pandas reset_index() – Rest Index on DataFrame

DataFrame.reset_index() method is used to reset the index on the pandas DataFrame. This takes level, drop, inplace, col_level, col_fill as parameters and returns DataFrame. When inplace=True is used, it returns None. Use pandas.DataFrame.set_index() to set column as index.

In this article, I will cover how to reset index on pandas DataFrame with several examples. We are usually required to reset the index post dropping some rows from DataFrame as it creates a gap in the index.

Key Points –

• reset_index() is used to reset the index of a DataFrame, converting it back to a default integer index.
• By default, reset_index() moves the existing index into a new column in the DataFrame.
• Use drop=True to reset the index without adding the old index as a new column.
• The inplace=True parameter allows you to modify the DataFrame directly without creating a copy.
• reset_index() can be combined with the rename() function to adjust column names after reset.
• After resetting the index, the DataFrame index is restored to the default range from 0 to n-1.

1. Quick Examples of Pandas Reset Index

Below are some quick examples of reset index on DataFrame.

# Quick examples of reset index

# Reset index
df2=df.reset_index()

# Reset index with out assigning to column
df.reset_index(inplace=True, drop=True)

# Set index column name
df.index.name = 'custom_index'

# Reset index starting from 1
df.index = np.arange(1, len(df) + 1)

2. pandas.DataFrame.reset_index() Syntax

Following is the syntax of the pandas.DataFrame.reset_index().

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

• level – Takes int, str, tuple, or list, default None
• drop – bool, default False. When set to True, ignores setting old index as column.
• inplace – bool, default False. When set to True, it updates the existing DataFrame object.
• col_level – If the columns have multiple levels, determines which level the labels are inserted into. 
• col_fill – If the columns have multiple levels, determines how the other levels are named.

Let’s create a pandas DataFrame with examples

import pandas as pd

# Create DataFrame from dict
df = pd.DataFrame({'Courses':['Spark','PySpark','Java','PHP'],
           'Fee':[20000,20000,15000,10000],
           'Duration':['35days','35days','40days','30days']})

df=df.drop([2])
print(df)

Yields below output

# Output:
   Courses    Fee Duration
0    Spark  20000   35days
1  PySpark  20000   35days
3      PHP  10000   30days

3. Pandas Rest Index on DataFrame Example

Let’s use the pandas.DataFrame.reset_index() syntax to reset the DataFrame index. When we reset the index, the old index is added as a column to DataFrame, and a new sequential index is used

# Reset index on DataFrame
df2=df.reset_index()
print(df2)

Yields below output.

# Output:
   index  Courses    Fee Duration
0      0    Spark  20000   35days
1      1  PySpark  20000   35days
2      3      PHP  10000   30days

If you don’t want to add the existing index as a column, use drop=True param and also use inplace=True to update the existing DataFrame.

# Drop index as column
df.reset_index(inplace=True, drop=True)
print(df)

Yields below output

# Output:
   Courses    Fee Duration
0    Spark  20000   35days
1  PySpark  20000   35days
2      PHP  10000   30days

4. Reset Index Name on DataFrame

Now, let’s see how to reset an index name on pandas DataFrame. By default, DataFrame creates with index name as ‘index’

# Set index name
df.index.name = 'custom_index'
print(df)

Yields below output.

# Output:
              Courses    Fee Duration
custom_index                         
0               Spark  20000   35days
1             PySpark  20000   35days
2                 PHP  10000   30days

5. Reset Index to Start from 1

When you reset the index it assigns a numerical value starting from zero, some times you may need to assign from 1, you can do this as shown in the below example.

# Reset index starting from 1
df.index = np.arange(1, len(df) + 1)
print(df)

Yields below output

# Output:
   Courses    Fee Duration
1    Spark  20000   35days
2  PySpark  20000   35days
3      PHP  10000   30days

Complete Example of Reset Index on DataFrame

import pandas as pd
import numpy as np

# Create DataFrame from dict
df = pd.DataFrame({'Courses':['Spark','PySpark','Java','PHP'],
           'Fee':[20000,20000,15000,10000],
           'Duration':['35days','35days','40days','30days']})

df=df.drop([2])
print(df)

# Reset index
df2=df.reset_index()
print(df2)

# Reset index with out assigning to column
df.reset_index(inplace=True, drop=True)
print(df)

# Set index column name
df.index.name = 'custom_index'
print(df)

# Reset index starting from 1
df.index = np.arange(1, len(df) + 1)
print(df)

FAQ on pandas reset_index() – Rest Index on DataFrame

Conclusion

In this article, you have learned how to reset the index on DataFrame by using different prams on the reset_index() method.

Related Articles

• Pandas Drop Index Column Explained
• pandas.DataFrame.sort_values() – Examples
• pandas DataFrame.sort_index() – Sort by Index
• Pandas set_index() – Set Index to DataFrame
• Pandas melt() DataFrame Example
• How to Transpose() DataFrame in Pandas?
• Pandas Drop Rows with NaN Values in DataFrame
• Series.reindex() – Change the Index Order in Pandas Series
  

References

• https://www.w3schools.com/python/pandas/default.asp