Overview

Dataset statistics

Number of variables5
Number of observations8562
Missing cells38
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory351.3 KiB
Average record size in memory42.0 B

Variable types

Categorical1
Text2
Numeric2

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15249/F/1/datasetView.do

Alerts

'대여건수' is highly overall correlated with '반납건수'High correlation
'반납건수' is highly overall correlated with '대여건수'High correlation

Reproduction

Analysis started2024-03-13 09:54:22.467775
Analysis finished2024-03-13 09:54:23.446128
Duration0.98 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

'대여일자'
Categorical

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size67.0 KiB
'201712'
1043 
'201711'
984 
'201710'
900 
'201709'
899 
'201708'
865 
Other values (7)
3871 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row'201701'
2nd row'201701'
3rd row'201701'
4th row'201701'
5th row'201701'

Common Values

ValueCountFrequency (%)
'201712' 1043
12.2%
'201711' 984
11.5%
'201710' 900
10.5%
'201709' 899
10.5%
'201708' 865
10.1%
'201707' 790
9.2%
'201706' 701
8.2%
'201705' 553
6.5%
'201704' 487
5.7%
'201703' 448
5.2%
Other values (2) 892
10.4%

Length

2024-03-13T18:54:23.497320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
201712 1043
12.2%
201711 984
11.5%
201710 900
10.5%
201709 899
10.5%
201708 865
10.1%
201707 790
9.2%
201706 701
8.2%
201705 553
6.5%
201704 487
5.7%
201703 448
5.2%
Other values (2) 892
10.4%

'대여소번호'
Text

Distinct1053
Distinct (%)12.3%
Missing19
Missing (%)0.2%
Memory size67.0 KiB
2024-03-13T18:54:23.871945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length5
Mean length5.3735222
Min length3

Characters and Unicode

Total characters45906
Distinct characters30
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique68 ?
Unique (%)0.8%

Sample

1st row'108'
2nd row'503'
3rd row'504'
4th row'505'
5th row'506'
ValueCountFrequency (%)
309 12
 
0.1%
573 12
 
0.1%
626 12
 
0.1%
135 12
 
0.1%
555 12
 
0.1%
623 12
 
0.1%
622 12
 
0.1%
137 12
 
0.1%
621 12
 
0.1%
577 12
 
0.1%
Other values (1045) 8437
98.6%
2024-03-13T18:54:24.359887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 17086
37.2%
1 6110
 
13.3%
2 4828
 
10.5%
3 3522
 
7.7%
0 2917
 
6.4%
5 2646
 
5.8%
4 2187
 
4.8%
6 1963
 
4.3%
8 1553
 
3.4%
7 1438
 
3.1%
Other values (20) 1656
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28595
62.3%
Other Punctuation 17086
37.2%
Other Letter 203
 
0.4%
Space Separator 14
 
< 0.1%
Lowercase Letter 8
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
센 23
11.3%
터 23
11.3%
콤 17
8.4%
트 17
8.4%
위 17
8.4%
비 14
 
6.9%
정 14
 
6.9%
중 13
 
6.4%
랑 13
 
6.4%
실 12
 
5.9%
Other values (5) 40
19.7%
Decimal Number
ValueCountFrequency (%)
1 6110
21.4%
2 4828
16.9%
3 3522
12.3%
0 2917
10.2%
5 2646
9.3%
4 2187
 
7.6%
6 1963
 
6.9%
8 1553
 
5.4%
7 1438
 
5.0%
9 1431
 
5.0%
Lowercase Letter
ValueCountFrequency (%)
t 4
50.0%
e 2
25.0%
s 2
25.0%
Other Punctuation
ValueCountFrequency (%)
' 17086
100.0%
Space Separator
ValueCountFrequency (%)
14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45695
99.5%
Hangul 203
 
0.4%
Latin 8
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
센 23
11.3%
터 23
11.3%
콤 17
8.4%
트 17
8.4%
위 17
8.4%
비 14
 
6.9%
정 14
 
6.9%
중 13
 
6.4%
랑 13
 
6.4%
실 12
 
5.9%
Other values (5) 40
19.7%
Common
ValueCountFrequency (%)
' 17086
37.4%
1 6110
 
13.4%
2 4828
 
10.6%
3 3522
 
7.7%
0 2917
 
6.4%
5 2646
 
5.8%
4 2187
 
4.8%
6 1963
 
4.3%
8 1553
 
3.4%
7 1438
 
3.1%
Other values (2) 1445
 
3.2%
Latin
ValueCountFrequency (%)
t 4
50.0%
e 2
25.0%
s 2
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45703
99.6%
Hangul 203
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 17086
37.4%
1 6110
 
13.4%
2 4828
 
10.6%
3 3522
 
7.7%
0 2917
 
6.4%
5 2646
 
5.8%
4 2187
 
4.8%
6 1963
 
4.3%
8 1553
 
3.4%
7 1438
 
3.1%
Other values (5) 1453
 
3.2%
Hangul
ValueCountFrequency (%)
센 23
11.3%
터 23
11.3%
콤 17
8.4%
트 17
8.4%
위 17
8.4%
비 14
 
6.9%
정 14
 
6.9%
중 13
 
6.4%
랑 13
 
6.4%
실 12
 
5.9%
Other values (5) 40
19.7%

'대여소'
Text

Distinct1053
Distinct (%)12.3%
Missing19
Missing (%)0.2%
Memory size67.0 KiB
2024-03-13T18:54:24.608145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length27
Mean length12.782629
Min length2

Characters and Unicode

Total characters109202
Distinct characters466
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique68 ?
Unique (%)0.8%

Sample

1st row' 서교동 사거리'
2nd row' 더샵스타시티 C동 앞'
3rd row' 신자초교입구교차로'
4th row' 자양사거리 광진아크로텔 앞'
5th row' 금호 어울림 아파트 앞'
ValueCountFrequency (%)
8422
30.3%
앞 2977
 
10.7%
옆 636
 
2.3%
사거리 348
 
1.3%
1번출구 321
 
1.2%
출구 287
 
1.0%
2번출구 279
 
1.0%
뒤 265
 
1.0%
4번출구 259
 
0.9%
3번출구 203
 
0.7%
Other values (1297) 13768
49.6%
2024-03-13T18:54:25.008445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
19236
 
17.6%
' 17086
 
15.6%
앞 3323
 
3.0%
구 3041
 
2.8%
역 2499
 
2.3%
출 2249
 
2.1%
번 2237
 
2.0%
동 1553
 
1.4%
교 1256
 
1.2%
리 1231
 
1.1%
Other values (456) 55491
50.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 66820
61.2%
Space Separator 19236
 
17.6%
Other Punctuation 17118
 
15.7%
Decimal Number 3780
 
3.5%
Uppercase Letter 1207
 
1.1%
Close Punctuation 455
 
0.4%
Open Punctuation 455
 
0.4%
Dash Punctuation 59
 
0.1%
Lowercase Letter 51
 
< 0.1%
Math Symbol 12
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
앞 3323
 
5.0%
구 3041
 
4.6%
역 2499
 
3.7%
출 2249
 
3.4%
번 2237
 
3.3%
동 1553
 
2.3%
교 1256
 
1.9%
리 1231
 
1.8%
원 1113
 
1.7%
사 1056
 
1.6%
Other values (406) 47262
70.7%
Uppercase Letter
ValueCountFrequency (%)
K 158
13.1%
C 141
11.7%
S 113
 
9.4%
M 84
 
7.0%
G 76
 
6.3%
T 76
 
6.3%
A 70
 
5.8%
B 68
 
5.6%
L 65
 
5.4%
D 60
 
5.0%
Other values (13) 296
24.5%
Decimal Number
ValueCountFrequency (%)
1 1029
27.2%
2 727
19.2%
3 502
13.3%
4 494
13.1%
5 240
 
6.3%
0 179
 
4.7%
7 179
 
4.7%
8 166
 
4.4%
6 160
 
4.2%
9 104
 
2.8%
Lowercase Letter
ValueCountFrequency (%)
e 16
31.4%
t 7
13.7%
l 7
13.7%
c 7
13.7%
o 7
13.7%
m 7
13.7%
Other Punctuation
ValueCountFrequency (%)
' 17086
99.8%
, 19
 
0.1%
@ 7
 
< 0.1%
& 6
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
~ 10
83.3%
+ 2
 
16.7%
Space Separator
ValueCountFrequency (%)
19236
100.0%
Close Punctuation
ValueCountFrequency (%)
) 455
100.0%
Open Punctuation
ValueCountFrequency (%)
( 455
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 59
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 66820
61.2%
Common 41124
37.7%
Latin 1258
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
앞 3323
 
5.0%
구 3041
 
4.6%
역 2499
 
3.7%
출 2249
 
3.4%
번 2237
 
3.3%
동 1553
 
2.3%
교 1256
 
1.9%
리 1231
 
1.8%
원 1113
 
1.7%
사 1056
 
1.6%
Other values (406) 47262
70.7%
Latin
ValueCountFrequency (%)
K 158
12.6%
C 141
11.2%
S 113
 
9.0%
M 84
 
6.7%
G 76
 
6.0%
T 76
 
6.0%
A 70
 
5.6%
B 68
 
5.4%
L 65
 
5.2%
D 60
 
4.8%
Other values (19) 347
27.6%
Common
ValueCountFrequency (%)
19236
46.8%
' 17086
41.5%
1 1029
 
2.5%
2 727
 
1.8%
3 502
 
1.2%
4 494
 
1.2%
) 455
 
1.1%
( 455
 
1.1%
5 240
 
0.6%
0 179
 
0.4%
Other values (11) 721
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 66820
61.2%
ASCII 42382
38.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
19236
45.4%
' 17086
40.3%
1 1029
 
2.4%
2 727
 
1.7%
3 502
 
1.2%
4 494
 
1.2%
) 455
 
1.1%
( 455
 
1.1%
5 240
 
0.6%
0 179
 
0.4%
Other values (40) 1979
 
4.7%
Hangul
ValueCountFrequency (%)
앞 3323
 
5.0%
구 3041
 
4.6%
역 2499
 
3.7%
출 2249
 
3.4%
번 2237
 
3.3%
동 1553
 
2.3%
교 1256
 
1.9%
리 1231
 
1.8%
원 1113
 
1.7%
사 1056
 
1.6%
Other values (406) 47262
70.7%

'대여건수'
Real number (ℝ)

HIGH CORRELATION 

Distinct1840
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean570.80927
Minimum0
Maximum9971
Zeros73
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size75.4 KiB
2024-03-13T18:54:25.141479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile36
Q1181
median409
Q3778
95-th percentile1611
Maximum9971
Range9971
Interquartile range (IQR)597

Descriptive statistics

Standard deviation582.30957
Coefficient of variation (CV)1.0201474
Kurtosis24.087459
Mean570.80927
Median Absolute Deviation (MAD)263
Skewness3.2447479
Sum4887269
Variance339084.43
MonotonicityNot monotonic
2024-03-13T18:54:25.257829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 73
 
0.9%
1 31
 
0.4%
72 20
 
0.2%
111 20
 
0.2%
411 19
 
0.2%
296 19
 
0.2%
156 19
 
0.2%
113 19
 
0.2%
208 19
 
0.2%
154 19
 
0.2%
Other values (1830) 8304
97.0%
ValueCountFrequency (%)
0 73
0.9%
1 31
0.4%
2 15
 
0.2%
3 6
 
0.1%
4 6
 
0.1%
5 9
 
0.1%
6 9
 
0.1%
7 16
 
0.2%
8 12
 
0.1%
9 5
 
0.1%
ValueCountFrequency (%)
9971 1
< 0.1%
7823 1
< 0.1%
7603 1
< 0.1%
6920 1
< 0.1%
6218 1
< 0.1%
5997 1
< 0.1%
5889 1
< 0.1%
5705 1
< 0.1%
5580 1
< 0.1%
5457 1
< 0.1%

'반납건수'
Real number (ℝ)

HIGH CORRELATION 

Distinct1831
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean570.7858
Minimum0
Maximum9724
Zeros13
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size75.4 KiB
2024-03-13T18:54:25.375476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile32.05
Q1173
median398
Q3780
95-th percentile1627
Maximum9724
Range9724
Interquartile range (IQR)607

Descriptive statistics

Standard deviation607.53418
Coefficient of variation (CV)1.0643821
Kurtosis24.980393
Mean570.7858
Median Absolute Deviation (MAD)266
Skewness3.4391166
Sum4887068
Variance369097.78
MonotonicityNot monotonic
2024-03-13T18:54:25.525344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 41
 
0.5%
51 22
 
0.3%
147 21
 
0.2%
231 21
 
0.2%
180 21
 
0.2%
111 20
 
0.2%
139 20
 
0.2%
112 20
 
0.2%
124 19
 
0.2%
74 19
 
0.2%
Other values (1821) 8338
97.4%
ValueCountFrequency (%)
0 13
 
0.2%
1 41
0.5%
2 18
0.2%
3 13
 
0.2%
4 12
 
0.1%
5 16
 
0.2%
6 12
 
0.1%
7 15
 
0.2%
8 13
 
0.2%
9 12
 
0.1%
ValueCountFrequency (%)
9724 1
< 0.1%
8519 1
< 0.1%
7678 1
< 0.1%
7252 1
< 0.1%
6949 1
< 0.1%
6828 1
< 0.1%
6194 1
< 0.1%
5961 1
< 0.1%
5867 1
< 0.1%
5511 1
< 0.1%

Interactions

2024-03-13T18:54:23.062475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T18:54:22.886010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T18:54:23.139611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T18:54:22.980571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T18:54:25.603238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
'대여일자''대여건수''반납건수'
'대여일자'1.0000.2970.287
'대여건수'0.2971.0000.948
'반납건수'0.2870.9481.000
2024-03-13T18:54:25.677348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
'대여건수''반납건수''대여일자'
'대여건수'1.0000.9730.130
'반납건수'0.9731.0000.124
'대여일자'0.1300.1241.000

Missing values

2024-03-13T18:54:23.237011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T18:54:23.321149image/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.
2024-03-13T18:54:23.398603image/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'
0'201701''108'' 서교동 사거리'246198
1'201701''503'' 더샵스타시티 C동 앞'246224
2'201701''504'' 신자초교입구교차로'232261
3'201701''505'' 자양사거리 광진아크로텔 앞'302313
4'201701''506'' 금호 어울림 아파트 앞'7277
5'201701''507'' 성수아이에스비즈타워 앞'169199
6'201701''508'' 성수아카데미타워 앞'156126
7'201701''509'' 이마트 버스정류소 옆'311359
8'201701''510'' 서울숲 남문 버스정류소 옆'5267
9'201701''511'' 서울숲역 4번 출구 옆'238219
'대여일자''대여소번호''대여소''대여건수''반납건수'
8552'201712''501'' 광진구의회 앞'576621
8553'201712''593''자양중앙나들목'177172
8554'201712''1250''문정2동 주민센터'2918
8555'201712''3501'' 광진구청 앞'307296
8556'201712''3502'' 중곡역 1번출구'4047
8557'201712''3503'' 광진유진스웰'3321
8558'201712''3504'' 원일교회'196159
8559'201712''3505'' 신양초교앞 교차로'124106
8560'201712''502'' 뚝섬유원지역 1번출구 앞'571719
8561'201712''722'' LG전자베스트샵 신정점 '10