如何将所有字符串（如“故障”）转换为唯一的浮点数？

Question

I have a DataFrame that has int , float , and object (strings with characters) items in it. I want a unique float for every unique object like so-

Exhuast
Fault
Probation
Exhaust
Fault
Motor

to

1.
2.
3.
1.
2.
4.

Also, will it work on all of the columns or would I have to do column by column?

Last question, will it also convert all the int 's to float 's as well?

Answer 1

As mentioned by Jon, you could make use of Series.factorize .

(s.factorize()[0]+1).astype('float')

To perform this column-wise over an entire DataFrame, just use apply .

Demo

>>> s = pd.Series(['Exhaust', 'Fault', 'Probation', 5, int,
                   'Exhaust', int, 'Fault', 'Motor'])

>>> s
0          Exhaust
1            Fault
2        Probation
3                5
4    <class 'int'>
5          Exhaust
6    <class 'int'>
7            Fault
8            Motor
dtype: object

>>> (s.factorize()[0]+1).astype('float')
array([ 1.,  2.,  3.,  4.,  5.,  1.,  5.,  2.,  6.])

---

A NumPy solution may be to use the return_inverse keyword arg of np.unique ,

(np.unique(s, return_inverse=True)[1]+1).astype('float')

however from some rough benchmarking the Pandas solution may be faster.