Boolean indexing to retain falsy values as NaN

Question

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?

Answer 1

To get Strings, you can use Boolean Indexing on the Data column and located where Floats is null.

df['Floats'] = pd.to_numeric(df.Data, errors='coerce')
df['Strings'] = df.Data.loc[df.Floats.isnull()]  # Optional: .astype(str)

>>> df
# Output:
#        Data     Floats Strings
# 1    246804  246804.00     NaN
# 2    135272  135272.00     NaN
# 3    898.01     898.01     NaN
# 4   3453.33    3453.33     NaN
# 5     shine        NaN   shine
# 6       add        NaN     add
# 7       522     522.00     NaN
# 8       Nan        NaN     Nan
# 9    string        NaN  string
# 10    29.11      29.11     NaN
# 11       20      20.00     NaN

Answer 2

floats = pd.to_numeric(df.Data, 'coerce')
pd.DataFrame(dict(
    floats=floats,
    strings=df.Data.mask(floats.notnull())
))

       floats strings
1   246804.00     NaN
2   135272.00     NaN
3      898.01     NaN
4     3453.33     NaN
5         NaN   shine
6         NaN     add
7      522.00     NaN
8         NaN     Nan
9         NaN  string
10      29.11     NaN
11      20.00     NaN

---

You can even make it more obvious within mask by passing an alternative

floats = pd.to_numeric(df.Data, 'coerce')
pd.DataFrame(dict(
    floats=floats,
    strings=df.Data.mask(floats.notnull(), '')
))

       floats strings
1   246804.00        
2   135272.00        
3      898.01        
4     3453.33        
5         NaN   shine
6         NaN     add
7      522.00        
8         NaN     Nan
9         NaN  string
10      29.11        
11      20.00