Htykuut

Is there a way to make a list of lists and then loop through them.

Essentially I need to use the elements of multiple lists as a condition but also use the name of the as a string / column name.

I Know the example below can be done in a easier way, but I think it needs this approach as my task is a bit more complex than the below

 df=



    name

0   Alice

1   Fred

2   George



male=['fred','george']

female=['alice','emily']





alllists=[male, female]



for i in alllists:

    df[i]=0



    df.loc[df['Name'].str.contains('|'.join(i),na=False),l]=1

Output df

    name    Male   Female  

0   Alice    0      1      

1   Fred    1       0      

2   George  1        0   

While there is a way, it's not recommended. Just use a dictionary:

d = {'male': ['fred', 'george'],

     'female': ['alice', 'emily']}



for k, v in d.items():

    mask = df['name'].str.lower().str.contains('|'.join(v), na=False)

    df[k.capitalize()] = mask.astype(int)

mask.astype(int) works because a Boolean array may be mapped directly to 1 / 0, just as bool is a subclass of int in regular Python.

Result:

print(df)



     name  Male  Female

0   Alice     0       1

1    Fred     1       0

2  George     1       0

You can use pandas.get_dummies.

>>> males = {'fred', 'george'}

>>> fm = pd.get_dummies(['Male' if name.lower() in males else 'Female' for name in df['name']])

>>> result = pd.concat([df, fm], axis=1)

>>> 

>>> result

     name  Female  Male

0   Alice       1     0

1    Fred       0     1

2  George       0     1

This can be done much more elegantly by using a better data structure, like a dict to map names to sexes:

>>> sex = {'Fred': 'Male', 'George': 'Male', 'Alice': 'Female', 'Emily': 'Female'}

>>> result = pd.concat([df, pd.get_dummies(df['name'].map(sex))], axis=1)

>>> result

     name  Female  Male

0   Alice       1     0

1    Fred       0     1

2  George       0     1

If we have to start from

male = ['fred','george']

female = ['alice','emily']

you can build sex like this:

>>> sex = {name.capitalize():s for names, s in [(male, 'Male'), (female, 'Female')]

...:                           for name in names}

...:                           

>>> sex

{'Alice': 'Female', 'Emily': 'Female', 'Fred': 'Male', 'George': 'Male'}

Finally, if the order of the columns is important, you can reindex the result.

>>> result = result.reindex(columns=['name', 'Male', 'Female'])

>>> result

     name  Male  Female

0   Alice     0       1

1    Fred     1       0

2  George     1       0