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 09:39:09.839941
Analysis finished2023-12-11 09:39:15.967972
Duration6.13 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

ROUTE_ID
Real number (ℝ)

Distinct657
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0377376 × 108
Minimum1.0000002 × 108
Maximum1.249 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T18:39:16.048779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.0000002 × 108
5-th percentile1.0010003 × 108
Q11.0010016 × 108
median1.0010036 × 108
Q31.0390001 × 108
95-th percentile1.209 × 108
Maximum1.249 × 108
Range24899986
Interquartile range (IQR)3799858

Descriptive statistics

Standard deviation6920695.4
Coefficient of variation (CV)0.066690224
Kurtosis1.4285043
Mean1.0377376 × 108
Median Absolute Deviation (MAD)245
Skewness1.6893833
Sum1.0377376 × 1012
Variance4.7896025 × 1013
MonotonicityNot monotonic
2023-12-11T18:39:16.191347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100100025 51
 
0.5%
100100499 49
 
0.5%
100100588 44
 
0.4%
117000002 44
 
0.4%
100100589 43
 
0.4%
111000016 42
 
0.4%
100100592 42
 
0.4%
100100603 40
 
0.4%
100100116 39
 
0.4%
100100410 38
 
0.4%
Other values (647) 9568
95.7%
ValueCountFrequency (%)
100000017 6
 
0.1%
100000018 4
 
< 0.1%
100100001 8
 
0.1%
100100006 17
0.2%
100100007 18
0.2%
100100008 17
0.2%
100100009 14
0.1%
100100010 26
0.3%
100100011 14
0.1%
100100012 26
0.3%
ValueCountFrequency (%)
124900003 16
0.2%
124900002 15
0.1%
124900001 16
0.2%
124000039 6
 
0.1%
124000038 21
0.2%
124000036 24
0.2%
124000013 16
0.2%
124000010 4
 
< 0.1%
124000008 10
0.1%
124000006 16
0.2%

노선명
Text

Distinct657
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T18:39:16.524978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length4
Mean length3.7269
Min length2

Characters and Unicode

Total characters37269
Distinct characters65
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)0.1%

Sample

1st row관악11
2nd row2112
3rd row5522A난곡
4th row양천03
5th row3420
ValueCountFrequency (%)
146 51
 
0.5%
7212 49
 
0.5%
n51 44
 
0.4%
n62 44
 
0.4%
n61 43
 
0.4%
n16 42
 
0.4%
n75 42
 
0.4%
542 40
 
0.4%
742 39
 
0.4%
502 38
 
0.4%
Other values (647) 9568
95.7%
2023-12-11T18:39:16.976634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 6726
18.0%
0 4739
12.7%
2 4198
11.3%
6 3832
10.3%
3 3082
8.3%
7 2858
7.7%
4 2852
7.7%
5 2815
7.6%
9 576
 
1.5%
8 546
 
1.5%
Other values (55) 5045
13.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 32224
86.5%
Other Letter 4178
 
11.2%
Uppercase Letter 786
 
2.1%
Dash Punctuation 81
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 381
 
9.1%
강 308
 
7.4%
동 307
 
7.3%
북 218
 
5.2%
성 201
 
4.8%
포 199
 
4.8%
초 190
 
4.5%
대 180
 
4.3%
작 163
 
3.9%
로 156
 
3.7%
Other values (37) 1875
44.9%
Decimal Number
ValueCountFrequency (%)
1 6726
20.9%
0 4739
14.7%
2 4198
13.0%
6 3832
11.9%
3 3082
9.6%
7 2858
8.9%
4 2852
8.9%
5 2815
8.7%
9 576
 
1.8%
8 546
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
N 467
59.4%
B 116
 
14.8%
A 107
 
13.6%
U 24
 
3.1%
T 24
 
3.1%
O 24
 
3.1%
R 24
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 81
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 32305
86.7%
Hangul 4178
 
11.2%
Latin 786
 
2.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 381
 
9.1%
강 308
 
7.4%
동 307
 
7.3%
북 218
 
5.2%
성 201
 
4.8%
포 199
 
4.8%
초 190
 
4.5%
대 180
 
4.3%
작 163
 
3.9%
로 156
 
3.7%
Other values (37) 1875
44.9%
Common
ValueCountFrequency (%)
1 6726
20.8%
0 4739
14.7%
2 4198
13.0%
6 3832
11.9%
3 3082
9.5%
7 2858
8.8%
4 2852
8.8%
5 2815
8.7%
9 576
 
1.8%
8 546
 
1.7%
Latin
ValueCountFrequency (%)
N 467
59.4%
B 116
 
14.8%
A 107
 
13.6%
U 24
 
3.1%
T 24
 
3.1%
O 24
 
3.1%
R 24
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33091
88.8%
Hangul 4178
 
11.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6726
20.3%
0 4739
14.3%
2 4198
12.7%
6 3832
11.6%
3 3082
9.3%
7 2858
8.6%
4 2852
8.6%
5 2815
8.5%
9 576
 
1.7%
8 546
 
1.6%
Other values (8) 867
 
2.6%
Hangul
ValueCountFrequency (%)
서 381
 
9.1%
강 308
 
7.4%
동 307
 
7.3%
북 218
 
5.2%
성 201
 
4.8%
포 199
 
4.8%
초 190
 
4.5%
대 180
 
4.3%
작 163
 
3.9%
로 156
 
3.7%
Other values (37) 1875
44.9%

순번
Real number (ℝ)

Distinct168
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.1611
Minimum1
Maximum183
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T18:39:17.119154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q116
median34
Q361
95-th percentile100
Maximum183
Range182
Interquartile range (IQR)45

Descriptive statistics

Standard deviation31.114035
Coefficient of variation (CV)0.75590873
Kurtosis0.43844206
Mean41.1611
Median Absolute Deviation (MAD)21
Skewness0.90958234
Sum411611
Variance968.08316
MonotonicityNot monotonic
2023-12-11T18:39:17.247326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 189
 
1.9%
13 184
 
1.8%
2 175
 
1.8%
4 175
 
1.8%
8 171
 
1.7%
5 170
 
1.7%
17 164
 
1.6%
7 164
 
1.6%
6 162
 
1.6%
14 162
 
1.6%
Other values (158) 8284
82.8%
ValueCountFrequency (%)
1 151
1.5%
2 175
1.8%
3 149
1.5%
4 175
1.8%
5 170
1.7%
6 162
1.6%
7 164
1.6%
8 171
1.7%
9 189
1.9%
10 157
1.6%
ValueCountFrequency (%)
183 1
< 0.1%
170 2
< 0.1%
169 2
< 0.1%
167 1
< 0.1%
166 1
< 0.1%
165 1
< 0.1%
164 2
< 0.1%
163 1
< 0.1%
162 2
< 0.1%
161 1
< 0.1%

NODE_ID
Real number (ℝ)

HIGH CORRELATION 

Distinct6232
Distinct (%)62.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2226153 × 108
Minimum1 × 108
Maximum2.7710425 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T18:39:17.381612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1 × 108
5-th percentile1.0100009 × 108
Q11.0800001 × 108
median1.15 × 108
Q31.210001 × 108
95-th percentile2.1300016 × 108
Maximum2.7710425 × 108
Range1.7710425 × 108
Interquartile range (IQR)13000094

Descriptive statistics

Standard deviation29721092
Coefficient of variation (CV)0.24309438
Kurtosis5.84947
Mean1.2226153 × 108
Median Absolute Deviation (MAD)6099986
Skewness2.6510719
Sum1.2226153 × 1012
Variance8.833433 × 1014
MonotonicityNot monotonic
2023-12-11T18:39:17.521898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
168000692 11
 
0.1%
101000043 10
 
0.1%
105000015 10
 
0.1%
107000063 9
 
0.1%
110000184 9
 
0.1%
121000014 9
 
0.1%
113000193 9
 
0.1%
110000266 8
 
0.1%
107000008 8
 
0.1%
107000057 8
 
0.1%
Other values (6222) 9909
99.1%
ValueCountFrequency (%)
100000002 3
< 0.1%
100000003 3
< 0.1%
100000005 4
< 0.1%
100000007 1
 
< 0.1%
100000008 2
< 0.1%
100000009 1
 
< 0.1%
100000012 1
 
< 0.1%
100000013 1
 
< 0.1%
100000015 1
 
< 0.1%
100000017 2
< 0.1%
ValueCountFrequency (%)
277104251 1
 
< 0.1%
277103813 1
 
< 0.1%
274115267 1
 
< 0.1%
274109998 1
 
< 0.1%
274109991 3
< 0.1%
235000834 1
 
< 0.1%
235000815 1
 
< 0.1%
235000358 1
 
< 0.1%
235000353 1
 
< 0.1%
235000325 1
 
< 0.1%

ARS-ID
Real number (ℝ)

HIGH CORRELATION 

Distinct6224
Distinct (%)62.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17376.893
Minimum1002
Maximum92702
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T18:39:17.656523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1002
5-th percentile2190
Q19010
median15718
Q322142.5
95-th percentile40061
Maximum92702
Range91700
Interquartile range (IQR)13132.5

Descriptive statistics

Standard deviation12911.648
Coefficient of variation (CV)0.74303547
Kurtosis9.6841946
Mean17376.893
Median Absolute Deviation (MAD)6560
Skewness2.4170135
Sum1.7376893 × 108
Variance1.6671065 × 108
MonotonicityNot monotonic
2023-12-11T18:39:18.046318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90617 11
 
0.1%
6015 10
 
0.1%
2142 10
 
0.1%
14287 9
 
0.1%
8153 9
 
0.1%
11284 9
 
0.1%
22014 9
 
0.1%
7418 8
 
0.1%
8008 8
 
0.1%
13032 8
 
0.1%
Other values (6214) 9909
99.1%
ValueCountFrequency (%)
1002 3
 
< 0.1%
1003 3
 
< 0.1%
1005 4
< 0.1%
1006 1
 
< 0.1%
1007 3
 
< 0.1%
1008 7
0.1%
1009 5
0.1%
1010 8
0.1%
1011 5
0.1%
1012 7
0.1%
ValueCountFrequency (%)
92702 5
0.1%
92701 5
0.1%
92652 6
0.1%
92646 4
< 0.1%
92644 6
0.1%
92641 6
0.1%
92638 1
 
< 0.1%
92630 4
< 0.1%
92626 2
 
< 0.1%
92619 1
 
< 0.1%

정류소명
Text

Distinct4767
Distinct (%)47.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T18:39:18.300909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length20
Mean length8.0363
Min length2

Characters and Unicode

Total characters80363
Distinct characters627
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2574 ?
Unique (%)25.7%

Sample

1st row성원홈아파트
2nd row성북동주민센터.동구마케팅고
3rd row난곡종점
4th row서울남부지법
5th row매봉삼성아파트SK리더스뷰
ValueCountFrequency (%)
모래내시장.가좌역 18
 
0.2%
신도림역 17
 
0.2%
래미안아파트.파이낸셜뉴스 15
 
0.1%
현대아파트 15
 
0.1%
사천교 15
 
0.1%
돈암사거리.성신여대입구 15
 
0.1%
광화문 15
 
0.1%
상암dmc홍보관.ytn 14
 
0.1%
북인천ic 14
 
0.1%
미아리고개.미아리예술극장 13
 
0.1%
Other values (4757) 9849
98.5%
2023-12-11T18:39:18.706363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 2744
 
3.4%
동 2342
 
2.9%
역 1881
 
2.3%
교 1879
 
2.3%
아 1781
 
2.2%
파 1751
 
2.2%
트 1651
 
2.1%
구 1634
 
2.0%
지 1442
 
1.8%
대 1371
 
1.7%
Other values (617) 61887
77.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 73706
91.7%
Other Punctuation 2776
 
3.5%
Decimal Number 2305
 
2.9%
Uppercase Letter 880
 
1.1%
Open Punctuation 308
 
0.4%
Close Punctuation 307
 
0.4%
Dash Punctuation 29
 
< 0.1%
Space Separator 27
 
< 0.1%
Lowercase Letter 25
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 2342
 
3.2%
역 1881
 
2.6%
교 1879
 
2.5%
아 1781
 
2.4%
파 1751
 
2.4%
트 1651
 
2.2%
구 1634
 
2.2%
지 1442
 
2.0%
대 1371
 
1.9%
학 1272
 
1.7%
Other values (571) 56702
76.9%
Uppercase Letter
ValueCountFrequency (%)
T 142
16.1%
C 141
16.0%
K 87
9.9%
S 62
 
7.0%
G 61
 
6.9%
I 58
 
6.6%
M 51
 
5.8%
D 48
 
5.5%
B 32
 
3.6%
L 30
 
3.4%
Other values (13) 168
19.1%
Decimal Number
ValueCountFrequency (%)
1 675
29.3%
2 446
19.3%
3 332
14.4%
4 204
 
8.9%
5 141
 
6.1%
6 116
 
5.0%
0 115
 
5.0%
7 112
 
4.9%
9 102
 
4.4%
8 62
 
2.7%
Other Punctuation
ValueCountFrequency (%)
. 2744
98.8%
· 19
 
0.7%
& 10
 
0.4%
, 2
 
0.1%
․ 1
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
e 21
84.0%
k 2
 
8.0%
s 1
 
4.0%
t 1
 
4.0%
Open Punctuation
ValueCountFrequency (%)
( 308
100.0%
Close Punctuation
ValueCountFrequency (%)
) 307
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 29
100.0%
Space Separator
ValueCountFrequency (%)
27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 73706
91.7%
Common 5752
 
7.2%
Latin 905
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 2342
 
3.2%
역 1881
 
2.6%
교 1879
 
2.5%
아 1781
 
2.4%
파 1751
 
2.4%
트 1651
 
2.2%
구 1634
 
2.2%
지 1442
 
2.0%
대 1371
 
1.9%
학 1272
 
1.7%
Other values (571) 56702
76.9%
Latin
ValueCountFrequency (%)
T 142
15.7%
C 141
15.6%
K 87
9.6%
S 62
 
6.9%
G 61
 
6.7%
I 58
 
6.4%
M 51
 
5.6%
D 48
 
5.3%
B 32
 
3.5%
L 30
 
3.3%
Other values (17) 193
21.3%
Common
ValueCountFrequency (%)
. 2744
47.7%
1 675
 
11.7%
2 446
 
7.8%
3 332
 
5.8%
( 308
 
5.4%
) 307
 
5.3%
4 204
 
3.5%
5 141
 
2.5%
6 116
 
2.0%
0 115
 
2.0%
Other values (9) 364
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 73706
91.7%
ASCII 6637
 
8.3%
None 19
 
< 0.1%
Punctuation 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 2744
41.3%
1 675
 
10.2%
2 446
 
6.7%
3 332
 
5.0%
( 308
 
4.6%
) 307
 
4.6%
4 204
 
3.1%
T 142
 
2.1%
5 141
 
2.1%
C 141
 
2.1%
Other values (34) 1197
18.0%
Hangul
ValueCountFrequency (%)
동 2342
 
3.2%
역 1881
 
2.6%
교 1879
 
2.5%
아 1781
 
2.4%
파 1751
 
2.4%
트 1651
 
2.2%
구 1634
 
2.2%
지 1442
 
2.0%
대 1371
 
1.9%
학 1272
 
1.7%
Other values (571) 56702
76.9%
None
ValueCountFrequency (%)
· 19
100.0%
Punctuation
ValueCountFrequency (%)
․ 1
100.0%

X좌표
Real number (ℝ)

Distinct6226
Distinct (%)62.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.97534
Minimum126.43402
Maximum127.18608
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T18:39:18.859686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.43402
5-th percentile126.82732
Q1126.90768
median126.97952
Q3127.05142
95-th percentile127.12608
Maximum127.18608
Range0.75205708
Interquartile range (IQR)0.1437394

Descriptive statistics

Standard deviation0.10115393
Coefficient of variation (CV)0.00079664238
Kurtosis2.667941
Mean126.97534
Median Absolute Deviation (MAD)0.071893315
Skewness-0.85852929
Sum1269753.4
Variance0.010232118
MonotonicityNot monotonic
2023-12-11T18:39:19.035450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.6064444917 11
 
0.1%
126.9819217706 10
 
0.1%
127.0454185705 10
 
0.1%
127.0347212515 9
 
0.1%
127.0670040201 9
 
0.1%
126.8914688176 9
 
0.1%
127.0237329243 9
 
0.1%
126.9780908549 8
 
0.1%
126.8936555576 8
 
0.1%
127.0666020029 8
 
0.1%
Other values (6216) 9909
99.1%
ValueCountFrequency (%)
126.4340182489 5
0.1%
126.4344306036 5
0.1%
126.4495156076 1
 
< 0.1%
126.4501560215 4
< 0.1%
126.4515057687 6
0.1%
126.458668546 3
 
< 0.1%
126.4598157152 1
 
< 0.1%
126.476113766 4
< 0.1%
126.4893195551 8
0.1%
126.4899916011 5
0.1%
ValueCountFrequency (%)
127.1860753273 1
< 0.1%
127.1830750982 1
< 0.1%
127.1815600278 1
< 0.1%
127.1802657501 2
< 0.1%
127.1800898791 1
< 0.1%
127.1796658097 1
< 0.1%
127.1795399999 1
< 0.1%
127.1792290002 1
< 0.1%
127.1791803843 2
< 0.1%
127.1791506667 1
< 0.1%

Y좌표
Real number (ℝ)

HIGH CORRELATION 

Distinct6226
Distinct (%)62.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.54667
Minimum37.302375
Maximum37.832682
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T18:39:19.214463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.302375
5-th percentile37.458705
Q137.499248
median37.548256
Q337.587708
95-th percentile37.651577
Maximum37.832682
Range0.5303073
Interquartile range (IQR)0.088459871

Descriptive statistics

Standard deviation0.063865542
Coefficient of variation (CV)0.0017009642
Kurtosis0.66553341
Mean37.54667
Median Absolute Deviation (MAD)0.04513514
Skewness0.06806263
Sum375466.7
Variance0.0040788074
MonotonicityNot monotonic
2023-12-11T18:39:19.387239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.5528517607 11
 
0.1%
37.5630081328 10
 
0.1%
37.5804121594 10
 
0.1%
37.5985785099 9
 
0.1%
37.6151943546 9
 
0.1%
37.577653985 9
 
0.1%
37.5063685121 9
 
0.1%
37.5702957096 8
 
0.1%
37.5116959807 8
 
0.1%
37.6394438999 8
 
0.1%
Other values (6216) 9909
99.1%
ValueCountFrequency (%)
37.3023750115 1
< 0.1%
37.3044514431 1
< 0.1%
37.3190251725 1
< 0.1%
37.3209725626 1
< 0.1%
37.3210324054 1
< 0.1%
37.3219424955 1
< 0.1%
37.3219562925 1
< 0.1%
37.3227833855 1
< 0.1%
37.323895112 1
< 0.1%
37.3241432189 1
< 0.1%
ValueCountFrequency (%)
37.8326823101 1
< 0.1%
37.8318099297 1
< 0.1%
37.8301807745 1
< 0.1%
37.8300739519 1
< 0.1%
37.8295990231 1
< 0.1%
37.8292626558 1
< 0.1%
37.8290025114 1
< 0.1%
37.8203056708 1
< 0.1%
37.8077359277 1
< 0.1%
37.7947449136 1
< 0.1%

Interactions

2023-12-11T18:39:15.187754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:11.774628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:12.511759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:13.180377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:13.909244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:14.543538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:15.281466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:11.901757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:12.609851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:13.305397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:14.031274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:14.649284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:15.373467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:12.017909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:12.726338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:13.424217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:14.148420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:14.756891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:15.473337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:12.157750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:12.858747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:13.536047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:14.260001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:14.877630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:15.561187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:12.280978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:12.954874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:13.665201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:14.354062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:14.964113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:15.674416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:12.401161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:13.073609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:13.803794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:14.458168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:39:15.085567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T18:39:19.525377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ROUTE_ID순번NODE_IDARS-IDX좌표Y좌표
ROUTE_ID1.0000.3690.3530.5220.5130.535
순번0.3691.0000.2370.2920.2140.350
NODE_ID0.3530.2371.0000.9010.7050.718
ARS-ID0.5220.2920.9011.0000.8730.835
X좌표0.5130.2140.7050.8731.0000.530
Y좌표0.5350.3500.7180.8350.5301.000
2023-12-11T18:39:19.660637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ROUTE_ID순번NODE_IDARS-IDX좌표Y좌표
ROUTE_ID1.000-0.2350.1450.142-0.135-0.119
순번-0.2351.0000.0050.0010.040-0.020
NODE_ID0.1450.0051.0000.988-0.137-0.570
ARS-ID0.1420.0010.9881.000-0.143-0.574
X좌표-0.1350.040-0.137-0.1431.0000.143
Y좌표-0.119-0.020-0.570-0.5740.1431.000

Missing values

2023-12-11T18:39:15.794189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T18:39:15.915915image/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
33719114900004관악111212000044921366성원홈아파트126.93431437.490208
66151001001912112491070001458235성북동주민센터.동구마케팅고127.00394937.590682
161521001002575522A난곡2812000044421809난곡종점126.91924737.464119
38510114900003양천032511490005915547서울남부지법126.86289537.521672
1164410010057934203012200021223316매봉삼성아파트SK리더스뷰127.04902137.48806
35656119900018동작07411990005020546남성초등학교.사당청소년문화의집126.97491437.486114
211201001002946514911400011515218신한은행신월동지점126.84177437.517908
71801001002022227271060001487243홈플러스면목동점127.0817237.581147
1815610010028757133011700000118001문성초등학교126.89867237.475363
26198100100112721611200009713180DMC.파크뷰자이126.91620337.573711
ROUTE_ID노선명순번NODE_IDARS-ID정류소명X좌표Y좌표
9135100100049273441000003791008서울역사박물관.경희궁앞126.97044237.569336
31474100100589N6113612200008623189봉은사.삼성1파출소앞127.05959337.515023
248931001004497013A1711300015914250망원시장.망원동월드컵시장입구126.90864837.557594
34990109900010노원15811000020811308인덕대학교127.05540437.628446
2012312200000961051011500054716848김포공항국제선126.80218637.565863
32673124900003강동025312400015625267명일1동주민센터127.14626837.550301
2586610010010770410023500035368208샘골126.97083537.704501
38203107900017성북10-2141080003399739미아사거리역2번출구127.03045837.613008
49051001000291506311700092418239시흥대교126.89721737.45336
162451001002595523812000010321205난곡우체국사거리126.91560237.477048