def timeChangeintoshixi(time): #150902 第0个时间段0~15min time=int(time/100)#1509 time=int(time/100)*4+int((time%100)/15) return time*15 for i in range(df.shape[0]): df.iloc[i, 1] = timeChangeintoshixi(df.iloc[i, 1])对于以上代码,如何改造成一个多线程的代码,从而提高for循环速度
时间: 2024-01-22 20:17:18 浏览: 21
以下是将代码改造为多线程的示例:
```python
import concurrent.futures
def timeChangeintoshixi(time):
time=int(time/100)
time=int(time/100)*4+int((time%100)/15)
return time*15
def process_row(row):
row[1] = timeChangeintoshixi(row[1])
return row
# 使用多线程处理数据
with concurrent.futures.ThreadPoolExecutor() as executor:
results = executor.map(process_row, df.values)
# 将处理后的结果转换为 DataFrame
df = pd.DataFrame(results, columns=df.columns)
```
使用 `ThreadPoolExecutor` 可以同时处理多个数据行,从而提高处理速度。在上面的示例中,我们定义了一个 `process_row` 函数来处理每一行数据,然后使用 `executor.map()` 方法来并发地处理所有数据行。最后,将处理后的结果转换为 DataFrame。
相关问题
for file in files: condent.append(pd.read_csv(file,sep=',')) print(file) df=pd.concat(condent,axis=0) print('ok') df.columns=['ID','date','time','chepai','jindu','weidu','v','passger'] df.drop(columns='ID',inplace=True)for i in range(df.shape[0]): df.iloc[i, 1] = timeChangeintoshixi(df.iloc[i, 1])def timeChangeintoshixi(time): #150902 第0个时间段0~15min time=int(time/100)#1509 time=int(time/100)*4+int((time%100)/15) return time*15将以上代码修改成多线程多进程模式,提高for循环速率
以下是使用多线程和多进程的代码示例:
使用多线程:
```python
import pandas as pd
import glob
import concurrent.futures
def read_csv(file):
return pd.read_csv(file, sep=',')
def main():
files = glob.glob('*.csv')
content = []
with concurrent.futures.ThreadPoolExecutor(max_workers=4) as executor:
futures = [executor.submit(read_csv, file) for file in files]
for future in concurrent.futures.as_completed(futures):
content.append(future.result())
df = pd.concat(content, axis=0)
df.columns = ['ID', 'date', 'time', 'chepai', 'jindu', 'weidu', 'v', 'passger']
df.drop(columns='ID', inplace=True)
for i in range(df.shape[0]):
df.iloc[i, 1] = timeChangeintoshixi(df.iloc[i, 1])
print('ok')
def timeChangeintoshixi(time):
time = int(time / 100)
time = int(time / 100) * 4 + int((time % 100) / 15)
return time * 15
if __name__ == '__main__':
main()
```
使用多进程:
```python
import pandas as pd
import glob
import concurrent.futures
def read_csv(file):
return pd.read_csv(file, sep=',')
def main():
files = glob.glob('*.csv')
content = []
with concurrent.futures.ProcessPoolExecutor(max_workers=4) as executor:
futures = [executor.submit(read_csv, file) for file in files]
for future in concurrent.futures.as_completed(futures):
content.append(future.result())
df = pd.concat(content, axis=0)
df.columns = ['ID', 'date', 'time', 'chepai', 'jindu', 'weidu', 'v', 'passger']
df.drop(columns='ID', inplace=True)
for i in range(df.shape[0]):
df.iloc[i, 1] = timeChangeintoshixi(df.iloc[i, 1])
print('ok')
def timeChangeintoshixi(time):
time = int(time / 100)
time = int(time / 100) * 4 + int((time % 100) / 15)
return time * 15
if __name__ == '__main__':
main()
```
注意:使用多进程时,需要在 `if __name__ == '__main__':` 条件下调用 `main()` 函数。
import random import numpy as np import math from sklearn.cluster import KMeans #定义状态空间,每个时间片是一个决策阶段,时间片结束点的时刻为决策点 # 划分时间片,划分成了从0-100min的10个决策片,也就是我们整个过程的决策点,在每个时间片的右区间做出决策 scene = [] time_slices = np.linspace(0, 100, 11) # def calculate_distance(p1, p2): # # 计算两点之间的欧几里得距离 # return ((p1[0] - p2[0]) ** 2 + (p1[1] - p2[1]) ** 2) ** 0.5 # 生成随机场景 for i in range(len(time_slices) - 1): start_time, end_time = time_slices[i], time_slices[i + 1] order_counts = np.random.poisson(4) # 每个时间段平均有4个订单 driver_counts = np.random.poisson(2) # 每个时间段平均有2个司机 decision_id = i + 1 decision_time = time_slices[i + 1] for j in range(order_counts): order_id =j+1 #order_time = int(random.uniform(start_time, end_time)) order_x, order_y = int(random.uniform(0, 10)), int(random.uniform(0, 10)) order_state = 1 for m in range(driver_counts): driver1_id = m + 1 # driver1_time = int(random.uniform(start_time, end_time)) driver1_x, driver1_y = int(random.uniform(0, 10)), int(random.uniform(0, 10)) driver1_deadline = 100 scene.append({"决策阶段":decision_id,"决策时间":decision_time,"订单编号":order_id,"坐标":(order_x, order_y),"订单状态":order_state},"司机编号":driver1_id,"司机目的地":(driver1_x, driver1_y),"初始路径":[(0,0),(driver1_x, driver1_y)],"最晚可用时间":driver1_deadline})
There seems to be a syntax error in your code. You need to properly format your code by adding indentation after the for loop statements. Here's the corrected code:
```
import random
import numpy as np
import math
from sklearn.cluster import KMeans
#定义状态空间,每个时间片是一个决策阶段,时间片结束点的时刻为决策点
# 划分时间片,划分成了从0-100min的10个决策片,也就是我们整个过程的决策点,在每个时间片的右区间做出决策
scene = []
time_slices = np.linspace(0, 100, 11)
# def calculate_distance(p1, p2):
# # 计算两点之间的欧几里得距离
# return ((p1[0] - p2[0]) ** 2 + (p1[1] - p2[1]) ** 2) ** 0.5
# 生成随机场景
for i in range(len(time_slices) - 1):
start_time, end_time = time_slices[i], time_slices[i + 1]
order_counts = np.random.poisson(4) # 每个时间段平均有4个订单
driver_counts = np.random.poisson(2) # 每个时间段平均有2个司机
decision_id = i + 1
decision_time = time_slices[i + 1]
for j in range(order_counts):
order_id =j+1
#order_time = int(random.uniform(start_time, end_time))
order_x, order_y = int(random.uniform(0, 10)), int(random.uniform(0, 10))
order_state = 1
for m in range(driver_counts):
driver1_id = m + 1
# driver1_time = int(random.uniform(start_time, end_time))
driver1_x, driver1_y = int(random.uniform(0, 10)), int(random.uniform(0, 10))
driver1_deadline = 100
scene.append({"决策阶段":decision_id,"决策时间":decision_time,"订单编号":order_id,"坐标":(order_x, order_y),"订单状态":order_state,"司机编号":driver1_id,"司机目的地":(driver1_x, driver1_y),"初始路径":[(0,0),(driver1_x, driver1_y)],"最晚可用时间":driver1_deadline})
```
相关推荐
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)