SciPy bridge lesson

You completed Pandas fundamentals. Continue to SciPy for statistical tests and numerical algorithms, deepen SQL for warehouse-scale queries, and keep Pandas as your in-Python wrangling layer.

What Pandas gave you

• Labeled tabular thinking and alignment
• EDA workflow: inspect, filter, groupby, merge
• Missing data and dtype discipline
• Bridge to NumPy, Matplotlib, sklearn, and SciPy

What comes next

• SciPy — hypothesis tests, optimization, sparse LA
• SQL — scale queries; pair with read_sql
• ML tracks — feature pipelines built on clean DataFrames

Recommended path

1. Python — language fluency
2. NumPy — ndarray foundation
3. Data Science — workflow and ethics
4. Pandas (this track) — tabular wrangling
5. SciPy — scientific computing
6. SQL — database analytics at scale

Bridge code

import pandas as pd
import numpy as np

df = pd.DataFrame({'x': np.arange(5), 'y': np.arange(5) ** 2})
arr = df.to_numpy()
print('Pandas → NumPy → SciPy pipeline ready')
print(arr.shape)

Important interview questions and answers

1. Q: When stay in Pandas?
   A: EDA, feature engineering, moderate-size transforms in Python notebooks and services.
2. Q: When add SciPy?
   A: Formal statistical inference, optimization, signal processing beyond groupby.

Self-check

1. Name three things you learned in this Pandas track.
2. What track covers hypothesis tests after Pandas?
3. How does SQL complement Pandas in production?

Tip: Continue at SciPy intro and SQL intro—wrangling done, scale up next.