How to resolve TypeError: Cannot cast scalar from dtype(‘float64’) to dtype(‘int64’) according to the rule ‘safe’

If you are working with NumPy arrays, you may encounter a TypeError when you try to convert a float array to an integer array using the astype() method. For example, if you have an array like this:

import numpy as np
arr = np.array([1.2, 3.4, 5.6])

and you try to do this:

arr_int = arr.astype(np.int64)

you will get an error message like this:

TypeError: Cannot cast scalar from dtype('float64') to dtype('int64') according to the rule 'safe'

This error means that NumPy cannot safely cast the float values to integer values without losing information or causing overflow. The ‘safe’ rule is the default casting rule that NumPy uses, which only allows casting between types that can preserve the values exactly.

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To fix this error, you have two options:

– You can use a different casting rule that allows for some loss of information or overflow. For example, you can use the ‘unsafe’ rule, which will cast the float values to the nearest integer values, even if they are outside the range of the integer type. To do this, you need to specify the casting argument in the astype() method:

arr_int = arr.astype(np.int64, casting='unsafe')

This will give you an array like this:

array([1, 3, 5])

Note that this may not be what you want, as you are losing the decimal part of the float values and potentially causing overflow if the float values are too large or too small for the integer type.

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– You can round the float values before casting them to integer values. This way, you can preserve the closest integer value without causing overflow. To do this, you can use the round() method on the array before using the astype() method:

arr_rounded = arr.round()
arr_int = arr_rounded.astype(np.int64)

This will give you an array like this:

array([1, 3, 6])

Note that this may still cause some loss of information, as you are rounding the float values to the nearest integer value, which may not be what you want in some cases.

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In summary, if you want to convert a float array to an integer array in NumPy, you need to be careful about the casting rule and the rounding method that you use. Depending on your use case and your data, you may prefer one option over another. I hope this was helpful for you to understand and resolve the TypeError: Cannot cast scalar from dtype(‘float64’) to dtype(‘int64’) according to the rule ‘safe’.