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 started2024-05-03 23:57:28.188825
Analysis finished2024-05-03 23:57:33.096596
Duration4.91 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
 
656
강남구
 
628
서초구
 
566
영등포구
 
566
강서구
 
552
Other values (22)
7032 

Length

Max length6
Median length3
Mean length3.0993
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row송파구
2nd row강남구
3rd row강동구
4th row서대문구
5th row마포구

Common Values

ValueCountFrequency (%)
송파구 656
 
6.6%
강남구 628
 
6.3%
서초구 566
 
5.7%
영등포구 566
 
5.7%
강서구 552
 
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

2024-05-03T23:57:33.323877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
송파구 656
 
6.6%
강남구 628
 
6.3%
서초구 566
 
5.7%
영등포구 566
 
5.7%
강서구 552
 
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
2024-05-03T23:57:33.931981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length31
Mean length15.4061
Min length1

Characters and Unicode

Total characters154061
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 row1245. 문정 법조단지9
2nd row2306. 압구정역 2번 출구 옆
3rd row1016. 해뜨는 주유소옆 리엔파크 109동앞
4th row171. 임광빌딩 앞
5th row427. 성산시영아파트
ValueCountFrequency (%)
앞 2561
 
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) 25479
83.2%
2024-05-03T23:57:35.014199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
20626
 
13.4%
. 10001
 
6.5%
1 8710
 
5.7%
2 6682
 
4.3%
3 4712
 
3.1%
5 3484
 
2.3%
구 3389
 
2.2%
4 3369
 
2.2%
0 3366
 
2.2%
앞 3093
 
2.0%
Other values (546) 86629
56.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 79466
51.6%
Decimal Number 40708
26.4%
Space Separator 20626
 
13.4%
Other Punctuation 10077
 
6.5%
Uppercase Letter 1240
 
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 3389
 
4.3%
앞 3093
 
3.9%
역 2525
 
3.2%
번 2230
 
2.8%
출 2195
 
2.8%
동 1954
 
2.5%
교 1659
 
2.1%
리 1401
 
1.8%
원 1243
 
1.6%
파 1212
 
1.5%
Other values (488) 58565
73.7%
Uppercase Letter
ValueCountFrequency (%)
K 177
14.3%
S 149
12.0%
C 121
9.8%
G 95
 
7.7%
L 92
 
7.4%
T 90
 
7.3%
A 70
 
5.6%
M 63
 
5.1%
B 54
 
4.4%
I 50
 
4.0%
Other values (14) 279
22.5%
Lowercase Letter
ValueCountFrequency (%)
e 44
33.8%
k 15
 
11.5%
l 12
 
9.2%
n 12
 
9.2%
t 12
 
9.2%
s 9
 
6.9%
c 6
 
4.6%
m 6
 
4.6%
o 6
 
4.6%
y 6
 
4.6%
Decimal Number
ValueCountFrequency (%)
1 8710
21.4%
2 6682
16.4%
3 4712
11.6%
5 3484
 
8.6%
4 3369
 
8.3%
0 3366
 
8.3%
6 3075
 
7.6%
7 2498
 
6.1%
9 2428
 
6.0%
8 2384
 
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 (%)
20626
100.0%
Open Punctuation
ValueCountFrequency (%)
( 856
100.0%
Close Punctuation
ValueCountFrequency (%)
) 856
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 79472
51.6%
Common 73219
47.5%
Latin 1370
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 3389
 
4.3%
앞 3093
 
3.9%
역 2525
 
3.2%
번 2230
 
2.8%
출 2195
 
2.8%
동 1954
 
2.5%
교 1659
 
2.1%
리 1401
 
1.8%
원 1243
 
1.6%
파 1212
 
1.5%
Other values (489) 58571
73.7%
Latin
ValueCountFrequency (%)
K 177
12.9%
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 54
 
3.9%
I 50
 
3.6%
Other values (25) 409
29.9%
Common
ValueCountFrequency (%)
20626
28.2%
. 10001
13.7%
1 8710
11.9%
2 6682
 
9.1%
3 4712
 
6.4%
5 3484
 
4.8%
4 3369
 
4.6%
0 3366
 
4.6%
6 3075
 
4.2%
7 2498
 
3.4%
Other values (12) 6696
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 79466
51.6%
ASCII 74583
48.4%
None 12
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
20626
27.7%
. 10001
13.4%
1 8710
11.7%
2 6682
 
9.0%
3 4712
 
6.3%
5 3484
 
4.7%
4 3369
 
4.5%
0 3366
 
4.5%
6 3075
 
4.1%
7 2498
 
3.3%
Other values (46) 8060
 
10.8%
Hangul
ValueCountFrequency (%)
구 3389
 
4.3%
앞 3093
 
3.9%
역 2525
 
3.2%
번 2230
 
2.8%
출 2195
 
2.8%
동 1954
 
2.5%
교 1659
 
2.1%
리 1401
 
1.8%
원 1243
 
1.6%
파 1212
 
1.5%
Other values (488) 58565
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
2024-05-03T23:57:35.396849image/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 deviation32.9404
Coefficient of variation (CV)0.00016308015
Kurtosis1.6481591
Mean201989.03
Median Absolute Deviation (MAD)1
Skewness-1.9068874
Sum2.0198903 × 109
Variance1085.07
MonotonicityNot monotonic
2024-05-03T23:57:35.799511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
202004 1879
18.8%
202005 1756
17.6%
202003 1730
17.3%
202002 1550
15.5%
201912 1544
15.4%
202001 1541
15.4%
ValueCountFrequency (%)
201912 1544
15.4%
202001 1541
15.4%
202002 1550
15.5%
202003 1730
17.3%
202004 1879
18.8%
202005 1756
17.6%
ValueCountFrequency (%)
202005 1756
17.6%
202004 1879
18.8%
202003 1730
17.3%
202002 1550
15.5%
202001 1541
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%
Mean860.9619
Minimum0
Maximum23174
Zeros23
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-03T23:57:36.196897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation1023.9822
Coefficient of variation (CV)1.1893467
Kurtosis66.623251
Mean860.9619
Median Absolute Deviation (MAD)347
Skewness5.4359335
Sum8609619
Variance1048539.6
MonotonicityNot monotonic
2024-05-03T23:57:36.649510image/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%
474 19
 
0.2%
479 19
 
0.2%
566 17
 
0.2%
253 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%
Mean860.3929
Minimum0
Maximum24256
Zeros17
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-03T23:57:37.148411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation1090.6387
Coefficient of variation (CV)1.2676054
Kurtosis71.216315
Mean860.3929
Median Absolute Deviation (MAD)368
Skewness5.79065
Sum8603929
Variance1189492.7
MonotonicityNot monotonic
2024-05-03T23:57:37.577326image/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%
84 15
 
0.1%
579 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

2024-05-03T23:57:31.615785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:57:29.638534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:57:30.647639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:57:31.955715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:57:29.988642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:57:30.918865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:57:32.270447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:57:30.324698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:57:31.240193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-03T23:57:37.854177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소 그룹일자 / 월대여 건수반납 건수
대여소 그룹1.000NaN0.1340.436
일자 / 월NaN1.000NaNNaN
대여 건수0.134NaN1.0000.992
반납 건수0.436NaN0.9921.000
2024-05-03T23:57:38.354560image/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

2024-05-03T23:57:32.624197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-03T23:57:32.963024image/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
2610송파구1245. 문정 법조단지9202001731733
8251강남구2306. 압구정역 2번 출구 옆20200511801157
6485강동구1016. 해뜨는 주유소옆 리엔파크 109동앞20200410941100
2342서대문구171. 임광빌딩 앞202001311313
2308마포구427. 성산시영아파트202001498558
7626송파구1217. 송파파인타운 7단지202004928921
3306강서구1131. 꿈돌이공원 앞202002738739
6322중랑구1410. 면목 대원칸타빌아파트20200322867
5397동작구237. 보라매 두산위브 건너편202003867871
6265중구336. 티마크 호텔 앞202003404366
대여소 그룹대여소 명일자 / 월대여 건수반납 건수
2268마포구150. 서강대역 2번출구 앞202001643808
4211송파구2620. 송파나루역 4번 출구옆20200211961331
7472성동구3531. 논골사거리(금호도서관 입구)20200410060
4554중구373. 청구 어린이공원2020025756
1478중구387. 훈련원공원주차장 앞201912549543
9310성동구514. 성수사거리 버스정류장 앞20200519701979
1731강북구1541. 삼양역20200118188
7759양천구749. 이대 목동병원 앞20200417641802
4903강서구1137. 등촌2동주민센터202003375195
8001은평구960. 구파발역 환승센터202004527572