注意：本文引用自专业人工智能社区Venus AI
更多AI知识请参考原站 （[www.aideeplearning.cn]）
项目背景
- 外卖服务的兴起: 随着互联网技术和移动应用的发展，外卖成为一种日益普及的餐饮服务方式。顾客通过餐厅、杂货店的网站或移动应用，或通过外卖订餐公司下单。
- 配送方式的多样性: 根据地理位置的不同，配送方式包括汽车、自行车或电动滑板车。在大城市，由于餐馆和住宅的密集，非机动车辆成为更常见的配送方式。
项目目标
- 优化配送效率: 研究不同区域和时间段的配送数据，以提高配送效率和客户满意度。
- 提升用户体验: 分析用户订单习惯，优化点餐和配送流程，提升整体用户体验。
- 预测配送时间: 预测外面的配送时间。
项目应用
- 智能配送系统: 利用数据分析和机器学习技术，开发智能配送系统，自动优化配送路线和时间。
- 客户行为分析: 分析客户的订单偏好和习惯，为餐厅和配送公司提供有价值的洞察，帮助他们更好地调整菜单和服务。
数据集
数据集主要特征
- Delivery_person_ID (配送员ID): 配送员的唯一标识符。
- Delivery_person_Age (配送员年龄): 配送员的年龄。
- Delivery_person_Ratings (配送员评分): 配送员的服务评分，反映其配送效率和服务质量。
- Restaurant_latitude, Restaurant_longitude (餐厅纬度, 餐厅经度): 餐厅的地理位置坐标。
- Delivery_location_latitude, Delivery_location_longitude (配送地点经纬度): 订单配送地点的地理位置坐标。
- Order_Date (订单日期): 下单的日期。
- Time_Orderd (订单下单时间): 客户下单的具体时间。
- Time_Order_picked (接单时间): 配送员接单的时间。
- Weatherconditions (天气状况): 下单当天的天气状况，可能影响配送速度和安全。
- Road_traffic_density (道路交通密度): 配送路线上的交通密度情况。
- Vehicle_condition (交通工具状况): 配送员使用的交通工具的状况。
- Type_of_order (订单类型): 订单的类型，如餐饮、杂货等。
- Type_of_vehicle (交通工具类型): 配送员用于配送的交通工具类型，如汽车、自行车或电动滑板车。
- multiple_deliveries (多重配送): 是否为一次性配送多个订单。
- Festival (节日): 下单当天是否为节日，节日可能影响订单量和配送速度。
- City (城市): 订单配送所在的城市。
- Time_taken(min) (配送用时): 从接单到完成配送所需的时间，以分钟计。
模型和依赖库
Models:
- RandomForestRegressor
- Linear Regression
- Lasso Regression
- Ensemble learning
Libraries:
- Pandas
- Numpy
- Seaborn
- Matplotlib
- sk-learn
- haversine
代码实现
import pandas as pd
import numpy as np
import haversine as hs
from datetime import datetime
import matplotlib.pyplot as plt
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)data = pd.read_csv('train.csv')
data| ID | Delivery_person_ID | Delivery_person_Age | Delivery_person_Ratings | Restaurant_latitude | Restaurant_longitude | Delivery_location_latitude | Delivery_location_longitude | Order_Date | Time_Orderd | Time_Order_picked | Weatherconditions | Road_traffic_density | Vehicle_condition | Type_of_order | Type_of_vehicle | multiple_deliveries | Festival | City | Time_taken(min) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0x4607 | INDORES13DEL02 | 37 | 4.9 | 22.745049 | 75.892471 | 22.765049 | 75.912471 | 19-03-2022 | 11:30:00 | 11:45:00 | conditions Sunny | High | 2 | Snack | motorcycle | 0 | No | Urban | (min) 24 |
| 1 | 0xb379 | BANGRES18DEL02 | 34 | 4.5 | 12.913041 | 77.683237 | 13.043041 | 77.813237 | 25-03-2022 | 19:45:00 | 19:50:00 | conditions Stormy | Jam | 2 | Snack | scooter | 1 | No | Metropolitian | (min) 33 |
| 2 | 0x5d6d | BANGRES19DEL01 | 23 | 4.4 | 12.914264 | 77.678400 | 12.924264 | 77.688400 | 19-03-2022 | 08:30:00 | 08:45:00 | conditions Sandstorms | Low | 0 | Drinks | motorcycle | 1 | No | Urban | (min) 26 |
| 3 | 0x7a6a | COIMBRES13DEL02 | 38 | 4.7 | 11.003669 | 76.976494 | 11.053669 | 77.026494 | 05-04-2022 | 18:00:00 | 18:10:00 | conditions Sunny | Medium | 0 | Buffet | motorcycle | 1 | No | Metropolitian | (min) 21 |
| 4 | 0x70a2 | CHENRES12DEL01 | 32 | 4.6 | 12.972793 | 80.249982 | 13.012793 | 80.289982 | 26-03-2022 | 13:30:00 | 13:45:00 | conditions Cloudy | High | 1 | Snack | scooter | 1 | No | Metropolitian | (min) 30 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 45588 | 0x7c09 | JAPRES04DEL01 | 30 | 4.8 | 26.902328 | 75.794257 | 26.912328 | 75.804257 | 24-03-2022 | 11:35:00 | 11:45:00 | conditions Windy | High | 1 | Meal | motorcycle | 0 | No | Metropolitian | (min) 32 |
| 45589 | 0xd641 | AGRRES16DEL01 | 21 | 4.6 | 0.000000 | 0.000000 | 0.070000 | 0.070000 | 16-02-2022 | 19:55:00 | 20:10:00 | conditions Windy | Jam | 0 | Buffet | motorcycle | 1 | No | Metropolitian | (min) 36 |
| 45590 | 0x4f8d | CHENRES08DEL03 | 30 | 4.9 | 13.022394 | 80.242439 | 13.052394 | 80.272439 | 11-03-2022 | 23:50:00 | 00:05:00 | conditions Cloudy | Low | 1 | Drinks | scooter | 0 | No | Metropolitian | (min) 16 |
| 45591 | 0x5eee | COIMBRES11DEL01 | 20 | 4.7 | 11.001753 | 76.986241 | 11.041753 | 77.026241 | 07-03-2022 | 13:35:00 | 13:40:00 | conditions Cloudy | High | 0 | Snack | motorcycle | 1 | No | Metropolitian | (min) 26 |
| 45592 | 0x5fb2 | RANCHIRES09DEL02 | 23 | 4.9 | 23.351058 | 85.325731 | 23.431058 | 85.405731 | 02-03-2022 | 17:10:00 | 17:15:00 | conditions Fog | Medium | 2 | Snack | scooter | 1 | No | Metropolitian | (min) 36 |
45593 rows × 20 columns
data.replace({"NaN": np.nan}, regex=True, inplace = True)
data| ID | Delivery_person_ID | Delivery_person_Age | Delivery_person_Ratings | Restaurant_latitude | Restaurant_longitude | Delivery_location_latitude | Delivery_location_longitude | Order_Date | Time_Orderd | Time_Order_picked | Weatherconditions | Road_traffic_density | Vehicle_condition | Type_of_order | Type_of_vehicle | multiple_deliveries | Festival | City | Time_taken(min) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0x4607 | INDORES13DEL02 | 37 | 4.9 | 22.745049 | 75.892471 | 22.765049 | 75.912471 | 19-03-2022 | 11:30:00 | 11:45:00 | conditions Sunny | High | 2 | Snack | motorcycle | 0 | No | Urban | (min) 24 |
| 1 | 0xb379 | BANGRES18DEL02 | 34 | 4.5 | 12.913041 | 77.683237 | 13.043041 | 77.813237 | 25-03-2022 | 19:45:00 | 19:50:00 | conditions Stormy | Jam | 2 | Snack | scooter | 1 | No | Metropolitian | (min) 33 |
| 2 | 0x5d6d | BANGRES19DEL01 | 23 | 4.4 | 12.914264 | 77.678400 | 12.924264 | 77.688400 | 19-03-2022 | 08:30:00 | 08:45:00 | conditions Sandstorms | Low | 0 | Drinks | motorcycle | 1 | No | Urban | (min) 26 |
| 3 | 0x7a6a | COIMBRES13DEL02 | 38 | 4.7 | 11.003669 | 76.976494 | 11.053669 | 77.026494 | 05-04-2022 | 18:00:00 | 18:10:00 | conditions Sunny | Medium | 0 | Buffet | motorcycle | 1 | No | Metropolitian | (min) 21 |
| 4 | 0x70a2 | CHENRES12DEL01 | 32 | 4.6 | 12.972793 | 80.249982 | 13.012793 | 80.289982 | 26-03-2022 | 13:30:00 | 13:45:00 | conditions Cloudy | High | 1 | Snack | scooter | 1 | No | Metropolitian | (min) 30 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 45588 | 0x7c09 | JAPRES04DEL01 | 30 | 4.8 | 26.902328 | 75.794257 | 26.912328 | 75.804257 | 24-03-2022 | 11:35:00 | 11:45:00 | conditions Windy | High | 1 | Meal | motorcycle | 0 | No | Metropolitian | (min) 32 |
| 45589 | 0xd641 | AGRRES16DEL01 | 21 | 4.6 | 0.000000 | 0.000000 | 0.070000 | 0.070000 | 16-02-2022 | 19:55:00 | 20:10:00 | conditions Windy | Jam | 0 | Buffet | motorcycle | 1 | No | Metropolitian | (min) 36 |
| 45590 | 0x4f8d | CHENRES08DEL03 | 30 | 4.9 | 13.022394 | 80.242439 | 13.052394 | 80.272439 | 11-03-2022 | 23:50:00 | 00:05:00 | conditions Cloudy | Low | 1 | Drinks | scooter | 0 | No | Metropolitian | (min) 16 |
| 45591 | 0x5eee | COIMBRES11DEL01 | 20 | 4.7 | 11.001753 | 76.986241 | 11.041753 | 77.026241 | 07-03-2022 | 13:35:00 | 13:40:00 | conditions Cloudy | High | 0 | Snack | motorcycle | 1 | No | Metropolitian | (min) 26 |
| 45592 | 0x5fb2 | RANCHIRES09DEL02 | 23 | 4.9 | 23.351058 | 85.325731 | 23.431058 | 85.405731 | 02-03-2022 | 17:10:00 | 17:15:00 | conditions Fog | Medium | 2 | Snack | scooter | 1 | No | Metropolitian | (min) 36 |
45593 rows × 20 columns
data.isna().sum()ID 0 Delivery_person_ID 0 Delivery_person_Age 1854 Delivery_person_Ratings 1908 Restaurant_latitude 0 Restaurant_longitude 0 Delivery_location_latitude 0 Delivery_location_longitude 0 Order_Date 0 Time_Orderd 1731 Time_Order_picked 0 Weatherconditions 616 Road_traffic_density 601 Vehicle_condition 0 Type_of_order 0 Type_of_vehicle 0 multiple_deliveries 993 Festival 228 City 1200 Time_taken(min) 0 dtype: int64
data['Distance'] = 0
data| ID | Delivery_person_ID | Delivery_person_Age | Delivery_person_Ratings | Restaurant_latitude | Restaurant_longitude | Delivery_location_latitude | Delivery_location_longitude | Order_Date | Time_Orderd | Time_Order_picked | Weatherconditions | Road_traffic_density | Vehicle_condition | Type_of_order | Type_of_vehicle | multiple_deliveries | Festival | City | Time_taken(min) | Distance | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0x4607 | INDORES13DEL02 | 37 | 4.9 | 22.745049 | 75.892471 | 22.765049 | 75.912471 | 19-03-2022 | 11:30:00 | 11:45:00 | conditions Sunny | High | 2 | Snack | motorcycle | 0 | No | Urban | (min) 24 | 0 |
| 1 | 0xb379 | BANGRES18DEL02 | 34 | 4.5 | 12.913041 | 77.683237 | 13.043041 | 77.813237 | 25-03-2022 | 19:45:00 | 19:50:00 | conditions Stormy | Jam | 2 | Snack | scooter | 1 | No | Metropolitian | (min) 33 | 0 |
| 2 | 0x5d6d | BANGRES19DEL01 | 23 | 4.4 | 12.914264 | 77.678400 | 12.924264 | 77.688400 | 19-03-2022 | 08:30:00 | 08:45:00 | conditions Sandstorms | Low | 0 | Drinks | motorcycle | 1 | No | Urban | (min) 26 | 0 |
| 3 | 0x7a6a | COIMBRES13DEL02 | 38 | 4.7 | 11.003669 | 76.976494 | 11.053669 | 77.026494 | 05-04-2022 | 18:00:00 | 18:10:00 | conditions Sunny | Medium | 0 | Buffet | motorcycle | 1 | No | Metropolitian | (min) 21 | 0 |
| 4 | 0x70a2 | CHENRES12DEL01 | 32 | 4.6 | 12.972793 | 80.249982 | 13.012793 | 80.289982 | 26-03-2022 | 13:30:00 | 13:45:00 | conditions Cloudy | High | 1 | Snack | scooter | 1 | No | Metropolitian | (min) 30 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 45588 | 0x7c09 | JAPRES04DEL01 | 30 | 4.8 | 26.902328 | 75.794257 | 26.912328 | 75.804257 | 24-03-2022 | 11:35:00 | 11:45:00 | conditions Windy | High | 1 | Meal | motorcycle | 0 | No | Metropolitian | (min) 32 | 0 |
| 45589 | 0xd641 | AGRRES16DEL01 | 21 | 4.6 | 0.000000 | 0.000000 | 0.070000 | 0.070000 | 16-02-2022 | 19:55:00 | 20:10:00 | conditions Windy | Jam | 0 | Buffet | motorcycle | 1 | No | Metropolitian | (min) 36 | 0 |
| 45590 | 0x4f8d | CHENRES08DEL03 | 30 | 4.9 | 13.022394 | 80.242439 | 13.052394 | 80.272439 | 11-03-2022 | 23:50:00 | 00:05:00 | conditions Cloudy | Low | 1 | Drinks | scooter | 0 | No | Metropolitian | (min) 16 | 0 |
| 45591 | 0x5eee | COIMBRES11DEL01 | 20 | 4.7 | 11.001753 | 76.986241 | 11.041753 | 77.026241 | 07-03-2022 | 13:35:00 | 13:40:00 | conditions Cloudy | High | 0 | Snack | motorcycle | 1 | No | Metropolitian | (min) 26 | 0 |
| 45592 | 0x5fb2 | RANCHIRES09DEL02 | 23 | 4.9 | 23.351058 | 85.325731 | 23.431058 | 85.405731 | 02-03-2022 | 17:10:00 | 17:15:00 | conditions Fog | Medium | 2 | Snack | scooter | 1 | No | Metropolitian | (min) 36 | 0 |
45593 rows × 21 columns
haversine 是一个 Python 库，用于计算地球上两点之间的大圆距离。这个距离是指在地球表面两点之间的最短路径，考虑到地球的曲率。这个库在进行地理位置数据的处理和分析时特别有用，比如在确定两个地理坐标（经度和纬度）之间的直线距离时。
def calculate_distance(row):
loc1 = (row['Restaurant_latitude'], row['Restaurant_longitude'])
loc2 = (row['Delivery_location_latitude'], row['Delivery_location_longitude'])
return hs.haversine(loc1, loc2)
# Apply the function to each row
data['Distance'] = data.apply(calculate_distance, axis=1)data['Time_prep'] = 0
data| ID | Delivery_person_ID | Delivery_person_Age | Delivery_person_Ratings | Restaurant_latitude | Restaurant_longitude | Delivery_location_latitude | Delivery_location_longitude | Order_Date | Time_Orderd | Time_Order_picked | Weatherconditions | Road_traffic_density | Vehicle_condition | Type_of_order | Type_of_vehicle | multiple_deliveries | Festival | City | Time_taken(min) | Distance | Time_prep | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0x4607 | INDORES13DEL02 | 37 | 4.9 | 22.745049 | 75.892471 | 22.765049 | 75.912471 | 19-03-2022 | 11:30:00 | 11:45:00 | conditions Sunny | High | 2 | Snack | motorcycle | 0 | No | Urban | (min) 24 | 3.025153 | 0 |
| 1 | 0xb379 | BANGRES18DEL02 | 34 | 4.5 | 12.913041 | 77.683237 | 13.043041 | 77.813237 | 25-03-2022 | 19:45:00 | 19:50:00 | conditions Stormy | Jam | 2 | Snack | scooter | 1 | No | Metropolitian | (min) 33 | 20.183558 | 0 |
| 2 | 0x5d6d | BANGRES19DEL01 | 23 | 4.4 | 12.914264 | 77.678400 | 12.924264 | 77.688400 | 19-03-2022 | 08:30:00 | 08:45:00 | conditions Sandstorms | Low | 0 | Drinks | motorcycle | 1 | No | Urban | (min) 26 | 1.552760 | 0 |
| 3 | 0x7a6a | COIMBRES13DEL02 | 38 | 4.7 | 11.003669 | 76.976494 | 11.053669 | 77.026494 | 05-04-2022 | 18:00:00 | 18:10:00 | conditions Sunny | Medium | 0 | Buffet | motorcycle | 1 | No | Metropolitian | (min) 21 | 7.790412 | 0 |
| 4 | 0x70a2 | CHENRES12DEL01 | 32 | 4.6 | 12.972793 | 80.249982 | 13.012793 | 80.289982 | 26-03-2022 | 13:30:00 | 13:45:00 | conditions Cloudy | High | 1 | Snack | scooter | 1 | No | Metropolitian | (min) 30 | 6.210147 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 45588 | 0x7c09 | JAPRES04DEL01 | 30 | 4.8 | 26.902328 | 75.794257 | 26.912328 | 75.804257 | 24-03-2022 | 11:35:00 | 11:45:00 | conditions Windy | High | 1 | Meal | motorcycle | 0 | No | Metropolitian | (min) 32 | 1.489848 | 0 |
| 45589 | 0xd641 | AGRRES16DEL01 | 21 | 4.6 | 0.000000 | 0.000000 | 0.070000 | 0.070000 | 16-02-2022 | 19:55:00 | 20:10:00 | conditions Windy | Jam | 0 | Buffet | motorcycle | 1 | No | Metropolitian | (min) 36 | 11.007750 | 0 |
| 45590 | 0x4f8d | CHENRES08DEL03 | 30 | 4.9 | 13.022394 | 80.242439 | 13.052394 | 80.272439 | 11-03-2022 | 23:50:00 | 00:05:00 | conditions Cloudy | Low | 1 | Drinks | scooter | 0 | No | Metropolitian | (min) 16 | 4.657202 | 0 |
| 45591 | 0x5eee | COIMBRES11DEL01 | 20 | 4.7 | 11.001753 | 76.986241 | 11.041753 | 77.026241 | 07-03-2022 | 13:35:00 | 13:40:00 | conditions Cloudy | High | 0 | Snack | motorcycle | 1 | No | Metropolitian | (min) 26 | 6.232402 | 0 |
| 45592 | 0x5fb2 | RANCHIRES09DEL02 | 23 | 4.9 | 23.351058 | 85.325731 | 23.431058 | 85.405731 | 02-03-2022 | 17:10:00 | 17:15:00 | conditions Fog | Medium | 2 | Snack | scooter | 1 | No | Metropolitian | (min) 36 | 12.074412 | 0 |
45593 rows × 22 columns
data.dropna(axis = 0, how = 'all', inplace = True)
data| ID | Delivery_person_ID | Delivery_person_Age | Delivery_person_Ratings | Restaurant_latitude | Restaurant_longitude | Delivery_location_latitude | Delivery_location_longitude | Order_Date | Time_Orderd | Time_Order_picked | Weatherconditions | Road_traffic_density | Vehicle_condition | Type_of_order | Type_of_vehicle | multiple_deliveries | Festival | City | Time_taken(min) | Distance | Time_prep | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0x4607 | INDORES13DEL02 | 37 | 4.9 | 22.745049 | 75.892471 | 22.765049 | 75.912471 | 19-03-2022 | 11:30:00 | 11:45:00 | conditions Sunny | High | 2 | Snack | motorcycle | 0 | No | Urban | (min) 24 | 3.025153 | 0 |
| 1 | 0xb379 | BANGRES18DEL02 | 34 | 4.5 | 12.913041 | 77.683237 | 13.043041 | 77.813237 | 25-03-2022 | 19:45:00 | 19:50:00 | conditions Stormy | Jam | 2 | Snack | scooter | 1 | No | Metropolitian | (min) 33 | 20.183558 | 0 |
| 2 | 0x5d6d | BANGRES19DEL01 | 23 | 4.4 | 12.914264 | 77.678400 | 12.924264 | 77.688400 | 19-03-2022 | 08:30:00 | 08:45:00 | conditions Sandstorms | Low | 0 | Drinks | motorcycle | 1 | No | Urban | (min) 26 | 1.552760 | 0 |
| 3 | 0x7a6a | COIMBRES13DEL02 | 38 | 4.7 | 11.003669 | 76.976494 | 11.053669 | 77.026494 | 05-04-2022 | 18:00:00 | 18:10:00 | conditions Sunny | Medium | 0 | Buffet | motorcycle | 1 | No | Metropolitian | (min) 21 | 7.790412 | 0 |
| 4 | 0x70a2 | CHENRES12DEL01 | 32 | 4.6 | 12.972793 | 80.249982 | 13.012793 | 80.289982 | 26-03-2022 | 13:30:00 | 13:45:00 | conditions Cloudy | High | 1 | Snack | scooter | 1 | No | Metropolitian | (min) 30 | 6.210147 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 45588 | 0x7c09 | JAPRES04DEL01 | 30 | 4.8 | 26.902328 | 75.794257 | 26.912328 | 75.804257 | 24-03-2022 | 11:35:00 | 11:45:00 | conditions Windy | High | 1 | Meal | motorcycle | 0 | No | Metropolitian | (min) 32 | 1.489848 | 0 |
| 45589 | 0xd641 | AGRRES16DEL01 | 21 | 4.6 | 0.000000 | 0.000000 | 0.070000 | 0.070000 | 16-02-2022 | 19:55:00 | 20:10:00 | conditions Windy | Jam | 0 | Buffet | motorcycle | 1 | No | Metropolitian | (min) 36 | 11.007750 | 0 |
| 45590 | 0x4f8d | CHENRES08DEL03 | 30 | 4.9 | 13.022394 | 80.242439 | 13.052394 | 80.272439 | 11-03-2022 | 23:50:00 | 00:05:00 | conditions Cloudy | Low | 1 | Drinks | scooter | 0 | No | Metropolitian | (min) 16 | 4.657202 | 0 |
| 45591 | 0x5eee | COIMBRES11DEL01 | 20 | 4.7 | 11.001753 | 76.986241 | 11.041753 | 77.026241 | 07-03-2022 | 13:35:00 | 13:40:00 | conditions Cloudy | High | 0 | Snack | motorcycle | 1 | No | Metropolitian | (min) 26 | 6.232402 | 0 |
| 45592 | 0x5fb2 | RANCHIRES09DEL02 | 23 | 4.9 | 23.351058 | 85.325731 | 23.431058 | 85.405731 | 02-03-2022 | 17:10:00 | 17:15:00 | conditions Fog | Medium | 2 | Snack | scooter | 1 | No | Metropolitian | (min) 36 | 12.074412 | 0 |
45593 rows × 22 columns
data = data[data["Time_Orderd"].str.contains("NaN") == False].reset_index()
data = data[data["Time_Order_picked"].str.contains("NaN") == False].reset_index()
data.head(50)| level_0 | index | ID | Delivery_person_ID | Delivery_person_Age | Delivery_person_Ratings | Restaurant_latitude | Restaurant_longitude | Delivery_location_latitude | Delivery_location_longitude | Order_Date | Time_Orderd | Time_Order_picked | Weatherconditions | Road_traffic_density | Vehicle_condition | Type_of_order | Type_of_vehicle | multiple_deliveries | Festival | City | Time_taken(min) | Distance | Time_prep | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 0x4607 | INDORES13DEL02 | 37 | 4.9 | 22.745049 | 75.892471 | 22.765049 | 75.912471 | 19-03-2022 | 11:30:00 | 11:45:00 | conditions Sunny | High | 2 | Snack | motorcycle | 0 | No | Urban | (min) 24 | 3.025153 | 0 |
| 1 | 1 | 1 | 0xb379 | BANGRES18DEL02 | 34 | 4.5 | 12.913041 | 77.683237 | 13.043041 | 77.813237 | 25-03-2022 | 19:45:00 | 19:50:00 | conditions Stormy | Jam | 2 | Snack | scooter | 1 | No | Metropolitian | (min) 33 | 20.183558 | 0 |
| 2 | 2 | 2 | 0x5d6d | BANGRES19DEL01 | 23 | 4.4 | 12.914264 | 77.678400 | 12.924264 | 77.688400 | 19-03-2022 | 08:30:00 | 08:45:00 | conditions Sandstorms | Low | 0 | Drinks | motorcycle | 1 | No | Urban | (min) 26 | 1.552760 | 0 |
| 3 | 3 | 3 | 0x7a6a | COIMBRES13DEL02 | 38 | 4.7 | 11.003669 | 76.976494 | 11.053669 | 77.026494 | 05-04-2022 | 18:00:00 | 18:10:00 | conditions Sunny | Medium | 0 | Buffet | motorcycle | 1 | No | Metropolitian | (min) 21 | 7.790412 | 0 |
| 4 | 4 | 4 | 0x70a2 | CHENRES12DEL01 | 32 | 4.6 | 12.972793 | 80.249982 | 13.012793 | 80.289982 | 26-03-2022 | 13:30:00 | 13:45:00 | conditions Cloudy | High | 1 | Snack | scooter | 1 | No | Metropolitian | (min) 30 | 6.210147 | 0 |
| 5 | 5 | 5 | 0x9bb4 | HYDRES09DEL03 | 22 | 4.8 | 17.431668 | 78.408321 | 17.461668 | 78.438321 | 11-03-2022 | 21:20:00 | 21:30:00 | conditions Cloudy | Jam | 0 | Buffet | motorcycle | 1 | No | Urban | (min) 26 | 4.610372 | 0 |
| 6 | 6 | 6 | 0x95b4 | RANCHIRES15DEL01 | 33 | 4.7 | 23.369746 | 85.339820 | 23.479746 | 85.449820 | 04-03-2022 | 19:15:00 | 19:30:00 | conditions Fog | Jam | 1 | Meal | scooter | 1 | No | Metropolitian | (min) 40 | 16.600384 | 0 |
| 7 | 7 | 7 | 0x9eb2 | MYSRES15DEL02 | 35 | 4.6 | 12.352058 | 76.606650 | 12.482058 | 76.736650 | 14-03-2022 | 17:25:00 | 17:30:00 | conditions Cloudy | Medium | 2 | Meal | motorcycle | 1 | No | Metropolitian | (min) 32 | 20.205281 | 0 |
| 8 | 8 | 8 | 0x1102 | HYDRES05DEL02 | 22 | 4.8 | 17.433809 | 78.386744 | 17.563809 | 78.516744 | 20-03-2022 | 20:55:00 | 21:05:00 | conditions Stormy | Jam | 0 | Buffet | motorcycle | 1 | No | Metropolitian | (min) 34 | 19.975548 | 0 |
| 9 | 9 | 9 | 0xcdcd | DEHRES17DEL01 | 36 | 4.2 | 30.327968 | 78.046106 | 30.397968 | 78.116106 | 12-02-2022 | 21:55:00 | 22:10:00 | conditions Fog | Jam | 2 | Snack | motorcycle | 3 | No | Metropolitian | (min) 46 | 10.280596 | 0 |
| 10 | 10 | 10 | 0xd987 | KOCRES16DEL01 | 21 | 4.7 | 10.003064 | 76.307589 | 10.043064 | 76.347589 | 13-02-2022 | 14:55:00 | 15:05:00 | conditions Stormy | High | 1 | Meal | motorcycle | 1 | No | Metropolitian | (min) 23 | 6.242327 | 0 |
| 11 | 11 | 11 | 0x2784 | PUNERES13DEL03 | 23 | 4.7 | 18.562450 | 73.916619 | 18.652450 | 74.006619 | 04-03-2022 | 17:30:00 | 17:40:00 | conditions Sandstorms | Medium | 1 | Drinks | scooter | 1 | No | Metropolitian | (min) 21 | 13.787879 | 0 |
| 12 | 12 | 12 | 0xc8b6 | LUDHRES15DEL02 | 34 | 4.3 | 30.899584 | 75.809346 | 30.919584 | 75.829346 | 13-02-2022 | 09:20:00 | 09:30:00 | conditions Sandstorms | Low | 0 | Buffet | motorcycle | 0 | No | Metropolitian | (min) 20 | 2.930262 | 0 |
| 13 | 13 | 13 | 0xdb64 | KNPRES14DEL02 | 24 | 4.7 | 26.463504 | 80.372929 | 26.593504 | 80.502929 | 14-02-2022 | 19:50:00 | 20:05:00 | conditions Fog | Jam | 1 | Snack | scooter | 1 | No | Metropolitian | (min) 41 | 19.396645 | 0 |
| 14 | 14 | 14 | 0x3af3 | MUMRES15DEL03 | 29 | 4.5 | 19.176269 | 72.836721 | 19.266269 | 72.926721 | 02-04-2022 | 20:25:00 | 20:35:00 | conditions Sandstorms | Jam | 2 | Buffet | electric_scooter | 1 | No | Metropolitian | (min) 20 | 13.763996 | 0 |
| 15 | 15 | 15 | 0x3aab | MYSRES01DEL01 | 35 | 4 | 12.311072 | 76.654878 | 12.351072 | 76.694878 | 01-03-2022 | 14:55:00 | 15:10:00 | conditions Windy | High | 1 | Meal | scooter | 1 | No | Metropolitian | (min) 33 | 6.218010 | 0 |
| 16 | 16 | 16 | 0x689b | PUNERES20DEL01 | 33 | 4.2 | 18.592718 | 73.773572 | 18.702718 | 73.883572 | 16-03-2022 | 20:30:00 | 20:40:00 | conditions Sandstorms | Jam | 2 | Snack | motorcycle | 1 | No | Metropolitian | (min) 40 | 16.849963 | 0 |
| 17 | 17 | 17 | 0x6f67 | HYDRES14DEL01 | 34 | 4.9 | 17.426228 | 78.407495 | 17.496228 | 78.477495 | 20-03-2022 | 20:40:00 | 20:50:00 | conditions Cloudy | Jam | 0 | Snack | motorcycle | NaN | No | Metropolitian | (min) 41 | 10.757124 | 0 |
| 18 | 18 | 18 | 0xc9cf | KOLRES15DEL03 | 21 | 4.7 | 22.552672 | 88.352885 | 22.582672 | 88.382885 | 15-02-2022 | 21:15:00 | 21:30:00 | conditions Windy | Jam | 0 | Meal | motorcycle | 1 | No | Urban | (min) 15 | 4.540581 | 0 |
| 19 | 19 | 19 | 0x36b8 | PUNERES19DEL02 | 25 | 4.1 | 18.563934 | 73.915367 | 18.643935 | 73.995367 | 16-03-2022 | 20:20:00 | 20:25:00 | conditions Sandstorms | Jam | 0 | Snack | motorcycle | 2 | No | Metropolitian | (min) 36 | 12.256093 | 0 |
| 20 | 20 | 20 | 0x5795 | RANCHIRES06DEL02 | 31 | 4.7 | 23.357804 | 85.325146 | 23.487804 | 85.455146 | 10-03-2022 | 22:30:00 | 22:45:00 | conditions Sandstorms | Low | 2 | Meal | electric_scooter | 0 | No | Metropolitian | (min) 26 | 19.618766 | 0 |
| 21 | 21 | 21 | 0x6c6b | COIMBRES13DEL01 | 37 | 5 | 11.003669 | 76.976494 | 11.013669 | 76.986494 | 11-03-2022 | 08:15:00 | 08:30:00 | conditions Sandstorms | Low | 1 | Snack | motorcycle | 1 | No | Metropolitian | (min) 20 | 1.558134 | 0 |
| 22 | 22 | 22 | 0xb816 | CHENRES19DEL02 | 33 | 4.3 | 12.986047 | 80.218114 | 13.116047 | 80.348114 | 27-03-2022 | 19:30:00 | 19:45:00 | conditions Windy | Jam | 2 | Meal | scooter | 1 | No | Metropolitian | (min) 39 | 20.180663 | 0 |
| 23 | 23 | 23 | 0x539b | MUMRES02DEL01 | 25 | 4 | 19.221315 | 72.862381 | 19.261315 | 72.902381 | 26-03-2022 | 12:25:00 | 12:30:00 | conditions Cloudy | High | 1 | Buffet | motorcycle | 1 | No | Metropolitian | (min) 34 | 6.116980 | 0 |
| 24 | 24 | 24 | 0xa1b2 | CHENRES01DEL01 | 29 | 4.5 | 13.005801 | 80.250744 | 13.115801 | 80.360744 | 27-03-2022 | 18:35:00 | 18:50:00 | conditions Sunny | Medium | 2 | Meal | electric_scooter | 1 | No | Metropolitian | (min) 15 | 17.075618 | 0 |
| 25 | 25 | 25 | 0x3231 | JAPRES16DEL03 | 27 | 5 | 26.849596 | 75.800512 | 26.879596 | 75.830512 | 05-04-2022 | 20:35:00 | 20:40:00 | conditions Stormy | Jam | 0 | Snack | motorcycle | 0 | No | Urban | (min) 18 | 4.470293 | 0 |
| 26 | 26 | 26 | 0x8bc0 | SURRES15DEL03 | 35 | 4.3 | 21.160522 | 72.771477 | 21.250522 | 72.861477 | 12-03-2022 | 23:20:00 | 23:30:00 | conditions Cloudy | Low | 1 | Drinks | scooter | 0 | No | Metropolitian | (min) 38 | 13.682063 | 0 |
| 27 | 27 | 27 | 0x2288 | BANGRES09DEL03 | 32 | 4 | 12.934179 | 77.615797 | 13.024179 | 77.705797 | 16-03-2022 | 21:20:00 | 21:35:00 | conditions Windy | Jam | 0 | Buffet | motorcycle | 1 | No | Metropolitian | (min) 47 | 13.973202 | 0 |
| 28 | 28 | 28 | 0x3c5e | PUNERES04DEL01 | 23 | 4.8 | 18.514210 | 73.838429 | 18.624210 | 73.948429 | 02-04-2022 | 23:35:00 | 23:45:00 | conditions Windy | Low | 2 | Buffet | electric_scooter | 0 | No | Urban | (min) 12 | 16.853642 | 0 |
| 29 | 29 | 29 | 0x3e60 | COIMBRES02DEL03 | 31 | 4.8 | 11.022477 | 76.995667 | 11.052477 | 77.025667 | 01-04-2022 | 22:35:00 | 22:50:00 | conditions Sandstorms | Low | 2 | Drinks | motorcycle | 1 | No | Metropolitian | (min) 26 | 4.674179 | 0 |
| 30 | 30 | 30 | 0xbff | SURRES16DEL02 | 36 | 4.1 | 21.160437 | 72.774209 | 21.210437 | 72.824209 | 05-03-2022 | 22:35:00 | 22:40:00 | conditions Stormy | Low | 0 | Drinks | motorcycle | 1 | No | Urban | (min) 22 | 7.601627 | 0 |
| 31 | 31 | 31 | 0xd936 | GOARES15DEL02 | 26 | 4.3 | 15.513150 | 73.783460 | 15.563150 | 73.833460 | 11-02-2022 | 23:25:00 | 23:35:00 | conditions Sandstorms | Low | 0 | Buffet | motorcycle | 0 | No | Urban | (min) 21 | 7.720335 | 0 |
| 32 | 32 | 32 | 0xd681 | GOARES07DEL01 | 38 | 4.9 | 15.561295 | 73.749478 | 15.601295 | 73.789478 | 11-02-2022 | 13:35:00 | 13:40:00 | conditions Cloudy | High | 1 | Drinks | scooter | 1 | No | Urban | (min) 25 | 6.175645 | 0 |
| 33 | 33 | 33 | 0x2876 | RANCHIRES02DEL01 | 32 | 3.5 | 0.000000 | 0.000000 | 0.110000 | 0.110000 | 08-03-2022 | 21:35:00 | 21:45:00 | conditions Stormy | Jam | 1 | Snack | scooter | 0 | No | Urban | (min) 35 | 17.297890 | 0 |
| 34 | 34 | 34 | 0x30c8 | PUNERES19DEL02 | 32 | 4.6 | 18.563934 | 73.915367 | 18.693935 | 74.045367 | 02-04-2022 | 22:35:00 | 22:45:00 | conditions Cloudy | Low | 2 | Drinks | scooter | 1 | No | Metropolitian | (min) 30 | 19.914714 | 0 |
| 35 | 35 | 35 | 0xb843 | PUNERES02DEL01 | 33 | 4.9 | 18.551440 | 73.804855 | 18.621440 | 73.874855 | 08-03-2022 | 18:55:00 | 19:10:00 | conditions Sunny | Medium | 1 | Snack | motorcycle | 1 | No | Metropolitian | (min) 22 | 10.724533 | 0 |
| 36 | 36 | 36 | 0xb3a0 | PUNERES18DEL01 | 20 | 4.7 | 18.593481 | 73.785901 | 18.633481 | 73.825901 | 03-04-2022 | 14:15:00 | 14:25:00 | conditions Windy | High | 1 | Snack | scooter | 0 | No | Urban | (min) 10 | 6.127844 | 0 |
| 37 | 37 | 37 | 0x6531 | SURRES08DEL01 | 20 | 4.8 | 21.173343 | 72.792731 | 21.183343 | 72.802731 | 30-03-2022 | 11:00:00 | 11:10:00 | conditions Sandstorms | Low | 2 | Meal | scooter | 1 | No | Metropolitian | (min) 19 | 1.520359 | 0 |
| 38 | 38 | 38 | 0x4bda | HYDRES17DEL02 | 35 | 5 | 17.451976 | 78.385883 | 17.471976 | 78.405883 | 01-04-2022 | 09:45:00 | 09:55:00 | conditions Sunny | Low | 2 | Snack | scooter | 1 | No | Urban | (min) 11 | 3.073458 | 0 |
| 39 | 39 | 39 | 0x9d26 | BANGRES17DEL02 | 26 | 4.9 | 12.972532 | 77.608179 | 12.992532 | 77.628179 | 28-03-2022 | 08:40:00 | 08:55:00 | conditions Stormy | Low | 2 | Buffet | scooter | 0 | No | Metropolitian | (min) 11 | 3.105136 | 0 |
| 40 | 40 | 40 | 0x9b18 | BANGRES17DEL01 | 22 | 4.8 | 12.972532 | 77.608179 | 13.042532 | 77.678179 | 18-03-2022 | 23:00:00 | 23:10:00 | conditions Fog | Low | 1 | Snack | motorcycle | 1 | No | Metropolitian | (min) 28 | 10.867442 | 0 |
| 41 | 41 | 41 | 0x5d99 | CHENRES11DEL01 | 35 | 4.3 | 13.064181 | 80.236442 | 13.134181 | 80.306442 | 14-03-2022 | 17:25:00 | 17:30:00 | conditions Cloudy | Medium | 1 | Snack | motorcycle | 1 | No | Metropolitian | (min) 33 | 10.865480 | 0 |
| 42 | 42 | 43 | 0xb796 | SURRES17DEL03 | 37 | 4.7 | 21.149569 | 72.772697 | 21.239569 | 72.862697 | 04-04-2022 | 19:45:00 | 19:50:00 | conditions Sandstorms | Jam | 0 | Snack | motorcycle | 3 | No | Metropolitian | (min) 52 | 13.682535 | 0 |
| 43 | 43 | 44 | 0x85b4 | MUMRES07DEL01 | 28 | 4.6 | 19.091458 | 72.827808 | 19.201458 | 72.937808 | 08-03-2022 | 19:10:00 | 19:25:00 | conditions Stormy | Jam | 1 | Snack | scooter | 1 | No | Metropolitian | (min) 22 | 16.826265 | 0 |
| 44 | 44 | 45 | 0xc644 | KOLRES16DEL02 | 37 | 4.9 | 22.539129 | 88.365507 | 22.559129 | 88.385507 | 13-02-2022 | 10:55:00 | 11:00:00 | conditions Fog | Low | 1 | Snack | scooter | 1 | No | Urban | (min) 16 | 3.027241 | 0 |
| 45 | 45 | 46 | 0x6999 | MUMRES02DEL03 | 23 | 4.9 | 19.221315 | 72.862381 | 19.281315 | 72.922381 | 24-03-2022 | 21:40:00 | 21:45:00 | conditions Sunny | Jam | 2 | Drinks | electric_scooter | 0 | No | Metropolitian | (min) 11 | 9.175207 | 0 |
| 46 | 46 | 47 | 0x63b6 | BANGRES05DEL03 | 27 | 4.9 | 12.970324 | 77.645748 | 13.030324 | 77.705748 | 19-03-2022 | 19:00:00 | 19:15:00 | conditions Windy | Medium | 1 | Drinks | scooter | 1 | No | Metropolitian | (min) 25 | 9.315082 | 0 |
| 47 | 47 | 48 | 0xa30b | CHENRES11DEL01 | 37 | 4.8 | 13.064181 | 80.236442 | 13.104181 | 80.276442 | 09-03-2022 | 16:45:00 | 16:55:00 | conditions Cloudy | Medium | 0 | Drinks | motorcycle | 1 | No | Urban | (min) 26 | 6.209030 | 0 |
| 48 | 48 | 49 | 0x3556 | SURRES09DEL01 | 33 | 5 | 21.175975 | 72.795503 | 21.185975 | 72.805503 | 19-03-2022 | 11:30:00 | 11:40:00 | conditions Sunny | High | 2 | Snack | scooter | 1 | No | Metropolitian | (min) 18 | 1.520347 | 0 |
| 49 | 49 | 50 | 0x5554 | RANCHIRES15DEL01 | 31 | 4.9 | 23.369746 | 85.339820 | 23.409746 | 85.379820 | 03-04-2022 | 15:10:00 | 15:15:00 | conditions Fog | Medium | 1 | Snack | motorcycle | 1 | No | Metropolitian | (min) 16 | 6.037234 | 0 |
# 计算配送时间
for i in range(len(data)):
t1 = datetime.strptime(data['Time_Orderd'][i], "%H:%M:%S")
t2 =datetime.strptime(data['Time_Order_picked'][i], "%H:%M:%S")
diff = t2-t1
sec = diff.total_seconds()
minute = sec/60
data['Time_prep'][i] = minute
data| level_0 | index | ID | Delivery_person_ID | Delivery_person_Age | Delivery_person_Ratings | Restaurant_latitude | Restaurant_longitude | Delivery_location_latitude | Delivery_location_longitude | Order_Date | Time_Orderd | Time_Order_picked | Weatherconditions | Road_traffic_density | Vehicle_condition | Type_of_order | Type_of_vehicle | multiple_deliveries | Festival | City | Time_taken(min) | Distance | Time_prep | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 0x4607 | INDORES13DEL02 | 37 | 4.9 | 22.745049 | 75.892471 | 22.765049 | 75.912471 | 19-03-2022 | 11:30:00 | 11:45:00 | conditions Sunny | High | 2 | Snack | motorcycle | 0 | No | Urban | (min) 24 | 3.025153 | 15 |
| 1 | 1 | 1 | 0xb379 | BANGRES18DEL02 | 34 | 4.5 | 12.913041 | 77.683237 | 13.043041 | 77.813237 | 25-03-2022 | 19:45:00 | 19:50:00 | conditions Stormy | Jam | 2 | Snack | scooter | 1 | No | Metropolitian | (min) 33 | 20.183558 | 5 |
| 2 | 2 | 2 | 0x5d6d | BANGRES19DEL01 | 23 | 4.4 | 12.914264 | 77.678400 | 12.924264 | 77.688400 | 19-03-2022 | 08:30:00 | 08:45:00 | conditions Sandstorms | Low | 0 | Drinks | motorcycle | 1 | No | Urban | (min) 26 | 1.552760 | 15 |
| 3 | 3 | 3 | 0x7a6a | COIMBRES13DEL02 | 38 | 4.7 | 11.003669 | 76.976494 | 11.053669 | 77.026494 | 05-04-2022 | 18:00:00 | 18:10:00 | conditions Sunny | Medium | 0 | Buffet | motorcycle | 1 | No | Metropolitian | (min) 21 | 7.790412 | 10 |
| 4 | 4 | 4 | 0x70a2 | CHENRES12DEL01 | 32 | 4.6 | 12.972793 | 80.249982 | 13.012793 | 80.289982 | 26-03-2022 | 13:30:00 | 13:45:00 | conditions Cloudy | High | 1 | Snack | scooter | 1 | No | Metropolitian | (min) 30 | 6.210147 | 15 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 43857 | 43857 | 45588 | 0x7c09 | JAPRES04DEL01 | 30 | 4.8 | 26.902328 | 75.794257 | 26.912328 | 75.804257 | 24-03-2022 | 11:35:00 | 11:45:00 | conditions Windy | High | 1 | Meal | motorcycle | 0 | No | Metropolitian | (min) 32 | 1.489848 | 10 |
| 43858 | 43858 | 45589 | 0xd641 | AGRRES16DEL01 | 21 | 4.6 | 0.000000 | 0.000000 | 0.070000 | 0.070000 | 16-02-2022 | 19:55:00 | 20:10:00 | conditions Windy | Jam | 0 | Buffet | motorcycle | 1 | No | Metropolitian | (min) 36 | 11.007750 | 15 |
| 43859 | 43859 | 45590 | 0x4f8d | CHENRES08DEL03 | 30 | 4.9 | 13.022394 | 80.242439 | 13.052394 | 80.272439 | 11-03-2022 | 23:50:00 | 00:05:00 | conditions Cloudy | Low | 1 | Drinks | scooter | 0 | No | Metropolitian | (min) 16 | 4.657202 | -1425 |
| 43860 | 43860 | 45591 | 0x5eee | COIMBRES11DEL01 | 20 | 4.7 | 11.001753 | 76.986241 | 11.041753 | 77.026241 | 07-03-2022 | 13:35:00 | 13:40:00 | conditions Cloudy | High | 0 | Snack | motorcycle | 1 | No | Metropolitian | (min) 26 | 6.232402 | 5 |
| 43861 | 43861 | 45592 | 0x5fb2 | RANCHIRES09DEL02 | 23 | 4.9 | 23.351058 | 85.325731 | 23.431058 | 85.405731 | 02-03-2022 | 17:10:00 | 17:15:00 | conditions Fog | Medium | 2 | Snack | scooter | 1 | No | Metropolitian | (min) 36 | 12.074412 | 5 |
43862 rows × 24 columns
data = data.drop(['Restaurant_latitude', 'Restaurant_longitude', 'Delivery_location_latitude', 'Delivery_location_longitude'], axis=1)
data = data.drop(['Time_Orderd', 'Time_Order_picked'], axis=1)
data| level_0 | index | ID | Delivery_person_ID | Delivery_person_Age | Delivery_person_Ratings | Order_Date | Weatherconditions | Road_traffic_density | Vehicle_condition | Type_of_order | Type_of_vehicle | multiple_deliveries | Festival | City | Time_taken(min) | Distance | Time_prep | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 0x4607 | INDORES13DEL02 | 37 | 4.9 | 19-03-2022 | conditions Sunny | High | 2 | Snack | motorcycle | 0 | No | Urban | (min) 24 | 3.025153 | 15 |
| 1 | 1 | 1 | 0xb379 | BANGRES18DEL02 | 34 | 4.5 | 25-03-2022 | conditions Stormy | Jam | 2 | Snack | scooter | 1 | No | Metropolitian | (min) 33 | 20.183558 | 5 |
| 2 | 2 | 2 | 0x5d6d | BANGRES19DEL01 | 23 | 4.4 | 19-03-2022 | conditions Sandstorms | Low | 0 | Drinks | motorcycle | 1 | No | Urban | (min) 26 | 1.552760 | 15 |
| 3 | 3 | 3 | 0x7a6a | COIMBRES13DEL02 | 38 | 4.7 | 05-04-2022 | conditions Sunny | Medium | 0 | Buffet | motorcycle | 1 | No | Metropolitian | (min) 21 | 7.790412 | 10 |
| 4 | 4 | 4 | 0x70a2 | CHENRES12DEL01 | 32 | 4.6 | 26-03-2022 | conditions Cloudy | High | 1 | Snack | scooter | 1 | No | Metropolitian | (min) 30 | 6.210147 | 15 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 43857 | 43857 | 45588 | 0x7c09 | JAPRES04DEL01 | 30 | 4.8 | 24-03-2022 | conditions Windy | High | 1 | Meal | motorcycle | 0 | No | Metropolitian | (min) 32 | 1.489848 | 10 |
| 43858 | 43858 | 45589 | 0xd641 | AGRRES16DEL01 | 21 | 4.6 | 16-02-2022 | conditions Windy | Jam | 0 | Buffet | motorcycle | 1 | No | Metropolitian | (min) 36 | 11.007750 | 15 |
| 43859 | 43859 | 45590 | 0x4f8d | CHENRES08DEL03 | 30 | 4.9 | 11-03-2022 | conditions Cloudy | Low | 1 | Drinks | scooter | 0 | No | Metropolitian | (min) 16 | 4.657202 | -1425 |
| 43860 | 43860 | 45591 | 0x5eee | COIMBRES11DEL01 | 20 | 4.7 | 07-03-2022 | conditions Cloudy | High | 0 | Snack | motorcycle | 1 | No | Metropolitian | (min) 26 | 6.232402 | 5 |
| 43861 | 43861 | 45592 | 0x5fb2 | RANCHIRES09DEL02 | 23 | 4.9 | 02-03-2022 | conditions Fog | Medium | 2 | Snack | scooter | 1 | No | Metropolitian | (min) 36 | 12.074412 | 5 |
43862 rows × 18 columns
# 将一个名为data的数据框(DataFrame)中的Weatherconditions列的值进行处理，然后将处理后的结果存储到一个名为Weather的新列中，最后删除原始的Weatherconditions列。
data['Weather'] = 0
for i in range(len(data)):
str1 = data['Weatherconditions'][i]
cond = str1.split(' ')
data['Weather'][i] = cond[1]
data = data.drop(['Weatherconditions'], axis=1)data = data.drop(['Order_Date'], axis=1)
data| level_0 | index | ID | Delivery_person_ID | Delivery_person_Age | Delivery_person_Ratings | Road_traffic_density | Vehicle_condition | Type_of_order | Type_of_vehicle | multiple_deliveries | Festival | City | Time_taken(min) | Distance | Time_prep | Weather | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 0x4607 | INDORES13DEL02 | 37 | 4.9 | High | 2 | Snack | motorcycle | 0 | No | Urban | (min) 24 | 3.025153 | 15 | Sunny |
| 1 | 1 | 1 | 0xb379 | BANGRES18DEL02 | 34 | 4.5 | Jam | 2 | Snack | scooter | 1 | No | Metropolitian | (min) 33 | 20.183558 | 5 | Stormy |
| 2 | 2 | 2 | 0x5d6d | BANGRES19DEL01 | 23 | 4.4 | Low | 0 | Drinks | motorcycle | 1 | No | Urban | (min) 26 | 1.552760 | 15 | Sandstorms |
| 3 | 3 | 3 | 0x7a6a | COIMBRES13DEL02 | 38 | 4.7 | Medium | 0 | Buffet | motorcycle | 1 | No | Metropolitian | (min) 21 | 7.790412 | 10 | Sunny |
| 4 | 4 | 4 | 0x70a2 | CHENRES12DEL01 | 32 | 4.6 | High | 1 | Snack | scooter | 1 | No | Metropolitian | (min) 30 | 6.210147 | 15 | Cloudy |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 43857 | 43857 | 45588 | 0x7c09 | JAPRES04DEL01 | 30 | 4.8 | High | 1 | Meal | motorcycle | 0 | No | Metropolitian | (min) 32 | 1.489848 | 10 | Windy |
| 43858 | 43858 | 45589 | 0xd641 | AGRRES16DEL01 | 21 | 4.6 | Jam | 0 | Buffet | motorcycle | 1 | No | Metropolitian | (min) 36 | 11.007750 | 15 | Windy |
| 43859 | 43859 | 45590 | 0x4f8d | CHENRES08DEL03 | 30 | 4.9 | Low | 1 | Drinks | scooter | 0 | No | Metropolitian | (min) 16 | 4.657202 | -1425 | Cloudy |
| 43860 | 43860 | 45591 | 0x5eee | COIMBRES11DEL01 | 20 | 4.7 | High | 0 | Snack | motorcycle | 1 | No | Metropolitian | (min) 26 | 6.232402 | 5 | Cloudy |
| 43861 | 43861 | 45592 | 0x5fb2 | RANCHIRES09DEL02 | 23 | 4.9 | Medium | 2 | Snack | scooter | 1 | No | Metropolitian | (min) 36 | 12.074412 | 5 | Fog |
43862 rows × 17 columns
data['Time_taken'] = 0
for i in range(len(data)):
list2 = data['Time_taken(min)'][i]
str2 = list2.split(' ')
data['Time_taken'][i] = int(str2[1])
data| level_0 | index | ID | Delivery_person_ID | Delivery_person_Age | Delivery_person_Ratings | Road_traffic_density | Vehicle_condition | Type_of_order | Type_of_vehicle | multiple_deliveries | Festival | City | Time_taken(min) | Distance | Time_prep | Weather | Time_taken | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 0x4607 | INDORES13DEL02 | 37 | 4.9 | High | 2 | Snack | motorcycle | 0 | No | Urban | (min) 24 | 3.025153 | 15 | Sunny | 24 |
| 1 | 1 | 1 | 0xb379 | BANGRES18DEL02 | 34 | 4.5 | Jam | 2 | Snack | scooter | 1 | No | Metropolitian | (min) 33 | 20.183558 | 5 | Stormy | 33 |
| 2 | 2 | 2 | 0x5d6d | BANGRES19DEL01 | 23 | 4.4 | Low | 0 | Drinks | motorcycle | 1 | No | Urban | (min) 26 | 1.552760 | 15 | Sandstorms | 26 |
| 3 | 3 | 3 | 0x7a6a | COIMBRES13DEL02 | 38 | 4.7 | Medium | 0 | Buffet | motorcycle | 1 | No | Metropolitian | (min) 21 | 7.790412 | 10 | Sunny | 21 |
| 4 | 4 | 4 | 0x70a2 | CHENRES12DEL01 | 32 | 4.6 | High | 1 | Snack | scooter | 1 | No | Metropolitian | (min) 30 | 6.210147 | 15 | Cloudy | 30 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 43857 | 43857 | 45588 | 0x7c09 | JAPRES04DEL01 | 30 | 4.8 | High | 1 | Meal | motorcycle | 0 | No | Metropolitian | (min) 32 | 1.489848 | 10 | Windy | 32 |
| 43858 | 43858 | 45589 | 0xd641 | AGRRES16DEL01 | 21 | 4.6 | Jam | 0 | Buffet | motorcycle | 1 | No | Metropolitian | (min) 36 | 11.007750 | 15 | Windy | 36 |
| 43859 | 43859 | 45590 | 0x4f8d | CHENRES08DEL03 | 30 | 4.9 | Low | 1 | Drinks | scooter | 0 | No | Metropolitian | (min) 16 | 4.657202 | -1425 | Cloudy | 16 |
| 43860 | 43860 | 45591 | 0x5eee | COIMBRES11DEL01 | 20 | 4.7 | High | 0 | Snack | motorcycle | 1 | No | Metropolitian | (min) 26 | 6.232402 | 5 | Cloudy | 26 |
| 43861 | 43861 | 45592 | 0x5fb2 | RANCHIRES09DEL02 | 23 | 4.9 | Medium | 2 | Snack | scooter | 1 | No | Metropolitian | (min) 36 | 12.074412 | 5 | Fog | 36 |
43862 rows × 18 columns
data = data.drop(['Time_taken(min)'], axis=1)
data| level_0 | index | ID | Delivery_person_ID | Delivery_person_Age | Delivery_person_Ratings | Road_traffic_density | Vehicle_condition | Type_of_order | Type_of_vehicle | multiple_deliveries | Festival | City | Distance | Time_prep | Weather | Time_taken | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 0x4607 | INDORES13DEL02 | 37 | 4.9 | High | 2 | Snack | motorcycle | 0 | No | Urban | 3.025153 | 15 | Sunny | 24 |
| 1 | 1 | 1 | 0xb379 | BANGRES18DEL02 | 34 | 4.5 | Jam | 2 | Snack | scooter | 1 | No | Metropolitian | 20.183558 | 5 | Stormy | 33 |
| 2 | 2 | 2 | 0x5d6d | BANGRES19DEL01 | 23 | 4.4 | Low | 0 | Drinks | motorcycle | 1 | No | Urban | 1.552760 | 15 | Sandstorms | 26 |
| 3 | 3 | 3 | 0x7a6a | COIMBRES13DEL02 | 38 | 4.7 | Medium | 0 | Buffet | motorcycle | 1 | No | Metropolitian | 7.790412 | 10 | Sunny | 21 |
| 4 | 4 | 4 | 0x70a2 | CHENRES12DEL01 | 32 | 4.6 | High | 1 | Snack | scooter | 1 | No | Metropolitian | 6.210147 | 15 | Cloudy | 30 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 43857 | 43857 | 45588 | 0x7c09 | JAPRES04DEL01 | 30 | 4.8 | High | 1 | Meal | motorcycle | 0 | No | Metropolitian | 1.489848 | 10 | Windy | 32 |
| 43858 | 43858 | 45589 | 0xd641 | AGRRES16DEL01 | 21 | 4.6 | Jam | 0 | Buffet | motorcycle | 1 | No | Metropolitian | 11.007750 | 15 | Windy | 36 |
| 43859 | 43859 | 45590 | 0x4f8d | CHENRES08DEL03 | 30 | 4.9 | Low | 1 | Drinks | scooter | 0 | No | Metropolitian | 4.657202 | -1425 | Cloudy | 16 |
| 43860 | 43860 | 45591 | 0x5eee | COIMBRES11DEL01 | 20 | 4.7 | High | 0 | Snack | motorcycle | 1 | No | Metropolitian | 6.232402 | 5 | Cloudy | 26 |
| 43861 | 43861 | 45592 | 0x5fb2 | RANCHIRES09DEL02 | 23 | 4.9 | Medium | 2 | Snack | scooter | 1 | No | Metropolitian | 12.074412 | 5 | Fog | 36 |
43862 rows × 17 columns
order_type = data['Type_of_order'].unique()
for i in range(len(order_type)):
data['Type_of_order'] = data['Type_of_order'].replace(order_type[i], i)
data| level_0 | index | ID | Delivery_person_ID | Delivery_person_Age | Delivery_person_Ratings | Road_traffic_density | Vehicle_condition | Type_of_order | Type_of_vehicle | multiple_deliveries | Festival | City | Distance | Time_prep | Weather | Time_taken | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 0x4607 | INDORES13DEL02 | 37 | 4.9 | High | 2 | 0 | motorcycle | 0 | No | Urban | 3.025153 | 15 | Sunny | 24 |
| 1 | 1 | 1 | 0xb379 | BANGRES18DEL02 | 34 | 4.5 | Jam | 2 | 0 | scooter | 1 | No | Metropolitian | 20.183558 | 5 | Stormy | 33 |
| 2 | 2 | 2 | 0x5d6d | BANGRES19DEL01 | 23 | 4.4 | Low | 0 | 1 | motorcycle | 1 | No | Urban | 1.552760 | 15 | Sandstorms | 26 |
| 3 | 3 | 3 | 0x7a6a | COIMBRES13DEL02 | 38 | 4.7 | Medium | 0 | 2 | motorcycle | 1 | No | Metropolitian | 7.790412 | 10 | Sunny | 21 |
| 4 | 4 | 4 | 0x70a2 | CHENRES12DEL01 | 32 | 4.6 | High | 1 | 0 | scooter | 1 | No | Metropolitian | 6.210147 | 15 | Cloudy | 30 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 43857 | 43857 | 45588 | 0x7c09 | JAPRES04DEL01 | 30 | 4.8 | High | 1 | 3 | motorcycle | 0 | No | Metropolitian | 1.489848 | 10 | Windy | 32 |
| 43858 | 43858 | 45589 | 0xd641 | AGRRES16DEL01 | 21 | 4.6 | Jam | 0 | 2 | motorcycle | 1 | No | Metropolitian | 11.007750 | 15 | Windy | 36 |
| 43859 | 43859 | 45590 | 0x4f8d | CHENRES08DEL03 | 30 | 4.9 | Low | 1 | 1 | scooter | 0 | No | Metropolitian | 4.657202 | -1425 | Cloudy | 16 |
| 43860 | 43860 | 45591 | 0x5eee | COIMBRES11DEL01 | 20 | 4.7 | High | 0 | 0 | motorcycle | 1 | No | Metropolitian | 6.232402 | 5 | Cloudy | 26 |
| 43861 | 43861 | 45592 | 0x5fb2 | RANCHIRES09DEL02 | 23 | 4.9 | Medium | 2 | 0 | scooter | 1 | No | Metropolitian | 12.074412 | 5 | Fog | 36 |
43862 rows × 17 columns
data['Type_of_vehicle'] = data['Type_of_vehicle'].replace('motorcycle ', 0)
data['Type_of_vehicle'] = data['Type_of_vehicle'].replace('scooter ', 1)
data['Type_of_vehicle'] = data['Type_of_vehicle'].replace('electric_scooter ', 2)
data| level_0 | index | ID | Delivery_person_ID | Delivery_person_Age | Delivery_person_Ratings | Road_traffic_density | Vehicle_condition | Type_of_order | Type_of_vehicle | multiple_deliveries | Festival | City | Distance | Time_prep | Weather | Time_taken | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 0x4607 | INDORES13DEL02 | 37 | 4.9 | High | 2 | 0 | 0 | 0 | No | Urban | 3.025153 | 15 | Sunny | 24 |
| 1 | 1 | 1 | 0xb379 | BANGRES18DEL02 | 34 | 4.5 | Jam | 2 | 0 | 1 | 1 | No | Metropolitian | 20.183558 | 5 | Stormy | 33 |
| 2 | 2 | 2 | 0x5d6d | BANGRES19DEL01 | 23 | 4.4 | Low | 0 | 1 | 0 | 1 | No | Urban | 1.552760 | 15 | Sandstorms | 26 |
| 3 | 3 | 3 | 0x7a6a | COIMBRES13DEL02 | 38 | 4.7 | Medium | 0 | 2 | 0 | 1 | No | Metropolitian | 7.790412 | 10 | Sunny | 21 |
| 4 | 4 | 4 | 0x70a2 | CHENRES12DEL01 | 32 | 4.6 | High | 1 | 0 | 1 | 1 | No | Metropolitian | 6.210147 | 15 | Cloudy | 30 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 43857 | 43857 | 45588 | 0x7c09 | JAPRES04DEL01 | 30 | 4.8 | High | 1 | 3 | 0 | 0 | No | Metropolitian | 1.489848 | 10 | Windy | 32 |
| 43858 | 43858 | 45589 | 0xd641 | AGRRES16DEL01 | 21 | 4.6 | Jam | 0 | 2 | 0 | 1 | No | Metropolitian | 11.007750 | 15 | Windy | 36 |
| 43859 | 43859 | 45590 | 0x4f8d | CHENRES08DEL03 | 30 | 4.9 | Low | 1 | 1 | 1 | 0 | No | Metropolitian | 4.657202 | -1425 | Cloudy | 16 |
| 43860 | 43860 | 45591 | 0x5eee | COIMBRES11DEL01 | 20 | 4.7 | High | 0 | 0 | 0 | 1 | No | Metropolitian | 6.232402 | 5 | Cloudy | 26 |
| 43861 | 43861 | 45592 | 0x5fb2 | RANCHIRES09DEL02 | 23 | 4.9 | Medium | 2 | 0 | 1 | 1 | No | Metropolitian | 12.074412 | 5 | Fog | 36 |
43862 rows × 17 columns
road_traffic = data['Road_traffic_density'].unique()
for i in range(len(road_traffic)):
data['Road_traffic_density'] = data['Road_traffic_density'].replace(road_traffic[i], i)
data| level_0 | index | ID | Delivery_person_ID | Delivery_person_Age | Delivery_person_Ratings | Road_traffic_density | Vehicle_condition | Type_of_order | Type_of_vehicle | multiple_deliveries | Festival | City | Distance | Time_prep | Weather | Time_taken | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 0x4607 | INDORES13DEL02 | 37 | 4.9 | 0 | 2 | 0 | 0 | 0 | No | Urban | 3.025153 | 15 | Sunny | 24 |
| 1 | 1 | 1 | 0xb379 | BANGRES18DEL02 | 34 | 4.5 | 1 | 2 | 0 | 1 | 1 | No | Metropolitian | 20.183558 | 5 | Stormy | 33 |
| 2 | 2 | 2 | 0x5d6d | BANGRES19DEL01 | 23 | 4.4 | 2 | 0 | 1 | 0 | 1 | No | Urban | 1.552760 | 15 | Sandstorms | 26 |
| 3 | 3 | 3 | 0x7a6a | COIMBRES13DEL02 | 38 | 4.7 | 3 | 0 | 2 | 0 | 1 | No | Metropolitian | 7.790412 | 10 | Sunny | 21 |
| 4 | 4 | 4 | 0x70a2 | CHENRES12DEL01 | 32 | 4.6 | 0 | 1 | 0 | 1 | 1 | No | Metropolitian | 6.210147 | 15 | Cloudy | 30 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 43857 | 43857 | 45588 | 0x7c09 | JAPRES04DEL01 | 30 | 4.8 | 0 | 1 | 3 | 0 | 0 | No | Metropolitian | 1.489848 | 10 | Windy | 32 |
| 43858 | 43858 | 45589 | 0xd641 | AGRRES16DEL01 | 21 | 4.6 | 1 | 0 | 2 | 0 | 1 | No | Metropolitian | 11.007750 | 15 | Windy | 36 |
| 43859 | 43859 | 45590 | 0x4f8d | CHENRES08DEL03 | 30 | 4.9 | 2 | 1 | 1 | 1 | 0 | No | Metropolitian | 4.657202 | -1425 | Cloudy | 16 |
| 43860 | 43860 | 45591 | 0x5eee | COIMBRES11DEL01 | 20 | 4.7 | 0 | 0 | 0 | 0 | 1 | No | Metropolitian | 6.232402 | 5 | Cloudy | 26 |
| 43861 | 43861 | 45592 | 0x5fb2 | RANCHIRES09DEL02 | 23 | 4.9 | 3 | 2 | 0 | 1 | 1 | No | Metropolitian | 12.074412 | 5 | Fog | 36 |
43862 rows × 17 columns
festival = data['Festival'].unique()
for i in range(len(festival)):
data['Festival'] = data['Festival'].replace(festival[i], i)
data| level_0 | index | ID | Delivery_person_ID | Delivery_person_Age | Delivery_person_Ratings | Road_traffic_density | Vehicle_condition | Type_of_order | Type_of_vehicle | multiple_deliveries | Festival | City | Distance | Time_prep | Weather | Time_taken | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 0x4607 | INDORES13DEL02 | 37 | 4.9 | 0 | 2 | 0 | 0 | 0 | 0.0 | Urban | 3.025153 | 15 | Sunny | 24 |
| 1 | 1 | 1 | 0xb379 | BANGRES18DEL02 | 34 | 4.5 | 1 | 2 | 0 | 1 | 1 | 0.0 | Metropolitian | 20.183558 | 5 | Stormy | 33 |
| 2 | 2 | 2 | 0x5d6d | BANGRES19DEL01 | 23 | 4.4 | 2 | 0 | 1 | 0 | 1 | 0.0 | Urban | 1.552760 | 15 | Sandstorms | 26 |
| 3 | 3 | 3 | 0x7a6a | COIMBRES13DEL02 | 38 | 4.7 | 3 | 0 | 2 | 0 | 1 | 0.0 | Metropolitian | 7.790412 | 10 | Sunny | 21 |
| 4 | 4 | 4 | 0x70a2 | CHENRES12DEL01 | 32 | 4.6 | 0 | 1 | 0 | 1 | 1 | 0.0 | Metropolitian | 6.210147 | 15 | Cloudy | 30 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 43857 | 43857 | 45588 | 0x7c09 | JAPRES04DEL01 | 30 | 4.8 | 0 | 1 | 3 | 0 | 0 | 0.0 | Metropolitian | 1.489848 | 10 | Windy | 32 |
| 43858 | 43858 | 45589 | 0xd641 | AGRRES16DEL01 | 21 | 4.6 | 1 | 0 | 2 | 0 | 1 | 0.0 | Metropolitian | 11.007750 | 15 | Windy | 36 |
| 43859 | 43859 | 45590 | 0x4f8d | CHENRES08DEL03 | 30 | 4.9 | 2 | 1 | 1 | 1 | 0 | 0.0 | Metropolitian | 4.657202 | -1425 | Cloudy | 16 |
| 43860 | 43860 | 45591 | 0x5eee | COIMBRES11DEL01 | 20 | 4.7 | 0 | 0 | 0 | 0 | 1 | 0.0 | Metropolitian | 6.232402 | 5 | Cloudy | 26 |
| 43861 | 43861 | 45592 | 0x5fb2 | RANCHIRES09DEL02 | 23 | 4.9 | 3 | 2 | 0 | 1 | 1 | 0.0 | Metropolitian | 12.074412 | 5 | Fog | 36 |
43862 rows × 17 columns
city = data['City'].unique()
for i in range(len(city)):
data['City'] = data['City'].replace(city[i], i)
data| level_0 | index | ID | Delivery_person_ID | Delivery_person_Age | Delivery_person_Ratings | Road_traffic_density | Vehicle_condition | Type_of_order | Type_of_vehicle | multiple_deliveries | Festival | City | Distance | Time_prep | Weather | Time_taken | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 0x4607 | INDORES13DEL02 | 37 | 4.9 | 0 | 2 | 0 | 0 | 0 | 0.0 | 0.0 | 3.025153 | 15 | Sunny | 24 |
| 1 | 1 | 1 | 0xb379 | BANGRES18DEL02 | 34 | 4.5 | 1 | 2 | 0 | 1 | 1 | 0.0 | 1.0 | 20.183558 | 5 | Stormy | 33 |
| 2 | 2 | 2 | 0x5d6d | BANGRES19DEL01 | 23 | 4.4 | 2 | 0 | 1 | 0 | 1 | 0.0 | 0.0 | 1.552760 | 15 | Sandstorms | 26 |
| 3 | 3 | 3 | 0x7a6a | COIMBRES13DEL02 | 38 | 4.7 | 3 | 0 | 2 | 0 | 1 | 0.0 | 1.0 | 7.790412 | 10 | Sunny | 21 |
| 4 | 4 | 4 | 0x70a2 | CHENRES12DEL01 | 32 | 4.6 | 0 | 1 | 0 | 1 | 1 | 0.0 | 1.0 | 6.210147 | 15 | Cloudy | 30 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 43857 | 43857 | 45588 | 0x7c09 | JAPRES04DEL01 | 30 | 4.8 | 0 | 1 | 3 | 0 | 0 | 0.0 | 1.0 | 1.489848 | 10 | Windy | 32 |
| 43858 | 43858 | 45589 | 0xd641 | AGRRES16DEL01 | 21 | 4.6 | 1 | 0 | 2 | 0 | 1 | 0.0 | 1.0 | 11.007750 | 15 | Windy | 36 |
| 43859 | 43859 | 45590 | 0x4f8d | CHENRES08DEL03 | 30 | 4.9 | 2 | 1 | 1 | 1 | 0 | 0.0 | 1.0 | 4.657202 | -1425 | Cloudy | 16 |
| 43860 | 43860 | 45591 | 0x5eee | COIMBRES11DEL01 | 20 | 4.7 | 0 | 0 | 0 | 0 | 1 | 0.0 | 1.0 | 6.232402 | 5 | Cloudy | 26 |
| 43861 | 43861 | 45592 | 0x5fb2 | RANCHIRES09DEL02 | 23 | 4.9 | 3 | 2 | 0 | 1 | 1 | 0.0 | 1.0 | 12.074412 | 5 | Fog | 36 |
43862 rows × 17 columns
weather = data['Weather'].unique()
for i in range(len(weather)):
data['Weather'] = data['Weather'].replace(weather[i], i)
data| level_0 | index | ID | Delivery_person_ID | Delivery_person_Age | Delivery_person_Ratings | Road_traffic_density | Vehicle_condition | Type_of_order | Type_of_vehicle | multiple_deliveries | Festival | City | Distance | Time_prep | Weather | Time_taken | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 0x4607 | INDORES13DEL02 | 37 | 4.9 | 0 | 2 | 0 | 0 | 0 | 0.0 | 0.0 | 3.025153 | 15 | 0 | 24 |
| 1 | 1 | 1 | 0xb379 | BANGRES18DEL02 | 34 | 4.5 | 1 | 2 | 0 | 1 | 1 | 0.0 | 1.0 | 20.183558 | 5 | 1 | 33 |
| 2 | 2 | 2 | 0x5d6d | BANGRES19DEL01 | 23 | 4.4 | 2 | 0 | 1 | 0 | 1 | 0.0 | 0.0 | 1.552760 | 15 | 2 | 26 |
| 3 | 3 | 3 | 0x7a6a | COIMBRES13DEL02 | 38 | 4.7 | 3 | 0 | 2 | 0 | 1 | 0.0 | 1.0 | 7.790412 | 10 | 0 | 21 |
| 4 | 4 | 4 | 0x70a2 | CHENRES12DEL01 | 32 | 4.6 | 0 | 1 | 0 | 1 | 1 | 0.0 | 1.0 | 6.210147 | 15 | 3 | 30 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 43857 | 43857 | 45588 | 0x7c09 | JAPRES04DEL01 | 30 | 4.8 | 0 | 1 | 3 | 0 | 0 | 0.0 | 1.0 | 1.489848 | 10 | 5 | 32 |
| 43858 | 43858 | 45589 | 0xd641 | AGRRES16DEL01 | 21 | 4.6 | 1 | 0 | 2 | 0 | 1 | 0.0 | 1.0 | 11.007750 | 15 | 5 | 36 |
| 43859 | 43859 | 45590 | 0x4f8d | CHENRES08DEL03 | 30 | 4.9 | 2 | 1 | 1 | 1 | 0 | 0.0 | 1.0 | 4.657202 | -1425 | 3 | 16 |
| 43860 | 43860 | 45591 | 0x5eee | COIMBRES11DEL01 | 20 | 4.7 | 0 | 0 | 0 | 0 | 1 | 0.0 | 1.0 | 6.232402 | 5 | 3 | 26 |
| 43861 | 43861 | 45592 | 0x5fb2 | RANCHIRES09DEL02 | 23 | 4.9 | 3 | 2 | 0 | 1 | 1 | 0.0 | 1.0 | 12.074412 | 5 | 4 | 36 |
43862 rows × 17 columns
data = data[data['Time_prep']>0]
data = data[data['Delivery_person_Age']!="NaN "]
data = data[data['Delivery_person_Ratings']!="NaN "]
data = data[data['Road_traffic_density']!="NaN "]
data = data[data['Vehicle_condition']!="NaN"]
data = data[data['Type_of_order']!="NaN"]
data = data[data['Type_of_vehicle']!="NaN"]
data = data[data['multiple_deliveries']!="NaN"]
data = data[data['Festival']!="NaN"]
data = data[data['City']!="NaN"]
data = data[data['Distance']!="NaN"]
data = data[data['Time_prep']!="NaN"]
data = data[data['Weather']!="NaN"]
data| level_0 | index | ID | Delivery_person_ID | Delivery_person_Age | Delivery_person_Ratings | Road_traffic_density | Vehicle_condition | Type_of_order | Type_of_vehicle | multiple_deliveries | Festival | City | Distance | Time_prep | Weather | Time_taken | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 0x4607 | INDORES13DEL02 | 37 | 4.9 | 0 | 2 | 0 | 0 | 0 | 0.0 | 0.0 | 3.025153 | 15 | 0 | 24 |
| 1 | 1 | 1 | 0xb379 | BANGRES18DEL02 | 34 | 4.5 | 1 | 2 | 0 | 1 | 1 | 0.0 | 1.0 | 20.183558 | 5 | 1 | 33 |
| 2 | 2 | 2 | 0x5d6d | BANGRES19DEL01 | 23 | 4.4 | 2 | 0 | 1 | 0 | 1 | 0.0 | 0.0 | 1.552760 | 15 | 2 | 26 |
| 3 | 3 | 3 | 0x7a6a | COIMBRES13DEL02 | 38 | 4.7 | 3 | 0 | 2 | 0 | 1 | 0.0 | 1.0 | 7.790412 | 10 | 0 | 21 |
| 4 | 4 | 4 | 0x70a2 | CHENRES12DEL01 | 32 | 4.6 | 0 | 1 | 0 | 1 | 1 | 0.0 | 1.0 | 6.210147 | 15 | 3 | 30 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 43856 | 43856 | 45587 | 0x1178 | RANCHIRES16DEL01 | 35 | 4.2 | 1 | 2 | 1 | 0 | 1 | 0.0 | 1.0 | 16.600295 | 10 | 5 | 33 |
| 43857 | 43857 | 45588 | 0x7c09 | JAPRES04DEL01 | 30 | 4.8 | 0 | 1 | 3 | 0 | 0 | 0.0 | 1.0 | 1.489848 | 10 | 5 | 32 |
| 43858 | 43858 | 45589 | 0xd641 | AGRRES16DEL01 | 21 | 4.6 | 1 | 0 | 2 | 0 | 1 | 0.0 | 1.0 | 11.007750 | 15 | 5 | 36 |
| 43860 | 43860 | 45591 | 0x5eee | COIMBRES11DEL01 | 20 | 4.7 | 0 | 0 | 0 | 0 | 1 | 0.0 | 1.0 | 6.232402 | 5 | 3 | 26 |
| 43861 | 43861 | 45592 | 0x5fb2 | RANCHIRES09DEL02 | 23 | 4.9 | 3 | 2 | 0 | 1 | 1 | 0.0 | 1.0 | 12.074412 | 5 | 4 | 36 |
43031 rows × 17 columns
data['Delivery_person_Age'] = pd.to_numeric(data['Delivery_person_Age'])
data['Delivery_person_Ratings'] = pd.to_numeric(data['Delivery_person_Ratings'])
data['multiple_deliveries'] = pd.to_numeric(data['multiple_deliveries'])
data| level_0 | index | ID | Delivery_person_ID | Delivery_person_Age | Delivery_person_Ratings | Road_traffic_density | Vehicle_condition | Type_of_order | Type_of_vehicle | multiple_deliveries | Festival | City | Distance | Time_prep | Weather | Time_taken | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 0x4607 | INDORES13DEL02 | 37.0 | 4.9 | 0 | 2 | 0 | 0 | 0.0 | 0.0 | 0.0 | 3.025153 | 15 | 0 | 24 |
| 1 | 1 | 1 | 0xb379 | BANGRES18DEL02 | 34.0 | 4.5 | 1 | 2 | 0 | 1 | 1.0 | 0.0 | 1.0 | 20.183558 | 5 | 1 | 33 |
| 2 | 2 | 2 | 0x5d6d | BANGRES19DEL01 | 23.0 | 4.4 | 2 | 0 | 1 | 0 | 1.0 | 0.0 | 0.0 | 1.552760 | 15 | 2 | 26 |
| 3 | 3 | 3 | 0x7a6a | COIMBRES13DEL02 | 38.0 | 4.7 | 3 | 0 | 2 | 0 | 1.0 | 0.0 | 1.0 | 7.790412 | 10 | 0 | 21 |
| 4 | 4 | 4 | 0x70a2 | CHENRES12DEL01 | 32.0 | 4.6 | 0 | 1 | 0 | 1 | 1.0 | 0.0 | 1.0 | 6.210147 | 15 | 3 | 30 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 43856 | 43856 | 45587 | 0x1178 | RANCHIRES16DEL01 | 35.0 | 4.2 | 1 | 2 | 1 | 0 | 1.0 | 0.0 | 1.0 | 16.600295 | 10 | 5 | 33 |
| 43857 | 43857 | 45588 | 0x7c09 | JAPRES04DEL01 | 30.0 | 4.8 | 0 | 1 | 3 | 0 | 0.0 | 0.0 | 1.0 | 1.489848 | 10 | 5 | 32 |
| 43858 | 43858 | 45589 | 0xd641 | AGRRES16DEL01 | 21.0 | 4.6 | 1 | 0 | 2 | 0 | 1.0 | 0.0 | 1.0 | 11.007750 | 15 | 5 | 36 |
| 43860 | 43860 | 45591 | 0x5eee | COIMBRES11DEL01 | 20.0 | 4.7 | 0 | 0 | 0 | 0 | 1.0 | 0.0 | 1.0 | 6.232402 | 5 | 3 | 26 |
| 43861 | 43861 | 45592 | 0x5fb2 | RANCHIRES09DEL02 | 23.0 | 4.9 | 3 | 2 | 0 | 1 | 1.0 | 0.0 | 1.0 | 12.074412 | 5 | 4 | 36 |
43031 rows × 17 columns
data = data.drop(['Delivery_person_ID', 'ID', 'index', 'level_0'], axis=1)
data| Delivery_person_Age | Delivery_person_Ratings | Road_traffic_density | Vehicle_condition | Type_of_order | Type_of_vehicle | multiple_deliveries | Festival | City | Distance | Time_prep | Weather | Time_taken | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 37.0 | 4.9 | 0 | 2 | 0 | 0 | 0.0 | 0.0 | 0.0 | 3.025153 | 15 | 0 | 24 |
| 1 | 34.0 | 4.5 | 1 | 2 | 0 | 1 | 1.0 | 0.0 | 1.0 | 20.183558 | 5 | 1 | 33 |
| 2 | 23.0 | 4.4 | 2 | 0 | 1 | 0 | 1.0 | 0.0 | 0.0 | 1.552760 | 15 | 2 | 26 |
| 3 | 38.0 | 4.7 | 3 | 0 | 2 | 0 | 1.0 | 0.0 | 1.0 | 7.790412 | 10 | 0 | 21 |
| 4 | 32.0 | 4.6 | 0 | 1 | 0 | 1 | 1.0 | 0.0 | 1.0 | 6.210147 | 15 | 3 | 30 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 43856 | 35.0 | 4.2 | 1 | 2 | 1 | 0 | 1.0 | 0.0 | 1.0 | 16.600295 | 10 | 5 | 33 |
| 43857 | 30.0 | 4.8 | 0 | 1 | 3 | 0 | 0.0 | 0.0 | 1.0 | 1.489848 | 10 | 5 | 32 |
| 43858 | 21.0 | 4.6 | 1 | 0 | 2 | 0 | 1.0 | 0.0 | 1.0 | 11.007750 | 15 | 5 | 36 |
| 43860 | 20.0 | 4.7 | 0 | 0 | 0 | 0 | 1.0 | 0.0 | 1.0 | 6.232402 | 5 | 3 | 26 |
| 43861 | 23.0 | 4.9 | 3 | 2 | 0 | 1 | 1.0 | 0.0 | 1.0 | 12.074412 | 5 | 4 | 36 |
43031 rows × 13 columns
data['Distance'].describe()
data = data[data['Distance']<100]
data = data.reset_index()
data| index | Delivery_person_Age | Delivery_person_Ratings | Road_traffic_density | Vehicle_condition | Type_of_order | Type_of_vehicle | multiple_deliveries | Festival | City | Distance | Time_prep | Weather | Time_taken | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 37.0 | 4.9 | 0 | 2 | 0 | 0 | 0.0 | 0.0 | 0.0 | 3.025153 | 15 | 0 | 24 |
| 1 | 1 | 34.0 | 4.5 | 1 | 2 | 0 | 1 | 1.0 | 0.0 | 1.0 | 20.183558 | 5 | 1 | 33 |
| 2 | 2 | 23.0 | 4.4 | 2 | 0 | 1 | 0 | 1.0 | 0.0 | 0.0 | 1.552760 | 15 | 2 | 26 |
| 3 | 3 | 38.0 | 4.7 | 3 | 0 | 2 | 0 | 1.0 | 0.0 | 1.0 | 7.790412 | 10 | 0 | 21 |
| 4 | 4 | 32.0 | 4.6 | 0 | 1 | 0 | 1 | 1.0 | 0.0 | 1.0 | 6.210147 | 15 | 3 | 30 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 42872 | 43856 | 35.0 | 4.2 | 1 | 2 | 1 | 0 | 1.0 | 0.0 | 1.0 | 16.600295 | 10 | 5 | 33 |
| 42873 | 43857 | 30.0 | 4.8 | 0 | 1 | 3 | 0 | 0.0 | 0.0 | 1.0 | 1.489848 | 10 | 5 | 32 |
| 42874 | 43858 | 21.0 | 4.6 | 1 | 0 | 2 | 0 | 1.0 | 0.0 | 1.0 | 11.007750 | 15 | 5 | 36 |
| 42875 | 43860 | 20.0 | 4.7 | 0 | 0 | 0 | 0 | 1.0 | 0.0 | 1.0 | 6.232402 | 5 | 3 | 26 |
| 42876 | 43861 | 23.0 | 4.9 | 3 | 2 | 0 | 1 | 1.0 | 0.0 | 1.0 | 12.074412 | 5 | 4 | 36 |
42877 rows × 14 columns
data.isna().sum()index 0 Delivery_person_Age 211 Delivery_person_Ratings 257 Road_traffic_density 0 Vehicle_condition 0 Type_of_order 0 Type_of_vehicle 0 multiple_deliveries 927 Festival 0 City 0 Distance 0 Time_prep 0 Weather 0 Time_taken 0 dtype: int64
data['multiple_deliveries'].head(30)0 0.0 1 1.0 2 1.0 3 1.0 4 1.0 5 1.0 6 1.0 7 1.0 8 1.0 9 3.0 10 1.0 11 1.0 12 0.0 13 1.0 14 1.0 15 1.0 16 1.0 17 NaN 18 1.0 19 2.0 20 0.0 21 1.0 22 1.0 23 1.0 24 1.0 25 0.0 26 0.0 27 1.0 28 0.0 29 1.0 Name: multiple_deliveries, dtype: float64
data['Delivery_person_Age'] = data['Delivery_person_Age'].fillna(data['Delivery_person_Age'].mean())
data['Delivery_person_Ratings'] = data['Delivery_person_Ratings'].fillna(data['Delivery_person_Ratings'].mean())
data['multiple_deliveries'].unique()
data['multiple_deliveries'].median()
data['multiple_deliveries'] = data['multiple_deliveries'].fillna(data['multiple_deliveries'].median())data.isna().sum()index 0 Delivery_person_Age 0 Delivery_person_Ratings 0 Road_traffic_density 0 Vehicle_condition 0 Type_of_order 0 Type_of_vehicle 0 multiple_deliveries 0 Festival 0 City 0 Distance 0 Time_prep 0 Weather 0 Time_taken 0 dtype: int64
import seaborn as snsplt.figure(figsize=(15,7))
ax = sns.heatmap(data.corr(), annot=True)
columns=data[['Delivery_person_Age','Delivery_person_Ratings','Road_traffic_density','Vehicle_condition','Type_of_order','Time_taken']]
sns.pairplot(columns)
plt.show()
from sklearn.model_selection import train_test_splitx = data.drop(['Time_taken', 'index'], axis=1)
x| Delivery_person_Age | Delivery_person_Ratings | Road_traffic_density | Vehicle_condition | Type_of_order | Type_of_vehicle | multiple_deliveries | Festival | City | Distance | Time_prep | Weather | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 37.0 | 4.9 | 0 | 2 | 0 | 0 | 0.0 | 0.0 | 0.0 | 3.025153 | 15 | 0 |
| 1 | 34.0 | 4.5 | 1 | 2 | 0 | 1 | 1.0 | 0.0 | 1.0 | 20.183558 | 5 | 1 |
| 2 | 23.0 | 4.4 | 2 | 0 | 1 | 0 | 1.0 | 0.0 | 0.0 | 1.552760 | 15 | 2 |
| 3 | 38.0 | 4.7 | 3 | 0 | 2 | 0 | 1.0 | 0.0 | 1.0 | 7.790412 | 10 | 0 |
| 4 | 32.0 | 4.6 | 0 | 1 | 0 | 1 | 1.0 | 0.0 | 1.0 | 6.210147 | 15 | 3 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 42872 | 35.0 | 4.2 | 1 | 2 | 1 | 0 | 1.0 | 0.0 | 1.0 | 16.600295 | 10 | 5 |
| 42873 | 30.0 | 4.8 | 0 | 1 | 3 | 0 | 0.0 | 0.0 | 1.0 | 1.489848 | 10 | 5 |
| 42874 | 21.0 | 4.6 | 1 | 0 | 2 | 0 | 1.0 | 0.0 | 1.0 | 11.007750 | 15 | 5 |
| 42875 | 20.0 | 4.7 | 0 | 0 | 0 | 0 | 1.0 | 0.0 | 1.0 | 6.232402 | 5 | 3 |
| 42876 | 23.0 | 4.9 | 3 | 2 | 0 | 1 | 1.0 | 0.0 | 1.0 | 12.074412 | 5 | 4 |
42877 rows × 12 columns
y = data[['Time_taken']]
y| Time_taken | |
|---|---|
| 0 | 24 |
| 1 | 33 |
| 2 | 26 |
| 3 | 21 |
| 4 | 30 |
| ... | ... |
| 42872 | 33 |
| 42873 | 32 |
| 42874 | 36 |
| 42875 | 26 |
| 42876 | 36 |
42877 rows × 1 columns
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=42)from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import GridSearchCV
from sklearn import metrics
import warningswarnings.filterwarnings('ignore')随机森林回归
forest = RandomForestRegressor(n_estimators= 200, min_samples_split= 4, min_samples_leaf= 1)
forest.fit(x_train, y_train)RandomForestRegressor
RandomForestRegressor(min_samples_split=4, n_estimators=200)
forest.score(x_test, y_test)0.8366806837953505
线性回归
from sklearn.linear_model import LinearRegression
reg = LinearRegression()
reg.fit(x_train, y_train)
reg.score(x_test, y_test)0.46966387660478515
from sklearn import linear_model
reg2=linear_model.LassoLars(alpha=.1, normalize=False)
reg2.fit(x_train,y_train)LassoLars
LassoLars(alpha=0.1, normalize=False)
reg2.score(x_train, y_train)0.4674006811969696
完整代码详见源网站：外卖配送时间预测项目-VenusAI (aideeplearning.cn)
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