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 09:39:24.613385
Analysis finished2023-12-11 09:39:30.267648
Duration5.65 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

ROUTE_ID
Real number (ℝ)

Distinct762
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.051159 × 108
Minimum1 × 108
Maximum1.249 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T18:39:30.342733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1 × 108
5-th percentile1.0010003 × 108
Q11.0010017 × 108
median1.0010042 × 108
Q31.0990001 × 108
95-th percentile1.2300001 × 108
Maximum1.249 × 108
Range24899999
Interquartile range (IQR)9799838

Descriptive statistics

Standard deviation8100455.5
Coefficient of variation (CV)0.077062132
Kurtosis0.097050573
Mean1.051159 × 108
Median Absolute Deviation (MAD)324
Skewness1.2930028
Sum1.051159 × 1012
Variance6.5617379 × 1013
MonotonicityNot monotonic
2023-12-11T18:39:30.486058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
124000025 46
 
0.5%
100100589 43
 
0.4%
100100588 40
 
0.4%
124000026 38
 
0.4%
111000017 38
 
0.4%
100100499 38
 
0.4%
111000016 38
 
0.4%
100100610 37
 
0.4%
100100592 36
 
0.4%
100100095 36
 
0.4%
Other values (752) 9610
96.1%
ValueCountFrequency (%)
100000004 6
 
0.1%
100000008 8
 
0.1%
100000009 9
0.1%
100000011 1
 
< 0.1%
100000015 17
0.2%
100000016 17
0.2%
100100001 4
 
< 0.1%
100100002 6
 
0.1%
100100003 6
 
0.1%
100100006 21
0.2%
ValueCountFrequency (%)
124900003 17
0.2%
124900002 18
0.2%
124900001 19
0.2%
124000039 9
 
0.1%
124000038 22
0.2%
124000036 18
0.2%
124000035 33
0.3%
124000034 15
0.1%
124000033 30
0.3%
124000032 34
0.3%

노선명
Text

Distinct759
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T18:39:30.803111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length4
Mean length3.8012
Min length2

Characters and Unicode

Total characters38012
Distinct characters73
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)0.2%

Sample

1st row654
2nd rowN26
3rd row9408
4th row702B용두초교
5th row5522B호암
ValueCountFrequency (%)
163 65
 
0.7%
n62b 46
 
0.5%
n61 43
 
0.4%
n62 40
 
0.4%
7212 38
 
0.4%
n75 38
 
0.4%
n61b 38
 
0.4%
n72 38
 
0.4%
n854 37
 
0.4%
n15 37
 
0.4%
Other values (749) 9580
95.8%
2023-12-11T18:39:31.275136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 6943
18.3%
0 4738
12.5%
6 4119
10.8%
2 4074
10.7%
3 3104
8.2%
7 2671
 
7.0%
5 2645
 
7.0%
4 2523
 
6.6%
N 908
 
2.4%
8 870
 
2.3%
Other values (63) 5417
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 32165
84.6%
Other Letter 4165
 
11.0%
Uppercase Letter 1444
 
3.8%
Dash Punctuation 198
 
0.5%
Open Punctuation 20
 
0.1%
Close Punctuation 20
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 349
 
8.4%
동 316
 
7.6%
강 299
 
7.2%
포 210
 
5.0%
북 202
 
4.8%
성 189
 
4.5%
초 171
 
4.1%
대 169
 
4.1%
작 164
 
3.9%
로 147
 
3.5%
Other values (46) 1949
46.8%
Decimal Number
ValueCountFrequency (%)
1 6943
21.6%
0 4738
14.7%
6 4119
12.8%
2 4074
12.7%
3 3104
9.7%
7 2671
 
8.3%
5 2645
 
8.2%
4 2523
 
7.8%
8 870
 
2.7%
9 478
 
1.5%
Uppercase Letter
ValueCountFrequency (%)
N 908
62.9%
B 321
 
22.2%
A 211
 
14.6%
S 4
 
0.3%
Dash Punctuation
ValueCountFrequency (%)
- 198
100.0%
Open Punctuation
ValueCountFrequency (%)
( 20
100.0%
Close Punctuation
ValueCountFrequency (%)
) 20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 32403
85.2%
Hangul 4165
 
11.0%
Latin 1444
 
3.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 349
 
8.4%
동 316
 
7.6%
강 299
 
7.2%
포 210
 
5.0%
북 202
 
4.8%
성 189
 
4.5%
초 171
 
4.1%
대 169
 
4.1%
작 164
 
3.9%
로 147
 
3.5%
Other values (46) 1949
46.8%
Common
ValueCountFrequency (%)
1 6943
21.4%
0 4738
14.6%
6 4119
12.7%
2 4074
12.6%
3 3104
9.6%
7 2671
 
8.2%
5 2645
 
8.2%
4 2523
 
7.8%
8 870
 
2.7%
9 478
 
1.5%
Other values (3) 238
 
0.7%
Latin
ValueCountFrequency (%)
N 908
62.9%
B 321
 
22.2%
A 211
 
14.6%
S 4
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33847
89.0%
Hangul 4165
 
11.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6943
20.5%
0 4738
14.0%
6 4119
12.2%
2 4074
12.0%
3 3104
9.2%
7 2671
 
7.9%
5 2645
 
7.8%
4 2523
 
7.5%
N 908
 
2.7%
8 870
 
2.6%
Other values (7) 1252
 
3.7%
Hangul
ValueCountFrequency (%)
서 349
 
8.4%
동 316
 
7.6%
강 299
 
7.2%
포 210
 
5.0%
북 202
 
4.8%
성 189
 
4.5%
초 171
 
4.1%
대 169
 
4.1%
작 164
 
3.9%
로 147
 
3.5%
Other values (46) 1949
46.8%

순번
Real number (ℝ)

Distinct174
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.6503
Minimum1
Maximum185
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T18:39:31.427832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q116
median34
Q359
95-th percentile99
Maximum185
Range184
Interquartile range (IQR)43

Descriptive statistics

Standard deviation30.533599
Coefficient of variation (CV)0.75112849
Kurtosis0.84495395
Mean40.6503
Median Absolute Deviation (MAD)20
Skewness1.0048995
Sum406503
Variance932.30064
MonotonicityNot monotonic
2023-12-11T18:39:31.619628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 185
 
1.8%
15 179
 
1.8%
13 176
 
1.8%
20 175
 
1.8%
8 173
 
1.7%
17 168
 
1.7%
22 166
 
1.7%
11 164
 
1.6%
7 163
 
1.6%
24 162
 
1.6%
Other values (164) 8289
82.9%
ValueCountFrequency (%)
1 160
1.6%
2 139
1.4%
3 138
1.4%
4 157
1.6%
5 150
1.5%
6 153
1.5%
7 163
1.6%
8 173
1.7%
9 153
1.5%
10 160
1.6%
ValueCountFrequency (%)
185 1
< 0.1%
179 1
< 0.1%
177 1
< 0.1%
175 1
< 0.1%
171 1
< 0.1%
170 1
< 0.1%
169 1
< 0.1%
167 1
< 0.1%
166 1
< 0.1%
165 1
< 0.1%

NODE_ID
Real number (ℝ)

HIGH CORRELATION 

Distinct5535
Distinct (%)55.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1399898 × 108
Minimum1 × 108
Maximum1.6801111 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T18:39:31.789010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1 × 108
5-th percentile1.0100004 × 108
Q11.079002 × 108
median1.1390027 × 108
Q31.2000001 × 108
95-th percentile1.239 × 108
Maximum1.6801111 × 108
Range68011110
Interquartile range (IQR)12099809

Descriptive statistics

Standard deviation9511636.4
Coefficient of variation (CV)0.083436154
Kurtosis10.068495
Mean1.1399898 × 108
Median Absolute Deviation (MAD)6000236
Skewness2.1367055
Sum1.1399898 × 1012
Variance9.0471228 × 1013
MonotonicityNot monotonic
2023-12-11T18:39:32.208924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129000083 17
 
0.2%
161000325 14
 
0.1%
113000505 11
 
0.1%
161000611 11
 
0.1%
161000381 11
 
0.1%
108000012 11
 
0.1%
167010604 11
 
0.1%
100000365 10
 
0.1%
161010557 10
 
0.1%
121000021 10
 
0.1%
Other values (5525) 9884
98.8%
ValueCountFrequency (%)
100000002 3
< 0.1%
100000003 5
0.1%
100000004 1
 
< 0.1%
100000005 3
< 0.1%
100000010 2
 
< 0.1%
100000011 1
 
< 0.1%
100000014 1
 
< 0.1%
100000016 3
< 0.1%
100000017 2
 
< 0.1%
100000018 4
< 0.1%
ValueCountFrequency (%)
168011112 9
0.1%
168011111 7
0.1%
168000737 1
 
< 0.1%
168000736 1
 
< 0.1%
168000693 10
0.1%
168000692 6
0.1%
168000632 1
 
< 0.1%
167010605 9
0.1%
167010604 11
0.1%
167010603 3
 
< 0.1%

ARS_ID
Real number (ℝ)

HIGH CORRELATION 

Distinct5534
Distinct (%)55.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15580.026
Minimum1002
Maximum92702
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T18:39:32.356707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1002
5-th percentile2145
Q18936.25
median14971.5
Q321112.25
95-th percentile24729.3
Maximum92702
Range91700
Interquartile range (IQR)12176

Descriptive statistics

Standard deviation11786.684
Coefficient of variation (CV)0.75652529
Kurtosis23.456433
Mean15580.026
Median Absolute Deviation (MAD)6131.5
Skewness3.8979477
Sum1.5580026 × 108
Variance1.3892592 × 108
MonotonicityNot monotonic
2023-12-11T18:39:32.495785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
92641 17
 
0.2%
92601 14
 
0.1%
14096 11
 
0.1%
92701 11
 
0.1%
92630 11
 
0.1%
9012 11
 
0.1%
41997 11
 
0.1%
92606 10
 
0.1%
20193 10
 
0.1%
90618 10
 
0.1%
Other values (5524) 9884
98.8%
ValueCountFrequency (%)
1002 3
< 0.1%
1003 5
0.1%
1004 1
 
< 0.1%
1005 3
< 0.1%
1006 1
 
< 0.1%
1007 6
0.1%
1008 1
 
< 0.1%
1009 4
< 0.1%
1010 4
< 0.1%
1011 3
< 0.1%
ValueCountFrequency (%)
92702 9
0.1%
92701 11
0.1%
92653 5
 
0.1%
92652 8
0.1%
92648 1
 
< 0.1%
92646 6
 
0.1%
92644 10
0.1%
92643 1
 
< 0.1%
92641 17
0.2%
92630 11
0.1%

정류소명
Text

Distinct4174
Distinct (%)41.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T18:39:32.764879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length19
Mean length8.0491
Min length2

Characters and Unicode

Total characters80491
Distinct characters604
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

Unique2034 ?
Unique (%)20.3%

Sample

1st row홍익병원목동관
2nd row신내교회.신내데시앙아파트
3rd row안골마을
4th row홍제역.서대문세무서
5th row신우초등학교
ValueCountFrequency (%)
서울역버스환승센터 21
 
0.2%
연희104고지앞.구성산회관 21
 
0.2%
노오지jc(가상 20
 
0.2%
미아사거리역 19
 
0.2%
고속터미널 19
 
0.2%
공항입구jc 18
 
0.2%
논현역 18
 
0.2%
홍대입구역(가상 18
 
0.2%
인천공항t1-1층 17
 
0.2%
신도림역 17
 
0.2%
Other values (4165) 9813
98.1%
2023-12-11T18:39:33.226323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 2658
 
3.3%
동 2338
 
2.9%
역 2143
 
2.7%
교 1928
 
2.4%
아 1799
 
2.2%
파 1757
 
2.2%
구 1731
 
2.2%
트 1605
 
2.0%
대 1455
 
1.8%
지 1330
 
1.7%
Other values (594) 61747
76.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 73831
91.7%
Other Punctuation 2673
 
3.3%
Decimal Number 2372
 
2.9%
Uppercase Letter 848
 
1.1%
Close Punctuation 341
 
0.4%
Open Punctuation 340
 
0.4%
Dash Punctuation 59
 
0.1%
Lowercase Letter 26
 
< 0.1%
Space Separator 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 2338
 
3.2%
역 2143
 
2.9%
교 1928
 
2.6%
아 1799
 
2.4%
파 1757
 
2.4%
구 1731
 
2.3%
트 1605
 
2.2%
대 1455
 
2.0%
지 1330
 
1.8%
학 1267
 
1.7%
Other values (549) 56478
76.5%
Uppercase Letter
ValueCountFrequency (%)
C 169
19.9%
T 143
16.9%
K 71
8.4%
S 69
8.1%
I 59
 
7.0%
M 58
 
6.8%
G 53
 
6.2%
D 52
 
6.1%
J 43
 
5.1%
B 32
 
3.8%
Other values (12) 99
11.7%
Decimal Number
ValueCountFrequency (%)
1 732
30.9%
2 475
20.0%
3 321
13.5%
4 192
 
8.1%
5 139
 
5.9%
6 122
 
5.1%
7 118
 
5.0%
0 109
 
4.6%
9 109
 
4.6%
8 55
 
2.3%
Other Punctuation
ValueCountFrequency (%)
. 2658
99.4%
· 7
 
0.3%
& 5
 
0.2%
, 2
 
0.1%
? 1
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
e 20
76.9%
k 3
 
11.5%
s 2
 
7.7%
t 1
 
3.8%
Close Punctuation
ValueCountFrequency (%)
) 341
100.0%
Open Punctuation
ValueCountFrequency (%)
( 340
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 59
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 73831
91.7%
Common 5786
 
7.2%
Latin 874
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 2338
 
3.2%
역 2143
 
2.9%
교 1928
 
2.6%
아 1799
 
2.4%
파 1757
 
2.4%
구 1731
 
2.3%
트 1605
 
2.2%
대 1455
 
2.0%
지 1330
 
1.8%
학 1267
 
1.7%
Other values (549) 56478
76.5%
Latin
ValueCountFrequency (%)
C 169
19.3%
T 143
16.4%
K 71
8.1%
S 69
7.9%
I 59
 
6.8%
M 58
 
6.6%
G 53
 
6.1%
D 52
 
5.9%
J 43
 
4.9%
B 32
 
3.7%
Other values (16) 125
14.3%
Common
ValueCountFrequency (%)
. 2658
45.9%
1 732
 
12.7%
2 475
 
8.2%
) 341
 
5.9%
( 340
 
5.9%
3 321
 
5.5%
4 192
 
3.3%
5 139
 
2.4%
6 122
 
2.1%
7 118
 
2.0%
Other values (9) 348
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 73831
91.7%
ASCII 6653
 
8.3%
None 7
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 2658
40.0%
1 732
 
11.0%
2 475
 
7.1%
) 341
 
5.1%
( 340
 
5.1%
3 321
 
4.8%
4 192
 
2.9%
C 169
 
2.5%
T 143
 
2.1%
5 139
 
2.1%
Other values (34) 1143
17.2%
Hangul
ValueCountFrequency (%)
동 2338
 
3.2%
역 2143
 
2.9%
교 1928
 
2.6%
아 1799
 
2.4%
파 1757
 
2.4%
구 1731
 
2.3%
트 1605
 
2.2%
대 1455
 
2.0%
지 1330
 
1.8%
학 1267
 
1.7%
Other values (549) 56478
76.5%
None
ValueCountFrequency (%)
· 7
100.0%

X좌표
Real number (ℝ)

Distinct5535
Distinct (%)55.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.97625
Minimum126.42987
Maximum127.18014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T18:39:33.386805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.42987
5-th percentile126.83572
Q1126.91498
median126.98248
Q3127.04852
95-th percentile127.12163
Maximum127.18014
Range0.75026595
Interquartile range (IQR)0.13354301

Descriptive statistics

Standard deviation0.10305471
Coefficient of variation (CV)0.00081160619
Kurtosis5.2686093
Mean126.97625
Median Absolute Deviation (MAD)0.066908827
Skewness-1.3836522
Sum1269762.5
Variance0.010620274
MonotonicityNot monotonic
2023-12-11T18:39:33.564871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.4513104918 17
 
0.2%
126.4501560215 14
 
0.1%
126.9218643721 11
 
0.1%
126.4344306036 11
 
0.1%
126.5421061082 11
 
0.1%
127.0302131064 11
 
0.1%
126.7535756822 11
 
0.1%
127.0120461121 10
 
0.1%
126.541634 10
 
0.1%
126.9958263651 10
 
0.1%
Other values (5525) 9884
98.8%
ValueCountFrequency (%)
126.4298719877 5
 
0.1%
126.4340182489 9
0.1%
126.4344306036 11
0.1%
126.4501560215 14
0.1%
126.4505374684 1
 
< 0.1%
126.4513104918 17
0.2%
126.4529707406 2
 
< 0.1%
126.4635022107 1
 
< 0.1%
126.4758382523 9
0.1%
126.4762125849 6
 
0.1%
ValueCountFrequency (%)
127.18013794 1
 
< 0.1%
127.1799002887 2
< 0.1%
127.1797196537 2
< 0.1%
127.1796504588 1
 
< 0.1%
127.1784008104 2
< 0.1%
127.177967 2
< 0.1%
127.177374 2
< 0.1%
127.1772712588 2
< 0.1%
127.1772337078 2
< 0.1%
127.1767618586 3
< 0.1%

Y좌표
Real number (ℝ)

HIGH CORRELATION 

Distinct5535
Distinct (%)55.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.549546
Minimum37.434643
Maximum37.692548
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T18:39:33.745102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.434643
5-th percentile37.470686
Q137.505143
median37.550739
Q337.58523
95-th percentile37.644801
Maximum37.692548
Range0.25790485
Interquartile range (IQR)0.080086944

Descriptive statistics

Standard deviation0.05335577
Coefficient of variation (CV)0.0014209432
Kurtosis-0.7356107
Mean37.549546
Median Absolute Deviation (MAD)0.04171954
Skewness0.24078276
Sum375495.46
Variance0.0028468382
MonotonicityNot monotonic
2023-12-11T18:39:33.912956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.4487989863 17
 
0.2%
37.445731623 14
 
0.1%
37.5552260552 11
 
0.1%
37.4664571532 11
 
0.1%
37.5213607131 11
 
0.1%
37.6128943078 11
 
0.1%
37.5704913424 11
 
0.1%
37.5719529259 10
 
0.1%
37.52179043 10
 
0.1%
37.5034152049 10
 
0.1%
Other values (5525) 9884
98.8%
ValueCountFrequency (%)
37.434643292 2
 
< 0.1%
37.4355241561 1
 
< 0.1%
37.4379594347 1
 
< 0.1%
37.439196853 1
 
< 0.1%
37.4394878782 1
 
< 0.1%
37.4401670279 1
 
< 0.1%
37.4402297095 1
 
< 0.1%
37.4402569123 1
 
< 0.1%
37.4411393093 6
0.1%
37.441730354 9
0.1%
ValueCountFrequency (%)
37.6925481467 1
< 0.1%
37.690177 1
< 0.1%
37.6899483575 1
< 0.1%
37.6893500743 1
< 0.1%
37.6893310475 1
< 0.1%
37.6891946492 1
< 0.1%
37.6890118581 1
< 0.1%
37.688568 2
< 0.1%
37.684132735 1
< 0.1%
37.6840520197 1
< 0.1%

Interactions

2023-12-11T18:39:29.427788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:26.110745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:26.890275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:27.548358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:28.230816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:28.819159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:29.533867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:26.239009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:26.993471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:27.643090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:28.340335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:28.907959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:29.636925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:26.391761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:27.099972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:27.735184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:28.446346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:29.025316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:29.729310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:26.533615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:27.220044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:27.869742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:28.547407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:29.127639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:29.806713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:26.637546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:27.329240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:27.976868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:28.631403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:29.220943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:29.926743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:26.779181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:27.444448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:28.122721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:28.728366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:29.326825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T18:39:34.006764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ROUTE_ID순번NODE_IDARS_IDX좌표Y좌표
ROUTE_ID1.0000.3610.4650.3780.5070.528
순번0.3611.0000.1260.0930.1500.216
NODE_ID0.4650.1261.0000.9660.8530.669
ARS_ID0.3780.0930.9661.0000.8260.577
X좌표0.5070.1500.8530.8261.0000.593
Y좌표0.5280.2160.6690.5770.5931.000
2023-12-11T18:39:34.105039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ROUTE_ID순번NODE_IDARS_IDX좌표Y좌표
ROUTE_ID1.000-0.1930.1930.193-0.088-0.147
순번-0.1931.000-0.035-0.0360.062-0.010
NODE_ID0.193-0.0351.0000.996-0.090-0.681
ARS_ID0.193-0.0360.9961.000-0.093-0.681
X좌표-0.0880.062-0.090-0.0931.0000.206
Y좌표-0.147-0.010-0.681-0.6810.2061.000

Missing values

2023-12-11T18:39:30.062540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T18:39:30.204802image/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
213081150000076547911400000115101홍익병원목동관126.86403837.530728
8549100100586N26691060002087303신내교회.신내데시앙아파트127.10101537.610538
3467910010039294087612100025322329안골마을127.06389237.455227
17779100100105702B용두초교4911200040813029홍제역.서대문세무서126.94532337.587843
321221001002585522B호암812000007521177신우초등학교126.93195637.462411
10202100100034162691070001718266아리랑고개.아리랑시네미디어센터127.01385737.600502
1542410010044670256811100000612006수색역앞126.89410337.583047
374321001000744714412200067323439세곡푸르지오127.09647837.465062
2693510010027756206111700009818184독산동정훈단지126.9020737.465438
312571230000096300-12211900030620996한강대교(가상)126.95719437.51412
ROUTE_ID노선명순번NODE_IDARS_ID정류소명X좌표Y좌표
26083116900015구로081211690031117516오류동삼거리126.84042737.495676
5967104900005광진01251049000135595장신대앞127.10513237.54763
42241001001892014231030000314130한양대정문앞127.0433237.556773
2141510010009564210411400002615126목동성원아파트126.86317537.542295
1578100900008종로02151009002111888종각.공평유적전시관126.98324337.571594
301031001000956424111900005020143서울공업고등학교126.92242937.499714
118631001003708111401080001369224삼양동벽산라이브파크미양초등학교127.01261137.620308
10779107900010성북14-1191079002038883꿈의숲코오롱하늘채아파트127.04583937.618984
1879111200000387733111200014213225서대문구보훈회관126.92953737.571831
3412910010022634144112100011422190논현역6번출구127.01921337.510291