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 397 (4.0%) zerosZeros

Reproduction

Analysis started2023-12-11 09:53:54.244380
Analysis finished2023-12-11 09:53:56.431099
Duration2.19 seconds
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
1826 
강변북로
1777 
한강교량
1595 
내부순환로
1338 
동부간선로
984 
Other values (5)
2480 

Length

Max length6
Median length5
Mean length4.6947
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
올림픽대로 1826
18.3%
강변북로 1777
17.8%
한강교량 1595
16.0%
내부순환로 1338
13.4%
동부간선로 984
9.8%
강남순환로 909
9.1%
북부간선로 462
 
4.6%
서부간선로 434
 
4.3%
분당수서로 356
 
3.6%
경부고속도로 319
 
3.2%

Length

2023-12-11T18:53:56.524964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T18:53:56.744215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
올림픽대로 1826
18.3%
강변북로 1777
17.8%
한강교량 1595
16.0%
내부순환로 1338
13.4%
동부간선로 984
9.8%
강남순환로 909
9.1%
북부간선로 462
 
4.6%
서부간선로 434
 
4.3%
분당수서로 356
 
3.6%
경부고속도로 319
 
3.2%

노선ID
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
LLL3000010
921 
LLR3000010
905 
LKL3000010
902 
LKR3000010
875 
LHU3000010
835 
Other values (15)
5562 

Length

Max length11
Median length10
Mean length10.0909
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLHD3000010
2nd rowLFD3000010
3rd rowLLR3000010
4th rowLKR3000010
5th rowLLL3000010

Common Values

ValueCountFrequency (%)
LLL3000010 921
 
9.2%
LLR3000010 905
 
9.0%
LKL3000010 902
 
9.0%
LKR3000010 875
 
8.8%
LHU3000010 835
 
8.3%
LHD3000010 760
 
7.6%
LRO3000010 689
 
6.9%
LRI3000010 649
 
6.5%
LDI3000010 503
 
5.0%
LDO3000010 481
 
4.8%
Other values (10) 2480
24.8%

Length

2023-12-11T18:53:56.932138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
lll3000010 921
 
9.2%
llr3000010 905
 
9.0%
lkl3000010 902
 
9.0%
lkr3000010 875
 
8.8%
lhu3000010 835
 
8.3%
lhd3000010 760
 
7.6%
lro3000010 689
 
6.9%
lri3000010 649
 
6.5%
ldi3000010 503
 
5.0%
ldo3000010 481
 
4.8%
Other values (10) 2480
24.8%

구간명
Text

Distinct216
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T18:53:57.185898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length16
Mean length11.6462
Min length5

Characters and Unicode

Total characters116462
Distinct characters110
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 row광진교 북단→광진교 남단
2nd row서초IC→양재IC
3rd row암사대교남단→강동대교남단
4th row한남대교북단→동호대교북단
5th row성산대교남단→가양대교남단
ValueCountFrequency (%)
서초터널 292
 
2.3%
출구부 276
 
2.2%
봉천터널 270
 
2.2%
관악터널 269
 
2.2%
중간부 263
 
2.1%
입구부 263
 
2.1%
잠실철교 193
 
1.5%
정릉터널 147
 
1.2%
남단 121
 
1.0%
중간부→서초터널 110
 
0.9%
Other values (221) 10288
82.4%
2023-12-11T18:53:57.569229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
교 12464
 
10.7%
→ 10000
 
8.6%
단 9963
 
8.6%
대 9533
 
8.2%
남 5748
 
4.9%
북 4867
 
4.2%
동 2615
 
2.2%
2492
 
2.1%
C 2202
 
1.9%
터 2149
 
1.8%
Other values (100) 54429
46.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 99193
85.2%
Math Symbol 10000
 
8.6%
Uppercase Letter 4404
 
3.8%
Space Separator 2492
 
2.1%
Decimal Number 373
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 12464
 
12.6%
단 9963
 
10.0%
대 9533
 
9.6%
남 5748
 
5.8%
북 4867
 
4.9%
동 2615
 
2.6%
터 2149
 
2.2%
널 2149
 
2.2%
구 1833
 
1.8%
성 1821
 
1.8%
Other values (94) 46051
46.4%
Uppercase Letter
ValueCountFrequency (%)
C 2202
50.0%
I 1193
27.1%
J 1009
22.9%
Math Symbol
ValueCountFrequency (%)
→ 10000
100.0%
Space Separator
ValueCountFrequency (%)
2492
100.0%
Decimal Number
ValueCountFrequency (%)
1 373
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 99193
85.2%
Common 12865
 
11.0%
Latin 4404
 
3.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 12464
 
12.6%
단 9963
 
10.0%
대 9533
 
9.6%
남 5748
 
5.8%
북 4867
 
4.9%
동 2615
 
2.6%
터 2149
 
2.2%
널 2149
 
2.2%
구 1833
 
1.8%
성 1821
 
1.8%
Other values (94) 46051
46.4%
Common
ValueCountFrequency (%)
→ 10000
77.7%
2492
 
19.4%
1 373
 
2.9%
Latin
ValueCountFrequency (%)
C 2202
50.0%
I 1193
27.1%
J 1009
22.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 99193
85.2%
Arrows 10000
 
8.6%
ASCII 7269
 
6.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
교 12464
 
12.6%
단 9963
 
10.0%
대 9533
 
9.6%
남 5748
 
5.8%
북 4867
 
4.9%
동 2615
 
2.6%
터 2149
 
2.2%
널 2149
 
2.2%
구 1833
 
1.8%
성 1821
 
1.8%
Other values (94) 46051
46.4%
Arrows
ValueCountFrequency (%)
→ 10000
100.0%
ASCII
ValueCountFrequency (%)
2492
34.3%
C 2202
30.3%
I 1193
16.4%
J 1009
13.9%
1 373
 
5.1%

구간ID
Text

Distinct222
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T18:53:57.901336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length10.0909
Min length10

Characters and Unicode

Total characters100909
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 rowLHD2000210
2nd rowLFD2000040
3rd rowLLR2000200
4th rowLKO2000110
5th rowLLL2000030
ValueCountFrequency (%)
ldo2000020 62
 
0.6%
ldi2000083 60
 
0.6%
lll2000030 59
 
0.6%
llr2000140 59
 
0.6%
lqrw2000030 59
 
0.6%
lhu2000040 58
 
0.6%
lri2000080 58
 
0.6%
lkl2000105 57
 
0.6%
lro2000010 57
 
0.6%
lqrw2000100 57
 
0.6%
Other values (212) 9414
94.1%
2023-12-11T18:53:58.352818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 45883
45.5%
L 13474
 
13.4%
2 11683
 
11.6%
1 5367
 
5.3%
R 3783
 
3.7%
D 2226
 
2.2%
O 1780
 
1.8%
K 1777
 
1.8%
I 1693
 
1.7%
H 1681
 
1.7%
Other values (15) 11562
 
11.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 70000
69.4%
Uppercase Letter 30909
30.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L 13474
43.6%
R 3783
 
12.2%
D 2226
 
7.2%
O 1780
 
5.8%
K 1777
 
5.7%
I 1693
 
5.5%
H 1681
 
5.4%
U 1028
 
3.3%
Q 909
 
2.9%
S 656
 
2.1%
Other values (5) 1902
 
6.2%
Decimal Number
ValueCountFrequency (%)
0 45883
65.5%
2 11683
 
16.7%
1 5367
 
7.7%
5 1529
 
2.2%
3 1315
 
1.9%
4 1019
 
1.5%
8 833
 
1.2%
9 819
 
1.2%
7 814
 
1.2%
6 738
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 70000
69.4%
Latin 30909
30.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
L 13474
43.6%
R 3783
 
12.2%
D 2226
 
7.2%
O 1780
 
5.8%
K 1777
 
5.7%
I 1693
 
5.5%
H 1681
 
5.4%
U 1028
 
3.3%
Q 909
 
2.9%
S 656
 
2.1%
Other values (5) 1902
 
6.2%
Common
ValueCountFrequency (%)
0 45883
65.5%
2 11683
 
16.7%
1 5367
 
7.7%
5 1529
 
2.2%
3 1315
 
1.9%
4 1019
 
1.5%
8 833
 
1.2%
9 819
 
1.2%
7 814
 
1.2%
6 738
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 100909
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 45883
45.5%
L 13474
 
13.4%
2 11683
 
11.6%
1 5367
 
5.3%
R 3783
 
3.7%
D 2226
 
2.2%
O 1780
 
1.8%
K 1777
 
1.8%
I 1693
 
1.7%
H 1681
 
1.7%
Other values (15) 11562
 
11.5%

년월일
Real number (ℝ)

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20170704
Minimum20170630
Maximum20170716
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T18:53:58.500795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20170630
5-th percentile20170630
Q120170704
median20170708
Q320170712
95-th percentile20170715
Maximum20170716
Range86
Interquartile range (IQR)8

Descriptive statistics

Standard deviation18.822895
Coefficient of variation (CV)9.3317987 × 10-7
Kurtosis10.667136
Mean20170704
Median Absolute Deviation (MAD)4
Skewness-3.4365789
Sum2.0170704 × 1011
Variance354.30136
MonotonicityNot monotonic
2023-12-11T18:53:58.607159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
20170703 639
 
6.4%
20170709 635
 
6.3%
20170708 631
 
6.3%
20170707 626
 
6.3%
20170701 623
 
6.2%
20170705 621
 
6.2%
20170704 620
 
6.2%
20170710 617
 
6.2%
20170712 616
 
6.2%
20170702 613
 
6.1%
Other values (7) 3759
37.6%
ValueCountFrequency (%)
20170630 584
5.8%
20170701 623
6.2%
20170702 613
6.1%
20170703 639
6.4%
20170704 620
6.2%
20170705 621
6.2%
20170706 580
5.8%
20170707 626
6.3%
20170708 631
6.3%
20170709 635
6.3%
ValueCountFrequency (%)
20170716 242
 
2.4%
20170715 581
5.8%
20170714 607
6.1%
20170713 584
5.8%
20170712 616
6.2%
20170711 581
5.8%
20170710 617
6.2%
20170709 635
6.3%
20170708 631
6.3%
20170707 626
6.3%

시간
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.4588
Minimum0
Maximum23
Zeros397
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T18:53:58.719197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median11
Q317
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.9106496
Coefficient of variation (CV)0.60308668
Kurtosis-1.2084914
Mean11.4588
Median Absolute Deviation (MAD)6
Skewness0.014490058
Sum114588
Variance47.757078
MonotonicityNot monotonic
2023-12-11T18:53:58.833304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
19 455
 
4.5%
6 450
 
4.5%
8 449
 
4.5%
10 436
 
4.4%
17 436
 
4.4%
23 428
 
4.3%
21 428
 
4.3%
3 427
 
4.3%
5 424
 
4.2%
2 423
 
4.2%
Other values (14) 5644
56.4%
ValueCountFrequency (%)
0 397
4.0%
1 422
4.2%
2 423
4.2%
3 427
4.3%
4 404
4.0%
5 424
4.2%
6 450
4.5%
7 412
4.1%
8 449
4.5%
9 413
4.1%
ValueCountFrequency (%)
23 428
4.3%
22 373
3.7%
21 428
4.3%
20 403
4.0%
19 455
4.5%
18 396
4.0%
17 436
4.4%
16 403
4.0%
15 410
4.1%
14 406
4.1%

속도
Real number (ℝ)

Distinct5458
Distinct (%)54.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.553041
Minimum6.66
Maximum116.53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T18:53:58.967381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.66
5-th percentile24.2
Q155.085
median72.73
Q383.2425
95-th percentile95.12
Maximum116.53
Range109.87
Interquartile range (IQR)28.1575

Descriptive statistics

Standard deviation21.341515
Coefficient of variation (CV)0.31592234
Kurtosis-0.23928012
Mean67.553041
Median Absolute Deviation (MAD)12.88
Skewness-0.72170012
Sum675530.41
Variance455.46026
MonotonicityNot monotonic
2023-12-11T18:53:59.113188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
83.17 9
 
0.1%
31.36 9
 
0.1%
75.7 8
 
0.1%
89.65 8
 
0.1%
84.26 7
 
0.1%
73.41 7
 
0.1%
79.3 7
 
0.1%
73.94 7
 
0.1%
79.82 7
 
0.1%
79.43 7
 
0.1%
Other values (5448) 9924
99.2%
ValueCountFrequency (%)
6.66 1
< 0.1%
8.66 1
< 0.1%
8.9 1
< 0.1%
8.93 1
< 0.1%
9.22 1
< 0.1%
10.11 1
< 0.1%
10.42 1
< 0.1%
10.5 1
< 0.1%
10.53 1
< 0.1%
10.72 1
< 0.1%
ValueCountFrequency (%)
116.53 1
< 0.1%
115.37 1
< 0.1%
110.93 1
< 0.1%
110.56 1
< 0.1%
110.3 1
< 0.1%
110.23 1
< 0.1%
109.74 1
< 0.1%
109.6 1
< 0.1%
109.35 1
< 0.1%
109.3 1
< 0.1%

Interactions

2023-12-11T18:53:55.815901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:53:55.179711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:53:55.479089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:53:55.916246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:53:55.267172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:53:55.602168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:53:56.037091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:53:55.360533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:53:55.712457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T18:53:59.208625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노선명노선ID년월일시간속도
노선명1.0001.0000.0330.0000.536
노선ID1.0001.0000.0110.0000.544
년월일0.0330.0111.0000.0100.029
시간0.0000.0000.0101.0000.456
속도0.5360.5440.0290.4561.000
2023-12-11T18:53:59.324534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노선명노선ID
노선명1.0000.999
노선ID0.9991.000
2023-12-11T18:53:59.419328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년월일시간속도노선명노선ID
년월일1.000-0.029-0.0150.0110.000
시간-0.0291.000-0.3400.0000.000
속도-0.015-0.3401.0000.1900.201
노선명0.0110.0000.1901.0000.999
노선ID0.0000.0000.2010.9991.000

Missing values

2023-12-11T18:53:56.189438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T18:53:56.364872image/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
54868한강교량LHD3000010광진교 북단→광진교 남단LHD200021020170710735.79
33067경부고속도로LFD3000010서초IC→양재ICLFD2000040201707065107.16
32302올림픽대로LLR3000010암사대교남단→강동대교남단LLR200020020170706190.52
66904강변북로LKR3000010한남대교북단→동호대교북단LKO2000110201707121528.19
23607올림픽대로LLL3000010성산대교남단→가양대교남단LLL2000030201707041077.9
10412한강교량LHU3000010서강대교남단→서강대교북단LHU2000060201707012277.34
7354강변북로LKL3000010잠실대교북단→청담대교북단LKL200008020170701983.48
58396강변북로LKR3000010반포대교북단→한남대교북단LKO200010020170711045.93
80281북부간선로LBR3000010묵동IC→신내ICLBR200004020170715462.53
49656강변북로LKL3000010영동대교북단→성수LKL200010020170709890.56
노선명노선ID구간명구간ID년월일시간속도
6587올림픽대로LLL3000010동호대교남단→한남대교남단LLL200011020170701576.98
41993북부간선로LBL3000010월릉JC→하월곡LBL2000040201707072138.9
39812올림픽대로LLL3000010잠실대교남단→청담대교남단LLL2000150201707071135.47
47878강변북로LKL3000010원효대교북단→마포대교북단LKI200014020170709083.55
53069올림픽대로LLL3000010반포대교남단→동작대교남단LLL2000090201707092360.02
43894강변북로LKL3000010동작대교북단→한강대교북단LKI200012020170708633.21
55931한강교량LHD3000010마포대교북단→마포대교남단LHD2000070201707101256.17
85394한강교량LHU3000010행주대교남단→행주대교북단LHU200001020170716369.04
82356강남순환로LQRW3000010선암영업소→서초터널 입구부LQRW2000015201707151487.2
85230강변북로LKL3000010성수→동호대교북단LKL200010520170716365.46