Overview

Dataset statistics

Number of variables5
Number of observations10000
Missing cells88
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory488.3 KiB
Average record size in memory50.0 B

Variable types

DateTime1
Text2
Numeric2

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15244/S/1/datasetView.do

Alerts

'대여건수' has 3012 (30.1%) zerosZeros
'반납건수' has 3026 (30.3%) zerosZeros

Reproduction

Analysis started2024-04-21 11:11:03.913911
Analysis finished2024-04-21 11:11:06.531535
Duration2.62 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

'대여일자'
Date

Distinct351
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2017-01-01 00:00:00
Maximum2017-12-31 00:00:00
2024-04-21T20:11:06.728308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:11:07.142848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

'대여소번호'
Text

Distinct628
Distinct (%)6.3%
Missing44
Missing (%)0.4%
Memory size156.2 KiB
2024-04-21T20:11:08.779342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length5
Mean length5.1477501
Min length5

Characters and Unicode

Total characters51251
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique124 ?
Unique (%)1.2%

Sample

1st row'320'
2nd row'119'
3rd row'402'
4th row'820'
5th row'2034'
ValueCountFrequency (%)
207 145
 
1.5%
113 134
 
1.3%
346 124
 
1.2%
324 113
 
1.1%
114 112
 
1.1%
347 109
 
1.1%
321 107
 
1.1%
327 99
 
1.0%
128 96
 
1.0%
322 94
 
0.9%
Other values (619) 8825
88.6%
2024-04-21T20:11:10.798075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 19912
38.9%
1 6554
 
12.8%
3 5357
 
10.5%
2 5223
 
10.2%
0 3002
 
5.9%
5 2478
 
4.8%
4 2328
 
4.5%
8 2079
 
4.1%
6 1661
 
3.2%
7 1376
 
2.7%
Other values (14) 1281
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 31273
61.0%
Other Punctuation 19912
38.9%
Other Letter 64
 
0.1%
Space Separator 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
콤 16
25.0%
트 16
25.0%
위 16
25.0%
상 2
 
3.1%
암 2
 
3.1%
센 2
 
3.1%
터 2
 
3.1%
정 2
 
3.1%
비 2
 
3.1%
실 2
 
3.1%
Other values (2) 2
 
3.1%
Decimal Number
ValueCountFrequency (%)
1 6554
21.0%
3 5357
17.1%
2 5223
16.7%
0 3002
9.6%
5 2478
 
7.9%
4 2328
 
7.4%
8 2079
 
6.6%
6 1661
 
5.3%
7 1376
 
4.4%
9 1215
 
3.9%
Other Punctuation
ValueCountFrequency (%)
' 19912
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 51187
99.9%
Hangul 64
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
' 19912
38.9%
1 6554
 
12.8%
3 5357
 
10.5%
2 5223
 
10.2%
0 3002
 
5.9%
5 2478
 
4.8%
4 2328
 
4.5%
8 2079
 
4.1%
6 1661
 
3.2%
7 1376
 
2.7%
Other values (2) 1217
 
2.4%
Hangul
ValueCountFrequency (%)
콤 16
25.0%
트 16
25.0%
위 16
25.0%
상 2
 
3.1%
암 2
 
3.1%
센 2
 
3.1%
터 2
 
3.1%
정 2
 
3.1%
비 2
 
3.1%
실 2
 
3.1%
Other values (2) 2
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51187
99.9%
Hangul 64
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 19912
38.9%
1 6554
 
12.8%
3 5357
 
10.5%
2 5223
 
10.2%
0 3002
 
5.9%
5 2478
 
4.8%
4 2328
 
4.5%
8 2079
 
4.1%
6 1661
 
3.2%
7 1376
 
2.7%
Other values (2) 1217
 
2.4%
Hangul
ValueCountFrequency (%)
콤 16
25.0%
트 16
25.0%
위 16
25.0%
상 2
 
3.1%
암 2
 
3.1%
센 2
 
3.1%
터 2
 
3.1%
정 2
 
3.1%
비 2
 
3.1%
실 2
 
3.1%
Other values (2) 2
 
3.1%

'대여소'
Text

Distinct628
Distinct (%)6.3%
Missing44
Missing (%)0.4%
Memory size156.2 KiB
2024-04-21T20:11:11.664008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length27
Mean length13.078646
Min length5

Characters and Unicode

Total characters130211
Distinct characters405
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

Unique124 ?
Unique (%)1.2%

Sample

1st row' 을지로입구역 4번출구 앞'
2nd row' 서강나루 공원'
3rd row' 상암월드컵파크 9단지 앞'
4th row' 청파동입구 교차로'
5th row' 사당역 7번출구쪽'
ValueCountFrequency (%)
9931
28.5%
앞 4515
 
13.0%
옆 1327
 
3.8%
1번출구 856
 
2.5%
뒤 509
 
1.5%
출구 500
 
1.4%
2번출구 487
 
1.4%
4번출구 421
 
1.2%
사거리 401
 
1.2%
5번출구 261
 
0.7%
Other values (791) 15627
44.9%
2024-04-21T20:11:12.789751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
24886
19.1%
' 19912
 
15.3%
구 4785
 
3.7%
앞 4695
 
3.6%
역 4394
 
3.4%
번 4026
 
3.1%
출 4011
 
3.1%
1 1579
 
1.2%
대 1482
 
1.1%
옆 1374
 
1.1%
Other values (395) 59067
45.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 76590
58.8%
Space Separator 24886
 
19.1%
Other Punctuation 19968
 
15.3%
Decimal Number 5699
 
4.4%
Uppercase Letter 1734
 
1.3%
Close Punctuation 589
 
0.5%
Open Punctuation 589
 
0.5%
Dash Punctuation 131
 
0.1%
Math Symbol 25
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 4785
 
6.2%
앞 4695
 
6.1%
역 4394
 
5.7%
번 4026
 
5.3%
출 4011
 
5.2%
대 1482
 
1.9%
옆 1374
 
1.8%
원 1361
 
1.8%
동 1346
 
1.8%
사 1263
 
1.6%
Other values (356) 47853
62.5%
Uppercase Letter
ValueCountFrequency (%)
K 310
17.9%
C 244
14.1%
B 196
11.3%
M 155
8.9%
D 141
8.1%
E 133
7.7%
S 120
 
6.9%
I 74
 
4.3%
A 55
 
3.2%
L 50
 
2.9%
Other values (10) 256
14.8%
Decimal Number
ValueCountFrequency (%)
1 1579
27.7%
2 1268
22.2%
4 696
12.2%
3 551
 
9.7%
5 355
 
6.2%
8 340
 
6.0%
7 298
 
5.2%
9 276
 
4.8%
6 249
 
4.4%
0 87
 
1.5%
Other Punctuation
ValueCountFrequency (%)
' 19912
99.7%
, 55
 
0.3%
@ 1
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
~ 24
96.0%
+ 1
 
4.0%
Space Separator
ValueCountFrequency (%)
24886
100.0%
Close Punctuation
ValueCountFrequency (%)
) 589
100.0%
Open Punctuation
ValueCountFrequency (%)
( 589
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 131
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 76590
58.8%
Common 51887
39.8%
Latin 1734
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 4785
 
6.2%
앞 4695
 
6.1%
역 4394
 
5.7%
번 4026
 
5.3%
출 4011
 
5.2%
대 1482
 
1.9%
옆 1374
 
1.8%
원 1361
 
1.8%
동 1346
 
1.8%
사 1263
 
1.6%
Other values (356) 47853
62.5%
Latin
ValueCountFrequency (%)
K 310
17.9%
C 244
14.1%
B 196
11.3%
M 155
8.9%
D 141
8.1%
E 133
7.7%
S 120
 
6.9%
I 74
 
4.3%
A 55
 
3.2%
L 50
 
2.9%
Other values (10) 256
14.8%
Common
ValueCountFrequency (%)
24886
48.0%
' 19912
38.4%
1 1579
 
3.0%
2 1268
 
2.4%
4 696
 
1.3%
) 589
 
1.1%
( 589
 
1.1%
3 551
 
1.1%
5 355
 
0.7%
8 340
 
0.7%
Other values (9) 1122
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 76590
58.8%
ASCII 53621
41.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
24886
46.4%
' 19912
37.1%
1 1579
 
2.9%
2 1268
 
2.4%
4 696
 
1.3%
) 589
 
1.1%
( 589
 
1.1%
3 551
 
1.0%
5 355
 
0.7%
8 340
 
0.6%
Other values (29) 2856
 
5.3%
Hangul
ValueCountFrequency (%)
구 4785
 
6.2%
앞 4695
 
6.1%
역 4394
 
5.7%
번 4026
 
5.3%
출 4011
 
5.2%
대 1482
 
1.9%
옆 1374
 
1.8%
원 1361
 
1.8%
동 1346
 
1.8%
사 1263
 
1.6%
Other values (356) 47853
62.5%

'대여건수'
Real number (ℝ)

ZEROS 

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1441
Minimum0
Maximum14
Zeros3012
Zeros (%)30.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T20:11:13.133757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum14
Range14
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.1848526
Coefficient of variation (CV)1.0356197
Kurtosis12.295028
Mean1.1441
Median Absolute Deviation (MAD)1
Skewness2.3708611
Sum11441
Variance1.4038756
MonotonicityNot monotonic
2024-04-21T20:11:13.491861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 4223
42.2%
0 3012
30.1%
2 1862
18.6%
3 494
 
4.9%
4 240
 
2.4%
5 90
 
0.9%
6 35
 
0.4%
7 12
 
0.1%
8 12
 
0.1%
9 8
 
0.1%
Other values (5) 12
 
0.1%
ValueCountFrequency (%)
0 3012
30.1%
1 4223
42.2%
2 1862
18.6%
3 494
 
4.9%
4 240
 
2.4%
5 90
 
0.9%
6 35
 
0.4%
7 12
 
0.1%
8 12
 
0.1%
9 8
 
0.1%
ValueCountFrequency (%)
14 2
 
< 0.1%
13 1
 
< 0.1%
12 3
 
< 0.1%
11 3
 
< 0.1%
10 3
 
< 0.1%
9 8
 
0.1%
8 12
 
0.1%
7 12
 
0.1%
6 35
 
0.4%
5 90
0.9%

'반납건수'
Real number (ℝ)

ZEROS 

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1416
Minimum0
Maximum15
Zeros3026
Zeros (%)30.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T20:11:13.841353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum15
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.191592
Coefficient of variation (CV)1.0437911
Kurtosis13.724903
Mean1.1416
Median Absolute Deviation (MAD)1
Skewness2.4941423
Sum11416
Variance1.4198914
MonotonicityNot monotonic
2024-04-21T20:11:14.203086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 4208
42.1%
0 3026
30.3%
2 1883
18.8%
3 475
 
4.8%
4 251
 
2.5%
5 72
 
0.7%
6 36
 
0.4%
7 16
 
0.2%
8 10
 
0.1%
10 7
 
0.1%
Other values (5) 16
 
0.2%
ValueCountFrequency (%)
0 3026
30.3%
1 4208
42.1%
2 1883
18.8%
3 475
 
4.8%
4 251
 
2.5%
5 72
 
0.7%
6 36
 
0.4%
7 16
 
0.2%
8 10
 
0.1%
9 7
 
0.1%
ValueCountFrequency (%)
15 1
 
< 0.1%
14 2
 
< 0.1%
12 3
 
< 0.1%
11 3
 
< 0.1%
10 7
 
0.1%
9 7
 
0.1%
8 10
 
0.1%
7 16
 
0.2%
6 36
0.4%
5 72
0.7%

Interactions

2024-04-21T20:11:05.170171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:11:04.626397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:11:05.442191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:11:04.897338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T20:11:14.447648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
'대여건수''반납건수'
'대여건수'1.0000.786
'반납건수'0.7861.000
2024-04-21T20:11:14.667601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
'대여건수''반납건수'
'대여건수'1.000-0.093
'반납건수'-0.0931.000

Missing values

2024-04-21T20:11:05.793628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T20:11:06.116171image/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.
2024-04-21T20:11:06.397204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

'대여일자''대여소번호''대여소''대여건수''반납건수'
11828'2017-11-11''320'' 을지로입구역 4번출구 앞'22
11063'2017-10-28''119'' 서강나루 공원'11
7745'2017-09-09''402'' 상암월드컵파크 9단지 앞'22
5877'2017-07-30''820'' 청파동입구 교차로'10
9980'2017-10-10''2034'' 사당역 7번출구쪽'02
4829'2017-06-27''112'' 극동방송국 앞'01
7797'2017-09-10''171'' 임광빌딩 앞'31
6513'2017-08-16''202'' 국민일보 앞'20
6244'2017-08-09''236'' 문래동자이아파트 앞'10
7527'2017-09-06''309'' 광화문역 6번출구 옆'22
'대여일자''대여소번호''대여소''대여건수''반납건수'
889'2017-03-25''171'' 임광빌딩 앞'22
8235'2017-09-17''584'' 광진광장 교통섬'20
8440'2017-09-21''109'' 제일빌딩 앞'10
2863'2017-05-15''803'' 한남초교 앞 보도육교'10
2142'2017-04-30''109'' 제일빌딩 앞'20
8563'2017-09-22''1215'' 올림픽공원역 1번출구 앞'44
3532'2017-05-30''346'' 맥스타일 앞'11
10522'2017-10-20''1118'' 증미역 3번출구뒤(등촌두산위브센티움오피스텔)'01
11727'2017-11-08''245'' 삼성생명 당산사옥 앞'20
6591'2017-08-18''111'' 상수역 2번출구 앞'10