Python | Pandas Series.combine()

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.combine() function combine the Series with a Series or scalar according to func. It combine the Series and other using func to perform element-wise selection for combined Series. fill_value is assumed when value is missing at some index from one of the two objects being combined.

Syntax: Series.combine(other, func, fill_value=None)

Parameter :
other : Series or scalar
func : Function that takes two scalars as inputs and returns an element.
fill_value : The value to assume when an index is missing from one Series or the other.

Returns : Series

Example #1: Use Series.combine() function to find the maximum value for each index labels in the two series object.

# importing pandas as pd
import pandas as pd

# Creating the first Series
sr1 = pd.Series([80, 25, 3, 25, 24, 6])

# Creating the second Series
sr2 = pd.Series([34, 5, 13, 32, 4, 15])

# Create the Index
index_ = ['Coca Cola', 'Sprite', 'Coke', 'Fanta', 'Dew', 'ThumbsUp']

# set the first index
sr1.index = index_

# set the second index
sr2.index = index_

# Print the first series
print(sr1)

# Print the second series
print(sr2)

Output :


Now we will use Series.combine() function to find the maximum value for each index labels in the two given series object.

# find the maximum element-wise
# among sr1 and sr2
result = sr1.combine(other = sr2, func = max)

# Print the result
print(result)

Output :

As we can see in the output, the Series.combine() function has successfully returned the maximum value for each index labels among the two series objects.

Example #2 : Use Series.combine() function to find the minimum value for each index labels in the two series object.

# importing pandas as pd
import pandas as pd

# Creating the first Series
sr1 = pd.Series([51, 10, 24, 18, None, 84, 12, 10, 5, 24, 2])

# Creating the second Series
sr2 = pd.Series([11, 21, 8, 18, 65, 18, 32, 10, 5, 32, None])

# Create the Index
index_ = pd.date_range('2010-10-09', periods = 11, freq ='M')

# set the first index
sr1.index = index_

# set the second index
sr2.index = index_

# Print the first series
print(sr1)

# Print the second series
print(sr2)

Output :


Now we will use Series.combine() function to find the minimum value for each index labels in the two given series object.

# find the minimum element-wise
# among sr1 and sr2
result = sr1.combine(other = sr2, func = min)

# Print the result
print(result)

Output :

As we can see in the output, the Series.combine() function has successfully returned the minimum value for each index labels among the two series objects.