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Use Case
Type: Data Science Stack
Typical Use Case: Scientific computing & data analysis
Famous Usage: Research & Analytics

📊 PyData Stack

NumPy · Pandas · SciPy · Jupyter

J

Jupyter

Interactive Notebooks
  • Exploratory coding & visuals
  • Reproducible workflows
  • Rich output (plots, tables)
↓
P

Pandas

DataFrames & ETL
  • Tabular data wrangling
  • GroupBy, joins, time series
  • IO for CSV/Parquet/DB
↓
N

NumPy

NDArray Math
  • Vectorized arrays
  • Linear algebra kernels
  • Broadcasting & ufuncs
↓
S

SciPy

Scientific Computing
  • Optimization & stats
  • Signal & image processing
  • Spatial, FFT, sparse ops
Workflow: Jupyter (explore) → Pandas (wrangle) → NumPy (arrays) → SciPy (algorithms) → Outputs back to Jupyter