Replacing pandas series values

Overview:

The mask() method accepts a Boolean condition and replaces each element of a pandas Series that satisfies the condition with the given value. More sophisticated replacement strategies shall be implemented using the callable parameter.
The replace() method replaces the occurrences of specified value with a given value. Regular expressions are supported for the string elements present in a pandas Series.

Example 1 - Using mask() method to replace elements satisfying a Boolean condition:

# Example Python program that masks specific values in a pandas Series
# that matches a condition
import pandas as pds

# Create a Series from a list
vals = [1, 2, 3, 4, 5]
series = pds.Series(data = vals)

# Replace all the even numbers with zero
resultant = series.mask(series%2 == 0, 0)

print("Original Series:")
print(series)

print("Resultant Series after masking:")
print(resultant)

Output:

Original Series:

0 1

1 2

2 3

3 4

4 5

dtype: int64

Resultant Series after masking:

0 1

1 0

2 3

3 0

4 5

dtype: int64

Example 2 - Using a callable to implement a replacement strategy:

# Example Python program that uses a callable
# to mask the values of a pandas Series

import pandas as pds

# A callable that is called once which returns a new Series with
# masked results
def meanReplace(data):
   d1 = data.copy(deep=True)
   for i in d1:
       if i > 5:
           d1[i] = sum(data) / len(data)
   return d1

# Create a Series
s1 = pds.Series(data = range(0, 10))
print("Original Series:")
print(s1)

# Mask the values greater than 5 with mean value
s2 = s1.mask(s1 > 5, meanReplace(s1))
print("Masked Series using a callable:")
print(s2)

Output:

Original Series:

0 0

1 1

2 2

3 3

4 4

5 5

6 6

7 7

8 8

9 9

dtype: int64

Masked Series using a callable:

0 0.0

1 1.0

2 2.0

3 3.0

4 4.0

5 5.0

6 4.5

7 4.5

8 4.5

9 4.5

dtype: float64

Example 3 - Change elements through replace() method:

# Example Python program that replaces
# specific occurrences of a value with a
# given value
import pandas as pds

# Create a pandas Series
values = [1, 3, 2, 1, 5, 2, 4, 1]
series = pds.Series(data = values)
print("Original data:")
print(series)

# Replace 1s with 0s
print("New data:")
newSeries = series.replace(1, 0)
print(newSeries)

Output:

Original data:

0 1

1 3

2 2

3 1

4 5

5 2

6 4

7 1

dtype: int64

New data:

0 0

1 3

2 2

3 0

4 5

5 2

6 4

7 0

dtype: int64