Python Pandas: How to Sort a Pandas DataFrame by Column Names or Row Index

Sorting a DataFrame by its labels - either row index labels or column names - is a common operation for organizing data for display, reporting, or further processing. Unlike sort_values(), which sorts by the data within columns, sort_index() sorts by the axis labels themselves.

This guide explains how to use sort_index() to sort both rows and columns alphabetically, in ascending or descending order, and in place.

Sample DataFrame

All examples use the following DataFrame with non-alphabetical row indices and column names:

import pandas as pd

students = [
    ('Ethan', 22, 'Texas', 'UCLA'),
    ('Olivia', 31, 'California', 'USC'),
    ('Liam', 16, 'New York', 'NYU'),
    ('Sophia', 41, 'Illinois', 'University of Chicago'),
    ('Noah', 33, 'Florida', 'University of Florida'),
    ('Ava', 35, 'Washington', 'University of Washington'),
    ('Mason', 35, 'Georgia', 'Georgia Tech'),
    ('Isabella', 35, 'Ohio', 'Ohio State'),
    ('Lucas', 35, 'Michigan', 'University of Michigan'),
    ('Mia', 35, 'Arizona', 'Arizona State')
]

df = pd.DataFrame(
    students,
    columns=['Name', 'Age', 'Place', 'College'],
    index=['b', 'c', 'a', 'e', 'f', 'g', 'i', 'j', 'k', 'd']
)
print(df)

Output:

       Name  Age       Place                   College
b     Ethan   22       Texas                      UCLA
c    Olivia   31  California                       USC
a      Liam   16    New York                       NYU
e    Sophia   41    Illinois     University of Chicago
f      Noah   33     Florida     University of Florida
g       Ava   35  Washington  University of Washington
i     Mason   35     Georgia              Georgia Tech
j  Isabella   35        Ohio                Ohio State
k     Lucas   35    Michigan    University of Michigan
d       Mia   35     Arizona             Arizona State

Notice the row index labels (b, c, a, e, f, g, i, j, k, d) are not in alphabetical order.

Understanding `sort_index()` Syntax

DataFrame.sort_index(axis=0, ascending=True, inplace=False, na_position='last')

Parameter	Description	Default
`axis`	`0` to sort rows by index labels, `1` to sort columns by column names	`0`
`ascending`	`True` for A→Z / 0→9, `False` for Z→A / 9→0	`True`
`inplace`	If `True`, modifies the DataFrame directly instead of returning a new one	`False`
`na_position`	Where to place `NaN` values: `'first'` or `'last'`	`'last'`

Sorting Rows by Index Labels

Ascending Order (Default)

import pandas as pd

students = [
    ('Ethan', 22, 'Texas', 'UCLA'),
    ('Olivia', 31, 'California', 'USC'),
    ('Liam', 16, 'New York', 'NYU'),
    ('Sophia', 41, 'Illinois', 'University of Chicago'),
    ('Noah', 33, 'Florida', 'University of Florida'),
    ('Ava', 35, 'Washington', 'University of Washington'),
    ('Mason', 35, 'Georgia', 'Georgia Tech'),
    ('Isabella', 35, 'Ohio', 'Ohio State'),
    ('Lucas', 35, 'Michigan', 'University of Michigan'),
    ('Mia', 35, 'Arizona', 'Arizona State')
]

df = pd.DataFrame(
    students,
    columns=['Name', 'Age', 'Place', 'College'],
    index=['b', 'c', 'a', 'e', 'f', 'g', 'i', 'j', 'k', 'd']
)

# sorting in ascending order
sorted_df = df.sort_index()
print(sorted_df)

Output:

       Name  Age       Place                   College
a      Liam   16    New York                       NYU
b     Ethan   22       Texas                      UCLA
c    Olivia   31  California                       USC
d       Mia   35     Arizona             Arizona State
e    Sophia   41    Illinois     University of Chicago
f      Noah   33     Florida     University of Florida
g       Ava   35  Washington  University of Washington
i     Mason   35     Georgia              Georgia Tech
j  Isabella   35        Ohio                Ohio State
k     Lucas   35    Michigan    University of Michigan

The rows are now arranged alphabetically by their index labels: a, b, c, d, e, f, g, i, j, k.

Descending Order

import pandas as pd

students = [
    ('Ethan', 22, 'Texas', 'UCLA'),
    ('Olivia', 31, 'California', 'USC'),
    ('Liam', 16, 'New York', 'NYU'),
    ('Sophia', 41, 'Illinois', 'University of Chicago'),
    ('Noah', 33, 'Florida', 'University of Florida'),
    ('Ava', 35, 'Washington', 'University of Washington'),
    ('Mason', 35, 'Georgia', 'Georgia Tech'),
    ('Isabella', 35, 'Ohio', 'Ohio State'),
    ('Lucas', 35, 'Michigan', 'University of Michigan'),
    ('Mia', 35, 'Arizona', 'Arizona State')
]

df = pd.DataFrame(
    students,
    columns=['Name', 'Age', 'Place', 'College'],
    index=['b', 'c', 'a', 'e', 'f', 'g', 'i', 'j', 'k', 'd']
)

# sorting in descending order
sorted_desc = df.sort_index(ascending=False)
print(sorted_desc)

Output:

       Name  Age       Place                   College
k     Lucas   35    Michigan    University of Michigan
j  Isabella   35        Ohio                Ohio State
i     Mason   35     Georgia              Georgia Tech
g       Ava   35  Washington  University of Washington
f      Noah   33     Florida     University of Florida
e    Sophia   41    Illinois     University of Chicago
d       Mia   35     Arizona             Arizona State
c    Olivia   31  California                       USC
b     Ethan   22       Texas                      UCLA
a      Liam   16    New York                       NYU

Sorting in Place

Use inplace=True to modify the original DataFrame directly without creating a copy:

import pandas as pd

students = [
    ('Ethan', 22, 'Texas', 'UCLA'),
    ('Olivia', 31, 'California', 'USC'),
    ('Liam', 16, 'New York', 'NYU'),
    ('Sophia', 41, 'Illinois', 'University of Chicago'),
    ('Noah', 33, 'Florida', 'University of Florida'),
    ('Ava', 35, 'Washington', 'University of Washington'),
    ('Mason', 35, 'Georgia', 'Georgia Tech'),
    ('Isabella', 35, 'Ohio', 'Ohio State'),
    ('Lucas', 35, 'Michigan', 'University of Michigan'),
    ('Mia', 35, 'Arizona', 'Arizona State')
]

df = pd.DataFrame(
    students,
    columns=['Name', 'Age', 'Place', 'College'],
    index=['b', 'c', 'a', 'e', 'f', 'g', 'i', 'j', 'k', 'd']
)

# sorting in place
df.sort_index(inplace=True)
print(df)

Output:

       Name  Age       Place                   College
a      Liam   16    New York                       NYU
b     Ethan   22       Texas                      UCLA
c    Olivia   31  California                       USC
d       Mia   35     Arizona             Arizona State
e    Sophia   41    Illinois     University of Chicago
f      Noah   33     Florida     University of Florida
g       Ava   35  Washington  University of Washington
i     Mason   35     Georgia              Georgia Tech
j  Isabella   35        Ohio                Ohio State
k     Lucas   35    Michigan    University of Michigan

The original df is now sorted. No new DataFrame is returned.

tip

Use inplace=True when you want to permanently reorder the DataFrame and don't need the original order. Otherwise, assign the result to a new variable to keep the original intact.

Sorting Columns by Column Names

To sort columns alphabetically, set axis=1. This rearranges the column order without changing any data values.

Ascending Order

import pandas as pd

students = [
    ('Ethan', 22, 'Texas', 'UCLA'),
    ('Olivia', 31, 'California', 'USC'),
    ('Liam', 16, 'New York', 'NYU'),
    ('Sophia', 41, 'Illinois', 'University of Chicago'),
    ('Noah', 33, 'Florida', 'University of Florida'),
    ('Ava', 35, 'Washington', 'University of Washington'),
    ('Mason', 35, 'Georgia', 'Georgia Tech'),
    ('Isabella', 35, 'Ohio', 'Ohio State'),
    ('Lucas', 35, 'Michigan', 'University of Michigan'),
    ('Mia', 35, 'Arizona', 'Arizona State')
]

df = pd.DataFrame(
    students,
    columns=['Name', 'Age', 'Place', 'College'],
    index=['b', 'c', 'a', 'e', 'f', 'g', 'i', 'j', 'k', 'd']
)

# sorting in ascending order
sorted_cols = df.sort_index(axis=1)
print(sorted_cols)

Output:

   Age                   College      Name       Place
b   22                      UCLA     Ethan       Texas
c   31                       USC    Olivia  California
a   16                       NYU      Liam    New York
e   41     University of Chicago    Sophia    Illinois
f   33     University of Florida      Noah     Florida
g   35  University of Washington       Ava  Washington
i   35              Georgia Tech     Mason     Georgia
j   35                Ohio State  Isabella        Ohio
k   35    University of Michigan     Lucas    Michigan
d   35             Arizona State       Mia     Arizona

The columns are now in alphabetical order: Age, College, Name, Place.

Descending Order

import pandas as pd

students = [
    ('Ethan', 22, 'Texas', 'UCLA'),
    ('Olivia', 31, 'California', 'USC'),
    ('Liam', 16, 'New York', 'NYU'),
    ('Sophia', 41, 'Illinois', 'University of Chicago'),
    ('Noah', 33, 'Florida', 'University of Florida'),
    ('Ava', 35, 'Washington', 'University of Washington'),
    ('Mason', 35, 'Georgia', 'Georgia Tech'),
    ('Isabella', 35, 'Ohio', 'Ohio State'),
    ('Lucas', 35, 'Michigan', 'University of Michigan'),
    ('Mia', 35, 'Arizona', 'Arizona State')
]

df = pd.DataFrame(
    students,
    columns=['Name', 'Age', 'Place', 'College'],
    index=['b', 'c', 'a', 'e', 'f', 'g', 'i', 'j', 'k', 'd']
)

# sorting in descending order
sorted_cols_desc = df.sort_index(axis=1, ascending=False)
print(sorted_cols_desc)

Output:

        Place      Name                   College  Age
b       Texas     Ethan                      UCLA   22
c  California    Olivia                       USC   31
a    New York      Liam                       NYU   16
e    Illinois    Sophia     University of Chicago   41
f     Florida      Noah     University of Florida   33
g  Washington       Ava  University of Washington   35
i     Georgia     Mason              Georgia Tech   35
j        Ohio  Isabella                Ohio State   35
k    Michigan     Lucas    University of Michigan   35
d     Arizona       Mia             Arizona State   35

Columns are now in reverse alphabetical order: Place, Name, College, Age.

Sorting Columns in Place

import pandas as pd

students = [
    ('Ethan', 22, 'Texas', 'UCLA'),
    ('Olivia', 31, 'California', 'USC'),
    ('Liam', 16, 'New York', 'NYU'),
    ('Sophia', 41, 'Illinois', 'University of Chicago'),
    ('Noah', 33, 'Florida', 'University of Florida'),
    ('Ava', 35, 'Washington', 'University of Washington'),
    ('Mason', 35, 'Georgia', 'Georgia Tech'),
    ('Isabella', 35, 'Ohio', 'Ohio State'),
    ('Lucas', 35, 'Michigan', 'University of Michigan'),
    ('Mia', 35, 'Arizona', 'Arizona State')
]

df = pd.DataFrame(
    students,
    columns=['Name', 'Age', 'Place', 'College'],
    index=['b', 'c', 'a', 'e', 'f', 'g', 'i', 'j', 'k', 'd']
)

# sorting in place
df.sort_index(axis=1, inplace=True)
print(df)

Output:

   Age                   College      Name       Place
b   22                      UCLA     Ethan       Texas
c   31                       USC    Olivia  California
a   16                       NYU      Liam    New York
e   41     University of Chicago    Sophia    Illinois
f   33     University of Florida      Noah     Florida
g   35  University of Washington       Ava  Washington
i   35              Georgia Tech     Mason     Georgia
j   35                Ohio State  Isabella        Ohio
k   35    University of Michigan     Lucas    Michigan
d   35             Arizona State       Mia     Arizona

Sorting Both Rows and Columns

You can sort both axes by chaining sort_index() calls:

import pandas as pd

students = [
    ('Ethan', 22, 'Texas', 'UCLA'),
    ('Olivia', 31, 'California', 'USC'),
    ('Liam', 16, 'New York', 'NYU'),
    ('Sophia', 41, 'Illinois', 'University of Chicago'),
    ('Noah', 33, 'Florida', 'University of Florida'),
    ('Ava', 35, 'Washington', 'University of Washington'),
    ('Mason', 35, 'Georgia', 'Georgia Tech'),
    ('Isabella', 35, 'Ohio', 'Ohio State'),
    ('Lucas', 35, 'Michigan', 'University of Michigan'),
    ('Mia', 35, 'Arizona', 'Arizona State')
]

df = pd.DataFrame(
    students,
    columns=['Name', 'Age', 'Place', 'College'],
    index=['b', 'c', 'a', 'e', 'f', 'g', 'i', 'j', 'k', 'd']
)

# Sort rows by index, then columns by name
result = df.sort_index(axis=0).sort_index(axis=1)
print(result)

Output:

   Age                   College      Name       Place
a   16                       NYU      Liam    New York
b   22                      UCLA     Ethan       Texas
c   31                       USC    Olivia  California
d   35             Arizona State       Mia     Arizona
e   41     University of Chicago    Sophia    Illinois
f   33     University of Florida      Noah     Florida
g   35  University of Washington       Ava  Washington
i   35              Georgia Tech     Mason     Georgia
j   35                Ohio State  Isabella        Ohio
k   35    University of Michigan     Lucas    Michigan

Both row indices and column names are now in alphabetical order.

Common Mistake: Confusing `sort_index()` with `sort_values()`

A frequent error is using sort_index() when you intend to sort by the data within a column, or vice versa:

import pandas as pd

df = pd.DataFrame({
    'Name': ['Charlie', 'Alice', 'Bob'],
    'Score': [78, 95, 88]
}, index=[2, 0, 1])

# WRONG: sort_index sorts by the index labels (2, 0, 1), not by Name
result = df.sort_index()
print("Sorted by index (not by Name):")
print(result)

Output:

Sorted by index (not by Name):
      Name  Score
0    Alice     95
1      Bob     88
2  Charlie     78

The rows are sorted by index values (0, 1, 2), not by the Name column alphabetically.

To sort by column values:

import pandas as pd

df = pd.DataFrame({
    'Name': ['Charlie', 'Alice', 'Bob'],
    'Score': [78, 95, 88]
}, index=[2, 0, 1])


# CORRECT: use sort_values to sort by data in a column
result = df.sort_values(by='Name')
print("Sorted by Name:")
print(result)

Output:

Sorted by Name:
      Name  Score
0    Alice     95
1      Bob     88
2  Charlie     78

info

Use sort_index() when you want to reorder rows by their index labels or columns by their column names.
Use sort_values(by='column') when you want to sort rows by the data inside a specific column.

Quick Reference

Goal	Code
Sort rows by index (ascending)	`df.sort_index()`
Sort rows by index (descending)	`df.sort_index(ascending=False)`
Sort columns by name (ascending)	`df.sort_index(axis=1)`
Sort columns by name (descending)	`df.sort_index(axis=1, ascending=False)`
Sort in place	`df.sort_index(inplace=True)`
Sort both rows and columns	`df.sort_index().sort_index(axis=1)`
Sort by column values instead	`df.sort_values(by='column_name')`

The sort_index() method provides a clean way to organize your DataFrame by its structural labels - row indices and column names - making your data easier to read, compare, and present.

Sample DataFrame​

Understanding sort_index() Syntax​

Sorting Rows by Index Labels​

Ascending Order (Default)​

Descending Order​

Sorting in Place​

Sorting Columns by Column Names​

Ascending Order​

Descending Order​

Sorting Columns in Place​

Sorting Both Rows and Columns​

Common Mistake: Confusing sort_index() with sort_values()​

Quick Reference​

Table of Contents

Sample DataFrame

Understanding `sort_index()` Syntax

Sorting Rows by Index Labels

Ascending Order (Default)

Descending Order

Sorting in Place

Sorting Columns by Column Names

Ascending Order

Descending Order

Sorting Columns in Place

Sorting Both Rows and Columns

Common Mistake: Confusing `sort_index()` with `sort_values()`

Quick Reference