32岁零基础学Python量化 第4周:NumPy核心语法自测5题,从掩码到模拟抛硬币

arr = np.random.randint(1,101,(10,10))arr[:,0] = 0arr[:,-1] = 0arr[0,:] = 0arr[-1,:] = 0print(arr)arr1 = arr[1:-1,1:-1] #切片print("内部最大值:",np.max(arr1))print("内部最小值:",np.min(arr1))  print("内部平均值:",np.mean(arr1))print("大于50的元素个数:",np.sum(arr>50)) # 统计整个矩阵中大于 50 的元素的个数 

scores = np.random.normal(75,10,(30,5))#1.计算课程和平均分标准差 mean_per_course = np.mean(scores,axis = 0)std_per_course = np.std(scores,axis = 0)print('每门课程平均分：',mean_per_course)print('每门课程标准差：',std_per_course)#2.标准化：normalized = (scores-mean_per_course)/std_per_courseprint(normalized)#3.裁剪到[-3, 3]normalized_clipped = np.clip(normalized,-3,3)print(normalized_clipped)#4.输出标准化且裁剪后形状和前两行数据 print(normalized_clipped.shape)  #shape在NumPy中是一个属性，不是方法，因此.shape不需要加括号 print(normalized_clipped[:2,:])

data = np.random.randint(0,100,200)mask = (data%3==0)&(data%5!=0)print('满足条件的索引：',np.where(mask))data[mask] = -1print(data)mask1 = data>50print('大于50的个数是：',np.sum(mask1))print('大于50的和是：',np.sum(data[mask1]))

a = np.random.rand(3,4)b = np.random.rand(4,2)c = np.dot(a,b)a = a.reshape(1,12) #这里必须赋值因为直接reshape不改变原数组的值b = b.reshape(8,1) #这里必须赋值因为直接reshape不改变原数组的值d= a*bprint(d)

import numpy as nparr1 = np.random.randint(0,2,10000)      print(np.sum(arr1))num = 0active = 0time = 0for num in range(0,len(arr1)-1):    b = arr1[num]+arr1[(num+1)]    if b==2:        active += 1    else:        if active >=4:            time += 1        active = 0print(time)

import numpy as np# 1. 抛硬币结果flips = np.random.randint(0, 2, 10000)# 2. 查找连续5个及以上正面的次数# 方法：对数组进行差分，标记连续块的开始和结束# 为方便处理，在头尾加0padded = np.concatenate(([0], flips, [0]))diff = np.diff(padded)starts = np.where(diff == 1)[0]   # 从0变1的位置ends = np.where(diff == -1)[0]    # 从1变0的位置lengths = ends - startscount_long = np.sum(lengths >= 5)print("出现连续5个及以上正面的次数:", count_long)# 3. 正面比例与0.5的误差prob_heads = np.mean(flips)error = abs(prob_heads - 0.5)print("正面比例:", prob_heads)print("与0.5的绝对误差:", error)