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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:
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]
