Pandas DataFrame.idxmax() Function
Minahil Noor
Jan 30, 2023
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Syntax of
pandas.DataFrame.idxmax(): -
Example Codes:
DataFrame.idxmax()Method to Find Indexes of Maximum Values Row-Wise -
Example Codes:
DataFrame.idxmax()Method to Find Indexes of Maximum Values Column-Wise
Python Pandas DataFrame.idxmax() function returns the index of the maximum value in rows or columns.
Syntax of pandas.DataFrame.idxmax():
DataFrame.idxmax(axis=0, skipna=True)
Parameters
axis |
It is an integer or string type parameter. It specifies the axis to use. 0 or index for rows, 1 or columns for columns. |
skipna |
It is a Boolean parameter. This parameter specifies excluding null values. If an entire row or column is null, the result will be NA. |
Return
It returns a Series that contains the indexes of maximum values along the specified axis.
Example Codes: DataFrame.idxmax() Method to Find Indexes of Maximum Values Row-Wise
import pandas as pd
dataframe=pd.DataFrame({'Attendance': {0: 60, 1: 100, 2: 80,3: 78,4: 95},
'Obtained Marks': {0: 90, 1: 75, 2: 82, 3: 64, 4: 45}})
print("The Original Data frame is: \n")
print(dataframe)
series = dataframe.idxmax()
print("The Indexes are: \n")
print(series)
Output:
The Original Data frame is:
Attendance Obtained Marks
0 60 90
1 100 75
2 80 82
3 78 64
4 95 45
The Indexes are:
Attendance 1
Obtained Marks 0
dtype: int64
The function has returned the indexes of maximum Attendance and Obtained Marks
Example Codes: DataFrame.idxmax() Method to Find Indexes of Maximum Values Column-Wise
import pandas as pd
dataframe=pd.DataFrame({'Attendance': {0: 60, 1: 100, 2: 80,3: 78,4: 95},
'Obtained Marks': {0: 90, 1: 75, 2: 82, 3: 64, 4: 45}})
print("The Original Data frame is: \n")
print(dataframe)
series = dataframe.idxmax(axis= 1)
print("The Indexes are: \n")
print(series)
Output:
The Original Data frame is:
Attendance Obtained Marks
0 60 90
1 100 75
2 80 82
3 78 64
4 95 45
The Indexes are:
0 Obtained Marks
1 Attendance
2 Obtained Marks
3 Attendance
4 Attendance
dtype: object
The function has returned the indexes column-wise.