Matplotlib and Pandas preview

Pandas plots wrap Matplotlib: call df.plot() or series.plot() for quick line, bar, and histogram charts. The index becomes the x-axis by default.

Quick plots (conceptual)

import pandas as pd
import numpy as np

s = pd.Series([1, 3, 2, 5], index=['Jan','Feb','Mar','Apr'])
# s.plot(kind='bar')  # requires matplotlib locally
print('Plot-ready Series:')
print(s)

Plot kinds

• kind='line' — default time series
• kind='bar' — categorical comparisons
• kind='hist' — distribution of one column
• kind='scatter' — requires x and y columns
• df.plot.scatter(x='a', y='b')

Playground note

Matplotlib display may not render in the server runner—focus on preparing tidy Series/DataFrame inputs. Run plots locally with pip install matplotlib.

Important interview questions and answers

1. Q: Why plot from Pandas?
   A: Index labels auto-label axes; integrates with groupby results.
2. Q: Seaborn?
   A: Statistical plots often accept DataFrames directly—built on Matplotlib.

Self-check

1. What becomes the x-axis when plotting a Series?
2. Name two plot kinds useful for EDA.

Tip: Tidy Series with meaningful index labels plot cleanly—run .plot() locally.