ChrisB - 1 year ago 138

Python Question

Consider the array

`x = np.array(['1', '2', 'a'])`

Tying to convert to a float array raises an exception

`x.astype(np.float)`

ValueError: could not convert string to float: a

Does numpy provide any efficient way to coerce this into a numeric array, replacing non-numeric values with something like NAN?

Alternatively, is there an efficient numpy function equivalent to

`np.isnan`

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Answer Source

You can convert an array of strings into an array of floats (with NaNs) using `np.genfromtxt`

:

```
In [83]: np.set_printoptions(precision=3, suppress=True)
In [84]: np.genfromtxt(np.array(['1','2','3.14','1e-3','b','nan','inf','-inf']))
Out[84]: array([ 1. , 2. , 3.14 , 0.001, nan, nan, inf, -inf])
```

Here is a way to identify "numeric" strings:

```
In [34]: x
Out[34]:
array(['1', '2', 'a'],
dtype='|S1')
In [35]: x.astype('unicode')
Out[35]:
array([u'1', u'2', u'a'],
dtype='<U1')
In [36]: np.char.isnumeric(x.astype('unicode'))
Out[36]: array([ True, True, False], dtype=bool)
```

Note that "numeric" means a unicode that contains only digit characters -- that is, characters that have the Unicode numeric value property. It does **not** include the decimal point. So `u'1.3'`

is not considered "numeric".

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