Overview

Dataset statistics

Number of variables7
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory654.3 KiB
Average record size in memory67.0 B

Variable types

Categorical2
Text2
Numeric3

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15247/F/1/datasetView.do

Alerts

노선명 is highly overall correlated with 노선IDHigh correlation
노선ID is highly overall correlated with 노선명High correlation
시간 has 428 (4.3%) zerosZeros

Reproduction

Analysis started2023-12-11 09:53:47.687743
Analysis finished2023-12-11 09:53:49.625501
Duration1.94 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

노선명
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
올림픽대로
1752 
강변북로
1726 
한강교량
1668 
내부순환로
1267 
동부간선로
1024 
Other values (5)
2563 

Length

Max length6
Median length5
Mean length4.6923
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row한강교량
2nd row올림픽대로
3rd row강변북로
4th row내부순환로
5th row강변북로

Common Values

ValueCountFrequency (%)
올림픽대로 1752
17.5%
강변북로 1726
17.3%
한강교량 1668
16.7%
내부순환로 1267
12.7%
동부간선로 1024
10.2%
강남순환로 993
9.9%
서부간선로 466
 
4.7%
북부간선로 448
 
4.5%
분당수서로 339
 
3.4%
경부고속도로 317
 
3.2%

Length

2023-12-11T18:53:49.719625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T18:53:49.886566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
올림픽대로 1752
17.5%
강변북로 1726
17.3%
한강교량 1668
16.7%
내부순환로 1267
12.7%
동부간선로 1024
10.2%
강남순환로 993
9.9%
서부간선로 466
 
4.7%
북부간선로 448
 
4.5%
분당수서로 339
 
3.4%
경부고속도로 317
 
3.2%

노선ID
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
LLL3000010
902 
LKL3000010
870 
LKR3000010
856 
LLR3000010
850 
LHU3000010
844 
Other values (15)
5678 

Length

Max length11
Median length10
Mean length10.0993
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLHD3000010
2nd rowLLR3000010
3rd rowLKL3000010
4th rowLRI3000010
5th rowLKL3000010

Common Values

ValueCountFrequency (%)
LLL3000010 902
 
9.0%
LKL3000010 870
 
8.7%
LKR3000010 856
 
8.6%
LLR3000010 850
 
8.5%
LHU3000010 844
 
8.4%
LHD3000010 824
 
8.2%
LRO3000010 671
 
6.7%
LRI3000010 596
 
6.0%
LQRE3000010 546
 
5.5%
LDI3000010 524
 
5.2%
Other values (10) 2517
25.2%

Length

2023-12-11T18:53:50.034547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
lll3000010 902
 
9.0%
lkl3000010 870
 
8.7%
lkr3000010 856
 
8.6%
llr3000010 850
 
8.5%
lhu3000010 844
 
8.4%
lhd3000010 824
 
8.2%
lro3000010 671
 
6.7%
lri3000010 596
 
6.0%
lqre3000010 546
 
5.5%
ldi3000010 524
 
5.2%
Other values (10) 2517
25.2%

구간명
Text

Distinct218
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T18:53:50.292282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length16
Mean length11.6259
Min length5

Characters and Unicode

Total characters116259
Distinct characters114
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row행주대교북단→행주대교남단
2nd row동호대교남단→성수대교남단
3rd row반포대교북단→동작대교북단
4th row마장램프→사근램프
5th row구리시계→천호대교북단
ValueCountFrequency (%)
봉천터널 287
 
2.3%
입구부 277
 
2.2%
출구부 276
 
2.2%
관악터널 273
 
2.2%
중간부 273
 
2.2%
서초터널 258
 
2.1%
잠실철교 160
 
1.3%
남단 121
 
1.0%
정릉터널 113
 
0.9%
중간부→봉천터널 102
 
0.8%
Other values (224) 10314
82.8%
2023-12-11T18:53:50.726182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
교 12426
 
10.7%
→ 10000
 
8.6%
단 9806
 
8.4%
대 9475
 
8.1%
남 5651
 
4.9%
북 4821
 
4.1%
동 2473
 
2.1%
2454
 
2.1%
C 2193
 
1.9%
널 2104
 
1.8%
Other values (104) 54856
47.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 99077
85.2%
Math Symbol 10000
 
8.6%
Uppercase Letter 4386
 
3.8%
Space Separator 2454
 
2.1%
Decimal Number 342
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 12426
 
12.5%
단 9806
 
9.9%
대 9475
 
9.6%
남 5651
 
5.7%
북 4821
 
4.9%
동 2473
 
2.5%
널 2104
 
2.1%
터 2104
 
2.1%
램 1814
 
1.8%
프 1814
 
1.8%
Other values (98) 46589
47.0%
Uppercase Letter
ValueCountFrequency (%)
C 2193
50.0%
I 1157
26.4%
J 1036
23.6%
Math Symbol
ValueCountFrequency (%)
→ 10000
100.0%
Space Separator
ValueCountFrequency (%)
2454
100.0%
Decimal Number
ValueCountFrequency (%)
1 342
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 99077
85.2%
Common 12796
 
11.0%
Latin 4386
 
3.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 12426
 
12.5%
단 9806
 
9.9%
대 9475
 
9.6%
남 5651
 
5.7%
북 4821
 
4.9%
동 2473
 
2.5%
널 2104
 
2.1%
터 2104
 
2.1%
램 1814
 
1.8%
프 1814
 
1.8%
Other values (98) 46589
47.0%
Common
ValueCountFrequency (%)
→ 10000
78.1%
2454
 
19.2%
1 342
 
2.7%
Latin
ValueCountFrequency (%)
C 2193
50.0%
I 1157
26.4%
J 1036
23.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 99077
85.2%
Arrows 10000
 
8.6%
ASCII 7182
 
6.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
교 12426
 
12.5%
단 9806
 
9.9%
대 9475
 
9.6%
남 5651
 
5.7%
북 4821
 
4.9%
동 2473
 
2.5%
널 2104
 
2.1%
터 2104
 
2.1%
램 1814
 
1.8%
프 1814
 
1.8%
Other values (98) 46589
47.0%
Arrows
ValueCountFrequency (%)
→ 10000
100.0%
ASCII
ValueCountFrequency (%)
2454
34.2%
C 2193
30.5%
I 1157
16.1%
J 1036
14.4%
1 342
 
4.8%

구간ID
Text

Distinct224
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T18:53:50.993800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length10.0993
Min length10

Characters and Unicode

Total characters100993
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLHD2000010
2nd rowLLR2000120
3rd rowLKI2000110
4th rowLRI2000135
5th rowLKL2000120
ValueCountFrequency (%)
lki2000110 64
 
0.6%
lbl2000050 61
 
0.6%
lhd2000040 60
 
0.6%
lri2000030 60
 
0.6%
lki2000170 59
 
0.6%
ldo2000120 57
 
0.6%
lhd2000200 57
 
0.6%
lqre2000070 57
 
0.6%
lro2000140 56
 
0.6%
lkr2000040 56
 
0.6%
Other values (214) 9413
94.1%
2023-12-11T18:53:51.392342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 45910
45.5%
L 13332
 
13.2%
2 11699
 
11.6%
1 5354
 
5.3%
R 3739
 
3.7%
D 2318
 
2.3%
O 1753
 
1.7%
H 1749
 
1.7%
K 1726
 
1.7%
I 1663
 
1.6%
Other values (15) 11750
 
11.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 70000
69.3%
Uppercase Letter 30993
30.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L 13332
43.0%
R 3739
 
12.1%
D 2318
 
7.5%
O 1753
 
5.7%
H 1749
 
5.6%
K 1726
 
5.6%
I 1663
 
5.4%
U 1030
 
3.3%
Q 993
 
3.2%
S 688
 
2.2%
Other values (5) 2002
 
6.5%
Decimal Number
ValueCountFrequency (%)
0 45910
65.6%
2 11699
 
16.7%
1 5354
 
7.6%
5 1563
 
2.2%
3 1223
 
1.7%
4 1049
 
1.5%
8 836
 
1.2%
7 812
 
1.2%
9 803
 
1.1%
6 751
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 70000
69.3%
Latin 30993
30.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
L 13332
43.0%
R 3739
 
12.1%
D 2318
 
7.5%
O 1753
 
5.7%
H 1749
 
5.6%
K 1726
 
5.6%
I 1663
 
5.4%
U 1030
 
3.3%
Q 993
 
3.2%
S 688
 
2.2%
Other values (5) 2002
 
6.5%
Common
ValueCountFrequency (%)
0 45910
65.6%
2 11699
 
16.7%
1 5354
 
7.6%
5 1563
 
2.2%
3 1223
 
1.7%
4 1049
 
1.5%
8 836
 
1.2%
7 812
 
1.2%
9 803
 
1.1%
6 751
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 100993
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 45910
45.5%
L 13332
 
13.2%
2 11699
 
11.6%
1 5354
 
5.3%
R 3739
 
3.7%
D 2318
 
2.3%
O 1753
 
1.7%
H 1749
 
1.7%
K 1726
 
1.7%
I 1663
 
1.6%
Other values (15) 11750
 
11.6%

년월일
Real number (ℝ)

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20180109
Minimum20180101
Maximum20180117
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T18:53:51.524699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20180101
5-th percentile20180101
Q120180105
median20180109
Q320180113
95-th percentile20180116
Maximum20180117
Range16
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.671561
Coefficient of variation (CV)2.3149335 × 10-7
Kurtosis-1.2095105
Mean20180109
Median Absolute Deviation (MAD)4
Skewness0.0062561379
Sum2.0180109 × 1011
Variance21.823482
MonotonicityNot monotonic
2023-12-11T18:53:51.646708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
20180106 672
 
6.7%
20180116 643
 
6.4%
20180112 637
 
6.4%
20180115 637
 
6.4%
20180101 635
 
6.3%
20180113 629
 
6.3%
20180105 627
 
6.3%
20180103 614
 
6.1%
20180109 614
 
6.1%
20180102 613
 
6.1%
Other values (7) 3679
36.8%
ValueCountFrequency (%)
20180101 635
6.3%
20180102 613
6.1%
20180103 614
6.1%
20180104 604
6.0%
20180105 627
6.3%
20180106 672
6.7%
20180107 601
6.0%
20180108 593
5.9%
20180109 614
6.1%
20180110 613
6.1%
ValueCountFrequency (%)
20180117 77
 
0.8%
20180116 643
6.4%
20180115 637
6.4%
20180114 596
6.0%
20180113 629
6.3%
20180112 637
6.4%
20180111 595
5.9%
20180110 613
6.1%
20180109 614
6.1%
20180108 593
5.9%

시간
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.405
Minimum0
Maximum23
Zeros428
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T18:53:51.766803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median11
Q317
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.9484821
Coefficient of variation (CV)0.60924876
Kurtosis-1.2177583
Mean11.405
Median Absolute Deviation (MAD)6
Skewness0.0040775353
Sum114050
Variance48.281403
MonotonicityNot monotonic
2023-12-11T18:53:51.878148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 459
 
4.6%
17 446
 
4.5%
4 442
 
4.4%
19 438
 
4.4%
2 436
 
4.4%
5 431
 
4.3%
0 428
 
4.3%
15 426
 
4.3%
7 424
 
4.2%
10 422
 
4.2%
Other values (14) 5648
56.5%
ValueCountFrequency (%)
0 428
4.3%
1 459
4.6%
2 436
4.4%
3 395
4.0%
4 442
4.4%
5 431
4.3%
6 383
3.8%
7 424
4.2%
8 416
4.2%
9 406
4.1%
ValueCountFrequency (%)
23 402
4.0%
22 405
4.0%
21 399
4.0%
20 410
4.1%
19 438
4.4%
18 401
4.0%
17 446
4.5%
16 419
4.2%
15 426
4.3%
14 418
4.2%

속도
Real number (ℝ)

Distinct5294
Distinct (%)52.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.774118
Minimum6
Maximum149.46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T18:53:52.003702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile30.298
Q161.9425
median75.525
Q385.3625
95-th percentile97.8205
Maximum149.46
Range143.46
Interquartile range (IQR)23.42

Descriptive statistics

Standard deviation19.739123
Coefficient of variation (CV)0.27501728
Kurtosis0.49708142
Mean71.774118
Median Absolute Deviation (MAD)11.14
Skewness-0.79781328
Sum717741.18
Variance389.63298
MonotonicityNot monotonic
2023-12-11T18:53:52.175758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73.52 9
 
0.1%
81.19 8
 
0.1%
87.26 8
 
0.1%
76.55 8
 
0.1%
82.02 8
 
0.1%
77.23 8
 
0.1%
84.51 8
 
0.1%
66.01 8
 
0.1%
75.49 8
 
0.1%
81.4 8
 
0.1%
Other values (5284) 9919
99.2%
ValueCountFrequency (%)
6.0 1
< 0.1%
7.5 1
< 0.1%
7.63 1
< 0.1%
7.67 1
< 0.1%
8.24 1
< 0.1%
8.76 1
< 0.1%
9.22 1
< 0.1%
9.65 1
< 0.1%
10.63 1
< 0.1%
10.7 1
< 0.1%
ValueCountFrequency (%)
149.46 1
< 0.1%
139.37 1
< 0.1%
136.51 1
< 0.1%
132.52 1
< 0.1%
132.21 1
< 0.1%
129.5 1
< 0.1%
127.46 1
< 0.1%
126.76 1
< 0.1%
125.85 1
< 0.1%
122.88 1
< 0.1%

Interactions

2023-12-11T18:53:49.026983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:53:48.340927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:53:48.706308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:53:49.165940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:53:48.461471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:53:48.826238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:53:49.268778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:53:48.576860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:53:48.921600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T18:53:52.273623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노선명노선ID년월일시간속도
노선명1.0001.0000.0290.0000.536
노선ID1.0001.0000.0280.0000.553
년월일0.0290.0281.0000.0880.162
시간0.0000.0000.0881.0000.451
속도0.5360.5530.1620.4511.000
2023-12-11T18:53:52.363249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노선명노선ID
노선명1.0000.999
노선ID0.9991.000
2023-12-11T18:53:52.466695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년월일시간속도노선명노선ID
년월일1.000-0.028-0.0620.0040.006
시간-0.0281.000-0.2810.0000.000
속도-0.062-0.2811.0000.1900.206
노선명0.0040.0000.1901.0000.999
노선ID0.0060.0000.2060.9991.000

Missing values

2023-12-11T18:53:49.413287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T18:53:49.557832image/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.

Sample

노선명노선ID구간명구간ID년월일시간속도
66937한강교량LHD3000010행주대교북단→행주대교남단LHD2000010201801131079.06
27280올림픽대로LLR3000010동호대교남단→성수대교남단LLR200012020180106187.08
4056강변북로LKL3000010반포대교북단→동작대교북단LKI2000110201801011863.5
69965내부순환로LRI3000010마장램프→사근램프LRI200013520180114061.39
59174강변북로LKL3000010구리시계→천호대교북단LKL200012020180112077.63
27898북부간선로LBR3000010종암JC→하월곡LBR200001020180106483.27
42034올림픽대로LLL3000010가양대교남단→방화대교남단LLL2000020201801081964.32
35669강변북로LKR3000010청담대교북단→잠실대교북단LKR2000180201801071583.41
64842동부간선로LDO3000010성수JC→성동JCLDO200001020180113167.26
20888강변북로LKR3000010올림픽대교북단→천호대교북단LKR2000210201801042179.68
노선명노선ID구간명구간ID년월일시간속도
3013동부간선로LDI3000010녹천교→월계1교LDI2000050201801011345.4
44315한강교량LHD3000010성산대교북단→성산대교남단LHD200004020180109571.82
2886한강교량LHD3000010동호대교북단→동호대교남단LHD2000130201801011281.24
71521내부순환로LRI3000010성산대교북단→성산램프LRI200001020180114775.49
7944동부간선로LDI3000010중랑교→장안교LDI2000083201801021179.66
37104서부간선로LSS3000010광명교→금천교LSS2100050201801072151.32
83127강변북로LKL3000010동작대교북단→한강대교북단LKI2000120201801161147.99
4581동부간선로LDI3000010녹천교→월계1교LDI2000050201801012045.08
55877동부간선로LDI3000010월릉JC→중랑교LDI200007020180111953.57
16594강남순환로LQRW3000010관악터널 중간부→관악터널 출구부LQRW2000090201801042108.77