Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells14299
Missing cells (%)9.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory129.0 B

Variable types

DateTime6
Categorical4
Text3
Unsupported1
Numeric1

Dataset

Description서울 장애인콜택시 탑승 내역 데이터 입니다.(예정일시 기준 탑승 한 내역입니다.)데이터 제공 내역 : 접수일시, 예정일시, 배차일시, 승차일시, 하차일시, 취소일시, 출발지구, 출발지동, 목적지구, 목적지동, 이용목적, 요금, 승차거리, 차량고유번호, 차량종류
Author서울시설공단
URLhttps://www.data.go.kr/data/15115859/fileData.do

Alerts

배차일시 has 846 (8.5%) missing valuesMissing
승차일시 has 1283 (12.8%) missing valuesMissing
하차일시 has 1279 (12.8%) missing valuesMissing
취소일시 has 8722 (87.2%) missing valuesMissing
승차거리 has 1320 (13.2%) missing valuesMissing
차량고유번호 has 849 (8.5%) missing valuesMissing
요금 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-04-13 11:20:24.931307
Analysis finished2024-04-13 11:20:29.912154
Duration4.98 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

접수일시
Date

Distinct9697
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2021-12-31 13:43:04
Maximum2022-01-26 13:45:42
2024-04-13T20:20:30.140560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T20:20:30.563946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

예정일시
Date

Distinct8459
Distinct (%)84.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2022-01-01 00:01:00
Maximum2022-01-26 13:46:00
2024-04-13T20:20:30.969771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T20:20:31.394633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

배차일시
Date

MISSING 

Distinct9106
Distinct (%)99.5%
Missing846
Missing (%)8.5%
Memory size156.2 KiB
Minimum2022-01-01 00:08:08
Maximum2022-01-26 13:49:41
2024-04-13T20:20:31.799677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T20:20:32.217646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

승차일시
Date

MISSING 

Distinct8673
Distinct (%)99.5%
Missing1283
Missing (%)12.8%
Memory size156.2 KiB
Minimum2022-01-01 02:10:17
Maximum2022-01-26 14:14:37
2024-04-13T20:20:32.625771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T20:20:33.206214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

하차일시
Date

MISSING 

Distinct8674
Distinct (%)99.5%
Missing1279
Missing (%)12.8%
Memory size156.2 KiB
Minimum2022-01-01 02:25:00
Maximum2022-01-26 15:03:07
2024-04-13T20:20:33.611242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T20:20:34.027403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

취소일시
Date

MISSING 

Distinct1272
Distinct (%)99.5%
Missing8722
Missing (%)87.2%
Memory size156.2 KiB
Minimum2022-01-01 00:11:20
Maximum2022-01-26 13:50:40
2024-04-13T20:20:34.430862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T20:20:34.858756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

출발지구
Categorical

Distinct41
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
노원구
1010 
강서구
787 
은평구
 
607
서대문구
 
592
강동구
 
558
Other values (36)
6446 

Length

Max length7
Median length3
Mean length3.1208
Min length2

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row금천구
2nd row서대문구
3rd row강북구
4th row구로구
5th row강서구

Common Values

ValueCountFrequency (%)
노원구 1010
 
10.1%
강서구 787
 
7.9%
은평구 607
 
6.1%
서대문구 592
 
5.9%
강동구 558
 
5.6%
마포구 537
 
5.4%
송파구 479
 
4.8%
강남구 442
 
4.4%
중랑구 407
 
4.1%
도봉구 379
 
3.8%
Other values (31) 4202
42.0%

Length

2024-04-13T20:20:35.273003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
노원구 1010
 
10.1%
강서구 787
 
7.9%
은평구 607
 
6.1%
서대문구 592
 
5.9%
강동구 558
 
5.6%
마포구 537
 
5.4%
송파구 479
 
4.8%
강남구 442
 
4.4%
중랑구 407
 
4.1%
도봉구 379
 
3.8%
Other values (31) 4202
42.0%

출발지동
Text

Distinct463
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-13T20:20:36.370888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length7
Mean length4.2269
Min length2

Characters and Unicode

Total characters42269
Distinct characters200
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)0.3%

Sample

1st row시흥제2동
2nd row북아현동
3rd row수유제2동
4th row오류제1동
5th row화곡제4동
ValueCountFrequency (%)
신촌동 209
 
2.1%
하계1동 209
 
2.1%
상계6.7동 205
 
2.1%
역촌동 159
 
1.6%
성산제2동 154
 
1.5%
연희동 139
 
1.4%
상암동 120
 
1.2%
이화동 120
 
1.2%
도봉제2동 97
 
1.0%
강일동 89
 
0.9%
Other values (453) 8499
85.0%
2024-04-13T20:20:37.881104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 9997
23.7%
제 4105
 
9.7%
2 2318
 
5.5%
1 2226
 
5.3%
3 1112
 
2.6%
계 923
 
2.2%
신 866
 
2.0%
상 705
 
1.7%
. 585
 
1.4%
촌 582
 
1.4%
Other values (190) 18850
44.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 34701
82.1%
Decimal Number 6983
 
16.5%
Other Punctuation 585
 
1.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 9997
28.8%
제 4105
 
11.8%
계 923
 
2.7%
신 866
 
2.5%
상 705
 
2.0%
촌 582
 
1.7%
화 548
 
1.6%
산 519
 
1.5%
가 494
 
1.4%
방 463
 
1.3%
Other values (179) 15499
44.7%
Decimal Number
ValueCountFrequency (%)
2 2318
33.2%
1 2226
31.9%
3 1112
15.9%
4 472
 
6.8%
6 330
 
4.7%
7 273
 
3.9%
5 169
 
2.4%
8 53
 
0.8%
9 18
 
0.3%
0 12
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 585
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 34701
82.1%
Common 7568
 
17.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 9997
28.8%
제 4105
 
11.8%
계 923
 
2.7%
신 866
 
2.5%
상 705
 
2.0%
촌 582
 
1.7%
화 548
 
1.6%
산 519
 
1.5%
가 494
 
1.4%
방 463
 
1.3%
Other values (179) 15499
44.7%
Common
ValueCountFrequency (%)
2 2318
30.6%
1 2226
29.4%
3 1112
14.7%
. 585
 
7.7%
4 472
 
6.2%
6 330
 
4.4%
7 273
 
3.6%
5 169
 
2.2%
8 53
 
0.7%
9 18
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 34701
82.1%
ASCII 7568
 
17.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 9997
28.8%
제 4105
 
11.8%
계 923
 
2.7%
신 866
 
2.5%
상 705
 
2.0%
촌 582
 
1.7%
화 548
 
1.6%
산 519
 
1.5%
가 494
 
1.4%
방 463
 
1.3%
Other values (179) 15499
44.7%
ASCII
ValueCountFrequency (%)
2 2318
30.6%
1 2226
29.4%
3 1112
14.7%
. 585
 
7.7%
4 472
 
6.2%
6 330
 
4.4%
7 273
 
3.6%
5 169
 
2.2%
8 53
 
0.7%
9 18
 
0.2%

목적지구
Categorical

Distinct48
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
노원구
950 
강서구
682 
은평구
 
593
서대문구
 
550
강동구
 
505
Other values (43)
6720 

Length

Max length7
Median length3
Mean length3.2098
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row영등포구
2nd row서대문구
3rd row노원구
4th row성남시분당구
5th row영등포구

Common Values

ValueCountFrequency (%)
노원구 950
 
9.5%
강서구 682
 
6.8%
은평구 593
 
5.9%
서대문구 550
 
5.5%
강동구 505
 
5.1%
마포구 482
 
4.8%
송파구 443
 
4.4%
중랑구 403
 
4.0%
강남구 393
 
3.9%
영등포구 350
 
3.5%
Other values (38) 4649
46.5%

Length

2024-04-13T20:20:38.303074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
노원구 950
 
9.5%
강서구 682
 
6.8%
은평구 593
 
5.9%
서대문구 550
 
5.5%
강동구 505
 
5.1%
마포구 482
 
4.8%
송파구 443
 
4.4%
중랑구 403
 
4.0%
강남구 393
 
3.9%
영등포구 350
 
3.5%
Other values (38) 4649
46.5%

목적지동
Text

Distinct592
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-13T20:20:39.465946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length7
Mean length4.1826
Min length2

Characters and Unicode

Total characters41826
Distinct characters216
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique56 ?
Unique (%)0.6%

Sample

1st row당산제1동
2nd row충현동
3rd row월계2동
4th row구미동
5th row여의동
ValueCountFrequency (%)
상계6.7동 196
 
2.0%
신촌동 187
 
1.9%
하계1동 185
 
1.8%
역촌동 139
 
1.4%
성산제2동 123
 
1.2%
구산동 109
 
1.1%
상암동 104
 
1.0%
연희동 101
 
1.0%
신내1동 101
 
1.0%
강일동 95
 
0.9%
Other values (582) 8660
86.6%
2024-04-13T20:20:41.058822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 9954
23.8%
제 3863
 
9.2%
1 2275
 
5.4%
2 2250
 
5.4%
3 1069
 
2.6%
신 932
 
2.2%
계 872
 
2.1%
상 681
 
1.6%
산 606
 
1.4%
촌 539
 
1.3%
Other values (206) 18785
44.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 34420
82.3%
Decimal Number 6877
 
16.4%
Other Punctuation 529
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 9954
28.9%
제 3863
 
11.2%
신 932
 
2.7%
계 872
 
2.5%
상 681
 
2.0%
산 606
 
1.8%
촌 539
 
1.6%
가 498
 
1.4%
화 482
 
1.4%
방 424
 
1.2%
Other values (195) 15569
45.2%
Decimal Number
ValueCountFrequency (%)
1 2275
33.1%
2 2250
32.7%
3 1069
15.5%
4 464
 
6.7%
6 310
 
4.5%
7 246
 
3.6%
5 172
 
2.5%
8 66
 
1.0%
0 14
 
0.2%
9 11
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 529
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 34420
82.3%
Common 7406
 
17.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 9954
28.9%
제 3863
 
11.2%
신 932
 
2.7%
계 872
 
2.5%
상 681
 
2.0%
산 606
 
1.8%
촌 539
 
1.6%
가 498
 
1.4%
화 482
 
1.4%
방 424
 
1.2%
Other values (195) 15569
45.2%
Common
ValueCountFrequency (%)
1 2275
30.7%
2 2250
30.4%
3 1069
14.4%
. 529
 
7.1%
4 464
 
6.3%
6 310
 
4.2%
7 246
 
3.3%
5 172
 
2.3%
8 66
 
0.9%
0 14
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 34420
82.3%
ASCII 7406
 
17.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 9954
28.9%
제 3863
 
11.2%
신 932
 
2.7%
계 872
 
2.5%
상 681
 
2.0%
산 606
 
1.8%
촌 539
 
1.6%
가 498
 
1.4%
화 482
 
1.4%
방 424
 
1.2%
Other values (195) 15569
45.2%
ASCII
ValueCountFrequency (%)
1 2275
30.7%
2 2250
30.4%
3 1069
14.4%
. 529
 
7.1%
4 464
 
6.3%
6 310
 
4.2%
7 246
 
3.3%
5 172
 
2.3%
8 66
 
0.9%
0 14
 
0.2%

이용목적
Categorical

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
기타
5483 
귀가
2187 
치료
1178 
재활
 
486
통학/출근
 
205
Other values (7)
 
461

Length

Max length7
Median length2
Mean length2.1419
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row기타
2nd row치료
3rd row기타
4th row치료
5th row종교

Common Values

ValueCountFrequency (%)
기타 5483
54.8%
귀가 2187
 
21.9%
치료 1178
 
11.8%
재활 486
 
4.9%
통학/출근 205
 
2.1%
예약치료 146
 
1.5%
예약기타 114
 
1.1%
종교 100
 
1.0%
예약재활 58
 
0.6%
예약통학/출근 32
 
0.3%
Other values (2) 11
 
0.1%

Length

2024-04-13T20:20:41.478657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
기타 5483
54.8%
귀가 2187
 
21.9%
치료 1178
 
11.8%
재활 486
 
4.9%
통학/출근 205
 
2.1%
예약치료 146
 
1.5%
예약기타 114
 
1.1%
종교 100
 
1.0%
예약재활 58
 
0.6%
예약통학/출근 32
 
0.3%
Other values (2) 11
 
0.1%

요금
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size156.2 KiB

승차거리
Real number (ℝ)

MISSING 

Distinct6683
Distinct (%)77.0%
Missing1320
Missing (%)13.2%
Infinite0
Infinite (%)0.0%
Mean8490.8524
Minimum2
Maximum89363
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-13T20:20:41.866881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile1434
Q13210.25
median5513
Q311002.75
95-th percentile25647.35
Maximum89363
Range89361
Interquartile range (IQR)7792.5

Descriptive statistics

Standard deviation8062.0397
Coefficient of variation (CV)0.94949709
Kurtosis6.1797732
Mean8490.8524
Median Absolute Deviation (MAD)2991
Skewness2.1185613
Sum73700599
Variance64996484
MonotonicityNot monotonic
2024-04-13T20:20:42.295055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2718 6
 
0.1%
4361 6
 
0.1%
4741 6
 
0.1%
2684 6
 
0.1%
1515 5
 
0.1%
2795 5
 
0.1%
4029 5
 
0.1%
3621 5
 
0.1%
3164 5
 
0.1%
2368 5
 
0.1%
Other values (6673) 8626
86.3%
(Missing) 1320
 
13.2%
ValueCountFrequency (%)
2 1
< 0.1%
5 1
< 0.1%
11 1
< 0.1%
12 1
< 0.1%
46 1
< 0.1%
49 1
< 0.1%
98 1
< 0.1%
133 1
< 0.1%
213 1
< 0.1%
287 1
< 0.1%
ValueCountFrequency (%)
89363 1
< 0.1%
71835 1
< 0.1%
63254 1
< 0.1%
62342 1
< 0.1%
59267 1
< 0.1%
57818 1
< 0.1%
56333 1
< 0.1%
56324 1
< 0.1%
55845 1
< 0.1%
55208 1
< 0.1%

차량고유번호
Text

MISSING 

Distinct698
Distinct (%)7.6%
Missing849
Missing (%)8.5%
Memory size156.2 KiB
2024-04-13T20:20:43.428014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length7
Mean length7.0052453
Min length7

Characters and Unicode

Total characters64105
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st row31자5689
2nd row72우1567
3rd row71버0802
4th row78무4994
5th row77라3614
ValueCountFrequency (%)
31사9681 31
 
0.3%
77라3596 29
 
0.3%
31아6994 29
 
0.3%
32사8373 28
 
0.3%
31사8420 27
 
0.3%
32아4102 26
 
0.3%
74노9128 25
 
0.3%
32사9020 24
 
0.3%
77라3551 24
 
0.3%
77라7936 24
 
0.3%
Other values (688) 8884
97.1%
2024-04-13T20:20:44.694988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 14046
21.9%
1 7899
12.3%
8 5602
 
8.7%
5 4732
 
7.4%
3 4464
 
7.0%
2 4120
 
6.4%
4 4050
 
6.3%
6 3774
 
5.9%
0 3244
 
5.1%
9 2975
 
4.6%
Other values (15) 9199
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 54906
85.7%
Other Letter 9199
 
14.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
우 2893
31.4%
라 2209
24.0%
마 1309
14.2%
자 510
 
5.5%
사 430
 
4.7%
무 421
 
4.6%
노 398
 
4.3%
버 322
 
3.5%
바 215
 
2.3%
주 192
 
2.1%
Other values (5) 300
 
3.3%
Decimal Number
ValueCountFrequency (%)
7 14046
25.6%
1 7899
14.4%
8 5602
 
10.2%
5 4732
 
8.6%
3 4464
 
8.1%
2 4120
 
7.5%
4 4050
 
7.4%
6 3774
 
6.9%
0 3244
 
5.9%
9 2975
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Common 54906
85.7%
Hangul 9199
 
14.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
우 2893
31.4%
라 2209
24.0%
마 1309
14.2%
자 510
 
5.5%
사 430
 
4.7%
무 421
 
4.6%
노 398
 
4.3%
버 322
 
3.5%
바 215
 
2.3%
주 192
 
2.1%
Other values (5) 300
 
3.3%
Common
ValueCountFrequency (%)
7 14046
25.6%
1 7899
14.4%
8 5602
 
10.2%
5 4732
 
8.6%
3 4464
 
8.1%
2 4120
 
7.5%
4 4050
 
7.4%
6 3774
 
6.9%
0 3244
 
5.9%
9 2975
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 54906
85.7%
Hangul 9199
 
14.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 14046
25.6%
1 7899
14.4%
8 5602
 
10.2%
5 4732
 
8.6%
3 4464
 
8.1%
2 4120
 
7.5%
4 4050
 
7.4%
6 3774
 
6.9%
0 3244
 
5.9%
9 2975
 
5.4%
Hangul
ValueCountFrequency (%)
우 2893
31.4%
라 2209
24.0%
마 1309
14.2%
자 510
 
5.5%
사 430
 
4.7%
무 421
 
4.6%
노 398
 
4.3%
버 322
 
3.5%
바 215
 
2.3%
주 192
 
2.1%
Other values (5) 300
 
3.3%

차량구분
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
특장차
8669 
임차택시
1331 

Length

Max length4
Median length3
Mean length3.1331
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row임차택시
2nd row특장차
3rd row특장차
4th row특장차
5th row특장차

Common Values

ValueCountFrequency (%)
특장차 8669
86.7%
임차택시 1331
 
13.3%

Length

2024-04-13T20:20:44.926102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T20:20:45.103603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
특장차 8669
86.7%
임차택시 1331
 
13.3%

Interactions

2024-04-13T20:20:28.227663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-13T20:20:45.216006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
출발지구목적지구이용목적승차거리차량구분
출발지구1.0000.8360.1570.4850.129
목적지구0.8361.0000.2590.5650.120
이용목적0.1570.2591.0000.0560.147
승차거리0.4850.5650.0561.0000.100
차량구분0.1290.1200.1470.1001.000
2024-04-13T20:20:45.388353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
이용목적출발지구차량구분목적지구
이용목적1.0000.0510.1140.078
출발지구0.0511.0000.1080.277
차량구분0.1140.1081.0000.096
목적지구0.0780.2770.0961.000
2024-04-13T20:20:45.549973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
승차거리출발지구목적지구이용목적차량구분
승차거리1.0000.1880.2290.0230.077
출발지구0.1881.0000.2770.0510.108
목적지구0.2290.2771.0000.0780.096
이용목적0.0230.0510.0781.0000.114
차량구분0.0770.1080.0960.1141.000

Missing values

2024-04-13T20:20:28.610959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-13T20:20:29.243009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-04-13T20:20:29.684858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

접수일시예정일시배차일시승차일시하차일시취소일시출발지구출발지동목적지구목적지동이용목적요금승차거리차량고유번호차량구분
788672022-01-21 07:48:10.0002022-01-21 07:49:00.0002022-01-21 08:03:53NaTNaT2022-01-21 08:07:01금천구시흥제2동영등포구당산제1동기타<NA>31자5689임차택시
834912022-01-22 03:05:00.9532022-01-22 05:10:00.0002022-01-22 04:51:312022-01-22 05:13:592022-01-22 05:22:53NaT서대문구북아현동서대문구충현동치료1500231472우1567특장차
562682022-01-15 13:49:44.3872022-01-15 13:49:44.3872022-01-15 13:51:022022-01-15 14:09:112022-01-15 14:27:41NaT강북구수유제2동노원구월계2동기타1500293171버0802특장차
433302022-01-12 13:53:06.0002022-01-12 13:54:00.0002022-01-12 13:56:082022-01-12 14:12:422022-01-12 15:33:06NaT구로구오류제1동성남시분당구구미동치료48003740678무4994특장차
14822022-01-02 09:31:33.0002022-01-02 09:32:00.0002022-01-02 09:36:272022-01-02 09:48:062022-01-02 10:18:27NaT강서구화곡제4동영등포구여의동종교2000636777라3614특장차
723872022-01-19 15:21:05.0002022-01-19 15:22:00.000NaTNaTNaT2022-01-19 15:21:25도봉구도봉제1동강북구송중동귀가<NA><NA>특장차
475032022-01-13 12:09:14.6272022-01-13 12:09:14.627NaTNaTNaT2022-01-13 12:10:04동작구사당제2동서초구내곡동기타<NA><NA>특장차
281812022-01-08 12:24:08.0002022-01-08 12:25:00.0002022-01-08 12:36:332022-01-08 13:07:212022-01-08 13:45:33NaT동작구사당제1동서초구반포1동기타2000669375마5723특장차
139922022-01-05 10:40:49.3402022-01-05 10:40:49.3402022-01-05 11:00:022022-01-05 11:23:122022-01-05 11:51:42NaT영등포구영등포동관악구청룡동기타2300759671우8014특장차
162812022-01-05 15:14:55.0002022-01-05 15:15:00.0002022-01-05 15:36:152022-01-05 15:57:372022-01-05 16:15:17NaT영등포구신길제5동영등포구양평제1동귀가1500465231바4183임차택시
접수일시예정일시배차일시승차일시하차일시취소일시출발지구출발지동목적지구목적지동이용목적요금승차거리차량고유번호차량구분
251432022-01-07 13:36:42.2432022-01-07 13:36:42.2432022-01-07 13:45:572022-01-07 14:01:452022-01-07 14:15:03NaT송파구오금동강남구수서동기타1500429577라7924특장차
312232022-01-10 08:47:25.0932022-01-10 08:47:25.0902022-01-10 09:23:242022-01-10 10:04:182022-01-10 10:22:16NaT강서구가양제1동강서구가양제1동기타1500313071우8158특장차
313692022-01-10 09:07:08.3932022-01-10 09:07:08.3902022-01-10 09:18:022022-01-10 09:31:382022-01-10 10:21:03NaT송파구장지동서초구반포4동기타35001871632아3294임차택시
425052022-01-12 12:02:38.0472022-01-12 12:02:38.0332022-01-12 12:11:562022-01-12 12:28:072022-01-12 13:13:36NaT서대문구북가좌제2동금천구시흥제4동기타36001946971우8143특장차
479352022-01-13 13:08:22.0002022-01-13 13:09:00.0002022-01-13 13:17:332022-01-13 13:45:122022-01-13 14:12:33NaT서초구반포4동동작구상도제1동귀가2000680178무4954특장차
361732022-01-11 08:34:05.0002022-01-11 08:35:00.000NaTNaTNaT2022-01-11 08:34:25강동구천호제3동강동구강일동기타<NA><NA>특장차
587842022-01-17 08:06:36.7972022-01-17 08:06:36.7972022-01-17 08:10:252022-01-17 08:30:552022-01-17 08:47:04NaT은평구증산동은평구갈현제2동기타1500333978무4951특장차
940702022-01-25 11:54:40.0002022-01-25 11:55:00.0002022-01-25 12:09:422022-01-25 12:46:552022-01-25 13:03:40NaT노원구상계5동노원구공릉1.3동귀가1500473475마5718특장차
509242022-01-14 08:21:54.0002022-01-14 08:22:00.0002022-01-14 08:43:442022-01-14 09:07:562022-01-14 09:54:23NaT성북구석관동송파구풍납2동치료38002201571우8048특장차
448092022-01-12 16:42:21.2772022-01-12 16:42:21.2772022-01-12 17:48:272022-01-12 18:16:572022-01-12 18:31:03NaT동작구신대방제1동동작구신대방제2동기타1500265471버0818특장차