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.rtruediv() function return the floating division of series and other, element-wise (binary operator rtruediv). The function is equivalent to other / series, but with support to substitute a fill_value for missing data in one of the inputs.
Syntax: Series.rtruediv(other, level=None, fill_value=None, axis=0)
Parameter :
other : Series or scalar value
fill_value : Fill existing missing (NaN) values
level : Broadcast across a level, matching Index values on the passed MultiIndex levelReturns : Series
Example #1: Use Series.rtruediv() function to perform reverse division of the given Series object with a scalar element-wise.
# importing pandas as pd import pandas as pd # Creating the Series sr = pd.Series([100, 25, 32, 118, 24, 65]) # Create the Index index_ = ['Coca Cola', 'Sprite', 'Coke', 'Fanta', 'Dew', 'ThumbsUp'] # set the index sr.index = index_ # Print the series print(sr) |
Output :
Now we will use Series.rtruediv() function to perform element-wise reverse floating division of the given Series object with a scalar.
# perform reverse floating division with 1000 selected_items = sr.rtruediv(other = 1000) # Print the returned Series object print(selected_items) |
Output :
As we can see in the output, the Series.rtruediv() function has successfully returned the reverse division of the given Series object with the scalar.
Example #2 : Use Series.rtruediv() function to perform reverse division of the given Series object with a scalar element-wise. The given Series object also contains some missing values.
# importing pandas as pd import pandas as pd # Creating the Series sr = pd.Series([19.5, 16.8, None, 22.78, None, 20.124, None, 18.1002, None]) # Print the series print(sr) |
Output :
Now we will use Series.rtruediv() function to perform element-wise reverse floating division of the given Series object with a scalar. We replace all the missing values with 100.
# perform reverse floating division with 1000 # Fill all the missing values with 100 selected_items = sr.rtruediv(other = 1000, fill_value = 100) # Print the returned Series object print(selected_items) |
Output :
As we can see in the output, the Series.rtruediv() function has successfully returned the reverse division of the given Series object with the scalar and it has also substituted 100 at the place of all the missing values.
