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有多个条件时替换 Numpy 数组中的元素

将所有大于 30 的元素替换为 0
将大于 30 小于 50 的所有元素替换为 0
给所有大于 40 的元素加 5
用 Nan 替换数组中大于 25 的所有元素
将数组中大于 25 的所有元素替换为 1，否则为 0

在 Python 中找到 Numpy 数组的维度
两个条件过滤 NumPy 数组

Example 1
Example 2
Example 3
Example 4
Example 5

对最后一列求和

第一列总和
第二列总和
第一列和第二列的总和
最后一列的总和

满足条件，则替换 Numpy 元素

将所有大于 30 的元素替换为 0
将大于 30 小于 50 的所有元素替换为 0
给所有大于 40 的元素加 5
用 Nan 替换数组中大于 25 的所有元素
将数组中大于 25 的所有元素替换为 1，否则为 0

从 Nump y数组中随机选择两行

Example 1
Example 2
Example 3

以给定的精度漂亮地打印一个 Numpy 数组

Example 1
Example 2
Example 3
Example 4
Example 5

提取 Numpy 矩阵的前 n 列

列范围1
列范围2
列范围3
特定列
特定行和列

从 NumPy 数组中删除值

Example 1
Example 2
Example 3

将满足条件的项目替换为 Numpy 数组中的另一个值

将所有大于 30 的元素替换为 0
将大于 30 小于 50 的所有元素替换为 0
给所有大于 40 的元素加 5
用 Nan 替换数组中大于 25 的所有元素
将数组中大于 25 的所有元素替换为 1，否则为 0

对 NumPy 数组中的所有元素求和
创建 3D NumPy 零数组
计算 NumPy 数组中每一行的总和
打印没有科学记数法的 NumPy 数组
获取numpy数组中所有NaN值的索引列表
检查 NumPy 数组中的所有元素都是 NaN
将列表添加到 Python 中的 NumPy 数组
在 Numpy 中抑制科学记数法
将具有 12 个元素的一维数组转换为 3 维数组

Example 1
Example 2
Example 3
Example 4

检查 NumPy 数组是否为空
在 Python 中重塑 3D 数组

Example 1
Example 2
Example 3
Example 4

在 Python 中重复 NumPy 数组中的一列
在 NumPy 数组中找到跨维度的平均值
检查 NumPy 数组中的 NaN 元素
格式化 NumPy 数组的打印方式

Example 1
Example 2
Example 3
Example 4
Example 5

乘以Numpy数组的每个元素

Example 1
Example 2
Example 3
Example 4

在 NumPy 中生成随机数

Example 1
Example 2
Example 3

Numpy 将具有 8 个元素的一维数组转换为 Python 中的二维数组

4 行 2 列
2 行 4 列

在 Python 中使用 numpy.all()
将一维数组转换为二维数组

4 行 2 列
2 行 4 列
Example 3
通过添加新轴将一维数组转换为二维数组
Example 5

计算 NumPy 数组中唯一值的频率
在一列中找到平均值
在 Numpy 数组的长度、维度、大小

Example 1
Example 2

在 NumPy 数组中找到最大值的索引
按降序对 NumPy 数组进行排序

按降序对 Numpy 进行排序
按降序对 2D Numpy 进行排序
按降序对 Numpy 进行排序

Numpy 从二维数组中获取随机的一组行

Example 1
Example 2
Example 3

将 Numpy 数组转换为 JSON
检查 NumPy 数组中是否存在值
创建一个 3D NumPy 数组
在numpy中将字符串数组转换为浮点数数组
从 Python 的 numpy 数组中随机选择

Example 1
Example 2
Example 3

不截断地打印完整的 NumPy 数组
将 Numpy 转换为列表
将字符串数组转换为浮点数数组
计算 NumPy 数组中每一列的总和
使用 Python 中的值创建 3D NumPy 数组
计算不同长度的 Numpy 数组的平均值
从 Numpy 数组中删除 nan 值

Example 1
Example 2

向 NumPy 数组添加一列
在 Numpy Array 中打印浮点值时如何抑制科学记数法
Numpy 将 1d 数组重塑为 1 列的 2d 数组
初始化 NumPy 数组
创建重复一行
将 NumPy 数组附加到 Python 中的空数组
找到 Numpy 数组的平均值

计算每列的平均值
计算每一行的平均值
仅第一列的平均值
仅第二列的平均值

检测 NumPy 数组是否包含至少一个非数字值
在 Python 中附加 NumPy 数组
使用 numpy.any()
获得 NumPy 数组的转置
获取和设置NumPy数组的数据类型
获得NumPy数组的形状
获得 1、2 或 3 维 NumPy 数组
重塑 NumPy 数组
调整 NumPy 数组的大小
将 List 或 Tuple 转换为 NumPy 数组
使用 arange 函数创建 NumPy 数组
使用 linspace() 创建 NumPy 数组
NumPy 日志空间数组示例
创建 Zeros NumPy 数组
NumPy One 数组示例
NumPy 完整数组示例
NumPy Eye 数组示例
NumPy 生成随机数数组
NumPy 标识和对角线数组示例
NumPy 索引示例
多维数组中的 NumPy 索引
NumPy 单维切片示例
NumPy 数组中的多维切片
翻转 NumPy 数组的轴顺序
NumPy 数组的连接和堆叠
NumPy 数组的算术运算
NumPy 数组上的标量算术运算
NumPy 初等数学函数
NumPy Element Wise 数学运算
NumPy 聚合和统计函数
Where 函数的 NumPy 示例
Select 函数的 NumPy 示例
选择函数的 NumPy 示例
NumPy 逻辑操作，用于根据给定条件从数组中选择性地选取值
标准集合操作的 NumPy 示例

1有多个条件时替换 Numpy 数组中的元素

将所有大于 30 的元素替换为 0

import numpy as np
the_array = np.array([49, 7, 44, 27, 13, 35, 71])
an_array = np.where(the_array > 30, 0, the_array)
print(an_array)

Output:

[ 0  7  0 27 13  0  0]

将大于 30 小于 50 的所有元素替换为 0

import numpy as np
the_array = np.array([49, 7, 44, 27, 13, 35, 71])
an_array = np.where((the_array > 30) & (the_array < 50), 0, the_array)
print(an_array)

Output:

[ 0  7  0 27 13  0 71]

给所有大于 40 的元素加 5

import numpy as np
the_array = np.array([49, 7, 44, 27, 13, 35, 71])
an_array = np.where(the_array > 40, the_array + 5, the_array)
print(an_array)

Output:

[54  7 49 27 13 35 76]

用 Nan 替换数组中大于 25 的所有元素

import numpy as np
the_array = np.array([49, 7, 44, 27, 13, 35, 71])
an_array = np.where(the_array > 25, np.NaN, the_array)
print(an_array)

Output:

[nan  7. nan nan 13. nan nan]

将数组中大于 25 的所有元素替换为 1，否则为 0

import numpy as np
the_array = np.array([49, 7, 44, 27, 13, 35, 71])
an_array = np.asarray([0 if val < 25 else 1 for val in the_array])
print(an_array)

Output:

[1 0 1 1 0 1 1]

2在 Python 中找到 Numpy 数组的维度

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

Output:

1
2
3

3两个条件过滤 NumPy 数组

Example 1

import numpy as np
the_array = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])
filter_arr = np.logical_and(np.greater(the_array, 3), np.less(the_array, 8))
print(the_array[filter_arr])

Output:

[4 5 6 7]

Example 2

import numpy as np
the_array = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])
filter_arr = np.logical_or(the_array < 3, the_array == 4)
print(the_array[filter_arr])

Output:

[1 2 4]

Example 3

import numpy as np
the_array = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])
filter_arr = np.logical_not(the_array > 1, the_array < 5)
print(the_array[filter_arr])

Output:

[1]

Example 4

import numpy as np
the_array = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])
filter_arr = np.logical_or(the_array == 8, the_array < 5)
print(the_array[filter_arr])

Output:

[1 2 3 4 8]

Example 5

import numpy as np
the_array = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])
filter_arr = np.logical_and(the_array == 8, the_array < 5)
print(the_array[filter_arr])

Output:

[]

4对最后一列求和

第一列总和

import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
newarr = arr.reshape(4, 3)
print(newarr)
column_sums = newarr[:, 0].sum()
print(column_sums)

Output:

[[ 1  2  3]
 [ 4  5  6]
 [ 7  8  9]
 [10 11 12]]
22

第二列总和

import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
newarr = arr.reshape(4, 3)
print(newarr)
column_sums = newarr[:, 1].sum()
print(column_sums)

Output:

[[ 1  2  3]
 [ 4  5  6]
 [ 7  8  9]
 [10 11 12]]
26

第一列和第二列的总和

import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
newarr = arr.reshape(4, 3)
print(newarr)
column_sums = newarr[:, 0:2].sum()
print(column_sums)

Output:

[[ 1  2  3]
 [ 4  5  6]
 [ 7  8  9]
 [10 11 12]]
48

最后一列的总和

import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
newarr = arr.reshape(4, 3)
print(newarr)
column_sums = newarr[:, -1].sum()
print(column_sums)

Output:

[[ 1  2  3]
 [ 4  5  6]
 [ 7  8  9]
 [10 11 12]]
30

5满足条件，则替换 Numpy 元素

将所有大于 30 的元素替换为 0

import numpy as np
the_array = np.array([49, 7, 44, 27, 13, 35, 71])
an_array = np.where(the_array > 30, 0, the_array)
print(an_array)

Output:

[ 0  7  0 27 13  0  0]

将大于 30 小于 50 的所有元素替换为 0

import numpy as np
the_array = np.array([49, 7, 44, 27, 13, 35, 71])
an_array = np.where((the_array > 30) & (the_array < 50), 0, the_array)
print(an_array)

Output:

[ 0  7  0 27 13  0 71]

给所有大于 40 的元素加 5

import numpy as np
the_array = np.array([49, 7, 44, 27, 13, 35, 71])
an_array = np.where(the_array > 40, the_array + 5, the_array)
print(an_array)

Output:

[54  7 49 27 13 35 76]

用 Nan 替换数组中大于 25 的所有元素

import numpy as np
the_array = np.array([49, 7, 44, 27, 13, 35, 71])
an_array = np.where(the_array > 25, np.NaN, the_array)
print(an_array)

Output:

[nan  7. nan nan 13. nan nan]

将数组中大于 25 的所有元素替换为 1，否则为 0

import numpy as np
the_array = np.array([49, 7, 44, 27, 13, 35, 71])
an_array = np.asarray([0 if val < 25 else 1 for val in the_array])
print(an_array)

Output:

[1 0 1 1 0 1 1]

6从 Nump y数组中随机选择两行

Example 1

import numpy as np
# create 2D array
the_array = np.arange(50).reshape((5, 10))
# row manipulation
np.random.shuffle(the_array)
# display random rows
rows = the_array[:2, :]
print(rows)

Output:

[[10 11 12 13 14 15 16 17 18 19]
 [ 0  1  2  3  4  5  6  7  8  9]]

Example 2

import random
import numpy as np
# create 2D array
the_array = np.arange(16).reshape((4, 4))
# row manipulation
rows_id = random.sample(range(0, the_array.shape[1] - 1), 2)
# display random rows
rows = the_array[rows_id, :]
print(rows)

Output:

[[ 4  5  6  7]
 [ 8  9 10 11]]

Example 3

import numpy as np
# create 2D array
the_array = np.arange(16).reshape((4, 4))
number_of_rows = the_array.shape[0]
random_indices = np.random.choice(number_of_rows,
                                  size=2,
                                  replace=False)
# display random rows
rows = the_array[random_indices, :]
print(rows)

Output:

[[ 4  5  6  7]
 [ 8  9 10 11]]

7以给定的精度漂亮地打印一个 Numpy 数组

Example 1

import numpy as np
x = np.array([[1.1, 0.9, 1e-6]] * 3)
print(x)
print(np.array_str(x, precision=1, suppress_small=True))

Output:

[[1.1e+00 9.0e-01 1.0e-06]
 [1.1e+00 9.0e-01 1.0e-06]
 [1.1e+00 9.0e-01 1.0e-06]]
[[1.1 0.9 0. ]
 [1.1 0.9 0. ]
 [1.1 0.9 0. ]]

Example 2

import numpy as np
x = np.random.random(10)
print(x)
np.set_printoptions(precision=3)
print(x)

Output:

[0.53828153 0.75848226 0.50046312 0.94723558 0.50415632 0.13899663
 0.80301141 0.40887872 0.24837485 0.83008548]
[0.538 0.758 0.5   0.947 0.504 0.139 0.803 0.409 0.248 0.83 ]

Example 3

import numpy as np
x = np.array([[1.1, 0.9, 1e-6]] * 3)
print(x)
np.set_printoptions(suppress=True)
print(x)

Output:

[[1.1e+00 9.0e-01 1.0e-06]
 [1.1e+00 9.0e-01 1.0e-06]
 [1.1e+00 9.0e-01 1.0e-06]]
[[1.1      0.9      0.000001]
 [1.1      0.9      0.000001]
 [1.1      0.9      0.000001]]

Example 4

import numpy as np
x = np.array([[1.1, 0.9, 1e-6]] * 3)
print(x)
np.set_printoptions(formatter={'float': '{: 0.3f}'.format})
print(x)

Output:

[[1.1e+00 9.0e-01 1.0e-06]
 [1.1e+00 9.0e-01 1.0e-06]
 [1.1e+00 9.0e-01 1.0e-06]]
[[ 1.100  0.900  0.000]
 [ 1.100  0.900  0.000]
 [ 1.100  0.900  0.000]]

Example 5

import numpy as np
x = np.random.random((3, 3)) * 9
print(np.array2string(x, formatter={'float_kind': '{0:.3f}'.format}))

Output:

[[3.479 1.490 5.674]
 [6.043 7.025 1.597]
 [0.261 8.530 2.298]]

8提取 Numpy 矩阵的前 n 列

列范围1

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

Output:

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

列范围2

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

Output:

[[ 0  2  3  5]
 [ 4  6  7  5]
 [ 8 10 11  4]]

列范围3

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

Output:

[[ 0  3  8]
 [ 4  7  5]
 [ 8 11  5]]

特定列

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

Output:

[1 5 9]

特定行和列

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

Output:

[[1 2]
 [5 6]]

9从 NumPy 数组中删除值

Example 1

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

Output:

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

Example 2

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

Output:

[1 2 3 4]
[1 3 4]

Example 3

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

Output:

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

10将满足条件的项目替换为 Numpy 数组中的另一个值

将所有大于 30 的元素替换为 0

import numpy as np
the_array = np.array([49, 7, 44, 27, 13, 35, 71])
an_array = np.where(the_array > 30, 0, the_array)
print(an_array)

Output:

[ 0  7  0 27 13  0  0]

将大于 30 小于 50 的所有元素替换为 0

import numpy as np
the_array = np.array([49, 7, 44, 27, 13, 35, 71])
an_array = np.where((the_array > 30) & (the_array < 50), 0, the_array)
print(an_array)

Output:

[ 0  7  0 27 13  0 71]

给所有大于 40 的元素加 5

import numpy as np
the_array = np.array([49, 7, 44, 27, 13, 35, 71])
an_array = np.where(the_array > 40, the_array + 5, the_array)
print(an_array)

Output:

[54  7 49 27 13 35 76]

用 Nan 替换数组中大于 25 的所有元素

import numpy as np
the_array = np.array([49, 7, 44, 27, 13, 35, 71])
an_array = np.where(the_array > 25, np.NaN, the_array)
print(an_array)

Output:

[nan  7. nan nan 13. nan nan]

将数组中大于 25 的所有元素替换为 1，否则为 0

import numpy as np
the_array = np.array([49, 7, 44, 27, 13, 35, 71])
an_array = np.asarray([0 if val < 25 else 1 for val in the_array])
print(an_array)

Output:

[1 0 1 1 0 1 1]

11对 NumPy 数组中的所有元素求和

import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
newarr = arr.reshape(4, 3)
column_sums = newarr[:, :].sum()
print(column_sums)

Output:

12创建 3D NumPy 零数组

import numpy as np
the_3d_array = np.zeros((2, 2, 2))
print(the_3d_array)

Output:

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

13计算 NumPy 数组中每一行的总和

import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
newarr = arr.reshape(4, 3)
print(newarr)
column_sums = newarr.sum(axis=1)
print(column_sums)

Output:

[[ 1  2  3]
 [ 4  5  6]
 [ 7  8  9]
 [10 11 12]]
[ 6 15 24 33]

14打印没有科学记数法的 NumPy 数组

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]

15获取numpy数组中所有NaN值的索引列表

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

Output:

[ True False False False]

16检查 NumPy 数组中的所有元素都是 NaN

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

Output:

False
True

17将列表添加到 Python 中的 NumPy 数组

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

Output:

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

18在 Numpy 中抑制科学记数法

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]

19将具有 12 个元素的一维数组转换为 3 维数组

Example 1

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

Output:

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

Example 2

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

Output:

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

Example 3

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

Output:

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

Example 4

import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
newarr = arr.reshape(-1, 2).T.reshape(-1, 3, 4)
print(newarr)

Output:

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

20检查 NumPy 数组是否为空

import numpy as np
the_array = np.array([])
is_empty = the_array.size == 0
print(is_empty)
the_array = np.array([1, 2, 3])
is_empty = the_array.size == 0
print(is_empty)

Output:

True
False

21在 Python 中重塑 3D 数组

Example 1

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

Output:

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

Example 2

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

Output:

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

Example 3

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

Output:

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

Example 4

import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
newarr = arr.reshape(-1, 2).T.reshape(-1, 3, 4)
print(newarr)

Output:

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

22在 Python 中重复 NumPy 数组中的一列

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

Output:

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

23在 NumPy 数组中找到跨维度的平均值

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.]

24检查 NumPy 数组中的 NaN 元素

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

25格式化 NumPy 数组的打印方式

Example 1

import numpy as np
x = np.array([[1.1, 0.9, 1e-6]] * 3)
print(x)
print(np.array_str(x, precision=1, suppress_small=True))

Output:

[[1.1e+00 9.0e-01 1.0e-06]
 [1.1e+00 9.0e-01 1.0e-06]
 [1.1e+00 9.0e-01 1.0e-06]]
[[1.1 0.9 0. ]
 [1.1 0.9 0. ]
 [1.1 0.9 0. ]]

Example 2

import numpy as np
x = np.random.random(10)
print(x)
np.set_printoptions(precision=3)
print(x)

Output:

[0.53828153 0.75848226 0.50046312 0.94723558 0.50415632 0.13899663
 0.80301141 0.40887872 0.24837485 0.83008548]
[0.538 0.758 0.5   0.947 0.504 0.139 0.803 0.409 0.248 0.83 ]

Example 3

import numpy as np
x = np.array([[1.1, 0.9, 1e-6]] * 3)
print(x)
np.set_printoptions(suppress=True)
print(x)

Output:

[[1.1e+00 9.0e-01 1.0e-06]
 [1.1e+00 9.0e-01 1.0e-06]
 [1.1e+00 9.0e-01 1.0e-06]]
[[1.1      0.9      0.000001]
 [1.1      0.9      0.000001]
 [1.1      0.9      0.000001]]

Example 4

import numpy as np
x = np.array([[1.1, 0.9, 1e-6]] * 3)
print(x)
np.set_printoptions(formatter={'float': '{: 0.3f}'.format})
print(x)

Output:

[[1.1e+00 9.0e-01 1.0e-06]
 [1.1e+00 9.0e-01 1.0e-06]
 [1.1e+00 9.0e-01 1.0e-06]]
[[ 1.100  0.900  0.000]
 [ 1.100  0.900  0.000]
 [ 1.100  0.900  0.000]]

Example 5

import numpy as np
x = np.random.random((3, 3)) * 9
print(np.array2string(x, formatter={'float_kind': '{0:.3f}'.format}))

Output:

[[3.479 1.490 5.674]
 [6.043 7.025 1.597]
 [0.261 8.530 2.298]]

26乘以Numpy数组的每个元素

Example 1

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

Output:

Example 2

import numpy as np
the_array = np.array([[1, 2, 3], [1, 2, 3]])
prod = np.prod(the_array, 0)
print(prod)

Output:

[1 4 9]

Example 3

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

Output:

[6, 6]

Example 4

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

Output:

27在 NumPy 中生成随机数

Example 1

import numpy as np
# create 2D array
the_array = np.arange(50).reshape((5, 10))
# row manipulation
np.random.shuffle(the_array)
# display random rows
rows = the_array[:2, :]
print(rows)

Output:

[[10 11 12 13 14 15 16 17 18 19]
 [ 0  1  2  3  4  5  6  7  8  9]]

Example 2

import random
import numpy as np
# create 2D array
the_array = np.arange(16).reshape((4, 4))
# row manipulation
rows_id = random.sample(range(0, the_array.shape[1] - 1), 2)
# display random rows
rows = the_array[rows_id, :]
print(rows)

Output:

[[ 4  5  6  7]
 [ 8  9 10 11]]

Example 3

import numpy as np
# create 2D array
the_array = np.arange(16).reshape((4, 4))
number_of_rows = the_array.shape[0]
random_indices = np.random.choice(number_of_rows,
                                  size=2,
                                  replace=False)
# display random rows
rows = the_array[random_indices, :]
print(rows)

Output:

[[ 4  5  6  7]
 [ 8  9 10 11]]

28Numpy 将具有 8 个元素的一维数组转换为 Python 中的二维数组

4 行 2 列

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

Output:

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

2 行 4 列

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

Output:

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

29在 Python 中使用 numpy.all()

import numpy as np
thelist = [[True, True], [True, True]]
thebool = np.all(thelist)
print(thebool)
thelist = [[False, False], [False, False]]
thebool = np.all(thelist)
print(thebool)
thelist = [[True, False], [True, False]]
thebool = np.all(thelist)
print(thebool)

Output:

True

30将一维数组转换为二维数组

4 行 2 列

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

Output:

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

2 行 4 列

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

Output:

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

Example 3

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

Output:

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

通过添加新轴将一维数组转换为二维数组

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

Output:

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

Example 5

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

Output:

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

31计算 NumPy 数组中唯一值的频率

import numpy as np
the_array = np.array([9, 7, 4, 7, 3, 5, 9])
frequencies = np.asarray((np.unique(the_array, return_counts=True))).T
print(frequencies)

Output:

[[3 1]
 [4 1]
 [5 1]
 [7 2]
 [9 2]]

32在一列中找到平均值

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.]

33在 Numpy 数组的长度、维度、大小

Example 1

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

Output:

1
(12,)
2
(3, 4)
3
(1, 3, 4)

Example 2

import numpy as np
arr = np.array([[1, 1, 1, 0], [0, 5, 0, 1], [2, 1, 3, 10]])
print(np.info(arr))

Output:

class:  ndarray
shape:  (3, 4)
strides:  (16, 4)
itemsize:  4
aligned:  True
contiguous:  True
fortran:  False
data pointer: 0x25da9fd5710
byteorder:  little
byteswap:  False
type: int32
None

34在 NumPy 数组中找到最大值的索引

import numpy as np
the_array = np.array([11, 22, 53, 14, 15])
max_index_col = np.argmax(the_array, axis=0)
print(max_index_col)

Output:

35按降序对 NumPy 数组进行排序

按降序对 Numpy 进行排序

import numpy as np
the_array = np.array([49, 7, 44, 27, 13, 35, 71])
sort_array = np.sort(the_array)[::-1]
print(sort_array)

Output:

[71 49 44 35 27 13  7]

按降序对 2D Numpy 进行排序

import numpy as np
the_array = np.array([[49, 7, 4], [27, 13, 35]])
sort_array = np.sort(the_array)[::1]
print(sort_array)

Output:

[[ 4  7 49]
 [13 27 35]]

按降序对 Numpy 进行排序

import numpy as np
the_array = np.array([[49, 7, 4], [27, 13, 35], [12, 3, 5]])
a_idx = np.argsort(-the_array)
sort_array = np.take_along_axis(the_array, a_idx, axis=1)
print(sort_array)

Output:

[[49  7  4]
 [35 27 13]
 [12  5  3]]

36Numpy 从二维数组中获取随机的一组行

Example 1

import numpy as np
# create 2D array
the_array = np.arange(50).reshape((5, 10))
# row manipulation
np.random.shuffle(the_array)
# display random rows
rows = the_array[:2, :]
print(rows)

Output:

[[10 11 12 13 14 15 16 17 18 19]
 [ 0  1  2  3  4  5  6  7  8  9]]

Example 2

import random
import numpy as np
# create 2D array
the_array = np.arange(16).reshape((4, 4))
# row manipulation
rows_id = random.sample(range(0, the_array.shape[1] - 1), 2)
# display random rows
rows = the_array[rows_id, :]
print(rows)

Output:

[[ 4  5  6  7]
 [ 8  9 10 11]]

Example 3

import numpy as np
# create 2D array
the_array = np.arange(16).reshape((4, 4))
number_of_rows = the_array.shape[0]
random_indices = np.random.choice(number_of_rows,
                                  size=2,
                                  replace=False)
# display random rows
rows = the_array[random_indices, :]
print(rows)

Output:

[[ 4  5  6  7]
 [ 8  9 10 11]]

37将 Numpy 数组转换为 JSON

import numpy as np
the_array = np.array([[49, 7, 44], [27, 13, 35], [27, 13, 35]])
lists = the_array.tolist()
print([{'x': x[0], 'y': x[1], 'z': x[2]} for i, x in enumerate(lists)])

Output:

[{'x': 49, 'y': 7, 'z': 44}, {'x': 27, 'y': 13, 'z': 35}, {'x': 27, 'y': 13, 'z': 35}]

38检查 NumPy 数组中是否存在值

import numpy as np
the_array = np.array([[1, 2], [3, 4]])
n = 3
if n in the_array:
    print(True)
else:
    print(False)

Output:

True
False

39创建一个 3D NumPy 数组

import numpy as np
the_3d_array = np.ones((2, 2, 2))
print(the_3d_array)

Output:

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

40在numpy中将字符串数组转换为浮点数数组

import numpy as np
string_arr = np.array(['1.1', '2.2', '3.3'])
float_arr = string_arr.astype(np.float64)
print(float_arr)

Output:

[1.1 2.2 3.3]

41从 Python 的 numpy 数组中随机选择

Example 1

import numpy as np
# create 2D array
the_array = np.arange(50).reshape((5, 10))
# row manipulation
np.random.shuffle(the_array)
# display random rows
rows = the_array[:2, :]
print(rows)

Output:

[[10 11 12 13 14 15 16 17 18 19]
 [ 0  1  2  3  4  5  6  7  8  9]]

Example 2

import random
import numpy as np
# create 2D array
the_array = np.arange(16).reshape((4, 4))
# row manipulation
rows_id = random.sample(range(0, the_array.shape[1] - 1), 2)
# display random rows
rows = the_array[rows_id, :]
print(rows)

Output:

[[ 4  5  6  7]
 [ 8  9 10 11]]

Example 3

import numpy as np
# create 2D array
the_array = np.arange(16).reshape((4, 4))
number_of_rows = the_array.shape[0]
random_indices = np.random.choice(number_of_rows,
                                  size=2,
                                  replace=False)
# display random rows
rows = the_array[random_indices, :]
print(rows)

Output:

[[ 4  5  6  7]
 [ 8  9 10 11]]

42不截断地打印完整的 NumPy 数组

import numpy as np
np.set_printoptions(threshold=np.inf)
the_array = np.arange(100)
print(the_array)

Output:

[ 0  1  2  3  4  5  6  7  8  9 10 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 36 37 38 39 40 41 42 43 44 45 46 47
 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71
 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95
 96 97 98 99]

43将 Numpy 转换为列表

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

Output:

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

44将字符串数组转换为浮点数数组

import numpy as np
string_arr = np.array(['1.1', '2.2', '3.3'])
float_arr = string_arr.astype(np.float64)
print(float_arr)

Output:

[1.1 2.2 3.3]

45计算 NumPy 数组中每一列的总和

import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
newarr = arr.reshape(4, 3)
print(newarr)
column_sums = newarr.sum(axis=0)
print(column_sums)

Output:

[[ 1  2  3]
 [ 4  5  6]
 [ 7  8  9]
 [10 11 12]]
[22 26 30]

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]