pandas assign with new column name as string

I recently discovered pandas "assign" method which I find very elegant. My issue is that the name of the new column is assigned as keyword, so it cannot have spaces or dashes in it.

df = DataFrame({'A': range(1, 11), 'B': np.random.randn(10)})
df.assign(ln_A = lambda x: np.log(x.A))
        A         B      ln_A
0   1  0.426905  0.000000
1   2 -0.780949  0.693147
2   3 -0.418711  1.098612
3   4 -0.269708  1.386294
4   5 -0.274002  1.609438
5   6 -0.500792  1.791759
6   7  1.649697  1.945910
7   8 -1.495604  2.079442
8   9  0.549296  2.197225
9  10 -0.758542  2.302585

but what if I want to name the new column "ln(A)" for example? E.g.

df.assign(ln(A) = lambda x: np.log(x.A))
df.assign("ln(A)" = lambda x: np.log(x.A))


File "<ipython-input-7-de0da86dce68>", line 1
df.assign(ln(A) = lambda x: np.log(x.A))
SyntaxError: keyword can't be an expression

I know I could rename the column right after the .assign call, but I want to understand more about this method and its syntax.

You can pass the keyword arguments to assign as a dictionary, like so:

kwargs = {"ln(A)" : lambda x: np.log(x.A)}
df.assign(**kwargs)

    A         B     ln(A)
0   1  0.500033  0.000000
1   2 -0.392229  0.693147
2   3  0.385512  1.098612
3   4 -0.029816  1.386294
4   5 -2.386748  1.609438
5   6 -1.828487  1.791759
6   7  0.096117  1.945910
7   8 -2.867469  2.079442
8   9 -0.731787  2.197225
9  10 -0.686110  2.302585

assign expects a bunch of key word arguments. It will, in turn, assign columns with the names of the key words. That's handy, but you can't pass an expression as the key word. This is spelled out by @EdChum in the comments with this link

use insert instead for inplace transformation

df.insert(2, 'ln(A)', np.log(df.A))
df

use concat if you don't want inplace

pd.concat([df, np.log(df.A).rename('log(A)')], axis=1)