PYTHON

convert categorical variable to numeric python

# get all categorical columns in the dataframe
catCols = [col for col in df1.columns if df1[col].dtype=="O"]

from sklearn.preprocessing import LabelEncoder

lb_make = LabelEncoder()

for item in catCols:
    df1[item] = lb_make.fit_transform(df1[item])

convert categorical data type to int in pandas

from sklearn import preprocessing

lab_encoder = preprocessing.LabelEncoder()
df['column'] = lab_encoder.fit_transform(df['column'])

panda categorical data into numerica

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

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 contionous data into categorical data in python

pd.cut(df.Age,bins=[0,2,17,65,99],labels=['Toddler/Baby','Child','Adult','Elderly'])
# where bins is cut off points of bins for the continuous data 
# and key things here is that no. of labels is always less than 1

how to convert categorical data to numerical data in python

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

how to convert categorical data to numerical data in python

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

Converting categorical variable to numeric variable in python

## Converting Age to numeric variable

df['Gender']=pd.get_dummies(df['Gender'],drop_first=1)
df.head()

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])