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 09:53:40.915781
Analysis finished2023-12-11 09:53:42.907987
Duration1.99 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
1847 
한강교량
1666 
강변북로
1658 
내부순환로
1265 
동부간선로
1051 
Other values (5)
2513 

Length

Max length6
Median length5
Mean length4.7007
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
올림픽대로 1847
18.5%
한강교량 1666
16.7%
강변북로 1658
16.6%
내부순환로 1265
12.7%
동부간선로 1051
10.5%
강남순환로 985
9.8%
북부간선로 435
 
4.3%
서부간선로 418
 
4.2%
분당수서로 344
 
3.4%
경부고속도로 331
 
3.3%

Length

2023-12-11T18:53:43.005152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T18:53:43.154380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
올림픽대로 1847
18.5%
한강교량 1666
16.7%
강변북로 1658
16.6%
내부순환로 1265
12.7%
동부간선로 1051
10.5%
강남순환로 985
9.8%
북부간선로 435
 
4.3%
서부간선로 418
 
4.2%
분당수서로 344
 
3.4%
경부고속도로 331
 
3.3%

노선ID
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
LLR3000010
942 
LLL3000010
905 
LHU3000010
837 
LKL3000010
831 
LHD3000010
829 
Other values (15)
5656 

Length

Max length11
Median length10
Mean length10.0985
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLHD3000010
2nd rowLRI3000010
3rd rowLBR3000010
4th rowLLR3000010
5th rowLKL3000010

Common Values

ValueCountFrequency (%)
LLR3000010 942
 
9.4%
LLL3000010 905
 
9.0%
LHU3000010 837
 
8.4%
LKL3000010 831
 
8.3%
LHD3000010 829
 
8.3%
LKR3000010 827
 
8.3%
LRO3000010 650
 
6.5%
LRI3000010 615
 
6.2%
LDI3000010 570
 
5.7%
LQRE3000010 512
 
5.1%
Other values (10) 2482
24.8%

Length

2023-12-11T18:53:43.621682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
llr3000010 942
 
9.4%
lll3000010 905
 
9.0%
lhu3000010 837
 
8.4%
lkl3000010 831
 
8.3%
lhd3000010 829
 
8.3%
lkr3000010 827
 
8.3%
lro3000010 650
 
6.5%
lri3000010 615
 
6.2%
ldi3000010 570
 
5.7%
lqre3000010 512
 
5.1%
Other values (10) 2482
24.8%

구간명
Text

Distinct218
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T18:53:43.904369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length16
Mean length11.693
Min length5

Characters and Unicode

Total characters116930
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 row성수대교북단→성수대교입구남단
2nd row홍제램프→홍은램프
3rd row종암JC→하월곡
4th row반포대교남단→한남대교남단
5th row잠실철교 북단→잠실대교북단
ValueCountFrequency (%)
서초터널 292
 
2.3%
입구부 291
 
2.3%
중간부 280
 
2.2%
관악터널 278
 
2.2%
봉천터널 278
 
2.2%
출구부 274
 
2.2%
잠실철교 180
 
1.4%
남단 140
 
1.1%
정릉터널 137
 
1.1%
광진교 102
 
0.8%
Other values (224) 10406
82.2%
2023-12-11T18:53:44.314916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
교 12455
 
10.7%
→ 10000
 
8.6%
단 9877
 
8.4%
대 9415
 
8.1%
남 5867
 
5.0%
북 4700
 
4.0%
2658
 
2.3%
동 2527
 
2.2%
C 2214
 
1.9%
터 2213
 
1.9%
Other values (104) 55004
47.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 99446
85.0%
Math Symbol 10000
 
8.6%
Uppercase Letter 4428
 
3.8%
Space Separator 2658
 
2.3%
Decimal Number 398
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 12455
 
12.5%
단 9877
 
9.9%
대 9415
 
9.5%
남 5867
 
5.9%
북 4700
 
4.7%
동 2527
 
2.5%
터 2213
 
2.2%
널 2213
 
2.2%
구 1883
 
1.9%
성 1767
 
1.8%
Other values (98) 46529
46.8%
Uppercase Letter
ValueCountFrequency (%)
C 2214
50.0%
I 1193
26.9%
J 1021
23.1%
Math Symbol
ValueCountFrequency (%)
→ 10000
100.0%
Space Separator
ValueCountFrequency (%)
2658
100.0%
Decimal Number
ValueCountFrequency (%)
1 398
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 99446
85.0%
Common 13056
 
11.2%
Latin 4428
 
3.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 12455
 
12.5%
단 9877
 
9.9%
대 9415
 
9.5%
남 5867
 
5.9%
북 4700
 
4.7%
동 2527
 
2.5%
터 2213
 
2.2%
널 2213
 
2.2%
구 1883
 
1.9%
성 1767
 
1.8%
Other values (98) 46529
46.8%
Common
ValueCountFrequency (%)
→ 10000
76.6%
2658
 
20.4%
1 398
 
3.0%
Latin
ValueCountFrequency (%)
C 2214
50.0%
I 1193
26.9%
J 1021
23.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 99446
85.0%
Arrows 10000
 
8.6%
ASCII 7484
 
6.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
교 12455
 
12.5%
단 9877
 
9.9%
대 9415
 
9.5%
남 5867
 
5.9%
북 4700
 
4.7%
동 2527
 
2.5%
터 2213
 
2.2%
널 2213
 
2.2%
구 1883
 
1.9%
성 1767
 
1.8%
Other values (98) 46529
46.8%
Arrows
ValueCountFrequency (%)
→ 10000
100.0%
ASCII
ValueCountFrequency (%)
2658
35.5%
C 2214
29.6%
I 1193
15.9%
J 1021
 
13.6%
1 398
 
5.3%

구간ID
Text

Distinct224
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T18:53:44.600096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length10.0985
Min length10

Characters and Unicode

Total characters100985
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 rowLHD2000140
2nd rowLRI2000040
3rd rowLBR2000010
4th rowLLR2000100
5th rowLKL2000070
ValueCountFrequency (%)
lhd2000130 64
 
0.6%
lro2000120 60
 
0.6%
lkr2000040 57
 
0.6%
lhu2000210 57
 
0.6%
llr2000010 57
 
0.6%
lko2000090 56
 
0.6%
lhu2000100 55
 
0.5%
lkl2000010 55
 
0.5%
lll2000190 55
 
0.5%
llr2000130 55
 
0.5%
Other values (214) 9429
94.3%
2023-12-11T18:53:44.999213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 45929
45.5%
L 13428
 
13.3%
2 11677
 
11.6%
1 5369
 
5.3%
R 3796
 
3.8%
D 2380
 
2.4%
H 1742
 
1.7%
O 1710
 
1.7%
I 1687
 
1.7%
K 1658
 
1.6%
Other values (15) 11609
 
11.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 70000
69.3%
Uppercase Letter 30985
30.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L 13428
43.3%
R 3796
 
12.3%
D 2380
 
7.7%
H 1742
 
5.6%
O 1710
 
5.5%
I 1687
 
5.4%
K 1658
 
5.4%
U 1012
 
3.3%
Q 985
 
3.2%
S 626
 
2.0%
Other values (5) 1961
 
6.3%
Decimal Number
ValueCountFrequency (%)
0 45929
65.6%
2 11677
 
16.7%
1 5369
 
7.7%
5 1512
 
2.2%
3 1285
 
1.8%
4 966
 
1.4%
9 856
 
1.2%
7 832
 
1.2%
8 826
 
1.2%
6 748
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 70000
69.3%
Latin 30985
30.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
L 13428
43.3%
R 3796
 
12.3%
D 2380
 
7.7%
H 1742
 
5.6%
O 1710
 
5.5%
I 1687
 
5.4%
K 1658
 
5.4%
U 1012
 
3.3%
Q 985
 
3.2%
S 626
 
2.0%
Other values (5) 1961
 
6.3%
Common
ValueCountFrequency (%)
0 45929
65.6%
2 11677
 
16.7%
1 5369
 
7.7%
5 1512
 
2.2%
3 1285
 
1.8%
4 966
 
1.4%
9 856
 
1.2%
7 832
 
1.2%
8 826
 
1.2%
6 748
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 100985
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 45929
45.5%
L 13428
 
13.3%
2 11677
 
11.6%
1 5369
 
5.3%
R 3796
 
3.8%
D 2380
 
2.4%
H 1742
 
1.7%
O 1710
 
1.7%
I 1687
 
1.7%
K 1658
 
1.6%
Other values (15) 11609
 
11.5%

년월일
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20180725
Minimum20180720
Maximum20180731
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T18:53:45.131821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20180720
5-th percentile20180720
Q120180722
median20180725
Q320180728
95-th percentile20180731
Maximum20180731
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4574753
Coefficient of variation (CV)1.7132562 × 10-7
Kurtosis-1.2215201
Mean20180725
Median Absolute Deviation (MAD)3
Skewness0.013031811
Sum2.0180725 × 1011
Variance11.954135
MonotonicityNot monotonic
2023-12-11T18:53:45.232967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
20180721 905
9.0%
20180724 867
8.7%
20180726 851
8.5%
20180729 850
8.5%
20180727 839
8.4%
20180731 839
8.4%
20180720 826
8.3%
20180722 824
8.2%
20180728 815
8.2%
20180723 807
8.1%
Other values (2) 1577
15.8%
ValueCountFrequency (%)
20180720 826
8.3%
20180721 905
9.0%
20180722 824
8.2%
20180723 807
8.1%
20180724 867
8.7%
20180725 789
7.9%
20180726 851
8.5%
20180727 839
8.4%
20180728 815
8.2%
20180729 850
8.5%
ValueCountFrequency (%)
20180731 839
8.4%
20180730 788
7.9%
20180729 850
8.5%
20180728 815
8.2%
20180727 839
8.4%
20180726 851
8.5%
20180725 789
7.9%
20180724 867
8.7%
20180723 807
8.1%
20180722 824
8.2%

시간
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.516
Minimum0
Maximum23
Zeros397
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T18:53:45.353114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median11
Q318
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.9106526
Coefficient of variation (CV)0.60009141
Kurtosis-1.2035899
Mean11.516
Median Absolute Deviation (MAD)6
Skewness0.0066758331
Sum115160
Variance47.75712
MonotonicityNot monotonic
2023-12-11T18:53:45.493011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
10 452
 
4.5%
19 448
 
4.5%
5 434
 
4.3%
2 430
 
4.3%
16 429
 
4.3%
8 428
 
4.3%
11 427
 
4.3%
23 426
 
4.3%
12 424
 
4.2%
3 422
 
4.2%
Other values (14) 5680
56.8%
ValueCountFrequency (%)
0 397
4.0%
1 398
4.0%
2 430
4.3%
3 422
4.2%
4 418
4.2%
5 434
4.3%
6 412
4.1%
7 403
4.0%
8 428
4.3%
9 405
4.0%
ValueCountFrequency (%)
23 426
4.3%
22 422
4.2%
21 398
4.0%
20 418
4.2%
19 448
4.5%
18 396
4.0%
17 408
4.1%
16 429
4.3%
15 413
4.1%
14 396
4.0%

속도
Real number (ℝ)

Distinct5653
Distinct (%)56.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.519502
Minimum6.12
Maximum129.12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T18:53:45.617174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.12
5-th percentile20.899
Q153.875
median73.43
Q384.5025
95-th percentile96.97
Maximum129.12
Range123
Interquartile range (IQR)30.6275

Descriptive statistics

Standard deviation23.048221
Coefficient of variation (CV)0.34135651
Kurtosis-0.31125787
Mean67.519502
Median Absolute Deviation (MAD)13.56
Skewness-0.71310837
Sum675195.02
Variance531.22051
MonotonicityNot monotonic
2023-12-11T18:53:45.777620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88.7 8
 
0.1%
83.21 7
 
0.1%
83.33 7
 
0.1%
76.41 7
 
0.1%
84.05 7
 
0.1%
84.09 7
 
0.1%
62.84 7
 
0.1%
81.91 7
 
0.1%
86.15 7
 
0.1%
83.86 7
 
0.1%
Other values (5643) 9929
99.3%
ValueCountFrequency (%)
6.12 1
< 0.1%
6.47 1
< 0.1%
6.94 1
< 0.1%
7.3 1
< 0.1%
7.37 1
< 0.1%
7.74 1
< 0.1%
7.79 1
< 0.1%
7.83 1
< 0.1%
8.18 1
< 0.1%
8.19 1
< 0.1%
ValueCountFrequency (%)
129.12 1
< 0.1%
128.81 1
< 0.1%
128.03 1
< 0.1%
127.47 1
< 0.1%
126.65 1
< 0.1%
126.45 1
< 0.1%
121.35 1
< 0.1%
119.37 1
< 0.1%
119.01 1
< 0.1%
118.47 1
< 0.1%

Interactions

2023-12-11T18:53:42.307599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:53:41.637749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:53:41.997464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:53:42.427017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:53:41.769241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:53:42.123306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:53:42.529563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:53:41.879018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:53:42.215725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T18:53:45.870767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노선명노선ID년월일시간속도
노선명1.0001.0000.0000.0000.525
노선ID1.0001.0000.0000.0000.537
년월일0.0000.0001.0000.0000.184
시간0.0000.0000.0001.0000.525
속도0.5250.5370.1840.5251.000
2023-12-11T18:53:45.976146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노선명노선ID
노선명1.0000.999
노선ID0.9991.000
2023-12-11T18:53:46.055908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년월일시간속도노선명노선ID
년월일1.0000.001-0.0140.0000.000
시간0.0011.000-0.3800.0000.000
속도-0.014-0.3801.0000.1850.198
노선명0.0000.0000.1851.0000.999
노선ID0.0000.0000.1980.9991.000

Missing values

2023-12-11T18:53:42.683952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T18:53:42.836002image/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
24208한강교량LHD3000010성수대교북단→성수대교입구남단LHD2000140201807241238.13
11371내부순환로LRI3000010홍제램프→홍은램프LRI200004020180722382.59
16777북부간선로LBR3000010종암JC→하월곡LBR200001020180723383.85
39316올림픽대로LLR3000010반포대교남단→한남대교남단LLR200010020180727836.3
4274강변북로LKL3000010잠실철교 북단→잠실대교북단LKL2000070201807201966.55
11196동부간선로LDO3000010노원교→수락지하차도LDO200014020180722271.22
41620강변북로LKL3000010잠실대교북단→청담대교북단LKL2000080201807271976.93
9235올림픽대로LLL3000010암사대교남단→천호대교남단LLL2000190201807211776.44
20792북부간선로LBR3000010신내IC→구리시계LBR2000050201807232172.68
25278올림픽대로LLL3000010동작대교남단→한강대교남단LLL2000080201807241731.62
노선명노선ID구간명구간ID년월일시간속도
25387강변북로LKL3000010성수→동호대교북단LKL2000105201807241885.24
34985내부순환로LRI3000010성산램프→연희램프LRI2000020201807261378.53
26682한강교량LHU3000010잠실철교 남단→잠실철교북단LHU2000180201807242368.47
7170동부간선로LDI3000010노원교→상계교LDI200002520180721837.44
47989강남순환로LQRE3000010서초터널 중간부→서초터널 출구부LQRE200010020180729097.69
36423올림픽대로LLR3000010양화대교남단→여의하류LLR2000050201807261982.94
9176동부간선로LDI3000010장안교→군자교LDI2000085201807211781.88
30525내부순환로LRI3000010성산램프→연희램프LRI2000020201807251714.14
27985올림픽대로LLR3000010천호대교남단→암사대교남단LLR200019020180725584.68
17012서부간선로LSN3000010금천교→광명교LSN210001020180723472.14