Answers for "how to convert categorical data to numerical data in python"

how to convert categorical data to numerical data in python

pd.get_dummies(obj_df, columns=["body_style", "drive_wheels"], prefix=["body", "drive"]).head()

panda categorical data into numerica

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

transform categorical variables python

from sklearn.preprocessing import LabelEncoder

lb_make = LabelEncoder()
obj_df["make_code"] = lb_make.fit_transform(obj_df["make"])
obj_df[["make", "make_code"]].head(11)

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'})

how to convert categorical data to numerical data in python

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

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()