The two libraries you can't avoid

• NumPy — fast numerical arrays and math. The engine under everything.
• pandas — spreadsheets in code: the DataFrame. Load, filter, group, and clean data.

A typical first look at a dataset

import pandas as pd
df = pd.read_csv("sales.csv")
df.head()          # first rows
df.describe()      # summary stats
df["region"].value_counts()
df.groupby("region")["revenue"].mean()

The everyday toolkit

• Selecting rows/columns, filtering with conditions.
• Handling missing values (dropna, fillna).
• Grouping & aggregating (groupby).
• Merging datasets (merge).

Takeaway: most "AI" work is actually data work — and pandas is where it happens.