Pandas ecosystem preview

Pandas sits between raw NumPy arrays and higher-level ML/visualization stacks. Most Python data workflows pass through a DataFrame at some stage.

Upstream and downstream

• NumPy — numeric columns stored as ndarrays; df.to_numpy() for ML
• Matplotlib / Seaborn — plot directly from Series and DataFrame columns
• scikit-learn — accepts DataFrames; pipelines use column names
• SciPy — stats and optimization on array exports
• SQL — databases feed DataFrames via connectors

File formats

• read_csv / to_csv — universal interchange
• read_parquet / to_parquet — columnar, typed, compressed
• read_json, read_excel — common in business data

Version check

import pandas as pd
import numpy as np
print('Pandas:', pd.__version__)
print('NumPy:', np.__version__)
df = pd.DataFrame({'x': np.arange(3)})
print(df['x'].values)  # underlying ndarray

Important interview questions and answers

1. Q: NumPy relationship?
   A: Numeric columns are backed by ndarrays; Pandas adds labels, alignment, and IO.
2. Q: Why learn SQL alongside Pandas?
   A: Production data often lives in databases—SQL extracts, Pandas transforms.

Self-check

1. Name three libraries that integrate with Pandas.
2. What method exposes a column as a NumPy array?

Tip: Numeric columns live on NumPy—check .values when debugging dtypes.