Overview

Dataset statistics

Number of variables4
Number of observations10000
Missing cells14592
Missing cells (%)36.5%
Duplicate rows1
Duplicate rows (%)< 0.1%
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

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
일시 has 3648 (36.5%) missing valuesMissing
대여소 has 3648 (36.5%) missing valuesMissing
대여건수 has 3648 (36.5%) missing valuesMissing
반납건수 has 3648 (36.5%) missing valuesMissing
대여건수 has 2260 (22.6%) zerosZeros
반납건수 has 2314 (23.1%) zerosZeros

Reproduction

Analysis started2023-12-11 10:02:08.416508
Analysis finished2023-12-11 10:02:09.567677
Duration1.15 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일시
Date

MISSING 

Distinct184
Distinct (%)2.9%
Missing3648
Missing (%)36.5%
Memory size156.2 KiB
Minimum2021-07-01 00:00:00
Maximum2021-12-31 00:00:00
2023-12-11T19:02:09.648571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T19:02:09.820699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

대여소
Text

MISSING 

Distinct1305
Distinct (%)20.5%
Missing3648
Missing (%)36.5%
Memory size156.2 KiB
2023-12-11T19:02:10.137495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length29
Mean length15.134131
Min length4

Characters and Unicode

Total characters96132
Distinct characters520
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique440 ?
Unique (%)6.9%

Sample

1st row510. 서울숲 남문 버스정류소 옆
2nd row3811.신사로입구
3rd row870.노들섬 서측 앞
4th row1210. 롯데월드타워(잠실역2번출구 쪽)
5th row1032. 선사고등학교
ValueCountFrequency (%)
앞 1995
 
10.3%
1번출구 409
 
2.1%
출구 378
 
2.0%
옆 360
 
1.9%
3번출구 216
 
1.1%
2번출구 167
 
0.9%
입구 167
 
0.9%
뒤 161
 
0.8%
5번출구 144
 
0.7%
4번출구 142
 
0.7%
Other values (2657) 15181
78.6%
2023-12-11T19:02:10.552642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
13044
 
13.6%
. 6391
 
6.6%
1 4477
 
4.7%
2 4154
 
4.3%
3 3211
 
3.3%
구 2759
 
2.9%
4 2742
 
2.9%
5 2394
 
2.5%
앞 2294
 
2.4%
0 2276
 
2.4%
Other values (510) 52390
54.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 49010
51.0%
Decimal Number 25759
26.8%
Space Separator 13044
 
13.6%
Other Punctuation 6439
 
6.7%
Uppercase Letter 733
 
0.8%
Open Punctuation 495
 
0.5%
Close Punctuation 495
 
0.5%
Dash Punctuation 77
 
0.1%
Lowercase Letter 54
 
0.1%
Math Symbol 21
 
< 0.1%
Other values (2) 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 2759
 
5.6%
앞 2294
 
4.7%
역 2242
 
4.6%
번 2092
 
4.3%
출 2056
 
4.2%
동 1023
 
2.1%
교 953
 
1.9%
대 908
 
1.9%
원 862
 
1.8%
서 734
 
1.5%
Other values (456) 33087
67.5%
Uppercase Letter
ValueCountFrequency (%)
K 84
11.5%
S 82
11.2%
C 69
 
9.4%
D 56
 
7.6%
I 53
 
7.2%
G 50
 
6.8%
L 45
 
6.1%
T 39
 
5.3%
A 37
 
5.0%
B 34
 
4.6%
Other values (12) 184
25.1%
Decimal Number
ValueCountFrequency (%)
1 4477
17.4%
2 4154
16.1%
3 3211
12.5%
4 2742
10.6%
5 2394
9.3%
0 2276
8.8%
6 1972
7.7%
8 1749
 
6.8%
7 1588
 
6.2%
9 1196
 
4.6%
Lowercase Letter
ValueCountFrequency (%)
k 16
29.6%
s 15
27.8%
e 9
16.7%
v 3
 
5.6%
t 3
 
5.6%
n 2
 
3.7%
a 2
 
3.7%
g 2
 
3.7%
l 1
 
1.9%
y 1
 
1.9%
Other Punctuation
ValueCountFrequency (%)
. 6391
99.3%
, 45
 
0.7%
· 2
 
< 0.1%
& 1
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
+ 12
57.1%
~ 9
42.9%
Space Separator
ValueCountFrequency (%)
13044
100.0%
Open Punctuation
ValueCountFrequency (%)
( 495
100.0%
Close Punctuation
ValueCountFrequency (%)
) 495
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 77
100.0%
Other Number
ValueCountFrequency (%)
① 4
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 49011
51.0%
Common 46334
48.2%
Latin 787
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 2759
 
5.6%
앞 2294
 
4.7%
역 2242
 
4.6%
번 2092
 
4.3%
출 2056
 
4.2%
동 1023
 
2.1%
교 953
 
1.9%
대 908
 
1.9%
원 862
 
1.8%
서 734
 
1.5%
Other values (457) 33088
67.5%
Latin
ValueCountFrequency (%)
K 84
 
10.7%
S 82
 
10.4%
C 69
 
8.8%
D 56
 
7.1%
I 53
 
6.7%
G 50
 
6.4%
L 45
 
5.7%
T 39
 
5.0%
A 37
 
4.7%
B 34
 
4.3%
Other values (22) 238
30.2%
Common
ValueCountFrequency (%)
13044
28.2%
. 6391
13.8%
1 4477
 
9.7%
2 4154
 
9.0%
3 3211
 
6.9%
4 2742
 
5.9%
5 2394
 
5.2%
0 2276
 
4.9%
6 1972
 
4.3%
8 1749
 
3.8%
Other values (11) 3924
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 49010
51.0%
ASCII 47115
49.0%
Enclosed Alphanum 4
 
< 0.1%
None 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
13044
27.7%
. 6391
13.6%
1 4477
 
9.5%
2 4154
 
8.8%
3 3211
 
6.8%
4 2742
 
5.8%
5 2394
 
5.1%
0 2276
 
4.8%
6 1972
 
4.2%
8 1749
 
3.7%
Other values (41) 4705
 
10.0%
Hangul
ValueCountFrequency (%)
구 2759
 
5.6%
앞 2294
 
4.7%
역 2242
 
4.6%
번 2092
 
4.3%
출 2056
 
4.2%
동 1023
 
2.1%
교 953
 
1.9%
대 908
 
1.9%
원 862
 
1.8%
서 734
 
1.5%
Other values (456) 33087
67.5%
Enclosed Alphanum
ValueCountFrequency (%)
① 4
100.0%
None
ValueCountFrequency (%)
· 2
66.7%
㈜ 1
33.3%

대여건수
Real number (ℝ)

MISSING  ZEROS 

Distinct14
Distinct (%)0.2%
Missing3648
Missing (%)36.5%
Infinite0
Infinite (%)0.0%
Mean0.947733
Minimum0
Maximum14
Zeros2260
Zeros (%)22.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T19:02:10.663027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum14
Range14
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0945426
Coefficient of variation (CV)1.1549061
Kurtosis17.217297
Mean0.947733
Median Absolute Deviation (MAD)1
Skewness2.9560201
Sum6020
Variance1.1980235
MonotonicityNot monotonic
2023-12-11T19:02:10.763093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1 2940
29.4%
0 2260
22.6%
2 768
 
7.7%
3 219
 
2.2%
4 73
 
0.7%
5 38
 
0.4%
6 20
 
0.2%
7 14
 
0.1%
8 10
 
0.1%
10 4
 
< 0.1%
Other values (4) 6
 
0.1%
(Missing) 3648
36.5%
ValueCountFrequency (%)
0 2260
22.6%
1 2940
29.4%
2 768
 
7.7%
3 219
 
2.2%
4 73
 
0.7%
5 38
 
0.4%
6 20
 
0.2%
7 14
 
0.1%
8 10
 
0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
14 1
 
< 0.1%
12 2
 
< 0.1%
11 1
 
< 0.1%
10 4
 
< 0.1%
9 2
 
< 0.1%
8 10
 
0.1%
7 14
 
0.1%
6 20
 
0.2%
5 38
0.4%
4 73
0.7%

반납건수
Real number (ℝ)

MISSING  ZEROS 

Distinct13
Distinct (%)0.2%
Missing3648
Missing (%)36.5%
Infinite0
Infinite (%)0.0%
Mean0.92443325
Minimum0
Maximum13
Zeros2314
Zeros (%)23.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T19:02:10.917127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum13
Range13
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0430603
Coefficient of variation (CV)1.1283241
Kurtosis13.313332
Mean0.92443325
Median Absolute Deviation (MAD)1
Skewness2.5286876
Sum5872
Variance1.0879748
MonotonicityNot monotonic
2023-12-11T19:02:11.030747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 2918
29.2%
0 2314
23.1%
2 744
 
7.4%
3 204
 
2.0%
4 83
 
0.8%
5 53
 
0.5%
6 20
 
0.2%
7 7
 
0.1%
8 4
 
< 0.1%
11 2
 
< 0.1%
Other values (3) 3
 
< 0.1%
(Missing) 3648
36.5%
ValueCountFrequency (%)
0 2314
23.1%
1 2918
29.2%
2 744
 
7.4%
3 204
 
2.0%
4 83
 
0.8%
5 53
 
0.5%
6 20
 
0.2%
7 7
 
0.1%
8 4
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
13 1
 
< 0.1%
12 1
 
< 0.1%
11 2
 
< 0.1%
9 1
 
< 0.1%
8 4
 
< 0.1%
7 7
 
0.1%
6 20
 
0.2%
5 53
 
0.5%
4 83
0.8%
3 204
2.0%

Interactions

2023-12-11T19:02:09.067677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T19:02:08.843977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T19:02:09.178091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T19:02:08.946719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T19:02:11.107543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여건수반납건수
대여건수1.0000.617
반납건수0.6171.000
2023-12-11T19:02:11.180695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여건수반납건수
대여건수1.000-0.312
반납건수-0.3121.000

Missing values

2023-12-11T19:02:09.299031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T19:02:09.395322image/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.
2023-12-11T19:02:09.501679image/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

일시대여소대여건수반납건수
15157<NA><NA><NA><NA>
15412021-07-23510. 서울숲 남문 버스정류소 옆11
18435<NA><NA><NA><NA>
39472021-08-293811.신사로입구10
17874<NA><NA><NA><NA>
112272021-11-11870.노들섬 서측 앞11
99692021-10-291210. 롯데월드타워(잠실역2번출구 쪽)10
21391<NA><NA><NA><NA>
98602021-10-291032. 선사고등학교01
135942021-12-20115. 마스타 빌딩 앞12
일시대여소대여건수반납건수
106772021-11-052725.서울식물원 식물문화센터20
20866<NA><NA><NA><NA>
42882021-09-03145. 공덕역 5번출구10
136162021-12-201461.동양엔파트 앞01
17478<NA><NA><NA><NA>
104532021-11-03440. 하늘공원 입구22
110802021-11-072025. 흑석역 1번출구42
19905<NA><NA><NA><NA>
132772021-12-114509. 서울영상고교 입구02
100382021-10-303011.경의선(노고산동)20

Duplicate rows

Most frequently occurring

일시대여소대여건수반납건수# duplicates
0<NA><NA><NA><NA>3648