Python | Pandas Series.rmod()
Last Updated :
10 Feb, 2019
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.rmod()
function perform the modulo of series and other, element-wise (binary operator rmul). The operation is equivalent to other % series, but with support to substitute a fill_value for missing data in one of the inputs.
Syntax: Series.rmod(other, level=None, fill_value=None, axis=0)
Parameter :
other : Series or scalar value
fill_value : Fill existing missing (NaN) value
level : Broadcast across a level,
Returns : result : Series
Example #1: Use Series.rmod()
function to perform the Modulo of a scalar with the given series object.
import pandas as pd
sr = pd.Series([ 10 , 25 , 3 , 11 , 24 , 6 ])
index_ = [ 'Coca Cola' , 'Sprite' , 'Coke' , 'Fanta' , 'Dew' , 'ThumbsUp' ]
sr.index = index_
print (sr)
|
Output :
Now we will use Series.rmod()
function to perform the Modulo of scalar with the series.
result = sr.rmod( 2000 )
print (result)
|
Output :
As we can see in the output, the Series.rmod()
function has returned the result of modulo of the given scalar with the series object.
Example #2: Use Series.rmod()
function to perform the modulo of a scalar with the given series object. The given series object contains some missing values.
import pandas as pd
sr = pd.Series([ 19.5 , 16.8 , None , 22.78 , None , 20.124 , None , 18.1002 , None ])
print (sr)
|
Output :
Now we will use Series.rmod()
function to perform the Modulo of scalar with the series. We will also fill 10 at the place of missing values.
result = sr.rmod( 2000 , fill_value = 10 )
print (result)
|
Output :
As we can see in the output, the Series.rmod()
function has returned the result of modulo of the given scalar with the series object.
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