Pandas series is a One-dimensional ndarray with axis labels. The labels need not be unique but must be a hashable type. The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index.
Pandas Series.argmin() function returns the row label of the minimum value in the given series object.
Syntax: Series.argmin(axis=0, skipna=True, *args, **kwargs)
Parameter :
skipna : Exclude NA/null values. If the entire Series is NA, the result will be NA.
axis : For compatibility with DataFrame.idxmin. Redundant for application on Series.
*args, **kwargs : Additional keywords have no effect but might be accepted for compatibility with NumPy.Returns : idxmin : Index of minimum of values.
Example #1: Use Series.argmin() function to return the row label of the minimum value in the given series object
# importing pandas as pd import pandas as pd # Creating the Series sr = pd.Series([34, 5, 13, 32, 4, 15]) # Create the Index index_ = ['Coca Cola', 'Sprite', 'Coke', 'Fanta', 'Dew', 'ThumbsUp'] # set the index sr.index = index_ # Print the series print(sr) |
Output :
Coca Cola 34 Sprite 5 Coke 13 Fanta 32 Dew 4 ThumbsUp 15 dtype: int64
Now we will use Series.argmin() function to return the row label of the minimum value in the given series object.
# return the row label for # the minimum value result = sr.argmin() # Print the result print(result) |
Output :
Dew
As we can see in the output, the Series.argmin() function has successfully returned the row label of the minimum value in the given series object.
Example #2 : Use Series.argmin() function to return the row label of the minimum value in the given series object.
# importing pandas as pd import pandas as pd # Creating the Series sr = pd.Series([11, 21, 8, 18, 65, 18, 32, 10, 5, 32, None]) # Create the Index # apply yearly frequency index_ = pd.date_range('2010-10-09 08:45', periods = 11, freq ='Y') # set the index sr.index = index_ # Print the series print(sr) |
Output :
2010-12-31 08:45:00 11.0 2011-12-31 08:45:00 21.0 2012-12-31 08:45:00 8.0 2013-12-31 08:45:00 18.0 2014-12-31 08:45:00 65.0 2015-12-31 08:45:00 18.0 2016-12-31 08:45:00 32.0 2017-12-31 08:45:00 10.0 2018-12-31 08:45:00 5.0 2019-12-31 08:45:00 32.0 2020-12-31 08:45:00 NaN Freq: A-DEC, dtype: float64
Now we will use Series.argmin() function to return the row label of the minimum value in the given series object.
# return the row label for # the minimum value result = sr.argmin() # Print the result print(result) |
Output :
2018-12-31 08:45:00
As we can see in the output, the Series.argmin() function has successfully returned the row label of the minimum value in the given series object.
