Overview

Dataset statistics

Number of variables7
Number of observations276
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.6 KiB
Average record size in memory61.5 B

Variable types

Numeric5
Text1
DateTime1

Dataset

Description서울교통공사 1-8호선 276개역 경위도 좌표(소숫점 6째자리까지) 정보 입니다. 본 데이터는 우리 공사는 철도 건설 등 측량업무를 별도로 수행하지 않으므로, 데이터의 주기적인 관리가 어려워, 갱신주기는 "발생시(신설, 이설, 폐쇄)"에 한정하여 관리하오니 양해해 주시기 바랍니다.
Author서울교통공사
URLhttps://www.data.go.kr/data/15099316/fileData.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
고유역번호(외부역코드) is highly overall correlated with 연번 and 1 other fieldsHigh correlation
연번 has unique valuesUnique
고유역번호(외부역코드) has unique valuesUnique

Reproduction

Analysis started2023-12-12 10:23:36.967802
Analysis finished2023-12-12 10:23:40.430494
Duration3.46 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct276
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean138.5
Minimum1
Maximum276
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2023-12-12T19:23:40.531105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14.75
Q169.75
median138.5
Q3207.25
95-th percentile262.25
Maximum276
Range275
Interquartile range (IQR)137.5

Descriptive statistics

Standard deviation79.818544
Coefficient of variation (CV)0.57630718
Kurtosis-1.2
Mean138.5
Median Absolute Deviation (MAD)69
Skewness0
Sum38226
Variance6371
MonotonicityStrictly increasing
2023-12-12T19:23:40.689033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.4%
184 1
 
0.4%
190 1
 
0.4%
189 1
 
0.4%
188 1
 
0.4%
187 1
 
0.4%
186 1
 
0.4%
185 1
 
0.4%
183 1
 
0.4%
175 1
 
0.4%
Other values (266) 266
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 (%)
276 1
0.4%
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%

호선
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6014493
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2023-12-12T19:23:40.869906image/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 deviation2.0055917
Coefficient of variation (CV)0.43586086
Kurtosis-1.1523668
Mean4.6014493
Median Absolute Deviation (MAD)2
Skewness-0.051736261
Sum1270
Variance4.0223979
MonotonicityIncreasing
2023-12-12T19:23:41.029280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
5 56
20.3%
2 51
18.5%
7 42
15.2%
6 39
14.1%
3 34
12.3%
4 26
9.4%
8 18
 
6.5%
1 10
 
3.6%
ValueCountFrequency (%)
1 10
 
3.6%
2 51
18.5%
3 34
12.3%
4 26
9.4%
5 56
20.3%
6 39
14.1%
7 42
15.2%
8 18
 
6.5%
ValueCountFrequency (%)
8 18
 
6.5%
7 42
15.2%
6 39
14.1%
5 56
20.3%
4 26
9.4%
3 34
12.3%
2 51
18.5%
1 10
 
3.6%

고유역번호(외부역코드)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct276
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1613.2101
Minimum150
Maximum2828
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2023-12-12T19:23:41.218710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum150
5-th percentile203.75
Q1316.75
median2527.5
Q32640.25
95-th percentile2814.25
Maximum2828
Range2678
Interquartile range (IQR)2323.5

Descriptive statistics

Standard deviation1175.3363
Coefficient of variation (CV)0.72856988
Kurtosis-1.9278139
Mean1613.2101
Median Absolute Deviation (MAD)284
Skewness-0.24448864
Sum445246
Variance1381415.5
MonotonicityNot monotonic
2023-12-12T19:23:41.407855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150 1
 
0.4%
2618 1
 
0.4%
2624 1
 
0.4%
2623 1
 
0.4%
2622 1
 
0.4%
2621 1
 
0.4%
2620 1
 
0.4%
2619 1
 
0.4%
2617 1
 
0.4%
2564 1
 
0.4%
Other values (266) 266
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

Distinct239
Distinct (%)86.6%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2023-12-12T19:23:41.757930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length2
Mean length2.923913
Min length2

Characters and Unicode

Total characters807
Distinct characters207
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique204 ?
Unique (%)73.9%

Sample

1st row서울
2nd row시청
3rd row종각
4th row종로3가
5th row종로5가
ValueCountFrequency (%)
종로3가 3
 
1.1%
동대문역사문화공원 3
 
1.1%
영등포구청 2
 
0.7%
삼각지 2
 
0.7%
서울 2
 
0.7%
가락시장 2
 
0.7%
까치산 2
 
0.7%
교대 2
 
0.7%
대림 2
 
0.7%
사당 2
 
0.7%
Other values (229) 254
92.0%
2023-12-12T19:23:42.329945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
대 33
 
4.1%
구 29
 
3.6%
신 25
 
3.1%
동 22
 
2.7%
산 20
 
2.5%
지 15
 
1.9%
로 15
 
1.9%
문 15
 
1.9%
입 15
 
1.9%
원 14
 
1.7%
Other values (197) 604
74.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 799
99.0%
Decimal Number 8
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 33
 
4.1%
구 29
 
3.6%
신 25
 
3.1%
동 22
 
2.8%
산 20
 
2.5%
지 15
 
1.9%
로 15
 
1.9%
문 15
 
1.9%
입 15
 
1.9%
원 14
 
1.8%
Other values (194) 596
74.6%
Decimal Number
ValueCountFrequency (%)
3 5
62.5%
4 2
 
25.0%
5 1
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 799
99.0%
Common 8
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 33
 
4.1%
구 29
 
3.6%
신 25
 
3.1%
동 22
 
2.8%
산 20
 
2.5%
지 15
 
1.9%
로 15
 
1.9%
문 15
 
1.9%
입 15
 
1.9%
원 14
 
1.8%
Other values (194) 596
74.6%
Common
ValueCountFrequency (%)
3 5
62.5%
4 2
 
25.0%
5 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 799
99.0%
ASCII 8
 
1.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
대 33
 
4.1%
구 29
 
3.6%
신 25
 
3.1%
동 22
 
2.8%
산 20
 
2.5%
지 15
 
1.9%
로 15
 
1.9%
문 15
 
1.9%
입 15
 
1.9%
원 14
 
1.8%
Other values (194) 596
74.6%
ASCII
ValueCountFrequency (%)
3 5
62.5%
4 2
 
25.0%
5 1
 
12.5%

위도
Real number (ℝ)

Distinct272
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.546719
Minimum37.433888
Maximum37.70015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2023-12-12T19:23:42.524204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.433888
5-th percentile37.479105
Q137.508824
median37.548566
Q337.5716
95-th percentile37.636425
Maximum37.70015
Range0.266262
Interquartile range (IQR)0.06277625

Descriptive statistics

Standard deviation0.048326142
Coefficient of variation (CV)0.0012870936
Kurtosis0.20123082
Mean37.546719
Median Absolute Deviation (MAD)0.0300615
Skewness0.38784581
Sum10362.895
Variance0.002335416
MonotonicityNot monotonic
2023-12-12T19:23:42.729160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.561302 2
 
0.7%
37.617319 2
 
0.7%
37.562182 2
 
0.7%
37.53181 2
 
0.7%
37.55315 1
 
0.4%
37.583989 1
 
0.4%
37.549033 1
 
0.4%
37.556031 1
 
0.4%
37.563535 1
 
0.4%
37.569439 1
 
0.4%
Other values (262) 262
94.9%
ValueCountFrequency (%)
37.433888 1
0.4%
37.437575 1
0.4%
37.440952 1
0.4%
37.445057 1
0.4%
37.451568 1
0.4%
37.456886 1
0.4%
37.462839 1
0.4%
37.464339 1
0.4%
37.471016 1
0.4%
37.47616 1
0.4%
ValueCountFrequency (%)
37.70015 1
0.4%
37.689131 1
0.4%
37.677804 1
0.4%
37.66956 1
0.4%
37.664985 1
0.4%
37.660576 1
0.4%
37.656274 1
0.4%
37.654478 1
0.4%
37.652993 1
0.4%
37.648281 1
0.4%

경도
Real number (ℝ)

Distinct272
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.00905
Minimum126.80127
Maximum127.22343
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2023-12-12T19:23:42.906466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.80127
5-th percentile126.85585
Q1126.94747
median127.01275
Q3127.07157
95-th percentile127.14313
Maximum127.22343
Range0.422154
Interquartile range (IQR)0.124097

Descriptive statistics

Standard deviation0.086508065
Coefficient of variation (CV)0.00068111731
Kurtosis-0.43266924
Mean127.00905
Median Absolute Deviation (MAD)0.0613855
Skewness-0.12070173
Sum35054.499
Variance0.0074836452
MonotonicityNot monotonic
2023-12-12T19:23:43.125232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.995473 2
 
0.7%
127.074741 2
 
0.7%
126.82693 2
 
0.7%
126.846706 2
 
0.7%
126.972533 1
 
0.4%
126.909785 1
 
0.4%
126.913546 1
 
0.4%
126.910129 1
 
0.4%
126.903326 1
 
0.4%
126.899077 1
 
0.4%
Other values (262) 262
94.9%
ValueCountFrequency (%)
126.801273 1
0.4%
126.806838 1
0.4%
126.812052 1
0.4%
126.812822 1
0.4%
126.823294 1
0.4%
126.82693 2
0.7%
126.83633 1
0.4%
126.838684 1
0.4%
126.840436 1
0.4%
126.846706 2
0.7%
ValueCountFrequency (%)
127.223427 1
0.4%
127.206901 1
0.4%
127.203897 1
0.4%
127.192954 1
0.4%
127.176018 1
0.4%
127.166381 1
0.4%
127.159845 1
0.4%
127.156735 1
0.4%
127.154214 1
0.4%
127.152784 1
0.4%

작성일자
Date

Distinct104
Distinct (%)37.7%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
Minimum1974-02-28 00:00:00
Maximum2021-12-31 00:00:00
2023-12-12T19:23:43.301770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:23:43.476277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-12-12T19:23:39.568383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:23:37.278855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:23:37.786992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:23:38.404814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:23:38.965061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:23:39.684737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:23:37.364329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:23:37.908763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:23:38.518546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:23:39.094012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:23:39.819834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:23:37.466977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:23:38.034000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:23:38.633078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:23:39.214993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:23:39.930636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:23:37.568973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:23:38.154588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:23:38.739911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:23:39.324743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:23:40.041896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:23:37.680682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:23:38.275164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:23:38.854506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:23:39.429396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:23:43.589671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번호선고유역번호(외부역코드)위도경도
연번1.0000.9170.9190.7170.790
호선0.9171.0000.9950.6340.593
고유역번호(외부역코드)0.9190.9951.0000.3920.535
위도0.7170.6340.3921.0000.628
경도0.7900.5930.5350.6281.000
2023-12-12T19:23:44.080420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번호선고유역번호(외부역코드)위도경도
연번1.0000.9880.998-0.0910.228
호선0.9881.0000.988-0.0310.193
고유역번호(외부역코드)0.9980.9881.000-0.0940.230
위도-0.091-0.031-0.0941.000-0.021
경도0.2280.1930.230-0.0211.000

Missing values

2023-12-12T19:23:40.190867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:23:40.377073image/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

연번호선고유역번호(외부역코드)역명위도경도작성일자
011150서울37.55315126.9725331974-02-28
121151시청37.56359126.9754071974-08-15
231152종각37.570203126.9831161974-08-15
341153종로3가37.570429126.9920951974-08-15
451154종로5가37.570971127.00191974-03-31
561155동대문37.57179127.0113831974-04-30
671159동묘앞37.573265127.0164592005-12-31
781156신설동37.576117127.024711974-03-30
891157제기동37.578116127.0349021974-03-30
9101158청량리37.580148127.0450631974-08-31
연번호선고유역번호(외부역코드)역명위도경도작성일자
26626782819문정37.485931127.1224731996-11-30
26726882820장지37.478609127.1262291996-11-30
26826982821복정37.471016127.1267461996-12-30
26927082828남위례37.462839127.1390472021-12-31
27027182822산성37.456886127.1499271996-10-31
27127282823남한산성입구37.451568127.1598451996-10-31
27227382824단대오거리37.445057127.1567351996-12-28
27327482825신흥37.440952127.147591996-12-28
27427582826수진37.437575127.1409361996-12-28
27527682827모란37.433888127.1299211996-11-30