再肝3天,整理了90个NumPy案例,不能不收藏!(下)

简介: 再肝3天,整理了90个NumPy案例,不能不收藏!(下)

46使用 Python 中的值创建 3D NumPy 数组


import numpy as np
the_3d_array = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
print(the_3d_array)

Output:

[[[1 2]
  [3 4]]
 [[5 6]
  [7 8]]]


47计算不同长度的 Numpy 数组的平均值


import numpy as np
x = np.array([[1, 2], [3, 4]])
y = np.array([[1, 2, 3], [3, 4, 5]])
z = np.array([[7], [8]])
arr = np.ma.empty((2, 3, 3))
arr.mask = True
arr[:x.shape[0], :x.shape[1], 0] = x
arr[:y.shape[0], :y.shape[1], 1] = y
arr[:z.shape[0], :z.shape[1], 2] = z
print(arr.mean(axis=2))

Output:

[[3.0 2.0 3.0]
 [4.666666666666667 4.0 5.0]]


48从 Numpy 数组中删除 nan 值


Example 1

import numpy as np
x = np.array([np.nan, 2, 3, 4])
x = x[~np.isnan(x)]
print(x)

Output:

[2. 3. 4.]

Example 2

import numpy as np
x = np.array([
    [5, np.nan],
    [np.nan, 0],
    [1, 2],
    [3, 4]
])
x = x[~np.isnan(x).any(axis=1)]
print(x)

Output:

[[1. 2.]
 [3. 4.]]


49向 NumPy 数组添加一列


import numpy as np
the_array = np.array([[1, 2], [3, 4]])
columns_to_append = np.array([[5], [6]])
the_array = np.append(the_array, columns_to_append, 1)
print(the_array)

Output:

[[1 2 5]
 [3 4 6]]


50在 Numpy Array 中打印浮点值时如何抑制科学记数法


import numpy as np
np.set_printoptions(suppress=True,
                    formatter={'float_kind': '{:f}'.format})
the_array = np.array([3.74, 5162, 13683628846.64, 12783387559.86, 1.81])
print(the_array)

Output:

[3.740000 5162.000000 13683628846.639999 12783387559.860001 1.810000]


51Numpy 将 1d 数组重塑为 1 列的 2d 数组


import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6, 7, 8])
newarr = arr.reshape(arr.shape[0], -1)
print(newarr)

Output:

[[1]
 [2]
 [3]
 [4]
 [5]
 [6]
 [7]
 [8]]


52初始化 NumPy 数组


import numpy as np
thearray = np.array([[1, 2], [3, 4], [5, 6]])
print(thearray)

Output:

[[1 2]
 [3 4]
 [5 6]]


53创建重复一行


import numpy as np
the_array = np.array([1, 2, 3])
repeat = 3
new_array = np.tile(the_array, (repeat, 1))
print(new_array)

Output:

[[1 2 3]
 [1 2 3]
 [1 2 3]]


54将 NumPy 数组附加到 Python 中的空数组


import numpy as np
the_array = np.array([1, 2, 3, 4])
empty_array = np.array([])
new_array = np.append(empty_array, the_array)
print(new_array)

Output:

[1. 2. 3. 4.]


55找到 Numpy 数组的平均值


计算每列的平均值

import numpy as np
the_array = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
mean_array = the_array.mean(axis=0)
print(mean_array)

Output:

[3. 4. 5. 6.]

计算每一行的平均值

import numpy as np
the_array = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
mean_array = the_array.mean(axis=1)
print(mean_array)

Output:

[2.5 6.5]

仅第一列的平均值

import numpy as np
the_array = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
mean_array = the_array[:, 0].mean()
print(mean_array)

Output:

3.0

仅第二列的平均值

import numpy as np
the_array = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
mean_array = the_array[:, 0].mean()
print(mean_array)

Output:

4.0


56检测 NumPy 数组是否包含至少一个非数字值


import numpy as np
the_array = np.array([np.nan, 2, 3, 4])
array_has_nan = np.isnan(the_array).any()
print(array_has_nan)
the_array = np.array([1, 2, 3, 4])
array_has_nan = np.isnan(the_array).any()
print(array_has_nan)

Output:

True
False


57在 Python 中附加 NumPy 数组


import numpy as np
the_array = np.array([[0, 1], [2, 3]])
row_to_append = np.array([[4, 5]])
the_array = np.append(the_array, row_to_append, 0)
print(the_array)
print('*' * 10)
columns_to_append = np.array([[7], [8], [9]])
the_array = np.append(the_array, columns_to_append, 1)
print(the_array)

Output:

[[0 1]
 [2 3]
 [4 5]]
**********
[[0 1 7]
 [2 3 8]
 [4 5 9]]


58使用 numpy.any()


import numpy as np
thearr = [[True, False], [True, True]]
thebool = np.any(thearr)
print(thebool)
thearr = [[False, False], [False, False]]
thebool = np.any(thearr)
print(thebool)

Output:

True
False


59获得 NumPy 数组的转置


import numpy as np
the_array = np.array([[1, 2], [3, 4]])
print(the_array)
print(the_array.T)

Output:

[[1 2]
 [3 4]]
[[1 3]
 [2 4]]


60获取和设置NumPy数组的数据类型


import numpy as np
type1 = np.array([1, 2, 3, 4, 5, 6])
type2 = np.array([1.5, 2.5, 0.5, 6])
type3 = np.array(['a', 'b', 'c'])
type4 = np.array(["Canada", "Australia"], dtype='U5')
type5 = np.array([555, 666], dtype=float)
print(type1.dtype)
print(type2.dtype)
print(type3.dtype)
print(type4.dtype)
print(type5.dtype)
print(type4)

Output:

int32
float64
<U1
<U5
float64
['Canad' 'Austr']


61获得NumPy数组的形状


import numpy as np
array1d = np.array([1, 2, 3, 4, 5, 6])
array2d = np.array([[1, 2, 3], [4, 5, 6]])
array3d = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]])
print(array1d.shape)
print(array2d.shape)
print(array3d.shape)

Output:

(6,)
(2, 3)
(2, 2, 3)


62获得 1、2 或 3 维 NumPy 数组


import numpy as np
array1d = np.array([1, 2, 3, 4, 5, 6])
print(array1d.ndim)  # 1
array2d = np.array([[1, 2, 3], [4, 5, 6]])
print(array2d.ndim)  # 2
array3d = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
array3d = array3d.reshape(2, 3, 2)
print(array3d.ndim)  # 3

Output:

1
2
3


63重塑 NumPy 数组


import numpy as np
thearray = np.array([1, 2, 3, 4, 5, 6, 7, 8])
thearray = thearray.reshape(2, 4)
print(thearray)
print("-" * 10)
thearray = thearray.reshape(4, 2)
print(thearray)
print("-" * 10)
thearray = thearray.reshape(8, 1)
print(thearray)

Output:

[[1 2 3 4]
 [5 6 7 8]]
----------
[[1 2]
 [3 4]
 [5 6]
 [7 8]]
----------
[[1]
 [2]
 [3]
 [4]
 [5]
 [6]
 [7]
 [8]]


64调整 NumPy 数组的大小


import numpy as np
thearray = np.array([1, 2, 3, 4, 5, 6, 7, 8])
thearray.resize(4)
print(thearray)
print("-" * 10)
thearray = np.array([1, 2, 3, 4, 5, 6, 7, 8])
thearray.resize(2, 4)
print(thearray)
print("-" * 10)
thearray = np.array([1, 2, 3, 4, 5, 6, 7, 8])
thearray.resize(3, 3)
print(thearray)

Output:

[1 2 3 4]
----------
[[1 2 3 4]
 [5 6 7 8]]
----------
[[1 2 3]
 [4 5 6]
 [7 8 0]]


65将 List 或 Tuple 转换为 NumPy 数组


import numpy as np
thelist = [1, 2, 3]
print(type(thelist))  # <class 'list'>
array1 = np.array(thelist)
print(type(array1))  # <class 'numpy.ndarray'>
thetuple = ((1, 2, 3))
print(type(thetuple))  # <class 'tuple'>
array2 = np.array(thetuple)
print(type(array2))  # <class 'numpy.ndarray'>
array3 = np.array([thetuple, thelist, array1])
print(array3)

Output:

<class 'list'>
<class 'numpy.ndarray'>
<class 'tuple'>
<class 'numpy.ndarray'>
[[1 2 3]
 [1 2 3]
 [1 2 3]]


66使用 arange 函数创建 NumPy 数组


import numpy as np
array1d = np.arange(5)  # 1 row and 5 columns
print(array1d)
array1d = np.arange(0, 12, 2)  # 1 row and 6 columns
print(array1d)
array2d = np.arange(0, 12, 2).reshape(2, 3)  # 2 rows 3 columns
print(array2d)
array3d = np.arange(9).reshape(3, 3)  # 3 rows and columns
print(array3d)

Output:

[0 1 2 3 4]
[ 0  2  4  6  8 10]
[[ 0  2  4]
 [ 6  8 10]]
[[0 1 2]
 [3 4 5]
 [6 7 8]]


67使用 linspace() 创建 NumPy 数组


import numpy as np
array1d = np.linspace(1, 12, 2)
print(array1d)
array1d = np.linspace(1, 12, 4)
print(array1d)
array2d = np.linspace(1, 12, 12).reshape(4, 3)
print(array2d)

Output:

[ 1. 12.]
[ 1.          4.66666667  8.33333333 12.        ]
[[ 1.  2.  3.]
 [ 4.  5.  6.]
 [ 7.  8.  9.]
 [10. 11. 12.]]


68NumPy 日志空间数组示例


import numpy as np
thearray = np.logspace(5, 10, num=10, base=10000000.0, dtype=float)
print(thearray)

Output:

[1.00000000e+35 7.74263683e+38 5.99484250e+42 4.64158883e+46
 3.59381366e+50 2.78255940e+54 2.15443469e+58 1.66810054e+62
 1.29154967e+66 1.00000000e+70]


69创建 Zeros NumPy 数组


import numpy as np
array1d = np.zeros(3)
print(array1d)
array2d = np.zeros((2, 4))
print(array2d)

Output:

[0. 0. 0.]
[[0. 0. 0. 0.]
 [0. 0. 0. 0.]]


70NumPy One 数组示例


import numpy as np
array1d = np.ones(3)
print(array1d)
array2d = np.ones((2, 4))
print(array2d)

Output:

[1. 1. 1.]
[[1. 1. 1. 1.]
 [1. 1. 1. 1.]]


71NumPy 完整数组示例


import numpy as np
array1d = np.full((3), 2)
print(array1d)
array2d = np.full((2, 4), 3)
print(array2d)

Output:

[2 2 2]
[[3 3 3 3]
 [3 3 3 3]]


72NumPy Eye 数组示例


import numpy as np
array1 = np.eye(3, dtype=int)
print(array1)
array2 = np.eye(5, k=2)
print(array2)

Output:

[[1 0 0]
 [0 1 0]
 [0 0 1]]
[[0. 0. 1. 0. 0.]
 [0. 0. 0. 1. 0.]
 [0. 0. 0. 0. 1.]
 [0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0.]]


73NumPy 生成随机数数组


import numpy as np
print(np.random.rand(3, 2))  # Uniformly distributed values.
print(np.random.randn(3, 2))  # Normally distributed values.
# Uniformly distributed integers in a given range.
print(np.random.randint(2, size=10))
print(np.random.randint(5, size=(2, 4)))

Output:

[[0.68428242 0.62467648]
 [0.28595395 0.96066372]
 [0.63394485 0.94036659]]
[[0.29458704 0.84015551]
 [0.42001253 0.89660667]
 [0.50442113 0.46681958]]
[0 1 1 0 0 0 0 1 0 0]
[[3 3 2 3]
 [2 1 2 0]]


74NumPy 标识和对角线数组示例


import numpy as np
print(np.identity(3))
print(np.diag(np.arange(0, 8, 2)))
print(np.diag(np.diag(np.arange(9).reshape((3,3)))))

Output:

[[1. 0. 0.]
 [0. 1. 0.]
 [0. 0. 1.]]
[[0 0 0 0]
 [0 2 0 0]
 [0 0 4 0]
 [0 0 0 6]]
[[0 0 0]
 [0 4 0]
 [0 0 8]]


75NumPy 索引示例


import numpy as np
array1d = np.array([1, 2, 3, 4, 5, 6])
print(array1d[0])   # Get first value
print(array1d[-1])  # Get last value
print(array1d[3])   # Get 4th value from first
print(array1d[-5])  # Get 5th value from last
# Get multiple values
print(array1d[[0, -1]])
print("-" * 10)
array2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print(array2d)
print("-" * 10)
print(array2d[0, 0])   # Get first row first col
print(array2d[0, 1])   # Get first row second col
print(array2d[0, 2])   # Get first row third col
print(array2d[0, 1])   # Get first row second col 
print(array2d[1, 1])   # Get second row second col
print(array2d[2, 1])   # Get third row second col

Output:

1
6
4
2
[1 6]
----------
[[1 2 3]
 [4 5 6]
 [7 8 9]]
----------
1
2
3
2
5
8


76多维数组中的 NumPy 索引


import numpy as np
array3d = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]])
print(array3d)
print(array3d[0, 0, 0])
print(array3d[0, 0, 1])
print(array3d[0, 0, 2])
print(array3d[0, 1, 0])
print(array3d[0, 1, 1])
print(array3d[0, 1, 2])
print(array3d[1, 0, 0])
print(array3d[1, 0, 1])
print(array3d[1, 0, 2])
print(array3d[1, 1, 0])
print(array3d[1, 1, 1])
print(array3d[1, 1, 2])

Output:

[[[ 1  2  3]
  [ 4  5  6]]
 [[ 7  8  9]
  [10 11 12]]]
1
2
3
4
5
6
7
8
9
10
11
12


77NumPy 单维切片示例


import numpy as np
array1d = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
print(array1d[4:])  # From index 4 to last index
print(array1d[:4])  # From index 0 to 4 index
print(array1d[4:7])  # From index 4(included) up to index 7(excluded)
print(array1d[:-1])  # Excluded last element
print(array1d[:-2])  # Up to second last index(negative index)
print(array1d[::-1])  # From last to first in reverse order(negative step)
print(array1d[::-2])  # All odd numbers in reversed order
print(array1d[-2::-2])  # All even numbers in reversed order
print(array1d[::])  # All elements

Output:

[4 5 6 7 8 9]
[0 1 2 3]
[4 5 6]
[0 1 2 3 4 5 6 7 8]
[0 1 2 3 4 5 6 7]
[9 8 7 6 5 4 3 2 1 0]
[9 7 5 3 1]
[8 6 4 2 0]
[0 1 2 3 4 5 6 7 8 9]


78NumPy 数组中的多维切片


import numpy as np
array2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print("-" * 10)
print(array2d[:, 0:2])  # 2nd and 3rd col
print("-" * 10)
print(array2d[1:3, 0:3])  # 2nd and 3rd row
print("-" * 10)
print(array2d[-1::-1, -1::-1])  # Reverse an array

Output:

----------
[[1 2]
 [4 5]
 [7 8]]
----------
[[4 5 6]
 [7 8 9]]
----------
[[9 8 7]
 [6 5 4]
 [3 2 1]]


79翻转 NumPy 数组的轴顺序


import numpy as np
array2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print(array2d)
print("-" * 10)
# Permute the dimensions of an array.
arrayT = np.transpose(array2d)
print(arrayT)
print("-" * 10)
# Flip array in the left/right direction.
arrayFlr = np.fliplr(array2d)
print(arrayFlr)
print("-" * 10)
# Flip array in the up/down direction.
arrayFud = np.flipud(array2d)
print(arrayFud)
print("-" * 10)
# Rotate an array by 90 degrees in the plane specified by axes.
arrayRot90 = np.rot90(array2d)
print(arrayRot90)

Output:

[[1 2 3]
 [4 5 6]
 [7 8 9]]
----------
[[1 4 7]
 [2 5 8]
 [3 6 9]]
----------
[[3 2 1]
 [6 5 4]
 [9 8 7]]
----------
[[7 8 9]
 [4 5 6]
 [1 2 3]]
----------
[[3 6 9]
 [2 5 8]
 [1 4 7]]


80NumPy 数组的连接和堆叠


import numpy as np
array1 = np.array([[1, 2, 3], [4, 5, 6]])
array2 = np.array([[7, 8, 9], [10, 11, 12]])
# Stack arrays in sequence horizontally (column wise).
arrayH = np.hstack((array1, array2))
print(arrayH)
print("-" * 10)
# Stack arrays in sequence vertically (row wise).
arrayV = np.vstack((array1, array2))
print(arrayV)
print("-" * 10)
# Stack arrays in sequence depth wise (along third axis).
arrayD = np.dstack((array1, array2))
print(arrayD)
print("-" * 10)
# Appending arrays after each other, along a given axis.
arrayC = np.concatenate((array1, array2))
print(arrayC)
print("-" * 10)
# Append values to the end of an array.
arrayA = np.append(array1, array2, axis=0)
print(arrayA)
print("-" * 10)
arrayA = np.append(array1, array2, axis=1)
print(arrayA)

Output:

[[ 1  2  3  7  8  9]
 [ 4  5  6 10 11 12]]
----------
[[ 1  2  3]
 [ 4  5  6]
 [ 7  8  9]
 [10 11 12]]
----------
[[[ 1  7]
  [ 2  8]
  [ 3  9]]
 [[ 4 10]
  [ 5 11]
  [ 6 12]]]
----------
[[ 1  2  3]
 [ 4  5  6]
 [ 7  8  9]
 [10 11 12]]
----------
[[ 1  2  3]
 [ 4  5  6]
 [ 7  8  9]
 [10 11 12]]
----------
[[ 1  2  3  7  8  9]
 [ 4  5  6 10 11 12]]


81NumPy 数组的算术运算


import numpy as np
array1 = np.array([[1, 2, 3], [4, 5, 6]])
array2 = np.array([[7, 8, 9], [10, 11, 12]])
print(array1 + array2)
print("-" * 20)
print(array1 - array2)
print("-" * 20)
print(array1 * array2)
print("-" * 20)
print(array2 / array1)
print("-" * 40)
print(array1 ** array2)
print("-" * 40)

Output:

[[ 8 10 12]
 [14 16 18]]
--------------------
[[-6 -6 -6]
 [-6 -6 -6]]
--------------------
[[ 7 16 27]
 [40 55 72]]
--------------------
[[7.  4.  3. ]
 [2.5 2.2 2. ]]
----------------------------------------
[[          1         256       19683]
 [    1048576    48828125 -2118184960]]
----------------------------------------


82NumPy 数组上的标量算术运算


import numpy as np
array1 = np.array([[10, 20, 30], [40, 50, 60]])
print(array1 + 2)
print("-" * 20)
print(array1 - 5)
print("-" * 20)
print(array1 * 2)
print("-" * 20)
print(array1 / 5)
print("-" * 20)
print(array1 ** 2)
print("-" * 20)

Output:

[[12 22 32]
 [42 52 62]]
--------------------
[[ 5 15 25]
 [35 45 55]]
--------------------
[[ 20  40  60]
 [ 80 100 120]]
--------------------
[[ 2.  4.  6.]
 [ 8. 10. 12.]]
--------------------
[[ 100  400  900]
 [1600 2500 3600]]
--------------------


83NumPy 初等数学函数


import numpy as np
array1 = np.array([[10, 20, 30], [40, 50, 60]])
print(np.sin(array1))
print("-" * 40)
print(np.cos(array1))
print("-" * 40)
print(np.tan(array1))
print("-" * 40)
print(np.sqrt(array1))
print("-" * 40)
print(np.exp(array1))
print("-" * 40)
print(np.log10(array1))
print("-" * 40)

Output:

[[-0.54402111  0.91294525 -0.98803162]
 [ 0.74511316 -0.26237485 -0.30481062]]
----------------------------------------
[[-0.83907153  0.40808206  0.15425145]
 [-0.66693806  0.96496603 -0.95241298]]
----------------------------------------
[[ 0.64836083  2.23716094 -6.4053312 ]
 [-1.11721493 -0.27190061  0.32004039]]
----------------------------------------
[[3.16227766 4.47213595 5.47722558]
 [6.32455532 7.07106781 7.74596669]]
----------------------------------------
[[2.20264658e+04 4.85165195e+08 1.06864746e+13]
 [2.35385267e+17 5.18470553e+21 1.14200739e+26]]
----------------------------------------
[[1.         1.30103    1.47712125]
 [1.60205999 1.69897    1.77815125]]
----------------------------------------


84NumPy Element Wise 数学运算


import numpy as np
array1 = np.array([[10, 20, 30], [40, 50, 60]])
array2 = np.array([[2, 3, 4], [4, 6, 8]])
array3 = np.array([[-2, 3.5, -4], [4.05, -6, 8]])
print(np.add(array1, array2))
print("-" * 40)
print(np.power(array1, array2))
print("-" * 40)
print(np.remainder((array2), 5))
print("-" * 40)
print(np.reciprocal(array3))
print("-" * 40)
print(np.sign(array3))
print("-" * 40)
print(np.ceil(array3))
print("-" * 40)
print(np.round(array3))
print("-" * 40)

Output:

[[12 23 34]
 [44 56 68]]
----------------------------------------
[[        100        8000      810000]
 [    2560000 -1554869184 -1686044672]]
----------------------------------------
[[2 3 4]
 [4 1 3]]
----------------------------------------
[[-0.5         0.28571429 -0.25      ]
 [ 0.24691358 -0.16666667  0.125     ]]
----------------------------------------
[[-1.  1. -1.]
 [ 1. -1.  1.]]
----------------------------------------
[[-2.  4. -4.]
 [ 5. -6.  8.]]
----------------------------------------
[[-2.  4. -4.]
 [ 4. -6.  8.]]
----------------------------------------


85NumPy 聚合和统计函数


import numpy as np
array1 = np.array([[10, 20, 30], [40, 50, 60]])
print("Mean: ", np.mean(array1))
print("Std: ", np.std(array1))
print("Var: ", np.var(array1))
print("Sum: ", np.sum(array1))
print("Prod: ", np.prod(array1))

Output:

Mean:  35.0
Std:  17.07825127659933
Var:  291.6666666666667
Sum:  210
Prod:  720000000


86Where 函数的 NumPy 示例


import numpy as np
before = np.array([[1, 2, 3], [4, 5, 6]])
# If element is less than 4, mul by 2 else by 3
after = np.where(before < 4, before * 2, before * 3)
print(after)

Output:

[[ 2  4  6]
 [12 15 18]]


87Select 函数的 NumPy 示例


import numpy as np
before = np.array([[1, 2, 3], [4, 5, 6]])
# If element is less than 4, mul by 2 else by 3
after = np.select([before < 4, before], [before * 2, before * 3])
print(after)

Output:

[[ 2  4  6]
 [12 15 18]]


88选择函数的 NumPy 示例


import numpy as np
before = np.array([[0, 1, 2], [2, 0, 1], [1, 2, 0]])
choices = [5, 10, 15]
after = np.choose(before, choices)
print(after)
print("-" * 10)
before = np.array([[0, 0, 0], [2, 2, 2], [1, 1, 1]])
choice1 = [5, 10, 15]
choice2 = [8, 16, 24]
choice3 = [9, 18, 27]
after = np.choose(before, (choice1, choice2, choice3))
print(after)

Output:

[[ 5 10 15]
 [15  5 10]
 [10 15  5]]
----------
[[ 5 10 15]
 [ 9 18 27]
 [ 8 16 24]]


89NumPy 逻辑操作,用于根据给定条件从数组中选择性地选取值


import numpy as np
thearray = np.array([[10, 20, 30], [14, 24, 36]])
print(np.logical_or(thearray < 10, thearray > 15))
print("-" * 30)
print(np.logical_and(thearray < 10, thearray > 15))
print("-" * 30)
print(np.logical_not(thearray < 20))
print("-" * 30)

Output:

[[False  True  True]
 [False  True  True]]
------------------------------
[[False False False]
 [False False False]]
------------------------------
[[False  True  True]
 [False  True  True]]
------------------------------


90标准集合操作的 NumPy 示例


import numpy as np
array1 = np.array([[10, 20, 30], [14, 24, 36]])
array2 = np.array([[20, 40, 50], [24, 34, 46]])
# Find the union of two arrays.
print(np.union1d(array1, array2))
# Find the intersection of two arrays.
print(np.intersect1d(array1, array2))
# Find the set difference of two arrays.
print(np.setdiff1d(array1, array2))

Output:

[10 14 20 24 30 34 36 40 46 50]
[20 24]
[10 14 30 36]
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