[Numpy-discussion] Different attributes for NumPy types

Bruce Southey bsouthey@gmail....
Thu May 22 10:10:12 CDT 2008

Is it bug if different NumPy types have different attributes?

Based on prior discussion, 'complex', 'float' and 'int' are Python types 
and others are NumPy types.  Consequently 'complex', 'float' and 'int' 
do not inherit from NumPy. However, an element from array created using 
dtype=numpy.float has the numpy.float64 type. So this is really a 
documentation issue than an implementation issue.

Also different NumPy types have different attributes, for example, 
'float64' contains attributes (eg __coerce__) that are not present in 
'float32' and 'float128' (these two have the same attributes). This can 
cause attribute errors in somewhat contrived examples that probably are 
unlikely to appear in practice because of the casting involved in array 

The 'uint' types all seem to have the same attributes so do not have 
these issues.

import numpy
len(dir(float))          #   47
len(dir(numpy.float))    #   47
len(dir(numpy.float32))  #  131
len(dir(numpy.float64))  #  135
len(dir(numpy.float128)) #  131

len(dir(int))            #   54
len(dir(numpy.int))      #   54
len(dir(numpy.int0))     #  135
len(dir(numpy.int16))    #  132
len(dir(numpy.int32))    #  132
len(dir(numpy.int64))    #  135
len(dir(numpy.int8))     #  132

print (numpy.float64(1234).size)  # 1
print (numpy.float(1234).size)  
''' prints error:
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: 'float' object has no attribute 'size'


More information about the Numpy-discussion mailing list