Utilize pd.get dummies() to transform the inputs X into one-hot embeddings in the transform() method.  Put the output into the variable one hot.

Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
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TODO 1.1: Utilize pd.get dummies() to transform the inputs X into one-hot embeddings in the transform() method.  Put the output into the variable one hot. 

TODO 1.2: For our one-hot encoding one_hot, save the column names so we may later retrieve them if necessary. The class variable self should be used to store the output. feature names

 

class OneHotEncoding(BaseEstimator, TransformerMixin):
def__init__(self):
self.feature_names = None
 
deffit(self, X: pd.DataFrame, y: pd.DataFrame = None):
# We don't need to set/learn any variables so
# we just need to return a reference to the object with 'self'
returnself
 
deftransform(self, X: pd.DataFrame, y: pd.DataFrame = None):
# TODO 1.1
one_hot =
 
# TODO 1.2
self.feature_names =
 
return one_hot
 
---
one_hot = OneHotEncoding()
ohe_df = one_hot.fit_transform(forestfire_df[['month', 'day']])
display(ohe_df)

todo_check([
(ohe_df.shape == (517,19),'ohe_df shape did not match (517, 19)'),
('month_nov'in ohe_df.columns,'month_nov not detected in column names!'),
('day_fri'in ohe_df.columns,'day_fri not detected in column names!'),
('month_nov'in one_hot.feature_names,'self.feature_names does not contain the right column name values!'),
])
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