Series and DataFrame intro

A Series is a one-dimensional labeled array; a DataFrame is a collection of Series sharing a row index. Both preserve labels through operations—unlike raw NumPy arrays.

Creating a Series

import pandas as pd
import numpy as np

s = pd.Series([10, 20, 30], index=['a', 'b', 'c'], name='score')
print(s)
print(s.index, s.dtype)

Creating a DataFrame

• From dict of lists: pd.DataFrame({'col': [1,2,3]})
• From dict of Series: columns align on index automatically
• From 2D NumPy array: pass columns= and index=
• From list of dicts (JSON-like records): pd.DataFrame(records)

Key attributes

• .index — row labels
• .columns — column names (DataFrame only)
• .values / .to_numpy() — underlying array
• .shape, .dtypes, .ndim

Important interview questions and answers

1. Q: Dict of lists vs list of dicts?
   A: Dict of lists = column-oriented; list of dicts = row-oriented (API JSON). Both produce DataFrames.
2. Q: Shared index?
   A: When building from Series, Pandas aligns rows by index label—missing labels become NaN.

Self-check

1. Create a Series with a custom index.
2. How do you list all column names?

Tip: Print type(df['col']) vs type(df[['col']]) once—it prevents selection bugs.