NumPy types with underscore: `int_`, `float_`, etc

Question

What is the significance of the underscore suffixing in int_, float_, etc.?

I would suppose it's so they don't clash with builtins. It's the same reason you wouldn't create your own class named list. — Steven Rumbalski
– Steven Rumbalski, Commented Jun 1, 2011 at 17:21

Steven Rumbalski · Accepted Answer · 2011-06-01 17:36:27Z

29

From page 21 of Guide to Numpy by TE Oliphant:

Names for the data types that would clash with standard Python object names are followed by a trailing underscore, ’ ’. These data types are so named because they use the same underlying precision as the corresponding Python data types.

. . .

The array types bool_, int_, complex_, float_, object_, unicode_, and str_ are enhanced-scalars. They are very similar to the standard Python types (without the trailing underscore) and inherit from them (except for bool_ and object_). They can be used in place of the standard Python types whenever desired. Whenever a data type is required, as an argument, the standard Python types are recognized as well.

edited Jun 1, 2011 at 17:36

answered Jun 1, 2011 at 17:24

Steven Rumbalski

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pic11 Over a year ago

Oliphant's book looks great. Thanks for posting it.

hsl · Accepted Answer · 2022-04-08 00:53:57Z

5

If you're unsure if your variable is scalar, list, or array, using the ones with "_" will ensure your code will work regardless (if that's the behavior you intended). See the example code below.

As of numpy version 1.20.0, np.float64 or np.float32 works on scalars and lists.

import numpy as np  # numpy >= 1.20.0
scalar = 3
L1 = [3]
L2 = [1, 2, 3]

np.float64(scalar)  # okay
np.float64(L1)  # okay
np.float64(L2)  # okay

np.float_(scalar)  # okay
np.float_(L1)  # okay
np.float_(L2)  # okay

Note that np.float_ is just an alias of np.double.

edited Apr 8, 2022 at 0:53

answered Dec 20, 2019 at 13:34

hsl

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2 Comments

wjandrea Over a year ago

How does this change now that np.float is deprecated?

hsl Over a year ago

Updated the answer to reflect the changes. np.float is deprecated but np.float_ isn't, though the latter is just an alias of np.double

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