Overview

Dataset statistics

Number of variables7
Number of observations272
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.6 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 21:09:48.932490
Analysis finished2024-04-29 21:09:53.840773
Duration4.91 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct272
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean136.5
Minimum1
Maximum272
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-04-30T06:09:53.903635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14.55
Q168.75
median136.5
Q3204.25
95-th percentile258.45
Maximum272
Range271
Interquartile range (IQR)135.5

Descriptive statistics

Standard deviation78.663842
Coefficient of variation (CV)0.57629188
Kurtosis-1.2
Mean136.5
Median Absolute Deviation (MAD)68
Skewness0
Sum37128
Variance6188
MonotonicityStrictly increasing
2024-04-30T06:09:54.213599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.4%
181 1
 
0.4%
187 1
 
0.4%
186 1
 
0.4%
185 1
 
0.4%
184 1
 
0.4%
183 1
 
0.4%
182 1
 
0.4%
180 1
 
0.4%
138 1
 
0.4%
Other values (262) 262
96.3%
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 (%)
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%
265 1
0.4%
264 1
0.4%
263 1
0.4%

호선
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6066176
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-04-30T06:09:54.320003image/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 deviation2.008201
Coefficient of variation (CV)0.43593828
Kurtosis-1.1478026
Mean4.6066176
Median Absolute Deviation (MAD)2
Skewness-0.052624328
Sum1253
Variance4.0328712
MonotonicityIncreasing
2024-04-30T06:09:54.425987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
5 56
20.6%
2 50
18.4%
7 42
15.4%
6 37
13.6%
3 33
12.1%
4 26
9.6%
8 18
 
6.6%
1 10
 
3.7%
ValueCountFrequency (%)
1 10
 
3.7%
2 50
18.4%
3 33
12.1%
4 26
9.6%
5 56
20.6%
6 37
13.6%
7 42
15.4%
8 18
 
6.6%
ValueCountFrequency (%)
8 18
 
6.6%
7 42
15.4%
6 37
13.6%
5 56
20.6%
4 26
9.6%
3 33
12.1%
2 50
18.4%
1 10
 
3.7%

역번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct272
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1615.6654
Minimum150
Maximum2828
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-04-30T06:09:54.546283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum150
5-th percentile204.55
Q1316.75
median2527.5
Q32640.25
95-th percentile2814.45
Maximum2828
Range2678
Interquartile range (IQR)2323.5

Descriptive statistics

Standard deviation1174.9919
Coefficient of variation (CV)0.7272495
Kurtosis-1.9259226
Mean1615.6654
Median Absolute Deviation (MAD)284
Skewness-0.24809565
Sum439461
Variance1380605.9
MonotonicityStrictly increasing
2024-04-30T06:09:54.665540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150 1
 
0.4%
2617 1
 
0.4%
2623 1
 
0.4%
2622 1
 
0.4%
2621 1
 
0.4%
2620 1
 
0.4%
2619 1
 
0.4%
2618 1
 
0.4%
2616 1
 
0.4%
2529 1
 
0.4%
Other values (262) 262
96.3%
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 (%)
2828 1
0.4%
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%

역명
Text

Distinct239
Distinct (%)87.9%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2024-04-30T06:09:54.885230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length4.4411765
Min length2

Characters and Unicode

Total characters1208
Distinct characters240
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique208 ?
Unique (%)76.5%

Sample

1st row서울역
2nd row시청
3rd row종각
4th row종로3가
5th row종로5가
ValueCountFrequency (%)
종로3가 3
 
1.1%
동대문역사문화공원(ddp 3
 
1.1%
동대문 2
 
0.7%
잠실(송파구청 2
 
0.7%
불광 2
 
0.7%
고속터미널 2
 
0.7%
가락시장 2
 
0.7%
충정로(경기대입구 2
 
0.7%
사당 2
 
0.7%
영등포구청 2
 
0.7%
Other values (229) 250
91.9%
2024-04-30T06:09:55.241157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
) 64
 
5.3%
( 64
 
5.3%
대 49
 
4.1%
구 49
 
4.1%
동 32
 
2.6%
청 30
 
2.5%
신 25
 
2.1%
원 23
 
1.9%
산 21
 
1.7%
문 20
 
1.7%
Other values (230) 831
68.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1059
87.7%
Close Punctuation 64
 
5.3%
Open Punctuation 64
 
5.3%
Uppercase Letter 9
 
0.7%
Decimal Number 8
 
0.7%
Other Punctuation 4
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 49
 
4.6%
구 49
 
4.6%
동 32
 
3.0%
청 30
 
2.8%
신 25
 
2.4%
원 23
 
2.2%
산 21
 
2.0%
문 20
 
1.9%
입 19
 
1.8%
성 16
 
1.5%
Other values (221) 775
73.2%
Decimal Number
ValueCountFrequency (%)
3 5
62.5%
4 2
 
25.0%
5 1
 
12.5%
Uppercase Letter
ValueCountFrequency (%)
D 6
66.7%
P 3
33.3%
Other Punctuation
ValueCountFrequency (%)
. 3
75.0%
· 1
 
25.0%
Close Punctuation
ValueCountFrequency (%)
) 64
100.0%
Open Punctuation
ValueCountFrequency (%)
( 64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1059
87.7%
Common 140
 
11.6%
Latin 9
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 49
 
4.6%
구 49
 
4.6%
동 32
 
3.0%
청 30
 
2.8%
신 25
 
2.4%
원 23
 
2.2%
산 21
 
2.0%
문 20
 
1.9%
입 19
 
1.8%
성 16
 
1.5%
Other values (221) 775
73.2%
Common
ValueCountFrequency (%)
) 64
45.7%
( 64
45.7%
3 5
 
3.6%
. 3
 
2.1%
4 2
 
1.4%
· 1
 
0.7%
5 1
 
0.7%
Latin
ValueCountFrequency (%)
D 6
66.7%
P 3
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1059
87.7%
ASCII 148
 
12.3%
None 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
) 64
43.2%
( 64
43.2%
D 6
 
4.1%
3 5
 
3.4%
P 3
 
2.0%
. 3
 
2.0%
4 2
 
1.4%
5 1
 
0.7%
Hangul
ValueCountFrequency (%)
대 49
 
4.6%
구 49
 
4.6%
동 32
 
3.0%
청 30
 
2.8%
신 25
 
2.4%
원 23
 
2.2%
산 21
 
2.0%
문 20
 
1.9%
입 19
 
1.8%
성 16
 
1.5%
Other values (221) 775
73.2%
None
ValueCountFrequency (%)
· 1
100.0%

경로
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct272
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean722962.08
Minimum78165
Maximum2917042
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-04-30T06:09:55.366466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum78165
5-th percentile170359.45
Q1393744
median614096.5
Q3887249
95-th percentile1738779.9
Maximum2917042
Range2838877
Interquartile range (IQR)493505

Descriptive statistics

Standard deviation484291.62
Coefficient of variation (CV)0.6698714
Kurtosis3.6474604
Mean722962.08
Median Absolute Deviation (MAD)243161.5
Skewness1.6678809
Sum1.9664569 × 108
Variance2.3453837 × 1011
MonotonicityNot monotonic
2024-04-30T06:09:55.487373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1738809 1
 
0.4%
809758 1
 
0.4%
370357 1
 
0.4%
753942 1
 
0.4%
685350 1
 
0.4%
574072 1
 
0.4%
525246 1
 
0.4%
663267 1
 
0.4%
475460 1
 
0.4%
583812 1
 
0.4%
Other values (262) 262
96.3%
ValueCountFrequency (%)
78165 1
0.4%
78620 1
0.4%
85221 1
0.4%
91964 1
0.4%
99557 1
0.4%
100763 1
0.4%
113536 1
0.4%
128031 1
0.4%
132712 1
0.4%
141188 1
0.4%
ValueCountFrequency (%)
2917042 1
0.4%
2764406 1
0.4%
2518370 1
0.4%
2514663 1
0.4%
2452955 1
0.4%
2038522 1
0.4%
1943248 1
0.4%
1875656 1
0.4%
1874012 1
0.4%
1831750 1
0.4%

장애
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct272
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean124179.76
Minimum10928
Maximum418217
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-04-30T06:09:55.599298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10928
5-th percentile30762.45
Q168148.5
median105213.5
Q3162098.5
95-th percentile293163.25
Maximum418217
Range407289
Interquartile range (IQR)93950

Descriptive statistics

Standard deviation81646.755
Coefficient of variation (CV)0.6574884
Kurtosis1.6760117
Mean124179.76
Median Absolute Deviation (MAD)40917
Skewness1.3123065
Sum33776896
Variance6.6661926 × 109
MonotonicityNot monotonic
2024-04-30T06:09:55.738251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
407971 1
 
0.4%
136081 1
 
0.4%
70012 1
 
0.4%
123015 1
 
0.4%
173537 1
 
0.4%
76509 1
 
0.4%
96439 1
 
0.4%
121567 1
 
0.4%
81207 1
 
0.4%
84316 1
 
0.4%
Other values (262) 262
96.3%
ValueCountFrequency (%)
10928 1
0.4%
11173 1
0.4%
12542 1
0.4%
13197 1
0.4%
15785 1
0.4%
17120 1
0.4%
17851 1
0.4%
19195 1
0.4%
24803 1
0.4%
25847 1
0.4%
ValueCountFrequency (%)
418217 1
0.4%
407971 1
0.4%
402867 1
0.4%
393181 1
0.4%
358409 1
0.4%
357505 1
0.4%
354410 1
0.4%
347635 1
0.4%
333570 1
0.4%
324073 1
0.4%

유공자
Real number (ℝ)

HIGH CORRELATION 

Distinct270
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8112.7463
Minimum293
Maximum40317
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-04-30T06:09:55.849639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum293
5-th percentile1678.35
Q13990
median6587
Q310055
95-th percentile20029.45
Maximum40317
Range40024
Interquartile range (IQR)6065

Descriptive statistics

Standard deviation5855.5773
Coefficient of variation (CV)0.72177498
Kurtosis4.5937158
Mean8112.7463
Median Absolute Deviation (MAD)3119.5
Skewness1.7751886
Sum2206667
Variance34287786
MonotonicityNot monotonic
2024-04-30T06:09:55.989540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7086 2
 
0.7%
2302 2
 
0.7%
6295 1
 
0.4%
4344 1
 
0.4%
5738 1
 
0.4%
6460 1
 
0.4%
5665 1
 
0.4%
8096 1
 
0.4%
6009 1
 
0.4%
31156 1
 
0.4%
Other values (260) 260
95.6%
ValueCountFrequency (%)
293 1
0.4%
546 1
0.4%
1110 1
0.4%
1190 1
0.4%
1422 1
0.4%
1448 1
0.4%
1483 1
0.4%
1484 1
0.4%
1506 1
0.4%
1527 1
0.4%
ValueCountFrequency (%)
40317 1
0.4%
31156 1
0.4%
27822 1
0.4%
27374 1
0.4%
25307 1
0.4%
25068 1
0.4%
24996 1
0.4%
24118 1
0.4%
22195 1
0.4%
21660 1
0.4%

Interactions

2024-04-30T06:09:53.192077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:50.799723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:51.279016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:51.727486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:52.189462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:52.667203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:53.262972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:50.913420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:51.348938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:51.801636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:52.254974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:52.761434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:53.345389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:50.991812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:51.427254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:51.879624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:52.343068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:52.858183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:53.414678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:51.064825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:51.491112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:51.940169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:52.409488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:52.937654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:53.499836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:51.132402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:51.561297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:52.011723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:52.485311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:53.017121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:53.595091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:51.204560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:51.644865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:52.101053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:52.568685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:53.106874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-30T06:09:56.071724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번호선역번호경로장애유공자
연번1.0000.9180.9160.3760.4230.373
호선0.9181.0000.9940.4670.4010.458
역번호0.9160.9941.0000.3260.3480.487
경로0.3760.4670.3261.0000.7920.927
장애0.4230.4010.3480.7921.0000.798
유공자0.3730.4580.4870.9270.7981.000
2024-04-30T06:09:56.160368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번호선역번호경로장애유공자
연번1.0000.9881.000-0.356-0.329-0.367
호선0.9881.0000.988-0.335-0.299-0.349
역번호1.0000.9881.000-0.356-0.329-0.367
경로-0.356-0.335-0.3561.0000.9390.908
장애-0.329-0.299-0.3290.9391.0000.895
유공자-0.367-0.349-0.3670.9080.8951.000

Missing values

2024-04-30T06:09:53.695525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T06:09:53.790459image/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
011150서울역173880940797131156
121151시청81407816144713011
231152종각128310224289618474
341153종로3가291704241821740317
451154종로5가251466332407325307
561155동대문11404551951159997
671156신설동112789018536510236
781157제기동276440631614420239
891158청량리(서울시립대입구)251837035840924996
9101159동묘앞145301425520716830
연번호선역번호역명경로장애유공자
26226382819문정5457241020715529
26326482820장지7224791446527879
26426582821복정358605741485455
26526682822산성302164440013369
26626782823남한산성입구(성남법원.검찰청)7270191433996614
26726882824단대오거리6536581420184509
26826982825신흥338571713452470
26927082826수진411929846053006
27027182827모란512846910994627
27127282828남위례244866357562815