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.divide() function performs floating division of series and other, element-wise (binary operator truediv). It is equivalent to series / other, but with support to substitute a fill_value for missing data in one of the inputs.
Syntax: Series.divide(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 : result : Series
Example #1: Use Series.divide() function to perform floating division of the given series object with a scalar.
# importing pandas as pd import pandas as pd # Creating the Series sr = pd.Series([80, 25, 3, 25, 24, 6]) # 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.divide() function to perform floating division of the given series object with a scalar.
# perform floating division result = sr.divide(other = 2) # Print the result print(result) |
Output :
As we can see in the output, the Series.divide() function has successfully performed the floating division of the given series object with a scalar.
Example #2 : Use Series.divide() function to perform floating division of the given series object with a scalar. The given series object contains some missing values.
# importing pandas as pd import pandas as pd # Creating the Series sr = pd.Series([100, None, None, 18, 65, None, 32, 10, 5, 24, None]) # Create the Index index_ = pd.date_range('2010-10-09', periods = 11, freq ='M') # set the index sr.index = index_ # Print the series print(sr) |
Output :
Now we will use Series.divide() function to perform floating division of the given series object with a scalar. We are going to fill 50 at the place of all the missing values.
# perform floating division # fill 50 at the place of missing values result = sr.divide(other = 2, fill_value = 50) # Print the result print(result) |
Output :
As we can see in the output, the Series.divide() function has successfully performed the floating division of the given series object with a scalar.
