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.

Task

Swipe to start coding

Import the correct function for creating a pipeline.
Make a ColumnTransformer with the OneHotEncoder applied only to columns 'sex' and 'island'.
Make sure that all other columns remain untouched.
Create a pipeline containing ct you just created, SimpleImputer that fills in missing values with the most frequent value and a StandardScaler as a last step.
Transform the X using the pipe you created.

Solution

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)

Everything was clear?

Thanks for your feedback!

Section 3. Chapter 4

single

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)

Ask AI

Ask anything or try one of the suggested questions to begin our chat