Data Science Foundations
📊 Data Science Foundations
Numerical computing in Python relies on Vectorization to achieve high performance, bypassing the GIL for compute-heavy tasks.
🟢 Level 1: Foundations
1. NumPy (Numerical Python)
The foundational library for all numerical computing in Python.
- NDArray: Contiguous memory arrays with fixed types.
- Broadcasting: Automatic element-wise operations on arrays of different shapes.
2. Pandas (Data Manipulation)
The industry standard for tabular data manipulation.
- Series & DataFrames: Labeled data structures.
- Split-Apply-Combine: Efficient grouping and aggregation.
🟡 Level 2: Visualization
Using matplotlib and seaborn to understand data distributions and identify outliers.
🔴 Level 3: Advanced Optimization
- Polars: A lightning-fast DataFrame library written in Rust.
- Numba: JIT compiler for Python that translates a subset of Python and NumPy code into fast machine code.