Course Content
Data Preprocessing
Data Preprocessing
2. Processing Quantitative Data
3. Processing Categorical Data
4. Time Series Data Processing
6. Moving on to Tasks
Label Encoding of the Target Variable
Let's go straight to the main thing - label encoding implements everything the same as ordinal encoder, but:
- Methods work with different data dimensions;
- The order of the categories is not important for label encoding.
How to use this method in Python:
from sklearn.preprocessing import LabelEncoder import pandas as pd # Simple categorical variable fruits = pd.Series(['apple', 'orange', 'banana', 'banana', 'apple', 'orange', 'banana']) # Create label encoder object le = LabelEncoder() # Fit and transform the categorical variable using label encoding fruits_encoded = le.fit_transform(fruits) # Print the encoded values print(fruits_encoded)
Task
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Read the dataset 'salary_and_gender.csv' and encode the output column 'Gender' with label encoding.
Solution
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Section 3. Chapter 4
Label Encoding of the Target Variable
Let's go straight to the main thing - label encoding implements everything the same as ordinal encoder, but:
- Methods work with different data dimensions;
- The order of the categories is not important for label encoding.
How to use this method in Python:
from sklearn.preprocessing import LabelEncoder import pandas as pd # Simple categorical variable fruits = pd.Series(['apple', 'orange', 'banana', 'banana', 'apple', 'orange', 'banana']) # Create label encoder object le = LabelEncoder() # Fit and transform the categorical variable using label encoding fruits_encoded = le.fit_transform(fruits) # Print the encoded values print(fruits_encoded)
Task
Swipe to show code editor
Read the dataset 'salary_and_gender.csv' and encode the output column 'Gender' with label encoding.
Solution
Switch to desktop for real-world practiceContinue from where you are using one of the options belowEverything was clear?
Thanks for your feedback!
Section 3. Chapter 4
Switch to desktop for real-world practiceContinue from where you are using one of the options below