Overview

Dataset statistics

Number of variables17
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory72.4 KiB
Average record size in memory148.3 B

Variable types

Categorical3
Numeric11
Text3

Dataset

Description샘플 데이터
Author서울시(스마트카드사)
URLhttps://bigdata.seoul.go.kr/data/selectSampleData.do?sample_data_seq=14

Alerts

조합구분 is highly overall correlated with 교통수단코드 and 5 other fieldsHigh correlation
교통수단명 is highly overall correlated with 교통수단코드 and 3 other fieldsHigh correlation
교통수단코드 is highly overall correlated with 조합구분 and 1 other fieldsHigh correlation
정류장ID is highly overall correlated with 조합구분 and 1 other fieldsHigh correlation
정류장수 is highly overall correlated with 정류장순서 and 3 other fieldsHigh correlation
정류장순서 is highly overall correlated with 정류장수 and 2 other fieldsHigh correlation
총누적거리(km) is highly overall correlated with 정류장수 and 4 other fieldsHigh correlation
정류장누적거리(m) is highly overall correlated with 정류장수 and 3 other fieldsHigh correlation
종료일자 is highly imbalanced (93.6%)Imbalance
정류장누적거리(m) has 7 (1.4%) zerosZeros
정류장간차이(m) has 7 (1.4%) zerosZeros

Reproduction

Analysis started2024-04-16 11:26:27.980476
Analysis finished2024-04-16 11:26:39.951983
Duration11.97 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

조합구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
시내
394 
마을
106 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row마을
2nd row마을
3rd row마을
4th row마을
5th row마을

Common Values

ValueCountFrequency (%)
시내 394
78.8%
마을 106
 
21.2%

Length

2024-04-16T20:26:40.015919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T20:26:40.093670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
시내 394
78.8%
마을 106
 
21.2%

버스노선ID
Real number (ℝ)

Distinct309
Distinct (%)61.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18192736
Minimum11110001
Maximum41110219
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-16T20:26:40.186127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110001
5-th percentile11110024
Q111110194
median11110582
Q311111158
95-th percentile41110155
Maximum41110219
Range30000218
Interquartile range (IQR)964.25

Descriptive statistics

Standard deviation12749981
Coefficient of variation (CV)0.70082813
Kurtosis-0.44630022
Mean18192736
Median Absolute Deviation (MAD)407
Skewness1.2471877
Sum9.096368 × 109
Variance1.6256202 × 1014
MonotonicityNot monotonic
2024-04-16T20:26:40.310349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11110193 6
 
1.2%
11110213 5
 
1.0%
11110039 5
 
1.0%
41110092 5
 
1.0%
11110268 5
 
1.0%
41110012 5
 
1.0%
41110216 5
 
1.0%
11110032 4
 
0.8%
11110070 4
 
0.8%
41110013 4
 
0.8%
Other values (299) 452
90.4%
ValueCountFrequency (%)
11110001 4
0.8%
11110002 2
0.4%
11110006 1
 
0.2%
11110009 2
0.4%
11110010 1
 
0.2%
11110011 3
0.6%
11110012 1
 
0.2%
11110013 2
0.4%
11110014 2
0.4%
11110016 2
0.4%
ValueCountFrequency (%)
41110219 4
0.8%
41110218 1
 
0.2%
41110216 5
1.0%
41110215 1
 
0.2%
41110214 1
 
0.2%
41110211 3
0.6%
41110210 2
 
0.4%
41110209 3
0.6%
41110206 1
 
0.2%
41110205 1
 
0.2%

교통수단코드
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.93
Minimum105
Maximum131
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-16T20:26:40.419018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum105
5-th percentile105
Q1115
median115
Q3120
95-th percentile130
Maximum131
Range26
Interquartile range (IQR)5

Descriptive statistics

Standard deviation6.9297289
Coefficient of variation (CV)0.059775113
Kurtosis-0.12428884
Mean115.93
Median Absolute Deviation (MAD)5
Skewness-0.050140305
Sum57965
Variance48.021142
MonotonicityNot monotonic
2024-04-16T20:26:40.519989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
120 180
36.0%
115 169
33.8%
105 106
21.2%
131 22
 
4.4%
130 15
 
3.0%
121 8
 
1.6%
ValueCountFrequency (%)
105 106
21.2%
115 169
33.8%
120 180
36.0%
121 8
 
1.6%
130 15
 
3.0%
131 22
 
4.4%
ValueCountFrequency (%)
131 22
 
4.4%
130 15
 
3.0%
121 8
 
1.6%
120 180
36.0%
115 169
33.8%
105 106
21.2%

교통수단명
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
지선버스(120)
180 
간선버스
169 
마을버스(105)
106 
심야버스(131)
22 
광역버스(130)
 
15

Length

Max length9
Median length9
Mean length7.31
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row마을버스(105)
2nd row마을버스(105)
3rd row마을버스(105)
4th row마을버스(105)
5th row마을버스(105)

Common Values

ValueCountFrequency (%)
지선버스(120) 180
36.0%
간선버스 169
33.8%
마을버스(105) 106
21.2%
심야버스(131) 22
 
4.4%
광역버스(130) 15
 
3.0%
지선버스(121) 8
 
1.6%

Length

2024-04-16T20:26:40.623557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T20:26:40.715516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지선버스(120 180
36.0%
간선버스 169
33.8%
마을버스(105 106
21.2%
심야버스(131 22
 
4.4%
광역버스(130 15
 
3.0%
지선버스(121 8
 
1.6%
Distinct309
Distinct (%)61.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-04-16T20:26:40.893018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length22
Mean length16.24
Min length12

Characters and Unicode

Total characters8120
Distinct characters271
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique191 ?
Unique (%)38.2%

Sample

1st row은평06(대성중.고등학교~불광동팀수양관)
2nd row금천01(적.독산역~벽산아파트)
3rd row금천10(독산역~디지털3단지운동장)
4th row양천01(동신아파트~당산역)
5th row종로13(평창동주민센터~부암동주민센터)
ValueCountFrequency (%)
2233번(면목동~옥수동 6
 
1.2%
덕정~종로5가 5
 
1.0%
360번(송파차고지~여의도 5
 
1.0%
542번(군포터미널~강남역 5
 
1.0%
6511번(구로동~서울대 5
 
1.0%
703번(문산선유리~서울역환승센터 5
 
1.0%
3413번(강일동공영차고지~수서경찰서 5
 
1.0%
108번(양주 5
 
1.0%
271번(면목동~상암동 4
 
0.8%
262번(중랑공영차고지~국회의사당 4
 
0.8%
Other values (307) 468
90.5%
2024-04-16T20:26:41.190258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
) 502
 
6.2%
( 502
 
6.2%
~ 500
 
6.2%
364
 
4.5%
339
 
4.2%
1 337
 
4.2%
0 230
 
2.8%
214
 
2.6%
2 212
 
2.6%
209
 
2.6%
Other values (261) 4711
58.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4918
60.6%
Decimal Number 1619
 
19.9%
Close Punctuation 502
 
6.2%
Open Punctuation 502
 
6.2%
Math Symbol 500
 
6.2%
Other Punctuation 30
 
0.4%
Uppercase Letter 29
 
0.4%
Space Separator 17
 
0.2%
Dash Punctuation 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
364
 
7.4%
339
 
6.9%
214
 
4.4%
209
 
4.2%
190
 
3.9%
159
 
3.2%
113
 
2.3%
110
 
2.2%
96
 
2.0%
87
 
1.8%
Other values (241) 3037
61.8%
Decimal Number
ValueCountFrequency (%)
1 337
20.8%
0 230
14.2%
2 212
13.1%
5 173
10.7%
6 172
10.6%
3 166
10.3%
7 130
 
8.0%
4 129
 
8.0%
9 42
 
2.6%
8 28
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
N 22
75.9%
A 4
 
13.8%
B 3
 
10.3%
Other Punctuation
ValueCountFrequency (%)
, 17
56.7%
. 13
43.3%
Close Punctuation
ValueCountFrequency (%)
) 502
100.0%
Open Punctuation
ValueCountFrequency (%)
( 502
100.0%
Math Symbol
ValueCountFrequency (%)
~ 500
100.0%
Space Separator
ValueCountFrequency (%)
17
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4918
60.6%
Common 3173
39.1%
Latin 29
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
364
 
7.4%
339
 
6.9%
214
 
4.4%
209
 
4.2%
190
 
3.9%
159
 
3.2%
113
 
2.3%
110
 
2.2%
96
 
2.0%
87
 
1.8%
Other values (241) 3037
61.8%
Common
ValueCountFrequency (%)
) 502
15.8%
( 502
15.8%
~ 500
15.8%
1 337
10.6%
0 230
7.2%
2 212
6.7%
5 173
 
5.5%
6 172
 
5.4%
3 166
 
5.2%
7 130
 
4.1%
Other values (7) 249
7.8%
Latin
ValueCountFrequency (%)
N 22
75.9%
A 4
 
13.8%
B 3
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4918
60.6%
ASCII 3202
39.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
) 502
15.7%
( 502
15.7%
~ 500
15.6%
1 337
10.5%
0 230
7.2%
2 212
6.6%
5 173
 
5.4%
6 172
 
5.4%
3 166
 
5.2%
7 130
 
4.1%
Other values (10) 278
8.7%
Hangul
ValueCountFrequency (%)
364
 
7.4%
339
 
6.9%
214
 
4.4%
209
 
4.2%
190
 
3.9%
159
 
3.2%
113
 
2.3%
110
 
2.2%
96
 
2.0%
87
 
1.8%
Other values (241) 3037
61.8%
Distinct304
Distinct (%)60.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-04-16T20:26:41.454640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length3.658
Min length3

Characters and Unicode

Total characters1829
Distinct characters46
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

Unique186 ?
Unique (%)37.2%

Sample

1st row은평06
2nd row금천01
3rd row금천10
4th row양천01
5th row종로13
ValueCountFrequency (%)
n61 7
 
1.4%
2233 6
 
1.2%
542 5
 
1.0%
703 5
 
1.0%
6511 5
 
1.0%
108 5
 
1.0%
3413 5
 
1.0%
360 5
 
1.0%
302 4
 
0.8%
2311 4
 
0.8%
Other values (294) 449
89.8%
2024-04-16T20:26:41.832349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 330
18.0%
0 230
12.6%
2 205
11.2%
6 172
9.4%
5 162
8.9%
3 160
8.7%
4 128
 
7.0%
7 125
 
6.8%
9 36
 
2.0%
8 27
 
1.5%
Other values (36) 254
13.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1575
86.1%
Other Letter 222
 
12.1%
Uppercase Letter 29
 
1.6%
Dash Punctuation 3
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
23
 
10.4%
18
 
8.1%
14
 
6.3%
14
 
6.3%
11
 
5.0%
9
 
4.1%
9
 
4.1%
8
 
3.6%
8
 
3.6%
8
 
3.6%
Other values (22) 100
45.0%
Decimal Number
ValueCountFrequency (%)
1 330
21.0%
0 230
14.6%
2 205
13.0%
6 172
10.9%
5 162
10.3%
3 160
10.2%
4 128
 
8.1%
7 125
 
7.9%
9 36
 
2.3%
8 27
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
N 22
75.9%
A 4
 
13.8%
B 3
 
10.3%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1578
86.3%
Hangul 222
 
12.1%
Latin 29
 
1.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
23
 
10.4%
18
 
8.1%
14
 
6.3%
14
 
6.3%
11
 
5.0%
9
 
4.1%
9
 
4.1%
8
 
3.6%
8
 
3.6%
8
 
3.6%
Other values (22) 100
45.0%
Common
ValueCountFrequency (%)
1 330
20.9%
0 230
14.6%
2 205
13.0%
6 172
10.9%
5 162
10.3%
3 160
10.1%
4 128
 
8.1%
7 125
 
7.9%
9 36
 
2.3%
8 27
 
1.7%
Latin
ValueCountFrequency (%)
N 22
75.9%
A 4
 
13.8%
B 3
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1607
87.9%
Hangul 222
 
12.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 330
20.5%
0 230
14.3%
2 205
12.8%
6 172
10.7%
5 162
10.1%
3 160
10.0%
4 128
 
8.0%
7 125
 
7.8%
9 36
 
2.2%
8 27
 
1.7%
Other values (4) 32
 
2.0%
Hangul
ValueCountFrequency (%)
23
 
10.4%
18
 
8.1%
14
 
6.3%
14
 
6.3%
11
 
5.0%
9
 
4.1%
9
 
4.1%
8
 
3.6%
8
 
3.6%
8
 
3.6%
Other values (22) 100
45.0%

정류장ID
Real number (ℝ)

HIGH CORRELATION 

Distinct486
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3956314.8
Minimum1911
Maximum9133550
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-16T20:26:41.971366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1911
5-th percentile8691.55
Q115580.75
median75141
Q39005895
95-th percentile9013845.2
Maximum9133550
Range9131639
Interquartile range (IQR)8990314.2

Descriptive statistics

Standard deviation4321263.5
Coefficient of variation (CV)1.0922446
Kurtosis-1.9464434
Mean3956314.8
Median Absolute Deviation (MAD)67027
Skewness0.20772669
Sum1.9781574 × 109
Variance1.8673318 × 1013
MonotonicityNot monotonic
2024-04-16T20:26:42.085878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73202 2
 
0.4%
12869 2
 
0.4%
8002663 2
 
0.4%
8922 2
 
0.4%
70141 2
 
0.4%
8002386 2
 
0.4%
9012353 2
 
0.4%
7404 2
 
0.4%
8501377 2
 
0.4%
72079 2
 
0.4%
Other values (476) 480
96.0%
ValueCountFrequency (%)
1911 1
0.2%
7197 2
0.4%
7256 1
0.2%
7404 2
0.4%
7452 1
0.2%
7479 1
0.2%
7569 1
0.2%
7634 1
0.2%
7651 1
0.2%
7684 1
0.2%
ValueCountFrequency (%)
9133550 1
0.2%
9113188 1
0.2%
9110211 1
0.2%
9107116 1
0.2%
9037014 1
0.2%
9036937 1
0.2%
9036500 1
0.2%
9036413 1
0.2%
9036229 1
0.2%
9036137 1
0.2%
Distinct465
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-04-16T20:26:42.279171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length15
Mean length6.726
Min length2

Characters and Unicode

Total characters3363
Distinct characters338
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique432 ?
Unique (%)86.4%

Sample

1st row새장골유래비
2nd row범일운수종점
3rd row구로세관
4th row신목중학교
5th row문방구
ValueCountFrequency (%)
월곡뉴타운 3
 
0.6%
논현역 3
 
0.6%
독립문역.한성과학고 2
 
0.4%
종로2가 2
 
0.4%
신내교회.신내데시앙아파트 2
 
0.4%
광화문 2
 
0.4%
종로5가 2
 
0.4%
방화2동우촌연립 2
 
0.4%
불광역 2
 
0.4%
신반포역 2
 
0.4%
Other values (457) 480
95.6%
2024-04-16T20:26:42.630975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
94
 
2.8%
79
 
2.3%
78
 
2.3%
76
 
2.3%
75
 
2.2%
75
 
2.2%
73
 
2.2%
. 70
 
2.1%
57
 
1.7%
53
 
1.6%
Other values (328) 2633
78.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3169
94.2%
Decimal Number 99
 
2.9%
Other Punctuation 71
 
2.1%
Uppercase Letter 22
 
0.7%
Space Separator 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
94
 
3.0%
79
 
2.5%
78
 
2.5%
76
 
2.4%
75
 
2.4%
75
 
2.4%
73
 
2.3%
57
 
1.8%
53
 
1.7%
51
 
1.6%
Other values (309) 2458
77.6%
Decimal Number
ValueCountFrequency (%)
1 25
25.3%
2 21
21.2%
3 11
11.1%
4 11
11.1%
5 10
 
10.1%
6 6
 
6.1%
7 5
 
5.1%
0 4
 
4.0%
9 3
 
3.0%
8 3
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
T 7
31.8%
K 6
27.3%
N 3
13.6%
S 3
13.6%
Y 2
 
9.1%
G 1
 
4.5%
Other Punctuation
ValueCountFrequency (%)
. 70
98.6%
& 1
 
1.4%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3169
94.2%
Common 172
 
5.1%
Latin 22
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
94
 
3.0%
79
 
2.5%
78
 
2.5%
76
 
2.4%
75
 
2.4%
75
 
2.4%
73
 
2.3%
57
 
1.8%
53
 
1.7%
51
 
1.6%
Other values (309) 2458
77.6%
Common
ValueCountFrequency (%)
. 70
40.7%
1 25
 
14.5%
2 21
 
12.2%
3 11
 
6.4%
4 11
 
6.4%
5 10
 
5.8%
6 6
 
3.5%
7 5
 
2.9%
0 4
 
2.3%
9 3
 
1.7%
Other values (3) 6
 
3.5%
Latin
ValueCountFrequency (%)
T 7
31.8%
K 6
27.3%
N 3
13.6%
S 3
13.6%
Y 2
 
9.1%
G 1
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3169
94.2%
ASCII 194
 
5.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
94
 
3.0%
79
 
2.5%
78
 
2.5%
76
 
2.4%
75
 
2.4%
75
 
2.4%
73
 
2.3%
57
 
1.8%
53
 
1.7%
51
 
1.6%
Other values (309) 2458
77.6%
ASCII
ValueCountFrequency (%)
. 70
36.1%
1 25
 
12.9%
2 21
 
10.8%
3 11
 
5.7%
4 11
 
5.7%
5 10
 
5.2%
T 7
 
3.6%
K 6
 
3.1%
6 6
 
3.1%
7 5
 
2.6%
Other values (9) 22
 
11.3%

정류장수
Real number (ℝ)

HIGH CORRELATION 

Distinct123
Distinct (%)24.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.52
Minimum11
Maximum182
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-16T20:26:42.754270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile24
Q150
median84
Q3113
95-th percentile154
Maximum182
Range171
Interquartile range (IQR)63

Descriptive statistics

Standard deviation39.56038
Coefficient of variation (CV)0.47366355
Kurtosis-0.53671618
Mean83.52
Median Absolute Deviation (MAD)30
Skewness0.23041268
Sum41760
Variance1565.0236
MonotonicityNot monotonic
2024-04-16T20:26:42.863908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73 13
 
2.6%
119 12
 
2.4%
87 12
 
2.4%
32 10
 
2.0%
111 10
 
2.0%
101 10
 
2.0%
113 10
 
2.0%
36 9
 
1.8%
99 9
 
1.8%
28 9
 
1.8%
Other values (113) 396
79.2%
ValueCountFrequency (%)
11 1
 
0.2%
12 1
 
0.2%
13 1
 
0.2%
14 2
0.4%
16 2
0.4%
18 3
0.6%
19 3
0.6%
20 1
 
0.2%
21 3
0.6%
22 1
 
0.2%
ValueCountFrequency (%)
182 5
1.0%
179 4
0.8%
178 3
0.6%
164 5
1.0%
160 3
0.6%
156 3
0.6%
154 3
0.6%
150 1
 
0.2%
147 1
 
0.2%
145 1
 
0.2%

정류장순서
Real number (ℝ)

HIGH CORRELATION 

Distinct125
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.43
Minimum1
Maximum170
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-16T20:26:42.976762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q116
median36
Q367
95-th percentile111.05
Maximum170
Range169
Interquartile range (IQR)51

Descriptive statistics

Standard deviation34.142885
Coefficient of variation (CV)0.76846466
Kurtosis0.062452795
Mean44.43
Median Absolute Deviation (MAD)23
Skewness0.83391096
Sum22215
Variance1165.7366
MonotonicityNot monotonic
2024-04-16T20:26:43.097904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 14
 
2.8%
2 13
 
2.6%
6 12
 
2.4%
10 10
 
2.0%
29 9
 
1.8%
24 9
 
1.8%
3 9
 
1.8%
28 8
 
1.6%
21 8
 
1.6%
40 8
 
1.6%
Other values (115) 400
80.0%
ValueCountFrequency (%)
1 7
1.4%
2 13
2.6%
3 9
1.8%
4 7
1.4%
5 6
1.2%
6 12
2.4%
7 5
 
1.0%
8 7
1.4%
9 6
1.2%
10 10
2.0%
ValueCountFrequency (%)
170 1
0.2%
148 1
0.2%
144 1
0.2%
143 1
0.2%
138 1
0.2%
136 1
0.2%
131 2
0.4%
129 1
0.2%
128 1
0.2%
127 1
0.2%

정류장X좌표
Real number (ℝ)

Distinct484
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12676.43
Minimum12643.808
Maximum12712.491
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-16T20:26:43.216470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12643.808
5-th percentile12649.995
Q112655.039
median12658.597
Q312702.662
95-th percentile12707.567
Maximum12712.491
Range68.6833
Interquartile range (IQR)47.622525

Descriptive statistics

Standard deviation24.459388
Coefficient of variation (CV)0.0019295171
Kurtosis-1.8910947
Mean12676.43
Median Absolute Deviation (MAD)8.3873
Skewness0.21674616
Sum6338215
Variance598.26168
MonotonicityNot monotonic
2024-04-16T20:26:43.326453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12659.677 2
 
0.4%
12700.6906 2
 
0.4%
12701.5418 2
 
0.4%
12657.7889 2
 
0.4%
12653.6741 2
 
0.4%
12648.6906 2
 
0.4%
12658.4331 2
 
0.4%
12658.188 2
 
0.4%
12701.6596 2
 
0.4%
12701.4251 2
 
0.4%
Other values (474) 480
96.0%
ValueCountFrequency (%)
12643.8078 1
0.2%
12644.7353 1
0.2%
12644.7564 1
0.2%
12645.9914 1
0.2%
12646.02 1
0.2%
12646.0402 1
0.2%
12646.1583 1
0.2%
12646.5123 1
0.2%
12646.8852 1
0.2%
12646.9371 1
0.2%
ValueCountFrequency (%)
12712.4911 1
0.2%
12710.7806 1
0.2%
12710.7161 1
0.2%
12710.397 1
0.2%
12710.224 1
0.2%
12709.4015 1
0.2%
12708.9101 1
0.2%
12708.7979 1
0.2%
12708.5269 1
0.2%
12708.5031 1
0.2%

정류장Y좌표
Real number (ℝ)

Distinct486
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3732.6729
Minimum3719.8596
Maximum3747.106
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-16T20:26:43.465600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3719.8596
5-th percentile3726.1892
Q13729.6795
median3732.7344
Q33735.5628
95-th percentile3739.38
Maximum3747.106
Range27.2464
Interquartile range (IQR)5.883275

Descriptive statistics

Standard deviation4.3563809
Coefficient of variation (CV)0.0011670942
Kurtosis0.49868803
Mean3732.6729
Median Absolute Deviation (MAD)2.9879
Skewness-0.0138393
Sum1866336.5
Variance18.978054
MonotonicityNot monotonic
2024-04-16T20:26:43.603337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3734.6996 2
 
0.4%
3736.3686 2
 
0.4%
3731.678 2
 
0.4%
3726.3295 2
 
0.4%
3730.3818 2
 
0.4%
3721.2411 2
 
0.4%
3732.8865 2
 
0.4%
3734.2036 2
 
0.4%
3738.29 2
 
0.4%
3728.5562 2
 
0.4%
Other values (476) 480
96.0%
ValueCountFrequency (%)
3719.8596 1
0.2%
3720.2931 1
0.2%
3720.3639 1
0.2%
3721.0153 1
0.2%
3721.0522 1
0.2%
3721.0864 1
0.2%
3721.2411 2
0.4%
3721.4494 1
0.2%
3721.8651 1
0.2%
3722.2093 1
0.2%
ValueCountFrequency (%)
3747.106 1
0.2%
3745.9321 1
0.2%
3745.2932 1
0.2%
3744.461 1
0.2%
3744.3719 1
0.2%
3743.2953 1
0.2%
3743.1467 1
0.2%
3742.7534 1
0.2%
3741.969 1
0.2%
3741.7461 1
0.2%

진입각도
Real number (ℝ)

Distinct261
Distinct (%)52.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180.084
Minimum0
Maximum359
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-16T20:26:43.713693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13.95
Q188
median178
Q3268
95-th percentile344.05
Maximum359
Range359
Interquartile range (IQR)180

Descriptive statistics

Standard deviation105.8859
Coefficient of variation (CV)0.58798063
Kurtosis-1.1910912
Mean180.084
Median Absolute Deviation (MAD)90
Skewness-0.0031536054
Sum90042
Variance11211.825
MonotonicityNot monotonic
2024-04-16T20:26:43.834336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75 6
 
1.2%
316 5
 
1.0%
232 5
 
1.0%
144 5
 
1.0%
3 5
 
1.0%
348 5
 
1.0%
338 5
 
1.0%
134 4
 
0.8%
96 4
 
0.8%
220 4
 
0.8%
Other values (251) 452
90.4%
ValueCountFrequency (%)
0 1
 
0.2%
1 1
 
0.2%
2 2
 
0.4%
3 5
1.0%
4 3
0.6%
5 1
 
0.2%
6 2
 
0.4%
8 1
 
0.2%
9 4
0.8%
10 2
 
0.4%
ValueCountFrequency (%)
359 3
0.6%
358 3
0.6%
356 1
 
0.2%
354 1
 
0.2%
353 2
 
0.4%
352 1
 
0.2%
350 1
 
0.2%
349 1
 
0.2%
348 5
1.0%
347 3
0.6%

총누적거리(km)
Real number (ℝ)

HIGH CORRELATION 

Distinct308
Distinct (%)61.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.838224
Minimum1.636
Maximum101.857
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-16T20:26:43.981876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.636
5-th percentile6.55085
Q118.1865
median38.6015
Q355.458
95-th percentile77.546
Maximum101.857
Range100.221
Interquartile range (IQR)37.2715

Descriptive statistics

Standard deviation23.201975
Coefficient of variation (CV)0.59740053
Kurtosis-0.68140122
Mean38.838224
Median Absolute Deviation (MAD)18.3745
Skewness0.27522696
Sum19419.112
Variance538.33167
MonotonicityNot monotonic
2024-04-16T20:26:44.107941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42.688 6
 
1.2%
55.757 5
 
1.0%
26.96 5
 
1.0%
88.542 5
 
1.0%
87.479 5
 
1.0%
38.616 5
 
1.0%
62.07 5
 
1.0%
51.611 4
 
0.8%
90.303 4
 
0.8%
77.546 4
 
0.8%
Other values (298) 452
90.4%
ValueCountFrequency (%)
1.636 1
0.2%
2.133 1
0.2%
2.741 2
0.4%
2.799 1
0.2%
3.364 1
0.2%
3.461 1
0.2%
3.625 1
0.2%
3.929 1
0.2%
4.094 1
0.2%
4.16 1
0.2%
ValueCountFrequency (%)
101.857 3
0.6%
90.303 4
0.8%
89.968 3
0.6%
88.542 5
1.0%
87.479 5
1.0%
79.247 1
 
0.2%
78.964 2
 
0.4%
77.546 4
0.8%
76.151 2
 
0.4%
75.999 3
0.6%

정류장누적거리(m)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct489
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20343.33
Minimum0
Maximum91512
Zeros7
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-16T20:26:44.216317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile520.6
Q15173
median14531.5
Q332442
95-th percentile53460.1
Maximum91512
Range91512
Interquartile range (IQR)27269

Descriptive statistics

Standard deviation18457.997
Coefficient of variation (CV)0.90732425
Kurtosis0.48903696
Mean20343.33
Median Absolute Deviation (MAD)11251
Skewness1.023164
Sum10171665
Variance3.4069764 × 108
MonotonicityNot monotonic
2024-04-16T20:26:44.319997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7
 
1.4%
2002 3
 
0.6%
6640 2
 
0.4%
16402 2
 
0.4%
7129 2
 
0.4%
67024 1
 
0.2%
29412 1
 
0.2%
30715 1
 
0.2%
39010 1
 
0.2%
24613 1
 
0.2%
Other values (479) 479
95.8%
ValueCountFrequency (%)
0 7
1.4%
61 1
 
0.2%
130 1
 
0.2%
161 1
 
0.2%
196 1
 
0.2%
212 1
 
0.2%
251 1
 
0.2%
256 1
 
0.2%
278 1
 
0.2%
279 1
 
0.2%
ValueCountFrequency (%)
91512 1
0.2%
87429 1
0.2%
77656 1
0.2%
74009 1
0.2%
72515 1
0.2%
71723 1
0.2%
71259 1
0.2%
70016 1
0.2%
69503 1
0.2%
69356 1
0.2%

정류장간차이(m)
Real number (ℝ)

ZEROS 

Distinct346
Distinct (%)69.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean423.074
Minimum0
Maximum3588
Zeros7
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-16T20:26:44.433669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile135.9
Q1250
median362
Q3510
95-th percentile837.7
Maximum3588
Range3588
Interquartile range (IQR)260

Descriptive statistics

Standard deviation304.70257
Coefficient of variation (CV)0.72021105
Kurtosis31.499345
Mean423.074
Median Absolute Deviation (MAD)124
Skewness4.2048165
Sum211537
Variance92843.656
MonotonicityNot monotonic
2024-04-16T20:26:44.856462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
243 7
 
1.4%
0 7
 
1.4%
362 5
 
1.0%
300 4
 
0.8%
410 4
 
0.8%
328 4
 
0.8%
262 4
 
0.8%
225 4
 
0.8%
539 3
 
0.6%
219 3
 
0.6%
Other values (336) 455
91.0%
ValueCountFrequency (%)
0 7
1.4%
59 1
 
0.2%
61 1
 
0.2%
88 1
 
0.2%
91 1
 
0.2%
93 1
 
0.2%
96 1
 
0.2%
99 2
 
0.4%
103 1
 
0.2%
105 1
 
0.2%
ValueCountFrequency (%)
3588 1
0.2%
2518 1
0.2%
2291 1
0.2%
1809 1
0.2%
1666 1
0.2%
1587 1
0.2%
1574 1
0.2%
1559 1
0.2%
1391 1
0.2%
1194 1
0.2%

종료일자
Categorical

IMBALANCE 

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
99991231
492 
20150903
 
3
20150824
 
2
20150901
 
2
20150803
 
1

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row99991231
2nd row20150903
3rd row20150824
4th row99991231
5th row99991231

Common Values

ValueCountFrequency (%)
99991231 492
98.4%
20150903 3
 
0.6%
20150824 2
 
0.4%
20150901 2
 
0.4%
20150803 1
 
0.2%

Length

2024-04-16T20:26:44.990487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T20:26:45.094943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
99991231 492
98.4%
20150903 3
 
0.6%
20150824 2
 
0.4%
20150901 2
 
0.4%
20150803 1
 
0.2%

Interactions

2024-04-16T20:26:38.419228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:29.018600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:29.982928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:30.868349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:31.748160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:32.690281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:33.645138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:34.828687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:35.728890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:36.671419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:37.538853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:38.525740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:29.096694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:30.064466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:30.966278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:31.835488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:32.770981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:33.733401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:34.915219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:35.811727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:36.750577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:37.626887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:38.607530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:29.188794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:30.145769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:31.051311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:31.911972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:32.851229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:33.812687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:34.991069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:35.888065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:36.841201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:37.716751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:38.697640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:29.258942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:30.227495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:31.117345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:31.999983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:32.948799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:33.890563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:35.068862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:35.979520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:36.915288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:37.784406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:38.772540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:29.337825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:30.308406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:31.195940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:32.097717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:33.030591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:33.980566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:35.149418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:36.073150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:37.000499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:37.860925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:38.854969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:29.416331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:30.384703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:31.300249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:32.219399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:33.111640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:34.076294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:35.227830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:36.162678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:37.076364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:37.942112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:38.933321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:29.511959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:30.462524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:31.383000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:32.300726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:33.189158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:34.165973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:35.306877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:36.267243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:37.164076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:38.021090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:39.020772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:29.604873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:30.538684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:31.452098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:32.387341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:33.279362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:34.501770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:35.381327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:36.343087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:37.240770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:38.097677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:39.113677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:29.701345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:30.637060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:31.529180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:32.464082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:33.374151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:34.593314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:35.458567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:36.422368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:37.320479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:38.172530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:39.195489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:29.807180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:30.713209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:31.601988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:32.539325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:33.478949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:34.670135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:35.564430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:36.496883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:37.390721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:38.243164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:39.276432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:29.892745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:30.785602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:31.671127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:32.608090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:33.550959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:34.739632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:35.640241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:36.591269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:37.460898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T20:26:38.312560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-16T20:26:45.174239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
조합구분버스노선ID교통수단코드교통수단명정류장ID정류장수정류장순서정류장X좌표정류장Y좌표진입각도총누적거리(km)정류장누적거리(m)정류장간차이(m)종료일자
조합구분1.0000.3951.0001.0000.4870.9280.5510.0620.1860.0000.9500.6720.2890.145
버스노선ID0.3951.0000.2790.5540.0440.5040.3780.1900.6230.1400.6650.4780.2180.044
교통수단코드1.0000.2791.0001.0000.5870.8220.5520.3490.1390.0000.9160.6740.2640.169
교통수단명1.0000.5541.0001.0000.8700.6900.4280.2350.2320.0000.7860.5370.2820.067
정류장ID0.4870.0440.5870.8701.0000.5570.3810.0940.0370.1440.6050.4440.3800.104
정류장수0.9280.5040.8220.6900.5571.0000.7640.3010.4630.1070.9310.7190.2590.195
정류장순서0.5510.3780.5520.4280.3810.7641.0000.2320.5930.1770.6640.9470.1540.000
정류장X좌표0.0620.1900.3490.2350.0940.3010.2321.0000.4810.1740.3540.1920.1070.151
정류장Y좌표0.1860.6230.1390.2320.0370.4630.5930.4811.0000.1730.4190.7090.0000.561
진입각도0.0000.1400.0000.0000.1440.1070.1770.1740.1731.0000.1270.1430.0000.000
총누적거리(km)0.9500.6650.9160.7860.6050.9310.6640.3540.4190.1271.0000.7670.2340.159
정류장누적거리(m)0.6720.4780.6740.5370.4440.7190.9470.1920.7090.1430.7671.0000.1860.000
정류장간차이(m)0.2890.2180.2640.2820.3800.2590.1540.1070.0000.0000.2340.1861.0000.000
종료일자0.1450.0440.1690.0670.1040.1950.0000.1510.5610.0000.1590.0000.0001.000
2024-04-16T20:26:45.296172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
종료일자조합구분교통수단명
종료일자1.0000.1760.045
조합구분0.1761.0000.996
교통수단명0.0450.9961.000
2024-04-16T20:26:45.380611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
버스노선ID교통수단코드정류장ID정류장수정류장순서정류장X좌표정류장Y좌표진입각도총누적거리(km)정류장누적거리(m)정류장간차이(m)조합구분교통수단명종료일자
버스노선ID1.0000.0750.1800.0570.025-0.137-0.1600.0680.0950.0410.0100.2560.3970.053
교통수단코드0.0751.000-0.4630.3880.2460.0020.007-0.0060.4030.2830.2460.9970.9990.064
정류장ID0.180-0.4631.000-0.366-0.271-0.074-0.0310.024-0.348-0.289-0.2200.7440.5710.078
정류장수0.0570.388-0.3661.0000.6170.0970.052-0.0130.9410.6490.2990.7680.4520.082
정류장순서0.0250.246-0.2710.6171.0000.0590.074-0.0320.5830.9750.2530.4220.2410.000
정류장X좌표-0.1370.002-0.0740.0970.0591.0000.179-0.0390.0600.0530.1300.0790.1620.057
정류장Y좌표-0.1600.007-0.0310.0520.0740.1791.000-0.0670.0020.040-0.0420.1430.1190.265
진입각도0.068-0.0060.024-0.013-0.032-0.039-0.0671.0000.006-0.0230.0540.0000.0000.000
총누적거리(km)0.0950.403-0.3480.9410.5830.0600.0020.0061.0000.6700.3690.8040.5660.066
정류장누적거리(m)0.0410.283-0.2890.6490.9750.0530.040-0.0230.6701.0000.3330.5190.3170.000
정류장간차이(m)0.0100.246-0.2200.2990.2530.130-0.0420.0540.3690.3331.0000.2870.1440.000
조합구분0.2560.9970.7440.7680.4220.0790.1430.0000.8040.5190.2871.0000.9960.176
교통수단명0.3970.9990.5710.4520.2410.1620.1190.0000.5660.3170.1440.9961.0000.045
종료일자0.0530.0640.0780.0820.0000.0570.2650.0000.0660.0000.0000.1760.0451.000

Missing values

2024-04-16T20:26:39.413256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-16T20:26:39.872521image/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

조합구분버스노선ID교통수단코드교통수단명노선명노선번호정류장ID정류장명정류장수정류장순서정류장X좌표정류장Y좌표진입각도총누적거리(km)정류장누적거리(m)정류장간차이(m)종료일자
0마을11110084105마을버스(105)은평06(대성중.고등학교~불광동팀수양관)은평069012124새장골유래비211512655.45833737.22782384.81332933199991231
1마을11110734105마을버스(105)금천01(적.독산역~벽산아파트)금천019012549범일운수종점281912654.66513727.03562788.028504122620150903
2마을11110751105마을버스(105)금천10(독산역~디지털3단지운동장)금천109037014구로세관201712653.10253728.34561565.494428424820150824
3마을11110696105마을버스(105)양천01(동신아파트~당산역)양천019007773신목중학교35812652.3183732.22522912.65183617499991231
4마을11110720105마을버스(105)종로13(평창동주민센터~부암동주민센터)종로139007311문방구37812657.47953736.37772337.642164011699991231
5마을11110685105마을버스(105)성북15(고려아파트~한미약국)성북159011172현대2차아파트18712701.91783735.8705832.1336789199991231
6마을11111163105마을버스(105)강남10(개포4단지7단지~양재역)강남109012819양재1동주민센터442012702.19223729.104526315.481638869999991231
7마을11110806105마을버스(105)서초18-1(서초네이처힐5단지~양재역)서초18-19012801교육개발원입구사거리231012702.31443728.77253417.774270628299991231
8마을11110599105마을버스(105)동작17(극동아파트~이수힐스테이트)동작179036937경문고교앞251112658.96023729.429535.754221235699991231
9마을11110517105마을버스(105)강북05(번동초등학교~한일아파트)강북059009013롯데백화점362612701.85633736.86743477.343503921499991231
조합구분버스노선ID교통수단코드교통수단명노선명노선번호정류장ID정류장명정류장수정류장순서정류장X좌표정류장Y좌표진입각도총누적거리(km)정류장누적거리(m)정류장간차이(m)종료일자
490시내11110010115간선버스130번(우이동~길동)13075114길동주유소844212708.40263732.45589349.1762437031699991231
491시내41110211115간선버스661번(부천상동~영등포역,신세계백화점)66115353팰리스카운티아파트122812645.99143729.289810053.182242036399991231
492시내11110059115간선버스604번(신월동~중구청)60471018광성중고등학교866212656.38573732.938329945.6163207130399991231
493시내11110354120지선버스(120)7212번(은평차고지~옥수동)72129002930신사초등학교14012712654.81323735.590831559.2135212537099991231
494시내11110892120지선버스(120)2235(능말~신이문역)22358003106우림시장.망우사거리441012705.92813735.982125414.777375225120150901
495시내11110048115간선버스406번(개포동~서울역)40675143방배프라자705012659.39563729.597315342.9473365342499991231
496시내11110213120지선버스(120)3413번(강일동공영차고지~수서경찰서)341312184동명약국앞876912708.26123731.84881638.6163225635199991231
497시내11110860120지선버스(120)6647(상사마을~송정중후문)66478100935상사마을기점46212648.10863735.321818819.5221666166699991231
498시내11110198120지선버스(120)3212번(강동공영차고지~강변역)321212119대순진리교622412707.87533731.822128925.872799828799991231
499시내11110070115간선버스752번(구산동~노량진)75275079남성역1014012658.24333729.097429651.6112108224399991231