Beginner

Series & DataFrames

The two core pandas structures, and what's actually inside a DataFrame.

A Series is a 1-D labeled array: think of it as a NumPy array with an index attached. A DataFrame is a 2-D table: a dict of Series that all share the same index, one per column.

Method	Shows	Example
`df.head(n)` / `df.tail(n)`	first / last n rows	`df.head(2)` → the first 2 rows as a DataFrame
`df.shape`	(rows, columns)	a 3-row, 2-column DataFrame → `(3, 2)`
`df.dtypes`	the dtype of every column	`{'age': dtype('int64'), 'name': dtype('O')}`-style Series
`df.describe()`	count/mean/std/min/quartiles/max for numeric columns	one row per stat, one column per numeric field
`df.info()`	dtypes + non-null counts in one summary	prints column names, non-null counts, and dtypes

💡 Tip: df['col'] returns a Series (1-D); df[['col']] with double brackets returns a one-column DataFrame (2-D). The bracket style changes the result type.

Now practice it

Exercises that use what this lesson just covered.

Import pandasEasy pandas VersionEasy Create a DataFrameEasy Summary of a DataFrameEasy First 3 RowsEasy

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