Challenge: Creating a Pipeline
In this challenge, you need to put all the preprocessing steps we did together into one pipeline. The dataset is the initial penguins.csv file we started from.
The first step is to remove two useless rows. Then you will have to create a pipeline containing encoding, imputing, and scaling.
You need to encode only two columns, 'sex' and 'island'. Since you do not want to encode the entire X, you must use a ColumnTransformer. Afterward, apply the SimpleImputer and StandardScaler to the entire X.
Here is a reminder of the make_column_transformer() and make_pipeline() functions you will use.
Tarefa
Swipe to start coding
- Import the correct function for creating a pipeline.
- Make a
ColumnTransformerwith theOneHotEncoderapplied only to columns'sex'and'island'. - Make sure that all other columns remain untouched.
- Create a pipeline containing
ctyou just created,SimpleImputerthat fills in missing values with the most frequent value and aStandardScaleras a last step. - Transform the
Xusing thepipeyou created.
Solução
import pandas as pd
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.compose import make_column_transformer
from sklearn.pipeline import make_pipeline
df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a65bbc96-309e-4df9-a790-a1eb8c815a1c/penguins.csv')
# Removing rows with more than 1 null
df = df[df.isna().sum(axis=1) < 2]
# Assigining X, y variables
X, y = df.drop('species', axis=1), df['species']
# Create the ColumnTransformer for encoding
ct = make_column_transformer((OneHotEncoder(), ['sex', 'island']), remainder='passthrough')
# Make a Pipeline of ct, SimpleImputer, and StandardScaler
pipe = make_pipeline(ct, SimpleImputer(strategy='most_frequent'), StandardScaler())
# Transform X using the pipeline and print transformed X
X_transformed = pipe.fit_transform(X)
print(X_transformed)
Tudo estava claro?
Obrigado pelo seu feedback!
Seção 3. Capítulo 4
import pandas as pd
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.compose import make_column_transformer
from ___ import ___
df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a65bbc96-309e-4df9-a790-a1eb8c815a1c/penguins.csv')
# Removing rows with more than 1 null
df = df[df.isna().sum(axis=1) < 2]
# Assigining X, y variables
X, y = df.drop('species', axis=1), df['species']
# Create the ColumnTransformer for encoding
ct = ___((___(), [___]), ___=___)
# Make a Pipeline of ct, SimpleImputer, and StandardScaler
pipe = ___(___, ___(___=___), ___())
# Transform X using the pipeline and print transformed X
X_transformed = ___
print(X_transformed)
