Overview

Dataset statistics

Number of variables8
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory761.7 KiB
Average record size in memory78.0 B

Variable types

Numeric6
Text2

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-1095/F/1/datasetView.do

Alerts

NODE_ID is highly overall correlated with ARS_ID and 1 other fieldsHigh correlation
ARS_ID is highly overall correlated with NODE_ID and 1 other fieldsHigh correlation
Y좌표 is highly overall correlated with NODE_ID and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-12-11 09:39:56.310898
Analysis finished2023-12-11 09:40:02.054161
Duration5.74 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

ROUTE_ID
Real number (ℝ)

Distinct763
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0514642 × 108
Minimum1 × 108
Maximum1.249 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T18:40:02.410443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1 × 108
5-th percentile1.0010003 × 108
Q11.0010018 × 108
median1.0010042 × 108
Q31.1 × 108
95-th percentile1.2300001 × 108
Maximum1.249 × 108
Range24899999
Interquartile range (IQR)9899826

Descriptive statistics

Standard deviation8121152.6
Coefficient of variation (CV)0.077236605
Kurtosis0.064753725
Mean1.0514642 × 108
Median Absolute Deviation (MAD)327
Skewness1.2821644
Sum1.0514642 × 1012
Variance6.595312 × 1013
MonotonicityNot monotonic
2023-12-11T18:40:02.607216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
111000016 50
 
0.5%
111000017 48
 
0.5%
100100589 47
 
0.5%
123000013 45
 
0.4%
124000035 44
 
0.4%
115000010 43
 
0.4%
110000004 40
 
0.4%
100100522 40
 
0.4%
124000026 39
 
0.4%
117000002 39
 
0.4%
Other values (753) 9565
95.7%
ValueCountFrequency (%)
100000004 3
 
< 0.1%
100000008 10
0.1%
100000009 8
0.1%
100000011 1
 
< 0.1%
100000012 1
 
< 0.1%
100000014 1
 
< 0.1%
100000015 17
0.2%
100000016 15
0.1%
100000020 2
 
< 0.1%
100100001 7
0.1%
ValueCountFrequency (%)
124900003 21
0.2%
124900002 19
0.2%
124900001 15
 
0.1%
124000039 7
 
0.1%
124000038 22
0.2%
124000036 14
 
0.1%
124000035 44
0.4%
124000034 27
0.3%
124000033 30
0.3%
124000032 28
0.3%

노선명
Text

Distinct760
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T18:40:03.057741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length4
Mean length3.813
Min length2

Characters and Unicode

Total characters38130
Distinct characters83
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)0.2%

Sample

1st row202
2nd row5626
3rd row8146
4th row472
5th row4312
ValueCountFrequency (%)
n75 50
 
0.5%
n72 48
 
0.5%
163 47
 
0.5%
n61 47
 
0.5%
n73 45
 
0.4%
n13b 44
 
0.4%
n64 43
 
0.4%
2016 40
 
0.4%
8146 40
 
0.4%
n62b 39
 
0.4%
Other values (750) 9557
95.6%
2023-12-11T18:40:03.583732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 7081
18.6%
0 4795
12.6%
2 4194
11.0%
6 4052
10.6%
3 3041
8.0%
7 2612
 
6.9%
5 2541
 
6.7%
4 2530
 
6.6%
N 885
 
2.3%
8 838
 
2.2%
Other values (73) 5561
14.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 32169
84.4%
Other Letter 4265
 
11.2%
Uppercase Letter 1427
 
3.7%
Dash Punctuation 228
 
0.6%
Open Punctuation 19
 
< 0.1%
Close Punctuation 19
 
< 0.1%
Lowercase Letter 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 330
 
7.7%
서 324
 
7.6%
강 302
 
7.1%
성 216
 
5.1%
포 215
 
5.0%
북 197
 
4.6%
로 179
 
4.2%
대 178
 
4.2%
작 152
 
3.6%
초 151
 
3.5%
Other values (52) 2021
47.4%
Decimal Number
ValueCountFrequency (%)
1 7081
22.0%
0 4795
14.9%
2 4194
13.0%
6 4052
12.6%
3 3041
9.5%
7 2612
 
8.1%
5 2541
 
7.9%
4 2530
 
7.9%
8 838
 
2.6%
9 485
 
1.5%
Uppercase Letter
ValueCountFrequency (%)
N 885
62.0%
B 328
 
23.0%
A 207
 
14.5%
S 6
 
0.4%
T 1
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
e 1
33.3%
s 1
33.3%
t 1
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 228
100.0%
Open Punctuation
ValueCountFrequency (%)
( 19
100.0%
Close Punctuation
ValueCountFrequency (%)
) 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 32435
85.1%
Hangul 4265
 
11.2%
Latin 1430
 
3.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 330
 
7.7%
서 324
 
7.6%
강 302
 
7.1%
성 216
 
5.1%
포 215
 
5.0%
북 197
 
4.6%
로 179
 
4.2%
대 178
 
4.2%
작 152
 
3.6%
초 151
 
3.5%
Other values (52) 2021
47.4%
Common
ValueCountFrequency (%)
1 7081
21.8%
0 4795
14.8%
2 4194
12.9%
6 4052
12.5%
3 3041
9.4%
7 2612
 
8.1%
5 2541
 
7.8%
4 2530
 
7.8%
8 838
 
2.6%
9 485
 
1.5%
Other values (3) 266
 
0.8%
Latin
ValueCountFrequency (%)
N 885
61.9%
B 328
 
22.9%
A 207
 
14.5%
S 6
 
0.4%
T 1
 
0.1%
e 1
 
0.1%
s 1
 
0.1%
t 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33865
88.8%
Hangul 4265
 
11.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 7081
20.9%
0 4795
14.2%
2 4194
12.4%
6 4052
12.0%
3 3041
9.0%
7 2612
 
7.7%
5 2541
 
7.5%
4 2530
 
7.5%
N 885
 
2.6%
8 838
 
2.5%
Other values (11) 1296
 
3.8%
Hangul
ValueCountFrequency (%)
동 330
 
7.7%
서 324
 
7.6%
강 302
 
7.1%
성 216
 
5.1%
포 215
 
5.0%
북 197
 
4.6%
로 179
 
4.2%
대 178
 
4.2%
작 152
 
3.6%
초 151
 
3.5%
Other values (52) 2021
47.4%

순번
Real number (ℝ)

Distinct169
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.2777
Minimum1
Maximum186
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T18:40:03.774767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q116
median33
Q359
95-th percentile99
Maximum186
Range185
Interquartile range (IQR)43

Descriptive statistics

Standard deviation30.700737
Coefficient of variation (CV)0.76222666
Kurtosis0.74667451
Mean40.2777
Median Absolute Deviation (MAD)20
Skewness0.98923602
Sum402777
Variance942.53524
MonotonicityNot monotonic
2023-12-11T18:40:03.927778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 179
 
1.8%
6 176
 
1.8%
10 176
 
1.8%
19 175
 
1.8%
9 174
 
1.7%
22 174
 
1.7%
4 170
 
1.7%
3 168
 
1.7%
20 167
 
1.7%
2 166
 
1.7%
Other values (159) 8275
82.8%
ValueCountFrequency (%)
1 179
1.8%
2 166
1.7%
3 168
1.7%
4 170
1.7%
5 159
1.6%
6 176
1.8%
7 147
1.5%
8 152
1.5%
9 174
1.7%
10 176
1.8%
ValueCountFrequency (%)
186 1
 
< 0.1%
183 1
 
< 0.1%
174 1
 
< 0.1%
170 2
< 0.1%
169 1
 
< 0.1%
167 1
 
< 0.1%
166 1
 
< 0.1%
165 1
 
< 0.1%
164 3
< 0.1%
163 1
 
< 0.1%

NODE_ID
Real number (ℝ)

HIGH CORRELATION 

Distinct5650
Distinct (%)56.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1390364 × 108
Minimum1 × 108
Maximum1.6801111 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T18:40:04.114606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1 × 108
5-th percentile1.0100005 × 108
Q11.070002 × 108
median1.1390005 × 108
Q31.2000001 × 108
95-th percentile1.2300065 × 108
Maximum1.6801111 × 108
Range68011111
Interquartile range (IQR)12999813

Descriptive statistics

Standard deviation9541891.4
Coefficient of variation (CV)0.08377161
Kurtosis10.129053
Mean1.1390364 × 108
Median Absolute Deviation (MAD)6099981.5
Skewness2.1416748
Sum1.1390364 × 1012
Variance9.1047691 × 1013
MonotonicityNot monotonic
2023-12-11T18:40:04.300512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
161000612 14
 
0.1%
129000083 14
 
0.1%
168011111 12
 
0.1%
161000332 12
 
0.1%
113000422 12
 
0.1%
167010603 12
 
0.1%
108000007 11
 
0.1%
161000333 11
 
0.1%
121000013 10
 
0.1%
107000010 10
 
0.1%
Other values (5640) 9882
98.8%
ValueCountFrequency (%)
100000001 2
 
< 0.1%
100000002 3
< 0.1%
100000003 5
0.1%
100000004 1
 
< 0.1%
100000005 5
0.1%
100000006 2
 
< 0.1%
100000007 1
 
< 0.1%
100000009 1
 
< 0.1%
100000012 1
 
< 0.1%
100000013 1
 
< 0.1%
ValueCountFrequency (%)
168011112 6
0.1%
168011111 12
0.1%
168000693 8
0.1%
168000692 10
0.1%
168000632 1
 
< 0.1%
168000496 1
 
< 0.1%
167010605 7
0.1%
167010604 7
0.1%
167010603 12
0.1%
167010602 8
0.1%

ARS_ID
Real number (ℝ)

HIGH CORRELATION 

Distinct5650
Distinct (%)56.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15434.564
Minimum1001
Maximum92702
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T18:40:04.450767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile2145
Q18313.25
median14676.5
Q321113.25
95-th percentile24651
Maximum92702
Range91701
Interquartile range (IQR)12800

Descriptive statistics

Standard deviation11567.72
Coefficient of variation (CV)0.7494685
Kurtosis23.390605
Mean15434.564
Median Absolute Deviation (MAD)6425.5
Skewness3.8189113
Sum1.5434564 × 108
Variance1.3381214 × 108
MonotonicityNot monotonic
2023-12-11T18:40:04.599596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
92702 14
 
0.1%
92641 14
 
0.1%
42994 12
 
0.1%
92606 12
 
0.1%
14015 12
 
0.1%
41998 12
 
0.1%
9007 11
 
0.1%
92652 11
 
0.1%
22013 10
 
0.1%
8010 10
 
0.1%
Other values (5640) 9882
98.8%
ValueCountFrequency (%)
1001 2
 
< 0.1%
1002 3
< 0.1%
1003 5
0.1%
1004 1
 
< 0.1%
1005 5
0.1%
1006 3
< 0.1%
1007 2
 
< 0.1%
1008 4
< 0.1%
1009 5
0.1%
1010 3
< 0.1%
ValueCountFrequency (%)
92702 14
0.1%
92701 7
0.1%
92653 1
 
< 0.1%
92652 11
0.1%
92647 1
 
< 0.1%
92646 8
0.1%
92644 6
0.1%
92643 2
 
< 0.1%
92641 14
0.1%
92630 7
0.1%

정류소명
Text

Distinct4245
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T18:40:04.941668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length19
Mean length8.0938
Min length2

Characters and Unicode

Total characters80938
Distinct characters619
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2118 ?
Unique (%)21.2%

Sample

1st row숭례문(가상)
2nd row용두연립
3rd row도봉구청
4th row한양아파트.압구정로데오역
5th row한양아파트.압구정로데오역
ValueCountFrequency (%)
김포공항ic(가상 23
 
0.2%
공항입구jc 23
 
0.2%
뱅뱅사거리 21
 
0.2%
홍대입구역 19
 
0.2%
북인천ic 18
 
0.2%
신공항tg(가상 18
 
0.2%
gs주유소(가상 17
 
0.2%
논현역 17
 
0.2%
동묘앞 16
 
0.2%
종로3가.탑골공원 16
 
0.2%
Other values (4236) 9814
98.1%
2023-12-11T18:40:05.500722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 2768
 
3.4%
동 2320
 
2.9%
역 2046
 
2.5%
교 1993
 
2.5%
파 1872
 
2.3%
아 1823
 
2.3%
트 1715
 
2.1%
구 1668
 
2.1%
대 1431
 
1.8%
지 1342
 
1.7%
Other values (609) 61960
76.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 74114
91.6%
Other Punctuation 2806
 
3.5%
Decimal Number 2300
 
2.8%
Uppercase Letter 974
 
1.2%
Close Punctuation 336
 
0.4%
Open Punctuation 335
 
0.4%
Dash Punctuation 49
 
0.1%
Lowercase Letter 22
 
< 0.1%
Space Separator 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 2320
 
3.1%
역 2046
 
2.8%
교 1993
 
2.7%
파 1872
 
2.5%
아 1823
 
2.5%
트 1715
 
2.3%
구 1668
 
2.3%
대 1431
 
1.9%
지 1342
 
1.8%
학 1291
 
1.7%
Other values (567) 56613
76.4%
Uppercase Letter
ValueCountFrequency (%)
C 190
19.5%
T 144
14.8%
I 80
8.2%
S 76
 
7.8%
K 69
 
7.1%
G 68
 
7.0%
M 67
 
6.9%
D 60
 
6.2%
B 50
 
5.1%
J 44
 
4.5%
Other values (11) 126
12.9%
Decimal Number
ValueCountFrequency (%)
1 663
28.8%
2 502
21.8%
3 324
14.1%
4 186
 
8.1%
5 163
 
7.1%
7 114
 
5.0%
0 111
 
4.8%
6 98
 
4.3%
9 82
 
3.6%
8 57
 
2.5%
Other Punctuation
ValueCountFrequency (%)
. 2768
98.6%
· 21
 
0.7%
& 11
 
0.4%
, 6
 
0.2%
Lowercase Letter
ValueCountFrequency (%)
e 20
90.9%
k 1
 
4.5%
t 1
 
4.5%
Close Punctuation
ValueCountFrequency (%)
) 336
100.0%
Open Punctuation
ValueCountFrequency (%)
( 335
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 49
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 74114
91.6%
Common 5828
 
7.2%
Latin 996
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 2320
 
3.1%
역 2046
 
2.8%
교 1993
 
2.7%
파 1872
 
2.5%
아 1823
 
2.5%
트 1715
 
2.3%
구 1668
 
2.3%
대 1431
 
1.9%
지 1342
 
1.8%
학 1291
 
1.7%
Other values (567) 56613
76.4%
Latin
ValueCountFrequency (%)
C 190
19.1%
T 144
14.5%
I 80
8.0%
S 76
 
7.6%
K 69
 
6.9%
G 68
 
6.8%
M 67
 
6.7%
D 60
 
6.0%
B 50
 
5.0%
J 44
 
4.4%
Other values (14) 148
14.9%
Common
ValueCountFrequency (%)
. 2768
47.5%
1 663
 
11.4%
2 502
 
8.6%
) 336
 
5.8%
( 335
 
5.7%
3 324
 
5.6%
4 186
 
3.2%
5 163
 
2.8%
7 114
 
2.0%
0 111
 
1.9%
Other values (8) 326
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 74114
91.6%
ASCII 6803
 
8.4%
None 21
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 2768
40.7%
1 663
 
9.7%
2 502
 
7.4%
) 336
 
4.9%
( 335
 
4.9%
3 324
 
4.8%
C 190
 
2.8%
4 186
 
2.7%
5 163
 
2.4%
T 144
 
2.1%
Other values (31) 1192
17.5%
Hangul
ValueCountFrequency (%)
동 2320
 
3.1%
역 2046
 
2.8%
교 1993
 
2.7%
파 1872
 
2.5%
아 1823
 
2.5%
트 1715
 
2.3%
구 1668
 
2.3%
대 1431
 
1.9%
지 1342
 
1.8%
학 1291
 
1.7%
Other values (567) 56613
76.4%
None
ValueCountFrequency (%)
· 21
100.0%

X좌표
Real number (ℝ)

Distinct5648
Distinct (%)56.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.9782
Minimum126.42987
Maximum127.18161
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T18:40:05.698723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.42987
5-th percentile126.83613
Q1126.91561
median126.98536
Q3127.05021
95-th percentile127.12475
Maximum127.18161
Range0.75174226
Interquartile range (IQR)0.13460197

Descriptive statistics

Standard deviation0.10226223
Coefficient of variation (CV)0.00080535262
Kurtosis4.7798462
Mean126.9782
Median Absolute Deviation (MAD)0.068192946
Skewness-1.2811267
Sum1269782
Variance0.010457564
MonotonicityNot monotonic
2023-12-11T18:40:05.892072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.4340182489 14
 
0.1%
126.4513104918 14
 
0.1%
126.9231959983 12
 
0.1%
126.6647190353 12
 
0.1%
126.7926774008 12
 
0.1%
126.5083067251 12
 
0.1%
126.508722936 11
 
0.1%
127.0266493048 11
 
0.1%
126.6064444917 10
 
0.1%
127.0230319441 10
 
0.1%
Other values (5638) 9882
98.8%
ValueCountFrequency (%)
126.4298719877 1
 
< 0.1%
126.4340182489 14
0.1%
126.4344306036 7
0.1%
126.4501560215 9
0.1%
126.4505374684 1
 
< 0.1%
126.4513104918 14
0.1%
126.4635022107 2
 
< 0.1%
126.4758382523 9
0.1%
126.4762125849 8
0.1%
126.4899916011 1
 
< 0.1%
ValueCountFrequency (%)
127.1816142452 1
 
< 0.1%
127.1797196537 1
 
< 0.1%
127.1784008104 1
 
< 0.1%
127.1779895218 1
 
< 0.1%
127.177967 2
< 0.1%
127.177918 1
 
< 0.1%
127.1777180853 4
< 0.1%
127.1774839621 1
 
< 0.1%
127.177374 2
< 0.1%
127.1772337078 1
 
< 0.1%

Y좌표
Real number (ℝ)

HIGH CORRELATION 

Distinct5648
Distinct (%)56.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.54911
Minimum37.392202
Maximum37.689948
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T18:40:06.076304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.392202
5-th percentile37.470685
Q137.504348
median37.55043
Q337.584677
95-th percentile37.643407
Maximum37.689948
Range0.29774599
Interquartile range (IQR)0.08032905

Descriptive statistics

Standard deviation0.053311782
Coefficient of variation (CV)0.0014197882
Kurtosis-0.71451043
Mean37.54911
Median Absolute Deviation (MAD)0.041269866
Skewness0.23248616
Sum375491.1
Variance0.0028421461
MonotonicityNot monotonic
2023-12-11T18:40:06.225621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.4665046753 14
 
0.1%
37.4487989863 14
 
0.1%
37.5563734241 12
 
0.1%
37.5699088357 12
 
0.1%
37.5839536338 12
 
0.1%
37.506165796 12
 
0.1%
37.5056530035 11
 
0.1%
37.6248696784 11
 
0.1%
37.486653 10
 
0.1%
37.500785 10
 
0.1%
Other values (5638) 9882
98.8%
ValueCountFrequency (%)
37.3922023703 1
 
< 0.1%
37.434378975 1
 
< 0.1%
37.434643292 3
< 0.1%
37.4347964213 1
 
< 0.1%
37.4349830389 2
 
< 0.1%
37.4355241561 1
 
< 0.1%
37.439196853 2
 
< 0.1%
37.4401670279 2
 
< 0.1%
37.4402569123 5
0.1%
37.440823 4
< 0.1%
ValueCountFrequency (%)
37.6899483575 2
< 0.1%
37.6893500743 1
 
< 0.1%
37.6893310475 1
 
< 0.1%
37.6891946492 3
< 0.1%
37.6890118581 1
 
< 0.1%
37.688568 3
< 0.1%
37.6879397664 1
 
< 0.1%
37.6872443546 1
 
< 0.1%
37.6872120941 1
 
< 0.1%
37.6840520197 3
< 0.1%

Interactions

2023-12-11T18:40:01.043574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:57.792062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:58.546273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:59.184893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:59.745637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:40:00.338926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:40:01.168205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:57.925410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:58.642886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:59.268224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:59.849705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:40:00.439700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:40:01.287464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:58.028007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:58.782357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:59.358852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:59.941458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:40:00.571735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:40:01.400099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:58.168491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:58.910028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:59.455759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:40:00.040034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:40:00.698345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:40:01.518523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:58.294734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:58.988973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:59.542632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:40:00.125452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:40:00.795295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:40:01.655759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:58.433826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:59.093426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:59.646490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:40:00.227827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:40:00.932123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T18:40:06.343471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ROUTE_ID순번NODE_IDARS_IDX좌표Y좌표
ROUTE_ID1.0000.3560.4760.3830.5110.530
순번0.3561.0000.1250.1060.1530.223
NODE_ID0.4760.1251.0000.9610.8570.671
ARS_ID0.3830.1060.9611.0000.8250.661
X좌표0.5110.1530.8570.8251.0000.644
Y좌표0.5300.2230.6710.6610.6441.000
2023-12-11T18:40:06.457439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ROUTE_ID순번NODE_IDARS_IDX좌표Y좌표
ROUTE_ID1.000-0.1760.2040.204-0.086-0.163
순번-0.1761.000-0.044-0.0460.048-0.016
NODE_ID0.204-0.0441.0000.997-0.085-0.674
ARS_ID0.204-0.0460.9971.000-0.088-0.674
X좌표-0.0860.048-0.085-0.0881.0000.190
Y좌표-0.163-0.016-0.674-0.6740.1901.000

Missing values

2023-12-11T18:40:01.845852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T18:40:01.993289image/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

ROUTE_ID노선명순번NODE_IDARS_ID정류소명X좌표Y좌표
1704100100039202601010003292099숭례문(가상)126.97435637.559797
2569410010028256266411600013017220용두연립126.85179537.499002
12669110000004814612810900004910134도봉구청127.04615437.669928
357591001000754725412200005723159한양아파트.압구정로데오역127.03925737.527929
3574310010050043126512200005623158한양아파트.압구정로데오역127.03957937.52827
1968610010045167165911300050614097홍대입구역(가상)126.92592537.558652
8560100100042260841060002117306능산지하차도127.10063837.600947
269821001000795042111700011118197모두의학교.금천문화예술정보학교126.90559337.47995
1965100100085600371010000382135시청.서소문청사126.97495437.563525
310100100061370401000003931018종로4가.종묘126.99528437.570627
ROUTE_ID노선명순번NODE_IDARS_ID정류소명X좌표Y좌표
6621100100024145261050000726158답십리역.동부시장127.05251937.567256
67361001001301017441050000906176우신향병원127.03229337.585548
16201111900012은평08-21411100008812176새절역.숭실고입구126.91400737.591583
13206109900002도봉072710900021410302방학3동주민센터127.02790437.659168
68861001001882013481050001196205동대문중학교127.05327737.574176
93981001003447211451070000298119숭덕초교127.01468337.603358
150741001001531138111000038911491흥안운수상계4동종점127.08227637.670834
3116010010057167043411900030420987동작대교(가상)126.97973437.505119
205731001003397019511300019514289월드컵파크5단지126.88651837.580333
302471001000976504311900007420167봉현초등학교126.95606937.491736