Overview

Dataset statistics

Number of variables7
Number of observations275
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.8 KiB
Average record size in memory62.5 B

Variable types

Numeric6
Text1

Dataset

Description파일 다운로드
Author서울교통공사
URLhttps://data.seoul.go.kr/dataList/OA-21720/F/1/datasetView.do

Alerts

연번 is highly overall correlated with 호선 and 1 other fieldsHigh correlation
호선 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
역번호 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
경로 is highly overall correlated with 장애 and 1 other fieldsHigh correlation
장애 is highly overall correlated with 경로 and 1 other fieldsHigh correlation
유공자 is highly overall correlated with 경로 and 1 other fieldsHigh correlation
연번 has unique valuesUnique
역번호 has unique valuesUnique
경로 has unique valuesUnique
장애 has unique valuesUnique

Reproduction

Analysis started2024-04-29 21:10:22.725514
Analysis finished2024-04-29 21:10:26.109683
Duration3.38 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct275
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean138
Minimum1
Maximum275
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-04-30T06:10:26.181015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14.7
Q169.5
median138
Q3206.5
95-th percentile261.3
Maximum275
Range274
Interquartile range (IQR)137

Descriptive statistics

Standard deviation79.529869
Coefficient of variation (CV)0.5763034
Kurtosis-1.2
Mean138
Median Absolute Deviation (MAD)69
Skewness0
Sum37950
Variance6325
MonotonicityStrictly increasing
2024-04-30T06:10:26.297421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.4%
183 1
 
0.4%
189 1
 
0.4%
188 1
 
0.4%
187 1
 
0.4%
186 1
 
0.4%
185 1
 
0.4%
184 1
 
0.4%
182 1
 
0.4%
174 1
 
0.4%
Other values (265) 265
96.4%
ValueCountFrequency (%)
1 1
0.4%
2 1
0.4%
3 1
0.4%
4 1
0.4%
5 1
0.4%
6 1
0.4%
7 1
0.4%
8 1
0.4%
9 1
0.4%
10 1
0.4%
ValueCountFrequency (%)
275 1
0.4%
274 1
0.4%
273 1
0.4%
272 1
0.4%
271 1
0.4%
270 1
0.4%
269 1
0.4%
268 1
0.4%
267 1
0.4%
266 1
0.4%

호선
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6654545
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-04-30T06:10:26.393825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median5
Q36
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.0318826
Coefficient of variation (CV)0.43551653
Kurtosis-1.2116408
Mean4.6654545
Median Absolute Deviation (MAD)2
Skewness-0.10095916
Sum1283
Variance4.1285468
MonotonicityIncreasing
2024-04-30T06:10:26.499542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
5 51
18.5%
7 51
18.5%
2 50
18.2%
6 37
13.5%
3 33
12.0%
4 26
9.5%
8 17
 
6.2%
1 10
 
3.6%
ValueCountFrequency (%)
1 10
 
3.6%
2 50
18.2%
3 33
12.0%
4 26
9.5%
5 51
18.5%
6 37
13.5%
7 51
18.5%
8 17
 
6.2%
ValueCountFrequency (%)
8 17
 
6.2%
7 51
18.5%
6 37
13.5%
5 51
18.5%
4 26
9.5%
3 33
12.0%
2 50
18.2%
1 10
 
3.6%

역번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct275
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1631.3673
Minimum150
Maximum2827
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-04-30T06:10:26.629492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum150
5-th percentile204.7
Q1317.5
median2529
Q32647.5
95-th percentile2813.3
Maximum2827
Range2677
Interquartile range (IQR)2330

Descriptive statistics

Standard deviation1177.3932
Coefficient of variation (CV)0.72172173
Kurtosis-1.9158526
Mean1631.3673
Median Absolute Deviation (MAD)231
Skewness-0.26764389
Sum448626
Variance1386254.8
MonotonicityStrictly increasing
2024-04-30T06:10:26.805795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150 1
 
0.4%
2624 1
 
0.4%
2630 1
 
0.4%
2629 1
 
0.4%
2628 1
 
0.4%
2627 1
 
0.4%
2626 1
 
0.4%
2625 1
 
0.4%
2623 1
 
0.4%
2614 1
 
0.4%
Other values (265) 265
96.4%
ValueCountFrequency (%)
150 1
0.4%
151 1
0.4%
152 1
0.4%
153 1
0.4%
154 1
0.4%
155 1
0.4%
156 1
0.4%
157 1
0.4%
158 1
0.4%
159 1
0.4%
ValueCountFrequency (%)
2827 1
0.4%
2826 1
0.4%
2825 1
0.4%
2824 1
0.4%
2823 1
0.4%
2822 1
0.4%
2821 1
0.4%
2820 1
0.4%
2819 1
0.4%
2818 1
0.4%

역명
Text

Distinct242
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2024-04-30T06:10:27.020434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length4.3127273
Min length2

Characters and Unicode

Total characters1186
Distinct characters236
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique211 ?
Unique (%)76.7%

Sample

1st row서울역
2nd row시청
3rd row종각
4th row종로3가
5th row종로5가
ValueCountFrequency (%)
종로3가 3
 
1.1%
동대문역사문화공원 3
 
1.1%
천호(풍납토성 2
 
0.7%
사당 2
 
0.7%
서울역 2
 
0.7%
영등포구청 2
 
0.7%
대림(구로구청 2
 
0.7%
불광 2
 
0.7%
약수 2
 
0.7%
오금 2
 
0.7%
Other values (232) 253
92.0%
2024-04-30T06:10:27.366908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
) 58
 
4.9%
( 58
 
4.9%
구 50
 
4.2%
대 49
 
4.1%
동 35
 
3.0%
청 31
 
2.6%
신 25
 
2.1%
원 22
 
1.9%
산 20
 
1.7%
문 20
 
1.7%
Other values (226) 818
69.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1059
89.3%
Close Punctuation 58
 
4.9%
Open Punctuation 58
 
4.9%
Decimal Number 8
 
0.7%
Other Punctuation 3
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 50
 
4.7%
대 49
 
4.6%
동 35
 
3.3%
청 31
 
2.9%
신 25
 
2.4%
원 22
 
2.1%
산 20
 
1.9%
문 20
 
1.9%
입 19
 
1.8%
로 16
 
1.5%
Other values (220) 772
72.9%
Decimal Number
ValueCountFrequency (%)
3 5
62.5%
4 2
 
25.0%
5 1
 
12.5%
Close Punctuation
ValueCountFrequency (%)
) 58
100.0%
Open Punctuation
ValueCountFrequency (%)
( 58
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1059
89.3%
Common 127
 
10.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 50
 
4.7%
대 49
 
4.6%
동 35
 
3.3%
청 31
 
2.9%
신 25
 
2.4%
원 22
 
2.1%
산 20
 
1.9%
문 20
 
1.9%
입 19
 
1.8%
로 16
 
1.5%
Other values (220) 772
72.9%
Common
ValueCountFrequency (%)
) 58
45.7%
( 58
45.7%
3 5
 
3.9%
. 3
 
2.4%
4 2
 
1.6%
5 1
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1059
89.3%
ASCII 127
 
10.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
) 58
45.7%
( 58
45.7%
3 5
 
3.9%
. 3
 
2.4%
4 2
 
1.6%
5 1
 
0.8%
Hangul
ValueCountFrequency (%)
구 50
 
4.7%
대 49
 
4.6%
동 35
 
3.3%
청 31
 
2.9%
신 25
 
2.4%
원 22
 
2.1%
산 20
 
1.9%
문 20
 
1.9%
입 19
 
1.8%
로 16
 
1.5%
Other values (220) 772
72.9%

경로
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct275
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean758058.96
Minimum59099
Maximum3685909
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-04-30T06:10:27.492322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum59099
5-th percentile170230.5
Q1403043.5
median614074
Q3903142
95-th percentile1877775.4
Maximum3685909
Range3626810
Interquartile range (IQR)500098.5

Descriptive statistics

Standard deviation561303.92
Coefficient of variation (CV)0.74044889
Kurtosis5.9306483
Mean758058.96
Median Absolute Deviation (MAD)242886
Skewness2.052748
Sum2.0846621 × 108
Variance3.1506209 × 1011
MonotonicityNot monotonic
2024-04-30T06:10:27.607466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1964968 1
 
0.4%
226810 1
 
0.4%
203900 1
 
0.4%
204962 1
 
0.4%
407342 1
 
0.4%
696311 1
 
0.4%
436190 1
 
0.4%
597453 1
 
0.4%
380586 1
 
0.4%
204684 1
 
0.4%
Other values (265) 265
96.4%
ValueCountFrequency (%)
59099 1
0.4%
76792 1
0.4%
95126 1
0.4%
98957 1
0.4%
99160 1
0.4%
102532 1
0.4%
115179 1
0.4%
130249 1
0.4%
142450 1
0.4%
149737 1
0.4%
ValueCountFrequency (%)
3685909 1
0.4%
3413844 1
0.4%
3322146 1
0.4%
2718723 1
0.4%
2601023 1
0.4%
2323720 1
0.4%
2253205 1
0.4%
2179579 1
0.4%
2118338 1
0.4%
2038213 1
0.4%

장애
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct275
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean168744.23
Minimum14972
Maximum644969
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-04-30T06:10:27.720520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14972
5-th percentile40506.7
Q190059.5
median138428
Q3211662.5
95-th percentile398415
Maximum644969
Range629997
Interquartile range (IQR)121603

Descriptive statistics

Standard deviation116848.37
Coefficient of variation (CV)0.69245847
Kurtosis2.7726028
Mean168744.23
Median Absolute Deviation (MAD)56141
Skewness1.5469953
Sum46404664
Variance1.3653542 × 1010
MonotonicityNot monotonic
2024-04-30T06:10:27.834800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
644969 1
 
0.4%
48461 1
 
0.4%
42244 1
 
0.4%
41466 1
 
0.4%
84188 1
 
0.4%
152411 1
 
0.4%
88985 1
 
0.4%
92619 1
 
0.4%
93413 1
 
0.4%
39295 1
 
0.4%
Other values (265) 265
96.4%
ValueCountFrequency (%)
14972 1
0.4%
18275 1
0.4%
19797 1
0.4%
20430 1
0.4%
20732 1
0.4%
21206 1
0.4%
24292 1
0.4%
26310 1
0.4%
27716 1
0.4%
33170 1
0.4%
ValueCountFrequency (%)
644969 1
0.4%
633436 1
0.4%
590176 1
0.4%
576792 1
0.4%
569897 1
0.4%
531625 1
0.4%
502462 1
0.4%
481082 1
0.4%
453907 1
0.4%
443097 1
0.4%

유공자
Real number (ℝ)

HIGH CORRELATION 

Distinct274
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12287.451
Minimum482
Maximum99725
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-04-30T06:10:28.189454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum482
5-th percentile2495.8
Q15479.5
median9461
Q315347.5
95-th percentile31705
Maximum99725
Range99243
Interquartile range (IQR)9868

Descriptive statistics

Standard deviation11461.637
Coefficient of variation (CV)0.93279211
Kurtosis17.773397
Mean12287.451
Median Absolute Deviation (MAD)4674
Skewness3.3801995
Sum3379049
Variance1.3136913 × 108
MonotonicityNot monotonic
2024-04-30T06:10:28.305118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7219 2
 
0.7%
50650 1
 
0.4%
5802 1
 
0.4%
9134 1
 
0.4%
4879 1
 
0.4%
12198 1
 
0.4%
6223 1
 
0.4%
6125 1
 
0.4%
3950 1
 
0.4%
7938 1
 
0.4%
Other values (264) 264
96.0%
ValueCountFrequency (%)
482 1
0.4%
743 1
0.4%
772 1
0.4%
1166 1
0.4%
1342 1
0.4%
1389 1
0.4%
1431 1
0.4%
1462 1
0.4%
1723 1
0.4%
1742 1
0.4%
ValueCountFrequency (%)
99725 1
0.4%
78821 1
0.4%
65557 1
0.4%
55513 1
0.4%
50650 1
0.4%
40047 1
0.4%
38620 1
0.4%
37250 1
0.4%
35426 1
0.4%
34994 1
0.4%

Interactions

2024-04-30T06:10:25.497668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:22.996105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:23.410666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:24.095842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:24.544683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:25.033559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:25.560286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:23.063718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:23.692530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:24.169172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:24.616698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:25.114130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:25.636342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:23.135488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:23.766078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:24.248051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:24.696758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:25.206134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:25.706906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:23.197530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:23.835620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:24.331149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:24.774862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:25.272710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:25.796982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:23.275440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:23.925520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:24.408827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:24.868702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:25.359446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:25.866053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:23.342930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:24.013914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:24.480285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:24.949339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:25.428632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-30T06:10:28.393184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번호선역번호경로장애유공자
연번1.0000.9180.9210.5280.4730.409
호선0.9181.0000.9960.5200.4440.583
역번호0.9210.9961.0000.4170.3760.466
경로0.5280.5200.4171.0000.9260.762
장애0.4730.4440.3760.9261.0000.790
유공자0.4090.5830.4660.7620.7901.000
2024-04-30T06:10:28.487019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번호선역번호경로장애유공자
연번1.0000.9881.000-0.430-0.369-0.389
호선0.9881.0000.988-0.402-0.338-0.359
역번호1.0000.9881.000-0.430-0.369-0.389
경로-0.430-0.402-0.4301.0000.9370.907
장애-0.369-0.338-0.3690.9371.0000.910
유공자-0.389-0.359-0.3890.9070.9101.000

Missing values

2024-04-30T06:10:25.971946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T06:10:26.072249image/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

연번호선역번호역명경로장애유공자
011150서울역196496864496950650
121151시청107790424836722747
231152종각148801436434930246
341153종로3가368590963343665557
451154종로5가271872344309735426
561155동대문130918429844816730
671156신설동117293626481116064
781157제기동332214642964728539
891158청량리(서울시립대입구)341384457679240047
9101159동묘앞136442930502922619
연번호선역번호역명경로장애유공자
26526682818가락시장523206976896451
26626782819문정3997191088115697
26726882820장지58598917405811439
26826982821복정4176281015869005
26927082822산성337061846333882
27027182823남한산성입구(성남법원.검찰청)7049781701649592
27127282824단대오거리6157511795115573
27227382825신흥4093551072194119
27327482826수진4154151078732810
27427582827모란5051811118555311