How to convert Categorical features to Numerical Features in Python?

How to convert Categorical features to Numerical Features in Python?

How to convert Categorical features to Numerical Features in Python?

This recipe helps you convert Categorical features to Numerical Features in Python


Recipe Objective

Machine Learning Models can not work on categorical variables in the form of strings, so we need to change it into numerical form. This can be done by making new features according to the categories by assigning it values.

So this is the recipe on how we can convert Categorical features to Numerical Features in Python

Step 1 - Import the library

import pandas as pd

We have only imported pandas this is reqired for dataset.

Step 2 - Setting up the Data

We have created a dictionary and passed it through the pd.DataFrame to create a dataframe with columns "name", "episodes", "gender". data = {"name": ["Sheldon", "Penny", "Amy", "Penny", "Raj", "Sheldon"], "episodes": [42, 24, 31, 29, 37, 40], "gender": ["male", "female", "female", "female", "male", "male"]} df = pd.DataFrame(data, columns = ["name","episodes", "gender"]) print(df)

Step 3 - Converting the values

We can clearly observe that in the column "gender" there are two categories male and female, so for that we can assign number to each categories like 1 to male and 2 to female. Now we are using LabelEncoder. We have first fitted the feature and transformed it. le = preprocessing.LabelEncoder()["gender"]) print(); print(list(le.classes_)) print(); print(le.transform(df["gender"])) So the output comes as:

Feature Matrix:
   Feature 1  Feature 2  Feature 3  Feature 4  Feature 5  Feature 6  
0  -1.867524   1.745983   2.952435  -0.177492  -3.088648   1.762311
1   0.450144  -2.106431  -1.065847  -1.958231  -0.451780  -1.990662
2  -4.647836  -4.214226  -1.830341  -1.714825  -6.590249  -0.315993
3   1.958901  -1.313546   1.409145  -2.069271   1.508912   3.774923
4   2.001750   0.879350  -2.041154   1.917629  -0.760137   1.310228

   Feature 7  Feature 8  Feature 9  Feature 10
0  -0.195266   1.029769   2.814171    0.071059
1  -2.530104  -1.377802  -0.013353   -2.849859
2   2.780038  -3.325841  -4.008319    2.001941
3   5.012315  -5.772415  -0.818187   -0.392333
4   0.671990   1.444606  -1.731576   -0.378597

Target Class:
0            1
1            2
2            1
3            0
4            0

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