Given a dataframe:
Data
1 246804
2 135272
3 898.01
4 3453.33
5 shine
6 add
7 522
8 Nan
9 string
10 29.11
11 20
I would like two new columns Floats and Strings, both having the same length as the original dataframe. Getting the Floats column is easy:
In [176]: pd.to_numeric(df.Data, errors='coerce')
Out[176]:
1 246804.00
2 135272.00
3 898.01
4 3453.33
5 NaN
6 NaN
7 522.00
8 NaN
9 NaN
10 29.11
11 20.00
Name: Data, dtype: float64
As you can see, non Floats are coerced to NaN, which is exactly what I want.
To get strings, this is what I do:
In [177]: df[df.Data.str.isalpha()]
Out[177]:
Data
5 shine
6 add
8 Nan
9 string
But as you can see, it does not retain the non-String values as NaN. I want something like this:
1 NaN
2 NaN
3 NaN
4 NaN
5 shine
6 add
7 NaN
8 Nan (not NaN)
9 string
10 NaN
11 NaN
How can I get it to do so?