How to reset index in Pandas dataframe?

In Pandas, the index serves as row labels for a DataFrame. Sometimes you need to reset the index back to the default integer sequence (0, 1, 2...) or convert a custom index into a regular column. The reset_index() method provides this functionality.

Basic reset_index() Usage

Let's start with a simple example showing how to reset a DataFrame's index ?

import pandas as pd

# Create DataFrame with default index
data = {'Name': ["Allen", "Jack", "Mark", "Vishal"], 
        'Marks': [85, 92, 99, 87]}
df = pd.DataFrame(data)
print("Original DataFrame:")
print(df)

Original DataFrame:
    Name  Marks
0  Allen     85
1   Jack     92
2   Mark     99
3  Vishal    87

Resetting Custom Index

When you have a custom index and want to convert it back to default integers ?

import pandas as pd

# Create DataFrame with custom index
data = {'Name': ["Allen", "Jack", "Mark", "Vishal"], 
        'Marks': [85, 92, 99, 87]}
custom_index = ['a', 'j', 'm', 'v']
df = pd.DataFrame(data, index=custom_index)

print("DataFrame with custom index:")
print(df)
print("\nAfter reset_index():")
df_reset = df.reset_index()
print(df_reset)

DataFrame with custom index:
     Name  Marks
a   Allen     85
j    Jack     92
m    Mark     99
v  Vishal     87

After reset_index():
  index   Name  Marks
0     a  Allen     85
1     j   Jack     92
2     m   Mark     99
3     v  Vishal     87

Using inplace Parameter

Use inplace=True to modify the original DataFrame instead of creating a new one ?

import pandas as pd

data = {'Name': ["Allen", "Jack", "Mark"], 'Marks': [85, 92, 99]}
df = pd.DataFrame(data, index=['x', 'y', 'z'])

print("Before inplace reset:")
print(df)

df.reset_index(inplace=True)
print("\nAfter inplace reset:")
print(df)

Before inplace reset:
   Name  Marks
x  Allen     85
y   Jack     92
z   Mark     99

After inplace reset:
  index   Name  Marks
0     x  Allen     85
1     y   Jack     92
2     z   Mark     99

Dropping the Old Index

Use drop=True to discard the old index instead of adding it as a column ?

import pandas as pd

data = {'Name': ["Allen", "Jack", "Mark"], 'Marks': [85, 92, 99]}
df = pd.DataFrame(data, index=['x', 'y', 'z'])

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

df_reset = df.reset_index(drop=True)
print("\nAfter reset_index(drop=True):")
print(df_reset)

Original DataFrame:
   Name  Marks
x  Allen     85
y   Jack     92
z   Mark     99

After reset_index(drop=True):
    Name  Marks
0  Allen     85
1   Jack     92
2   Mark     99

Key Parameters

Conclusion

Use reset_index() to convert custom indices back to default integers. Set drop=True to discard the old index, or inplace=True to modify the original DataFrame directly.