Pandas categorizing age variable into groups

Question

I have a dataframe df with age and I am working on categorizing the file into age groups with 0s and 1s.

df:

User_ID | Age
35435      22
45345      36
63456      18
63523      55

I tried the following

df['Age_GroupA'] = 0
df['Age_GroupA'][(df['Age'] >= 1) & (df['Age'] <= 25)] = 1

but get this error

SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

To avoid it, I am going for .loc

df['Age_GroupA'] = 0
df['Age_GroupA'] = df.loc[(df['Age'] >= 1) & (df['Age'] <= 25)] = 1

However, this marks all ages as 1

This is what I get

User_ID | Age | Age_GroupA
35435      22       1
45345      36       1
63456      18       1
63523      55       1

while this is the goal

User_ID | Age | Age_GroupA
35435      22       1
45345      36       0
63456      18       1
63523      55       0

Thank you

Answer 1

Due to peer pressure (@DSM), I feel compelled to breakdown your error:

df['Age_GroupA'][(df['Age'] >= 1) & (df['Age'] <= 25)] = 1

this is chained indexing/assignment

so what you tried next:

df['Age_GroupA'] = df.loc[(df['Age'] >= 1) & (df['Age'] <= 25)] = 1

is incorrect form, when using loc you want:

df.loc[<boolean mask>, cols of interest] = some scalar or calculated value

like this:

df.loc[(df['Age_MDB_S'] >= 1) & (df['Age_MDB_S'] <= 25), 'Age_GroupA'] = 1

You could also have done this using np.where:

df['Age_GroupA'] = np.where( (df['Age_MDB_S'] >= 1) & (df['Age_MDB_S'] <= 25), 1, 0)

To do this in 1 line, there are many ways to do this