Python's Data Science Stack
NumPy and Pandas are the two foundational libraries for data science in Python. NumPy provides fast numerical arrays, while Pandas adds labelled, tabular data structures that make data wrangling intuitive.
NumPy Arrays
NumPy arrays are homogeneous, fixed-size containers optimised for vectorised arithmetic. Operations on arrays run in compiled C, making them orders of magnitude faster than Python loops.
Pandas DataFrames
- pd.read_csv() — load tabular data from files
- df.head() / df.info() — quick inspection
- df.describe() — summary statistics
- df['col'] — select a single column as a Series
Tip: Always inspect your data with .info() and .describe() before doing any analysis. Catching data type issues early saves hours of debugging.
Key Takeaway
NumPy handles raw numerical computation; Pandas adds labels, indexing, and I/O — together they form the backbone of every data science workflow.