Menu
Basically, I am using python x32 bit to load from file a list object containing several numpy arrays (previously saved inside a pickle using python x64).
I can load them properly and check the contents but I cannot use them.
TypeError: Cannot cast array data from dtype('int64') to dtype('int32') How can I convert the arrays element type from within the list to int32 so I can use them with python x32.
The error comes when I try to execute the following part:
a=np.bincount(np.hstack(data['Y'])) Looking at what is inside data['Y'] 
929
Int32 is used to represents 32-bit signed integers . Int64 is used to represents 64-bit signed integers. 2. Int16 stands for signed integer. Int32 also stands for signed integer.
int32 is an integer between -2^31 and 2^31 - 1. This range requires 32 bits of memory, and hence the name. Numpy allows for a variety of data types that are not present in regular Python, whose int type can be really, really large. Moreover, unlike Python's list, all elements of a Numpy array must be of the same type.
Convert Column to int (Integer)Use pandas DataFrame. astype() function to convert column to int (integer), you can apply this on a specific column or on an entire DataFrame. To cast the data type to 64-bit signed integer, you can use numpy. int64 , numpy.
As others have said, 32-bit versions of numpy still support 64-bit dtypes. But if you really need to convert to int32, you can use the astype function:
>>> import numpy as np >>> x = np.array([1,2,3], dtype=np.int64) >>> x array([1, 2, 3]) >>> x.dtype dtype('int64') >>> y = x.astype(np.int32) >>> y array([1, 2, 3], dtype=int32) If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With
