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Python | Pandas Series.divide()

  • Last Updated : 15 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.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 level

Returns : 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.

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