Overview

Dataset statistics

Number of variables5
Number of observations7012
Missing cells18
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory287.7 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 09:54:18.588675
Analysis finished2024-03-13 09:54:19.527187
Duration0.94 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

'대여일자'
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size54.9 KiB
'201805'
1273 
'201806'
1272 
'201804'
1268 
'201803'
1121 
'201802'
1045 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row'201801'
2nd row'201801'
3rd row'201801'
4th row'201801'
5th row'201801'

Common Values

ValueCountFrequency (%)
'201805' 1273
18.2%
'201806' 1272
18.1%
'201804' 1268
18.1%
'201803' 1121
16.0%
'201802' 1045
14.9%
'201801' 1033
14.7%

Length

2024-03-13T18:54:19.584544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T18:54:19.679142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
201805 1273
18.2%
201806 1272
18.1%
201804 1268
18.1%
201803 1121
16.0%
201802 1045
14.9%
201801 1033
14.7%

'대여소번호'
Text

Distinct1276
Distinct (%)18.2%
Missing9
Missing (%)0.1%
Memory size54.9 KiB
2024-03-13T18:54:20.014911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length6
Mean length5.5707554
Min length5

Characters and Unicode

Total characters39012
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

Unique4 ?
Unique (%)0.1%

Sample

1st row'108'
2nd row'503'
3rd row'731'
4th row'504'
5th row'740'
ValueCountFrequency (%)
516 6
 
0.1%
104 6
 
0.1%
1825 6
 
0.1%
1833 6
 
0.1%
1504 6
 
0.1%
1503 6
 
0.1%
1819 6
 
0.1%
1835 6
 
0.1%
1834 6
 
0.1%
215 6
 
0.1%
Other values (1267) 6949
99.1%
2024-03-13T18:54:20.505802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 14006
35.9%
1 5497
 
14.1%
2 4336
 
11.1%
3 2971
 
7.6%
0 2286
 
5.9%
5 2170
 
5.6%
4 1943
 
5.0%
6 1736
 
4.4%
7 1396
 
3.6%
9 1312
 
3.4%
Other values (14) 1359
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24931
63.9%
Other Punctuation 14006
35.9%
Other Letter 69
 
0.2%
Space Separator 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
센 12
17.4%
터 12
17.4%
실 6
8.7%
비 6
8.7%
정 6
8.7%
랑 6
8.7%
암 6
8.7%
상 6
8.7%
중 6
8.7%
위 1
 
1.4%
Other values (2) 2
 
2.9%
Decimal Number
ValueCountFrequency (%)
1 5497
22.0%
2 4336
17.4%
3 2971
11.9%
0 2286
9.2%
5 2170
 
8.7%
4 1943
 
7.8%
6 1736
 
7.0%
7 1396
 
5.6%
9 1312
 
5.3%
8 1284
 
5.2%
Other Punctuation
ValueCountFrequency (%)
' 14006
100.0%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 38943
99.8%
Hangul 69
 
0.2%

Most frequent character per script

Common
ValueCountFrequency (%)
' 14006
36.0%
1 5497
 
14.1%
2 4336
 
11.1%
3 2971
 
7.6%
0 2286
 
5.9%
5 2170
 
5.6%
4 1943
 
5.0%
6 1736
 
4.5%
7 1396
 
3.6%
9 1312
 
3.4%
Other values (2) 1290
 
3.3%
Hangul
ValueCountFrequency (%)
센 12
17.4%
터 12
17.4%
실 6
8.7%
비 6
8.7%
정 6
8.7%
랑 6
8.7%
암 6
8.7%
상 6
8.7%
중 6
8.7%
위 1
 
1.4%
Other values (2) 2
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 38943
99.8%
Hangul 69
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 14006
36.0%
1 5497
 
14.1%
2 4336
 
11.1%
3 2971
 
7.6%
0 2286
 
5.9%
5 2170
 
5.6%
4 1943
 
5.0%
6 1736
 
4.5%
7 1396
 
3.6%
9 1312
 
3.4%
Other values (2) 1290
 
3.3%
Hangul
ValueCountFrequency (%)
센 12
17.4%
터 12
17.4%
실 6
8.7%
비 6
8.7%
정 6
8.7%
랑 6
8.7%
암 6
8.7%
상 6
8.7%
중 6
8.7%
위 1
 
1.4%
Other values (2) 2
 
2.9%

'대여소'
Text

Distinct1276
Distinct (%)18.2%
Missing9
Missing (%)0.1%
Memory size54.9 KiB
2024-03-13T18:54:20.759577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length27
Mean length12.711838
Min length5

Characters and Unicode

Total characters89021
Distinct characters488
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

Unique4 ?
Unique (%)0.1%

Sample

1st row' 서교동 사거리'
2nd row' 더샵스타시티 C동 앞'
3rd row' 서울시 도로환경관리센터'
4th row' 신자초교입구교차로'
5th row' 으뜸공원'
ValueCountFrequency (%)
6935
31.2%
앞 1981
 
8.9%
옆 399
 
1.8%
1번출구 265
 
1.2%
출구 248
 
1.1%
사거리 217
 
1.0%
뒤 202
 
0.9%
2번출구 196
 
0.9%
교차로 166
 
0.7%
4번출구 165
 
0.7%
Other values (1536) 11450
51.5%
2024-03-13T18:54:21.110070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15246
 
17.1%
' 14006
 
15.7%
구 2490
 
2.8%
앞 2340
 
2.6%
역 1926
 
2.2%
번 1742
 
2.0%
출 1722
 
1.9%
동 1347
 
1.5%
교 1118
 
1.3%
리 951
 
1.1%
Other values (478) 46133
51.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 54873
61.6%
Space Separator 15246
 
17.1%
Other Punctuation 14045
 
15.8%
Decimal Number 3121
 
3.5%
Uppercase Letter 818
 
0.9%
Close Punctuation 392
 
0.4%
Open Punctuation 392
 
0.4%
Lowercase Letter 62
 
0.1%
Dash Punctuation 48
 
0.1%
Math Symbol 18
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 2490
 
4.5%
앞 2340
 
4.3%
역 1926
 
3.5%
번 1742
 
3.2%
출 1722
 
3.1%
동 1347
 
2.5%
교 1118
 
2.0%
리 951
 
1.7%
원 887
 
1.6%
사 824
 
1.5%
Other values (425) 39526
72.0%
Uppercase Letter
ValueCountFrequency (%)
K 110
13.4%
S 99
12.1%
C 94
11.5%
T 53
 
6.5%
G 52
 
6.4%
M 51
 
6.2%
L 49
 
6.0%
A 48
 
5.9%
B 42
 
5.1%
I 36
 
4.4%
Other values (13) 184
22.5%
Decimal Number
ValueCountFrequency (%)
1 895
28.7%
2 569
18.2%
3 423
13.6%
4 370
11.9%
5 212
 
6.8%
0 164
 
5.3%
7 146
 
4.7%
6 136
 
4.4%
8 134
 
4.3%
9 72
 
2.3%
Lowercase Letter
ValueCountFrequency (%)
e 22
35.5%
t 9
14.5%
m 6
 
9.7%
o 6
 
9.7%
c 6
 
9.7%
l 6
 
9.7%
k 5
 
8.1%
s 2
 
3.2%
Other Punctuation
ValueCountFrequency (%)
' 14006
99.7%
, 24
 
0.2%
& 6
 
< 0.1%
@ 6
 
< 0.1%
? 3
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
~ 12
66.7%
+ 6
33.3%
Space Separator
ValueCountFrequency (%)
15246
100.0%
Close Punctuation
ValueCountFrequency (%)
) 392
100.0%
Open Punctuation
ValueCountFrequency (%)
( 392
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 48
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 54873
61.6%
Common 33268
37.4%
Latin 880
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 2490
 
4.5%
앞 2340
 
4.3%
역 1926
 
3.5%
번 1742
 
3.2%
출 1722
 
3.1%
동 1347
 
2.5%
교 1118
 
2.0%
리 951
 
1.7%
원 887
 
1.6%
사 824
 
1.5%
Other values (425) 39526
72.0%
Latin
ValueCountFrequency (%)
K 110
12.5%
S 99
11.2%
C 94
 
10.7%
T 53
 
6.0%
G 52
 
5.9%
M 51
 
5.8%
L 49
 
5.6%
A 48
 
5.5%
B 42
 
4.8%
I 36
 
4.1%
Other values (21) 246
28.0%
Common
ValueCountFrequency (%)
15246
45.8%
' 14006
42.1%
1 895
 
2.7%
2 569
 
1.7%
3 423
 
1.3%
) 392
 
1.2%
( 392
 
1.2%
4 370
 
1.1%
5 212
 
0.6%
0 164
 
0.5%
Other values (12) 599
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 54873
61.6%
ASCII 34148
38.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
15246
44.6%
' 14006
41.0%
1 895
 
2.6%
2 569
 
1.7%
3 423
 
1.2%
) 392
 
1.1%
( 392
 
1.1%
4 370
 
1.1%
5 212
 
0.6%
0 164
 
0.5%
Other values (43) 1479
 
4.3%
Hangul
ValueCountFrequency (%)
구 2490
 
4.5%
앞 2340
 
4.3%
역 1926
 
3.5%
번 1742
 
3.2%
출 1722
 
3.1%
동 1347
 
2.5%
교 1118
 
2.0%
리 951
 
1.7%
원 887
 
1.6%
사 824
 
1.5%
Other values (425) 39526
72.0%

'대여건수'
Real number (ℝ)

HIGH CORRELATION 

Distinct1615
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean502.89062
Minimum0
Maximum8707
Zeros43
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size61.8 KiB
2024-03-13T18:54:21.235905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile38
Q1143
median325
Q3665.25
95-th percentile1518.9
Maximum8707
Range8707
Interquartile range (IQR)522.25

Descriptive statistics

Standard deviation569.17611
Coefficient of variation (CV)1.131809
Kurtosis28.869588
Mean502.89062
Median Absolute Deviation (MAD)216
Skewness3.7470037
Sum3526269
Variance323961.44
MonotonicityNot monotonic
2024-03-13T18:54:21.359131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 43
 
0.6%
127 26
 
0.4%
170 22
 
0.3%
54 21
 
0.3%
73 21
 
0.3%
128 20
 
0.3%
36 20
 
0.3%
50 19
 
0.3%
75 19
 
0.3%
43 18
 
0.3%
Other values (1605) 6783
96.7%
ValueCountFrequency (%)
0 43
0.6%
1 8
 
0.1%
2 4
 
0.1%
3 2
 
< 0.1%
4 4
 
0.1%
5 6
 
0.1%
6 4
 
0.1%
7 4
 
0.1%
8 2
 
< 0.1%
9 4
 
0.1%
ValueCountFrequency (%)
8707 1
< 0.1%
8632 1
< 0.1%
7438 1
< 0.1%
7131 1
< 0.1%
6426 1
< 0.1%
5962 1
< 0.1%
5649 1
< 0.1%
5608 1
< 0.1%
5252 1
< 0.1%
5242 1
< 0.1%

'반납건수'
Real number (ℝ)

HIGH CORRELATION 

Distinct1615
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean502.7905
Minimum0
Maximum8639
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size61.8 KiB
2024-03-13T18:54:21.481195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33
Q1136
median315.5
Q3661
95-th percentile1524.45
Maximum8639
Range8639
Interquartile range (IQR)525

Descriptive statistics

Standard deviation597.98716
Coefficient of variation (CV)1.1893366
Kurtosis30.713529
Mean502.7905
Median Absolute Deviation (MAD)218.5
Skewness3.9528073
Sum3525567
Variance357588.64
MonotonicityNot monotonic
2024-03-13T18:54:21.630568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
82 25
 
0.4%
63 21
 
0.3%
97 21
 
0.3%
24 20
 
0.3%
61 20
 
0.3%
70 20
 
0.3%
45 20
 
0.3%
136 19
 
0.3%
133 19
 
0.3%
50 19
 
0.3%
Other values (1605) 6808
97.1%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 18
0.3%
2 9
0.1%
3 6
 
0.1%
4 4
 
0.1%
5 12
0.2%
6 4
 
0.1%
7 10
0.1%
8 8
0.1%
9 11
0.2%
ValueCountFrequency (%)
8639 1
< 0.1%
8495 1
< 0.1%
8153 1
< 0.1%
8142 1
< 0.1%
7875 1
< 0.1%
6291 1
< 0.1%
5799 1
< 0.1%
5725 1
< 0.1%
5633 1
< 0.1%
5344 1
< 0.1%

Interactions

2024-03-13T18:54:19.099989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T18:54:18.950624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T18:54:19.185831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T18:54:19.025169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T18:54:21.706051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
'대여일자''대여건수''반납건수'
'대여일자'1.0000.3400.362
'대여건수'0.3401.0000.925
'반납건수'0.3620.9251.000
2024-03-13T18:54:21.781759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
'대여건수''반납건수''대여일자'
'대여건수'1.0000.9790.186
'반납건수'0.9791.0000.189
'대여일자'0.1860.1891.000

Missing values

2024-03-13T18:54:19.295792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T18:54:19.383088image/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.
2024-03-13T18:54:19.481179image/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'
0'201801''108'' 서교동 사거리'355422
1'201801''503'' 더샵스타시티 C동 앞'287256
2'201801''731'' 서울시 도로환경관리센터'04
3'201801''504'' 신자초교입구교차로'370368
4'201801''740'' 으뜸공원'01
5'201801''746'' 목동2단지 상가'01
6'201801''505'' 자양사거리 광진아크로텔 앞'349328
7'201801''506'' 금호 어울림 아파트 앞'8964
8'201801''949'' 연신내역 1번 출구 '01
9'201801''507'' 성수아이에스비즈타워 앞'180225
'대여일자''대여소번호''대여소''대여건수''반납건수'
7002'201806''3504'' 원일교회'1204992
7003'201806''3505'' 신양초교앞 교차로'1085902
7004'201806''502'' 뚝섬유원지역 1번출구 앞'74388495
7005'201806''424'' 롯데하이마트 (상암월드컵점) '342252
7006'201806''425'' DMC첨단산업센터'351378
7007'201806''722'' LG전자베스트샵 신정점 '8751003
7008'201806''723'' SBS방송국'14411610
7009'201806''724'' 계남공원 입구 주출입구 좌측'204199
7010'201806''725'' 양강중학교앞 교차로'435178
7011'201806''726'' 목동3단지 시내버스정류장'726719