Overview

Dataset statistics

Number of variables5
Number of observations2265
Missing cells10
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory93.0 KiB
Average record size in memory42.1 B

Variable types

Categorical1
Text2
Numeric2

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15242/S/1/datasetView.do

Alerts

'대여건수' is highly overall correlated with '반납건수'High correlation
'반납건수' is highly overall correlated with '대여건수'High correlation
'대여건수' has 375 (16.6%) zerosZeros
'반납건수' has 444 (19.6%) zerosZeros

Reproduction

Analysis started2023-12-11 06:57:41.173728
Analysis finished2023-12-11 06:57:42.435146
Duration1.26 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

'대여일자'
Categorical

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size17.8 KiB
'201806'
562 
'201805'
533 
'201804'
489 
'201803'
354 
'201802'
176 

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 (%)
'201806' 562
24.8%
'201805' 533
23.5%
'201804' 489
21.6%
'201803' 354
15.6%
'201802' 176
 
7.8%
'201801' 151
 
6.7%

Length

2023-12-11T15:57:42.530741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T15:57:42.685183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
201806 562
24.8%
201805 533
23.5%
201804 489
21.6%
201803 354
15.6%
201802 176
 
7.8%
201801 151
 
6.7%

'대여소번호'
Text

Distinct774
Distinct (%)34.2%
Missing5
Missing (%)0.2%
Memory size17.8 KiB
2023-12-11T15:57:43.235885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.3774336
Min length5

Characters and Unicode

Total characters12153
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

Unique227 ?
Unique (%)10.0%

Sample

1st row'108'
2nd row'1041'
3rd row'510'
4th row'515'
5th row'300'
ValueCountFrequency (%)
108 6
 
0.3%
2301 6
 
0.3%
2308 6
 
0.3%
561 6
 
0.3%
134 6
 
0.3%
186 6
 
0.3%
387 6
 
0.3%
385 6
 
0.3%
812 6
 
0.3%
811 6
 
0.3%
Other values (764) 2200
97.3%
2023-12-11T15:57:44.015985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 4520
37.2%
1 1499
 
12.3%
2 1437
 
11.8%
3 1131
 
9.3%
0 737
 
6.1%
5 687
 
5.7%
4 507
 
4.2%
8 480
 
3.9%
6 430
 
3.5%
9 380
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7633
62.8%
Other Punctuation 4520
37.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1499
19.6%
2 1437
18.8%
3 1131
14.8%
0 737
9.7%
5 687
9.0%
4 507
 
6.6%
8 480
 
6.3%
6 430
 
5.6%
9 380
 
5.0%
7 345
 
4.5%
Other Punctuation
ValueCountFrequency (%)
' 4520
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12153
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
' 4520
37.2%
1 1499
 
12.3%
2 1437
 
11.8%
3 1131
 
9.3%
0 737
 
6.1%
5 687
 
5.7%
4 507
 
4.2%
8 480
 
3.9%
6 430
 
3.5%
9 380
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12153
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 4520
37.2%
1 1499
 
12.3%
2 1437
 
11.8%
3 1131
 
9.3%
0 737
 
6.1%
5 687
 
5.7%
4 507
 
4.2%
8 480
 
3.9%
6 430
 
3.5%
9 380
 
3.1%

'대여소'
Text

Distinct774
Distinct (%)34.2%
Missing5
Missing (%)0.2%
Memory size17.8 KiB
2023-12-11T15:57:44.370728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length26
Mean length12.810619
Min length6

Characters and Unicode

Total characters28952
Distinct characters430
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

Unique227 ?
Unique (%)10.0%

Sample

1st row' 서교동 사거리'
2nd row' 묘곡초등학교'
3rd row' 서울숲 남문 버스정류소 옆'
4th row' 광양중학교 앞'
5th row' 정동사거리'
ValueCountFrequency (%)
2247
30.0%
앞 767
 
10.3%
옆 192
 
2.6%
1번출구 142
 
1.9%
출구 116
 
1.6%
뒤 97
 
1.3%
2번출구 81
 
1.1%
사거리 77
 
1.0%
4번출구 72
 
1.0%
3번출구 62
 
0.8%
Other values (969) 3627
48.5%
2023-12-11T15:57:44.974388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5226
 
18.1%
' 4520
 
15.6%
구 1024
 
3.5%
역 868
 
3.0%
앞 864
 
3.0%
출 791
 
2.7%
번 789
 
2.7%
동 343
 
1.2%
1 320
 
1.1%
교 319
 
1.1%
Other values (420) 13888
48.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 17383
60.0%
Space Separator 5226
 
18.1%
Other Punctuation 4527
 
15.6%
Decimal Number 1186
 
4.1%
Uppercase Letter 326
 
1.1%
Open Punctuation 136
 
0.5%
Close Punctuation 136
 
0.5%
Dash Punctuation 19
 
0.1%
Math Symbol 10
 
< 0.1%
Lowercase Letter 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 1024
 
5.9%
역 868
 
5.0%
앞 864
 
5.0%
출 791
 
4.6%
번 789
 
4.5%
동 343
 
2.0%
교 319
 
1.8%
원 290
 
1.7%
리 287
 
1.7%
대 283
 
1.6%
Other values (381) 11525
66.3%
Uppercase Letter
ValueCountFrequency (%)
C 45
13.8%
K 45
13.8%
S 27
 
8.3%
M 24
 
7.4%
A 24
 
7.4%
L 20
 
6.1%
D 19
 
5.8%
I 17
 
5.2%
B 17
 
5.2%
E 17
 
5.2%
Other values (10) 71
21.8%
Decimal Number
ValueCountFrequency (%)
1 320
27.0%
2 232
19.6%
3 149
12.6%
4 140
11.8%
5 84
 
7.1%
7 66
 
5.6%
8 58
 
4.9%
6 56
 
4.7%
9 47
 
4.0%
0 34
 
2.9%
Other Punctuation
ValueCountFrequency (%)
' 4520
99.8%
, 7
 
0.2%
Math Symbol
ValueCountFrequency (%)
~ 6
60.0%
+ 4
40.0%
Space Separator
ValueCountFrequency (%)
5226
100.0%
Open Punctuation
ValueCountFrequency (%)
( 136
100.0%
Close Punctuation
ValueCountFrequency (%)
) 136
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 17383
60.0%
Common 11240
38.8%
Latin 329
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 1024
 
5.9%
역 868
 
5.0%
앞 864
 
5.0%
출 791
 
4.6%
번 789
 
4.5%
동 343
 
2.0%
교 319
 
1.8%
원 290
 
1.7%
리 287
 
1.7%
대 283
 
1.6%
Other values (381) 11525
66.3%
Latin
ValueCountFrequency (%)
C 45
13.7%
K 45
13.7%
S 27
 
8.2%
M 24
 
7.3%
A 24
 
7.3%
L 20
 
6.1%
D 19
 
5.8%
I 17
 
5.2%
B 17
 
5.2%
E 17
 
5.2%
Other values (11) 74
22.5%
Common
ValueCountFrequency (%)
5226
46.5%
' 4520
40.2%
1 320
 
2.8%
2 232
 
2.1%
3 149
 
1.3%
4 140
 
1.2%
( 136
 
1.2%
) 136
 
1.2%
5 84
 
0.7%
7 66
 
0.6%
Other values (8) 231
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 17383
60.0%
ASCII 11569
40.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5226
45.2%
' 4520
39.1%
1 320
 
2.8%
2 232
 
2.0%
3 149
 
1.3%
4 140
 
1.2%
( 136
 
1.2%
) 136
 
1.2%
5 84
 
0.7%
7 66
 
0.6%
Other values (29) 560
 
4.8%
Hangul
ValueCountFrequency (%)
구 1024
 
5.9%
역 868
 
5.0%
앞 864
 
5.0%
출 791
 
4.6%
번 789
 
4.5%
동 343
 
2.0%
교 319
 
1.8%
원 290
 
1.7%
리 287
 
1.7%
대 283
 
1.6%
Other values (381) 11525
66.3%

'대여건수'
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.989404
Minimum0
Maximum112
Zeros375
Zeros (%)16.6%
Negative0
Negative (%)0.0%
Memory size20.0 KiB
2023-12-11T15:57:45.177400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q35
95-th percentile15
Maximum112
Range112
Interquartile range (IQR)4

Descriptive statistics

Standard deviation6.3111232
Coefficient of variation (CV)1.5819715
Kurtosis78.241328
Mean3.989404
Median Absolute Deviation (MAD)1
Skewness6.3718666
Sum9036
Variance39.830276
MonotonicityNot monotonic
2023-12-11T15:57:45.689816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
1 557
24.6%
2 391
17.3%
0 375
16.6%
3 216
 
9.5%
4 141
 
6.2%
5 93
 
4.1%
6 86
 
3.8%
7 71
 
3.1%
8 46
 
2.0%
11 37
 
1.6%
Other values (28) 252
11.1%
ValueCountFrequency (%)
0 375
16.6%
1 557
24.6%
2 391
17.3%
3 216
 
9.5%
4 141
 
6.2%
5 93
 
4.1%
6 86
 
3.8%
7 71
 
3.1%
8 46
 
2.0%
9 33
 
1.5%
ValueCountFrequency (%)
112 1
 
< 0.1%
100 1
 
< 0.1%
86 1
 
< 0.1%
49 2
 
0.1%
39 1
 
< 0.1%
35 2
 
0.1%
32 1
 
< 0.1%
30 6
0.3%
29 3
0.1%
28 3
0.1%

'반납건수'
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct44
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9889625
Minimum0
Maximum100
Zeros444
Zeros (%)19.6%
Negative0
Negative (%)0.0%
Memory size20.0 KiB
2023-12-11T15:57:45.860972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q35
95-th percentile15
Maximum100
Range100
Interquartile range (IQR)4

Descriptive statistics

Standard deviation6.4463238
Coefficient of variation (CV)1.6160402
Kurtosis53.94995
Mean3.9889625
Median Absolute Deviation (MAD)2
Skewness5.4077996
Sum9035
Variance41.55509
MonotonicityNot monotonic
2023-12-11T15:57:46.024130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
1 554
24.5%
0 444
19.6%
2 368
16.2%
3 177
 
7.8%
4 147
 
6.5%
5 83
 
3.7%
6 81
 
3.6%
8 57
 
2.5%
7 57
 
2.5%
9 42
 
1.9%
Other values (34) 255
11.3%
ValueCountFrequency (%)
0 444
19.6%
1 554
24.5%
2 368
16.2%
3 177
 
7.8%
4 147
 
6.5%
5 83
 
3.7%
6 81
 
3.6%
7 57
 
2.5%
8 57
 
2.5%
9 42
 
1.9%
ValueCountFrequency (%)
100 1
< 0.1%
94 1
< 0.1%
79 1
< 0.1%
61 1
< 0.1%
53 1
< 0.1%
44 1
< 0.1%
39 1
< 0.1%
38 2
0.1%
37 1
< 0.1%
34 1
< 0.1%

Interactions

2023-12-11T15:57:41.775605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:57:41.528788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:57:41.913349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:57:41.643824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T15:57:46.148786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
'대여일자''대여건수''반납건수'
'대여일자'1.0000.0830.089
'대여건수'0.0831.0000.883
'반납건수'0.0890.8831.000
2023-12-11T15:57:46.254460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
'대여건수''반납건수''대여일자'
'대여건수'1.0000.5950.046
'반납건수'0.5951.0000.044
'대여일자'0.0460.0441.000

Missing values

2023-12-11T15:57:42.075785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T15:57:42.222754image/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.
2023-12-11T15:57:42.369097image/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'20
1'201801''1041'' 묘곡초등학교'11
2'201801''510'' 서울숲 남문 버스정류소 옆'30
3'201801''515'' 광양중학교 앞'11
4'201801''300'' 정동사거리'01
5'201801''2361'' 압구정역 교차로'20
6'201801''2362'' 신사동 가로수길 입구'20
7'201801''302'' 경복궁역 4번출구 뒤'41
8'201801''2377'' 수서역 5번출구 뒤'11
9'201801''3101'' 서대문구청'01
'대여일자''대여소번호''대여소''대여건수''반납건수'
2255'201806''590'' 건국대학교 (입학정보관)'36
2256'201806''591'' 건국대학교 (행정관)'11
2257'201806''592'' 건국대학교 학생회관'411
2258'201806''450'' 효자동 삼거리'96
2259'201806''451'' 청와대앞길'79
2260'201806''501'' 광진구의회 앞'12
2261'201806''3500'' 군자역2번출구'01
2262'201806''3504'' 원일교회'02
2263'201806''3505'' 신양초교앞 교차로'23
2264'201806''502'' 뚝섬유원지역 1번출구 앞'2430