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 09:40:27.500633
Analysis finished2023-12-11 09:40:33.038903
Duration5.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

ROUTE_ID
Real number (ℝ)

Distinct764
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.048803 × 108
Minimum1 × 108
Maximum1.249 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T18:40:33.122812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1 × 108
5-th percentile1.0010003 × 108
Q11.0010017 × 108
median1.0010041 × 108
Q31.099 × 108
95-th percentile1.2300001 × 108
Maximum1.249 × 108
Range24899999
Interquartile range (IQR)9799832

Descriptive statistics

Standard deviation7952676.4
Coefficient of variation (CV)0.07582622
Kurtosis0.31131856
Mean1.048803 × 108
Median Absolute Deviation (MAD)304
Skewness1.3658327
Sum1.048803 × 1012
Variance6.3245062 × 1013
MonotonicityNot monotonic
2023-12-11T18:40:33.322579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100100589 47
 
0.5%
111000017 42
 
0.4%
124000026 41
 
0.4%
100100035 40
 
0.4%
115000010 40
 
0.4%
100100593 39
 
0.4%
111000016 37
 
0.4%
110000004 36
 
0.4%
100100013 36
 
0.4%
100100032 36
 
0.4%
Other values (754) 9606
96.1%
ValueCountFrequency (%)
100000004 3
 
< 0.1%
100000008 8
0.1%
100000009 6
 
0.1%
100000012 1
 
< 0.1%
100000014 1
 
< 0.1%
100000015 18
0.2%
100000016 13
0.1%
100000020 2
 
< 0.1%
100100001 3
 
< 0.1%
100100002 9
0.1%
ValueCountFrequency (%)
124900003 21
0.2%
124900002 15
0.1%
124900001 16
0.2%
124000039 10
 
0.1%
124000038 25
0.2%
124000036 25
0.2%
124000035 28
0.3%
124000034 33
0.3%
124000033 29
0.3%
124000032 31
0.3%

노선명
Text

Distinct759
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T18:40:33.714425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length4
Mean length3.7689
Min length2

Characters and Unicode

Total characters37689
Distinct characters80
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

Unique10 ?
Unique (%)0.1%

Sample

1st row8774
2nd row9711
3rd row9404
4th row606
5th row152
ValueCountFrequency (%)
163 76
 
0.8%
703 57
 
0.6%
n61 47
 
0.5%
n72 42
 
0.4%
n61b 41
 
0.4%
n64 40
 
0.4%
n13 39
 
0.4%
n75 37
 
0.4%
108 36
 
0.4%
8146 36
 
0.4%
Other values (749) 9549
95.5%
2023-12-11T18:40:34.250784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 6752
17.9%
0 4793
12.7%
6 3930
10.4%
2 3901
10.4%
3 3095
8.2%
7 2931
7.8%
5 2746
7.3%
4 2654
 
7.0%
8 886
 
2.4%
N 811
 
2.2%
Other values (70) 5190
13.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 32294
85.7%
Other Letter 3871
 
10.3%
Uppercase Letter 1323
 
3.5%
Dash Punctuation 194
 
0.5%
Lowercase Letter 3
 
< 0.1%
Close Punctuation 2
 
< 0.1%
Open Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 344
 
8.9%
동 318
 
8.2%
강 304
 
7.9%
성 185
 
4.8%
북 173
 
4.5%
작 166
 
4.3%
대 162
 
4.2%
포 155
 
4.0%
초 153
 
4.0%
로 136
 
3.5%
Other values (49) 1775
45.9%
Decimal Number
ValueCountFrequency (%)
1 6752
20.9%
0 4793
14.8%
6 3930
12.2%
2 3901
12.1%
3 3095
9.6%
7 2931
9.1%
5 2746
8.5%
4 2654
 
8.2%
8 886
 
2.7%
9 606
 
1.9%
Uppercase Letter
ValueCountFrequency (%)
N 811
61.3%
B 302
 
22.8%
A 206
 
15.6%
S 3
 
0.2%
T 1
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
e 1
33.3%
s 1
33.3%
t 1
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 194
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 32492
86.2%
Hangul 3871
 
10.3%
Latin 1326
 
3.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 344
 
8.9%
동 318
 
8.2%
강 304
 
7.9%
성 185
 
4.8%
북 173
 
4.5%
작 166
 
4.3%
대 162
 
4.2%
포 155
 
4.0%
초 153
 
4.0%
로 136
 
3.5%
Other values (49) 1775
45.9%
Common
ValueCountFrequency (%)
1 6752
20.8%
0 4793
14.8%
6 3930
12.1%
2 3901
12.0%
3 3095
9.5%
7 2931
9.0%
5 2746
8.5%
4 2654
 
8.2%
8 886
 
2.7%
9 606
 
1.9%
Other values (3) 198
 
0.6%
Latin
ValueCountFrequency (%)
N 811
61.2%
B 302
 
22.8%
A 206
 
15.5%
S 3
 
0.2%
T 1
 
0.1%
e 1
 
0.1%
s 1
 
0.1%
t 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33818
89.7%
Hangul 3871
 
10.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6752
20.0%
0 4793
14.2%
6 3930
11.6%
2 3901
11.5%
3 3095
9.2%
7 2931
8.7%
5 2746
8.1%
4 2654
 
7.8%
8 886
 
2.6%
N 811
 
2.4%
Other values (11) 1319
 
3.9%
Hangul
ValueCountFrequency (%)
서 344
 
8.9%
동 318
 
8.2%
강 304
 
7.9%
성 185
 
4.8%
북 173
 
4.5%
작 166
 
4.3%
대 162
 
4.2%
포 155
 
4.0%
초 153
 
4.0%
로 136
 
3.5%
Other values (49) 1775
45.9%

순번
Real number (ℝ)

Distinct175
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.8887
Minimum1
Maximum185
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T18:40:34.411027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q116
median35
Q361
95-th percentile105
Maximum185
Range184
Interquartile range (IQR)45

Descriptive statistics

Standard deviation32.445502
Coefficient of variation (CV)0.77456454
Kurtosis0.72252616
Mean41.8887
Median Absolute Deviation (MAD)22
Skewness0.99894671
Sum418887
Variance1052.7106
MonotonicityNot monotonic
2023-12-11T18:40:34.579221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 179
 
1.8%
8 178
 
1.8%
9 176
 
1.8%
5 173
 
1.7%
3 171
 
1.7%
2 169
 
1.7%
1 165
 
1.7%
6 164
 
1.6%
18 163
 
1.6%
7 158
 
1.6%
Other values (165) 8304
83.0%
ValueCountFrequency (%)
1 165
1.7%
2 169
1.7%
3 171
1.7%
4 179
1.8%
5 173
1.7%
6 164
1.6%
7 158
1.6%
8 178
1.8%
9 176
1.8%
10 142
1.4%
ValueCountFrequency (%)
185 1
 
< 0.1%
183 1
 
< 0.1%
179 1
 
< 0.1%
177 1
 
< 0.1%
176 2
< 0.1%
175 1
 
< 0.1%
174 1
 
< 0.1%
173 1
 
< 0.1%
170 3
< 0.1%
169 2
< 0.1%

NODE_ID
Real number (ℝ)

HIGH CORRELATION 

Distinct6017
Distinct (%)60.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2348369 × 108
Minimum1 × 108
Maximum2.771039 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T18:40:34.745988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1 × 108
5-th percentile1.0100008 × 108
Q11.080001 × 108
median1.1500011 × 108
Q31.2100031 × 108
95-th percentile2.1800001 × 108
Maximum2.771039 × 108
Range1.771039 × 108
Interquartile range (IQR)13000214

Descriptive statistics

Standard deviation31102639
Coefficient of variation (CV)0.25187649
Kurtosis4.8707004
Mean1.2348369 × 108
Median Absolute Deviation (MAD)6899927
Skewness2.4779718
Sum1.2348369 × 1012
Variance9.6737416 × 1014
MonotonicityNot monotonic
2023-12-11T18:40:34.927281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
113000422 13
 
0.1%
168000693 12
 
0.1%
168011112 12
 
0.1%
129000083 11
 
0.1%
168011111 11
 
0.1%
168000692 11
 
0.1%
108000007 10
 
0.1%
107000003 10
 
0.1%
161010557 9
 
0.1%
161010558 9
 
0.1%
Other values (6007) 9892
98.9%
ValueCountFrequency (%)
100000001 1
 
< 0.1%
100000002 3
< 0.1%
100000003 5
0.1%
100000004 2
 
< 0.1%
100000005 4
< 0.1%
100000006 1
 
< 0.1%
100000007 2
 
< 0.1%
100000008 2
 
< 0.1%
100000009 1
 
< 0.1%
100000010 1
 
< 0.1%
ValueCountFrequency (%)
277103898 1
< 0.1%
277103814 1
< 0.1%
277103813 1
< 0.1%
274109998 1
< 0.1%
274109991 2
< 0.1%
235001215 1
< 0.1%
235001210 1
< 0.1%
235001208 1
< 0.1%
235001169 1
< 0.1%
235000907 1
< 0.1%

ARS_ID
Real number (ℝ)

HIGH CORRELATION 

Distinct6010
Distinct (%)60.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18038.778
Minimum1001
Maximum92702
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T18:40:35.124011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile2183
Q19171
median16149
Q322308.25
95-th percentile42707.15
Maximum92702
Range91701
Interquartile range (IQR)13137.25

Descriptive statistics

Standard deviation13889.06
Coefficient of variation (CV)0.76995571
Kurtosis8.9494302
Mean18038.778
Median Absolute Deviation (MAD)6431.5
Skewness2.42851
Sum1.8038778 × 108
Variance1.9290599 × 108
MonotonicityNot monotonic
2023-12-11T18:40:35.292819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14015 13
 
0.1%
90618 12
 
0.1%
42748 12
 
0.1%
92641 11
 
0.1%
90617 11
 
0.1%
42994 11
 
0.1%
9007 10
 
0.1%
8003 10
 
0.1%
16989 9
 
0.1%
92570 9
 
0.1%
Other values (6000) 9892
98.9%
ValueCountFrequency (%)
1001 1
 
< 0.1%
1002 3
< 0.1%
1003 5
0.1%
1004 2
 
< 0.1%
1005 4
< 0.1%
1006 2
 
< 0.1%
1007 4
< 0.1%
1008 3
< 0.1%
1009 3
< 0.1%
1010 5
0.1%
ValueCountFrequency (%)
92702 6
0.1%
92701 2
 
< 0.1%
92697 1
 
< 0.1%
92653 3
 
< 0.1%
92652 7
0.1%
92650 1
 
< 0.1%
92646 9
0.1%
92644 9
0.1%
92641 11
0.1%
92630 5
0.1%

정류소명
Text

Distinct4620
Distinct (%)46.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T18:40:35.577641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length19
Mean length8.0706
Min length2

Characters and Unicode

Total characters80706
Distinct characters626
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

Unique2471 ?
Unique (%)24.7%

Sample

1st row신사동고개사거리
2nd row상암DMC홍보관.YTN
3rd row뱅뱅사거리
4th row복사골문화센터.반달마을
5th row동작구청.노량진초등학교앞
ValueCountFrequency (%)
북인천ic 23
 
0.2%
신공항tg(가상 23
 
0.2%
홍대입구역 22
 
0.2%
서울역버스환승센터 19
 
0.2%
미아역.신일중고 19
 
0.2%
gs주유소(가상 18
 
0.2%
현대아파트 17
 
0.2%
동묘앞 17
 
0.2%
잠실종합운동장 17
 
0.2%
노오지jc(가상 17
 
0.2%
Other values (4610) 9808
98.1%
2023-12-11T18:40:36.292269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 2813
 
3.5%
동 2293
 
2.8%
역 2041
 
2.5%
교 1892
 
2.3%
아 1813
 
2.2%
파 1750
 
2.2%
트 1682
 
2.1%
구 1634
 
2.0%
지 1439
 
1.8%
대 1345
 
1.7%
Other values (616) 62004
76.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 73656
91.3%
Other Punctuation 2841
 
3.5%
Decimal Number 2330
 
2.9%
Uppercase Letter 1035
 
1.3%
Close Punctuation 374
 
0.5%
Open Punctuation 374
 
0.5%
Dash Punctuation 39
 
< 0.1%
Lowercase Letter 29
 
< 0.1%
Space Separator 28
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 2293
 
3.1%
역 2041
 
2.8%
교 1892
 
2.6%
아 1813
 
2.5%
파 1750
 
2.4%
트 1682
 
2.3%
구 1634
 
2.2%
지 1439
 
2.0%
대 1345
 
1.8%
학 1255
 
1.7%
Other values (573) 56512
76.7%
Uppercase Letter
ValueCountFrequency (%)
C 186
18.0%
T 155
15.0%
S 93
9.0%
K 92
8.9%
G 74
 
7.1%
I 66
 
6.4%
M 64
 
6.2%
D 53
 
5.1%
B 44
 
4.3%
J 43
 
4.2%
Other values (11) 165
15.9%
Decimal Number
ValueCountFrequency (%)
1 679
29.1%
2 485
20.8%
3 309
13.3%
4 193
 
8.3%
5 149
 
6.4%
7 128
 
5.5%
6 113
 
4.8%
0 113
 
4.8%
9 93
 
4.0%
8 68
 
2.9%
Other Punctuation
ValueCountFrequency (%)
. 2813
99.0%
· 15
 
0.5%
& 9
 
0.3%
, 4
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
e 25
86.2%
k 2
 
6.9%
s 1
 
3.4%
t 1
 
3.4%
Close Punctuation
ValueCountFrequency (%)
) 374
100.0%
Open Punctuation
ValueCountFrequency (%)
( 374
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 39
100.0%
Space Separator
ValueCountFrequency (%)
28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 73656
91.3%
Common 5986
 
7.4%
Latin 1064
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 2293
 
3.1%
역 2041
 
2.8%
교 1892
 
2.6%
아 1813
 
2.5%
파 1750
 
2.4%
트 1682
 
2.3%
구 1634
 
2.2%
지 1439
 
2.0%
대 1345
 
1.8%
학 1255
 
1.7%
Other values (573) 56512
76.7%
Latin
ValueCountFrequency (%)
C 186
17.5%
T 155
14.6%
S 93
8.7%
K 92
8.6%
G 74
 
7.0%
I 66
 
6.2%
M 64
 
6.0%
D 53
 
5.0%
B 44
 
4.1%
J 43
 
4.0%
Other values (15) 194
18.2%
Common
ValueCountFrequency (%)
. 2813
47.0%
1 679
 
11.3%
2 485
 
8.1%
) 374
 
6.2%
( 374
 
6.2%
3 309
 
5.2%
4 193
 
3.2%
5 149
 
2.5%
7 128
 
2.1%
6 113
 
1.9%
Other values (8) 369
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 73656
91.3%
ASCII 7035
 
8.7%
None 15
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 2813
40.0%
1 679
 
9.7%
2 485
 
6.9%
) 374
 
5.3%
( 374
 
5.3%
3 309
 
4.4%
4 193
 
2.7%
C 186
 
2.6%
T 155
 
2.2%
5 149
 
2.1%
Other values (32) 1318
18.7%
Hangul
ValueCountFrequency (%)
동 2293
 
3.1%
역 2041
 
2.8%
교 1892
 
2.6%
아 1813
 
2.5%
파 1750
 
2.4%
트 1682
 
2.3%
구 1634
 
2.2%
지 1439
 
2.0%
대 1345
 
1.8%
학 1255
 
1.7%
Other values (573) 56512
76.7%
None
ValueCountFrequency (%)
· 15
100.0%

X좌표
Real number (ℝ)

Distinct6016
Distinct (%)60.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.97406
Minimum126.42987
Maximum127.18352
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T18:40:36.450456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.42987
5-th percentile126.82181
Q1126.90913
median126.97875
Q3127.05107
95-th percentile127.12543
Maximum127.18352
Range0.75364833
Interquartile range (IQR)0.14194038

Descriptive statistics

Standard deviation0.10484232
Coefficient of variation (CV)0.00082569875
Kurtosis3.263553
Mean126.97406
Median Absolute Deviation (MAD)0.071125324
Skewness-1.0394036
Sum1269740.6
Variance0.010991912
MonotonicityNot monotonic
2023-12-11T18:40:36.632906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.9231959983 13
 
0.1%
126.6649121544 12
 
0.1%
126.606550195 12
 
0.1%
126.6647190353 11
 
0.1%
126.6064444917 11
 
0.1%
126.4513104918 11
 
0.1%
127.0266493048 10
 
0.1%
127.0241681657 10
 
0.1%
126.4758382523 9
 
0.1%
126.4762125849 9
 
0.1%
Other values (6006) 9892
98.9%
ValueCountFrequency (%)
126.4298719877 3
 
< 0.1%
126.4340182489 6
0.1%
126.4344306036 2
 
< 0.1%
126.4493489201 1
 
< 0.1%
126.4501560215 8
0.1%
126.4513104918 11
0.1%
126.4529707406 3
 
< 0.1%
126.4587311191 2
 
< 0.1%
126.4758382523 9
0.1%
126.4762125849 9
0.1%
ValueCountFrequency (%)
127.183520315 1
 
< 0.1%
127.18013794 2
< 0.1%
127.1799002887 2
< 0.1%
127.1793979417 1
 
< 0.1%
127.179383 1
 
< 0.1%
127.17919 1
 
< 0.1%
127.1788491927 1
 
< 0.1%
127.177967 2
< 0.1%
127.177918 2
< 0.1%
127.1774839621 3
< 0.1%

Y좌표
Real number (ℝ)

HIGH CORRELATION 

Distinct6015
Distinct (%)60.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.549374
Minimum37.3029
Maximum37.855967
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T18:40:36.800976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.3029
5-th percentile37.45699
Q137.500442
median37.5498
Q337.590733
95-th percentile37.658048
Maximum37.855967
Range0.55306668
Interquartile range (IQR)0.090291411

Descriptive statistics

Standard deviation0.067008611
Coefficient of variation (CV)0.0017845467
Kurtosis0.80927733
Mean37.549374
Median Absolute Deviation (MAD)0.04608864
Skewness0.19696557
Sum375493.74
Variance0.0044901539
MonotonicityNot monotonic
2023-12-11T18:40:36.964101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.5563734241 13
 
0.1%
37.5525517673 12
 
0.1%
37.5692880569 12
 
0.1%
37.4487989863 11
 
0.1%
37.5528517607 11
 
0.1%
37.5699088357 11
 
0.1%
37.6248696784 10
 
0.1%
37.6036262275 10
 
0.1%
37.6241099542 9
 
0.1%
37.5742434484 9
 
0.1%
Other values (6005) 9892
98.9%
ValueCountFrequency (%)
37.3029004959 1
< 0.1%
37.3044514428 1
< 0.1%
37.3059242439 1
< 0.1%
37.3140566796 1
< 0.1%
37.3196931883 1
< 0.1%
37.3239481995 1
< 0.1%
37.3241432186 1
< 0.1%
37.3241806416 1
< 0.1%
37.3291532274 1
< 0.1%
37.3329436645 1
< 0.1%
ValueCountFrequency (%)
37.8559671712 1
< 0.1%
37.8495626064 1
< 0.1%
37.8380844776 1
< 0.1%
37.8379020922 1
< 0.1%
37.8357539946 1
< 0.1%
37.8325908165 1
< 0.1%
37.8316442083 1
< 0.1%
37.8269240724 1
< 0.1%
37.8266167059 1
< 0.1%
37.8228003107 1
< 0.1%

Interactions

2023-12-11T18:40:32.182955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:40:28.855777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:40:29.483633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:40:30.133413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:40:30.816090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:40:31.451569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:40:32.287270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:40:28.971874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:40:29.594226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:40:30.236884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:40:30.932771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:40:31.557267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:40:32.397401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:40:29.067209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:40:29.727429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:40:30.350420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:40:31.045221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:40:31.680233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:40:32.511177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:40:29.161511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:40:29.845761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:40:30.472268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:40:31.150868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:40:31.803247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:40:32.599164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:40:29.251068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:40:29.937566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:40:30.581552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:40:31.238638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:40:31.929954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:40:32.719181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:40:29.387767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:40:30.042846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:40:30.704553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:40:31.352759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:40:32.078382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T18:40:37.074702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ROUTE_ID순번NODE_IDARS_IDX좌표Y좌표
ROUTE_ID1.0000.3380.3550.3890.5130.489
순번0.3381.0000.2450.2540.2110.448
NODE_ID0.3550.2451.0000.8860.6990.747
ARS_ID0.3890.2540.8861.0000.7770.729
X좌표0.5130.2110.6990.7771.0000.540
Y좌표0.4890.4480.7470.7290.5401.000
2023-12-11T18:40:37.199583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ROUTE_ID순번NODE_IDARS_IDX좌표Y좌표
ROUTE_ID1.000-0.1830.1390.140-0.087-0.138
순번-0.1831.0000.007-0.0020.0340.005
NODE_ID0.1390.0071.0000.986-0.142-0.514
ARS_ID0.140-0.0020.9861.000-0.149-0.517
X좌표-0.0870.034-0.142-0.1491.0000.112
Y좌표-0.1380.005-0.514-0.5170.1121.000

Missing values

2023-12-11T18:40:32.832975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T18:40:32.970628image/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
2664510010038787744111100007112159신사동고개사거리126.90987137.596033
1154110010060797111611300019314287상암DMC홍보관.YTN126.89128837.577461
2413710010039194043612100000522005뱅뱅사거리127.03243237.487667
3542100100091606521000007046606복사골문화센터.반달마을126.75287737.494005
278451001000311524411900009720190동작구청.노량진초등학교앞126.94003737.510373
259551001000956424011800025519346보라매역126.9197437.499735
36625100100026147671030000564155금호역127.0162137.54787
30337111000016N75501020000603154용산구청126.99110637.529612
10369100100032163141080000079007미아역.신일중고127.02664937.62487
2850210010057367024511300048114664서강대교북단(가상)126.92891837.54371
ROUTE_ID노선명순번NODE_IDARS_ID정류소명X좌표Y좌표
18240122900003강남012612200013323236대치동은마아파트127.06839737.498429
19827100100105702B용두초교2811200041613030홍제삼거리.인왕산한신휴플러스126.94784337.585207
22190124900002강동01321040000645157현대아파트앞127.09425437.539585
34471100100048272601000000331128사직단126.96818937.574855
37704111000016N757112100026322340방배동래미안타워.동덕여중고126.99055137.474718
142981040000091112020700028461053장암역.석림사입구127.05335737.700016
16328116900015구로082311600012517215세곡초등학교126.84984237.502668
1523310010022233162112400035725011강동역127.13257137.535847
10429115900001강서041411590006916564등촌현대아파트126.85508637.556617
1171610010026855361611600003717123고용노동부관악지청.이마트구로점126.89755837.483634