Pandas DataFrame.reset_index() Function

  1. Syntax of pandas.DataFrame.replace_index():
  2. Example Codes: DataFrame.reset_index() Method to Reset the Index of a Dataframe
  3. Example Codes: DataFrame.reset_index() Method to Reset the Index of a MultiIndex Dataframe

Python Pandas DataFrame.reset_index() function resets the index of the given data frame. It replaces the old index with the default index. If the given data frame has a MultiIndex, then this method removes all levels.

Syntax of pandas.DataFrame.replace_index():

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

Parameters

level It is an integer, string, tuple, or list type parameter. If passed, then the function will remove the passed level.
drop It is a Boolean parameter. It specifies inserting index into data frame column. It resets the index to the default integer index.
inplace It is a Boolean parameter. It specifies modifying the given data frame or creating a new object.
col_level It is an integer or string type parameter. It tells which level the labels are inserted into if the columns have multiple levels.
col_fill It is an object type parameter. It tells how the other levels are named if the columns have multiple levels.

Return

It returns the Dataframe with the new index or None if inplace=True.

Example Codes: DataFrame.reset_index() Method to Reset the Index of a Dataframe

import pandas as pd

dataframe=pd.DataFrame({'Attendance': {0: 60, 1: 100, 2: 80,3: 78,4: 95},
                        'Name': {0: 'Olivia', 1: 'John', 2: 'Laura',3: 'Ben',4: 'Kevin'},
                        'Obtained Marks': {0: 90, 1: 75, 2: 82, 3: 64, 4: 45}})
print("The Original Data frame is: \n")
print(dataframe)

dataframe1 = dataframe.reset_index()
print("The Modified Data frame is: \n")
print(dataframe1)

Output:

The Original Data frame is: 

   Attendance    Name  Obtained Marks
0          60  Olivia              90
1         100    John              75
2          80   Laura              82
3          78     Ben              64
4          95   Kevin              45
The Modified Data frame is: 

   index  Attendance    Name  Obtained Marks
0      0          60  Olivia              90
1      1         100    John              75
2      2          80   Laura              82
3      3          78     Ben              64
4      4          95   Kevin              45

The function has returned the data frame with a new index.

If you do not wish to see another index column, then you can set the parameter drop= True. It will reset the index to the default index column.

import pandas as pd

dataframe=pd.DataFrame({'Attendance': {0: 60, 1: 100, 2: 80,3: 78,4: 95},
                        'Name': {0: 'Olivia', 1: 'John', 2: 'Laura',3: 'Ben',4: 'Kevin'},
                        'Obtained Marks': {0: 90, 1: 75, 2: 82, 3: 64, 4: 45}})
print("The Original Data frame is: \n")
print(dataframe)

dataframe1 = dataframe.reset_index(drop= True)
print("The Modified Data frame is: \n")
print(dataframe1)

Output:

The Original Data frame is: 

   Attendance    Name  Obtained Marks
0          60  Olivia              90
1         100    John              75
2          80   Laura              82
3          78     Ben              64
4          95   Kevin              45
The Modified Data frame is: 

   Attendance    Name  Obtained Marks
0          60  Olivia              90
1         100    John              75
2          80   Laura              82
3          78     Ben              64
4          95   Kevin              45

Example Codes: DataFrame.reset_index() Method to Reset the Index of a MultiIndex Dataframe

import pandas as pd
import numpy as np

index = pd.MultiIndex.from_tuples([(1, 'Sarah'),
                                   (1, 'Peter'),
                                   (2, 'Harry'),
                                   (2, 'Monika')],
                                  names=['class', 'name'])
columns = pd.MultiIndex.from_tuples([('Performance', 'max'),
                                     ('Grade', 'type')])
dataframe = pd.DataFrame([('Good', 'A'),
                   ( 'Best', 'A+'),
                   ( 'Bad', 'C'),
                   (np.nan, 'F')],
                  index=index,
                  columns=columns)            
print("The Original Data frame is: \n")
print(dataframe)

dataframe1 = dataframe.reset_index(drop= True)
print("The Modified Data frame is: \n")
print(dataframe1)

Output:

The Original Data frame is: 

             Performance Grade
                     max  type
class name                    
1     Sarah         Good     A
      Peter         Best    A+
2     Harry          Bad     C
      Monika         NaN     F
The Modified Data frame is: 

  Performance Grade
          max  type
0        Good     A
1        Best    A+
2         Bad     C
3         NaN     F

The function has reset the index and added the default integer index.

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