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Python Pandas: How to Sort a Pandas DataFrame by Multiple Columns

Learn how to sort a Pandas DataFrame by multiple columns using sort_values(), nlargest(), nsmallest(), and other methods with practical examples and parameter guidance.

Sorting a DataFrame by multiple columns is a fundamental operation in data analysis. It allows you to organize data hierarchically - for example, sorting employees first by department and then by salary within each department. Pandas offers several methods to accomplish this, from the versatile sort_values() to specialized functions like nlargest() and nsmallest().

This guide covers each approach with practical examples, outputs, and guidance on when to use which method.

Sample DataFrame

All examples use the following DataFrame:

import pandas as pd
import numpy as np

df = pd.DataFrame({
    'Name': ['Ryan', 'Aiden', 'Noah', 'Caleb', 'Emma', 'Chloe'],
    'Age': [20, 22, 21, 19, 17, 23],
    'Rank': [1, np.nan, 8, 9, 4, np.nan]
})

print(df)

Output:

    Name  Age  Rank
0   Ryan   20   1.0
1  Aiden   22   NaN
2   Noah   21   8.0
3  Caleb   19   9.0
4   Emma   17   4.0
5  Chloe   23   NaN

Using sort_values() - The Most Flexible Method

The sort_values() method is the standard way to sort a DataFrame by one or more columns. It supports independent ascending/descending order per column and configurable NaN placement.

Sorting by Multiple Columns

import pandas as pd
import numpy as np

df = pd.DataFrame({
    'Name': ['Ryan', 'Aiden', 'Noah', 'Caleb', 'Emma', 'Chloe'],
    'Age': [20, 22, 21, 19, 17, 23],
    'Rank': [1, np.nan, 8, 9, 4, np.nan]
})

result = df.sort_values(
    by=['Rank', 'Age'],
    ascending=[True, False],
    na_position='last'
)
print(result)

Output:

    Name  Age  Rank
0   Ryan   20   1.0
4   Emma   17   4.0
2   Noah   21   8.0
3  Caleb   19   9.0
5  Chloe   23   NaN
1  Aiden   22   NaN

Key parameters:

Parameter Description
by List of column names to sort by, in priority order
ascending List of booleans - True for ascending, False for descending - one per column
na_position 'last' (default) or 'first' - where to place NaN values
ignore_index If True, resets the index to 0, 1, 2, ... after sorting

Sorting with Reset Index

When you want a clean index after sorting:

import pandas as pd
import numpy as np

df = pd.DataFrame({
    'Name': ['Ryan', 'Aiden', 'Noah', 'Caleb', 'Emma', 'Chloe'],
    'Age': [20, 22, 21, 19, 17, 23],
    'Rank': [1, np.nan, 8, 9, 4, np.nan]
})

result = df.sort_values(
    by=['Rank', 'Age'],
    ascending=[True, False],
    na_position='last',
    ignore_index=True
)
print(result)

Output:

    Name  Age  Rank
0   Ryan   20   1.0
1   Emma   17   4.0
2   Noah   21   8.0
3  Caleb   19   9.0
4  Chloe   23   NaN
5  Aiden   22   NaN

Using nlargest() - Top N Rows by Value

The nlargest() method is optimized to retrieve the top N rows with the highest values in specified columns. It is faster than sorting the entire DataFrame when you only need a few top results.

import pandas as pd
import numpy as np

df = pd.DataFrame({
    'Name': ['Ryan', 'Aiden', 'Noah', 'Caleb', 'Emma', 'Chloe'],
    'Age': [20, 22, 21, 19, 17, 23],
    'Rank': [1, np.nan, 8, 9, 4, np.nan]
})

top_3 = df.nlargest(3, 'Rank')
print(top_3)

Output:

    Name  Age  Rank
3  Caleb   19   9.0
2   Noah   21   8.0
4   Emma   17   4.0

NaN values are automatically ignored. You can also sort by multiple columns - nlargest uses the second column as a tiebreaker:

import pandas as pd
import numpy as np

df = pd.DataFrame({
    'Name': ['Ryan', 'Aiden', 'Noah', 'Caleb', 'Emma', 'Chloe'],
    'Age': [20, 22, 21, 19, 17, 23],
    'Rank': [1, np.nan, 8, 9, 4, np.nan]
})

top_3 = df.nlargest(3, ['Rank', 'Age'])
print(top_3)

Output:

    Name  Age  Rank
3  Caleb   19   9.0
2   Noah   21   8.0
4   Emma   17   4.0

Using nsmallest() - Bottom N Rows by Value

The counterpart to nlargest(), this method retrieves the lowest N values:

import pandas as pd
import numpy as np

df = pd.DataFrame({
    'Name': ['Ryan', 'Aiden', 'Noah', 'Caleb', 'Emma', 'Chloe'],
    'Age': [20, 22, 21, 19, 17, 23],
    'Rank': [1, np.nan, 8, 9, 4, np.nan]
})

bottom_3 = df.nsmallest(3, 'Rank')
print(bottom_3)

Output:

   Name  Age  Rank
0  Ryan   20   1.0
4  Emma   17   4.0
2  Noah   21   8.0

Using sort_index()

The sort_index() method sorts by the DataFrame's index rather than column values. This is useful after operations that disorder the index, such as filtering, grouping, or setting a custom index.

import pandas as pd
import numpy as np

df = pd.DataFrame({
    'Name': ['Ryan', 'Aiden', 'Noah', 'Caleb', 'Emma', 'Chloe'],
    'Age': [20, 22, 21, 19, 17, 23],
    'Rank': [1, np.nan, 8, 9, 4, np.nan]
})

result = df.sort_index(ascending=False)
print(result)

Output:

    Name  Age  Rank
5  Chloe   23   NaN
4   Emma   17   4.0
3  Caleb   19   9.0
2   Noah   21   8.0
1  Aiden   22   NaN
0   Ryan   20   1.0

Sorting by a MultiIndex

sort_index() becomes powerful with a MultiIndex:

import pandas as pd

df = pd.DataFrame({
    'Score': [88, 92, 75, 81, 95, 70]
}, index=pd.MultiIndex.from_tuples([
    ('Math', 'Alice'), ('Math', 'Bob'),
    ('Science', 'Alice'), ('Science', 'Bob'),
    ('English', 'Alice'), ('English', 'Bob')
], names=['Subject', 'Student']))

result = df.sort_index(level=['Subject', 'Student'], ascending=[True, False])
print(result)

Output:

                 Score
Subject Student       
English Bob         70
        Alice       95
Math    Bob         92
        Alice       88
Science Bob         81
        Alice       75

Using argsort() with NumPy

For performance-critical scenarios, np.argsort() computes sorted indices directly on the underlying NumPy array, which can be faster than Pandas sorting for very large datasets:

import pandas as pd
import numpy as np

df = pd.DataFrame({
    'Name': ['Ryan', 'Aiden', 'Noah', 'Caleb', 'Emma', 'Chloe'],
    'Age': [20, 22, 21, 19, 17, 23],
    'Rank': [1, np.nan, 8, 9, 4, np.nan]
})

sorted_idx = np.argsort(df['Rank'].values)
result = df.iloc[sorted_idx]
print(result)

Output:

    Name  Age  Rank
0   Ryan   20   1.0
4   Emma   17   4.0
2   Noah   21   8.0
3  Caleb   19   9.0
1  Aiden   22   NaN
5  Chloe   23   NaN

Common Mistake: Mismatched ascending List Length

When sorting by multiple columns, the ascending parameter must have the same length as the by parameter. Passing a single boolean applies it to all columns, which may not be intended:

import pandas as pd

df = pd.DataFrame({
    'Department': ['Sales', 'Sales', 'Engineering', 'Engineering'],
    'Salary': [50000, 60000, 70000, 55000]
})

result = df.sort_values(by=['Department', 'Salary'], ascending=False)
print(result)

Output:

    Department  Salary
1        Sales   60000
0        Sales   50000
2  Engineering   70000
3  Engineering   55000

If the intent was to sort by Department ascending and Salary descending, the result above is incorrect.

The correct approach:

import pandas as pd

df = pd.DataFrame({
    'Department': ['Sales', 'Sales', 'Engineering', 'Engineering'],
    'Salary': [50000, 60000, 70000, 55000]
})

result = df.sort_values(by=['Department', 'Salary'], ascending=[True, False])
print(result)

Output:

    Department  Salary
2  Engineering   70000
3  Engineering   55000
1        Sales   60000
0        Sales   50000

Method Comparison

Method Sorts By Multi-Column NaN Handling Best For
sort_values() Column values Yes Configurable (na_position) General-purpose sorting - recommended
nlargest() Column values (descending) Yes (tiebreaker) Ignores NaN Getting top N rows efficiently
nsmallest() Column values (ascending) Yes (tiebreaker) Ignores NaN Getting bottom N rows efficiently
sort_index() Index values Yes (MultiIndex) N/A Reordering by row index
np.argsort() Single column (NumPy) No Pushes to end Performance-critical, single-column sorting

For most multi-column sorting tasks, sort_values() is the recommended method. It offers the most flexibility with independent sort directions, configurable NaN placement, and clean syntax for any number of columns.

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