Statistical Operations

Moving Beyond Basic Averages

In Data Science, understanding the spread of your data is just as important as the average.

Key NumPy Stats Methods

• np.mean(): Average
• np.median(): Middle value (robust to outliers)
• np.std(): Standard Deviation (spread)
• np.var(): Variance (std squared)
• np.percentile(): Find thresholds (e.g., top 10%)

The axis Argument

When working with 2D arrays (matrices), you can calculate stats per row or per column:

• axis=0: Operate down the rows (result per column)
• axis=1: Operate across the columns (result per row)

matrix = np.array([[1, 2, 3],
                   [4, 5, 6]])
matrix.mean(axis=0) # → [2.5, 3.5, 4.5] (column means)