Pandas - using assign and if-else statement in method chaining

I come from an R background and I'm trying to replicate the mutate() function from dplyr in pandas.

I have a dataframe that looks like this:

data = {'name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'], 
        'age': [42, 52, 36, 24, 73], 
        'preTestScore': [4, 24, 31, 2, 3],
        'postTestScore': [25, 94, 57, 62, 70]}
df = pd.DataFrame(data, columns = ['name', 'age', 'preTestScore', 'postTestScore'])

I am now trying to create a new column called age_bracket using assign method as follows:

(df.
    assign(age_bracket= lambda x: "under 25" if x['age'] < 25 else
        ("25-34" if x['age'] < 35 else "35+"))

And this is throwing the following error which I'm not able to understand:

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()

I am not interested the following solution:

df['age_bracket'] = np.where(df.age < 25, 'under 25',
     (np.where(df.age < 35, "25-34", "35+")))

As I do not want the underlying df to change. I'm trying to get better at method chaining where I can quickly explore my df in different ways without changing the underlying df.

Any suggestions?

Solution

It is possible, but not recommended, because loops (under the hood of apply function):

df = (df.
    assign(age_bracket= lambda x: x['age'].apply(lambda y: "under 25" if y < 25 else
        ("25-34" if y < 35 else "35+"))))
print (df)
    name  age  preTestScore  postTestScore age_bracket
0  Jason   42             4             25         35+
1  Molly   52            24             94         35+
2   Tina   36            31             57         35+
3   Jake   24             2             62    under 25
4    Amy   73             3             70         35+

Or numpy.select:

df = df.assign(age_bracket= np.select([df.age < 25,df.age < 35], ['under 25', "25-34"], "35+"))

But better is use cut here:

df = (df.assign(age_bracket= lambda x: pd.cut(x['age'], 
                                              bins=[0, 25, 35, 150],
                                              labels=["under 25", "25-34", "35+"])))