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Python numpy ndarray运算

2023-7-20 myluzh Python

0x01 逻辑运算 import numpy as np # 生成40-100的10行5列数据 arr1 = np.random.randint(40, 100, (10, 5)) """ #查看下arr1 arr1 Out[3]: array([[95, 65, 74, 76, 64], [95, 62, 74, 81, 59], [48, 93, 87, 40, 63], [99, 68, 57, 95, 51], [75, 78, 43, 50, 49], [75, 74, 86, 50, 98], [62, 61, 53, 40, 73], [58, 63, 99, 76, 85], [52, 44, 65, 77, 51], [90, 74, 93, 78, 46]]) """ # 取6行开始取,每行取0-5列 arr2 = arr1[6:, 0:5] """ # 查看下arr2 arr2 Out[4]: array([[62, 61...

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标签: python ndarray

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Python numpy ndarray介绍

2023-7-13 myluzh Python

0x01 ndarray属性 属性名字 属性解释 ndarray.shape 数组维度的元组 ndarray.ndim 数组维数 ndarray.size 数组中的元素数量 ndarray.itemsize 个数组元素的长度 (字节) ndarray.dtype 数组元素的类型 0x02 ndarray形状 import numpy as np a = np.array([[1,2,3],[4,5,6]]) b = np.array([1,2,3,4]) c = np.array([[[1,2,3], [4,5,6]],[[1,2,3],...

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标签: python numpy ndarray

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Python numpy中ndarray与原生list对比

2023-7-13 myluzh Python

0x01 ndarray与list运行时长对比 import random import numpy as np import timeit a = [] for i in range(100000000): a.append(random.random()) def sum_list(): return sum(a) def sum_numpy(): b = np.array(a) return np.sum(b) time_list = timeit.timeit(sum_list, number=1) time_numpy = timeit.timeit(sum_numpy, number=1) print("List sum time:", time_list) print("Numpy sum time:", time_numpy) List sum time: 6.816154757 Numpy sum time: 4.999972694 0x02 ndarray的优势 (1)ndarray在存储数据的时候,数据...

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标签: numpy ndarray

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