CODEWITHSUNDEEP
python
Thoa Ng
Thoa Ng

Reputation: 49

Age Range to Age Numerical value(python)

I want to transform age range to age numerical value. I used def Age(x) & If statement to transform, but it doesn't work and give the wrong result. I attached the images of the step that I did and the result. The dataset that I used is BlackFriday. Please help me to clarify the mistakes. Thank you!

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Upvotes: 2

Views: 2958

Answers (2)

Kusumashree V P
Kusumashree V P

Reputation: 1

A simple function to modifiy age_range to mean:

Here is the age ranges we have

temp_df['age_range'].unique()

array([70, '18-25', '26-35', '36-45', '46-55', '56-70'], dtype=object)

Function to modify age

def mod_age(df):
for i in range(df.shape[0]):
    if(df.loc[i,'age_range']==70):
        df.loc[i,'age_range']=70
    elif(df.loc[i,'age_range']=='18-25'):
        df.loc[i,'age_range']=(18+25)//2
    elif(df.loc[i,'age_range']=='26-35'):
        df.loc[i,'age_range']=(26+35)//2
    elif(df.loc[i,'age_range']=='36-45'):
        df.loc[i,'age_range']=(36+45)//2
    elif(df.loc[i,'age_range']=='46-55'):
        df.loc[i,'age_range']=(46+55)//2
    elif(df.loc[i,'age_range']=='56-70'):
        df.loc[i,'age_range']=(56+75)//2

 age_range  family_size marital_status  sum
2   70         2        Single          4
25  40         4        Single          2
5   21         2        Married         4
32  50         3        Single          3
13  30         2        Single          5

Upvotes: 0

yatu
yatu

Reputation: 88285

Given what is shown from the result of value_counts, it seems like a simple str.extract with a fillna for ages of 55+ will do:

df.Age.str.extract(r'(?<=-)(\d+)').fillna(56)

Lets consider the following example:

df = pd.DataFrame({'Age':['26-35','36-45', '55+']})

    Age
0  26-35
1  36-45
2    55+

df.Age.str.extract(r'(?<=-)(\d+)').fillna(56).rename(columns={0:'Age'})

   Age
0  35
1  45
2  56

Upvotes: 1

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