04 数据分析与挖掘练习题

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1.0 背景

该数据集是澳大利亚某公司无人机送货的记录（2018年8月之前），主要包括以下的列：

• 'Id' ： 记录的ID
• 'Drone Type' ： 无人机的类别分 1类 2类 3类
• 'Post Type' ： 运送的类别 0为普通运送 1为速运
• 'Package Weight' ：包裹的重量
• 'Origin Region' ：出发地的区域代码
• 'Destination Region' ：目的地的区域代码
• 'Origin Latitude' ：出发纬度
• 'Origin Longitude' ：出发经度
• 'Destination Latitude' ：目的地纬度
• 'Destination Longitude' ：目的地经度
• 'Journey Distance' ：运送距离
• 'Departure Date' ：出发日期
• 'Departure Time' ：出发时间
• 'Travel Time' ：飞行时间
• 'Delivery Time' ：到达时间
• 'Delivery Fare' ：运送费用

pd.options.display.max_rows = 10

2.0 载入包和数据

#loading library
import pandas as pd
import re
import matplotlib.pyplot as plt
#import seaborn as sns  !pip intall seaborn
import scipy.stats as st
import numpy as np
import math
from math import *
from datetime import datetime,timedelta

任务1：载入名为‘data.csv’的数据

data = pd.read_csv('data.csv')

DataFrame
Series

type(data)

pandas.core.frame.DataFrame

3.0 数据初步探索

任务2：找出数据有多少行列

data.shape

(37903, 16)

任务3：查看列的统计信息

提示：describe()

data.describe()

	Drone Type	Post Type	Package Weight	Origin Region	Destination Region	Origin Latitude	Origin Longitude	Destination Latitude	Destination Longitude	Journey Distance	Travel Time	Delivery Fare
count	37893.000000	37883.000000	37903.000000	37893.000000	37893.000000	37903.000000	37903.000000	37903.000000	37903.000000	37903.000000	37863.000000	37874.000000
mean	1.699285	0.298709	25.669901	20.476684	20.452722	-37.728867	145.423058	-37.722054	145.434035	221.954150	208.794518	126.814976
std	0.779845	0.457698	12.107150	11.501110	11.509311	1.899183	6.923993	1.895621	6.909055	116.604355	107.612447	59.314445
min	1.000000	0.000000	5.001000	1.000000	1.000000	-39.006941	-148.337157	-39.006941	-147.691902	0.664000	7.420000	54.020000
25%	1.000000	0.000000	15.199000	11.000000	11.000000	-38.443034	143.965002	-38.431293	143.951543	131.044500	125.165000	97.440000
50%	2.000000	0.000000	25.446000	20.000000	20.000000	-37.707244	145.423386	-37.700695	145.450794	209.796000	196.370000	120.045000
75%	2.000000	1.000000	35.953500	30.000000	30.000000	-37.094433	147.170334	-37.080256	147.216886	302.052000	281.250000	145.800000
max	3.000000	1.000000	55.992000	40.000000	40.000000	38.986998	148.450576	38.989473	148.450576	556.637000	545.460000	1217.690000

任务4：找出每个列名称

data.columns

Index(['Id', 'Drone Type', 'Post Type', 'Package Weight', 'Origin Region',
       'Destination Region', 'Origin Latitude', 'Origin Longitude',
       'Destination Latitude', 'Destination Longitude', 'Journey Distance',
       'Departure Date', 'Departure Time', 'Travel Time', 'Delivery Time',
       'Delivery Fare'],
      dtype='object')

任务5：找出每个列的属性

是Obeject 还是 float

data.info()
data['Drone Type'] = data['Drone Type'].astype('str')

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 37903 entries, 0 to 37902
Data columns (total 16 columns):
Id                       37878 non-null object
Drone Type               37893 non-null float64
Post Type                37883 non-null float64
Package Weight           37903 non-null float64
Origin Region            37893 non-null float64
Destination Region       37893 non-null float64
Origin Latitude          37903 non-null float64
Origin Longitude         37903 non-null float64
Destination Latitude     37903 non-null float64
Destination Longitude    37903 non-null float64
Journey Distance         37903 non-null float64
Departure Date           37903 non-null object
Departure Time           37903 non-null object
Travel Time              37863 non-null float64
Delivery Time            37903 non-null object
Delivery Fare            37874 non-null float64
dtypes: float64(12), object(4)
memory usage: 4.6+ MB

data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 37903 entries, 0 to 37902
Data columns (total 16 columns):
Id                       37878 non-null object
Drone Type               37903 non-null object
Post Type                37883 non-null float64
Package Weight           37903 non-null float64
Origin Region            37893 non-null float64
Destination Region       37893 non-null float64
Origin Latitude          37903 non-null float64
Origin Longitude         37903 non-null float64
Destination Latitude     37903 non-null float64
Destination Longitude    37903 non-null float64
Journey Distance         37903 non-null float64
Departure Date           37903 non-null object
Departure Time           37903 non-null object
Travel Time              37863 non-null float64
Delivery Time            37903 non-null object
Delivery Fare            37874 non-null float64
dtypes: float64(11), object(5)
memory usage: 4.6+ MB

任务6：找出数据的前5行和后5行

data.head()

	Id	Drone Type	Post Type	Package Weight	Origin Region	Destination Region	Origin Latitude	Origin Longitude	Destination Latitude	Destination Longitude	Journey Distance	Departure Date	Departure Time	Travel Time	Delivery Time	Delivery Fare
0	ID1645282128	2.0	0.0	21.686	19.0	38.0	-37.089338	144.429529	-37.639134	142.891391	149.212	2018-01-16	09:38:17	140.19	11:58:28	99.25
1	ID1697620764	nan	0.0	39.075	15.0	15.0	-38.481935	146.009567	-38.585528	146.199827	20.185	2018-02-10	04:28:17	22.84	4:51:07	149.04
2	ID1543933503	2.0	0.0	7.243	33.0	28.0	-38.754167	144.509664	-38.242224	147.855342	296.975	2018-05-05	01:38:03	272.52	6:10:34	141.48
3	ID1756517608	2.0	0.0	13.383	10.0	38.0	-37.240526	147.568019	-37.687178	142.991188	407.396	2018-06-11	11:43:04	371.40	17:54:27	122.82
4	ID1832325834	2.0	0.0	8.123	1.0	8.0	-38.143985	143.798292	-38.548315	144.769228	95.974	2018-03-16	14:50:25	92.51	16:22:55	111.97
5	ID1802448576	2.0	0.0	32.859	2.0	28.0	-37.421211	148.044072	-38.159627	148.194048	83.250	2018-05-15	16:35:50	81.12	17:56:57	113.88
6	ID1940231408	1.0	0.0	20.616	29.0	36.0	-37.173949	143.140662	-37.021605	145.197043	183.363	2018-04-01	19:31:12	184.22	22:35:25	85.60
7	ID1299303958	2.0	0.0	44.577	36.0	31.0	-37.123190	145.236196	-37.667199	143.877650	134.543	2018-05-01	18:39:36	127.05	20:46:38	114.22
8	ID1752722028	1.0	0.0	15.363	20.0	30.0	-38.850561	148.317253	-38.024914	144.823938	318.132	2018-05-27	14:48:17	314.64	20:02:55	87.39
9	ID5995243590	1.0	1.0	36.190	18.0	28.0	-38.070189	142.950207	-37.996817	148.026520	445.106	2018-06-17	12:53:02	437.52	20:10:33	142.95

data.tail()

	Id	Drone Type	Post Type	Package Weight	Origin Region	Destination Region	Origin Latitude	Origin Longitude	Destination Latitude	Destination Longitude	Journey Distance	Departure Date	Departure Time	Travel Time	Delivery Time	Delivery Fare
37898	NaN	3.0	1.0	27.153	39.0	16.0	-38.446310	148.292498	-36.739777	143.604529	454.968	2018-07-23	08:29:19	366.09	14:35:24	188.49
37899	ID5862552991	1.0	1.0	40.363	9.0	38.0	-38.983710	145.320518	-37.673908	142.879230	258.259	2018-06-26	15:55:37	256.70	20:12:18	122.98
37900	ID5339104082	1.0	1.0	35.955	13.0	32.0	-38.292301	147.562013	-36.605285	148.293183	198.597	2018-03-19	16:41:10	198.97	20:00:08	118.47
37901	ID5468787866	2.0	1.0	29.566	33.0	23.0	-38.853243	144.508346	-37.727691	145.662270	160.816	2018-02-26	04:22:30	150.58	6:53:04	161.96
37902	ID1448126768	3.0	0.0	44.070	36.0	34.0	-37.129313	145.266426	-38.428477	143.341632	222.687	2018-07-07	08:01:42	182.71	11:04:24	144.41

任务7：找出所有列的缺失值个数并且按照多到少排列

隐藏任务：可视化缺失值的列

data.isnull().sum().sort_values(ascending=False)

Travel Time              40
Delivery Fare            29
Id                       25
Post Type                20
Destination Region       10
Origin Region            10
Delivery Time             0
Departure Time            0
Departure Date            0
Journey Distance          0
Destination Longitude     0
Destination Latitude      0
Origin Longitude          0
Origin Latitude           0
Package Weight            0
Drone Type                0
dtype: int64

count = {}
for col in data.columns:
    count_null = data[col].isnull().sum()
    count[col] = count_null
for i,j in sorted(count.items(),key = lambda s: s[1], reverse=True):
    print('列名：%s，存在缺失值 %s 个'%(i,j))

列名：Travel Time，存在缺失值 40 个
列名：Delivery Fare，存在缺失值 29 个
列名：Id，存在缺失值 25 个
列名：Post Type，存在缺失值 20 个
列名：Drone Type，存在缺失值 10 个
列名：Origin Region，存在缺失值 10 个
列名：Destination Region，存在缺失值 10 个
列名：Package Weight，存在缺失值 0 个
列名：Origin Latitude，存在缺失值 0 个
列名：Origin Longitude，存在缺失值 0 个
列名：Destination Latitude，存在缺失值 0 个
列名：Destination Longitude，存在缺失值 0 个
列名：Journey Distance，存在缺失值 0 个
列名：Departure Date，存在缺失值 0 个
列名：Departure Time，存在缺失值 0 个
列名：Delivery Time，存在缺失值 0 个

任务8：找出所有至少含有一个缺失值的行，并统计有多少行

data.isnull().any(axis=1)  # 判断至少有一个缺失值

0        False
1         True
2        False
3        False
4        False
         ...  
37898     True
37899    False
37900    False
37901    False
37902    False
Length: 37903, dtype: bool

data.drop(data.iloc[0,2])

	Id	Drone Type	Post Type	Package Weight	Origin Region	Destination Region	Origin Latitude	Origin Longitude	Destination Latitude	Destination Longitude	Journey Distance	Departure Date	Departure Time	Travel Time	Delivery Time	Delivery Fare
1	ID1697620764	NaN	0.0	39.075	15.0	15.0	-38.481935	146.009567	-38.585528	146.199827	20.185	2018-02-10	04:28:17	22.84	4:51:07	149.04
2	ID1543933503	2.0	0.0	7.243	33.0	28.0	-38.754167	144.509664	-38.242224	147.855342	296.975	2018-05-05	01:38:03	272.52	6:10:34	141.48
3	ID1756517608	2.0	0.0	13.383	10.0	38.0	-37.240526	147.568019	-37.687178	142.991188	407.396	2018-06-11	11:43:04	371.40	17:54:27	122.82
4	ID1832325834	2.0	0.0	8.123	1.0	8.0	-38.143985	143.798292	-38.548315	144.769228	95.974	2018-03-16	14:50:25	92.51	16:22:55	111.97
5	ID1802448576	2.0	0.0	32.859	2.0	28.0	-37.421211	148.044072	-38.159627	148.194048	83.250	2018-05-15	16:35:50	81.12	17:56:57	113.88
6	ID1940231408	1.0	0.0	20.616	29.0	36.0	-37.173949	143.140662	-37.021605	145.197043	183.363	2018-04-01	19:31:12	184.22	22:35:25	85.60
7	ID1299303958	2.0	0.0	44.577	36.0	31.0	-37.123190	145.236196	-37.667199	143.877650	134.543	2018-05-01	18:39:36	127.05	20:46:38	114.22
8	ID1752722028	1.0	0.0	15.363	20.0	30.0	-38.850561	148.317253	-38.024914	144.823938	318.132	2018-05-27	14:48:17	314.64	20:02:55	87.39
9	ID5995243590	1.0	1.0	36.190	18.0	28.0	-38.070189	142.950207	-37.996817	148.026520	445.106	2018-06-17	12:53:02	437.52	20:10:33	142.95
10	ID1483358088	2.0	0.0	23.172	13.0	27.0	-38.225456	147.425515	-37.642798	147.124104	70.051	2018-03-19	09:59:10	69.30	11:08:27	96.95
11	ID1626798395	2.0	0.0	19.754	23.0	26.0	-37.625368	145.838281	-36.789955	147.133916	147.791	2018-02-28	17:40:59	138.92	19:59:54	117.48
12	ID5277549009	3.0	1.0	12.807	4.0	6.0	-36.855984	142.929596	-36.906838	145.696986	246.465	2018-03-26	07:55:48	201.49	11:17:17	173.32
13	ID1950928883	2.0	0.0	22.332	33.0	19.0	-38.894115	144.457143	-37.173740	144.152105	193.365	2018-06-28	16:05:55	179.73	19:05:38	117.67
14	ID5143738648	2.0	1.0	25.880	33.0	5.0	-38.872372	144.606034	-37.553304	145.120753	153.580	2018-05-09	08:33:29	144.10	10:57:34	132.98
15	ID5132897910	2.0	1.0	38.691	7.0	15.0	-38.844622	144.093195	-38.476630	145.849992	158.102	2018-01-05	15:55:00	148.15	18:23:09	147.73
16	ID1290889802	1.0	0.0	30.742	19.0	14.0	-37.178059	144.403991	-37.713867	146.382965	184.783	2018-06-05	13:03:43	185.60	16:09:18	84.82
17	ID5226355535	1.0	1.0	18.055	10.0	18.0	-37.141728	147.256091	-37.983127	143.290272	362.227	2018-05-04	07:42:51	357.32	13:40:10	116.35
18	ID1898978312	1.0	0.0	5.986	23.0	18.0	-37.706960	145.718119	-37.983127	143.182464	224.997	2018-05-23	12:53:12	224.51	16:37:42	82.47
19	ID5284908619	2.0	1.0	30.664	13.0	14.0	-38.250238	147.366610	-37.774617	146.503568	92.371	2018-07-11	12:52:57	89.29	14:22:14	143.96
20	ID1585556406	3.0	0.0	24.942	3.0	39.0	-38.322643	145.505910	-38.453191	148.300405	244.251	2018-03-14	22:16:33	199.74	1:36:17	167.45
21	ID1901962779	1.0	0.0	34.012	27.0	37.0	-37.516090	146.969053	-38.852488	147.816987	166.236	2018-07-17	16:26:55	167.65	19:14:34	88.98
22	ID5590279060	1.0	1.0	55.229	7.0	6.0	-38.876145	143.911302	-36.875012	145.759049	275.633	2018-05-01	12:42:19	273.52	17:15:50	688.24
23	ID1473718059	3.0	0.0	40.741	33.0	17.0	-38.756634	144.375564	-38.817727	147.071495	234.014	2018-06-21	01:04:15	191.66	4:15:54	165.37
24	ID5551646734	3.0	1.0	15.419	35.0	21.0	-36.922413	146.362349	-37.240137	143.768208	233.068	2018-01-05	07:19:48	190.91	10:30:42	174.52
25	ID1772122934	2.0	0.0	10.663	29.0	1.0	-37.019910	142.798295	-38.358124	143.943955	179.929	2018-02-27	04:42:03	167.70	7:29:44	124.95
26	ID1987608852	1.0	0.0	20.685	9.0	35.0	-38.970092	145.435801	-37.068624	146.317717	225.349	2018-05-15	15:35:10	224.85	19:20:00	80.66
27	ID1249352358	2.0	0.0	10.272	8.0	34.0	-38.485641	144.522135	-38.505484	143.311788	105.471	2018-02-05	06:13:29	101.02	7:54:30	102.09
28	ID1611614450	1.0	0.0	7.373	16.0	38.0	-36.600877	143.566811	-37.804113	142.793139	150.483	2018-06-18	14:01:42	152.40	16:34:06	82.11
29	ID1262379299	2.0	0.0	31.358	13.0	16.0	-38.195911	147.436921	-36.734698	143.764279	362.941	2018-04-24	22:40:54	331.59	4:12:29	147.21
30	ID5498216777	2.0	1.0	6.383	40.0	29.0	-37.692167	147.890721	-37.273398	142.951899	438.698	2018-05-09	10:31:10	399.43	17:10:35	152.50
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
37873	NaN	1.0	0.0	39.038	7.0	22.0	-38.664403	143.702592	-36.670478	144.312065	228.361	2018-02-02	18:03:25	227.77	21:51:11	87.87
37874	NaN	1.0	0.0	7.171	14.0	1.0	-37.589382	146.559422	-38.229669	143.710836	260.125	2018-02-03	22:11:31	258.51	2:30:01	106.47
37875	NaN	1.0	0.0	39.141	5.0	1.0	-37.491746	145.168879	-38.168680	143.936761	131.959	2018-04-19	22:54:02	134.48	1:08:30	94.83
37876	NaN	2.0	0.0	42.330	25.0	5.0	-36.618297	147.643165	-37.563856	145.038804	254.069	2018-01-27	11:06:09	234.09	15:00:14	116.10
37877	NaN	1.0	1.0	43.965	25.0	38.0	-36.542082	147.863736	-37.549355	142.980000	448.104	2018-06-14	22:15:50	440.42	5:36:15	149.26
37878	ID1525565031	2.0	0.0	27.560	7.0	35.0	-38.689638	144.093996	-37.046945	146.460389	276.894	2018-02-22	23:43:48	254.53	3:58:19	135.68
37879	NaN	1.0	0.0	18.540	36.0	18.0	-36.974023	145.036046	-38.015640	142.955101	217.299	2018-05-28	07:14:50	217.06	10:51:53	76.31
37880	ID1272392458	1.0	0.0	44.226	40.0	3.0	-37.740012	147.768225	-38.276119	145.403536	215.813	2018-02-21	21:28:22	215.63	1:03:59	105.89
37881	ID1909131399	2.0	0.0	6.415	25.0	27.0	-36.585246	147.853062	-37.700285	147.027318	144.135	2018-01-16	08:26:33	135.64	10:42:11	100.45
37882	ID5420355345	3.0	1.0	30.471	3.0	15.0	-38.345519	145.542105	-38.712933	145.977537	55.772	2018-07-22	00:13:54	50.94	1:04:50	183.73
37883	ID5164753016	2.0	1.0	6.553	5.0	15.0	-37.625311	145.097611	-38.630132	146.040376	139.018	2018-01-14	16:51:56	131.06	19:02:59	145.71
37884	ID1122103211	1.0	0.0	37.850	38.0	10.0	-37.510578	142.826103	-37.337327	147.315144	397.278	2018-04-27	14:54:35	391.24	21:25:49	102.88
37885	NaN	1.0	0.0	13.544	34.0	38.0	-38.528632	143.386140	-37.561137	142.835312	118.028	2018-04-09	11:48:46	120.99	13:49:45	69.88
37886	ID1970402579	2.0	0.0	38.656	1.0	32.0	-38.171542	143.871582	-36.756484	148.322553	423.585	2018-03-18	01:01:33	385.90	7:27:26	142.19
37887	ID1388385049	1.0	0.0	23.699	10.0	10.0	-37.352794	147.476662	-37.104700	147.417027	28.118	2018-05-24	00:41:08	33.99	1:15:07	83.77
37888	NaN	1.0	1.0	34.923	36.0	28.0	-37.044972	144.920592	-38.218799	148.050871	305.307	2018-07-20	03:13:54	302.23	8:16:07	130.70
37889	ID1281653747	1.0	0.0	34.130	8.0	36.0	-38.434373	144.730220	-37.047801	145.309517	162.554	2018-02-06	06:27:55	164.08	9:11:59	69.44
37890	ID5349085772	1.0	1.0	18.286	22.0	14.0	-36.720129	144.588398	-37.695893	146.430793	196.154	2018-02-15	17:04:44	196.60	20:21:19	121.11
37891	ID5972337482	1.0	1.0	11.538	33.0	2.0	-38.836802	144.357057	-37.549931	148.306793	374.024	2018-05-26	02:07:50	368.73	8:16:33	141.06
37892	NaN	3.0	1.0	5.416	20.0	33.0	-38.959090	148.294700	-38.930545	144.661887	314.514	2018-04-27	20:53:02	255.21	1:08:14	185.96
37893	ID1539650034	2.0	0.0	34.355	8.0	39.0	-38.520278	144.408786	-38.447195	148.416066	349.251	2018-05-09	07:26:49	319.33	12:46:08	121.30
37894	NaN	2.0	0.0	41.232	38.0	39.0	-37.657406	142.777301	-38.622040	148.366529	500.901	2018-07-02	08:59:29	455.14	16:34:37	139.79
37895	ID1796943211	1.0	0.0	44.341	23.0	24.0	-37.777223	146.024184	-38.913981	142.913934	299.552	2018-02-20	05:08:12	296.66	10:04:51	113.70
37896	ID5429883749	2.0	1.0	17.798	11.0	40.0	-38.045551	146.736254	-37.633007	147.639273	91.711	2018-05-03	10:19:32	88.70	11:48:14	130.51
37897	NaN	1.0	0.0	8.865	9.0	2.0	-38.839254	145.226776	-37.695101	148.251214	293.394	2018-03-11	12:18:21	290.70	17:09:02	88.71
37898	NaN	3.0	1.0	27.153	39.0	16.0	-38.446310	148.292498	-36.739777	143.604529	454.968	2018-07-23	08:29:19	366.09	14:35:24	188.49
37899	ID5862552991	1.0	1.0	40.363	9.0	38.0	-38.983710	145.320518	-37.673908	142.879230	258.259	2018-06-26	15:55:37	256.70	20:12:18	122.98
37900	ID5339104082	1.0	1.0	35.955	13.0	32.0	-38.292301	147.562013	-36.605285	148.293183	198.597	2018-03-19	16:41:10	198.97	20:00:08	118.47
37901	ID5468787866	2.0	1.0	29.566	33.0	23.0	-38.853243	144.508346	-37.727691	145.662270	160.816	2018-02-26	04:22:30	150.58	6:53:04	161.96
37902	ID1448126768	3.0	0.0	44.070	36.0	34.0	-37.129313	145.266426	-38.428477	143.341632	222.687	2018-07-07	08:01:42	182.71	11:04:24	144.41

37902 rows × 16 columns

# axis=1针对的是行；=0针对的是列
data[data.isnull().any(axis=1)].shape
#（data.isnull().sum(axis=1) >= 1）.sum()
data[data.isnull().any(axis=1)].shape
data.isnull().any(axis=1)

0        False
1         True
2        False
3        False
4        False
5        False
6        False
7        False
8        False
9        False
10       False
11       False
12       False
13       False
14       False
15       False
16       False
17       False
18       False
19       False
20       False
21       False
22       False
23       False
24       False
25       False
26       False
27       False
28       False
29       False
         ...  
37873     True
37874     True
37875     True
37876     True
37877     True
37878    False
37879     True
37880    False
37881    False
37882    False
37883    False
37884    False
37885     True
37886    False
37887    False
37888     True
37889    False
37890    False
37891    False
37892     True
37893    False
37894     True
37895    False
37896    False
37897     True
37898     True
37899    False
37900    False
37901    False
37902    False
dtype: bool

4.0 数据清洗

任务9： 填补 'Id'列的空值

任务9.1 统计‘id’列有多少个空值

data['Id'].isnull().sum()

25

任务9.2 找出所有‘id’为空的行

data[data['Id'].isnull()]

	Id	Drone Type	Post Type	Package Weight	Origin Region	Destination Region	Origin Latitude	Origin Longitude	Destination Latitude	Destination Longitude	Journey Distance	Departure Date	Departure Time	Travel Time	Delivery Time	Delivery Fare
37844	NaN	1.0	0.0	22.498	30.0	15.0	-37.885792	144.875305	-38.680341	145.874064	124.252	2018-01-28	13:07:09	127.02	15:14:10	74.69
37845	NaN	3.0	0.0	32.300	16.0	36.0	-36.571169	143.741010	-36.993435	144.983048	120.297	2018-03-26	02:34:49	101.88	4:16:41	162.20
37846	NaN	3.0	0.0	18.601	38.0	29.0	-37.694132	142.851548	-37.058014	142.873698	70.838	2018-03-02	20:33:11	62.83	21:36:00	142.10
37850	NaN	2.0	1.0	28.203	18.0	21.0	-38.139260	143.345778	-37.279348	143.604189	98.391	2018-04-17	03:28:41	94.68	5:03:21	152.30
37851	NaN	2.0	1.0	45.696	28.0	1.0	-38.152835	147.793072	-38.162103	144.043047	328.220	2018-03-25	13:50:57	300.50	18:51:27	167.39
37852	NaN	1.0	0.0	27.143	38.0	32.0	-37.613135	142.854194	-36.713765	148.383062	500.500	2018-05-12	08:54:52	491.13	17:05:59	97.44
37854	NaN	1.0	0.0	13.002	27.0	3.0	-37.428594	147.056992	-38.383631	145.590528	167.005	2018-02-20	03:35:43	168.39	6:24:06	92.12
37857	NaN	1.0	0.0	19.468	5.0	31.0	-37.570514	145.253281	-37.824303	143.862472	125.716	2018-04-20	11:48:14	128.43	13:56:39	69.86
37860	NaN	1.0	0.0	10.647	21.0	5.0	-37.239046	143.524656	-37.546884	145.204154	152.434	2018-06-13	23:05:39	154.29	1:39:56	95.65
37861	NaN	2.0	0.0	40.775	22.0	19.0	-36.678761	144.345069	-37.167099	144.257043	54.922	2018-03-27	04:31:43	55.75	5:27:28	120.70
37863	NaN	1.0	0.0	35.410	9.0	16.0	-39.006941	145.406988	-36.525200	143.517906	322.399	2018-07-21	06:48:39	318.77	12:07:25	88.14
37866	NaN	1.0	1.0	40.151	14.0	34.0	-37.644061	146.625820	-38.518928	143.369714	301.447	2018-04-14	07:29:13	298.50	12:27:43	118.89
37869	NaN	3.0	1.0	44.559	33.0	10.0	-38.733722	144.474460	-37.220132	147.498884	314.323	2018-04-18	04:57:37	255.06	9:12:40	201.89
37873	NaN	1.0	0.0	39.038	7.0	22.0	-38.664403	143.702592	-36.670478	144.312065	228.361	2018-02-02	18:03:25	227.77	21:51:11	87.87
37874	NaN	1.0	0.0	7.171	14.0	1.0	-37.589382	146.559422	-38.229669	143.710836	260.125	2018-02-03	22:11:31	258.51	2:30:01	106.47
37875	NaN	1.0	0.0	39.141	5.0	1.0	-37.491746	145.168879	-38.168680	143.936761	131.959	2018-04-19	22:54:02	134.48	1:08:30	94.83
37876	NaN	2.0	0.0	42.330	25.0	5.0	-36.618297	147.643165	-37.563856	145.038804	254.069	2018-01-27	11:06:09	234.09	15:00:14	116.10
37877	NaN	1.0	1.0	43.965	25.0	38.0	-36.542082	147.863736	-37.549355	142.980000	448.104	2018-06-14	22:15:50	440.42	5:36:15	149.26
37879	NaN	1.0	0.0	18.540	36.0	18.0	-36.974023	145.036046	-38.015640	142.955101	217.299	2018-05-28	07:14:50	217.06	10:51:53	76.31
37885	NaN	1.0	0.0	13.544	34.0	38.0	-38.528632	143.386140	-37.561137	142.835312	118.028	2018-04-09	11:48:46	120.99	13:49:45	69.88
37888	NaN	1.0	1.0	34.923	36.0	28.0	-37.044972	144.920592	-38.218799	148.050871	305.307	2018-07-20	03:13:54	302.23	8:16:07	130.70
37892	NaN	3.0	1.0	5.416	20.0	33.0	-38.959090	148.294700	-38.930545	144.661887	314.514	2018-04-27	20:53:02	255.21	1:08:14	185.96
37894	NaN	2.0	0.0	41.232	38.0	39.0	-37.657406	142.777301	-38.622040	148.366529	500.901	2018-07-02	08:59:29	455.14	16:34:37	139.79
37897	NaN	1.0	0.0	8.865	9.0	2.0	-38.839254	145.226776	-37.695101	148.251214	293.394	2018-03-11	12:18:21	290.70	17:09:02	88.71
37898	NaN	3.0	1.0	27.153	39.0	16.0	-38.446310	148.292498	-36.739777	143.604529	454.968	2018-07-23	08:29:19	366.09	14:35:24	188.49

9.2.1 删除 除ID列之外其余数据重复的行

data[
    ['Drone Type', 'Post Type', 'Package Weight', 'Origin Region',
       'Destination Region', 'Origin Latitude', 'Origin Longitude',
       'Destination Latitude', 'Destination Longitude', 'Journey Distance',
       'Departure Date', 'Departure Time', 'Travel Time', 'Delivery Time',
       'Delivery Fare']
]

	Drone Type	Post Type	Package Weight	Origin Region	Destination Region	Origin Latitude	Origin Longitude	Destination Latitude	Destination Longitude	Journey Distance	Departure Date	Departure Time	Travel Time	Delivery Time	Delivery Fare
0	2.0	0.0	21.686	19.0	38.0	-37.089338	144.429529	-37.639134	142.891391	149.212	2018-01-16	09:38:17	140.19	11:58:28	99.25
1	NaN	0.0	39.075	15.0	15.0	-38.481935	146.009567	-38.585528	146.199827	20.185	2018-02-10	04:28:17	22.84	4:51:07	149.04
2	2.0	0.0	7.243	33.0	28.0	-38.754167	144.509664	-38.242224	147.855342	296.975	2018-05-05	01:38:03	272.52	6:10:34	141.48
3	2.0	0.0	13.383	10.0	38.0	-37.240526	147.568019	-37.687178	142.991188	407.396	2018-06-11	11:43:04	371.40	17:54:27	122.82
4	2.0	0.0	8.123	1.0	8.0	-38.143985	143.798292	-38.548315	144.769228	95.974	2018-03-16	14:50:25	92.51	16:22:55	111.97
5	2.0	0.0	32.859	2.0	28.0	-37.421211	148.044072	-38.159627	148.194048	83.250	2018-05-15	16:35:50	81.12	17:56:57	113.88
6	1.0	0.0	20.616	29.0	36.0	-37.173949	143.140662	-37.021605	145.197043	183.363	2018-04-01	19:31:12	184.22	22:35:25	85.60
7	2.0	0.0	44.577	36.0	31.0	-37.123190	145.236196	-37.667199	143.877650	134.543	2018-05-01	18:39:36	127.05	20:46:38	114.22
8	1.0	0.0	15.363	20.0	30.0	-38.850561	148.317253	-38.024914	144.823938	318.132	2018-05-27	14:48:17	314.64	20:02:55	87.39
9	1.0	1.0	36.190	18.0	28.0	-38.070189	142.950207	-37.996817	148.026520	445.106	2018-06-17	12:53:02	437.52	20:10:33	142.95
10	2.0	0.0	23.172	13.0	27.0	-38.225456	147.425515	-37.642798	147.124104	70.051	2018-03-19	09:59:10	69.30	11:08:27	96.95
11	2.0	0.0	19.754	23.0	26.0	-37.625368	145.838281	-36.789955	147.133916	147.791	2018-02-28	17:40:59	138.92	19:59:54	117.48
12	3.0	1.0	12.807	4.0	6.0	-36.855984	142.929596	-36.906838	145.696986	246.465	2018-03-26	07:55:48	201.49	11:17:17	173.32
13	2.0	0.0	22.332	33.0	19.0	-38.894115	144.457143	-37.173740	144.152105	193.365	2018-06-28	16:05:55	179.73	19:05:38	117.67
14	2.0	1.0	25.880	33.0	5.0	-38.872372	144.606034	-37.553304	145.120753	153.580	2018-05-09	08:33:29	144.10	10:57:34	132.98
15	2.0	1.0	38.691	7.0	15.0	-38.844622	144.093195	-38.476630	145.849992	158.102	2018-01-05	15:55:00	148.15	18:23:09	147.73
16	1.0	0.0	30.742	19.0	14.0	-37.178059	144.403991	-37.713867	146.382965	184.783	2018-06-05	13:03:43	185.60	16:09:18	84.82
17	1.0	1.0	18.055	10.0	18.0	-37.141728	147.256091	-37.983127	143.290272	362.227	2018-05-04	07:42:51	357.32	13:40:10	116.35
18	1.0	0.0	5.986	23.0	18.0	-37.706960	145.718119	-37.983127	143.182464	224.997	2018-05-23	12:53:12	224.51	16:37:42	82.47
19	2.0	1.0	30.664	13.0	14.0	-38.250238	147.366610	-37.774617	146.503568	92.371	2018-07-11	12:52:57	89.29	14:22:14	143.96
20	3.0	0.0	24.942	3.0	39.0	-38.322643	145.505910	-38.453191	148.300405	244.251	2018-03-14	22:16:33	199.74	1:36:17	167.45
21	1.0	0.0	34.012	27.0	37.0	-37.516090	146.969053	-38.852488	147.816987	166.236	2018-07-17	16:26:55	167.65	19:14:34	88.98
22	1.0	1.0	55.229	7.0	6.0	-38.876145	143.911302	-36.875012	145.759049	275.633	2018-05-01	12:42:19	273.52	17:15:50	688.24
23	3.0	0.0	40.741	33.0	17.0	-38.756634	144.375564	-38.817727	147.071495	234.014	2018-06-21	01:04:15	191.66	4:15:54	165.37
24	3.0	1.0	15.419	35.0	21.0	-36.922413	146.362349	-37.240137	143.768208	233.068	2018-01-05	07:19:48	190.91	10:30:42	174.52
25	2.0	0.0	10.663	29.0	1.0	-37.019910	142.798295	-38.358124	143.943955	179.929	2018-02-27	04:42:03	167.70	7:29:44	124.95
26	1.0	0.0	20.685	9.0	35.0	-38.970092	145.435801	-37.068624	146.317717	225.349	2018-05-15	15:35:10	224.85	19:20:00	80.66
27	2.0	0.0	10.272	8.0	34.0	-38.485641	144.522135	-38.505484	143.311788	105.471	2018-02-05	06:13:29	101.02	7:54:30	102.09
28	1.0	0.0	7.373	16.0	38.0	-36.600877	143.566811	-37.804113	142.793139	150.483	2018-06-18	14:01:42	152.40	16:34:06	82.11
29	2.0	0.0	31.358	13.0	16.0	-38.195911	147.436921	-36.734698	143.764279	362.941	2018-04-24	22:40:54	331.59	4:12:29	147.21
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
37873	1.0	0.0	39.038	7.0	22.0	-38.664403	143.702592	-36.670478	144.312065	228.361	2018-02-02	18:03:25	227.77	21:51:11	87.87
37874	1.0	0.0	7.171	14.0	1.0	-37.589382	146.559422	-38.229669	143.710836	260.125	2018-02-03	22:11:31	258.51	2:30:01	106.47
37875	1.0	0.0	39.141	5.0	1.0	-37.491746	145.168879	-38.168680	143.936761	131.959	2018-04-19	22:54:02	134.48	1:08:30	94.83
37876	2.0	0.0	42.330	25.0	5.0	-36.618297	147.643165	-37.563856	145.038804	254.069	2018-01-27	11:06:09	234.09	15:00:14	116.10
37877	1.0	1.0	43.965	25.0	38.0	-36.542082	147.863736	-37.549355	142.980000	448.104	2018-06-14	22:15:50	440.42	5:36:15	149.26
37878	2.0	0.0	27.560	7.0	35.0	-38.689638	144.093996	-37.046945	146.460389	276.894	2018-02-22	23:43:48	254.53	3:58:19	135.68
37879	1.0	0.0	18.540	36.0	18.0	-36.974023	145.036046	-38.015640	142.955101	217.299	2018-05-28	07:14:50	217.06	10:51:53	76.31
37880	1.0	0.0	44.226	40.0	3.0	-37.740012	147.768225	-38.276119	145.403536	215.813	2018-02-21	21:28:22	215.63	1:03:59	105.89
37881	2.0	0.0	6.415	25.0	27.0	-36.585246	147.853062	-37.700285	147.027318	144.135	2018-01-16	08:26:33	135.64	10:42:11	100.45
37882	3.0	1.0	30.471	3.0	15.0	-38.345519	145.542105	-38.712933	145.977537	55.772	2018-07-22	00:13:54	50.94	1:04:50	183.73
37883	2.0	1.0	6.553	5.0	15.0	-37.625311	145.097611	-38.630132	146.040376	139.018	2018-01-14	16:51:56	131.06	19:02:59	145.71
37884	1.0	0.0	37.850	38.0	10.0	-37.510578	142.826103	-37.337327	147.315144	397.278	2018-04-27	14:54:35	391.24	21:25:49	102.88
37885	1.0	0.0	13.544	34.0	38.0	-38.528632	143.386140	-37.561137	142.835312	118.028	2018-04-09	11:48:46	120.99	13:49:45	69.88
37886	2.0	0.0	38.656	1.0	32.0	-38.171542	143.871582	-36.756484	148.322553	423.585	2018-03-18	01:01:33	385.90	7:27:26	142.19
37887	1.0	0.0	23.699	10.0	10.0	-37.352794	147.476662	-37.104700	147.417027	28.118	2018-05-24	00:41:08	33.99	1:15:07	83.77
37888	1.0	1.0	34.923	36.0	28.0	-37.044972	144.920592	-38.218799	148.050871	305.307	2018-07-20	03:13:54	302.23	8:16:07	130.70
37889	1.0	0.0	34.130	8.0	36.0	-38.434373	144.730220	-37.047801	145.309517	162.554	2018-02-06	06:27:55	164.08	9:11:59	69.44
37890	1.0	1.0	18.286	22.0	14.0	-36.720129	144.588398	-37.695893	146.430793	196.154	2018-02-15	17:04:44	196.60	20:21:19	121.11
37891	1.0	1.0	11.538	33.0	2.0	-38.836802	144.357057	-37.549931	148.306793	374.024	2018-05-26	02:07:50	368.73	8:16:33	141.06
37892	3.0	1.0	5.416	20.0	33.0	-38.959090	148.294700	-38.930545	144.661887	314.514	2018-04-27	20:53:02	255.21	1:08:14	185.96
37893	2.0	0.0	34.355	8.0	39.0	-38.520278	144.408786	-38.447195	148.416066	349.251	2018-05-09	07:26:49	319.33	12:46:08	121.30
37894	2.0	0.0	41.232	38.0	39.0	-37.657406	142.777301	-38.622040	148.366529	500.901	2018-07-02	08:59:29	455.14	16:34:37	139.79
37895	1.0	0.0	44.341	23.0	24.0	-37.777223	146.024184	-38.913981	142.913934	299.552	2018-02-20	05:08:12	296.66	10:04:51	113.70
37896	2.0	1.0	17.798	11.0	40.0	-38.045551	146.736254	-37.633007	147.639273	91.711	2018-05-03	10:19:32	88.70	11:48:14	130.51
37897	1.0	0.0	8.865	9.0	2.0	-38.839254	145.226776	-37.695101	148.251214	293.394	2018-03-11	12:18:21	290.70	17:09:02	88.71
37898	3.0	1.0	27.153	39.0	16.0	-38.446310	148.292498	-36.739777	143.604529	454.968	2018-07-23	08:29:19	366.09	14:35:24	188.49
37899	1.0	1.0	40.363	9.0	38.0	-38.983710	145.320518	-37.673908	142.879230	258.259	2018-06-26	15:55:37	256.70	20:12:18	122.98
37900	1.0	1.0	35.955	13.0	32.0	-38.292301	147.562013	-36.605285	148.293183	198.597	2018-03-19	16:41:10	198.97	20:00:08	118.47
37901	2.0	1.0	29.566	33.0	23.0	-38.853243	144.508346	-37.727691	145.662270	160.816	2018-02-26	04:22:30	150.58	6:53:04	161.96
37902	3.0	0.0	44.070	36.0	34.0	-37.129313	145.266426	-38.428477	143.341632	222.687	2018-07-07	08:01:42	182.71	11:04:24	144.41

37903 rows × 15 columns

# drop_duplicates返回一个dataframe，重复的行会标为False
# data_1 = data.drop_duplicates(data[['Drone Type', 'Post Type', 'Package Weight', 'Origin Region',
#        'Destination Region', 'Origin Latitude', 'Origin Longitude',
#        'Destination Latitude', 'Destination Longitude', 'Journey Distance',
#        'Departure Date', 'Departure Time', 'Travel Time', 'Delivery Time',
#        'Delivery Fare']])
# 2
data_1 = data.drop_duplicates(['Drone Type', 'Post Type', 'Package Weight', 'Origin Region',
    'Destination Region', 'Origin Latitude', 'Origin Longitude',
    'Destination Latitude', 'Destination Longitude', 'Journey Distance',
    'Departure Date', 'Departure Time', 'Travel Time', 'Delivery Time',
    'Delivery Fare'])
data_1

	Id	Drone Type	Post Type	Package Weight	Origin Region	Destination Region	Origin Latitude	Origin Longitude	Destination Latitude	Destination Longitude	Journey Distance	Departure Date	Departure Time	Travel Time	Delivery Time	Delivery Fare
0	ID1645282128	2.0	0.0	21.686	19.0	38.0	-37.089338	144.429529	-37.639134	142.891391	149.212	2018-01-16	09:38:17	140.19	11:58:28	99.25
1	ID1697620764	NaN	0.0	39.075	15.0	15.0	-38.481935	146.009567	-38.585528	146.199827	20.185	2018-02-10	04:28:17	22.84	4:51:07	149.04
2	ID1543933503	2.0	0.0	7.243	33.0	28.0	-38.754167	144.509664	-38.242224	147.855342	296.975	2018-05-05	01:38:03	272.52	6:10:34	141.48
3	ID1756517608	2.0	0.0	13.383	10.0	38.0	-37.240526	147.568019	-37.687178	142.991188	407.396	2018-06-11	11:43:04	371.40	17:54:27	122.82
4	ID1832325834	2.0	0.0	8.123	1.0	8.0	-38.143985	143.798292	-38.548315	144.769228	95.974	2018-03-16	14:50:25	92.51	16:22:55	111.97
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
37839	ID1879423081	2.0	0.0	17.081	20.0	14.0	-38.860358	148.174855	-37.562224	146.443991	209.278	2018-02-15	12:09:15	193.98	15:23:13	121.33
37840	ID5705840841	1.0	1.0	35.164	29.0	23.0	-37.250331	142.837244	-37.739285	145.963420	281.403	2018-01-01	00:07:35	279.10	4:46:41	136.58
37841	ID1276239209	3.0	0.0	36.704	12.0	11.0	-36.578568	145.273744	-38.305557	146.997297	245.269	2018-01-24	06:48:05	200.54	10:08:37	146.69
37842	ID1432868583	3.0	0.0	13.195	37.0	21.0	-38.755802	147.744770	-37.487866	143.585293	390.599	2018-04-07	18:57:52	315.28	0:13:08	170.52
37889	ID1281653747	1.0	0.0	34.130	8.0	36.0	-38.434373	144.730220	-37.047801	145.309517	162.554	2018-02-06	06:27:55	164.08	9:11:59	69.44

37844 rows × 16 columns

9.2.2 设置ID列相同，但其余数据不重复的ID为np.nan

# 返回一个布尔型的series，表示id是否重复行
data_1['Id'].duplicated()

0        False
1        False
2        False
3        False
4        False
         ...  
37839    False
37840    False
37841    False
37842    False
37889     True
Name: Id, Length: 37844, dtype: bool

(data_1['Id'].duplicated()) & ~(data_1[['Drone Type', 'Post Type', 'Package Weight', 'Origin Region',
       'Destination Region', 'Origin Latitude', 'Origin Longitude',
       'Destination Latitude', 'Destination Longitude', 'Journey Distance',
       'Departure Date', 'Departure Time', 'Travel Time', 'Delivery Time',
       'Delivery Fare']].duplicated())

0        False
1        False
2        False
3        False
4        False
         ...  
37839    False
37840    False
37841    False
37842    False
37889     True
Length: 37844, dtype: bool

data_1.loc[(data_1['Id'].duplicated()) & ~(data_1[['Drone Type', 'Post Type', 'Package Weight', 'Origin Region',
       'Destination Region', 'Origin Latitude', 'Origin Longitude',
       'Destination Latitude', 'Destination Longitude', 'Journey Distance',
       'Departure Date', 'Departure Time', 'Travel Time', 'Delivery Time',
       'Delivery Fare']].duplicated()),'Id'] = np.nan

data_1['Id'].value_counts()

ID1858018960    1
ID1792956453    1
ID1574575344    1
ID1146475421    1
ID1897230811    1
               ..
ID5349642032    1
ID5451065609    1
ID5131869013    1
ID1550669799    1
ID5260585195    1
Name: Id, Length: 37843, dtype: int64

任务9.3 想出空值的填补的2种方法，并且选一种应用

#方法1 ：随机数
import random
# 'Id'+str(random.randint(1000000000,2000000000)) in data_1['Id']
#方法2 ：随机数填补

def fill_id(Id):
    Id_out = Id
    while Id_out in data_1['Id'].tolist():
        Id_out = 'ID'+str(random.randint(1000000000,2000000000))
        return Id_out
data_1.loc[(data_1['Post Type']==0)&(data_1['Id'].isnull()),'Id'].apply(fill_id)

37889    ID1117118084
Name: Id, dtype: object

data_1.loc[(data_1['Post Type']==0)&(data_1['Id'].isnull()),'Id']

37889    NaN
Name: Id, dtype: object

data_1['Id']

0        ID1645282128
1        ID1697620764
2        ID1543933503
3        ID1756517608
4        ID1832325834
             ...     
37839    ID1879423081
37840    ID5705840841
37841    ID1276239209
37842    ID1432868583
37889             NaN
Name: Id, Length: 37844, dtype: object

任务9.4 检查‘id’是否还有空值

data_1['Id'].isnull().sum()

1

任务10：找出所有重复的id

data[data.Id.duplicated()].Id

37843    ID1874340610
37845             NaN
37846             NaN
37847    ID5156350605
37848    ID1176413101
             ...     
37898             NaN
37899    ID5862552991
37900    ID5339104082
37901    ID5468787866
37902    ID1448126768
Name: Id, Length: 59, dtype: object

data['Id'].value_counts() >= 2

ID5281864060     True
ID1796943211     True
ID1877344172     True
ID5122284320     True
ID1238297934     True
                ...  
ID5672029782    False
ID1114364309    False
ID5495523518    False
ID1874532678    False
ID5260585195    False
Name: Id, Length: 37843, dtype: bool

任务11：删除重复行

data.duplicated()

0        False
1        False
2        False
3        False
4        False
5        False
6        False
7        False
8        False
9        False
10       False
11       False
12       False
13       False
14       False
15       False
16       False
17       False
18       False
19       False
20       False
21       False
22       False
23       False
24       False
25       False
26       False
27       False
28       False
29       False
         ...  
37873    False
37874    False
37875    False
37876    False
37877    False
37878     True
37879    False
37880     True
37881     True
37882     True
37883     True
37884     True
37885    False
37886     True
37887     True
37888    False
37889    False
37890     True
37891     True
37892    False
37893     True
37894    False
37895     True
37896     True
37897    False
37898    False
37899     True
37900     True
37901     True
37902     True
dtype: bool

data.drop_duplicates()

	Id	Drone Type	Post Type	Package Weight	Origin Region	Destination Region	Origin Latitude	Origin Longitude	Destination Latitude	Destination Longitude	Journey Distance	Departure Date	Departure Time	Travel Time	Delivery Time	Delivery Fare
0	ID1645282128	2.0	0.0	21.686	19.0	38.0	-37.089338	144.429529	-37.639134	142.891391	149.212	2018-01-16	09:38:17	140.19	11:58:28	99.25
1	ID1697620764	NaN	0.0	39.075	15.0	15.0	-38.481935	146.009567	-38.585528	146.199827	20.185	2018-02-10	04:28:17	22.84	4:51:07	149.04
2	ID1543933503	2.0	0.0	7.243	33.0	28.0	-38.754167	144.509664	-38.242224	147.855342	296.975	2018-05-05	01:38:03	272.52	6:10:34	141.48
3	ID1756517608	2.0	0.0	13.383	10.0	38.0	-37.240526	147.568019	-37.687178	142.991188	407.396	2018-06-11	11:43:04	371.40	17:54:27	122.82
4	ID1832325834	2.0	0.0	8.123	1.0	8.0	-38.143985	143.798292	-38.548315	144.769228	95.974	2018-03-16	14:50:25	92.51	16:22:55	111.97
5	ID1802448576	2.0	0.0	32.859	2.0	28.0	-37.421211	148.044072	-38.159627	148.194048	83.250	2018-05-15	16:35:50	81.12	17:56:57	113.88
6	ID1940231408	1.0	0.0	20.616	29.0	36.0	-37.173949	143.140662	-37.021605	145.197043	183.363	2018-04-01	19:31:12	184.22	22:35:25	85.60
7	ID1299303958	2.0	0.0	44.577	36.0	31.0	-37.123190	145.236196	-37.667199	143.877650	134.543	2018-05-01	18:39:36	127.05	20:46:38	114.22
8	ID1752722028	1.0	0.0	15.363	20.0	30.0	-38.850561	148.317253	-38.024914	144.823938	318.132	2018-05-27	14:48:17	314.64	20:02:55	87.39
9	ID5995243590	1.0	1.0	36.190	18.0	28.0	-38.070189	142.950207	-37.996817	148.026520	445.106	2018-06-17	12:53:02	437.52	20:10:33	142.95
10	ID1483358088	2.0	0.0	23.172	13.0	27.0	-38.225456	147.425515	-37.642798	147.124104	70.051	2018-03-19	09:59:10	69.30	11:08:27	96.95
11	ID1626798395	2.0	0.0	19.754	23.0	26.0	-37.625368	145.838281	-36.789955	147.133916	147.791	2018-02-28	17:40:59	138.92	19:59:54	117.48
12	ID5277549009	3.0	1.0	12.807	4.0	6.0	-36.855984	142.929596	-36.906838	145.696986	246.465	2018-03-26	07:55:48	201.49	11:17:17	173.32
13	ID1950928883	2.0	0.0	22.332	33.0	19.0	-38.894115	144.457143	-37.173740	144.152105	193.365	2018-06-28	16:05:55	179.73	19:05:38	117.67
14	ID5143738648	2.0	1.0	25.880	33.0	5.0	-38.872372	144.606034	-37.553304	145.120753	153.580	2018-05-09	08:33:29	144.10	10:57:34	132.98
15	ID5132897910	2.0	1.0	38.691	7.0	15.0	-38.844622	144.093195	-38.476630	145.849992	158.102	2018-01-05	15:55:00	148.15	18:23:09	147.73
16	ID1290889802	1.0	0.0	30.742	19.0	14.0	-37.178059	144.403991	-37.713867	146.382965	184.783	2018-06-05	13:03:43	185.60	16:09:18	84.82
17	ID5226355535	1.0	1.0	18.055	10.0	18.0	-37.141728	147.256091	-37.983127	143.290272	362.227	2018-05-04	07:42:51	357.32	13:40:10	116.35
18	ID1898978312	1.0	0.0	5.986	23.0	18.0	-37.706960	145.718119	-37.983127	143.182464	224.997	2018-05-23	12:53:12	224.51	16:37:42	82.47
19	ID5284908619	2.0	1.0	30.664	13.0	14.0	-38.250238	147.366610	-37.774617	146.503568	92.371	2018-07-11	12:52:57	89.29	14:22:14	143.96
20	ID1585556406	3.0	0.0	24.942	3.0	39.0	-38.322643	145.505910	-38.453191	148.300405	244.251	2018-03-14	22:16:33	199.74	1:36:17	167.45
21	ID1901962779	1.0	0.0	34.012	27.0	37.0	-37.516090	146.969053	-38.852488	147.816987	166.236	2018-07-17	16:26:55	167.65	19:14:34	88.98
22	ID5590279060	1.0	1.0	55.229	7.0	6.0	-38.876145	143.911302	-36.875012	145.759049	275.633	2018-05-01	12:42:19	273.52	17:15:50	688.24
23	ID1473718059	3.0	0.0	40.741	33.0	17.0	-38.756634	144.375564	-38.817727	147.071495	234.014	2018-06-21	01:04:15	191.66	4:15:54	165.37
24	ID5551646734	3.0	1.0	15.419	35.0	21.0	-36.922413	146.362349	-37.240137	143.768208	233.068	2018-01-05	07:19:48	190.91	10:30:42	174.52
25	ID1772122934	2.0	0.0	10.663	29.0	1.0	-37.019910	142.798295	-38.358124	143.943955	179.929	2018-02-27	04:42:03	167.70	7:29:44	124.95
26	ID1987608852	1.0	0.0	20.685	9.0	35.0	-38.970092	145.435801	-37.068624	146.317717	225.349	2018-05-15	15:35:10	224.85	19:20:00	80.66
27	ID1249352358	2.0	0.0	10.272	8.0	34.0	-38.485641	144.522135	-38.505484	143.311788	105.471	2018-02-05	06:13:29	101.02	7:54:30	102.09
28	ID1611614450	1.0	0.0	7.373	16.0	38.0	-36.600877	143.566811	-37.804113	142.793139	150.483	2018-06-18	14:01:42	152.40	16:34:06	82.11
29	ID1262379299	2.0	0.0	31.358	13.0	16.0	-38.195911	147.436921	-36.734698	143.764279	362.941	2018-04-24	22:40:54	331.59	4:12:29	147.21
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
37839	ID1879423081	2.0	0.0	17.081	20.0	14.0	-38.860358	148.174855	-37.562224	146.443991	209.278	2018-02-15	12:09:15	193.98	15:23:13	121.33
37840	ID5705840841	1.0	1.0	35.164	29.0	23.0	-37.250331	142.837244	-37.739285	145.963420	281.403	2018-01-01	00:07:35	279.10	4:46:41	136.58
37841	ID1276239209	3.0	0.0	36.704	12.0	11.0	-36.578568	145.273744	-38.305557	146.997297	245.269	2018-01-24	06:48:05	200.54	10:08:37	146.69
37842	ID1432868583	3.0	0.0	13.195	37.0	21.0	-38.755802	147.744770	-37.487866	143.585293	390.599	2018-04-07	18:57:52	315.28	0:13:08	170.52
37844	NaN	1.0	0.0	22.498	30.0	15.0	-37.885792	144.875305	-38.680341	145.874064	124.252	2018-01-28	13:07:09	127.02	15:14:10	74.69
37845	NaN	3.0	0.0	32.300	16.0	36.0	-36.571169	143.741010	-36.993435	144.983048	120.297	2018-03-26	02:34:49	101.88	4:16:41	162.20
37846	NaN	3.0	0.0	18.601	38.0	29.0	-37.694132	142.851548	-37.058014	142.873698	70.838	2018-03-02	20:33:11	62.83	21:36:00	142.10
37850	NaN	2.0	1.0	28.203	18.0	21.0	-38.139260	143.345778	-37.279348	143.604189	98.391	2018-04-17	03:28:41	94.68	5:03:21	152.30
37851	NaN	2.0	1.0	45.696	28.0	1.0	-38.152835	147.793072	-38.162103	144.043047	328.220	2018-03-25	13:50:57	300.50	18:51:27	167.39
37852	NaN	1.0	0.0	27.143	38.0	32.0	-37.613135	142.854194	-36.713765	148.383062	500.500	2018-05-12	08:54:52	491.13	17:05:59	97.44
37854	NaN	1.0	0.0	13.002	27.0	3.0	-37.428594	147.056992	-38.383631	145.590528	167.005	2018-02-20	03:35:43	168.39	6:24:06	92.12
37857	NaN	1.0	0.0	19.468	5.0	31.0	-37.570514	145.253281	-37.824303	143.862472	125.716	2018-04-20	11:48:14	128.43	13:56:39	69.86
37860	NaN	1.0	0.0	10.647	21.0	5.0	-37.239046	143.524656	-37.546884	145.204154	152.434	2018-06-13	23:05:39	154.29	1:39:56	95.65
37861	NaN	2.0	0.0	40.775	22.0	19.0	-36.678761	144.345069	-37.167099	144.257043	54.922	2018-03-27	04:31:43	55.75	5:27:28	120.70
37863	NaN	1.0	0.0	35.410	9.0	16.0	-39.006941	145.406988	-36.525200	143.517906	322.399	2018-07-21	06:48:39	318.77	12:07:25	88.14
37866	NaN	1.0	1.0	40.151	14.0	34.0	-37.644061	146.625820	-38.518928	143.369714	301.447	2018-04-14	07:29:13	298.50	12:27:43	118.89
37869	NaN	3.0	1.0	44.559	33.0	10.0	-38.733722	144.474460	-37.220132	147.498884	314.323	2018-04-18	04:57:37	255.06	9:12:40	201.89
37873	NaN	1.0	0.0	39.038	7.0	22.0	-38.664403	143.702592	-36.670478	144.312065	228.361	2018-02-02	18:03:25	227.77	21:51:11	87.87
37874	NaN	1.0	0.0	7.171	14.0	1.0	-37.589382	146.559422	-38.229669	143.710836	260.125	2018-02-03	22:11:31	258.51	2:30:01	106.47
37875	NaN	1.0	0.0	39.141	5.0	1.0	-37.491746	145.168879	-38.168680	143.936761	131.959	2018-04-19	22:54:02	134.48	1:08:30	94.83
37876	NaN	2.0	0.0	42.330	25.0	5.0	-36.618297	147.643165	-37.563856	145.038804	254.069	2018-01-27	11:06:09	234.09	15:00:14	116.10
37877	NaN	1.0	1.0	43.965	25.0	38.0	-36.542082	147.863736	-37.549355	142.980000	448.104	2018-06-14	22:15:50	440.42	5:36:15	149.26
37879	NaN	1.0	0.0	18.540	36.0	18.0	-36.974023	145.036046	-38.015640	142.955101	217.299	2018-05-28	07:14:50	217.06	10:51:53	76.31
37885	NaN	1.0	0.0	13.544	34.0	38.0	-38.528632	143.386140	-37.561137	142.835312	118.028	2018-04-09	11:48:46	120.99	13:49:45	69.88
37888	NaN	1.0	1.0	34.923	36.0	28.0	-37.044972	144.920592	-38.218799	148.050871	305.307	2018-07-20	03:13:54	302.23	8:16:07	130.70
37889	ID1281653747	1.0	0.0	34.130	8.0	36.0	-38.434373	144.730220	-37.047801	145.309517	162.554	2018-02-06	06:27:55	164.08	9:11:59	69.44
37892	NaN	3.0	1.0	5.416	20.0	33.0	-38.959090	148.294700	-38.930545	144.661887	314.514	2018-04-27	20:53:02	255.21	1:08:14	185.96
37894	NaN	2.0	0.0	41.232	38.0	39.0	-37.657406	142.777301	-38.622040	148.366529	500.901	2018-07-02	08:59:29	455.14	16:34:37	139.79
37897	NaN	1.0	0.0	8.865	9.0	2.0	-38.839254	145.226776	-37.695101	148.251214	293.394	2018-03-11	12:18:21	290.70	17:09:02	88.71
37898	NaN	3.0	1.0	27.153	39.0	16.0	-38.446310	148.292498	-36.739777	143.604529	454.968	2018-07-23	08:29:19	366.09	14:35:24	188.49

37869 rows × 16 columns

任务12：填补 'Post Type'的空值

提示：可能与id有关

data_1[data_1['Post Type'].isnull()]

	Id	Drone Type	Post Type	Package Weight	Origin Region	Destination Region	Origin Latitude	Origin Longitude	Destination Latitude	Destination Longitude	Journey Distance	Departure Date	Departure Time	Travel Time	Delivery Time	Delivery Fare
2179	ID5742860733	1.0	NaN	27.126	22.0	34.0	-36.679647	144.463364	-38.469625	143.202877	228.181	2018-02-28	14:01:50	227.59	17:49:25	114.27
4212	ID1710452907	2.0	NaN	37.466	22.0	21.0	-36.695380	144.474875	-37.357030	143.508873	113.113	2018-07-28	11:30:33	107.86	13:18:24	103.59
4219	ID1787036788	1.0	NaN	30.877	3.0	6.0	-38.311620	145.478355	-36.832092	145.600747	165.050	2018-07-18	11:58:01	166.50	14:44:31	75.01
4253	ID1680028038	2.0	NaN	13.860	22.0	19.0	-36.837363	144.515369	-37.089526	144.531402	28.106	2018-06-17	04:43:00	31.74	5:14:44	120.43
6299	ID1377767619	1.0	NaN	45.416	23.0	13.0	-37.720326	146.008038	-38.222304	147.498125	142.197	2018-01-13	09:36:02	144.38	12:00:24	69.85
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
26778	ID1571528676	1.0	NaN	15.062	33.0	9.0	-38.965718	144.650812	-38.962812	145.292314	55.525	2018-04-28	21:13:25	60.51	22:13:55	89.97
28691	ID5883461461	1.0	NaN	40.325	4.0	29.0	-36.542055	143.024959	-37.232887	142.980514	77.003	2018-05-01	04:18:09	81.29	5:39:26	124.92
30861	ID1971183564	3.0	NaN	42.700	19.0	4.0	-37.120747	144.287009	-36.762751	143.078632	114.656	2018-02-25	16:19:23	97.43	17:56:48	144.33
32921	ID5291598933	2.0	NaN	27.189	2.0	7.0	-37.564903	148.097962	-38.797131	143.873724	394.211	2018-01-09	12:39:15	359.59	18:38:50	165.54
36991	ID1484986030	1.0	NaN	27.517	37.0	36.0	-38.938504	147.755657	-37.060130	144.993660	320.003	2018-01-07	20:24:23	316.45	1:40:49	99.58

20 rows × 16 columns

#用投递速度来区分
def speed(df):
    speed = df['Journey Distance']/df['Travel Time']
    return round(speed,2)

data_1.loc[data_1['Post Type']==0].apply(speed,axis=1)

0        1.06
1        0.88
2        1.09
3        1.10
4        1.04
         ... 
37837    0.99
37839    1.08
37841    1.22
37842    1.24
37889    0.99
Length: 26529, dtype: float64

#用投递价格来区分
def price(df):
    price = df['Delivery Fare']/df['Package Weight']
    return round(price,2)
data_1.loc[data_1['Post Type']==0,['Delivery Fare','Package Weight']].apply(price,axis=1)

0         4.58
1         3.81
2        19.53
3         9.18
4        13.78
         ...  
37837    13.20
37839     7.10
37841     4.00
37842    12.92
37889     2.03
Length: 26529, dtype: float64

#ID以5还是1开头为区别
data_1.loc[data_1['Post Type'].isnull(),'Post Type'] = data_1.loc[data_1['Post Type'].isnull(),'Id'].apply(lambda s:s[2]=='1')

任务13：修复 'Origin Longitude'与 'Origin Latitude' 列中错误的值

data_1['Origin Longitude'].describe()
#有负值

count    37844.000000
mean       145.423081
std          6.929107
min       -148.337157
25%        143.964265
50%        145.424189
75%        147.171954
max        148.450576
Name: Origin Longitude, dtype: float64

def fix_Longitude_Latitude(flt):
    if flt<0:
        return -flt
    else:
        return flt
data_1['Origin Longitude'].apply(fix_Longitude_Latitude).describe()

count    37844.000000
mean       145.577375
std          1.764044
min        142.769991
25%        143.966143
50%        145.426161
75%        147.172965
max        148.450576
Name: Origin Longitude, dtype: float64

data_1['Origin Longitude'] = data_1['Origin Longitude'].apply(fix_Longitude_Latitude)

/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  """Entry point for launching an IPython kernel.

data_1['Origin Latitude'].describe()

count    37844.000000
mean       -37.728738
std          1.900393
min        -39.006941
25%        -38.442905
50%        -37.707244
75%        -37.094433
max         38.986998
Name: Origin Latitude, dtype: float64

data_1['Origin Latitude'] = data_1['Origin Latitude'].apply(fix_Longitude_Latitude)

/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  """Entry point for launching an IPython kernel.

任务14：修复 'Destination Latitude' 与 'Destination Longitude'列中错误的值

data_1['Destination Latitude'] = data_1['Destination Latitude'].apply(fix_Longitude_Latitude)

/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  """Entry point for launching an IPython kernel.

data_1['Destination Longitude'] = data_1['Destination Longitude'].apply(fix_Longitude_Latitude)

/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  """Entry point for launching an IPython kernel.

任务 15：填补'Origin Region'和‘Destination Region’中的空值

data_1[data_1['Origin Region'].isnull()]

	Id	Drone Type	Post Type	Package Weight	Origin Region	Destination Region	Origin Latitude	Origin Longitude	Destination Latitude	Destination Longitude	Journey Distance	Departure Date	Departure Time	Travel Time	Delivery Time	Delivery Fare
2080	ID1634835235	1.0	0	31.871	NaN	22.0	37.300712	143.610855	36.794684	144.280083	81.904	2018-06-09	03:21:49	86.04	4:47:51	95.70
4442	ID1321659107	2.0	0	12.991	NaN	10.0	38.180248	146.775985	37.396662	147.313624	99.224	2018-04-12	11:25:54	95.42	13:01:19	98.48
8220	ID1522212019	1.0	0	25.282	NaN	23.0	36.756457	148.437792	37.665363	145.818608	253.275	2018-03-27	17:56:31	251.88	22:08:23	94.56
8228	ID1946204979	2.0	0	12.779	NaN	25.0	37.391203	148.116452	36.520786	147.557258	108.914	2018-07-08	10:57:12	104.10	12:41:17	102.49
15023	ID1891262263	1.0	0	36.466	NaN	7.0	37.537619	145.202427	38.723869	143.685908	187.265	2018-03-13	07:21:08	188.00	10:29:08	72.78
20513	ID1490968406	1.0	0	42.648	NaN	9.0	36.654443	143.691689	38.849982	145.479151	290.643	2018-02-16	18:46:02	288.04	23:34:04	99.81
20515	ID5148310393	1.0	1	44.505	NaN	7.0	38.973121	142.989422	38.823172	143.990199	88.293	2018-04-18	23:35:27	92.22	1:07:40	119.24
28710	ID5941350307	1.0	1	17.232	NaN	32.0	37.616289	146.052853	36.696768	148.280560	222.563	2018-02-22	02:17:46	222.16	5:59:55	125.46
34853	ID5234294750	2.0	1	29.063	NaN	7.0	37.048542	142.843945	38.852320	143.963431	223.542	2018-07-14	13:22:02	206.75	16:48:47	151.47
36904	ID1724943602	1.0	0	31.774	NaN	31.0	36.736858	143.688416	37.548149	143.885770	91.993	2018-02-12	14:31:24	95.80	16:07:11	81.57

data_1.loc[data_1['Origin Region'].notnull()]

	Id	Drone Type	Post Type	Package Weight	Origin Region	Destination Region	Origin Latitude	Origin Longitude	Destination Latitude	Destination Longitude	Journey Distance	Departure Date	Departure Time	Travel Time	Delivery Time	Delivery Fare
0	ID1645282128	2.0	0	21.686	19.0	38.0	37.089338	144.429529	37.639134	142.891391	149.212	2018-01-16	09:38:17	140.19	11:58:28	99.25
1	ID1697620764	NaN	0	39.075	15.0	15.0	38.481935	146.009567	38.585528	146.199827	20.185	2018-02-10	04:28:17	22.84	4:51:07	149.04
2	ID1543933503	2.0	0	7.243	33.0	28.0	38.754167	144.509664	38.242224	147.855342	296.975	2018-05-05	01:38:03	272.52	6:10:34	141.48
3	ID1756517608	2.0	0	13.383	10.0	38.0	37.240526	147.568019	37.687178	142.991188	407.396	2018-06-11	11:43:04	371.40	17:54:27	122.82
4	ID1832325834	2.0	0	8.123	1.0	8.0	38.143985	143.798292	38.548315	144.769228	95.974	2018-03-16	14:50:25	92.51	16:22:55	111.97
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
37839	ID1879423081	2.0	0	17.081	20.0	14.0	38.860358	148.174855	37.562224	146.443991	209.278	2018-02-15	12:09:15	193.98	15:23:13	121.33
37840	ID5705840841	1.0	1	35.164	29.0	23.0	37.250331	142.837244	37.739285	145.963420	281.403	2018-01-01	00:07:35	279.10	4:46:41	136.58
37841	ID1276239209	3.0	0	36.704	12.0	11.0	36.578568	145.273744	38.305557	146.997297	245.269	2018-01-24	06:48:05	200.54	10:08:37	146.69
37842	ID1432868583	3.0	0	13.195	37.0	21.0	38.755802	147.744770	37.487866	143.585293	390.599	2018-04-07	18:57:52	315.28	0:13:08	170.52
37889	NaN	1.0	0	34.130	8.0	36.0	38.434373	144.730220	37.047801	145.309517	162.554	2018-02-06	06:27:55	164.08	9:11:59	69.44

37834 rows × 16 columns

data_2 = data_1.loc[data_1['Origin Region'].notnull()]
def fill_region(df):
    longitude,latitude = df['Origin Longitude'],df['Origin Latitude']
    distance_0 = 100
    region_out = ''

    for index in data_2.index:
        longitude_ = data_2.loc[index,'Origin Longitude']
        latitude_ = data_2.loc[index,'Origin Latitude']
        region = data_2.loc[index,'Origin Region']
        
        distance = (longitude-longitude_)**2+(latitude_-latitude)**2
        if distance<distance_0 :
            region_out = region
            distance_0 = distance
    return region_out

data_1.loc[data_1['Origin Region'].isnull()].apply(fill_region,axis=1)

2080     21.0
4442     11.0
8220     32.0
8228      2.0
15023     5.0
20513    16.0
20515    24.0
28710    23.0
34853    29.0
36904    16.0
dtype: float64

data_1.loc[data_1['Origin Region'].isnull()]

	Id	Drone Type	Post Type	Package Weight	Origin Region	Destination Region	Origin Latitude	Origin Longitude	Destination Latitude	Destination Longitude	Journey Distance	Departure Date	Departure Time	Travel Time	Delivery Time	Delivery Fare
2080	ID1634835235	1.0	0	31.871	NaN	22.0	37.300712	143.610855	36.794684	144.280083	81.904	2018-06-09	03:21:49	86.04	4:47:51	95.70
4442	ID1321659107	2.0	0	12.991	NaN	10.0	38.180248	146.775985	37.396662	147.313624	99.224	2018-04-12	11:25:54	95.42	13:01:19	98.48
8220	ID1522212019	1.0	0	25.282	NaN	23.0	36.756457	148.437792	37.665363	145.818608	253.275	2018-03-27	17:56:31	251.88	22:08:23	94.56
8228	ID1946204979	2.0	0	12.779	NaN	25.0	37.391203	148.116452	36.520786	147.557258	108.914	2018-07-08	10:57:12	104.10	12:41:17	102.49
15023	ID1891262263	1.0	0	36.466	NaN	7.0	37.537619	145.202427	38.723869	143.685908	187.265	2018-03-13	07:21:08	188.00	10:29:08	72.78
20513	ID1490968406	1.0	0	42.648	NaN	9.0	36.654443	143.691689	38.849982	145.479151	290.643	2018-02-16	18:46:02	288.04	23:34:04	99.81
20515	ID5148310393	1.0	1	44.505	NaN	7.0	38.973121	142.989422	38.823172	143.990199	88.293	2018-04-18	23:35:27	92.22	1:07:40	119.24
28710	ID5941350307	1.0	1	17.232	NaN	32.0	37.616289	146.052853	36.696768	148.280560	222.563	2018-02-22	02:17:46	222.16	5:59:55	125.46
34853	ID5234294750	2.0	1	29.063	NaN	7.0	37.048542	142.843945	38.852320	143.963431	223.542	2018-07-14	13:22:02	206.75	16:48:47	151.47
36904	ID1724943602	1.0	0	31.774	NaN	31.0	36.736858	143.688416	37.548149	143.885770	91.993	2018-02-12	14:31:24	95.80	16:07:11	81.57

任务16：找出 'Departure Date'中错误的值

data_1['Departure Date'].sort_values(ascending=False)

31975    2018-30-06
4990     2018-28-06
17911    2018-28-06
28350    2018-28-05
35740    2018-28-05
            ...    
12702    2018-01-01
36755    2018-01-01
12684    2018-01-01
36782    2018-01-01
27230    2018-01-01
Name: Departure Date, Length: 37844, dtype: object

def fix_date(dt):
    split_ = dt.split('-')
    year,month,day = split_[0],split_[1],split_[2]
    if month > '08':
        return year+'-'+day+'-'+month
    else:
        return dt
data_1['Departure Date'] = data_1['Departure Date'].apply(fix_date)

/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy

data_1['Departure Date'].sort_values(ascending=False)

17849    2018-07-28
17570    2018-07-28
988      2018-07-28
592      2018-07-28
12772    2018-07-28
            ...    
29092    2018-01-01
5232     2018-01-01
12584    2018-01-01
29041    2018-01-01
5519     2018-01-01
Name: Departure Date, Length: 37844, dtype: object

任务17：输出数据集为‘solution.csv’到当前目录下面

data_1.to_csv('solution.csv')