What is Pandas?

Pandas (Panel Data) provides high-performance, easy-to-use data structures for structured data. The two workhorses are Series (1D labeled array) and DataFrame (2D labeled table).

Core concepts

• Series — one column with an index (row labels)
• DataFrame — table of Series sharing the same index
• Index — row labels; can be integers, strings, or datetimes
• Alignment — operations match on labels, not just position
• Vectorization — column math delegates to NumPy under the hood

Typical use cases

• Exploratory data analysis (EDA) on CSV/Parquet/SQL results
• Cleaning, filtering, and aggregating business metrics
• Feature engineering before machine learning
• Time-series analysis (sales, sensors, logs)

Import convention

import pandas as pd
import numpy as np

s = pd.Series([10, 20, 30], index=['a', 'b', 'c'])
df = pd.DataFrame({'x': s, 'y': [1, 2, 3]})
print(s)
print(df)

Important interview questions and answers

1. Q: Series vs DataFrame?
   A: Series is 1D with one index; DataFrame is 2D with shared row index and multiple named columns.
2. Q: Why import as pd?
   A: Universal convention across documentation, Stack Overflow, and production codebases.

Self-check

1. Name the two primary Pandas data structures.
2. Give one real-world use case for a DataFrame.

Tip: Remember: one bracket df['col'] → Series; two brackets → DataFrame.