Numpy实践_数组的创建

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依据现有数据来创建 ndarray

（a）通过array()函数进行创建

import numpy as np

# 创建一维数组
a = np.array([0, 1, 2, 3, 4])
b = np.array((0, 1, 2, 3, 4))
print(a, type(a))
# [0 1 2 3 4] <class 'numpy.ndarray'>
print(b, type(b))
# [0 1 2 3 4] <class 'numpy.ndarray'>

# 创建二维数组
c = np.array([[11, 12, 13, 14, 15],
              [16, 17, 18, 19, 20],
              [21, 22, 23, 24, 25],
              [26, 27, 28, 29, 30],
              [31, 32, 33, 34, 35]])
print(c, type(c))
# [[11 12 13 14 15]
#  [16 17 18 19 20]
#  [21 22 23 24 25]
#  [26 27 28 29 30]
#  [31 32 33 34 35]] <class 'numpy.ndarray'>

# 创建三维数组
d = np.array([[(1.5, 2, 3), (4, 5, 6)],
              [(3, 2, 1), (4, 5, 6)]])
print(d, type(d))
# [[[1.5 2.  3. ]
#   [4.  5.  6. ]]
#
#  [[3.  2.  1. ]
#   [4.  5.  6. ]]] <class 'numpy.ndarray'>

（b）通过asarray()函数进行创建

1.array()和asarray()都可以将结构数据转化为 ndarray

import numpy as np

x = [[1, 1, 1], [1, 1, 1], [1, 1, 1]]
y = np.array(x)
z = np.asarray(x)
x[1][2] = 2
print(x,type(x))
# [[1, 1, 1], [1, 1, 2], [1, 1, 1]] <class 'list'>

print(y,type(y))
# [[1 1 1]
#  [1 1 1]
#  [1 1 1]] <class 'numpy.ndarray'>

print(z,type(z))
# [[1 1 1]
#  [1 1 1]
#  [1 1 1]] <class 'numpy.ndarray'>

2.array()和asarray()的区别。(array()和asarray()主要区别就是当数据源是ndarray 时，array()仍然会 copy 出一个副本，占用新的内存，但不改变 dtype 时 asarray()不会。)

import numpy as np

x = np.array([[1, 1, 1], [1, 1, 1], [1, 1, 1]])
y = np.array(x)
z = np.asarray(x)
w = np.asarray(x, dtype=np.int)
x[1][2] = 2
print(x,type(x),x.dtype)
# [[1 1 1]
#  [1 1 2]
#  [1 1 1]] <class 'numpy.ndarray'> int32

print(y,type(y),y.dtype)
# [[1 1 1]
#  [1 1 1]
#  [1 1 1]] <class 'numpy.ndarray'> int32

print(z,type(z),z.dtype)
# [[1 1 1]
#  [1 1 2]
#  [1 1 1]] <class 'numpy.ndarray'> int32

print(w,type(w),w.dtype)
# [[1 1 1]
#  [1 1 2]
#  [1 1 1]] <class 'numpy.ndarray'> int32

3.更改为较大的dtype时，其大小必须是array的最后一个axis的总大小（以字节为单位）的除数

import numpy as np

x = np.array([[1, 1, 1], [1, 1, 1], [1, 1, 1]])
print(x, x.dtype)
# [[1 1 1]
#  [1 1 1]
#  [1 1 1]] int32
x.dtype = np.float

# ValueError: When changing to a larger dtype, its size must be a divisor of the total size in bytes of the last axis of the array.

（c）通过fromfunction()函数进行创建

1.通过在每个坐标上执行一个函数来构造数组

import numpy as np

def f(x, y):
    return 10 * x + y

x = np.fromfunction(f, (5, 4), dtype=int)
print(x)
# [[ 0  1  2  3]
#  [10 11 12 13]
#  [20 21 22 23]
#  [30 31 32 33]
#  [40 41 42 43]]

x = np.fromfunction(lambda i, j: i == j, (3, 3), dtype=int)
print(x)
# [[ True False False]
#  [False  True False]
#  [False False  True]]

x = np.fromfunction(lambda i, j: i + j, (3, 3), dtype=int)
print(x)
# [[0 1 2]
#  [1 2 3]
#  [2 3 4]]

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依据 ones 和 zeros 填充方式

（a）零数组

import numpy as np

x = np.zeros(5)
print(x)  # [0. 0. 0. 0. 0.]
x = np.zeros([2, 3])
print(x)
# [[0. 0. 0.]
#  [0. 0. 0.]]

x = np.array([[1, 2, 3], [4, 5, 6]])
y = np.zeros_like(x)
print(y)
# [[0 0 0]
#  [0 0 0]]

（b）数组

import numpy as np

x = np.ones(5)
print(x)  # [1. 1. 1. 1. 1.]
x = np.ones([2, 3])
print(x)
# [[1. 1. 1.]
#  [1. 1. 1.]]

x = np.array([[1, 2, 3], [4, 5, 6]])
y = np.ones_like(x)
print(y)
# [[1 1 1]
#  [1 1 1]]

（c）空数组

import numpy as np

x = np.empty(5)
print(x)
# [1.95821574e-306 1.60219035e-306 1.37961506e-306 
#  9.34609790e-307 1.24610383e-306]

x = np.empty((3, 2))
print(x)
# [[1.60220393e-306 9.34587382e-307]
#  [8.45599367e-307 7.56598449e-307]
#  [1.33509389e-306 3.59412896e-317]]

x = np.array([[1, 2, 3], [4, 5, 6]])
y = np.empty_like(x)
print(y)
# [[  7209029   6422625   6619244]
#  [      100 707539280       504]]

（d）单位数组

import numpy as np

x = np.eye(4)
print(x)
# [[1. 0. 0. 0.]
#  [0. 1. 0. 0.]
#  [0. 0. 1. 0.]
#  [0. 0. 0. 1.]]

x = np.eye(2, 3)
print(x)
# [[1. 0. 0.]
#  [0. 1. 0.]]

x = np.identity(4)
print(x)
# [[1. 0. 0. 0.]
#  [0. 1. 0. 0.]
#  [0. 0. 1. 0.]
#  [0. 0. 0. 1.]]

（e）对角数组

import numpy as np

x = np.arange(9).reshape((3, 3))
print(x)
# [[0 1 2]
#  [3 4 5]
#  [6 7 8]]
print(np.diag(x))  # [0 4 8]
print(np.diag(x, k=1))  # [1 5]
print(np.diag(x, k=-1))  # [3 7]

v = [1, 3, 5, 7]
x = np.diag(v)
print(x)
# [[1 0 0 0]
#  [0 3 0 0]
#  [0 0 5 0]
#  [0 0 0 7]]

（f）常数数组

# import numpy as np

x = np.full((2,), 7)
print(x)
# [7 7]

x = np.full(2, 7)
print(x)
# [7 7]

x = np.full((2, 7), 7)
print(x)
# [[7 7 7 7 7 7 7]
#  [7 7 7 7 7 7 7]]

x = np.array([[1, 2, 3], [4, 5, 6]])
y = np.full_like(x, 7)
print(y)
# [[7 7 7]
#  [7 7 7]]

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利用数值范围来创建ndarray

import numpy as np

x = np.arange(5)
print(x)  # [0 1 2 3 4]

x = np.arange(3, 7, 2)
print(x)  # [3 5]

x = np.linspace(start=0, stop=2, num=9)
print(x)  
# [0.   0.25 0.5  0.75 1.   1.25 1.5  1.75 2.  ]

x = np.logspace(0, 1, 5)
print(np.around(x, 2))
# [ 1.    1.78  3.16  5.62 10.  ]            
                                    #np.around 返回四舍五入后的值，可指定精度。
                                   # around(a, decimals=0, out=None)
                                   # a 输入数组
                                   # decimals 要舍入的小数位数。 默认值为0。 如果为负，整数将四舍五入到小数点左侧的位置


x = np.linspace(start=0, stop=1, num=5)
x = [10 ** i for i in x]
print(np.around(x, 2))
# [ 1.    1.78  3.16  5.62 10.  ]

x = np.random.random(5)
print(x)
# [0.41768753 0.16315577 0.80167915 0.99690199 0.11812291]

x = np.random.random([2, 3])
print(x)
# [[0.41151858 0.93785153 0.57031309]
#  [0.13482333 0.20583516 0.45429181]]

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结构数组的创建

（a）利用字典来定义结构

import numpy as np

personType = np.dtype({
    'names': ['name', 'age', 'weight'],
    'formats': ['U30', 'i8', 'f8']})

a = np.array([('Liming', 24, 63.9), ('Mike', 15, 67.), ('Jan', 34, 45.8)],
             dtype=personType)
print(a, type(a))
# [('Liming', 24, 63.9) ('Mike', 15, 67. ) ('Jan', 34, 45.8)]
# <class 'numpy.ndarray'>

（b）利用包含多个元组的列表来定义结构

import numpy as np

personType = np.dtype([('name', 'U30'), ('age', 'i8'), ('weight', 'f8')])
a = np.array([('Liming', 24, 63.9), ('Mike', 15, 67.), ('Jan', 34, 45.8)],
             dtype=personType)
print(a, type(a))
# [('Liming', 24, 63.9) ('Mike', 15, 67. ) ('Jan', 34, 45.8)]
# <class 'numpy.ndarray'>

# 结构数组的取值方式和一般数组差不多，可以通过下标取得元素：
print(a[0])
# ('Liming', 24, 63.9)

print(a[-2:])
# [('Mike', 15, 67. ) ('Jan', 34, 45.8)]

# 我们可以使用字段名作为下标获取对应的值
print(a['name'])
# ['Liming' 'Mike' 'Jan']
print(a['age'])
# [24 15 34]
print(a['weight'])
# [63.9 67.  45.8]

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数组的属性

import numpy as np

a = np.array([1, 2, 3, 4, 5])
print(a.shape)  # (5,)
print(a.dtype)  # int32
print(a.size)  # 5
print(a.ndim)  # 1
print(a.itemsize)  # 4

b = np.array([[1, 2, 3], [4, 5, 6.0]])
print(b.shape)  # (2, 3)
print(b.dtype)  # float64
print(b.size)  # 6
print(b.ndim)  # 2
print(b.itemsize)  # 8

import numpy as np

a = np.array([1, 2, 3, 4, 5])
print(a)  # [1 2 3 4 5]
b = np.array([1, 2, 3, 4, '5'])
print(b)  # ['1' '2' '3' '4' '5']
c = np.array([1, 2, 3, 4, 5.0])
print(c)  # [1. 2. 3. 4. 5.]

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参考：阿里云天池