SciPy and Pandas teaser

Export Pandas columns to NumPy arrays for SciPy routines: hypothesis tests (scipy.stats), optimization, and sparse linear algebra beyond Pandas' scope.

Typical handoff

import pandas as pd
import numpy as np

a = pd.Series([1.2, 2.1, 1.9, 2.3])
b = pd.Series([2.0, 2.5, 2.1, 2.8])
x = a.to_numpy()
y = b.to_numpy()
# from scipy import stats
# stats.ttest_ind(x, y)  # local with scipy installed
print('Arrays ready for SciPy:', x.shape, y.shape)

What SciPy adds

• Statistical tests (t-test, chi-square, ANOVA)
• Probability distributions and sampling
• Optimization and root finding
• Sparse matrices and advanced linear algebra

Continue learning

After mastering Pandas wrangling, the SciPy track covers scientific algorithms. Use Pandas for EDA and cleaning; SciPy for inferential stats and numerical methods.

Important interview questions and answers

1. Q: Pandas describe vs SciPy?
   A: describe gives sample stats; SciPy runs formal tests with p-values.
2. Q: When SciPy?
   A: Need p-values, fit distributions, optimize parameters—not just groupby means.

Self-check

1. How do you pass a Series to a SciPy function?
2. Name one SciPy module useful after Pandas EDA.

Tip: Continue inferential stats at SciPy intro after cleaning in Pandas.