Python | Pandas Series.mask()

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.mask() function is used for masking purpose. This function replace values where the passed condition is True. Otherwise the value remains same.

Syntax: Series.mask(cond, other=nan, inplace=False, axis=None, level=None, errors=’raise’, try_cast=False, raise_on_error=None)

Parameter :
cond : Where cond is False, keep the original value. Where True, replace with corresponding value from other.
other : Entries where cond is True are replaced with corresponding value from other.
inplace : Whether to perform the operation in place on the data.
axis : Alignment axis if needed.
level : Alignment level if needed.

Returns : wh : same type as caller

Example #1: Use Series.mask() function to replace the ‘Rio’ city in the given series object.

# importing pandas as pd
import pandas as pd

# Creating the Series
sr = pd.Series(['New York', 'Chicago', 'Toronto', 'Lisbon', 'Rio'])

# Create the Index
index_ = ['City 1', 'City 2', 'City 3', 'City 4', 'City 5']

# set the index
sr.index = index_

# Print the series
print(sr)

Output :

Now we will use Series.mask() function to replace the ‘Rio’ city in the given series object.

# replace 'Rio' with 'Tokyo'
result = sr.mask(lambda x : x =='Rio', other = 'Tokyo')

# Print the result
print(result)

Output :

As we can see in the output, the Series.mask() function has successfully replaced the ‘Rio’ city with ‘Tokyo’ in the given series object.

Example #2: Use Series.mask() function to mask all the values in the given series object which are greater than 50.

# importing pandas as pd
import pandas as pd

# Creating the Series
sr = pd.Series([11, 21, 8, 18, 65, 84, 32, 10, 5, 24, 32])

# Print the series
print(sr)

Output :

Now we will use Series.mask() function to mask all the values greater than 50 in the given series object.

# mask values greater than 50
result = sr.mask(sr > 50)

# Print the result
print(result)

Output :

As we can see in the output, the Series.mask() function has successfully masked all the values greater than 50 in the given series object.