如下所示：

# -*- coding: utf-8 -*-# @Time  : 2018/5/17 15:05# @Author : Sizer# @Site  : # @File  : test.py# @Software: PyCharmimport timeimport numpy as np# data = np.array([# [5.0, 3.0, 4.0, 4.0, 0.0],# [3.0, 1.0, 2.0, 3.0, 3.0],# [4.0, 3.0, 4.0, 3.0, 5.0],# [3.0, 3.0, 1.0, 5.0, 4.0],# [1.0, 5.0, 5.0, 2.0, 1.0]# ])data = np.random.random((1000, 1000))print(data.shape)start_time = time.time()# avg = [float(np.mean(data[i, :])) for i in range(data.shape[0])]# print(avg)start_time = time.time()avg = []for i in range(data.shape[0]):  sum = 0  cnt = 0  for rx in data[i, :]:   if rx > 0:     sum += rx     cnt += 1  if cnt > 0:   avg.append(sum/cnt)  else:   avg.append(0)end_time = time.time()print("op 1:", end_time - start_time)start_time = time.time()avg = []isexist = (data > 0) * 1for i in range(data.shape[0]):  sum = np.dot(data[i, :], isexist[i, :])  cnt = np.sum(isexist[i, :])  if cnt > 0:   avg.append(sum / cnt)  else:   avg.append(0)end_time = time.time()print("op 2:", end_time - start_time)## print(avg)factor = np.mat(np.ones(data.shape[1])).T# print("facotr :")# print(factor)exist = np.mat((data > 0) * 1.0)# print("exist :")# print(exist)# print("res  :")res = np.array(exist * factor)end_time = time.time()print("op 3:", end_time-start_time)start_time = time.time()exist = (data > 0) * 1.0factor = np.ones(data.shape[1])res = np.dot(exist, factor)end_time = time.time()print("op 4:", end_time - start_time)

經過多次驗證, 第四種實現方式的事件效率最高！

以上這篇python 判斷矩陣中每行非零個數的方法就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支持武林站長站。