts
• dropna()
• fillna()
• Filling with mean
• Forward fill
• Backward fill
• Handling missing data by column

Duplicates

• duplicated()
• drop_duplicates()
• Duplicate detection by subset
• Keeping first or last duplicate

Data Type Conversion

• astype()
• pd.to_numeric()
• errors="coerce"
• Handling invalid numeric values

String Cleaning

• .str accessor
• str.strip()
• str.lower()
• str.split()
• str.replace()
• str.contains()
• str.startswith()
• str.endswith()
• str.len()
• str.extract()
• Basic regular expressions
• Mapping categories
• .map()
• .replace()

GroupBy and Aggregation

• groupby()
• Split-apply-combine concept
• Mean
• Sum
• Min
• Max
• Count
• Multiple aggregations
• .agg()
• Dictionary-based aggregation
• Named aggregation
• Grouping by multiple keys
• MultiIndex results
• reset_index()

Concatenation and Merging

• pd.concat()
• Vertical concatenation
• Horizontal concatenation
• Index alignment
• merge()
• Inner joins
• Left joins
• Right joins
• Outer joins
• Cross joins
• left_on
• right_on
• right_index
• Merge indicators
• Merge validation
• One-to-one
• One-to-many
• Many-to-one
• Many-to-many
• Duplicate-key row expansion
• Merge suffixes

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Pandas Time-Series Analysis

Topics covered:

Datetime Basics

• pd.to_datetime()
• datetime64[ns]
• .dt accessor
• Extracting:
  • Year
  • Month
  • Day
  • Hour
  • Minute
  • Second
  • Day name
  • Day of week
• Weekend detection

DatetimeIndex

• Setting timestamps as index
• Sorting datetime indexes
• Partial datetime selection
• Datetime slicing
• Date-based filtering

Resampling

• resample()
• Hourly resampling
• Daily resampling
• Mean aggregation
• Sum aggregation
• Multiple aggregations
• Frequency aliases

Power and Energy Calculations

• Power vs energy distinction
• Converting power samples to energy
• Regular sampling intervals
• 30-minute measurements
• Energy_MWh = Power_MW × interval_hours
• Hourly power averages
• Hourly energy totals

Rolling Windows

• Row-based rolling windows
• Time-based rolling windows
• Rolling mean
• Rolling maximum
• Rolling minimum
• Rolling range
• min_periods
• Regular vs irregular sampling
• Time-window boundary behavior
• closed="both"

Lag Features

• shift()
• Lag-1 features
• Lag-2 features
• Lag-3 features
• Previous-value features
• Power differences
• Percentage changes
• pct_change()
• Forecasting feature intuition

Regular Time-Series Creation

• pd.date_range()
• periods
• freq
• 30-minute sampling
• Regular time indexes

Interpolation

• Linear interpolation
• Time-based interpolation
• Interpolation vs extrapolation
• Interpolation with one missing value
• Interpolation with multiple missing values
• Position-based interpolation
• Time-weighted interpolation
• Regular vs irregular timestamps

Missing Timestamps

• Difference between missing values and missing rows
• Restoring expected frequencies with asfreq()
• Creating expected time indexes
• Detecting missing timestamps with Index.difference()
• Dynamic expected indexes using:
  • .index.min()
  • .index.max()
• Missing timestamp masks
• Missing-value counts

Reusable Time-Series Utilities

Built helper functions for:

• Detecting missing timestamps
• Handling empty DataFrames
• Validating DatetimeIndex
• Raising TypeError
• Combining validation with:
  • try
  • except
  • else