统计每个User的用餐人数和消费金额。利用info_user表中的'number_consumers','expenditure'两列来计算用餐人数和消费金额,使用groupby语句,对'USER_ID'进行分组,计算'number_consumers','expenditure'的sum和,新的数据存于info_user2中,使用info_user2 = info_user2.reset_index()语句,使得'USER_ID'也成为info_user2的一列,使用语句info_user2.columns = ['USER_ID', 'numbers', 'amount'],对三列数据进行重命名。打印info_user2,结果如下:
时间: 2024-01-30 07:01:51 浏览: 20
抱歉,我无法直接访问您的数据库或数据表。但是,我可以提供相应的SQL语句供您参考:
```
SELECT USER_ID, SUM(number_consumers) AS numbers, SUM(expenditure) AS amount
FROM info_user
GROUP BY USER_ID;
```
这条SQL语句会根据`USER_ID`对`info_user`表中的记录进行分组,并计算每个分组内`number_consumers`和`expenditure`的总和。结果会包括`USER_ID`、用餐人数和消费金额三列,其中用餐人数和消费金额的列会分别命名为`numbers`和`amount`。您可以使用类似以下的代码将查询结果存储到`info_user2`数据框中:
```python
import pandas as pd
# 连接数据库并查询数据
conn = create_engine('数据库连接信息')
info_user2 = pd.read_sql_query('SELECT USER_ID, SUM(number_consumers) AS numbers, SUM(expenditure) AS amount FROM info_user GROUP BY USER_ID;', conn)
# 重命名列
info_user2.columns = ['USER_ID', 'numbers', 'amount']
# 打印结果
print(info_user2)
```
相关问题
编写代码producer_consumer_many.c,实现多个生产者和多个消费者对缓冲区的操作。
好的,以下是一个简单的实现:
```
#include <stdio.h>
#include <stdlib.h>
#include <pthread.h>
#define BUFFER_SIZE 5
#define NUM_PRODUCERS 3
#define NUM_CONSUMERS 2
#define MAX_ITERATIONS 20
int buffer[BUFFER_SIZE];
int count = 0;
int in = 0, out = 0;
pthread_mutex_t mutex = PTHREAD_MUTEX_INITIALIZER;
pthread_cond_t empty = PTHREAD_COND_INITIALIZER;
pthread_cond_t full = PTHREAD_COND_INITIALIZER;
void *producer(void *id) {
int producer_id = *(int *)id;
int i, item;
for (i = 0; i < MAX_ITERATIONS; i++) {
item = rand() % 100; // generate a random number
pthread_mutex_lock(&mutex);
while (count == BUFFER_SIZE) { // buffer full
printf("Producer %d: buffer is full, waiting...\n", producer_id);
pthread_cond_wait(&empty, &mutex);
}
buffer[in] = item;
printf("Producer %d: produced item %d, buffer count is now %d\n", producer_id, item, count + 1);
in = (in + 1) % BUFFER_SIZE;
count++;
pthread_cond_signal(&full); // signal to consumers that buffer is not empty
pthread_mutex_unlock(&mutex);
}
printf("Producer %d finished!\n", producer_id);
pthread_exit(NULL);
}
void *consumer(void *id) {
int consumer_id = *(int *)id;
int i, item;
for (i = 0; i < MAX_ITERATIONS; i++) {
pthread_mutex_lock(&mutex);
while (count == 0) { // buffer empty
printf("Consumer %d: buffer is empty, waiting...\n", consumer_id);
pthread_cond_wait(&full, &mutex);
}
item = buffer[out];
printf("Consumer %d: consumed item %d, buffer count is now %d\n", consumer_id, item, count - 1);
out = (out + 1) % BUFFER_SIZE;
count--;
pthread_cond_signal(&empty); // signal to producers that buffer is not full
pthread_mutex_unlock(&mutex);
}
printf("Consumer %d finished!\n", consumer_id);
pthread_exit(NULL);
}
int main() {
pthread_t producers[NUM_PRODUCERS], consumers[NUM_CONSUMERS];
int producer_ids[NUM_PRODUCERS], consumer_ids[NUM_CONSUMERS];
int i;
// create producers
for (i = 0; i < NUM_PRODUCERS; i++) {
producer_ids[i] = i;
pthread_create(&producers[i], NULL, producer, &producer_ids[i]);
}
// create consumers
for (i = 0; i < NUM_CONSUMERS; i++) {
consumer_ids[i] = i;
pthread_create(&consumers[i], NULL, consumer, &consumer_ids[i]);
}
// join threads
for (i = 0; i < NUM_PRODUCERS; i++) {
pthread_join(producers[i], NULL);
}
for (i = 0; i < NUM_CONSUMERS; i++) {
pthread_join(consumers[i], NULL);
}
printf("Main thread exiting...\n");
return 0;
}
```
该代码实现了多个生产者和多个消费者对缓冲区的操作,其中缓冲区大小为5,有3个生产者和2个消费者。生产者随机生成一个数字作为生产的物品,消费者从缓冲区中取出物品。当缓冲区满时,生产者会等待,直到有消费者取出物品后才会继续生产;当缓冲区为空时,消费者会等待,直到有生产者生产物品后才会继续消费。在每个生产者和消费者线程完成MAX_ITERATIONS轮操作后,它们会退出线程。最后,主线程会等待所有生产者和消费者线程结束后再退出。
jupyter绘制每月对应的消费人数
好的,首先你需要明确数据的格式。假设你有一个包含日期和消费人数的数据集,你可以按照如下步骤在 Jupyter Notebook 中绘制每月对应的消费人数。
1. 导入必要的库
```python
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
```
2. 读取数据集
```python
df = pd.read_csv('your_data.csv')
```
3. 将日期转换为 Pandas 中的日期格式
```python
df['date'] = pd.to_datetime(df['date'])
```
4. 按照月份对数据进行分组并计算每月的消费人数
```python
monthly_data = df.groupby(df['date'].dt.strftime('%Y-%m'))['consumers'].sum()
```
5. 绘制每月对应的消费人数的折线图
```python
monthly_data.plot(kind='line')
plt.xlabel('Month')
plt.ylabel('Number of Consumers')
plt.title('Monthly Consumer Trends')
plt.show()
```
以上步骤将生成一个折线图,展示每个月的消费人数。注意,你需要将 `your_data.csv` 替换为你的数据集文件名,并且确保文件在相同的目录下。