<details> <summary>高级命令</summary> importnumpyasnp# 创建一个示例数组array_data=np.arange(100).reshape((10,10))# 保存为二进制文件np.save('array_data.npy',array_data)# 保存多个数组np.savez('arrays_data.npz',array1=array_data,array2=array_data) 1. 2. 3. 4. 5. 6. 7. 8. 9....
array([q.close for q in quotes]).astype(np.float) print(close.shape) 使用对数收益率作为指标来计算不同股票之间的相似度。 我们正在要做的是计算数据点的欧几里德距离: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 logreturns = np.diff(np.log(close)) print(logreturns.shape) logreturns_...
我们可以通过以下代码块进行单元测试。 # 单元测试代码块importunittestimportnumpyasnpclassTestDataProcessing(unittest.TestCase):deftest_preprocess_data(self):data=np.array([[1,2],[3,np.inf],[5,6]])processed_data=preprocess_data(data)self.assertTrue(np.all(np.isfinite(processed_data)),"应确保...
start:the first value of the array stop:where the array ends step:the increment or decrement dtype:the type of the elements of the array You also learned how NumPyarange()compares with the Python built-in classrangewhen you’re creating sequences and generating values to iterate over. ...
print(np.sum(my_array, axis = 1)) # Get sum of array rows # [ 6 15]Video, Further Resources & SummaryHave a look at the following video on my YouTube channel. I illustrate the Python programming codes of this post in the video....
classStatisticalArray(CustomArray):defmean(self):# 计算均值returnnp.mean(self)defstd_dev(self):# 计算标准差returnnp.std(self)defsummary(self):# 返回统计摘要return{"mean":self.mean(),"std_dev":self.std_dev(),"min":np.min(self),"max":np.max(self),}# 创建带统计功能的数组 ...
Weeks indices initial [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19]Weeks indices after split [array([0, 1, 2, 3, 4], dtype=int64), array([5, 6, 7, 8, 9], dtype=int64), array([10, 11, 12, 13, 14], dtype=int64), array([15, 16, 17, 18, 19],...
Weeks indices after split [array([0,1, 2, 3, 4], dtype=int64), array([5, 6, 7, 8, 9], dtype=int64), array([10, 11, 12, 13, 14], dtype=int64), array([15, 16, 17, 18, 19], dtype=int64)] NumPy中,数组的维度也被称作轴。apply_along_axis 函数会调用另外一个由我们给出...
整理后,得到:[array([[0],[3],[6]]), array([[1], [4],[7]]), array([[2],[5],[8]])]也就是将0,3,6归为一组,注意:都是分别用"[ ]"和","分开的。 2、深度分割 import numpy as np #导入numpy包,并另命令为np c = np.array(np.arange(27).reshape(3, 3, 3)) #创建一个...
array_1d = np.array([1, 2, 3, 4, 5]) print(f"1维数组:\n{array_1d}") print(f"形状 (shape): {array_1d.shape}") # 输出: (5,) (注意:只有一个元素,表示1维数组,长度为 5) array_2d = np.array([[1, 2, 3], [4, 5, 6]]) print(f"2维数组:\n{array_2d}") print(f...