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

Learn how to use sort_index() in Pandas to alphabetically sort DataFrame rows by index labels or columns by names in ascending/descending order, with practical examples.

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']
)

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']
)

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']
)

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.

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']
)

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']
)

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']
)

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']
)

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])

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])


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

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.

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