I'm watching a PyData talk from 2017 in which the speaker provides this example for how to use FunctionTransformer for sklearn.preprocessing

from sklearn.preprocessing import FunctionTransformer
logger = FunctionTransformer(np.log1p)
X_log = logger.transform(X)

In other words, she's applying a function over the rows of a column. I assumed this could be done more simply using .apply(). I feel that there must be something more to the reason why a data analyst would import FunctionTransformer. Could someone help me understand what differentiates the .apply() method from FunctionTransformer?


FunctionTransformer is useful because it allows you to apply a custom function in a pipeline. Because Pipeline() from sklearn.pipeline only works with objects that implement the .transform() and .fit() methods, you use FunctionTransformer to change your custom function to allow .transform() and/or .fit() to be used on it.

You could transform a DataFrame or Series by using .apply() (or something similar like a list comprehension), but you wouldn't be able to use that function in Pipeline() without first using Function Transformer.

(answer adapted from a DataCamp module "Multiple types of processing: FunctionTransformer" from the class "Machine Learning with the Experts: School Budgets")


# Import FunctionTransformer
from sklearn.preprocessing import FunctionTransformer

# Obtain the text data: get_text_data
get_text_data = FunctionTransformer(lambda x: x['text'], validate=False)

# Obtain the numeric data: get_numeric_data
get_numeric_data = FunctionTransformer(lambda x: x[['numeric', 'with_missing']], validate=False)

# Fit and transform the text data: just_text_data
just_text_data = get_text_data.fit_transform(sample_df)

# Fit and transform the numeric data: just_numeric_data
just_numeric_data = get_numeric_data.fit_transform(sample_df)

# Print head to check results
print('Text Data')
print('\nNumeric Data')
<script.py> output:
    Text Data
    1        foo
    2    foo bar
    4    foo bar
    Name: text, dtype: object

    Numeric Data
         numeric  with_missing
    0 -10.856306      4.433240
    1   9.973454      4.310229
    2   2.829785      2.469828
    3 -15.062947      2.852981
    4  -5.786003      1.826475


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