Overview

Dataset statistics

Number of variables9
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory37.7 KiB
Average record size in memory77.3 B

Variable types

Categorical1
Text4
Numeric4

Dataset

Description샘플 데이터
Author지하철 : 서울시버스정류장 : 서울시(스마트카드사)
URLhttps://bigdata.seoul.go.kr/data/selectSampleData.do?sample_data_seq=15

Alerts

기준연월(YYYY_MM) has constant value ""Constant

Reproduction

Analysis started2023-12-10 14:53:13.285117
Analysis finished2023-12-10 14:53:16.374542
Duration3.09 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준연월(YYYY_MM)
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
201810
500 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row201810
2nd row201810
3rd row201810
4th row201810
5th row201810

Common Values

ValueCountFrequency (%)
201810 500
100.0%

Length

2023-12-10T23:53:16.459032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:53:16.593651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
201810 500
100.0%

버스노선버스_설명(LINE_NO_EXP)
Text

Distinct315
Distinct (%)63.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-10T23:53:16.886058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length22
Mean length16.578
Min length12

Characters and Unicode

Total characters8289
Distinct characters276
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

Unique187 ?
Unique (%)37.4%

Sample

1st row172번(하계동~월드컵2.3단지)
2nd row7723번(진관공영차고지~구파발역)
3rd row606번(부천상동~종로1가)
4th rowN37번(진관공영차고지~송파공영차고지)
5th row영등포13(대방천사거리~신도림역)
ValueCountFrequency (%)
n62번(면목동차고지~양천공영차고지 6
 
1.2%
2221번(자양동~신설동 4
 
0.8%
n15번(우이동~사당역 4
 
0.8%
2224번(성수동~강변역 4
 
0.8%
5623번(군포 4
 
0.8%
n65(범일차고지~개화공영차고지 4
 
0.8%
공영차고지~여의도 4
 
0.8%
6516번(양천차고지~박미고개 4
 
0.8%
6712번(방화동~대흥사거리 4
 
0.8%
242번(중랑공영차고지~개포시영아파트 4
 
0.8%
Other values (311) 472
91.8%
2023-12-10T23:53:17.494279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
) 503
 
6.1%
( 503
 
6.1%
~ 502
 
6.1%
번 381
 
4.6%
동 347
 
4.2%
1 336
 
4.1%
2 256
 
3.1%
지 239
 
2.9%
0 222
 
2.7%
고 214
 
2.6%
Other values (266) 4786
57.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5057
61.0%
Decimal Number 1635
 
19.7%
Close Punctuation 503
 
6.1%
Open Punctuation 503
 
6.1%
Math Symbol 502
 
6.1%
Uppercase Letter 44
 
0.5%
Other Punctuation 28
 
0.3%
Space Separator 14
 
0.2%
Dash Punctuation 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
번 381
 
7.5%
동 347
 
6.9%
지 239
 
4.7%
고 214
 
4.2%
역 200
 
4.0%
차 196
 
3.9%
영 130
 
2.6%
공 121
 
2.4%
대 93
 
1.8%
서 91
 
1.8%
Other values (245) 3045
60.2%
Decimal Number
ValueCountFrequency (%)
1 336
20.6%
2 256
15.7%
0 222
13.6%
6 180
11.0%
3 151
9.2%
7 144
8.8%
5 144
8.8%
4 139
8.5%
9 36
 
2.2%
8 27
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
N 31
70.5%
B 7
 
15.9%
A 5
 
11.4%
T 1
 
2.3%
Other Punctuation
ValueCountFrequency (%)
, 19
67.9%
. 9
32.1%
Close Punctuation
ValueCountFrequency (%)
) 503
100.0%
Open Punctuation
ValueCountFrequency (%)
( 503
100.0%
Math Symbol
ValueCountFrequency (%)
~ 502
100.0%
Space Separator
ValueCountFrequency (%)
14
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5057
61.0%
Common 3188
38.5%
Latin 44
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
번 381
 
7.5%
동 347
 
6.9%
지 239
 
4.7%
고 214
 
4.2%
역 200
 
4.0%
차 196
 
3.9%
영 130
 
2.6%
공 121
 
2.4%
대 93
 
1.8%
서 91
 
1.8%
Other values (245) 3045
60.2%
Common
ValueCountFrequency (%)
) 503
15.8%
( 503
15.8%
~ 502
15.7%
1 336
10.5%
2 256
8.0%
0 222
7.0%
6 180
 
5.6%
3 151
 
4.7%
7 144
 
4.5%
5 144
 
4.5%
Other values (7) 247
7.7%
Latin
ValueCountFrequency (%)
N 31
70.5%
B 7
 
15.9%
A 5
 
11.4%
T 1
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5057
61.0%
ASCII 3232
39.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
) 503
15.6%
( 503
15.6%
~ 502
15.5%
1 336
10.4%
2 256
7.9%
0 222
6.9%
6 180
 
5.6%
3 151
 
4.7%
7 144
 
4.5%
5 144
 
4.5%
Other values (11) 291
9.0%
Hangul
ValueCountFrequency (%)
번 381
 
7.5%
동 347
 
6.9%
지 239
 
4.7%
고 214
 
4.2%
역 200
 
4.0%
차 196
 
3.9%
영 130
 
2.6%
공 121
 
2.4%
대 93
 
1.8%
서 91
 
1.8%
Other values (245) 3045
60.2%

버스노선번호(LINE_NO)
Text

Distinct310
Distinct (%)62.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-10T23:53:17.902082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.428
Min length2

Characters and Unicode

Total characters2214
Distinct characters49
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

Unique196 ?
Unique (%)39.2%

Sample

1st row9711A번
2nd row양천02
3rd row7025번
4th row3214번
5th row106번
ValueCountFrequency (%)
5531번 6
 
1.2%
n13번 5
 
1.0%
6628번 5
 
1.0%
201번 5
 
1.0%
2016번 5
 
1.0%
4318번 5
 
1.0%
643번 4
 
0.8%
105번 4
 
0.8%
n16번 4
 
0.8%
173번 4
 
0.8%
Other values (300) 453
90.6%
2023-12-10T23:53:18.492980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
번 388
17.5%
1 337
15.2%
0 224
10.1%
2 211
9.5%
3 166
7.5%
6 160
7.2%
7 152
 
6.9%
5 136
 
6.1%
4 135
 
6.1%
9 34
 
1.5%
Other values (39) 271
12.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1588
71.7%
Other Letter 589
 
26.6%
Uppercase Letter 31
 
1.4%
Dash Punctuation 6
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
번 388
65.9%
서 20
 
3.4%
강 19
 
3.2%
동 15
 
2.5%
포 14
 
2.4%
성 12
 
2.0%
천 11
 
1.9%
북 10
 
1.7%
초 9
 
1.5%
로 7
 
1.2%
Other values (25) 84
 
14.3%
Decimal Number
ValueCountFrequency (%)
1 337
21.2%
0 224
14.1%
2 211
13.3%
3 166
10.5%
6 160
10.1%
7 152
9.6%
5 136
8.6%
4 135
8.5%
9 34
 
2.1%
8 33
 
2.1%
Uppercase Letter
ValueCountFrequency (%)
N 24
77.4%
A 4
 
12.9%
B 3
 
9.7%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1594
72.0%
Hangul 589
 
26.6%
Latin 31
 
1.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
번 388
65.9%
서 20
 
3.4%
강 19
 
3.2%
동 15
 
2.5%
포 14
 
2.4%
성 12
 
2.0%
천 11
 
1.9%
북 10
 
1.7%
초 9
 
1.5%
로 7
 
1.2%
Other values (25) 84
 
14.3%
Common
ValueCountFrequency (%)
1 337
21.1%
0 224
14.1%
2 211
13.2%
3 166
10.4%
6 160
10.0%
7 152
9.5%
5 136
8.5%
4 135
8.5%
9 34
 
2.1%
8 33
 
2.1%
Latin
ValueCountFrequency (%)
N 24
77.4%
A 4
 
12.9%
B 3
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1625
73.4%
Hangul 589
 
26.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
번 388
65.9%
서 20
 
3.4%
강 19
 
3.2%
동 15
 
2.5%
포 14
 
2.4%
성 12
 
2.0%
천 11
 
1.9%
북 10
 
1.7%
초 9
 
1.5%
로 7
 
1.2%
Other values (25) 84
 
14.3%
ASCII
ValueCountFrequency (%)
1 337
20.7%
0 224
13.8%
2 211
13.0%
3 166
10.2%
6 160
9.8%
7 152
9.4%
5 136
8.4%
4 135
8.3%
9 34
 
2.1%
8 33
 
2.0%
Other values (4) 37
 
2.3%

버스노선설명(LINE_EXP)
Text

Distinct323
Distinct (%)64.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-10T23:53:18.798885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length17
Mean length10.436
Min length6

Characters and Unicode

Total characters5218
Distinct characters281
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

Unique191 ?
Unique (%)38.2%

Sample

1st row두산.한신아파트~불광역
2nd row하계동~장암동
3rd row중랑공영차고지~신사역사거리
4th row신도림역~구로디지털단지역
5th row구로동~서울대
ValueCountFrequency (%)
송파공영차고지~진관공영차고지 6
 
1.2%
양천공영차고지~상계동차고지 6
 
1.2%
군포버스공영차고지~구로디지탈단지역 5
 
1.0%
분당~영등포 4
 
0.8%
신월동~상왕십리 4
 
0.8%
중랑공영차고지~강서공영차고지 4
 
0.8%
중랑공영차고지~석계역 4
 
0.8%
구로동~개포동 4
 
0.8%
은평공영차고지~홍제역 4
 
0.8%
성남 4
 
0.8%
Other values (319) 467
91.2%
2023-12-10T23:53:19.331994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
~ 502
 
9.6%
동 295
 
5.7%
지 259
 
5.0%
역 232
 
4.4%
고 215
 
4.1%
차 201
 
3.9%
공 140
 
2.7%
영 140
 
2.7%
대 89
 
1.7%
서 70
 
1.3%
Other values (271) 3075
58.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4599
88.1%
Math Symbol 502
 
9.6%
Decimal Number 45
 
0.9%
Other Punctuation 35
 
0.7%
Uppercase Letter 24
 
0.5%
Space Separator 12
 
0.2%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 295
 
6.4%
지 259
 
5.6%
역 232
 
5.0%
고 215
 
4.7%
차 201
 
4.4%
공 140
 
3.0%
영 140
 
3.0%
대 89
 
1.9%
서 70
 
1.5%
구 65
 
1.4%
Other values (251) 2893
62.9%
Decimal Number
ValueCountFrequency (%)
1 17
37.8%
2 15
33.3%
5 5
 
11.1%
3 3
 
6.7%
9 2
 
4.4%
7 1
 
2.2%
8 1
 
2.2%
4 1
 
2.2%
Uppercase Letter
ValueCountFrequency (%)
A 6
25.0%
B 6
25.0%
H 3
12.5%
L 3
12.5%
D 2
 
8.3%
M 2
 
8.3%
C 2
 
8.3%
Other Punctuation
ValueCountFrequency (%)
, 25
71.4%
. 10
 
28.6%
Math Symbol
ValueCountFrequency (%)
~ 502
100.0%
Space Separator
ValueCountFrequency (%)
12
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4599
88.1%
Common 594
 
11.4%
Latin 25
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 295
 
6.4%
지 259
 
5.6%
역 232
 
5.0%
고 215
 
4.7%
차 201
 
4.4%
공 140
 
3.0%
영 140
 
3.0%
대 89
 
1.9%
서 70
 
1.5%
구 65
 
1.4%
Other values (251) 2893
62.9%
Common
ValueCountFrequency (%)
~ 502
84.5%
, 25
 
4.2%
1 17
 
2.9%
2 15
 
2.5%
12
 
2.0%
. 10
 
1.7%
5 5
 
0.8%
3 3
 
0.5%
9 2
 
0.3%
7 1
 
0.2%
Other values (2) 2
 
0.3%
Latin
ValueCountFrequency (%)
A 6
24.0%
B 6
24.0%
H 3
12.0%
L 3
12.0%
D 2
 
8.0%
M 2
 
8.0%
C 2
 
8.0%
e 1
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4599
88.1%
ASCII 619
 
11.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
~ 502
81.1%
, 25
 
4.0%
1 17
 
2.7%
2 15
 
2.4%
12
 
1.9%
. 10
 
1.6%
A 6
 
1.0%
B 6
 
1.0%
5 5
 
0.8%
3 3
 
0.5%
Other values (10) 18
 
2.9%
Hangul
ValueCountFrequency (%)
동 295
 
6.4%
지 259
 
5.6%
역 232
 
5.0%
고 215
 
4.7%
차 201
 
4.4%
공 140
 
3.0%
영 140
 
3.0%
대 89
 
1.9%
서 70
 
1.5%
구 65
 
1.4%
Other values (251) 2893
62.9%

순서(SEQ_NO)
Real number (ℝ)

Distinct120
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.974
Minimum1
Maximum159
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:53:19.505541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q116
median36
Q360
95-th percentile106
Maximum159
Range158
Interquartile range (IQR)44

Descriptive statistics

Standard deviation31.818141
Coefficient of variation (CV)0.75804406
Kurtosis0.46639093
Mean41.974
Median Absolute Deviation (MAD)21
Skewness0.97967804
Sum20987
Variance1012.3941
MonotonicityNot monotonic
2023-12-10T23:53:19.690877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 17
 
3.4%
12 12
 
2.4%
10 12
 
2.4%
21 11
 
2.2%
16 10
 
2.0%
36 10
 
2.0%
19 10
 
2.0%
38 9
 
1.8%
3 9
 
1.8%
37 9
 
1.8%
Other values (110) 391
78.2%
ValueCountFrequency (%)
1 6
1.2%
2 6
1.2%
3 9
1.8%
4 7
1.4%
5 5
1.0%
6 3
 
0.6%
7 9
1.8%
8 7
1.4%
9 6
1.2%
10 12
2.4%
ValueCountFrequency (%)
159 1
0.2%
147 1
0.2%
144 1
0.2%
142 1
0.2%
138 1
0.2%
135 1
0.2%
132 1
0.2%
126 1
0.2%
124 1
0.2%
122 1
0.2%

버스정류장명(BUS_STA_NM)
Text

Distinct459
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-10T23:53:19.956814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length15
Mean length7.49
Min length3

Characters and Unicode

Total characters3745
Distinct characters360
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

Unique424 ?
Unique (%)84.8%

Sample

1st row강남구청역
2nd row안산초등학교
3rd row삼성래미안아파트
4th row서부병원
5th row명신초.삼선푸르지오.힐스테이트
ValueCountFrequency (%)
마포중앙도서관 4
 
0.8%
롯데백화점 3
 
0.6%
수색교 3
 
0.6%
녹번역 3
 
0.6%
중앙대후문 3
 
0.6%
안양중앙시장 2
 
0.4%
무악재역 2
 
0.4%
노원우체국 2
 
0.4%
노들역 2
 
0.4%
강서초등학교 2
 
0.4%
Other values (450) 475
94.8%
2023-12-10T23:53:20.368234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 115
 
3.1%
동 104
 
2.8%
교 95
 
2.5%
역 93
 
2.5%
아 86
 
2.3%
파 84
 
2.2%
트 82
 
2.2%
구 67
 
1.8%
학 67
 
1.8%
대 60
 
1.6%
Other values (350) 2892
77.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3525
94.1%
Other Punctuation 115
 
3.1%
Decimal Number 75
 
2.0%
Uppercase Letter 12
 
0.3%
Open Punctuation 7
 
0.2%
Close Punctuation 7
 
0.2%
Lowercase Letter 2
 
0.1%
Dash Punctuation 1
 
< 0.1%
Space Separator 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 104
 
3.0%
교 95
 
2.7%
역 93
 
2.6%
아 86
 
2.4%
파 84
 
2.4%
트 82
 
2.3%
구 67
 
1.9%
학 67
 
1.9%
대 60
 
1.7%
리 59
 
1.7%
Other values (327) 2728
77.4%
Decimal Number
ValueCountFrequency (%)
1 21
28.0%
2 14
18.7%
3 11
14.7%
4 9
12.0%
6 6
 
8.0%
5 5
 
6.7%
7 4
 
5.3%
9 3
 
4.0%
8 1
 
1.3%
0 1
 
1.3%
Uppercase Letter
ValueCountFrequency (%)
L 3
25.0%
G 3
25.0%
K 2
16.7%
C 2
16.7%
B 1
 
8.3%
E 1
 
8.3%
Lowercase Letter
ValueCountFrequency (%)
k 1
50.0%
t 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 115
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3525
94.1%
Common 206
 
5.5%
Latin 14
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 104
 
3.0%
교 95
 
2.7%
역 93
 
2.6%
아 86
 
2.4%
파 84
 
2.4%
트 82
 
2.3%
구 67
 
1.9%
학 67
 
1.9%
대 60
 
1.7%
리 59
 
1.7%
Other values (327) 2728
77.4%
Common
ValueCountFrequency (%)
. 115
55.8%
1 21
 
10.2%
2 14
 
6.8%
3 11
 
5.3%
4 9
 
4.4%
( 7
 
3.4%
) 7
 
3.4%
6 6
 
2.9%
5 5
 
2.4%
7 4
 
1.9%
Other values (5) 7
 
3.4%
Latin
ValueCountFrequency (%)
L 3
21.4%
G 3
21.4%
K 2
14.3%
C 2
14.3%
k 1
 
7.1%
t 1
 
7.1%
B 1
 
7.1%
E 1
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3525
94.1%
ASCII 220
 
5.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 115
52.3%
1 21
 
9.5%
2 14
 
6.4%
3 11
 
5.0%
4 9
 
4.1%
( 7
 
3.2%
) 7
 
3.2%
6 6
 
2.7%
5 5
 
2.3%
7 4
 
1.8%
Other values (13) 21
 
9.5%
Hangul
ValueCountFrequency (%)
동 104
 
3.0%
교 95
 
2.7%
역 93
 
2.6%
아 86
 
2.4%
파 84
 
2.4%
트 82
 
2.3%
구 67
 
1.9%
학 67
 
1.9%
대 60
 
1.7%
리 59
 
1.7%
Other values (327) 2728
77.4%

X좌표(X_COORD)
Real number (ℝ)

Distinct479
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.98464
Minimum126.72818
Maximum127.18007
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:53:20.546398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.72818
5-th percentile126.83317
Q1126.91221
median127.00216
Q3127.05413
95-th percentile127.13134
Maximum127.18007
Range0.45189
Interquartile range (IQR)0.141915

Descriptive statistics

Standard deviation0.092986657
Coefficient of variation (CV)0.00073226695
Kurtosis-0.71865002
Mean126.98464
Median Absolute Deviation (MAD)0.07071
Skewness-0.20766049
Sum63492.321
Variance0.0086465184
MonotonicityNot monotonic
2023-12-10T23:53:20.702369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.97254 2
 
0.4%
127.02373 2
 
0.4%
126.90284 2
 
0.4%
127.02576 2
 
0.4%
127.06219 2
 
0.4%
126.98294 2
 
0.4%
126.86281 2
 
0.4%
127.04779 2
 
0.4%
127.00176 2
 
0.4%
126.84097 2
 
0.4%
Other values (469) 480
96.0%
ValueCountFrequency (%)
126.72818 1
0.2%
126.74459 1
0.2%
126.75954 1
0.2%
126.77209 1
0.2%
126.78153 1
0.2%
126.78617 1
0.2%
126.78951 1
0.2%
126.79286 1
0.2%
126.79981 1
0.2%
126.80287 1
0.2%
ValueCountFrequency (%)
127.18007 1
0.2%
127.17833 1
0.2%
127.17463 1
0.2%
127.17365 1
0.2%
127.16485 1
0.2%
127.1624 1
0.2%
127.15837 1
0.2%
127.15679 1
0.2%
127.15645 1
0.2%
127.15409 2
0.4%

Y좌표(Y_COORD)
Real number (ℝ)

Distinct471
Distinct (%)94.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.549293
Minimum37.34057
Maximum37.83706
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:53:21.202746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.34057
5-th percentile37.454197
Q137.499795
median37.54713
Q337.595033
95-th percentile37.657934
Maximum37.83706
Range0.49649
Interquartile range (IQR)0.0952375

Descriptive statistics

Standard deviation0.069405354
Coefficient of variation (CV)0.0018483798
Kurtosis0.92186608
Mean37.549293
Median Absolute Deviation (MAD)0.047655
Skewness0.22325957
Sum18774.647
Variance0.0048171031
MonotonicityNot monotonic
2023-12-10T23:53:21.489179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.57021 3
 
0.6%
37.5745 3
 
0.6%
37.47003 2
 
0.4%
37.5705 2
 
0.4%
37.50478 2
 
0.4%
37.64815 2
 
0.4%
37.52533 2
 
0.4%
37.61711 2
 
0.4%
37.55228 2
 
0.4%
37.50342 2
 
0.4%
Other values (461) 478
95.6%
ValueCountFrequency (%)
37.34057 1
0.2%
37.34835 1
0.2%
37.34853 1
0.2%
37.35182 1
0.2%
37.36736 1
0.2%
37.37009 1
0.2%
37.37381 1
0.2%
37.38204 1
0.2%
37.38333 1
0.2%
37.38495 1
0.2%
ValueCountFrequency (%)
37.83706 1
0.2%
37.80791 1
0.2%
37.76425 1
0.2%
37.75146 1
0.2%
37.74817 1
0.2%
37.74121 1
0.2%
37.73586 1
0.2%
37.68857 1
0.2%
37.68394 1
0.2%
37.68026 1
0.2%

버스정류장_ARS번호(ARSID)
Real number (ℝ)

Distinct485
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17253.782
Minimum1037
Maximum68260
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:53:21.672618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1037
5-th percentile2218.45
Q19521.75
median15757
Q321350.75
95-th percentile42318.15
Maximum68260
Range67223
Interquartile range (IQR)11829

Descriptive statistics

Standard deviation11920.602
Coefficient of variation (CV)0.69089793
Kurtosis4.3915001
Mean17253.782
Median Absolute Deviation (MAD)5862.5
Skewness1.7969378
Sum8626891
Variance1.4210076 × 108
MonotonicityNot monotonic
2023-12-10T23:53:21.846459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6114 2
 
0.4%
13026 2
 
0.4%
8164 2
 
0.4%
19231 2
 
0.4%
48023 2
 
0.4%
13030 2
 
0.4%
13005 2
 
0.4%
18859 2
 
0.4%
16151 2
 
0.4%
19105 2
 
0.4%
Other values (475) 480
96.0%
ValueCountFrequency (%)
1037 1
0.2%
1044 1
0.2%
1104 1
0.2%
1110 1
0.2%
1120 1
0.2%
1125 1
0.2%
1157 1
0.2%
1198 1
0.2%
1214 1
0.2%
1227 1
0.2%
ValueCountFrequency (%)
68260 1
0.2%
68145 1
0.2%
63884 1
0.2%
63629 1
0.2%
63264 1
0.2%
63178 1
0.2%
63097 1
0.2%
61617 1
0.2%
61033 1
0.2%
61029 1
0.2%

Interactions

2023-12-10T23:53:15.497983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:13.966017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:14.443147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:14.990773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:15.650752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:14.097518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:14.575597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:15.135072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:15.773444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:14.211512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:14.689453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:15.241147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:15.896749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:14.325165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:14.830918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:53:15.350331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:53:21.966897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순서(SEQ_NO)X좌표(X_COORD)Y좌표(Y_COORD)버스정류장_ARS번호(ARSID)
순서(SEQ_NO)1.0000.1530.4180.000
X좌표(X_COORD)0.1531.0000.0000.104
Y좌표(Y_COORD)0.4180.0001.0000.000
버스정류장_ARS번호(ARSID)0.0000.1040.0001.000
2023-12-10T23:53:22.092693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순서(SEQ_NO)X좌표(X_COORD)Y좌표(Y_COORD)버스정류장_ARS번호(ARSID)
순서(SEQ_NO)1.000-0.0380.0660.039
X좌표(X_COORD)-0.0381.000-0.0360.018
Y좌표(Y_COORD)0.066-0.0361.000-0.057
버스정류장_ARS번호(ARSID)0.0390.018-0.0571.000

Missing values

2023-12-10T23:53:16.090251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:53:16.291748image/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

기준연월(YYYY_MM)버스노선버스_설명(LINE_NO_EXP)버스노선번호(LINE_NO)버스노선설명(LINE_EXP)순서(SEQ_NO)버스정류장명(BUS_STA_NM)X좌표(X_COORD)Y좌표(Y_COORD)버스정류장_ARS번호(ARSID)
0201810172번(하계동~월드컵2.3단지)9711A번두산.한신아파트~불광역38강남구청역127.0157537.6198821360
12018107723번(진관공영차고지~구파발역)양천02하계동~장암동44안산초등학교127.0020937.5724617123
2201810606번(부천상동~종로1가)7025번중랑공영차고지~신사역사거리42삼성래미안아파트126.9419837.570218004
3201810N37번(진관공영차고지~송파공영차고지)3214번신도림역~구로디지털단지역12서부병원127.0691637.657735140
4201810영등포13(대방천사거리~신도림역)106번구로동~서울대2명신초.삼선푸르지오.힐스테이트127.0483237.570216207
5201810101번(화계사~동대문)3011번난곡차고지~중앙대학17우리은행청담지점.청담삼익아파트127.0361437.6197612794
62018104412(개포동~삼성의료원)노원09하얏트호텔~용산전자상가25송파공영차고지127.1315637.5579721178
7201810동작11(사자암~노량진역)542번방화동~노들역32독산동정훈단지127.0259637.5379619264
8201810N16번(온수동차고지~도봉산공영차고지)강서06봉천역~숭실대29성당앞126.8828537.5794324138
92018106647(개화역환승센터~마곡나루역)영등포06B,국민대방향,정릉~정릉1고덕주공3단지127.045537.4955713007
기준연월(YYYY_MM)버스노선버스_설명(LINE_NO_EXP)버스노선번호(LINE_NO)버스노선설명(LINE_EXP)순서(SEQ_NO)버스정류장명(BUS_STA_NM)X좌표(X_COORD)Y좌표(Y_COORD)버스정류장_ARS번호(ARSID)
490201810120번(우이동~청량리)강남10부천상동~영등포역,신세계백화점15국민약국126.8972937.5484210599
491201810N15번(우이동~사당역)강남05대방천사거리~신도림역3수락리버시티3.4단지126.9756237.5023719323
4922018107728번(대화동~신촌)4318번A,고려대방향,정릉~정릉8강서초등학교126.7445937.5074360109
493201810603번(신월동~시청)130번문래동~양재동71한신아파트127.0327637.5834515501
494201810104번(강북청소년수련관난나~숭례문)2016번군포 공영차고지~여의도29진성빌라사거리127.0103837.628351227
4952018108772번(휴일,구파발~대서문입구)5536번장지공영차고지~삼성역28우림시장.망우사거리127.0404437.6132168145
4962018107022번(구산동~서울역)571번장지공영차고지~고속터미널25성수1가새마을금고126.9167737.5529723513
497201810240번(중랑공영차고지~신사역사거리)5519번은평차고지~연신내역55방화동동부센트레빌아파트126.9072237.6208735629
498201810146번(상계주공7단지~강남역)강북05B,국민대방향,정릉~정릉86안산초등학교126.8353837.5729238215
499201810서초15(롯데캐슬헤론아파트~사당역)4318번상계동~동대문12홍제역126.8961437.5489315377