Pandas DataFrame.reset_index: Reset Index of a DataFrame

Pandas DataFrame.reset_index

The DataFrame.reset_index method in pandas is used to reset the index of a DataFrame, replacing it with the default integer index. If the DataFrame has a MultiIndex, this method can remove one or more levels.

Syntax

The syntax for DataFrame.reset_index is:

DataFrame.reset_index(level=None, drop=False, inplace=False, col_level=0, col_fill='', allow_duplicates=, names=None)

Here, DataFrame refers to the pandas DataFrame whose index is being reset.

Parameters

Returns

A new DataFrame with the reset index, or None if inplace=True.

Examples

Resetting the Index of a DataFrame

By default, reset_index converts the existing index into a column and sets a new default integer index.

Python Program

import pandas as pd

# Create a DataFrame with a custom index
df = pd.DataFrame({'A': [10, 20, 30], 'B': [100, 200, 300]}, index=['x', 'y', 'z'])
print("Original DataFrame:")
print(df)

# Reset index
df_reset = df.reset_index()
print("\nDataFrame after reset_index:")
print(df_reset)

Output

Original DataFrame:
     A    B
x   10  100
y   20  200
z   30  300

DataFrame after reset_index:
  index   A    B
0     x  10  100
1     y  20  200
2     z  30  300

Resetting Index Without Adding Index as a Column

By setting drop=True, the index is removed without being added as a column.

Python Program

import pandas as pd

# Create a DataFrame with a custom index
df = pd.DataFrame({'A': [10, 20, 30], 'B': [100, 200, 300]}, index=['x', 'y', 'z'])

# Reset index and drop the existing index
df_reset_drop = df.reset_index(drop=True)
print(df_reset_drop)

Output

    A    B
0  10  100
1  20  200
2  30  300

Reset Index of a MultiIndex DataFrame

For MultiIndex DataFrames, reset_index removes one or more levels.

Python Program

# Create a MultiIndex DataFrame
arrays = [['A', 'A', 'B', 'B'], [1, 2, 1, 2]]
index = pd.MultiIndex.from_arrays(arrays, names=('Group', 'Number'))
df_multi = pd.DataFrame({'Values': [100, 200, 300, 400]}, index=index)
print("Original MultiIndex DataFrame:")
print(df_multi)

# Reset MultiIndex
df_reset_multi = df_multi.reset_index()
print("\nDataFrame after reset_index:")
print(df_reset_multi)

Output

Original MultiIndex DataFrame:
                Values
Group Number        
A     1        100
      2        200
B     1        300
      2        400

DataFrame after reset_index:
  Group  Number  Values
0     A       1     100
1     A       2     200
2     B       1     300
3     B       2     400

Reset Index In-Place

Using inplace=True modifies the DataFrame directly instead of returning a new one.

Python Program

# Create a MultiIndex DataFrame
arrays = [['A', 'A', 'B', 'B'], [1, 2, 1, 2]]
index = pd.MultiIndex.from_arrays(arrays, names=('Group', 'Number'))
df_multi = pd.DataFrame({'Values': [100, 200, 300, 400]}, index=index)
print("Original MultiIndex DataFrame:")
print(df_multi)

# Reset MultiIndex
df_reset_multi = df_multi.reset_index()

# Reset index in-place
df.reset_index(inplace=True)
print(df)

Output

  index   A    B
0     x  10  100
1     y  20  200
2     z  30  300

Renaming the Index Column After Reset

Using the names parameter, the new column containing the previous index can be renamed.

Python Program

# Rename the index column
df_reset_rename = df.reset_index(names='Custom_Index')
print(df_reset_rename)

Output

  Custom_Index   A    B
0           x  10  100
1           y  20  200
2           z  30  300

Summary

In this tutorial, we explored the DataFrame.reset_index method in pandas. Key takeaways include:

• By default, reset_index moves the current index into a new column and assigns a default integer index.
• Setting drop=True removes the index without adding it as a column.
• MultiIndex DataFrames can have one or more levels removed.
• Using inplace=True modifies the DataFrame directly.
• The names parameter allows renaming the column containing the previous index.