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 started2024-04-21 01:20:35.163060
Analysis finished2024-04-21 01:20:47.431689
Duration12.27 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

ROUTE_ID
Real number (ℝ)

Distinct759
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0509178 × 108
Minimum1 × 108
Maximum1.249 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T10:20:47.645272image/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)9799837

Descriptive statistics

Standard deviation8105449.1
Coefficient of variation (CV)0.077127334
Kurtosis0.11737301
Mean1.0509178 × 108
Median Absolute Deviation (MAD)330
Skewness1.3036384
Sum1.0509178 × 1012
Variance6.5698306 × 1013
MonotonicityNot monotonic
2024-04-21T10:20:48.107801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100100589 51
 
0.5%
124000026 46
 
0.5%
100100044 42
 
0.4%
124000035 40
 
0.4%
100100071 40
 
0.4%
100100035 37
 
0.4%
123000013 37
 
0.4%
100100595 37
 
0.4%
111000016 36
 
0.4%
100100033 35
 
0.4%
Other values (749) 9599
96.0%
ValueCountFrequency (%)
100000004 5
 
0.1%
100000008 9
0.1%
100000009 7
 
0.1%
100000011 1
 
< 0.1%
100000015 14
0.1%
100000016 18
0.2%
100100001 4
 
< 0.1%
100100002 7
 
0.1%
100100003 7
 
0.1%
100100006 9
0.1%
ValueCountFrequency (%)
124900003 21
0.2%
124900002 7
 
0.1%
124900001 17
0.2%
124000039 11
 
0.1%
124000038 26
0.3%
124000036 19
0.2%
124000035 40
0.4%
124000034 29
0.3%
124000033 30
0.3%
124000032 28
0.3%

노선명
Text

Distinct755
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-21T10:20:49.660050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length4
Mean length3.8027
Min length2

Characters and Unicode

Total characters38027
Distinct characters77
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

Unique18 ?
Unique (%)0.2%

Sample

1st row0411
2nd row금천04
3rd row6516
4th row8561
5th row171
ValueCountFrequency (%)
163 64
 
0.6%
n61 51
 
0.5%
n61b 46
 
0.5%
262 42
 
0.4%
n13b 40
 
0.4%
461 40
 
0.4%
n73 37
 
0.4%
241 37
 
0.4%
n75 36
 
0.4%
n72 35
 
0.4%
Other values (745) 9572
95.7%
2024-04-21T10:20:51.550011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 6976
18.3%
0 4829
12.7%
2 4145
10.9%
6 4006
10.5%
3 3065
8.1%
7 2662
 
7.0%
4 2581
 
6.8%
5 2534
 
6.7%
N 876
 
2.3%
8 829
 
2.2%
Other values (67) 5524
14.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 32086
84.4%
Other Letter 4267
 
11.2%
Uppercase Letter 1416
 
3.7%
Dash Punctuation 209
 
0.5%
Close Punctuation 23
 
0.1%
Open Punctuation 23
 
0.1%
Lowercase Letter 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 358
 
8.4%
동 316
 
7.4%
강 286
 
6.7%
성 215
 
5.0%
북 211
 
4.9%
포 191
 
4.5%
초 188
 
4.4%
작 168
 
3.9%
로 164
 
3.8%
대 156
 
3.7%
Other values (46) 2014
47.2%
Decimal Number
ValueCountFrequency (%)
1 6976
21.7%
0 4829
15.1%
2 4145
12.9%
6 4006
12.5%
3 3065
9.6%
7 2662
 
8.3%
4 2581
 
8.0%
5 2534
 
7.9%
8 829
 
2.6%
9 459
 
1.4%
Uppercase Letter
ValueCountFrequency (%)
N 876
61.9%
B 318
 
22.5%
A 213
 
15.0%
S 8
 
0.6%
T 1
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
e 1
33.3%
s 1
33.3%
t 1
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 209
100.0%
Close Punctuation
ValueCountFrequency (%)
) 23
100.0%
Open Punctuation
ValueCountFrequency (%)
( 23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 32341
85.0%
Hangul 4267
 
11.2%
Latin 1419
 
3.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 358
 
8.4%
동 316
 
7.4%
강 286
 
6.7%
성 215
 
5.0%
북 211
 
4.9%
포 191
 
4.5%
초 188
 
4.4%
작 168
 
3.9%
로 164
 
3.8%
대 156
 
3.7%
Other values (46) 2014
47.2%
Common
ValueCountFrequency (%)
1 6976
21.6%
0 4829
14.9%
2 4145
12.8%
6 4006
12.4%
3 3065
9.5%
7 2662
 
8.2%
4 2581
 
8.0%
5 2534
 
7.8%
8 829
 
2.6%
9 459
 
1.4%
Other values (3) 255
 
0.8%
Latin
ValueCountFrequency (%)
N 876
61.7%
B 318
 
22.4%
A 213
 
15.0%
S 8
 
0.6%
T 1
 
0.1%
e 1
 
0.1%
s 1
 
0.1%
t 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33760
88.8%
Hangul 4267
 
11.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6976
20.7%
0 4829
14.3%
2 4145
12.3%
6 4006
11.9%
3 3065
9.1%
7 2662
 
7.9%
4 2581
 
7.6%
5 2534
 
7.5%
N 876
 
2.6%
8 829
 
2.5%
Other values (11) 1257
 
3.7%
Hangul
ValueCountFrequency (%)
서 358
 
8.4%
동 316
 
7.4%
강 286
 
6.7%
성 215
 
5.0%
북 211
 
4.9%
포 191
 
4.5%
초 188
 
4.4%
작 168
 
3.9%
로 164
 
3.8%
대 156
 
3.7%
Other values (46) 2014
47.2%

순번
Real number (ℝ)

Distinct170
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.2289
Minimum1
Maximum183
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T10:20:51.955363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q116
median33
Q359
95-th percentile98.05
Maximum183
Range182
Interquartile range (IQR)43

Descriptive statistics

Standard deviation30.451313
Coefficient of variation (CV)0.75695116
Kurtosis0.80534902
Mean40.2289
Median Absolute Deviation (MAD)20
Skewness0.98904478
Sum402289
Variance927.28243
MonotonicityNot monotonic
2024-04-21T10:20:52.399948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 183
 
1.8%
14 179
 
1.8%
7 175
 
1.8%
16 173
 
1.7%
15 170
 
1.7%
8 169
 
1.7%
4 167
 
1.7%
2 167
 
1.7%
12 164
 
1.6%
24 163
 
1.6%
Other values (160) 8290
82.9%
ValueCountFrequency (%)
1 153
1.5%
2 167
1.7%
3 159
1.6%
4 167
1.7%
5 159
1.6%
6 149
1.5%
7 175
1.8%
8 169
1.7%
9 183
1.8%
10 144
1.4%
ValueCountFrequency (%)
183 1
< 0.1%
181 1
< 0.1%
179 1
< 0.1%
177 1
< 0.1%
176 1
< 0.1%
175 2
< 0.1%
173 1
< 0.1%
170 2
< 0.1%
169 1
< 0.1%
168 2
< 0.1%

NODE_ID
Real number (ℝ)

HIGH CORRELATION 

Distinct5634
Distinct (%)56.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1402161 × 108
Minimum1 × 108
Maximum1.6801111 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T10:20:52.830554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1 × 108
5-th percentile1.0100006 × 108
Q11.0700027 × 108
median1.1390007 × 108
Q31.2000002 × 108
95-th percentile1.2300066 × 108
Maximum1.6801111 × 108
Range68011111
Interquartile range (IQR)12999752

Descriptive statistics

Standard deviation9750430.1
Coefficient of variation (CV)0.08551388
Kurtosis10.106957
Mean1.1402161 × 108
Median Absolute Deviation (MAD)6099966.5
Skewness2.2161488
Sum1.1402161 × 1012
Variance9.5070887 × 1013
MonotonicityNot monotonic
2024-04-21T10:20:53.287689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
167010605 13
 
0.1%
161010558 13
 
0.1%
168011111 12
 
0.1%
161000325 12
 
0.1%
108000007 11
 
0.1%
167010604 11
 
0.1%
161000381 10
 
0.1%
121000014 10
 
0.1%
161010559 10
 
0.1%
129000083 10
 
0.1%
Other values (5624) 9888
98.9%
ValueCountFrequency (%)
100000001 1
 
< 0.1%
100000002 4
< 0.1%
100000003 3
< 0.1%
100000004 2
< 0.1%
100000005 4
< 0.1%
100000006 1
 
< 0.1%
100000007 3
< 0.1%
100000009 2
< 0.1%
100000010 1
 
< 0.1%
100000014 1
 
< 0.1%
ValueCountFrequency (%)
168011112 10
0.1%
168011111 12
0.1%
168000693 8
0.1%
168000692 7
0.1%
168000495 1
 
< 0.1%
167010605 13
0.1%
167010604 11
0.1%
167010603 5
 
0.1%
167010602 7
0.1%
164020604 1
 
< 0.1%

ARS_ID
Real number (ℝ)

HIGH CORRELATION 

Distinct5633
Distinct (%)56.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15515.681
Minimum1001
Maximum92702
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T10:20:53.711576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile2153
Q18380.75
median14722.5
Q321126
95-th percentile24727.05
Maximum92702
Range91701
Interquartile range (IQR)12745.25

Descriptive statistics

Standard deviation11699.037
Coefficient of variation (CV)0.75401376
Kurtosis23.117756
Mean15515.681
Median Absolute Deviation (MAD)6396.5
Skewness3.8279909
Sum1.5515681 × 108
Variance1.3686746 × 108
MonotonicityNot monotonic
2024-04-21T10:20:54.126085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41996 13
 
0.1%
92570 13
 
0.1%
42994 12
 
0.1%
92601 12
 
0.1%
9007 11
 
0.1%
41997 11
 
0.1%
92630 10
 
0.1%
22014 10
 
0.1%
92646 10
 
0.1%
92641 10
 
0.1%
Other values (5623) 9888
98.9%
ValueCountFrequency (%)
1001 1
 
< 0.1%
1002 4
< 0.1%
1003 3
< 0.1%
1004 2
< 0.1%
1005 4
< 0.1%
1006 3
< 0.1%
1007 4
< 0.1%
1008 1
 
< 0.1%
1009 4
< 0.1%
1010 2
< 0.1%
ValueCountFrequency (%)
92702 10
0.1%
92701 9
0.1%
92696 1
 
< 0.1%
92653 5
0.1%
92652 8
0.1%
92649 1
 
< 0.1%
92646 10
0.1%
92644 9
0.1%
92641 10
0.1%
92630 10
0.1%

정류소명
Text

Distinct4235
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-21T10:20:54.896635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length18
Mean length8.0133
Min length2

Characters and Unicode

Total characters80133
Distinct characters608
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

Unique2117 ?
Unique (%)21.2%

Sample

1st row구반포역.세화고등학교
2nd row박미마을
3rd row독산동쌈지공원
4th row공군호텔
5th row혜화동로터리.여운형활동터
ValueCountFrequency (%)
노오지jc(가상 24
 
0.2%
gs주유소(가상 23
 
0.2%
신공항tg(가상 22
 
0.2%
현대아파트 21
 
0.2%
공항입구jc 18
 
0.2%
연희104고지앞.구성산회관 18
 
0.2%
논현역 18
 
0.2%
고속터미널 17
 
0.2%
독립문역.한성과학고 17
 
0.2%
무악재역 17
 
0.2%
Other values (4227) 9807
98.1%
2024-04-21T10:20:56.075698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 2622
 
3.3%
동 2300
 
2.9%
역 2100
 
2.6%
교 1972
 
2.5%
아 1756
 
2.2%
파 1754
 
2.2%
구 1646
 
2.1%
트 1623
 
2.0%
대 1431
 
1.8%
지 1351
 
1.7%
Other values (598) 61578
76.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 73413
91.6%
Other Punctuation 2657
 
3.3%
Decimal Number 2259
 
2.8%
Uppercase Letter 1003
 
1.3%
Close Punctuation 362
 
0.5%
Open Punctuation 361
 
0.5%
Dash Punctuation 52
 
0.1%
Lowercase Letter 24
 
< 0.1%
Space Separator 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 2300
 
3.1%
역 2100
 
2.9%
교 1972
 
2.7%
아 1756
 
2.4%
파 1754
 
2.4%
구 1646
 
2.2%
트 1623
 
2.2%
대 1431
 
1.9%
지 1351
 
1.8%
학 1295
 
1.8%
Other values (554) 56185
76.5%
Uppercase Letter
ValueCountFrequency (%)
C 193
19.2%
T 163
16.3%
K 89
8.9%
S 76
 
7.6%
G 74
 
7.4%
I 66
 
6.6%
M 64
 
6.4%
D 59
 
5.9%
J 50
 
5.0%
B 41
 
4.1%
Other values (13) 128
12.8%
Decimal Number
ValueCountFrequency (%)
1 635
28.1%
2 484
21.4%
3 303
13.4%
4 196
 
8.7%
5 154
 
6.8%
0 128
 
5.7%
7 105
 
4.6%
9 102
 
4.5%
6 99
 
4.4%
8 53
 
2.3%
Other Punctuation
ValueCountFrequency (%)
. 2622
98.7%
· 18
 
0.7%
& 14
 
0.5%
, 3
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
e 22
91.7%
s 1
 
4.2%
k 1
 
4.2%
Close Punctuation
ValueCountFrequency (%)
) 362
100.0%
Open Punctuation
ValueCountFrequency (%)
( 361
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 52
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 73413
91.6%
Common 5693
 
7.1%
Latin 1027
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 2300
 
3.1%
역 2100
 
2.9%
교 1972
 
2.7%
아 1756
 
2.4%
파 1754
 
2.4%
구 1646
 
2.2%
트 1623
 
2.2%
대 1431
 
1.9%
지 1351
 
1.8%
학 1295
 
1.8%
Other values (554) 56185
76.5%
Latin
ValueCountFrequency (%)
C 193
18.8%
T 163
15.9%
K 89
8.7%
S 76
 
7.4%
G 74
 
7.2%
I 66
 
6.4%
M 64
 
6.2%
D 59
 
5.7%
J 50
 
4.9%
B 41
 
4.0%
Other values (16) 152
14.8%
Common
ValueCountFrequency (%)
. 2622
46.1%
1 635
 
11.2%
2 484
 
8.5%
) 362
 
6.4%
( 361
 
6.3%
3 303
 
5.3%
4 196
 
3.4%
5 154
 
2.7%
0 128
 
2.2%
7 105
 
1.8%
Other values (8) 343
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 73413
91.6%
ASCII 6702
 
8.4%
None 18
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 2622
39.1%
1 635
 
9.5%
2 484
 
7.2%
) 362
 
5.4%
( 361
 
5.4%
3 303
 
4.5%
4 196
 
2.9%
C 193
 
2.9%
T 163
 
2.4%
5 154
 
2.3%
Other values (33) 1229
18.3%
Hangul
ValueCountFrequency (%)
동 2300
 
3.1%
역 2100
 
2.9%
교 1972
 
2.7%
아 1756
 
2.4%
파 1754
 
2.4%
구 1646
 
2.2%
트 1623
 
2.2%
대 1431
 
1.9%
지 1351
 
1.8%
학 1295
 
1.8%
Other values (554) 56185
76.5%
None
ValueCountFrequency (%)
· 18
100.0%

X좌표
Real number (ℝ)

Distinct5632
Distinct (%)56.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.97763
Minimum126.42987
Maximum127.18014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T10:20:56.486326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.42987
5-th percentile126.83456
Q1126.91594
median126.98666
Q3127.04986
95-th percentile127.12163
Maximum127.18014
Range0.75026595
Interquartile range (IQR)0.133917

Descriptive statistics

Standard deviation0.10284734
Coefficient of variation (CV)0.00080996419
Kurtosis5.0763114
Mean126.97763
Median Absolute Deviation (MAD)0.067870768
Skewness-1.3695748
Sum1269776.3
Variance0.010577574
MonotonicityNot monotonic
2024-04-21T10:20:56.949080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.7535381313 13
 
0.1%
126.4758382523 13
 
0.1%
126.6647190353 12
 
0.1%
126.4501560215 12
 
0.1%
127.0266493048 11
 
0.1%
126.7535756822 11
 
0.1%
126.5083067251 10
 
0.1%
126.4762125849 10
 
0.1%
127.0196720085 10
 
0.1%
126.4513104918 10
 
0.1%
Other values (5622) 9888
98.9%
ValueCountFrequency (%)
126.4298719877 5
 
0.1%
126.4340182489 10
0.1%
126.4344306036 9
0.1%
126.4501560215 12
0.1%
126.4505374684 1
 
< 0.1%
126.4513104918 10
0.1%
126.4529707406 1
 
< 0.1%
126.4758382523 13
0.1%
126.4762125849 10
0.1%
126.4893195551 2
 
< 0.1%
ValueCountFrequency (%)
127.18013794 2
< 0.1%
127.1799002887 3
< 0.1%
127.1797196537 1
 
< 0.1%
127.1794626974 3
< 0.1%
127.17919 2
< 0.1%
127.1784008104 1
 
< 0.1%
127.1779895218 1
 
< 0.1%
127.177967 1
 
< 0.1%
127.177918 2
< 0.1%
127.1777180853 2
< 0.1%

Y좌표
Real number (ℝ)

HIGH CORRELATION 

Distinct5633
Distinct (%)56.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.549721
Minimum37.388243
Maximum37.690177
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T10:20:57.372061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.388243
5-th percentile37.471014
Q137.504393
median37.550976
Q337.58637
95-th percentile37.644509
Maximum37.690177
Range0.30193435
Interquartile range (IQR)0.081976592

Descriptive statistics

Standard deviation0.053664058
Coefficient of variation (CV)0.0014291467
Kurtosis-0.74797477
Mean37.549721
Median Absolute Deviation (MAD)0.042167524
Skewness0.21712372
Sum375497.21
Variance0.0028798311
MonotonicityNot monotonic
2024-04-21T10:20:58.025198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.5701937118 13
 
0.1%
37.441730354 13
 
0.1%
37.5699088357 12
 
0.1%
37.445731623 12
 
0.1%
37.6248696784 11
 
0.1%
37.5704913424 11
 
0.1%
37.5213607131 10
 
0.1%
37.506367 10
 
0.1%
37.4411393093 10
 
0.1%
37.4487989863 10
 
0.1%
Other values (5623) 9888
98.9%
ValueCountFrequency (%)
37.3882426464 1
< 0.1%
37.3921238822 1
< 0.1%
37.4092596392 1
< 0.1%
37.4349830389 1
< 0.1%
37.4379594347 1
< 0.1%
37.4394878782 1
< 0.1%
37.4402297095 1
< 0.1%
37.4402569123 1
< 0.1%
37.440823 2
< 0.1%
37.440931611 1
< 0.1%
ValueCountFrequency (%)
37.690177 2
< 0.1%
37.6899483575 1
< 0.1%
37.6893500743 1
< 0.1%
37.6891946492 2
< 0.1%
37.6890118581 1
< 0.1%
37.688568 1
< 0.1%
37.6872120941 1
< 0.1%
37.6864731679 1
< 0.1%
37.684132735 1
< 0.1%
37.6840520197 2
< 0.1%

Interactions

2024-04-21T10:20:45.125897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:20:36.995533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:20:38.600842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:20:40.239611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:20:41.908349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:20:43.266387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:20:45.392457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:20:37.253772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:20:38.867787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:20:40.515720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:20:42.165919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:20:43.540899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:20:45.664472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:20:37.523759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:20:39.138411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:20:40.797084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:20:42.435166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:20:43.825730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:20:45.943225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:20:37.798232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:20:39.424747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:20:41.077751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:20:42.712902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:20:44.112545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:20:46.196265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:20:38.051267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:20:39.677352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:20:41.339658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:20:42.883337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:20:44.558171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:20:46.480898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:20:38.333645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:20:39.966008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:20:41.628775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:20:43.059599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:20:44.845698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T10:20:58.298685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ROUTE_ID순번NODE_IDARS_IDX좌표Y좌표
ROUTE_ID1.0000.3650.4850.3900.5260.527
순번0.3651.0000.1420.1010.1760.197
NODE_ID0.4850.1421.0000.9670.8530.687
ARS_ID0.3900.1010.9671.0000.8300.634
X좌표0.5260.1760.8530.8301.0000.690
Y좌표0.5270.1970.6870.6340.6901.000
2024-04-21T10:20:58.572290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ROUTE_ID순번NODE_IDARS_IDX좌표Y좌표
ROUTE_ID1.000-0.2140.2090.209-0.090-0.163
순번-0.2141.000-0.050-0.0520.074-0.019
NODE_ID0.209-0.0501.0000.998-0.088-0.669
ARS_ID0.209-0.0520.9981.000-0.090-0.668
X좌표-0.0900.074-0.088-0.0901.0000.193
Y좌표-0.163-0.019-0.669-0.6680.1931.000

Missing values

2024-04-21T10:20:46.841421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T10:20:47.258112image/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
3355510400001204116312100002322023구반포역.세화고등학교126.99002637.501945
27169117900002금천042311790009118540박미마을126.90316937.440932
2693910010057665166911700010618192독산동쌈지공원126.90318137.471174
298091160000058561911900002120107공군호텔126.92454137.510011
5210010003617191000000051005혜화동로터리.여운형활동터127.00174437.586243
22623114900003양천032111490001315536신정1동주민센터126.85471337.518365
17088111900009은평091211190011212524연신내역.물빛공원126.92006437.617937
2020910010036377374911300009614187성미약수터126.91510537.561797
371571001000624011412200029723404수서역KT수서지점127.10073637.487189
340781001000966434612100011722193신논현역.구교보타워사거리127.02302337.504227
ROUTE_ID노선명순번NODE_IDARS_ID정류소명X좌표Y좌표
287551001000886032611800019319278선유도공원126.90291537.542025
9857100100610N151241070000868176삼선동주민센터127.01471537.590692
270110010000305191010001492254남산북측순환로입구126.99882237.550699
2948710010057167043711800058319994여의2교(가상)126.91161137.526608
3930102900002용산0241029000063510우리들가정의원126.98779537.543232
30686119900014동작061711990000620600사당문화복지관126.96949637.485223
3422210010038385412312100016522241사당역126.9820337.478875
18585111000015N8773311200011313196명지대삼거리126.92599737.58082
1485610010015611414711000031811419중계주공9단지후문127.07660837.64355
1583710010036277337511100111712113구파발역.롯데몰126.91862937.636778