Numpy 얘기 좀 하자.array에서 [:]와 [::]의 차이점은 무엇입니까?

array([:])

>>> import numpy as np
>>>
>>> x=np.array([1,2,3,4,5,6,7,8,9,10,11,12])
>>> print(x[1:5])# index 1~5 ， 
[2 3 4 5]
>>> print(x[3:])# index=3 ， index=3
[ 4  5  6  7  8  9 10 11 12]
>>> print(x[:9])# index=9 ， index=9
[1 2 3 4 5 6 7 8 9]
>>> print(x[1:-2])# index=1 2 index 
[ 2  3  4  5  6  7  8  9 10]
>>> print(x[-9:-2])# 9 index 2 index ， 
[ 4  5  6  7  8  9 10]

array([::])

>>> print(x[1::3])# index=1 ， 3
[ 2  5  8 11]
>>> print(x[::3])# index=0 ， 3
[ 1  4  7 10]
>>> print(x[3::])# [3:] 
[ 4  5  6  7  8  9 10 11 12]
>>> print(x[::-1])# ， index ， 1
[12 11 10  9  8  7  6  5  4  3  2  1]
>>> print(x[::-3])# ， index ， 3
[12  9  6  3]
>>> print(x[7:2:-1])# index=2( ) index=7 
[8 7 6 5 4]

1. Numpy.array () 상세 정보

1.1 함수 형식

numpy.array(object, 
    dtype=None, 
    copy=True, 
    order='K', 
    subok=False, 
    ndmin=0)

1.2 매개변수 상세 정보

1.3 구체적인 사용법

import numpy as np
 
arr01 = np.array([1,2,3])
print(arr01) #[1 2 3]
print(type(arr01))  #<class 'numpy.ndarray'>
print(arr01.dtype)  #int32
 
#Upcasting
arr02 = np.array([1.,2.,3.])
print(arr02) #[1. 2. 3.]
print(arr02.dtype)  #float64
 
#More than one dimension:
arr03 = np.array([[1,2],[3,4]])
print(arr03)
"""
[[1 2]
 [3 4]]
"""

import numpy as np
 
# 
DYX= np.array([1,2,3],dtype = complex)
print(DYX) #[1.+0.j 2.+0.j 3.+0.j]
print(DYX.dtype)  #complex128
 
# ：
HXH = np.array([(1,2),(3,4)],dtype=[('a','<i4'),('b','<i8')])
print(HXH)  #[(1, 2) (3, 4)]
# ， 。
print(HXH["a"]) #[1 3]
print(HXH["b"])  #[2 4]
print(HXH.dtype)  #[('a', '<i4'), ('b', '<i8')]
print(HXH["a"].dtype) #int32
print(HXH["b"].dtype) #int64
 
TSL = np.array([(1,2,3),(4,5,6)],dtype=[("a","i"),("b","i"),("c","i")])
print(TSL["a"]) #[1 4]
print(TSL["a"].dtype)  #int32

import numpy as np
 
DYX = np.array(np.mat('1 2; 3 4'))
# subok ， False
print(DYX)
# 
print(type(DYX))  #<class 'numpy.ndarray'>
"""
[[1 2]
 [3 4]]
"""
 
HXH = np.array(np.mat('1 2; 3 4'), subok=True)
print(HXH)
# 
print(type(HXH))  #<class 'numpy.matrixlib.defmatrix.matrix'>
"""
[[1 2]
 [3 4]]
"""

import numpy as np
 
DYX = np.array([1,2,3],ndmin=0)
print(DYX,DYX.shape) #[1 2 3] (3,)
 
HXH = np.array([1,2,3],ndmin=1)
print(HXH,HXH.shape) #[1 2 3] (3,)
 
TSL = np.array([1,2,3],ndmin=2)
print(TSL,TSL.shape) #[[1 2 3]] (1, 3)

import numpy as np
 
data = [1,1,1]
print(type(data)) #<class 'list'>  
arr_ar = np.array(data)
arr_as = np.asarray(data)
 
# 
print(arr_ar) #[1 1 1]
print(arr_as) #[1 1 1]
 
data[1]=2
# arr_ar arr_as 
print(arr_ar) #[1 1 1]
print(arr_as) #[1 1 1]
 
# data [1, 2, 1]
# arr_ar arr_as 
arr_ar[1]=3
print(data) #[1, 2, 1]
arr_as[1]=3
print(data)  #[1, 2, 1]

import numpy as np 
data = np.ones((3,))
#print(type(data)) #<class 'numpy.ndarray'>  
arr_ar = np.array(data)
arr_as = np.asarray(data)
 
print(arr_ar) #[1. 1. 1.]
print(arr_as) #[1. 1. 1.]
 
"""
 。 ，
np.array() ，
 np.asarray() 
"""
data[1]=2
print(arr_ar) #[1. 1. 1.]
print(arr_as) #[1. 2. 1.]  !!!
 
"""
 ， array ， 
 asarray ， 
"""
# data=[1. 2. 1.]
arr_ar[2]=3
print(data)  #[1. 2. 1.]
arr_as[2]=3
print(data)  #[1. 2. 3.]