importnumpyasnp# 创建一个空数组arr=np.empty((5,))# 使用for-loop给数组赋值foriinrange(5):arr[i]=iprint(arr) 上述代码中,首先导入了numpy库,并使用np.empty()函数创建了一个空数组arr,该数组有5个元素。然后使用for循环遍历索引值i,并将其赋值给数组的对应位置。最后打印输出数组arr。
1importnumpy as np2myarray=np.array(mylist)3myarray 6- Use a “for loop” to find the maximum value in “mylist” 1maxvalue =mylist[0]2foriinrange(len_mylist):3ifmaxvalue <mylist[i]:4maxvalue =mylist[i]5print('The maximum value is', maxvalue) 7- Use a “for loop” to ...
import timeimport numpy as npx1 = np.random.rand(1000000)x2 = np.random.rand(1000000)##使用循环计算向量点积tic = time.process_time()dot = 0for i in range(len(x1)): dot+= x1[i]*x2[i]toc = time.process_time()print ("dot = " + str(dot) + "\n for loop--- Computation ti...
比起Python的内置序列,NumPy数组使用的内存更少。 NumPy可以在整个数组上执行复杂的计算,而不需要Python的for循环。要搞明白具体的性能差距,考察一个包含一百万整数的数组,和一个等价的Python列表:In [7]: import numpy as np In [8]: my_arr = np.arange(1000000) In [9]: my_list = list(range(...
In [240]: from random import normalvariate In [241]: N = 1000000 In [242]: %timeit samples = [normalvariate(0, 1) for _ in range(N)] 1.77 s +- 126 ms per loop (mean +- std. dev. of 7 runs, 1 loop each) In [243]: %timeit np.random.normal(size=N) 61.7 ms +- 1.32 ...
简单的 for 循环如下 sum=0forxinrange(10):sum+=x 但我们知道 这种 1,2,3...n 相加有公式的...
In [1]: L = range(1000) In [2]: %timeit [i**2 for i in L] 1000 loops, best of 3: 403 usper loopIn [3]: a = np.arange(1000) In [4]: %timeit a**2 100000 loops, best of 3: 12.7 us per loop NumPy 参考文档
X_train = np.array([standardize(X_raw_train[row,:], X_scalers[row]) forrow in range(X_num_row)]) y_scalers = [get_scaler(y_raw_train[row,:]) for row inrange(y_num_row)] y_train = np.array([standardize(y_raw_train[row,:], y_scalers[row]) forrow in range(y_num_ro...
foriinrange(len(x1)): dot+= x1[i]*x2[i] toc = time.process_time() print("dot = "+ str(dot) +"n for loop--- Computation time = "+ str(1000*(toc - tic)) +"ms") ##使用numpy函数求点积 tic = time.process_time() dot...
In [240]: from random import normalvariate In [241]: N = 1000000 In [242]: %timeit samples = [normalvariate(0, 1) for _ in range(N)] 1.77 s +- 126 ms per loop (mean +- std. dev. of 7 runs, 1 loop each) In [243]: %timeit np.random.normal(size=N) 61.7 ms +- 1.32 ...