Python Pandas: How to Reset the Index of a Pandas DataFrame in Python

Learn how to use Pandas' reset_index() method to fix gaps, duplicates, or non-sequential indices after dropping rows, filtering, sorting, or concatenating DataFrames in Python.

When you manipulate a Pandas DataFrame - by dropping rows, filtering, sorting, or slicing - the original index values are preserved. This can leave gaps in the index (e.g., 0, 3, 5, 7 instead of 0, 1, 2, 3), which can cause confusion and issues in downstream operations. The reset_index() method reassigns a clean, sequential integer index starting from 0.

In this guide, you will learn how to reset a DataFrame's index, control whether the old index is kept or discarded, and understand when resetting the index is necessary.

The Problem: Gaps in the Index After Dropping Rows

When you drop rows from a DataFrame, the index retains the original values:

import pandas as pd

df = pd.DataFrame({
    'name': ['Jordan', 'Brady', 'James', 'Bryant',
             'Rodriguez', 'Jeter', 'Williams', 'Ali',
             'Phelps', 'Woods'],
    'runs': [32292, 89214, 38652, 33643,
             3115, 3465, 365, 56,
             28, 82]
})

print("Original DataFrame:")
print(df)

Output:

Original DataFrame:
        name   runs
0     Jordan  32292
1      Brady  89214
2      James  38652
3     Bryant  33643
4  Rodriguez   3115
5      Jeter   3465
6   Williams    365
7        Ali     56
8     Phelps     28
9      Woods     82

After dropping rows at index 0 and 1:

df = df.drop([0, 1])

print("After dropping rows 0 and 1:")
print(df)

Output:

After dropping rows 0 and 1:
        name   runs
2      James  38652
3     Bryant  33643
4  Rodriguez   3115
5      Jeter   3465
6   Williams    365
7        Ali     56
8     Phelps     28
9      Woods     82

The index now starts at 2 instead of 0, and positions 0 and 1 are missing. This can cause issues with positional operations like iloc or when iterating with range(len(df)).

Resetting the Index With `reset_index()`

The reset_index() method creates a new sequential integer index:

import pandas as pd

df = pd.DataFrame({
    'name': ['Jordan', 'Brady', 'James', 'Bryant',
             'Rodriguez', 'Jeter', 'Williams', 'Ali',
             'Phelps', 'Woods'],
    'runs': [32292, 89214, 38652, 33643,
             3115, 3465, 365, 56,
             28, 82]
})

print("Original DataFrame:")
print(df)
print("\nAfter dropping rows 0 and 1:")
print(df)

df = df.reset_index(drop=True)

print("After resetting the index:")
print(df)

Output:

Original DataFrame:
        name   runs
0     Jordan  32292
1      Brady  89214
2      James  38652
3     Bryant  33643
4  Rodriguez   3115
5      Jeter   3465
6   Williams    365
7        Ali     56
8     Phelps     28
9      Woods     82

After dropping rows 0 and 1:
        name   runs
0     Jordan  32292
1      Brady  89214
2      James  38652
3     Bryant  33643
4  Rodriguez   3115
5      Jeter   3465
6   Williams    365
7        Ali     56
8     Phelps     28
9      Woods     82
After resetting the index:
        name   runs
0     Jordan  32292
1      Brady  89214
2      James  38652
3     Bryant  33643
4  Rodriguez   3115
5      Jeter   3465
6   Williams    365
7        Ali     56
8     Phelps     28
9      Woods     82

The index is now clean and sequential: 0, 1, 2, ..., 7.

Key Parameter: `drop`

The drop parameter controls what happens to the old index:

`drop=True` - Discard the Old Index

The old index is removed completely:

df_reset = df.reset_index(drop=True)

This is the most common usage when the old index has no meaningful information.

`drop=False` (Default) - Keep the Old Index as a Column

The old index is preserved as a new column named index:

import pandas as pd 

df = pd.DataFrame({'value': [10, 20, 30]}, index=[5, 10, 15])

df_reset = df.reset_index()  # drop=False by default
print(df_reset)

Output:

   index  value
0      5     10
1     10     20
2     15     30

The old index [5, 10, 15] is now a column called index.

When to keep the old index

Keep the old index (drop=False) when it contains meaningful data - for example, if the index represents dates, IDs, or category labels that you want to preserve as a regular column.

import pandas as pd

df = pd.DataFrame({'price': [100, 105, 102]},
                  index=pd.to_datetime(['2024-01-01', '2024-01-02', '2024-01-03']))

df_reset = df.reset_index()  # Old index becomes a 'index' column (or the index name)
print(df_reset)

Output:

       index  price
0 2024-01-01    100
1 2024-01-02    105
2 2024-01-03    102

Resetting the Index In Place

By default, reset_index() returns a new DataFrame. To modify the original DataFrame directly, use inplace=True:

df.reset_index(drop=True, inplace=True)

Warning

Using inplace=True modifies the original DataFrame and returns None. Do not assign the result to a variable:

df = df.reset_index(drop=True, inplace=True)
print(df)  # None!

df.reset_index(drop=True, inplace=True)

df = df.reset_index(drop=True)

Common Scenarios for Resetting the Index

After Filtering Rows

import pandas as pd

df = pd.DataFrame({
    'name': ['Alice', 'Bob', 'Charlie', 'Diana', 'Eve'],
    'score': [85, 42, 91, 67, 73]
})

passed = df[df['score'] > 70]
print("Before reset:")
print(passed)

passed = passed.reset_index(drop=True)
print("\nAfter reset:")
print(passed)

Output:

Before reset:
      name  score
0    Alice     85
2  Charlie     91
4      Eve     73

After reset:
      name  score
0    Alice     85
1  Charlie     91
2      Eve     73

After Sorting

import pandas as pd

df = pd.DataFrame({
    'name': ['Charlie', 'Alice', 'Bob'],
    'score': [91, 85, 42]
})

df_sorted = df.sort_values('name').reset_index(drop=True)
print(df_sorted)

Output:

      name  score
0    Alice     85
1      Bob     42
2  Charlie     91

After Concatenation

import pandas as pd

df1 = pd.DataFrame({'value': [1, 2, 3]})
df2 = pd.DataFrame({'value': [4, 5, 6]})

combined = pd.concat([df1, df2])
print("Before reset:")
print(combined)

combined = combined.reset_index(drop=True)
print("\nAfter reset:")
print(combined)

Output:

Before reset:
   value
0      1
1      2
2      3
0      4
1      5
2      6

After reset:
   value
0      1
1      2
2      3
3      4
4      5
5      6

Without resetting, the concatenated DataFrame has duplicate index values (two 0s, two 1s, two 2s), which can cause unexpected behavior with loc[].

After GroupBy Aggregation

import pandas as pd

df = pd.DataFrame({
    'department': ['Sales', 'Sales', 'Engineering', 'Engineering'],
    'salary': [50000, 60000, 80000, 90000]
})

avg_salary = df.groupby('department')['salary'].mean().reset_index()
print(avg_salary)

Output:

    department   salary
0  Engineering  85000.0
1        Sales  55000.0

Without reset_index(), the department name would be the index, not a column.

Complete Example

import pandas as pd

df = pd.DataFrame({
    'name': ['Jordan', 'Brady', 'James', 'Bryant',
             'Rodriguez', 'Jeter', 'Williams', 'Ali',
             'Phelps', 'Woods'],
    'runs': [32292, 89214, 38652, 33643,
             3115, 3465, 365, 56,
             28, 82]
})

print("Original DataFrame:")
print(df)

df = df.drop([0, 1])
print("\nAfter dropping rows 0 and 1:")
print(df)

df = df.reset_index(drop=True)
print("\nAfter resetting the index:")
print(df)

Output:

Original DataFrame:
        name   runs
0     Jordan  32292
1      Brady  89214
2      James  38652
3     Bryant  33643
4  Rodriguez   3115
5      Jeter   3465
6   Williams    365
7        Ali     56
8     Phelps     28
9      Woods     82

After dropping rows 0 and 1:
        name   runs
2      James  38652
3     Bryant  33643
4  Rodriguez   3115
5      Jeter   3465
6   Williams    365
7        Ali     56
8     Phelps     28
9      Woods     82

After resetting the index:
        name   runs
0      James  38652
1     Bryant  33643
2  Rodriguez   3115
3      Jeter   3465
4   Williams    365
5        Ali     56
6     Phelps     28
7      Woods     82

Quick Reference

Scenario	Code
Reset and discard old index	`df.reset_index(drop=True)`
Reset and keep old index as column	`df.reset_index()`
Reset in place	`df.reset_index(drop=True, inplace=True)`
Reset after filtering	`df[condition].reset_index(drop=True)`
Reset after concat	`pd.concat([df1, df2]).reset_index(drop=True)`
Reset after groupby	`df.groupby('col').agg(...).reset_index()`

Conclusion

The reset_index() method is essential for maintaining a clean, sequential index after operations that alter the DataFrame's structure, such as dropping rows, filtering, sorting, concatenating, or aggregating.

Use drop=True when the old index has no analytical value, and omit drop (or set it to False) when the old index contains meaningful data you want to preserve as a column.

Resetting the index prevents bugs caused by gaps, duplicates, or unexpected index values in downstream operations.

Python Pandas: How to Reset the Index of a Pandas DataFrame in Python

The Problem: Gaps in the Index After Dropping Rows

Resetting the Index With reset_index()

Key Parameter: drop

drop=True - Discard the Old Index

drop=False (Default) - Keep the Old Index as a Column

Resetting the Index In Place

Common Scenarios for Resetting the Index

After Filtering Rows

After Sorting

After Concatenation

After GroupBy Aggregation

Complete Example

Quick Reference

Conclusion

Resetting the Index With `reset_index()`

Key Parameter: `drop`

`drop=True` - Discard the Old Index

`drop=False` (Default) - Keep the Old Index as a Column