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 21:10:16.593120
Analysis finished2024-04-29 21:10:19.848933
Duration3.26 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
2024-04-30T06:10:19.908819image/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
2024-04-30T06:10:20.013113image/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
2024-04-30T06:10:20.120134image/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
2024-04-30T06:10:20.228038image/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
2024-04-30T06:10:20.342878image/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
2024-04-30T06:10:20.455567image/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
2024-04-30T06:10:20.642872image/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%
2024-04-30T06:10:20.963701image/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%
Mean773975.29
Minimum65798
Maximum3755786
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-04-30T06:10:21.087352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum65798
5-th percentile171192.8
Q1410687.5
median634172
Q3923288
95-th percentile1954591.6
Maximum3755786
Range3689988
Interquartile range (IQR)512600.5

Descriptive statistics

Standard deviation567302.74
Coefficient of variation (CV)0.73297267
Kurtosis6.046397
Mean773975.29
Median Absolute Deviation (MAD)252127
Skewness2.0618372
Sum2.128432 × 108
Variance3.218324 × 1011
MonotonicityNot monotonic
2024-04-30T06:10:21.203802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1960487 1
 
0.4%
226030 1
 
0.4%
199957 1
 
0.4%
173408 1
 
0.4%
406270 1
 
0.4%
715849 1
 
0.4%
470491 1
 
0.4%
598495 1
 
0.4%
403206 1
 
0.4%
210025 1
 
0.4%
Other values (265) 265
96.4%
ValueCountFrequency (%)
65798 1
0.4%
80851 1
0.4%
92290 1
0.4%
101117 1
0.4%
104786 1
0.4%
106490 1
0.4%
109386 1
0.4%
139474 1
0.4%
140856 1
0.4%
156907 1
0.4%
ValueCountFrequency (%)
3755786 1
0.4%
3532050 1
0.4%
3282323 1
0.4%
2760605 1
0.4%
2685403 1
0.4%
2289648 1
0.4%
2250342 1
0.4%
2181299 1
0.4%
2120884 1
0.4%
2072221 1
0.4%

장애
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct275
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean163517.83
Minimum14106
Maximum606567
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-04-30T06:10:21.325519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14106
5-th percentile38192.6
Q188937.5
median135983
Q3206040
95-th percentile389509.2
Maximum606567
Range592461
Interquartile range (IQR)117102.5

Descriptive statistics

Standard deviation111324.95
Coefficient of variation (CV)0.68081229
Kurtosis2.4290181
Mean163517.83
Median Absolute Deviation (MAD)55813
Skewness1.4822155
Sum44967403
Variance1.2393244 × 1010
MonotonicityNot monotonic
2024-04-30T06:10:21.449229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
593788 1
 
0.4%
42791 1
 
0.4%
40848 1
 
0.4%
39367 1
 
0.4%
79630 1
 
0.4%
149225 1
 
0.4%
91942 1
 
0.4%
85971 1
 
0.4%
96253 1
 
0.4%
37090 1
 
0.4%
Other values (265) 265
96.4%
ValueCountFrequency (%)
14106 1
0.4%
19657 1
0.4%
20103 1
0.4%
20674 1
0.4%
20834 1
0.4%
21117 1
0.4%
23048 1
0.4%
23184 1
0.4%
25956 1
0.4%
32149 1
0.4%
ValueCountFrequency (%)
606567 1
0.4%
593788 1
0.4%
555845 1
0.4%
540005 1
0.4%
516051 1
0.4%
495089 1
0.4%
487012 1
0.4%
473527 1
0.4%
441996 1
0.4%
439436 1
0.4%

유공자
Real number (ℝ)

HIGH CORRELATION 

Distinct273
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11770.062
Minimum468
Maximum91219
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-04-30T06:10:21.562576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum468
5-th percentile2268.7
Q15172
median9229
Q314415
95-th percentile30667.1
Maximum91219
Range90751
Interquartile range (IQR)9243

Descriptive statistics

Standard deviation10835.421
Coefficient of variation (CV)0.9205917
Kurtosis16.658963
Mean11770.062
Median Absolute Deviation (MAD)4485
Skewness3.297367
Sum3236767
Variance1.1740635 × 108
MonotonicityNot monotonic
2024-04-30T06:10:21.676771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11878 2
 
0.7%
4651 2
 
0.7%
47379 1
 
0.4%
8192 1
 
0.4%
4474 1
 
0.4%
11754 1
 
0.4%
5596 1
 
0.4%
8234 1
 
0.4%
3789 1
 
0.4%
5680 1
 
0.4%
Other values (263) 263
95.6%
ValueCountFrequency (%)
468 1
0.4%
572 1
0.4%
867 1
0.4%
1312 1
0.4%
1353 1
0.4%
1380 1
0.4%
1567 1
0.4%
1689 1
0.4%
1705 1
0.4%
1777 1
0.4%
ValueCountFrequency (%)
91219 1
0.4%
75843 1
0.4%
64739 1
0.4%
53347 1
0.4%
47379 1
0.4%
37395 1
0.4%
36932 1
0.4%
33811 1
0.4%
33659 1
0.4%
33176 1
0.4%

Interactions

2024-04-30T06:10:19.292422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:16.849647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:17.292047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:17.741964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:18.176301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:18.839978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:19.369332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:16.934127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:17.364420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:17.816559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:18.252569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:18.905468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:19.443007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:17.010851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:17.439112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:17.889207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:18.328314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:18.985009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:19.502951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:17.078319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:17.511210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:17.950808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:18.400507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:19.052647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:19.571335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:17.160998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:17.590983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:18.031603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:18.485074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:19.131779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:19.638952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:17.227038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:17.662395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:18.102417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:18.562121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:19.210339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-30T06:10:21.766431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번호선역번호경로장애유공자
연번1.0000.9180.9210.4560.4620.332
호선0.9181.0000.9960.5080.4390.402
역번호0.9210.9961.0000.3860.3710.304
경로0.4560.5080.3861.0000.9270.793
장애0.4620.4390.3710.9271.0000.763
유공자0.3320.4020.3040.7930.7631.000
2024-04-30T06:10:21.865847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번호선역번호경로장애유공자
연번1.0000.9881.000-0.411-0.360-0.377
호선0.9881.0000.988-0.385-0.328-0.349
역번호1.0000.9881.000-0.411-0.360-0.377
경로-0.411-0.385-0.4111.0000.9370.904
장애-0.360-0.328-0.3600.9371.0000.901
유공자-0.377-0.349-0.3770.9040.9011.000

Missing values

2024-04-30T06:10:19.726255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T06:10:19.814528image/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
011150서울역196048759378847379
121151시청95344822642319220
231152종각149845635049027428
341153종로3가375578660656764739
451154종로5가276060542883933811
561155동대문130322728657215091
671156신설동118202025294916538
781157제기동353205043943629509
891158청량리(서울시립대입구)328232354000536932
9101159동묘앞140216629681320281
연번호선역번호역명경로장애유공자
26526682818가락시장554845963126484
26626782819문정4749961177286929
26726882820장지66646518006613625
26826982821복정46011910449210126
26927082822산성319624786033787
27027182823남한산성입구(성남법원.검찰청)7238551702399155
27127282824단대오거리6149931720364541
27227382825신흥402273995153560
27327482826수진4454811085612701
27427582827모란5189371110895243