Overview

Dataset statistics

Number of variables7
Number of observations281
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.1 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
유공자 has unique valuesUnique

Reproduction

Analysis started2024-04-29 21:09:57.165289
Analysis finished2024-04-29 21:10:00.599275
Duration3.43 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct281
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean141
Minimum1
Maximum281
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-04-30T06:10:00.655903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile15
Q171
median141
Q3211
95-th percentile267
Maximum281
Range280
Interquartile range (IQR)140

Descriptive statistics

Standard deviation81.261922
Coefficient of variation (CV)0.57632569
Kurtosis-1.2
Mean141
Median Absolute Deviation (MAD)70
Skewness0
Sum39621
Variance6603.5
MonotonicityStrictly increasing
2024-04-30T06:10:00.761622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.4%
186 1
 
0.4%
192 1
 
0.4%
191 1
 
0.4%
190 1
 
0.4%
189 1
 
0.4%
188 1
 
0.4%
187 1
 
0.4%
185 1
 
0.4%
194 1
 
0.4%
Other values (271) 271
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 (%)
281 1
0.4%
280 1
0.4%
279 1
0.4%
278 1
0.4%
277 1
0.4%
276 1
0.4%
275 1
0.4%
274 1
0.4%
273 1
0.4%
272 1
0.4%

호선
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.683274
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-04-30T06:10:00.856924image/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 deviation2.0202646
Coefficient of variation (CV)0.4313787
Kurtosis-1.1788697
Mean4.683274
Median Absolute Deviation (MAD)2
Skewness-0.11088965
Sum1316
Variance4.0814692
MonotonicityIncreasing
2024-04-30T06:10:00.943006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
5 56
19.9%
7 51
18.1%
2 50
17.8%
6 37
13.2%
3 33
11.7%
4 26
9.3%
8 18
 
6.4%
1 10
 
3.6%
ValueCountFrequency (%)
1 10
 
3.6%
2 50
17.8%
3 33
11.7%
4 26
9.3%
5 56
19.9%
6 37
13.2%
7 51
18.1%
8 18
 
6.4%
ValueCountFrequency (%)
8 18
 
6.4%
7 51
18.1%
6 37
13.2%
5 56
19.9%
4 26
9.3%
3 33
11.7%
2 50
17.8%
1 10
 
3.6%

역번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct281
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1652.2206
Minimum150
Maximum2828
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-04-30T06:10:01.064499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum150
5-th percentile205
Q1319
median2532
Q32647
95-th percentile2814
Maximum2828
Range2678
Interquartile range (IQR)2328

Descriptive statistics

Standard deviation1173.3538
Coefficient of variation (CV)0.71016775
Kurtosis-1.8931459
Mean1652.2206
Median Absolute Deviation (MAD)226
Skewness-0.30543982
Sum464274
Variance1376759.2
MonotonicityStrictly increasing
2024-04-30T06:10:01.192179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150 1
 
0.4%
2622 1
 
0.4%
2628 1
 
0.4%
2627 1
 
0.4%
2626 1
 
0.4%
2625 1
 
0.4%
2624 1
 
0.4%
2623 1
 
0.4%
2621 1
 
0.4%
2630 1
 
0.4%
Other values (271) 271
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 (%)
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

Distinct248
Distinct (%)88.3%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2024-04-30T06:10:01.410407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length4.4163701
Min length2

Characters and Unicode

Total characters1241
Distinct characters243
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

Unique217 ?
Unique (%)77.2%

Sample

1st row서울역
2nd row시청
3rd row종각
4th row종로3가
5th row종로5가
ValueCountFrequency (%)
종로3가 3
 
1.1%
동대문역사문화공원(ddp 3
 
1.1%
을지로4가 2
 
0.7%
사당 2
 
0.7%
서울역 2
 
0.7%
대림(구로구청 2
 
0.7%
불광 2
 
0.7%
교대(법원.검찰청 2
 
0.7%
노원 2
 
0.7%
잠실(송파구청 2
 
0.7%
Other values (238) 259
92.2%
2024-04-30T06:10:01.737234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 64
 
5.2%
) 64
 
5.2%
구 50
 
4.0%
대 49
 
3.9%
동 35
 
2.8%
청 32
 
2.6%
신 26
 
2.1%
원 23
 
1.9%
산 22
 
1.8%
문 20
 
1.6%
Other values (233) 856
69.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1092
88.0%
Open Punctuation 64
 
5.2%
Close Punctuation 64
 
5.2%
Uppercase Letter 9
 
0.7%
Decimal Number 8
 
0.6%
Other Punctuation 4
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 50
 
4.6%
대 49
 
4.5%
동 35
 
3.2%
청 32
 
2.9%
신 26
 
2.4%
원 23
 
2.1%
산 22
 
2.0%
문 20
 
1.8%
입 19
 
1.7%
로 16
 
1.5%
Other values (224) 800
73.3%
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%
Open Punctuation
ValueCountFrequency (%)
( 64
100.0%
Close Punctuation
ValueCountFrequency (%)
) 64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1092
88.0%
Common 140
 
11.3%
Latin 9
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 50
 
4.6%
대 49
 
4.5%
동 35
 
3.2%
청 32
 
2.9%
신 26
 
2.4%
원 23
 
2.1%
산 22
 
2.0%
문 20
 
1.8%
입 19
 
1.7%
로 16
 
1.5%
Other values (224) 800
73.3%
Common
ValueCountFrequency (%)
( 64
45.7%
) 64
45.7%
3 5
 
3.6%
. 3
 
2.1%
4 2
 
1.4%
5 1
 
0.7%
· 1
 
0.7%
Latin
ValueCountFrequency (%)
D 6
66.7%
P 3
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1092
88.0%
ASCII 148
 
11.9%
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 (%)
구 50
 
4.6%
대 49
 
4.5%
동 35
 
3.2%
청 32
 
2.9%
신 26
 
2.4%
원 23
 
2.1%
산 22
 
2.0%
문 20
 
1.8%
입 19
 
1.7%
로 16
 
1.5%
Other values (224) 800
73.3%
None
ValueCountFrequency (%)
· 1
100.0%

경로
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct281
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean607726.54
Minimum6178
Maximum2489647
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-04-30T06:10:01.858543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6178
5-th percentile153982
Q1322108
median519002
Q3742675
95-th percentile1458120
Maximum2489647
Range2483469
Interquartile range (IQR)420567

Descriptive statistics

Standard deviation422099.26
Coefficient of variation (CV)0.69455459
Kurtosis4.3043538
Mean607726.54
Median Absolute Deviation (MAD)204652
Skewness1.7965571
Sum1.7077116 × 108
Variance1.7816778 × 1011
MonotonicityNot monotonic
2024-04-30T06:10:01.975003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1460872 1
 
0.4%
700301 1
 
0.4%
322108 1
 
0.4%
605707 1
 
0.4%
367409 1
 
0.4%
377835 1
 
0.4%
180386 1
 
0.4%
318100 1
 
0.4%
593026 1
 
0.4%
150345 1
 
0.4%
Other values (271) 271
96.4%
ValueCountFrequency (%)
6178 1
0.4%
62778 1
0.4%
68680 1
0.4%
76384 1
0.4%
80199 1
0.4%
86231 1
0.4%
90356 1
0.4%
93552 1
0.4%
107244 1
0.4%
112234 1
0.4%
ValueCountFrequency (%)
2489647 1
0.4%
2452804 1
0.4%
2358285 1
0.4%
2303767 1
0.4%
2188055 1
0.4%
1820075 1
0.4%
1792211 1
0.4%
1586915 1
0.4%
1536611 1
0.4%
1531379 1
0.4%

장애
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct281
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116968.67
Minimum1202
Maximum406955
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-04-30T06:10:02.082431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1202
5-th percentile27844
Q162634
median97350
Q3153572
95-th percentile282633
Maximum406955
Range405753
Interquartile range (IQR)90938

Descriptive statistics

Standard deviation78933.472
Coefficient of variation (CV)0.67482579
Kurtosis1.9671453
Mean116968.67
Median Absolute Deviation (MAD)38748
Skewness1.3838752
Sum32868195
Variance6.230493 × 109
MonotonicityNot monotonic
2024-04-30T06:10:02.200867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
388272 1
 
0.4%
121000 1
 
0.4%
58602 1
 
0.4%
109169 1
 
0.4%
61623 1
 
0.4%
56009 1
 
0.4%
27995 1
 
0.4%
63926 1
 
0.4%
167030 1
 
0.4%
29547 1
 
0.4%
Other values (271) 271
96.4%
ValueCountFrequency (%)
1202 1
0.4%
10298 1
0.4%
12587 1
0.4%
12820 1
0.4%
13377 1
0.4%
15270 1
0.4%
16658 1
0.4%
17609 1
0.4%
23290 1
0.4%
23566 1
0.4%
ValueCountFrequency (%)
406955 1
0.4%
405814 1
0.4%
388272 1
0.4%
383345 1
0.4%
364559 1
0.4%
353724 1
0.4%
342727 1
0.4%
321227 1
0.4%
318872 1
0.4%
310723 1
0.4%

유공자
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct281
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7504.79
Minimum76
Maximum34588
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-04-30T06:10:02.339840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum76
5-th percentile1702
Q13583
median6153
Q39509
95-th percentile19721
Maximum34588
Range34512
Interquartile range (IQR)5926

Descriptive statistics

Standard deviation5452.9029
Coefficient of variation (CV)0.72658967
Kurtosis3.5704153
Mean7504.79
Median Absolute Deviation (MAD)2951
Skewness1.656351
Sum2108846
Variance29734150
MonotonicityNot monotonic
2024-04-30T06:10:02.468110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29791 1
 
0.4%
5760 1
 
0.4%
3471 1
 
0.4%
7634 1
 
0.4%
6052 1
 
0.4%
5659 1
 
0.4%
2863 1
 
0.4%
4837 1
 
0.4%
6141 1
 
0.4%
1722 1
 
0.4%
Other values (271) 271
96.4%
ValueCountFrequency (%)
76 1
0.4%
282 1
0.4%
550 1
0.4%
929 1
0.4%
1025 1
0.4%
1189 1
0.4%
1234 1
0.4%
1291 1
0.4%
1336 1
0.4%
1377 1
0.4%
ValueCountFrequency (%)
34588 1
0.4%
29791 1
0.4%
25689 1
0.4%
24058 1
0.4%
23967 1
0.4%
23192 1
0.4%
22791 1
0.4%
22076 1
0.4%
22069 1
0.4%
21137 1
0.4%

Interactions

2024-04-30T06:09:59.893018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:57.415964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:57.874049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:58.320315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:58.764068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:59.220202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:59.989825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:57.484008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:57.944600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:58.384525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:58.840217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:59.288732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:00.099895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:57.571269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:58.015365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:58.456812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:58.917356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:59.576695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:00.178554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:57.640164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:58.080072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:58.524189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:58.983692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:59.643376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:00.262801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:57.715679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:58.148239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:58.590363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:59.071651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:59.723998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:00.354247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:57.792550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:58.232705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:58.666028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:59.148351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:09:59.804614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-30T06:10:02.555400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번호선역번호경로장애유공자
연번1.0000.9230.9310.5310.4220.492
호선0.9231.0000.9950.5300.4030.465
역번호0.9310.9951.0000.3470.3400.378
경로0.5310.5300.3471.0000.9180.884
장애0.4220.4030.3400.9181.0000.880
유공자0.4920.4650.3780.8840.8801.000
2024-04-30T06:10:02.636839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번호선역번호경로장애유공자
연번1.0000.9881.000-0.372-0.330-0.382
호선0.9881.0000.988-0.343-0.296-0.354
역번호1.0000.9881.000-0.372-0.330-0.382
경로-0.372-0.343-0.3721.0000.9380.907
장애-0.330-0.296-0.3300.9381.0000.898
유공자-0.382-0.354-0.3820.9070.8981.000

Missing values

2024-04-30T06:10:00.476989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T06:10:00.565180image/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
011150서울역146087238827229791
121151시청56541413637811388
231152종각103204721972516035
341153종로3가248964740581434588
451154종로5가230376732122723192
561155동대문10361701999319011
671156신설동9963561802219934
781157제기동245280430770118213
891158청량리(서울시립대입구)235828536455923967
9101159동묘앞135086625195216072
연번호선역번호역명경로장애유공자
27127282819문정454496983006240
27227382820장지6066061357508783
27327482821복정370027809735505
27427582822산성298976493353397
27527682823남한산성입구(성남법원.검찰청)6512391395096359
27627782824단대오거리5623451332884211
27727882825신흥306458730082452
27827982826수진339066778842871
27928082827모란443197859204251
28028182828남위례6178120276