how to convert categorical data to numerical data code example

Example 1: pandas categorical to numeric

#this will label the different catagories as 0,1,2,3....
dataset["sex"] = dataset["sex"].astype('category').cat.codes

Example 2: using df.astype to select categorical data and numerical data

df = pd.DataFrame({'vertebrates': ['Bird', 'Bird', 'Mammal', 'Fish', 'Amphibian', 'Reptile', 'Mammal']})

df.vertebrates.astype("category").cat.codes

Example 3: panda categorical data into numerica

sex = train_dataset['Sex'].replace(['female','male'],[0,1])
print(sex)

Example 4: pandas categorical to numeric

#this will label as one hot vectors (origin is split into 3 columns - USA, Europe, Japan and any one place will be 1 while the others are 0)
dataset['Origin'] = dataset['Origin'].map({1: 'USA', 2: 'Europe', 3: 'Japan'})

Example 5: 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 6: how to convert categorical data to numerical data in r

Type_peau<-as.factor(c("Mixte","Normale","Sèche","Mixte","Normale","Mixte"))
Type_peau
unclass(Type_peau)