Index and MultiIndex

Pandas Basics

2 min read

Published Sep 29 2025, updated Aug 17 2026

PandasPython

What is an Index?

Every Pandas Series or DataFrame has an index.
It labels rows, allowing you to access, slice, and align data.
By default, a DataFrame gets a RangeIndex (0, 1, 2, …).

Example:

import pandas as pddf = pd.DataFrame({    "Name": ["Alice", "Bob", "Charlie"],    "Age": [25, 30, 35]})print(df)

Output:

      Name  Age0    Alice   251      Bob   302  Charlie   35

0,1,2 is the default index.

Custom Index

You can set a column as the index using .set_index():

df.set_index("Name", inplace=True)print(df)

Output:

         AgeName        Alice     25Bob       30Charlie   35

Now the index is Name, which can be used for label-based access:

df.loc["Bob"]

Output:

Age    30Name: Bob, dtype: int64

Index Properties and Methods

Attribute / Method	Description
`df.index`	Returns the index object
`df.columns`	Returns column labels (also an Index object)
`df.reset_index()`	Resets the index to default RangeIndex, optionally keeping old index as column
`df.set_index("col")`	Sets a column as index
`df.sort_index()`	Sorts rows by index labels
`df.index.name`	Set or get the name of the index
`df.index.values`	Get array of index values
`df.index.is_unique`	Check if index has unique labels

MultiIndex (Hierarchical Index)

MultiIndex allows multiple levels of indexing in rows or columns.
Useful for grouped, hierarchical, or panel-like data.
MultiIndex works well with .groupby(), .pivot_table(), and aggregation.

Creating MultiIndex - from arrays

arrays = [    ["East", "East", "West", "West"],    ["Store1", "Store2", "Store1", "Store2"]]index = pd.MultiIndex.from_arrays(arrays, names=("Region", "Store"))df = pd.DataFrame({    "Sales": [100, 150, 200, 250]}, index=index)print(df)

Output:

               SalesRegion Store        East   Store1    100       Store2    150West   Store1    200       Store2    250

Region is level 0, Store is level 1.

Creating MultiIndex - from tuples

tuples = [("East", "Store1"), ("East", "Store2"), ("West", "Store1"), ("West", "Store2")]index = pd.MultiIndex.from_tuples(tuples, names=("Region", "Store"))df = pd.DataFrame({"Sales": [100,150,200,250]}, index=index)

Accessing MultiIndex data

Single level:

df.loc["East"]

Output:

        SalesStore        Store1    100Store2    150

Specific row:

df.loc[("East","Store2")]

Output:

Sales    150Name: (East, Store2), dtype: int64

Slicing levels:

df.loc[pd.IndexSlice["East":"West", "Store1":"Store2"], :]

Resetting and swapping levels

# moves MultiIndex back to columnsdf.reset_index()  # swaps level 0 and level 1df.swaplevel()

Sorting MultiIndex

# sort by first leveldf.sort_index(level=0)  # sort by second level, then firstdf.sort_index(level=[1,0])

Index and MultiIndex

Pandas Basics

2 min read

Published Sep 29 2025, updated Aug 17 2026

Guide Sections

Guide Comments

What is an Index?

MultiIndex (Hierarchical Index)