1. Introduction

Pandas gives you labeled, heterogeneous, tabular data on top of NumPy.

Pandas gives you labeled, heterogeneous, tabular data on top of NumPy.

flowchart TD
    DF["DataFrame (2-D table)"] --> IDX["Index — row labels: 0, 1, 2"]
    DF --> C1["Series 'name' · object"]
    DF --> C2["Series 'age' · int64"]
    DF --> C3["Series 'city' · object"]
    IDX -. shared by every column .-> C1
    IDX -. shared by every column .-> C2
    IDX -. shared by every column .-> C3

Object	What it is
Series	1-D labeled array (a single column)
DataFrame	2-D labeled table (rows × columns), a dict of Series
Index	The row/column labels (can be integer, string, datetime, or MultiIndex)

s = pd.Series([10, 20, 30], index=['a', 'b', 'c'])
# a    10
# b    20
# c    30

df = pd.DataFrame({
    'name': ['Ann', 'Bob', 'Cy'],
    'age':  [25, 30, 35],
    'city': ['NYC', 'LA', 'SF']
})
print(df)
#   name  age city
# 0  Ann   25  NYC
# 1  Bob   30   LA
# 2   Cy   35   SF

📌 Remember: A DataFrame is a dict of Series sharing one Index. Every column is a Series; every column has a dtype.

⭐ Interview Question: Series vs DataFrame? A Series is 1-D (one column + index); a DataFrame is 2-D (multiple aligned columns).