[numpy] 타입변환 dtype | astype

1. dtype 속성 

'''
   타입 변환하는 2가지 방법

   1. dtype 속성

   2. astype 함수
'''
import numpy as np

# 1. int --> float
arr = np.array([10, 20, 30])
arr2 = np.array([10, 20, 30], dtype=np.float64)
print(arr, arr.dtype)  # [10 20 30] int32  ( 저장된 데이터를 보고 타입을 자동으로 설정 )
print(arr2, arr2.dtype)  # [10. 20. 30.] float64 ( 정수를 지정했으나 dtype에 의해서 float으로 변경됨 )
print()

# 2. float --> int
arr = np.array([1.34, 2.5, 3.8])
arr2 = np.array([1.34, 2.5, 3.8], dtype=np.int32)
print(arr, arr.dtype)  # [1.34 2.5  3.8 ] float64 ( 저장된 데이터를 보고 타입을 자동으로 설정 )
print(arr2, arr2.dtype)  # [1 2 3] int32 ( 실수를 지정했으나 dtype에 의해서 int으로 변경됨 )

# 3. int --> 문자열 ( bytes, 유니코드 )
arr = np.array([10, 20, 30])
arr2 = np.array([10, 20, 30], dtype=np.string_)  # bytes 타입, [b'10' b'20' b'30']
arr3 = np.array([10, 20, 30], dtype=np.str_)  # 유니코드타입, ['10' '20' '30']
print(arr2)
print(arr3)

# 4. 문자열 --> int
arr = np.array(['10', '20', '30'])
arr2 = np.array(['10', '20', '30'], dtype=np.int32)
print(arr, arr.dtype)  # ['10' '20' '30'] <U2
print(arr2, arr2.dtype)  # [10 20 30] int32

 

2. astype 함수

 

'''
   타입 변환하는 2가지 방법

   1. dtype 속성

   2. astype 함수
'''
'''
['T', '__abs__', '__add__', '__and__', '__array__', '__array_finalize__', '__array_function__', 
'__array_interface__', '__array_prepare__', '__array_priority__', '__array_struct__', 
'__array_ufunc__', '__array_wrap__', '__bool__', '__class__', '__class_getitem__', 
'__complex__', '__contains__', '__copy__', '__deepcopy__', '__delattr__', '__delitem__', 
'__dir__', '__divmod__', '__dlpack__', '__dlpack_device__', '__doc__', '__eq__', '__float__', 
'__floordiv__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__gt__', '__hash__', 
'__iadd__', '__iand__', '__ifloordiv__', '__ilshift__', '__imatmul__', '__imod__', '__imul__', 
'__index__', '__init__', '__init_subclass__', '__int__', '__invert__', '__ior__', '__ipow__', 
'__irshift__', '__isub__', '__iter__', '__itruediv__', '__ixor__', '__le__', '__len__', 
'__lshift__', '__lt__', '__matmul__', '__mod__', '__mul__', '__ne__', '__neg__', '__new__', 
'__or__', '__pos__', '__pow__', '__radd__', '__rand__', '__rdivmod__', '__reduce__', 
'__reduce_ex__', '__repr__', '__rfloordiv__', '__rlshift__', '__rmatmul__', '__rmod__', 
'__rmul__', '__ror__', '__rpow__', '__rrshift__', '__rshift__', '__rsub__', '__rtruediv__', 
'__rxor__', '__setattr__', '__setitem__', '__setstate__', '__sizeof__', '__str__', '__sub__', 
'__subclasshook__', '__truediv__', '__xor__', 
'all', 'any', 'argmax', 'argmin', 'argpartition', 'argsort', 'astype', 'base', 'byteswap', 
'choose', 'clip', 'compress', 'conj', 'conjugate', 'copy', 'ctypes', 'cumprod', 'cumsum', 
'data', 'diagonal', 'dot', 'dtype', 'dump', 'dumps', 'fill', 'flags', 'flat', 'flatten', 
'getfield', 'imag', 'item', 'itemset', 'itemsize', 'max', 'mean', 'min', 'nbytes', 'ndim', 
'newbyteorder', 'nonzero', 'partition', 'prod', 'ptp', 'put', 'ravel', 'real', 'repeat', 
'reshape', 'resize', 'round', 'searchsorted', 'setfield', 'setflags', 'shape', 'size', 
'sort', 'squeeze', 'std', 'strides', 'sum', 'swapaxes', 'take', 'tobytes', 'tofile', 
'tolist', 'tostring', 'trace', 'transpose', 'var', 'view']
'''

import numpy as np

# 1. int --> float
arr = np.array([10, 20, 30])
arr2 = arr.astype(np.float64)
arr3 = np.array([10, 20, 30]).astype(np.float64)
print(arr2, arr2.dtype)  # [10. 20. 30.] float64
print(arr3, arr3.dtype)  # [10. 20. 30.] float64

# 2. float --> int
arr = np.array([1.34, 2.5, 3.8])
arr2 = arr.astype(np.int32)
arr3 = np.array([1.34, 2.5, 3.8]).astype(np.int32)
print(arr2, arr2.dtype)  # [1 2 3] int32
print(arr3, arr3.dtype)  # [1 2 3] int32

# 3. int --> 문자열 ( bytes, 유니코드 )
arr = np.array([10, 20, 30])
arr2 = arr.astype(np.string_)
arr3 = arr.astype(np.str_)
print(arr2, arr2.dtype)  # [b'10' b'20' b'30'] |S11
print(arr3, arr3.dtype)  # ['10' '20' '30'] <U11

# 4. 문자열 --> int
arr = np.array(['10', '20', '30'])
arr2 = arr.astype(np.int64)
print(arr2, arr2.dtype)  # [10 20 30] int64