Overview

Dataset statistics

Number of variables5
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory498.0 KiB
Average record size in memory51.0 B

Variable types

Categorical1
Text1
Numeric3

Dataset

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

Alerts

대여 건수 is highly overall correlated with 반납 건수High correlation
반납 건수 is highly overall correlated with 대여 건수High correlation

Reproduction

Analysis started2024-03-13 09:53:27.031628
Analysis finished2024-03-13 09:53:28.529375
Duration1.5 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대여소 그룹
Categorical

Distinct27
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
송파구
 
655
강남구
 
629
서초구
 
566
영등포구
 
566
강서구
 
553
Other values (22)
7031 

Length

Max length6
Median length3
Mean length3.0994
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강서구
2nd row강북구
3rd row강남구
4th row동대문구
5th row서초구

Common Values

ValueCountFrequency (%)
송파구 655
 
6.6%
강남구 629
 
6.3%
서초구 566
 
5.7%
영등포구 566
 
5.7%
강서구 553
 
5.5%
마포구 512
 
5.1%
종로구 458
 
4.6%
노원구 436
 
4.4%
구로구 403
 
4.0%
성동구 398
 
4.0%
Other values (17) 4824
48.2%

Length

2024-03-13T18:53:28.597736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
송파구 655
 
6.5%
강남구 629
 
6.3%
서초구 566
 
5.7%
영등포구 566
 
5.7%
강서구 553
 
5.5%
마포구 512
 
5.1%
종로구 458
 
4.6%
노원구 436
 
4.4%
구로구 403
 
4.0%
성동구 398
 
4.0%
Other values (18) 4829
48.3%

대여소 명
Text

Distinct2026
Distinct (%)20.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-13T18:53:28.833295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length31
Mean length15.4057
Min length1

Characters and Unicode

Total characters154057
Distinct characters556
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique254 ?
Unique (%)2.5%

Sample

1st row1197. 엘펠리체 호텔 건너편
2nd row1506. 강북문화예술회관
3rd row2339. 현대아파트 정문 앞
4th row640. KEB하나은행 청량리역지점
5th row2268. 서초4동주민센터
ValueCountFrequency (%)
앞 2562
 
8.4%
옆 475
 
1.6%
출구 364
 
1.2%
1번출구 302
 
1.0%
사거리 256
 
0.8%
입구 253
 
0.8%
교차로 250
 
0.8%
뒤 237
 
0.8%
2번출구 231
 
0.8%
3번출구 216
 
0.7%
Other values (4031) 25481
83.2%
2024-03-13T18:53:29.228173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
20629
 
13.4%
. 10001
 
6.5%
1 8709
 
5.7%
2 6681
 
4.3%
3 4713
 
3.1%
5 3483
 
2.3%
구 3388
 
2.2%
4 3369
 
2.2%
0 3365
 
2.2%
앞 3093
 
2.0%
Other values (546) 86626
56.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 79461
51.6%
Decimal Number 40703
26.4%
Space Separator 20629
 
13.4%
Other Punctuation 10077
 
6.5%
Uppercase Letter 1243
 
0.8%
Open Punctuation 856
 
0.6%
Close Punctuation 856
 
0.6%
Lowercase Letter 130
 
0.1%
Dash Punctuation 60
 
< 0.1%
Math Symbol 30
 
< 0.1%
Other values (2) 12
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 3388
 
4.3%
앞 3093
 
3.9%
역 2526
 
3.2%
번 2229
 
2.8%
출 2194
 
2.8%
동 1953
 
2.5%
교 1659
 
2.1%
리 1402
 
1.8%
원 1244
 
1.6%
파 1212
 
1.5%
Other values (488) 58561
73.7%
Uppercase Letter
ValueCountFrequency (%)
K 178
14.3%
S 149
12.0%
C 121
9.7%
G 95
 
7.6%
L 92
 
7.4%
T 90
 
7.2%
A 70
 
5.6%
M 63
 
5.1%
B 55
 
4.4%
I 50
 
4.0%
Other values (14) 280
22.5%
Lowercase Letter
ValueCountFrequency (%)
e 44
33.8%
k 15
 
11.5%
t 12
 
9.2%
l 12
 
9.2%
n 12
 
9.2%
s 9
 
6.9%
y 6
 
4.6%
c 6
 
4.6%
o 6
 
4.6%
m 6
 
4.6%
Decimal Number
ValueCountFrequency (%)
1 8709
21.4%
2 6681
16.4%
3 4713
11.6%
5 3483
 
8.6%
4 3369
 
8.3%
0 3365
 
8.3%
6 3073
 
7.5%
7 2498
 
6.1%
9 2429
 
6.0%
8 2383
 
5.9%
Other Punctuation
ValueCountFrequency (%)
. 10001
99.2%
, 44
 
0.4%
? 13
 
0.1%
& 13
 
0.1%
· 6
 
0.1%
Math Symbol
ValueCountFrequency (%)
~ 24
80.0%
+ 6
 
20.0%
Space Separator
ValueCountFrequency (%)
20629
100.0%
Open Punctuation
ValueCountFrequency (%)
( 856
100.0%
Close Punctuation
ValueCountFrequency (%)
) 856
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 60
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 6
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 79467
51.6%
Common 73217
47.5%
Latin 1373
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 3388
 
4.3%
앞 3093
 
3.9%
역 2526
 
3.2%
번 2229
 
2.8%
출 2194
 
2.8%
동 1953
 
2.5%
교 1659
 
2.1%
리 1402
 
1.8%
원 1244
 
1.6%
파 1212
 
1.5%
Other values (489) 58567
73.7%
Latin
ValueCountFrequency (%)
K 178
13.0%
S 149
 
10.9%
C 121
 
8.8%
G 95
 
6.9%
L 92
 
6.7%
T 90
 
6.6%
A 70
 
5.1%
M 63
 
4.6%
B 55
 
4.0%
I 50
 
3.6%
Other values (25) 410
29.9%
Common
ValueCountFrequency (%)
20629
28.2%
. 10001
13.7%
1 8709
11.9%
2 6681
 
9.1%
3 4713
 
6.4%
5 3483
 
4.8%
4 3369
 
4.6%
0 3365
 
4.6%
6 3073
 
4.2%
7 2498
 
3.4%
Other values (12) 6696
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 79461
51.6%
ASCII 74584
48.4%
None 12
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
20629
27.7%
. 10001
13.4%
1 8709
11.7%
2 6681
 
9.0%
3 4713
 
6.3%
5 3483
 
4.7%
4 3369
 
4.5%
0 3365
 
4.5%
6 3073
 
4.1%
7 2498
 
3.3%
Other values (46) 8063
 
10.8%
Hangul
ValueCountFrequency (%)
구 3388
 
4.3%
앞 3093
 
3.9%
역 2526
 
3.2%
번 2229
 
2.8%
출 2194
 
2.8%
동 1953
 
2.5%
교 1659
 
2.1%
리 1402
 
1.8%
원 1244
 
1.6%
파 1212
 
1.5%
Other values (488) 58561
73.7%
None
ValueCountFrequency (%)
· 6
50.0%
㈜ 6
50.0%

일자 / 월
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201989.03
Minimum201912
Maximum202005
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T18:53:29.683074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201912
5-th percentile201912
Q1202001
median202003
Q3202004
95-th percentile202005
Maximum202005
Range93
Interquartile range (IQR)3

Descriptive statistics

Standard deviation32.940415
Coefficient of variation (CV)0.00016308022
Kurtosis1.6481516
Mean201989.03
Median Absolute Deviation (MAD)1
Skewness-1.9068836
Sum2.0198903 × 109
Variance1085.071
MonotonicityNot monotonic
2024-03-13T18:53:29.764283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
202004 1877
18.8%
202005 1758
17.6%
202003 1729
17.3%
202002 1550
15.5%
201912 1544
15.4%
202001 1542
15.4%
ValueCountFrequency (%)
201912 1544
15.4%
202001 1542
15.4%
202002 1550
15.5%
202003 1729
17.3%
202004 1877
18.8%
202005 1758
17.6%
ValueCountFrequency (%)
202005 1758
17.6%
202004 1877
18.8%
202003 1729
17.3%
202002 1550
15.5%
202001 1542
15.4%
201912 1544
15.4%

대여 건수
Real number (ℝ)

HIGH CORRELATION 

Distinct2520
Distinct (%)25.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean861.2879
Minimum0
Maximum23174
Zeros23
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T18:53:29.875089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q1294
median574
Q31088.25
95-th percentile2552.1
Maximum23174
Range23174
Interquartile range (IQR)794.25

Descriptive statistics

Standard deviation1024.4655
Coefficient of variation (CV)1.1894577
Kurtosis66.49574
Mean861.2879
Median Absolute Deviation (MAD)347
Skewness5.4306589
Sum8612879
Variance1049529.6
MonotonicityNot monotonic
2024-03-13T18:53:30.003778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 354
 
3.5%
2 75
 
0.8%
3 31
 
0.3%
4 25
 
0.2%
0 23
 
0.2%
479 19
 
0.2%
474 19
 
0.2%
269 17
 
0.2%
253 17
 
0.2%
566 17
 
0.2%
Other values (2510) 9403
94.0%
ValueCountFrequency (%)
0 23
 
0.2%
1 354
3.5%
2 75
 
0.8%
3 31
 
0.3%
4 25
 
0.2%
5 9
 
0.1%
6 8
 
0.1%
7 7
 
0.1%
9 1
 
< 0.1%
10 4
 
< 0.1%
ValueCountFrequency (%)
23174 1
< 0.1%
20641 1
< 0.1%
17524 1
< 0.1%
16546 1
< 0.1%
15080 1
< 0.1%
14584 1
< 0.1%
12155 1
< 0.1%
11480 1
< 0.1%
9927 1
< 0.1%
9652 1
< 0.1%

반납 건수
Real number (ℝ)

HIGH CORRELATION 

Distinct2538
Distinct (%)25.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean860.7351
Minimum0
Maximum24256
Zeros17
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T18:53:30.122044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q1263
median568
Q31088
95-th percentile2610.1
Maximum24256
Range24256
Interquartile range (IQR)825

Descriptive statistics

Standard deviation1091.1083
Coefficient of variation (CV)1.2676471
Kurtosis71.090292
Mean860.7351
Median Absolute Deviation (MAD)368
Skewness5.7851802
Sum8607351
Variance1190517.4
MonotonicityNot monotonic
2024-03-13T18:53:30.269970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 356
 
3.6%
2 76
 
0.8%
3 28
 
0.3%
4 20
 
0.2%
320 18
 
0.2%
0 17
 
0.2%
369 16
 
0.2%
396 16
 
0.2%
132 15
 
0.1%
336 15
 
0.1%
Other values (2528) 9423
94.2%
ValueCountFrequency (%)
0 17
 
0.2%
1 356
3.6%
2 76
 
0.8%
3 28
 
0.3%
4 20
 
0.2%
5 9
 
0.1%
6 13
 
0.1%
7 8
 
0.1%
8 1
 
< 0.1%
9 8
 
0.1%
ValueCountFrequency (%)
24256 1
< 0.1%
21073 1
< 0.1%
20030 1
< 0.1%
17256 1
< 0.1%
17062 1
< 0.1%
15781 1
< 0.1%
15317 1
< 0.1%
12715 1
< 0.1%
12713 1
< 0.1%
12063 1
< 0.1%

Interactions

2024-03-13T18:53:28.110653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T18:53:27.588897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T18:53:27.822483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T18:53:28.224323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T18:53:27.666120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T18:53:27.900311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T18:53:28.305415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T18:53:27.743508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T18:53:28.008774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T18:53:30.362236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소 그룹일자 / 월대여 건수반납 건수
대여소 그룹1.000NaN0.1350.436
일자 / 월NaN1.000NaNNaN
대여 건수0.135NaN1.0000.992
반납 건수0.436NaN0.9921.000
2024-03-13T18:53:30.450309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자 / 월대여 건수반납 건수대여소 그룹
일자 / 월1.0000.2960.2750.000
대여 건수0.2961.0000.9860.049
반납 건수0.2750.9861.0000.173
대여소 그룹0.0000.0490.1731.000

Missing values

2024-03-13T18:53:28.407768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T18:53:28.491295image/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.

Sample

대여소 그룹대여소 명일자 / 월대여 건수반납 건수
6667강서구1197. 엘펠리체 호텔 건너편20200429232961
158강북구1506. 강북문화예술회관201912531514
8283강남구2339. 현대아파트 정문 앞202005576560
7120동대문구640. KEB하나은행 청량리역지점202004747794
874서초구2268. 서초4동주민센터201912339346
6386강남구2320. 도곡역 대치지구대 방향20200411961176
9763은평구914. 새절역 2번출구20200550455314
759마포구419. 홈플러스 앞2019121142891
3169강남구2392. 구룡산 입구 (구룡산 서울둘레길 입구)2020027943
4142송파구1233. 잠실3거리(갤러리아팰리스)202002292266
대여소 그룹대여소 명일자 / 월대여 건수반납 건수
5610서초구2504. 신사역 4번출구 뒤202003714676
8440강북구1501. 미아역 3번,4번 출구 사이20200516771660
8626관악구2183. 동방1교20200559805935
9566양천구735. 영도초등학교20200522842411
4035성동구558. 성동광진 교육지원청 앞202002576559
1634강남구2404. 대모산입구역 4번 출구 앞202001326333
5143구로구2815.항동지구 7단지 정문20200311
2444서초구2501. 서초 포레스타5단지202001284290
9082마포구152. 마포구민체육센터 앞2020051215512715
907서초구2506. LG유플러스 (방배사옥)201912198102