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/A/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 started2024-05-18 01:18:21.215070
Analysis finished2024-05-18 01:18:25.567966
Duration4.35 seconds
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
영등포구
 
566
서초구
 
565
강서구
 
553
Other values (22)
7032 

Length

Max length6
Median length3
Mean length3.0994
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row성북구
2nd row금천구
3rd row서대문구
4th row서대문구
5th row노원구

Common Values

ValueCountFrequency (%)
송파구 655
 
6.6%
강남구 629
 
6.3%
영등포구 566
 
5.7%
서초구 565
 
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) 4825
48.2%

Length

2024-05-18T10:18:25.819645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
송파구 655
 
6.5%
강남구 629
 
6.3%
영등포구 566
 
5.7%
서초구 565
 
5.6%
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) 4830
48.3%

대여소 명
Text

Distinct2026
Distinct (%)20.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T10:18:26.388993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length31
Mean length15.4059
Min length1

Characters and Unicode

Total characters154059
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 row1316. 고려사대부속중고 건너편
2nd row1820. 신한은행 시흥대로금융센터지점
3rd row139. 연세대 정문 건너편
4th row179. 가좌역 4번출구 앞
5th row1608. 공릉역 1번 출구 앞
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) 25480
83.2%
2024-05-18T10:18:27.286813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
20628
 
13.4%
. 10001
 
6.5%
1 8709
 
5.7%
2 6677
 
4.3%
3 4713
 
3.1%
5 3483
 
2.3%
구 3388
 
2.2%
4 3369
 
2.2%
0 3367
 
2.2%
앞 3094
 
2.0%
Other values (546) 86630
56.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 79467
51.6%
Decimal Number 40704
26.4%
Space Separator 20628
 
13.4%
Other Punctuation 10077
 
6.5%
Uppercase Letter 1243
 
0.8%
Open Punctuation 854
 
0.6%
Close Punctuation 854
 
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 3094
 
3.9%
역 2526
 
3.2%
번 2230
 
2.8%
출 2195
 
2.8%
동 1953
 
2.5%
교 1657
 
2.1%
리 1403
 
1.8%
원 1244
 
1.6%
파 1211
 
1.5%
Other values (488) 58566
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%
n 12
 
9.2%
t 12
 
9.2%
l 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 6677
16.4%
3 4713
11.6%
5 3483
 
8.6%
4 3369
 
8.3%
0 3367
 
8.3%
6 3076
 
7.6%
7 2499
 
6.1%
9 2429
 
6.0%
8 2382
 
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 (%)
20628
100.0%
Open Punctuation
ValueCountFrequency (%)
( 854
100.0%
Close Punctuation
ValueCountFrequency (%)
) 854
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 60
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 6
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 79473
51.6%
Common 73213
47.5%
Latin 1373
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 3388
 
4.3%
앞 3094
 
3.9%
역 2526
 
3.2%
번 2230
 
2.8%
출 2195
 
2.8%
동 1953
 
2.5%
교 1657
 
2.1%
리 1403
 
1.8%
원 1244
 
1.6%
파 1211
 
1.5%
Other values (489) 58572
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 (%)
20628
28.2%
. 10001
13.7%
1 8709
11.9%
2 6677
 
9.1%
3 4713
 
6.4%
5 3483
 
4.8%
4 3369
 
4.6%
0 3367
 
4.6%
6 3076
 
4.2%
7 2499
 
3.4%
Other values (12) 6691
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 79467
51.6%
ASCII 74580
48.4%
None 12
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
20628
27.7%
. 10001
13.4%
1 8709
11.7%
2 6677
 
9.0%
3 4713
 
6.3%
5 3483
 
4.7%
4 3369
 
4.5%
0 3367
 
4.5%
6 3076
 
4.1%
7 2499
 
3.4%
Other values (46) 8058
 
10.8%
Hangul
ValueCountFrequency (%)
구 3388
 
4.3%
앞 3094
 
3.9%
역 2526
 
3.2%
번 2230
 
2.8%
출 2195
 
2.8%
동 1953
 
2.5%
교 1657
 
2.1%
리 1403
 
1.8%
원 1244
 
1.6%
파 1211
 
1.5%
Other values (488) 58566
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
2024-05-18T10:18:27.559883image/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 deviation32.940702
Coefficient of variation (CV)0.00016308164
Kurtosis1.6481579
Mean201989.03
Median Absolute Deviation (MAD)1
Skewness-1.9068869
Sum2.0198903 × 109
Variance1085.0898
MonotonicityNot monotonic
2024-05-18T10:18:27.747941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
202004 1879
18.8%
202005 1758
17.6%
202003 1730
17.3%
202002 1549
15.5%
201912 1544
15.4%
202001 1540
15.4%
ValueCountFrequency (%)
201912 1544
15.4%
202001 1540
15.4%
202002 1549
15.5%
202003 1730
17.3%
202004 1879
18.8%
202005 1758
17.6%
ValueCountFrequency (%)
202005 1758
17.6%
202004 1879
18.8%
202003 1730
17.3%
202002 1549
15.5%
202001 1540
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%
Mean861.2094
Minimum0
Maximum23174
Zeros23
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T10:18:28.045275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q1293.75
median574
Q31088.25
95-th percentile2552.1
Maximum23174
Range23174
Interquartile range (IQR)794.5

Descriptive statistics

Standard deviation1024.4441
Coefficient of variation (CV)1.1895412
Kurtosis66.503124
Mean861.2094
Median Absolute Deviation (MAD)347
Skewness5.4310605
Sum8612094
Variance1049485.7
MonotonicityNot monotonic
2024-05-18T10:18:28.354213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 355
 
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%
566 17
 
0.2%
475 17
 
0.2%
269 17
 
0.2%
Other values (2510) 9402
94.0%
ValueCountFrequency (%)
0 23
 
0.2%
1 355
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%
Mean860.644
Minimum0
Maximum24256
Zeros17
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T10:18:28.704993image/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 deviation1091.1017
Coefficient of variation (CV)1.2677735
Kurtosis71.093983
Mean860.644
Median Absolute Deviation (MAD)368
Skewness5.7854155
Sum8606440
Variance1190502.9
MonotonicityNot monotonic
2024-05-18T10:18:28.966906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 357
 
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%
586 15
 
0.1%
336 15
 
0.1%
Other values (2528) 9422
94.2%
ValueCountFrequency (%)
0 17
 
0.2%
1 357
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

2024-05-18T10:18:24.227621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:18:22.581464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:18:23.387873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:18:24.496050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:18:22.860321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:18:23.667727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:18:24.753314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:18:23.113632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:18:23.958649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T10:18:29.215265image/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
2024-05-18T10:18:29.431422image/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

2024-05-18T10:18:25.073959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T10:18:25.416484image/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
5716성북구1316. 고려사대부속중고 건너편202003225156
463금천구1820. 신한은행 시흥대로금융센터지점2019129699
9153서대문구139. 연세대 정문 건너편202005385298
806서대문구179. 가좌역 4번출구 앞201912937986
504노원구1608. 공릉역 1번 출구 앞20191214691585
1714강북구1522. 월드전기조명인테리어(우이동)202001196136
5909양천구720. 서울강월초등학교 앞202003313117
8507강서구1132. 등촌역 7번출구20200522482199
815서대문구3100. 북성초교20191218478
4486종로구339. 종로4가 사거리202002521579
대여소 그룹대여소 명일자 / 월대여 건수반납 건수
8487강서구1109. 공항시장역 4번출구202005888888
6017영등포구275. 신동아아파트20200319491914
8241중랑구1477.면목삼익아파트 앞20200411
4852강북구1527. 미아역 1번 출구 뒤202003573575
7588성북구1366. 일신초등학교 옆202004535506
61강남구2365. K+ 타워 앞201912448528
5302도봉구1741. 제일강산수산입구20200317721667
5431마포구151. 망원1동주민센터20200319892211
8975동대문구657. 동대문롯데캐슬아파트 앞20200512681285
4471종로구306. 광화문역 7번출구 앞202002634499