Pandas DataFrame.reset_index() Function
-
Syntax of
pandas.DataFrame.replace_index(): -
Example Codes:
DataFrame.reset_index()Method to Reset the Index of a Dataframe -
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.
Contribute
DelftStack is a collective effort contributed by software geeks like you. If you like the article and would like to contribute to DelftStack by writing paid articles, you can check the write for us page.
