panda categorical data into numerica
sex = train_dataset['Sex'].replace(['female','male'],[0,1])
print(sex)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'})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])how to store categorical variables in separate dataframe
df.loc[:,df.dtypes==np.object]Copyright © 2021 Codeinu
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