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])

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)

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 change value of categorical variable in python

df['Gender'].str[0].str.upper().map({'M':'Male', 'F':'Female'})

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()