数据源：http://grouplens.org/dataset/movieslens,观影人员评价总体可视化的代码

is waiting...\n");
return;
}

buffer--;
printf("Consumer consumes a data item. Buffer size = %d\n", buffer);

if (buffer == 0) {
buffer_empty = 1;
}

if (buffer == buffer_size -你可以使用Python中的pandas和matplotlib库对该数据集进行处理和可视化。以下是一个简单的代码 1) {
buffer_full = 0;
}
}

int main() {
int i, time = 0, completed =示例：

import pandas as pd
import matplotlib.pyplot as plt

# 读取数据集
movies = pd.read_csv 0;
    float avg_turnaround_time = 0.0, avg_waiting_time = 0.0;
    PCB processes[MAX_PROCESS];

    // 初始化生产者和消费者进程
    processes[0].pid = 1;
    processes[0].('movies.csv')
ratings = pd.read_csv('ratings.csv')

# 合并数据集
movie_ratings = pd.merge(movies, ratings,arrival_time = 0;
    processes[0].burst_time = 0;
    processes[0].remaining_time = 0;
    processes[0].type = 0;
    processes[0].buffer = 0;

    processes[1].pid = 2;
 on='movieId')

# 按照电影评分计算平均评分和评分次数
mean_ratings = movie_ratings    processes[1].arrival_time = 0;
    processes[1].burst_time = 0;
    processes[1].remaining_time.groupby('title')['rating'].mean()
count_ratings = movie_ratings.groupby('title')['rating'].count()

# 绘制直方图
plt = 0;
    processes[1].type = 1;
    processes[1].buffer = 0;

    // 将生产.hist(mean_ratings, bins=50)
plt.xlabel('Average Rating')
plt.ylabel('Number of Movies')
plt.title('Distribution of Movie Ratings')
者和消费者进程加入到就绪队列中
    enqueue(processes[0]);
    enqueue(processes[1]);

    while (completed < MAX_PROCESS) {
        PCB process = dequeue();

        if (process.type == 0) {
            //plt.show()

# 绘制散点图
plt.scatter(mean_ratings, count_ratings)
plt.xlabel('Average Rating')
plt.ylabel('Number of 生产者进程
            printf("Time %d: Producer process %d is running...\n", time, process.pid);
            Ratings')
plt.title('Movie Ratings vs. Number of Ratings')
plt.show()

这段代码将读取movies.csv和ratings.csv produce();
} else {
// 消费者进程
printf("Time %d: Consumer process %d is running...\n", time, process.pid);
consume();
}

time++;

process.remaining_time--;

if (process.remaining_time > 两个文件，将它们合并成一个数据集，并计算每部电影的平均评分和评分次0) {
enqueue(process);
} else {
completed++;
process.completion_time = time;
process.turnaround_time =数。然后，它将使用matplotlib库绘制两个图表：一个是电影评分的分布直方图 process.completion_time - process.arrival_time;
process.waiting_time = process.turnaround_time - process.burst_time;
avg，另一个是电影评分与评分次数的散点图。这两个图表可以帮助你进行总_turnaround_time += process.turnaround_time;
avg_waiting_time += process.waiting_time;
}

if (front != rear)体可视化分析。