Overview

Dataset statistics

Number of variables5
Number of observations8339
Missing cells68
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory342.2 KiB
Average record size in memory42.0 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 2699 (32.4%) zerosZeros
'반납건수' has 2772 (33.2%) zerosZeros

Reproduction

Analysis started2023-12-11 10:02:23.402232
Analysis finished2023-12-11 10:02:24.537552
Duration1.14 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

'대여일자'
Date

Distinct177
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size65.3 KiB
Minimum2018-01-01 00:00:00
Maximum2018-06-28 00:00:00
2023-12-11T19:02:24.606844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T19:02:24.737522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

'대여소번호'
Text

Distinct779
Distinct (%)9.4%
Missing34
Missing (%)0.4%
Memory size65.3 KiB
2023-12-11T19:02:25.119851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.2747742
Min length5

Characters and Unicode

Total characters43807
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique176 ?
Unique (%)2.1%

Sample

1st row'2377'
2nd row'116'
3rd row'324'
4th row'347'
5th row'114'
ValueCountFrequency (%)
207 103
 
1.2%
113 86
 
1.0%
140 74
 
0.9%
324 72
 
0.9%
346 71
 
0.9%
2219 71
 
0.9%
1210 66
 
0.8%
128 64
 
0.8%
816 63
 
0.8%
114 63
 
0.8%
Other values (769) 7572
91.2%
2023-12-11T19:02:25.636347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 16610
37.9%
1 5537
 
12.6%
2 5020
 
11.5%
3 4294
 
9.8%
0 2705
 
6.2%
5 2198
 
5.0%
4 1796
 
4.1%
8 1758
 
4.0%
6 1486
 
3.4%
7 1219
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 27197
62.1%
Other Punctuation 16610
37.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5537
20.4%
2 5020
18.5%
3 4294
15.8%
0 2705
9.9%
5 2198
 
8.1%
4 1796
 
6.6%
8 1758
 
6.5%
6 1486
 
5.5%
7 1219
 
4.5%
9 1184
 
4.4%
Other Punctuation
ValueCountFrequency (%)
' 16610
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 43807
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
' 16610
37.9%
1 5537
 
12.6%
2 5020
 
11.5%
3 4294
 
9.8%
0 2705
 
6.2%
5 2198
 
5.0%
4 1796
 
4.1%
8 1758
 
4.0%
6 1486
 
3.4%
7 1219
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43807
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 16610
37.9%
1 5537
 
12.6%
2 5020
 
11.5%
3 4294
 
9.8%
0 2705
 
6.2%
5 2198
 
5.0%
4 1796
 
4.1%
8 1758
 
4.0%
6 1486
 
3.4%
7 1219
 
2.8%

'대여소'
Text

Distinct779
Distinct (%)9.4%
Missing34
Missing (%)0.4%
Memory size65.3 KiB
2023-12-11T19:02:25.858284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length26
Mean length12.99109
Min length6

Characters and Unicode

Total characters107891
Distinct characters433
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique176 ?
Unique (%)2.1%

Sample

1st row' 수서역 5번출구 뒤'
2nd row' 일진아이윌아파트 옆'
3rd row' 신세계백화점 본점 앞'
4th row' 동대문역사문화공원역 9번출구 앞'
5th row' 홍대입구역 8번출구 앞'
ValueCountFrequency (%)
8288
29.2%
앞 3170
 
11.2%
옆 882
 
3.1%
1번출구 738
 
2.6%
출구 450
 
1.6%
뒤 447
 
1.6%
4번출구 365
 
1.3%
2번출구 363
 
1.3%
사거리 287
 
1.0%
7번출구 216
 
0.8%
Other values (975) 13183
46.4%
2023-12-11T19:02:26.204613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
20135
18.7%
' 16610
 
15.4%
구 4280
 
4.0%
역 3676
 
3.4%
번 3455
 
3.2%
앞 3432
 
3.2%
출 3416
 
3.2%
1 1330
 
1.2%
대 1203
 
1.1%
동 1174
 
1.1%
Other values (423) 49180
45.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 63837
59.2%
Space Separator 20135
 
18.7%
Other Punctuation 16682
 
15.5%
Decimal Number 4906
 
4.5%
Uppercase Letter 1168
 
1.1%
Close Punctuation 484
 
0.4%
Open Punctuation 484
 
0.4%
Dash Punctuation 152
 
0.1%
Math Symbol 40
 
< 0.1%
Lowercase Letter 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 4280
 
6.7%
역 3676
 
5.8%
번 3455
 
5.4%
앞 3432
 
5.4%
출 3416
 
5.4%
대 1203
 
1.9%
동 1174
 
1.8%
원 1033
 
1.6%
사 1031
 
1.6%
교 1011
 
1.6%
Other values (384) 40126
62.9%
Uppercase Letter
ValueCountFrequency (%)
C 160
13.7%
K 155
13.3%
I 99
8.5%
A 92
7.9%
B 83
 
7.1%
L 77
 
6.6%
M 75
 
6.4%
G 69
 
5.9%
D 66
 
5.7%
E 65
 
5.6%
Other values (10) 227
19.4%
Decimal Number
ValueCountFrequency (%)
1 1330
27.1%
2 1064
21.7%
4 564
11.5%
3 471
 
9.6%
8 371
 
7.6%
5 314
 
6.4%
7 262
 
5.3%
6 259
 
5.3%
9 187
 
3.8%
0 84
 
1.7%
Other Punctuation
ValueCountFrequency (%)
' 16610
99.6%
, 72
 
0.4%
Math Symbol
ValueCountFrequency (%)
~ 32
80.0%
+ 8
 
20.0%
Space Separator
ValueCountFrequency (%)
20135
100.0%
Close Punctuation
ValueCountFrequency (%)
) 484
100.0%
Open Punctuation
ValueCountFrequency (%)
( 484
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 152
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 63837
59.2%
Common 42883
39.7%
Latin 1171
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 4280
 
6.7%
역 3676
 
5.8%
번 3455
 
5.4%
앞 3432
 
5.4%
출 3416
 
5.4%
대 1203
 
1.9%
동 1174
 
1.8%
원 1033
 
1.6%
사 1031
 
1.6%
교 1011
 
1.6%
Other values (384) 40126
62.9%
Latin
ValueCountFrequency (%)
C 160
13.7%
K 155
13.2%
I 99
8.5%
A 92
 
7.9%
B 83
 
7.1%
L 77
 
6.6%
M 75
 
6.4%
G 69
 
5.9%
D 66
 
5.6%
E 65
 
5.6%
Other values (11) 230
19.6%
Common
ValueCountFrequency (%)
20135
47.0%
' 16610
38.7%
1 1330
 
3.1%
2 1064
 
2.5%
4 564
 
1.3%
) 484
 
1.1%
( 484
 
1.1%
3 471
 
1.1%
8 371
 
0.9%
5 314
 
0.7%
Other values (8) 1056
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 63837
59.2%
ASCII 44054
40.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
20135
45.7%
' 16610
37.7%
1 1330
 
3.0%
2 1064
 
2.4%
4 564
 
1.3%
) 484
 
1.1%
( 484
 
1.1%
3 471
 
1.1%
8 371
 
0.8%
5 314
 
0.7%
Other values (29) 2227
 
5.1%
Hangul
ValueCountFrequency (%)
구 4280
 
6.7%
역 3676
 
5.8%
번 3455
 
5.4%
앞 3432
 
5.4%
출 3416
 
5.4%
대 1203
 
1.9%
동 1174
 
1.8%
원 1033
 
1.6%
사 1031
 
1.6%
교 1011
 
1.6%
Other values (384) 40126
62.9%

'대여건수'
Real number (ℝ)

ZEROS 

Distinct14
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.11764
Minimum0
Maximum15
Zeros2699
Zeros (%)32.4%
Negative0
Negative (%)0.0%
Memory size73.4 KiB
2023-12-11T19:02:26.311237image/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 deviation1.2155345
Coefficient of variation (CV)1.0875903
Kurtosis12.521036
Mean1.11764
Median Absolute Deviation (MAD)1
Skewness2.4829901
Sum9320
Variance1.477524
MonotonicityNot monotonic
2023-12-11T19:02:26.405783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1 3391
40.7%
0 2699
32.4%
2 1526
18.3%
3 378
 
4.5%
4 192
 
2.3%
5 69
 
0.8%
6 40
 
0.5%
7 15
 
0.2%
9 11
 
0.1%
10 7
 
0.1%
Other values (4) 11
 
0.1%
ValueCountFrequency (%)
0 2699
32.4%
1 3391
40.7%
2 1526
18.3%
3 378
 
4.5%
4 192
 
2.3%
5 69
 
0.8%
6 40
 
0.5%
7 15
 
0.2%
8 4
 
< 0.1%
9 11
 
0.1%
ValueCountFrequency (%)
15 1
 
< 0.1%
12 3
 
< 0.1%
11 3
 
< 0.1%
10 7
 
0.1%
9 11
 
0.1%
8 4
 
< 0.1%
7 15
 
0.2%
6 40
 
0.5%
5 69
 
0.8%
4 192
2.3%

'반납건수'
Real number (ℝ)

ZEROS 

Distinct15
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.11764
Minimum0
Maximum16
Zeros2772
Zeros (%)33.2%
Negative0
Negative (%)0.0%
Memory size73.4 KiB
2023-12-11T19:02:26.494196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum16
Range16
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2512213
Coefficient of variation (CV)1.1195208
Kurtosis16.895555
Mean1.11764
Median Absolute Deviation (MAD)1
Skewness2.7879537
Sum9320
Variance1.5655547
MonotonicityNot monotonic
2023-12-11T19:02:26.585805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 3313
39.7%
0 2772
33.2%
2 1506
18.1%
3 382
 
4.6%
4 209
 
2.5%
5 79
 
0.9%
6 32
 
0.4%
7 14
 
0.2%
8 9
 
0.1%
9 8
 
0.1%
Other values (5) 15
 
0.2%
ValueCountFrequency (%)
0 2772
33.2%
1 3313
39.7%
2 1506
18.1%
3 382
 
4.6%
4 209
 
2.5%
5 79
 
0.9%
6 32
 
0.4%
7 14
 
0.2%
8 9
 
0.1%
9 8
 
0.1%
ValueCountFrequency (%)
16 1
 
< 0.1%
15 3
 
< 0.1%
12 5
 
0.1%
11 3
 
< 0.1%
10 3
 
< 0.1%
9 8
 
0.1%
8 9
 
0.1%
7 14
 
0.2%
6 32
0.4%
5 79
0.9%

Interactions

2023-12-11T19:02:24.021996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T19:02:23.806679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T19:02:24.178334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T19:02:23.900339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T19:02:26.656370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
'대여건수''반납건수'
'대여건수'1.0000.576
'반납건수'0.5761.000
2023-12-11T19:02:26.990856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
'대여건수''반납건수'
'대여건수'1.000-0.165
'반납건수'-0.1651.000

Missing values

2023-12-11T19:02:24.318134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T19:02:24.408260image/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.
2023-12-11T19:02:24.492208image/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'
0'2018-01-01''2377'' 수서역 5번출구 뒤'11
1'2018-01-01''116'' 일진아이윌아파트 옆'11
2'2018-01-01''324'' 신세계백화점 본점 앞'10
3'2018-01-01''347'' 동대문역사문화공원역 9번출구 앞'20
4'2018-01-01''114'' 홍대입구역 8번출구 앞'11
5'2018-01-01''146'' 마포역 1번출구 뒤'01
6'2018-01-01''247'' 당산역 10번출구 앞'02
7'2018-01-01''805'' 문배어린이공원 앞'02
8'2018-01-01''810'' 이태원지하보도'11
9'2018-01-01''811'' 녹사평역1번출구'10
'대여일자''대여소번호''대여소''대여건수''반납건수'
8329'2018-06-27''1232'' 롯데마트 주차장 옆'10
8330'2018-06-27''210'' IFC몰'10
8331'2018-06-27''207'' 여의나루역 1번출구 앞'20
8332'2018-06-27''2262'' 한신16차아파트 119동 앞'01
8333'2018-06-27''2316'' 삼성역 8번출구'01
8334'2018-06-27''107'' 신한은행 서교동금융센터점 앞'10
8335'2018-06-27''2215'' 반포종합운동장 입구'10
8336'2018-06-28''320'' 을지로입구역 4번출구 앞'01
8337'2018-06-28''359'' 원남동사거리'10
8338'2018-06-28''201'' 진미파라곤 앞'11