再肝3天，整理了90个NumPy案例，不能不收藏！
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前情回顾：
又肝了3天，整理了80个Python DateTime 例子，必须收藏！
- 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:
78
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:
36
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:
6
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:
2
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]
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