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

대여건수 is highly overall correlated with 반납건수High correlation
반납건수 is highly overall correlated with 대여건수High correlation

Reproduction

Analysis started2024-05-18 01:15:00.032571
Analysis finished2024-05-18 01:15:04.345221
Duration4.31 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

자치구
Categorical

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
송파구
797 
강서구
 
656
강남구
 
623
영등포구
 
588
노원구
 
500
Other values (20)
6836 

Length

Max length4
Median length3
Mean length3.0874
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row마포구
2nd row서초구
3rd row금천구
4th row성동구
5th row광진구

Common Values

ValueCountFrequency (%)
송파구 797
 
8.0%
강서구 656
 
6.6%
강남구 623
 
6.2%
영등포구 588
 
5.9%
노원구 500
 
5.0%
서초구 498
 
5.0%
마포구 450
 
4.5%
강동구 442
 
4.4%
구로구 420
 
4.2%
양천구 392
 
3.9%
Other values (15) 4634
46.3%

Length

2024-05-18T10:15:04.648835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
송파구 797
 
8.0%
강서구 656
 
6.6%
강남구 623
 
6.2%
영등포구 588
 
5.9%
노원구 500
 
5.0%
서초구 498
 
5.0%
마포구 450
 
4.5%
강동구 442
 
4.4%
구로구 420
 
4.2%
양천구 392
 
3.9%
Other values (15) 4634
46.3%

대여소명
Text

Distinct2759
Distinct (%)27.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T10:15:05.295115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length32
Mean length15.5989
Min length4

Characters and Unicode

Total characters155989
Distinct characters588
Distinct categories13 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique108 ?
Unique (%)1.1%

Sample

1st row114. 홍대입구역 8번출구 앞
2nd row2272. 교대입구 교차로
3rd row3963. 에이스 하드웨어(시흥대로 396) 앞
4th row569. 응봉현대아파트 정류소
5th row3864. 광진소방서 앞
ValueCountFrequency (%)
앞 2696
 
9.2%
출구 389
 
1.3%
옆 351
 
1.2%
입구 276
 
0.9%
1번출구 249
 
0.9%
사거리 239
 
0.8%
교차로 237
 
0.8%
3번출구 176
 
0.6%
2번출구 174
 
0.6%
뒤 169
 
0.6%
Other values (5487) 24191
83.0%
2024-05-18T10:15:06.316590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
19303
 
12.4%
. 10023
 
6.4%
1 7295
 
4.7%
2 5912
 
3.8%
4 5059
 
3.2%
3 5057
 
3.2%
5 3844
 
2.5%
0 3591
 
2.3%
6 3442
 
2.2%
앞 3129
 
2.0%
Other values (578) 89334
57.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 80565
51.6%
Decimal Number 42814
27.4%
Space Separator 19303
 
12.4%
Other Punctuation 10141
 
6.5%
Uppercase Letter 1336
 
0.9%
Open Punctuation 805
 
0.5%
Close Punctuation 805
 
0.5%
Lowercase Letter 142
 
0.1%
Dash Punctuation 51
 
< 0.1%
Math Symbol 13
 
< 0.1%
Other values (3) 14
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
앞 3129
 
3.9%
구 3062
 
3.8%
동 2227
 
2.8%
역 2213
 
2.7%
번 1903
 
2.4%
출 1867
 
2.3%
교 1668
 
2.1%
파 1594
 
2.0%
아 1516
 
1.9%
트 1458
 
1.8%
Other values (516) 59928
74.4%
Uppercase Letter
ValueCountFrequency (%)
S 143
10.7%
A 126
9.4%
C 120
9.0%
K 111
 
8.3%
T 105
 
7.9%
B 104
 
7.8%
G 96
 
7.2%
D 89
 
6.7%
M 77
 
5.8%
L 70
 
5.2%
Other values (14) 295
22.1%
Lowercase Letter
ValueCountFrequency (%)
e 50
35.2%
s 28
19.7%
k 26
18.3%
n 6
 
4.2%
f 5
 
3.5%
r 5
 
3.5%
h 5
 
3.5%
v 5
 
3.5%
t 4
 
2.8%
y 3
 
2.1%
Other values (3) 5
 
3.5%
Decimal Number
ValueCountFrequency (%)
1 7295
17.0%
2 5912
13.8%
4 5059
11.8%
3 5057
11.8%
5 3844
9.0%
0 3591
8.4%
6 3442
8.0%
7 3060
7.1%
8 2978
7.0%
9 2576
 
6.0%
Other Punctuation
ValueCountFrequency (%)
. 10023
98.8%
, 68
 
0.7%
& 25
 
0.2%
· 15
 
0.1%
? 10
 
0.1%
Math Symbol
ValueCountFrequency (%)
~ 10
76.9%
+ 3
 
23.1%
Other Number
ValueCountFrequency (%)
② 4
66.7%
① 2
33.3%
Space Separator
ValueCountFrequency (%)
19303
100.0%
Open Punctuation
ValueCountFrequency (%)
( 805
100.0%
Close Punctuation
ValueCountFrequency (%)
) 805
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 51
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 7
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 80566
51.6%
Common 73945
47.4%
Latin 1478
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
앞 3129
 
3.9%
구 3062
 
3.8%
동 2227
 
2.8%
역 2213
 
2.7%
번 1903
 
2.4%
출 1867
 
2.3%
교 1668
 
2.1%
파 1594
 
2.0%
아 1516
 
1.9%
트 1458
 
1.8%
Other values (517) 59929
74.4%
Latin
ValueCountFrequency (%)
S 143
 
9.7%
A 126
 
8.5%
C 120
 
8.1%
K 111
 
7.5%
T 105
 
7.1%
B 104
 
7.0%
G 96
 
6.5%
D 89
 
6.0%
M 77
 
5.2%
L 70
 
4.7%
Other values (27) 437
29.6%
Common
ValueCountFrequency (%)
19303
26.1%
. 10023
13.6%
1 7295
 
9.9%
2 5912
 
8.0%
4 5059
 
6.8%
3 5057
 
6.8%
5 3844
 
5.2%
0 3591
 
4.9%
6 3442
 
4.7%
7 3060
 
4.1%
Other values (14) 7359
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 80565
51.6%
ASCII 75402
48.3%
None 16
 
< 0.1%
Enclosed Alphanum 6
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
19303
25.6%
. 10023
13.3%
1 7295
 
9.7%
2 5912
 
7.8%
4 5059
 
6.7%
3 5057
 
6.7%
5 3844
 
5.1%
0 3591
 
4.8%
6 3442
 
4.6%
7 3060
 
4.1%
Other values (48) 8816
11.7%
Hangul
ValueCountFrequency (%)
앞 3129
 
3.9%
구 3062
 
3.8%
동 2227
 
2.8%
역 2213
 
2.7%
번 1903
 
2.4%
출 1867
 
2.3%
교 1668
 
2.1%
파 1594
 
2.0%
아 1516
 
1.9%
트 1458
 
1.8%
Other values (516) 59928
74.4%
None
ValueCountFrequency (%)
· 15
93.8%
㈜ 1
 
6.2%
Enclosed Alphanum
ValueCountFrequency (%)
② 4
66.7%
① 2
33.3%

기준년월
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean202309.5
Minimum202307
Maximum202312
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T10:15:06.642920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum202307
5-th percentile202307
Q1202308
median202309
Q3202311
95-th percentile202312
Maximum202312
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7052511
Coefficient of variation (CV)8.4289224 × 10-6
Kurtosis-1.2656369
Mean202309.5
Median Absolute Deviation (MAD)1
Skewness0.0063878295
Sum2.023095 × 109
Variance2.9078812
MonotonicityNot monotonic
2024-05-18T10:15:07.194107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
202308 1695
17.0%
202310 1678
16.8%
202309 1664
16.6%
202312 1664
16.6%
202307 1651
16.5%
202311 1648
16.5%
ValueCountFrequency (%)
202307 1651
16.5%
202308 1695
17.0%
202309 1664
16.6%
202310 1678
16.8%
202311 1648
16.5%
202312 1664
16.6%
ValueCountFrequency (%)
202312 1664
16.6%
202311 1648
16.5%
202310 1678
16.8%
202309 1664
16.6%
202308 1695
17.0%
202307 1651
16.5%

대여건수
Real number (ℝ)

HIGH CORRELATION 

Distinct3457
Distinct (%)34.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1415.4547
Minimum1
Maximum21106
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T10:15:07.625279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile174
Q1520
median1029
Q31873.25
95-th percentile3904.1
Maximum21106
Range21105
Interquartile range (IQR)1353.25

Descriptive statistics

Standard deviation1343.5418
Coefficient of variation (CV)0.94919451
Kurtosis17.687159
Mean1415.4547
Median Absolute Deviation (MAD)610
Skewness2.9038312
Sum14154547
Variance1805104.6
MonotonicityNot monotonic
2024-05-18T10:15:08.024717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
682 15
 
0.1%
867 13
 
0.1%
511 13
 
0.1%
272 12
 
0.1%
337 12
 
0.1%
325 12
 
0.1%
352 12
 
0.1%
549 11
 
0.1%
579 11
 
0.1%
801 11
 
0.1%
Other values (3447) 9878
98.8%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 3
< 0.1%
4 2
< 0.1%
5 2
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
12 4
< 0.1%
15 3
< 0.1%
ValueCountFrequency (%)
21106 1
< 0.1%
18026 1
< 0.1%
15901 1
< 0.1%
15551 1
< 0.1%
13370 1
< 0.1%
13287 1
< 0.1%
12609 1
< 0.1%
12594 1
< 0.1%
12116 1
< 0.1%
11399 1
< 0.1%

반납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct3510
Distinct (%)35.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1408.6868
Minimum0
Maximum21201
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T10:15:08.429615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile115
Q1467
median1010
Q31886
95-th percentile4006
Maximum21201
Range21201
Interquartile range (IQR)1419

Descriptive statistics

Standard deviation1396.857
Coefficient of variation (CV)0.99160225
Kurtosis16.830558
Mean1408.6868
Median Absolute Deviation (MAD)648
Skewness2.8365509
Sum14086868
Variance1951209.5
MonotonicityNot monotonic
2024-05-18T10:15:08.833149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
172 13
 
0.1%
323 12
 
0.1%
478 12
 
0.1%
231 12
 
0.1%
285 12
 
0.1%
276 11
 
0.1%
379 11
 
0.1%
181 11
 
0.1%
326 11
 
0.1%
368 11
 
0.1%
Other values (3500) 9884
98.8%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 2
 
< 0.1%
2 1
 
< 0.1%
3 4
< 0.1%
4 4
< 0.1%
5 8
0.1%
6 2
 
< 0.1%
7 2
 
< 0.1%
8 2
 
< 0.1%
9 6
0.1%
ValueCountFrequency (%)
21201 1
< 0.1%
18832 1
< 0.1%
17425 1
< 0.1%
15991 1
< 0.1%
13359 1
< 0.1%
13004 1
< 0.1%
12750 1
< 0.1%
12532 1
< 0.1%
12238 1
< 0.1%
11892 1
< 0.1%

Interactions

2024-05-18T10:15:02.867358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:15:01.287474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:15:02.059089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:15:03.151359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:15:01.564644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:15:02.339558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:15:03.389554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:15:01.795622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:15:02.603653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T10:15:09.089272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
자치구기준년월대여건수반납건수
자치구1.0000.0000.3030.292
기준년월0.0001.0000.2830.277
대여건수0.3030.2831.0000.994
반납건수0.2920.2770.9941.000
2024-05-18T10:15:09.365684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년월대여건수반납건수자치구
기준년월1.000-0.175-0.1630.000
대여건수-0.1751.0000.9840.111
반납건수-0.1630.9841.0000.107
자치구0.0000.1110.1071.000

Missing values

2024-05-18T10:15:03.822588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T10:15:04.231730image/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

자치구대여소명기준년월대여건수반납건수
6364마포구114. 홍대입구역 8번출구 앞20230930493100
5606서초구2272. 교대입구 교차로202309666661
12384금천구3963. 에이스 하드웨어(시흥대로 396) 앞2023119771086
13075성동구569. 응봉현대아파트 정류소202311334244
4105광진구3864. 광진소방서 앞20230816891580
4505동대문구4125. 회기역 2-①번출구202308615629
15040송파구4458. 가락2동주민센터 인근202312401401
3514송파구2637. 아시아지하보도 14번 출구20230829312931
9256서대문구3119.연희 브라운스톤 아파트 앞20231029122917
9319동대문구676.FITI시험연구원 앞20231010031031
자치구대여소명기준년월대여건수반납건수
4011용산구4616. 신용산지하차도 앞202308651685
11431강남구2407. 역삼서초삼성 세무서 앞 (역삼빌딩 앞)20231113831427
3146도봉구1737. 신창시장20230820502306
7249중랑구4829. 신내동 한살림 중랑지구 건너편202309582576
13953성북구1355. 보문2교20231214031394
10948은평구939. 은평구청 교차로202311725684
798강남구2414. 도곡역 아카데미스위트 앞20230715371544
10168영등포구5858. 영등포역5번출구20231075478344
15062송파구4486. 홈플러스 잠실점20231214361433
9244용산구866.브라운스톤 남산아파트202310659588