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 09:38:53.638605
Analysis finished2023-12-11 09:38:59.778948
Duration6.14 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

ROUTE_ID
Real number (ℝ)

Distinct658
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0384387 × 108
Minimum1.0000002 × 108
Maximum1.249 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T18:38:59.880166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.0000002 × 108
5-th percentile1.0010003 × 108
Q11.0010016 × 108
median1.0010036 × 108
Q31.0400001 × 108
95-th percentile1.219 × 108
Maximum1.249 × 108
Range24899986
Interquartile range (IQR)3899845

Descriptive statistics

Standard deviation6994481.1
Coefficient of variation (CV)0.067355743
Kurtosis1.400536
Mean1.0384387 × 108
Median Absolute Deviation (MAD)245
Skewness1.6804805
Sum1.0384387 × 1012
Variance4.8922766 × 1013
MonotonicityNot monotonic
2023-12-11T18:39:00.089235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100100589 54
 
0.5%
111000016 44
 
0.4%
100100499 43
 
0.4%
111000012 43
 
0.4%
111000017 42
 
0.4%
100100522 42
 
0.4%
100100592 41
 
0.4%
100100039 40
 
0.4%
104000011 38
 
0.4%
100100588 38
 
0.4%
Other values (648) 9575
95.8%
ValueCountFrequency (%)
100000017 4
 
< 0.1%
100000018 5
 
0.1%
100100001 7
 
0.1%
100100006 21
0.2%
100100007 16
0.2%
100100008 13
 
0.1%
100100009 18
0.2%
100100010 17
0.2%
100100011 23
0.2%
100100012 35
0.4%
ValueCountFrequency (%)
124900003 18
0.2%
124900002 11
 
0.1%
124900001 18
0.2%
124000039 8
 
0.1%
124000038 33
0.3%
124000036 27
0.3%
124000013 11
 
0.1%
124000010 4
 
< 0.1%
124000008 13
 
0.1%
124000006 18
0.2%

노선명
Text

Distinct658
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T18:39:00.619220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length4
Mean length3.725
Min length2

Characters and Unicode

Total characters37250
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

Unique8 ?
Unique (%)0.1%

Sample

1st row500
2nd row653
3rd row9408
4th row5620
5th row1227
ValueCountFrequency (%)
n61 54
 
0.5%
n75 44
 
0.4%
7212 43
 
0.4%
774 43
 
0.4%
n72 42
 
0.4%
2016 42
 
0.4%
n16 41
 
0.4%
202 40
 
0.4%
n31 38
 
0.4%
n62 38
 
0.4%
Other values (648) 9575
95.8%
2023-12-11T18:39:01.295495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 6682
17.9%
0 4648
12.5%
2 4267
11.5%
6 3718
10.0%
3 3141
8.4%
4 2912
7.8%
7 2874
7.7%
5 2736
7.3%
8 609
 
1.6%
9 579
 
1.6%
Other values (55) 5084
13.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 32166
86.4%
Other Letter 4177
 
11.2%
Uppercase Letter 815
 
2.2%
Dash Punctuation 92
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 376
 
9.0%
동 304
 
7.3%
강 300
 
7.2%
성 223
 
5.3%
북 210
 
5.0%
포 193
 
4.6%
대 189
 
4.5%
초 184
 
4.4%
로 161
 
3.9%
작 142
 
3.4%
Other values (37) 1895
45.4%
Decimal Number
ValueCountFrequency (%)
1 6682
20.8%
0 4648
14.5%
2 4267
13.3%
6 3718
11.6%
3 3141
9.8%
4 2912
9.1%
7 2874
8.9%
5 2736
8.5%
8 609
 
1.9%
9 579
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
N 516
63.3%
A 112
 
13.7%
B 95
 
11.7%
T 23
 
2.8%
R 23
 
2.8%
U 23
 
2.8%
O 23
 
2.8%
Dash Punctuation
ValueCountFrequency (%)
- 92
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 32258
86.6%
Hangul 4177
 
11.2%
Latin 815
 
2.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 376
 
9.0%
동 304
 
7.3%
강 300
 
7.2%
성 223
 
5.3%
북 210
 
5.0%
포 193
 
4.6%
대 189
 
4.5%
초 184
 
4.4%
로 161
 
3.9%
작 142
 
3.4%
Other values (37) 1895
45.4%
Common
ValueCountFrequency (%)
1 6682
20.7%
0 4648
14.4%
2 4267
13.2%
6 3718
11.5%
3 3141
9.7%
4 2912
9.0%
7 2874
8.9%
5 2736
8.5%
8 609
 
1.9%
9 579
 
1.8%
Latin
ValueCountFrequency (%)
N 516
63.3%
A 112
 
13.7%
B 95
 
11.7%
T 23
 
2.8%
R 23
 
2.8%
U 23
 
2.8%
O 23
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33073
88.8%
Hangul 4177
 
11.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6682
20.2%
0 4648
14.1%
2 4267
12.9%
6 3718
11.2%
3 3141
9.5%
4 2912
8.8%
7 2874
8.7%
5 2736
8.3%
8 609
 
1.8%
9 579
 
1.8%
Other values (8) 907
 
2.7%
Hangul
ValueCountFrequency (%)
서 376
 
9.0%
동 304
 
7.3%
강 300
 
7.2%
성 223
 
5.3%
북 210
 
5.0%
포 193
 
4.6%
대 189
 
4.5%
초 184
 
4.4%
로 161
 
3.9%
작 142
 
3.4%
Other values (37) 1895
45.4%

순번
Real number (ℝ)

Distinct174
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.6541
Minimum1
Maximum187
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T18:39:01.449355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q116
median34
Q361
95-th percentile103
Maximum187
Range186
Interquartile range (IQR)45

Descriptive statistics

Standard deviation31.60603
Coefficient of variation (CV)0.75877357
Kurtosis0.59013621
Mean41.6541
Median Absolute Deviation (MAD)21
Skewness0.95081688
Sum416541
Variance998.94115
MonotonicityNot monotonic
2023-12-11T18:39:01.602696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 174
 
1.7%
5 172
 
1.7%
7 166
 
1.7%
6 165
 
1.7%
14 164
 
1.6%
8 164
 
1.6%
18 163
 
1.6%
2 162
 
1.6%
15 162
 
1.6%
10 159
 
1.6%
Other values (164) 8349
83.5%
ValueCountFrequency (%)
1 152
1.5%
2 162
1.6%
3 174
1.7%
4 151
1.5%
5 172
1.7%
6 165
1.7%
7 166
1.7%
8 164
1.6%
9 153
1.5%
10 159
1.6%
ValueCountFrequency (%)
187 1
< 0.1%
186 1
< 0.1%
185 1
< 0.1%
184 1
< 0.1%
183 1
< 0.1%
177 1
< 0.1%
176 1
< 0.1%
175 1
< 0.1%
174 1
< 0.1%
172 1
< 0.1%

NODE_ID
Real number (ℝ)

HIGH CORRELATION 

Distinct6205
Distinct (%)62.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2269232 × 108
Minimum1 × 108
Maximum2.7710425 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T18:39:01.816472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1 × 108
5-th percentile1.0100008 × 108
Q11.0790032 × 108
median1.1400027 × 108
Q31.2100021 × 108
95-th percentile2.1300025 × 108
Maximum2.7710425 × 108
Range1.7710425 × 108
Interquartile range (IQR)13099886

Descriptive statistics

Standard deviation30546240
Coefficient of variation (CV)0.24896619
Kurtosis5.5340973
Mean1.2269232 × 108
Median Absolute Deviation (MAD)6999781
Skewness2.5885144
Sum1.2269232 × 1012
Variance9.3307281 × 1014
MonotonicityNot monotonic
2023-12-11T18:39:01.999460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000367 11
 
0.1%
101000043 11
 
0.1%
113000421 9
 
0.1%
108000004 8
 
0.1%
161000329 8
 
0.1%
106000241 8
 
0.1%
118000076 8
 
0.1%
106000467 8
 
0.1%
161000333 8
 
0.1%
161000325 8
 
0.1%
Other values (6195) 9913
99.1%
ValueCountFrequency (%)
100000002 4
< 0.1%
100000003 3
< 0.1%
100000004 2
 
< 0.1%
100000005 5
0.1%
100000006 2
 
< 0.1%
100000007 1
 
< 0.1%
100000008 1
 
< 0.1%
100000010 2
 
< 0.1%
100000013 1
 
< 0.1%
100000014 1
 
< 0.1%
ValueCountFrequency (%)
277104252 1
 
< 0.1%
277104251 1
 
< 0.1%
277104250 1
 
< 0.1%
277104249 1
 
< 0.1%
274199481 1
 
< 0.1%
274115267 1
 
< 0.1%
274109998 4
< 0.1%
274109991 2
< 0.1%
274000024 1
 
< 0.1%
235000834 1
 
< 0.1%

ARS-ID
Real number (ℝ)

HIGH CORRELATION 

Distinct6197
Distinct (%)62.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17420.28
Minimum1002
Maximum92702
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T18:39:02.180560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1002
5-th percentile2174
Q18908.25
median15351.5
Q322190
95-th percentile40193.45
Maximum92702
Range91700
Interquartile range (IQR)13281.75

Descriptive statistics

Standard deviation13222.301
Coefficient of variation (CV)0.75901771
Kurtosis9.2722435
Mean17420.28
Median Absolute Deviation (MAD)6780.5
Skewness2.4013794
Sum1.742028 × 108
Variance1.7482924 × 108
MonotonicityNot monotonic
2023-12-11T18:39:02.344168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1044 11
 
0.1%
2142 11
 
0.1%
14061 9
 
0.1%
7550 8
 
0.1%
92609 8
 
0.1%
22014 8
 
0.1%
92601 8
 
0.1%
14062 8
 
0.1%
92652 8
 
0.1%
41996 8
 
0.1%
Other values (6187) 9913
99.1%
ValueCountFrequency (%)
1002 4
< 0.1%
1003 3
< 0.1%
1004 2
 
< 0.1%
1005 5
0.1%
1006 1
 
< 0.1%
1007 5
0.1%
1008 3
< 0.1%
1009 7
0.1%
1010 4
< 0.1%
1011 7
0.1%
ValueCountFrequency (%)
92702 5
0.1%
92701 4
< 0.1%
92653 1
 
< 0.1%
92652 8
0.1%
92646 7
0.1%
92644 4
< 0.1%
92641 7
0.1%
92638 2
 
< 0.1%
92630 6
0.1%
92616 1
 
< 0.1%

정류소명
Text

Distinct4711
Distinct (%)47.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T18:39:02.610152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length19
Mean length8.0181
Min length2

Characters and Unicode

Total characters80181
Distinct characters626
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

Unique2523 ?
Unique (%)25.2%

Sample

1st row구암초등학교
2nd row화곡역
3rd row대방역
4th row당산유원제일1차아파트
5th row지하철7호선중화역
ValueCountFrequency (%)
뱅뱅사거리 18
 
0.2%
동묘앞 18
 
0.2%
노오지jc(가상 15
 
0.1%
공항입구jc 15
 
0.1%
연희104고지앞.구성산회관 15
 
0.1%
현대아파트 14
 
0.1%
홍대입구역 13
 
0.1%
지하철2호선강남역 13
 
0.1%
중랑공영차고지.신내역 13
 
0.1%
신도림역 13
 
0.1%
Other values (4701) 9853
98.5%
2023-12-11T18:39:03.081438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 2747
 
3.4%
동 2230
 
2.8%
역 1945
 
2.4%
교 1885
 
2.4%
아 1835
 
2.3%
파 1808
 
2.3%
트 1689
 
2.1%
구 1653
 
2.1%
지 1442
 
1.8%
대 1320
 
1.6%
Other values (616) 61627
76.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 73629
91.8%
Other Punctuation 2781
 
3.5%
Decimal Number 2239
 
2.8%
Uppercase Letter 849
 
1.1%
Open Punctuation 303
 
0.4%
Close Punctuation 301
 
0.4%
Dash Punctuation 32
 
< 0.1%
Lowercase Letter 26
 
< 0.1%
Space Separator 21
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 2230
 
3.0%
역 1945
 
2.6%
교 1885
 
2.6%
아 1835
 
2.5%
파 1808
 
2.5%
트 1689
 
2.3%
구 1653
 
2.2%
지 1442
 
2.0%
대 1320
 
1.8%
학 1271
 
1.7%
Other values (571) 56551
76.8%
Uppercase Letter
ValueCountFrequency (%)
C 150
17.7%
T 131
15.4%
K 84
9.9%
S 69
8.1%
G 61
7.2%
I 51
 
6.0%
M 47
 
5.5%
D 42
 
4.9%
B 37
 
4.4%
J 35
 
4.1%
Other values (12) 142
16.7%
Decimal Number
ValueCountFrequency (%)
1 654
29.2%
2 440
19.7%
3 312
13.9%
4 200
 
8.9%
5 157
 
7.0%
7 124
 
5.5%
6 105
 
4.7%
0 102
 
4.6%
9 88
 
3.9%
8 57
 
2.5%
Other Punctuation
ValueCountFrequency (%)
. 2747
98.8%
· 21
 
0.8%
& 10
 
0.4%
, 2
 
0.1%
․ 1
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
e 22
84.6%
k 2
 
7.7%
s 1
 
3.8%
t 1
 
3.8%
Open Punctuation
ValueCountFrequency (%)
( 303
100.0%
Close Punctuation
ValueCountFrequency (%)
) 301
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 32
100.0%
Space Separator
ValueCountFrequency (%)
21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 73629
91.8%
Common 5677
 
7.1%
Latin 875
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 2230
 
3.0%
역 1945
 
2.6%
교 1885
 
2.6%
아 1835
 
2.5%
파 1808
 
2.5%
트 1689
 
2.3%
구 1653
 
2.2%
지 1442
 
2.0%
대 1320
 
1.8%
학 1271
 
1.7%
Other values (571) 56551
76.8%
Latin
ValueCountFrequency (%)
C 150
17.1%
T 131
15.0%
K 84
9.6%
S 69
7.9%
G 61
 
7.0%
I 51
 
5.8%
M 47
 
5.4%
D 42
 
4.8%
B 37
 
4.2%
J 35
 
4.0%
Other values (16) 168
19.2%
Common
ValueCountFrequency (%)
. 2747
48.4%
1 654
 
11.5%
2 440
 
7.8%
3 312
 
5.5%
( 303
 
5.3%
) 301
 
5.3%
4 200
 
3.5%
5 157
 
2.8%
7 124
 
2.2%
6 105
 
1.8%
Other values (9) 334
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 73629
91.8%
ASCII 6530
 
8.1%
None 21
 
< 0.1%
Punctuation 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 2747
42.1%
1 654
 
10.0%
2 440
 
6.7%
3 312
 
4.8%
( 303
 
4.6%
) 301
 
4.6%
4 200
 
3.1%
5 157
 
2.4%
C 150
 
2.3%
T 131
 
2.0%
Other values (33) 1135
17.4%
Hangul
ValueCountFrequency (%)
동 2230
 
3.0%
역 1945
 
2.6%
교 1885
 
2.6%
아 1835
 
2.5%
파 1808
 
2.5%
트 1689
 
2.3%
구 1653
 
2.2%
지 1442
 
2.0%
대 1320
 
1.8%
학 1271
 
1.7%
Other values (571) 56551
76.8%
None
ValueCountFrequency (%)
· 21
100.0%
Punctuation
ValueCountFrequency (%)
․ 1
100.0%

X좌표
Real number (ℝ)

Distinct6201
Distinct (%)62.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.97632
Minimum126.42993
Maximum127.18628
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T18:39:03.269913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.42993
5-th percentile126.82961
Q1126.91035
median126.98107
Q3127.0521
95-th percentile127.12642
Maximum127.18628
Range0.75634483
Interquartile range (IQR)0.14174547

Descriptive statistics

Standard deviation0.10264509
Coefficient of variation (CV)0.00080837981
Kurtosis3.1458076
Mean126.97632
Median Absolute Deviation (MAD)0.070810253
Skewness-0.96536762
Sum1269763.2
Variance0.010536015
MonotonicityNot monotonic
2023-12-11T18:39:03.431247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.9819217706 11
 
0.1%
127.0196389758 11
 
0.1%
126.9284303238 9
 
0.1%
127.104237 8
 
0.1%
127.0237329243 8
 
0.1%
126.4893195551 8
 
0.1%
126.4501560215 8
 
0.1%
127.1020071933 8
 
0.1%
126.508722936 8
 
0.1%
127.0329932458 8
 
0.1%
Other values (6191) 9913
99.1%
ValueCountFrequency (%)
126.4299335622 1
 
< 0.1%
126.4340182489 5
0.1%
126.4344306036 4
< 0.1%
126.4495156076 4
< 0.1%
126.4501560215 8
0.1%
126.4515057687 7
0.1%
126.458668546 2
 
< 0.1%
126.4598157152 2
 
< 0.1%
126.476113766 7
0.1%
126.4893195551 8
0.1%
ValueCountFrequency (%)
127.1862783918 1
< 0.1%
127.1860753273 1
< 0.1%
127.1817875631 1
< 0.1%
127.1815600278 1
< 0.1%
127.1809032389 1
< 0.1%
127.1800898791 2
< 0.1%
127.1792290002 1
< 0.1%
127.1791803843 1
< 0.1%
127.1790170977 1
< 0.1%
127.178849193 1
< 0.1%

Y좌표
Real number (ℝ)

HIGH CORRELATION 

Distinct6201
Distinct (%)62.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.546867
Minimum37.302375
Maximum37.832663
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T18:39:03.645878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.302375
5-th percentile37.456798
Q137.499703
median37.548756
Q337.588454
95-th percentile37.65233
Maximum37.832663
Range0.53028848
Interquartile range (IQR)0.088750619

Descriptive statistics

Standard deviation0.064895327
Coefficient of variation (CV)0.001728382
Kurtosis0.72387548
Mean37.546867
Median Absolute Deviation (MAD)0.044867744
Skewness0.041831737
Sum375468.67
Variance0.0042114035
MonotonicityNot monotonic
2023-12-11T18:39:03.840228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.5630081328 11
 
0.1%
37.57430372 11
 
0.1%
37.5580111593 9
 
0.1%
37.613099 8
 
0.1%
37.5063685121 8
 
0.1%
37.4575756331 8
 
0.1%
37.445731623 8
 
0.1%
37.6136093277 8
 
0.1%
37.5056530035 8
 
0.1%
37.4868970819 8
 
0.1%
Other values (6191) 9913
99.1%
ValueCountFrequency (%)
37.3023750115 1
< 0.1%
37.3044514431 1
< 0.1%
37.3058257758 2
< 0.1%
37.3059242438 1
< 0.1%
37.3102527272 1
< 0.1%
37.3190251725 2
< 0.1%
37.3219424955 1
< 0.1%
37.3239481996 1
< 0.1%
37.3241806417 1
< 0.1%
37.3253100067 1
< 0.1%
ValueCountFrequency (%)
37.83266349 1
< 0.1%
37.8325564671 1
< 0.1%
37.8318099297 1
< 0.1%
37.8316442084 1
< 0.1%
37.8295990231 1
< 0.1%
37.8290858364 1
< 0.1%
37.8290025114 1
< 0.1%
37.8208189367 1
< 0.1%
37.8203056708 1
< 0.1%
37.8150838335 1
< 0.1%

Interactions

2023-12-11T18:38:58.597695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:38:55.105670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:38:55.846184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:38:56.532754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:38:57.204699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:38:57.850122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:38:58.708806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:38:55.215322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:38:55.947692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:38:56.636998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:38:57.314244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:38:57.984365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:38:59.060431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:38:55.355980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:38:56.085130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:38:56.747245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:38:57.430369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:38:58.094156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:38:59.216908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:38:55.511935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:38:56.207340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:38:56.855785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:38:57.540055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:38:58.236573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:38:59.332913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:38:55.631211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:38:56.326509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:38:56.952587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:38:57.624016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:38:58.356728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:38:59.452089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:38:55.742944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:38:56.441719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:38:57.086735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:38:57.742931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:38:58.490651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T18:39:03.942935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ROUTE_ID순번NODE_IDARS-IDX좌표Y좌표
ROUTE_ID1.0000.3780.3690.5260.5170.534
순번0.3781.0000.2120.2700.2300.318
NODE_ID0.3690.2121.0000.9040.7090.719
ARS-ID0.5260.2700.9041.0000.8800.836
X좌표0.5170.2300.7090.8801.0000.593
Y좌표0.5340.3180.7190.8360.5931.000
2023-12-11T18:39:04.065855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ROUTE_ID순번NODE_IDARS-IDX좌표Y좌표
ROUTE_ID1.000-0.2160.1470.144-0.111-0.117
순번-0.2161.000-0.001-0.0070.041-0.017
NODE_ID0.147-0.0011.0000.985-0.128-0.561
ARS-ID0.144-0.0070.9851.000-0.137-0.564
X좌표-0.1110.041-0.128-0.1371.0000.117
Y좌표-0.117-0.017-0.561-0.5640.1171.000

Missing values

2023-12-11T18:38:59.573642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T18:38:59.703454image/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
144871001000765002212000015221254구암초등학교126.94615837.489198
216791001004976534711500005216148화곡역126.83713337.540815
2970510010039294087511800001519015대방역126.9248337.512672
1724810010027756204011800009719182당산유원제일1차아파트126.89773537.528658
37991001001841227531060000147108지하철7호선중화역127.07934737.602829
204921001000936403912100001322013논현역127.02301237.50722
31093104000011N3112712400006325163천호역127.12251537.536027
2226111600000266153911400026715373양천공영차고지126.83737437.508527
2442610010045167168311400019815302신정1동주민센터.신오새마을금고126.85390237.51761
9587100100053303721040000825175광진구청127.08333937.538502
ROUTE_ID노선명순번NODE_IDARS-ID정류소명X좌표Y좌표
1141510010022834163212200031523422세곡동사거리127.10850337.465693
72211001002022227681040000485141능동사거리.군자역127.07850937.555215
66241001001912112581070001368226성북구립미술관.쌍다리앞126.99589737.593618
34697117900002금천041611790022318804롯데캐슬2차126.89353137.457522
380810010018412276211000002411124동부아파트127.07832937.627437
640210010052220161031039002134256성수사거리127.06246437.542522
37256121900008서초022012100006622142서초동삼성아파트127.0091137.488556
1809510010028657126011500004816144한국폴리텍1.서울강서대학교126.84480937.545812
38674107900009성북22171079003308850돈암코오롱하늘채아파트127.01140737.598973
1027610010022133157112300065524424호반써밋송파1차127.14057437.484325