ChrisB - 1 year ago 138
Python Question

# forced conversion of non-numeric numpy arrays with NAN replacement

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`
, but which also tests for non-numeric elements like letters?

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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