Overview

Dataset statistics

Number of variables4
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory410.2 KiB
Average record size in memory42.0 B

Variable types

DateTime1
Text1
Numeric2

Dataset

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

Alerts

대여건수 has 3336 (33.4%) zerosZeros
반납건수 has 3487 (34.9%) zerosZeros

Reproduction

Analysis started2023-12-11 10:02:19.820207
Analysis finished2023-12-11 10:02:20.786317
Duration0.97 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일시
Date

Distinct330
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2019-01-01 00:00:00
Maximum2019-11-30 00:00:00
2023-12-11T19:02:20.871622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T19:02:21.022858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

대여소
Text

Distinct1050
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T19:02:21.285597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length30
Mean length15.5187
Min length8

Characters and Unicode

Total characters155187
Distinct characters477
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique242 ?
Unique (%)2.4%

Sample

1st row831. 이태원관광특구입구
2nd row358. 성대입구 사거리
3rd row1297. 석촌호수교차로(동호 팔각정 앞)
4th row560. 비전교회 앞
5th row3515. 서울숲 관리사무소
ValueCountFrequency (%)
앞 3385
 
9.9%
옆 964
 
2.8%
1번출구 681
 
2.0%
출구 634
 
1.9%
2번출구 481
 
1.4%
뒤 468
 
1.4%
5번출구 376
 
1.1%
사거리 329
 
1.0%
4번출구 302
 
0.9%
3번출구 287
 
0.8%
Other values (2337) 26152
76.8%
2023-12-11T19:02:21.770142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
24138
 
15.6%
. 10042
 
6.5%
1 7921
 
5.1%
2 7524
 
4.8%
3 5830
 
3.8%
구 5016
 
3.2%
역 4414
 
2.8%
번 4083
 
2.6%
출 3961
 
2.6%
앞 3726
 
2.4%
Other values (467) 78532
50.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 78705
50.7%
Decimal Number 39374
25.4%
Space Separator 24139
 
15.6%
Other Punctuation 10156
 
6.5%
Uppercase Letter 1141
 
0.7%
Close Punctuation 696
 
0.4%
Open Punctuation 696
 
0.4%
Dash Punctuation 168
 
0.1%
Math Symbol 67
 
< 0.1%
Lowercase Letter 42
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 5016
 
6.4%
역 4414
 
5.6%
번 4083
 
5.2%
출 3961
 
5.0%
앞 3726
 
4.7%
동 1448
 
1.8%
사 1446
 
1.8%
대 1364
 
1.7%
원 1245
 
1.6%
리 1192
 
1.5%
Other values (415) 50810
64.6%
Uppercase Letter
ValueCountFrequency (%)
K 222
19.5%
C 119
10.4%
E 117
10.3%
S 117
10.3%
B 111
9.7%
I 60
 
5.3%
H 44
 
3.9%
N 42
 
3.7%
A 40
 
3.5%
T 40
 
3.5%
Other values (12) 229
20.1%
Decimal Number
ValueCountFrequency (%)
1 7921
20.1%
2 7524
19.1%
3 5830
14.8%
5 3406
8.7%
4 3116
 
7.9%
0 3082
 
7.8%
8 2591
 
6.6%
6 2241
 
5.7%
9 1895
 
4.8%
7 1768
 
4.5%
Lowercase Letter
ValueCountFrequency (%)
e 12
28.6%
k 9
21.4%
n 6
14.3%
t 6
14.3%
y 3
 
7.1%
l 3
 
7.1%
s 3
 
7.1%
Other Punctuation
ValueCountFrequency (%)
. 10042
98.9%
, 106
 
1.0%
& 6
 
0.1%
․ 1
 
< 0.1%
@ 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
24138
> 99.9%
  1
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
~ 41
61.2%
+ 26
38.8%
Close Punctuation
ValueCountFrequency (%)
) 696
100.0%
Open Punctuation
ValueCountFrequency (%)
( 696
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 168
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 78705
50.7%
Common 75299
48.5%
Latin 1183
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 5016
 
6.4%
역 4414
 
5.6%
번 4083
 
5.2%
출 3961
 
5.0%
앞 3726
 
4.7%
동 1448
 
1.8%
사 1446
 
1.8%
대 1364
 
1.7%
원 1245
 
1.6%
리 1192
 
1.5%
Other values (415) 50810
64.6%
Latin
ValueCountFrequency (%)
K 222
18.8%
C 119
10.1%
E 117
9.9%
S 117
9.9%
B 111
9.4%
I 60
 
5.1%
H 44
 
3.7%
N 42
 
3.6%
A 40
 
3.4%
T 40
 
3.4%
Other values (19) 271
22.9%
Common
ValueCountFrequency (%)
24138
32.1%
. 10042
13.3%
1 7921
 
10.5%
2 7524
 
10.0%
3 5830
 
7.7%
5 3406
 
4.5%
4 3116
 
4.1%
0 3082
 
4.1%
8 2591
 
3.4%
6 2241
 
3.0%
Other values (13) 5408
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 78705
50.7%
ASCII 76480
49.3%
Punctuation 1
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
24138
31.6%
. 10042
13.1%
1 7921
 
10.4%
2 7524
 
9.8%
3 5830
 
7.6%
5 3406
 
4.5%
4 3116
 
4.1%
0 3082
 
4.0%
8 2591
 
3.4%
6 2241
 
2.9%
Other values (40) 6589
 
8.6%
Hangul
ValueCountFrequency (%)
구 5016
 
6.4%
역 4414
 
5.6%
번 4083
 
5.2%
출 3961
 
5.0%
앞 3726
 
4.7%
동 1448
 
1.8%
사 1446
 
1.8%
대 1364
 
1.7%
원 1245
 
1.6%
리 1192
 
1.5%
Other values (415) 50810
64.6%
Punctuation
ValueCountFrequency (%)
․ 1
100.0%
None
ValueCountFrequency (%)
  1
100.0%

대여건수
Real number (ℝ)

ZEROS 

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1183
Minimum0
Maximum19
Zeros3336
Zeros (%)33.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T19:02:21.897696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum19
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2454157
Coefficient of variation (CV)1.1136687
Kurtosis13.848318
Mean1.1183
Median Absolute Deviation (MAD)1
Skewness2.5256792
Sum11183
Variance1.5510602
MonotonicityNot monotonic
2023-12-11T19:02:22.009955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 4005
40.1%
0 3336
33.4%
2 1705
17.1%
3 513
 
5.1%
4 237
 
2.4%
5 98
 
1.0%
6 40
 
0.4%
7 33
 
0.3%
8 15
 
0.1%
10 8
 
0.1%
Other values (5) 10
 
0.1%
ValueCountFrequency (%)
0 3336
33.4%
1 4005
40.1%
2 1705
17.1%
3 513
 
5.1%
4 237
 
2.4%
5 98
 
1.0%
6 40
 
0.4%
7 33
 
0.3%
8 15
 
0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
19 1
 
< 0.1%
14 1
 
< 0.1%
12 3
 
< 0.1%
11 3
 
< 0.1%
10 8
 
0.1%
9 2
 
< 0.1%
8 15
 
0.1%
7 33
 
0.3%
6 40
0.4%
5 98
1.0%

반납건수
Real number (ℝ)

ZEROS 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0944
Minimum0
Maximum21
Zeros3487
Zeros (%)34.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T19:02:22.142483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum21
Range21
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2452485
Coefficient of variation (CV)1.1378367
Kurtosis18.659065
Mean1.0944
Median Absolute Deviation (MAD)1
Skewness2.7635609
Sum10944
Variance1.5506437
MonotonicityNot monotonic
2023-12-11T19:02:22.282281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 3864
38.6%
0 3487
34.9%
2 1742
17.4%
3 479
 
4.8%
4 232
 
2.3%
5 92
 
0.9%
6 54
 
0.5%
7 19
 
0.2%
8 13
 
0.1%
10 4
 
< 0.1%
Other values (7) 14
 
0.1%
ValueCountFrequency (%)
0 3487
34.9%
1 3864
38.6%
2 1742
17.4%
3 479
 
4.8%
4 232
 
2.3%
5 92
 
0.9%
6 54
 
0.5%
7 19
 
0.2%
8 13
 
0.1%
9 4
 
< 0.1%
ValueCountFrequency (%)
21 1
 
< 0.1%
17 1
 
< 0.1%
15 1
 
< 0.1%
13 1
 
< 0.1%
12 2
 
< 0.1%
11 4
 
< 0.1%
10 4
 
< 0.1%
9 4
 
< 0.1%
8 13
0.1%
7 19
0.2%

Interactions

2023-12-11T19:02:20.415666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T19:02:20.193095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T19:02:20.524322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T19:02:20.321065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T19:02:22.398833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여건수반납건수
대여건수1.0000.748
반납건수0.7481.000
2023-12-11T19:02:22.488200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여건수반납건수
대여건수1.000-0.187
반납건수-0.1871.000

Missing values

2023-12-11T19:02:20.660436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T19:02:20.746360image/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

일시대여소대여건수반납건수
228292019-11-16831. 이태원관광특구입구23
228792019-11-18358. 성대입구 사거리01
216012019-11-011297. 석촌호수교차로(동호 팔각정 앞)20
137302019-08-17560. 비전교회 앞01
138242019-08-183515. 서울숲 관리사무소02
23522019-04-07131. 증산2교22
29062019-04-15112. 극동방송국 앞10
149082019-08-291153. 발산역 1번, 9번 인근 대여소10
18092019-03-262160. 관악구 보건소01
191482019-10-10353. 재동초교 앞 삼거리10
일시대여소대여건수반납건수
140722019-08-211986. 태평양물산빌딩10
17122019-03-24163. 명지전문대학교 정문 앞01
63482019-05-25104. 합정역 1번출구 앞01
183522019-10-033541. 커먼그라운드11
152982019-09-01272. 당산육갑문10
141022019-08-21328. 탑골공원 앞02
20032019-03-30605. 신설동역8번출구11
16432019-03-22331. 을지로2가 사거리 북측20
136722019-08-172259. 잠원역 3번-4번 출구사이20
85582019-06-193515. 서울숲 관리사무소10