array()創(chuàng)建多維數(shù)組

>>> arr = array([[1,2,3],[4,5,6],[7,8,9]])
>>> print arr
[[1 2 3]
 [4 5 6]
 [7 8 9]]

arange()用于創(chuàng)建等差數(shù)組**與range語法相同arange(start,end,step)

>>> arr = arange(0,10,2)
>>> print arr
[0 2 4 6 8]

數(shù)組維度

# 2×3 矩陣  
>>> arr = array([arange(3),arange(3)])
>>> arr.shape
(2, 3)
# 維的個數(shù)
>>> arr.ndim
2

數(shù)據(jù)類型

NumPy支持的數(shù)據(jù)類型有整型，浮點型和復數(shù)型，同時支持不同的精度。

查看數(shù)據(jù)類型

>>> arr = array([1+2j,3+4j,5+6j]) #arr.real 實部 arr.imag 虛部
>>> arr.dtype
dtype('complex128')
#類型所占字節(jié)數(shù)  
>>> arr.dtype.itemsize
16

指定數(shù)據(jù)類型

>>> arange(5,dtype=float32)
array([ 0.,  1.,  2.,  3.,  4.], dtype=float32)

創(chuàng)建自定義數(shù)據(jù)類型

>>> t = dtype([('name',str,32),('grade',int)])
>>> student = array([('weiss',90),('ruby',89)],dtype=t)
>>> print student
[('weiss', 90) ('ruby', 89)]
>>> student.shape
(2,)

轉換元素的類型

arr = arange(4)
arr.astype(float64)
array([ 0., 1., 2., 3.])

多維數(shù)組的切片

和python列表相似

# reshape() 改變數(shù)組維度  ravel()將數(shù)組展開成一維
>>> arr = arange(24).reshape(2,3,4)
>>> print arr
[[[ 0  1  2  3]
  [ 4  5  6  7]
  [ 8  9 10 11]]

 [[12 13 14 15]
  [16 17 18 19]
  [20 21 22 23]]]
>>> arr[0,0,0]
0
>>> arr[0,:,:]
array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])
>>> arr[0,...]
array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])
>>> arr[::-1,...]
array([[[12, 13, 14, 15],
        [16, 17, 18, 19],
        [20, 21, 22, 23]],

       [[ 0,  1,  2,  3],
        [ 4,  5,  6,  7],
        [ 8,  9, 10, 11]]])
>>> arr[0,::-1,-1]
array([11,  7,  3])

矩陣轉置

>>> arr = array([[1,2],[3,4]])
>>> arr.transpose()
array([[1, 3],
       [2, 4]])

數(shù)組合并

#水平組合  
>>> a = arange(9).reshape(3,3)
>>> b = a * 2
>>> hstack((a,b))
array([[ 0,  1,  2,  0,  2,  4],
       [ 3,  4,  5,  6,  8, 10],
       [ 6,  7,  8, 12, 14, 16]])
       
#垂直組合         
>>> vstack((a,b))
array([[ 0,  1,  2],
       [ 3,  4,  5],
       [ 6,  7,  8],
       [ 0,  2,  4],
       [ 6,  8, 10],
       [12, 14, 16]])
       
# 也可以通過concatenate函數(shù)來進行組合  
# NumPy中維度(dimensions)叫做軸(axis)，軸的個數(shù)叫做秩(rank)  
>>> concatenate((a,b),axis=0)
array([[ 0,  1,  2],
       [ 3,  4,  5],
       [ 6,  7,  8],
       [ 0,  2,  4],
       [ 6,  8, 10],
       [12, 14, 16]])

# 深度組合  
>>> dstack((a,b))
array([[[ 0,  0],
        [ 1,  2],
        [ 2,  4]],

       [[ 3,  6],
        [ 4,  8],
        [ 5, 10]],

       [[ 6, 12],
        [ 7, 14],
        [ 8, 16]]])

數(shù)組分割

>>> a = arange(9).reshape(3,3)
>>> print a
[[0 1 2]
 [3 4 5]
 [6 7 8]]
 
# 水平分割  
>>> hsplit(a,3)
[array([[0],
       [3],
       [6]]), array([[1],
       [4],
       [7]]), array([[2],
       [5],
       [8]])]
       
# 垂直分割  
>>> vsplit(a,3)
[array([[0, 1, 2]]), array([[3, 4, 5]]), array([[6, 7, 8]])]

# 通過split 指定軸進行分割  
>>> split(a,3,axis=1)
[array([[0],
       [3],
       [6]]), array([[1],
       [4],
       [7]]), array([[2],
       [5],
       [8]])]

# 深度分割  
# 必須三個維度以上的數(shù)組， 
>>> a = arange(24).reshape(2,3,4)
>>> dsplit(a,2)
[array([[[ 0,  1],
        [ 4,  5],
        [ 8,  9]],

       [[12, 13],
        [16, 17],
        [20, 21]]]), array([[[ 2,  3],
        [ 6,  7],
        [10, 11]],

       [[14, 15],
        [18, 19],
        [22, 23]]])]

flat屬性

flat屬性將返回一個flatiter對象，可以讓我們像遍歷一維數(shù)組一樣遍歷多維數(shù)組

>>> a = arange(24).reshape(2,3,4)
>>> print a.flat[10]
10  
# 獲取多個元素  
>>> a.flat[[1,2,3,4]]
array([1, 2, 3, 4])

# 對flat屬性賦值將導致整個數(shù)組的元素都被覆蓋
>>> a.flat = 6  
>>> print a
[[[6 6 6 6]
  [6 6 6 6]
  [6 6 6 6]]

 [[6 6 6 6]
  [6 6 6 6]
  [6 6 6 6]]]

# 指定元素進行覆蓋
>>> a.flat[[2,4,6,8,10]] = 1
>>> a
array([[[6, 6, 1, 6],
        [1, 6, 1, 6],
        [1, 6, 1, 6]],

       [[6, 6, 6, 6],
        [6, 6, 6, 6],
        [6, 6, 6, 6]]])

數(shù)組轉換列表

>>> arr = arange(4).reshape(2,2)
>>> arr.tolist()
[[0, 1], [2, 3]]

創(chuàng)建單位矩陣

>>> a = eye(2)
>>> a
array([[ 1.,  0.],
       [ 0.,  1.]])
# dtype float64
>>> a.dtype
dtype('float64')