Numpy 是什么就不太过多介绍了，懂的人都懂！
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[TOC]
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 npthe_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]在 Python 中找到 Numpy 数组的维度
import numpy as nparr = 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:
123两个条件过滤 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:
[]对最初一列求和
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最初一列的总和
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]从 Nump y数组中随机抉择两行
Example 1
import numpy as np # create 2D arraythe_array = np.arange(50).reshape((5, 10)) # row manipulationnp.random.shuffle(the_array) # display random rowsrows = 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 randomimport numpy as np # create 2D arraythe_array = np.arange(16).reshape((4, 4)) # row manipulationrows_id = random.sample(range(0, the_array.shape[1] - 1), 2) # display random rowsrows = the_array[rows_id, :]print(rows)Output:
[[ 4 5 6 7] [ 8 9 10 11]]Example 3
import numpy as np # create 2D arraythe_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 rowsrows = the_array[random_indices, :]print(rows)Output:
[[ 4 5 6 7] [ 8 9 10 11]]以给定的精度丑陋地打印一个 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)) * 9print(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]]提取 Numpy 矩阵的前 n 列
列范畴1
import numpy as npthe_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]]列范畴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]]列范畴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]]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]将满足条件的我的项目替换为 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]对 NumPy 数组中的所有元素求和
import numpy as nparr = 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创立 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.]]]计算 NumPy 数组中每一行的总和
import numpy as nparr = 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]打印没有迷信记数法的 NumPy 数组
import numpy as npnp.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]获取numpy数组中所有NaN值的索引列表
import numpy as npthe_array = np.array([np.nan, 2, 3, 4])array_has_nan = np.isnan(the_array)print(array_has_nan)Output:
[ True False False False]查看 NumPy 数组中的所有元素都是 NaN
import numpy as npthe_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:
FalseTrue将列表增加到 Python 中的 NumPy 数组
import numpy as npthe_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]]在 Numpy 中克制迷信记数法
import numpy as npnp.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]将具备 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 nparr = 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]]]查看 NumPy 数组是否为空
import numpy as npthe_array = np.array([])is_empty = the_array.size == 0print(is_empty)the_array = np.array([1, 2, 3])is_empty = the_array.size == 0print(is_empty)Output:
TrueFalse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]]]在 Python 中反复 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]]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.]查看 NumPy 数组中的 NaN 元素
import numpy as npthe_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:
TrueFalse格式化 NumPy 数组的打印形式
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)) * 9print(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]]乘以Numpy数组的每个元素
Example 1
import numpy as npthe_array = np.array([[1, 2, 3], [1, 2, 3]])prod = np.prod(the_array)print(prod)Output:
36Example 2
import numpy as npthe_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 npthe_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 npthe_array = np.array([1, 2, 3])prod = np.prod(the_array)print(prod)Output:
6在 NumPy 中生成随机数
Example 1
import numpy as np# create 2D arraythe_array = np.arange(50).reshape((5, 10))# row manipulationnp.random.shuffle(the_array)# display random rowsrows = 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 randomimport numpy as np # create 2D arraythe_array = np.arange(16).reshape((4, 4)) # row manipulationrows_id = random.sample(range(0, the_array.shape[1] - 1), 2) # display random rowsrows = the_array[rows_id, :]print(rows)Output:
[[ 4 5 6 7] [ 8 9 10 11]]Example 3
import numpy as np # create 2D arraythe_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 rowsrows = the_array[random_indices, :]print(rows)Output:
[[ 4 5 6 7] [ 8 9 10 11]]Numpy 将具备 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]]在 Python 中应用 numpy.all()
import numpy as npthelist = [[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]]通过增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]]计算 NumPy 数组中惟一值的频率
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))).Tprint(frequencies)Output:
[[3 1] [4 1] [5 1] [7 2] [9 2]]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.]在 Numpy 数组的长度、维度、大小
Example 1
import numpy as nparr = 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: ndarrayshape: (3, 4)strides: (16, 4)itemsize: 4aligned: Truecontiguous: Truefortran: Falsedata pointer: 0x25da9fd5710byteorder: littlebyteswap: Falsetype: int32None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]]Numpy 从二维数组中获取随机的一组行
Example 1
import numpy as np# create 2D arraythe_array = np.arange(50).reshape((5, 10))# row manipulationnp.random.shuffle(the_array)# display random rowsrows = 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 randomimport numpy as np # create 2D arraythe_array = np.arange(16).reshape((4, 4)) # row manipulationrows_id = random.sample(range(0, the_array.shape[1] - 1), 2) # display random rowsrows = the_array[rows_id, :]print(rows)Output:
[[ 4 5 6 7] [ 8 9 10 11]]Example 3
import numpy as np # create 2D arraythe_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 rowsrows = the_array[random_indices, :]print(rows)Output:
[[ 4 5 6 7] [ 8 9 10 11]]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}]查看 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:
TrueFalse创立一个 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.]]]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]从 Python 的 numpy 数组中随机抉择
Example 1
import numpy as np # create 2D arraythe_array = np.arange(50).reshape((5, 10)) # row manipulationnp.random.shuffle(the_array) # display random rowsrows = 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 randomimport numpy as np # create 2D arraythe_array = np.arange(16).reshape((4, 4)) # row manipulationrows_id = random.sample(range(0, the_array.shape[1] - 1), 2) # display random rowsrows = the_array[rows_id, :]print(rows)Output:
[[ 4 5 6 7] [ 8 9 10 11]]Example 3
import numpy as np # create 2D arraythe_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 rowsrows = the_array[random_indices, :]print(rows)Output:
[[ 4 5 6 7] [ 8 9 10 11]]不截断地打印残缺的 NumPy 数组
import numpy as npnp.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]将 Numpy 转换为列表
import numpy as npthe_array = np.array([[1, 2], [3, 4]])print(the_array.tolist())Output:
[[1, 2], [3, 4]]将字符串数组转换为浮点数数组
import numpy as npstring_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]计算 NumPy 数组中每一列的总和
import numpy as nparr = 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]应用 Python 中的值创立 3D NumPy 数组
import numpy as npthe_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]]]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 = Truearr[:x.shape[0], :x.shape[1], 0] = xarr[:y.shape[0], :y.shape[1], 1] = yarr[:z.shape[0], :z.shape[1], 2] = zprint(arr.mean(axis=2))Output:
[[3.0 2.0 3.0] [4.666666666666667 4.0 5.0]]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.]]向 NumPy 数组增加一列
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]]在 Numpy Array 中打印浮点值时如何克制迷信记数法
import numpy as npnp.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]Numpy 将 1d 数组重塑为 1 列的 2d 数组
import numpy as nparr = 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]]初始化 NumPy 数组
import numpy as npthearray = np.array([[1, 2], [3, 4], [5, 6]])print(thearray)Output:
[[1 2] [3 4] [5 6]]创立反复一行
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]]将 NumPy 数组附加到 Python 中的空数组
import numpy as npthe_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.]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检测 NumPy 数组是否蕴含至多一个非数字值
import numpy as npthe_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:
TrueFalse在 Python 中附加 NumPy 数组
import numpy as npthe_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]]应用 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:
TrueFalse取得 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]]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:
int32float64<U1<U5float64['Canad' 'Austr']取得NumPy数组的形态
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)取得 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) # 3Output:
123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]]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]]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]]应用 arange 函数创立 NumPy 数组
import numpy as np array1d = np.arange(5) # 1 row and 5 columnsprint(array1d) array1d = np.arange(0, 12, 2) # 1 row and 6 columnsprint(array1d) array2d = np.arange(0, 12, 2).reshape(2, 3) # 2 rows 3 columnsprint(array2d) array3d = np.arange(9).reshape(3, 3) # 3 rows and columnsprint(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]]应用 linspace() 创立 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.]]NumPy 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]创立 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.]]NumPy 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.]]NumPy 残缺数组示例
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]]NumPy 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.]]NumPy 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]]NumPy 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]]NumPy 索引示例
import numpy as np array1d = np.array([1, 2, 3, 4, 5, 6])print(array1d[0]) # Get first valueprint(array1d[-1]) # Get last valueprint(array1d[3]) # Get 4th value from firstprint(array1d[-5]) # Get 5th value from last # Get multiple valuesprint(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 colprint(array2d[0, 1]) # Get first row second colprint(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 colprint(array2d[2, 1]) # Get third row second colOutput:
1642[1 6]----------[[1 2 3] [4 5 6] [7 8 9]]----------123258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]]]123456789101112NumPy 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 elementsOutput:
[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]NumPy 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 arrayOutput:
----------[[1 2] [4 5] [7 8]]----------[[4 5 6] [7 8 9]]----------[[9 8 7] [6 5 4] [3 2 1]]翻转 NumPy 数组的轴程序
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]]NumPy 数组的连贯和重叠
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]]NumPy 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]]----------------------------------------NumPy 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]]--------------------NumPy 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]]----------------------------------------NumPy 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.]]----------------------------------------NumPy 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: 35.0Std: 17.07825127659933Var: 291.6666666666667Sum: 210Prod: 720000000Where 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 3after = np.where(before < 4, before * 2, before * 3) print(after)Output:
[[ 2 4 6] [12 15 18]]Select 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 3after = np.select([before < 4, before], [before * 2, before * 3]) print(after)Output:
[[ 2 4 6] [12 15 18]]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]]NumPy 逻辑操作，用于依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]]------------------------------规范汇合操作的 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:
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