The code
np.array([100,200,300],dtype=str)
returns:
array(['1', '2', '3'],
dtype='|S1')
The documentation says:
dtype : data-type, optional
The desired data-type for the array. If not given, then the type will be determined as the minimum type required to hold the objects in the sequence.
Is this a bug?
I still cannot find the question, but to get around it:
>>> a=[100,200,300]
>>> np.char.mod('%d', a)
array(['100', '200', '300'],
dtype='|S3')
This circumvents your problem:
>>> a=[100,200,3005]
>>> np.char.mod('%d', a)
array(['100', '200', '3005'],
dtype='|S4')
The obscure documentation, it should be noted that this is roughly 4 times slower then choosing dtype="S..", but non-linearly faster then using np.array(map(str,a)) methods.
You can also do some neat things:
>>> a
[1234.5, 123.4, 12345]
>>> np.char.mod('%s',a)
array(['1234.5', '123.4', '12345.0'],
dtype='|S7')
>>> np.char.mod('%f',a)
array(['1234.500000', '123.400000', '12345.000000'],
dtype='|S12')
>>> np.char.mod('%d',a) #Note the truncation of decimals here.
array(['1234', '123', '12345'],
dtype='|S5')
>>> np.char.mod('%s.stuff',a)
array(['1234.5.stuff', '123.4.stuff', '12345.0.stuff'],
dtype='|S13')
Additional information can be found here.
The reason you see this behavior is that you have to specify the size of each string element e.g. using:
>>> np.array([100,200,300],dtype='S3')
array(['100', '200', '300'],
dtype='|S3')
Otherwise the size of each element string will default to 1.
More info here: Numpy converting array from float to strings
dtype='|S3'and see if that gives what you expect?numpytag should lead you to it.'|S3'worksnp.array([101,201,301],dtype=str)instead?