how to convert labels in numerical categorical data code example

Example 1: how to convert categorical data to numerical data in python

obj_df["body_style"] = obj_df["body_style"].astype('category')
obj_df.dtypes

Example 2: how to convert categorical data to numerical data in python

import pandas as pd
import numpy as np

# Define the headers since the data does not have any
headers = ["symboling", "normalized_losses", "make", "fuel_type", "aspiration",
           "num_doors", "body_style", "drive_wheels", "engine_location",
           "wheel_base", "length", "width", "height", "curb_weight",
           "engine_type", "num_cylinders", "engine_size", "fuel_system",
           "bore", "stroke", "compression_ratio", "horsepower", "peak_rpm",
           "city_mpg", "highway_mpg", "price"]

# Read in the CSV file and convert "?" to NaN
df = pd.read_csv("https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data",
                  header=None, names=headers, na_values="?" )
df.head()

Example 3: To convert categorical data to numerical

cat_cols = ['Item_Identifier', 'Item_Fat_Content', 'Item_Type', 'Outlet_Identifier', 
         'Outlet_Size', 'Outlet_Location_Type', 'Outlet_Type', 'Item_Type_Combined']
enc = LabelEncoder()

for col in cat_cols:
    train[col] = train[col].astype('str')
    test[col] = test[col].astype('str')
    train[col] = enc.fit_transform(train[col])
    test[col] = enc.transform(test[col])