numpy.ptp(a, axis=None, out=None, keepdims=np._NoValue)Range of values (maximum - minimum) along an axis. The name of the function comes from the acronym for ‘peak to peak’. 【例】计算极差 import numpy as np np.random.seed(20201124) x = np.random.randint(0, 20, size=[4,5]...
3, 5] insert_positions = np.searchsorted(sorted_array, values_to_insert) print(insert_positions)...
Out[105]: array([ 1.2464, 0.477 ]) # ~和!=都可以表示否定 In [106]: names != 'Bob' Out[106]: array([False, True, True, False, True, True, True]) In [107]: data[~(names == 'Bob')] Out[107]: array([[ 1.0072, -1.2962, 0.275 , 0.2289], [ 1.3529, 0.8864, -2.0016, -...
3 对列表应用clip函数进行截取 为了对比,对列表应用clip函数进行截取,代码如下: list1 = [1, 2, 3, 4] np.clip(list1, 2, 3) 得到结果: array([2, 2, 3, 3]) 可以发现,clip函数把高于3的数值截取为3,低于2的数值截取为2。 4 对数据框应用clip函数进行截取 为了对比,对数据框应用clip函数进行截取...
arange是Python内置函数range的数组版: 代码语言:javascript 复制 In [32]: np.arange(15) Out[32]: array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]) 表4-1列出了一些数组创建函数。由于NumPy关注的是数值计算,因此,如果没有特别指定,数据类型基本都是float64(浮点数)。表...
import numpy as np np.random.seed(0) def compute_reciprocals(values): output = np.empty(len(values)) for i in range(len(values)): output[i] = 1.0 / values[i] return output values = np.random.randint(1, 10, size=5) compute_reciprocals(values) 输出为:Output...
B_list = [B[B[:, 0] == i, 1] for i in range(A.shape[0])] >>> B_list [array([7, 6, 4, 9]), array([8, 2, 9, 3]), array([1, 5, 8]), array([1, 8, 9]), array([9, 0, 8, 9]), array([2, 6, 8, 7]), ...
b = np.array([1.j + 1, 2.j + 3]) print(b) Out: array([ 1.+1.j, 3.+2.j]) b.astype(int) /* d:\tools\python37\lib\site-packages\ipykernel_launcher.py:1: ComplexWarning: Casting complex values to real discards the imaginary part """Entry point for launching an IPython...
for i in range(1, N):atr[i] = (N - 1) * atr[i - 1] + truerange[i]atr[i] /= N 示例代码如下: import numpy as npfrom datetime import datetimedef datestr2num(s): #定义一个函数 return datetime.strptime(s.decode('ascii'),"%Y-%m-%d").date().weekday()dates, opens, high, ...
列表数据:ls=[1,2,3,4]numpy数组:ar=np.array([1,2,3,4]) 1. (2)容器内数据可更新 Mutable 列表的添加功能:ls.append()/ls.extend()numpy数组添加:ar.append() 1. (3)可索引搜索 Can be indexed 列表的索引:ls[0]numpy数组的索引:ar[0] ...