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

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

Reproduction

Analysis started2024-03-13 09:54:56.083309
Analysis finished2024-03-13 09:54:57.632742
Duration1.55 second
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
812 
강서구
687 
강남구
 
624
영등포구
 
561
서초구
 
514
Other values (20)
6802 

Length

Max length4
Median length3
Mean length3.0845
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강동구
2nd row강남구
3rd row강서구
4th row중구
5th row강서구

Common Values

ValueCountFrequency (%)
송파구 812
 
8.1%
강서구 687
 
6.9%
강남구 624
 
6.2%
영등포구 561
 
5.6%
서초구 514
 
5.1%
노원구 508
 
5.1%
구로구 431
 
4.3%
마포구 426
 
4.3%
양천구 410
 
4.1%
강동구 409
 
4.1%
Other values (15) 4618
46.2%

Length

2024-03-13T18:54:57.695623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
송파구 812
 
8.1%
강서구 687
 
6.9%
강남구 624
 
6.2%
영등포구 561
 
5.6%
서초구 514
 
5.1%
노원구 508
 
5.1%
구로구 431
 
4.3%
마포구 426
 
4.3%
양천구 410
 
4.1%
강동구 409
 
4.1%
Other values (15) 4618
46.2%

대여소명
Text

Distinct2744
Distinct (%)27.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-13T18:54:57.941982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length32
Mean length15.6158
Min length4

Characters and Unicode

Total characters156158
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

Unique102 ?
Unique (%)1.0%

Sample

1st row1077.강동역 1번출구 앞
2nd row2330. 역삼월드메르디앙아파트 앞
3rd row5064. 양천향교역8번출구
4th row442.서울역 서부
5th row3758. 서울남부출입국관리소
ValueCountFrequency (%)
앞 2701
 
9.2%
출구 407
 
1.4%
옆 336
 
1.1%
입구 290
 
1.0%
1번출구 264
 
0.9%
교차로 246
 
0.8%
사거리 237
 
0.8%
뒤 178
 
0.6%
3번출구 176
 
0.6%
건너편 168
 
0.6%
Other values (5459) 24261
82.9%
2024-03-13T18:54:58.373154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
19440
 
12.4%
. 10025
 
6.4%
1 7345
 
4.7%
2 6000
 
3.8%
3 5106
 
3.3%
4 5077
 
3.3%
5 3815
 
2.4%
0 3526
 
2.3%
6 3369
 
2.2%
앞 3136
 
2.0%
Other values (578) 89319
57.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 80531
51.6%
Decimal Number 42887
27.5%
Space Separator 19440
 
12.4%
Other Punctuation 10152
 
6.5%
Uppercase Letter 1381
 
0.9%
Close Punctuation 766
 
0.5%
Open Punctuation 766
 
0.5%
Lowercase Letter 159
 
0.1%
Dash Punctuation 50
 
< 0.1%
Math Symbol 14
 
< 0.1%
Other values (3) 12
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
앞 3136
 
3.9%
구 3037
 
3.8%
역 2227
 
2.8%
동 2219
 
2.8%
번 1930
 
2.4%
출 1885
 
2.3%
교 1651
 
2.1%
파 1569
 
1.9%
아 1527
 
1.9%
트 1432
 
1.8%
Other values (516) 59918
74.4%
Uppercase Letter
ValueCountFrequency (%)
A 145
10.5%
K 145
10.5%
S 138
10.0%
C 124
9.0%
T 118
 
8.5%
B 102
 
7.4%
G 92
 
6.7%
D 75
 
5.4%
M 72
 
5.2%
L 65
 
4.7%
Other values (14) 305
22.1%
Lowercase Letter
ValueCountFrequency (%)
e 51
32.1%
s 30
18.9%
k 28
17.6%
n 10
 
6.3%
t 6
 
3.8%
l 5
 
3.1%
y 5
 
3.1%
h 5
 
3.1%
r 5
 
3.1%
f 5
 
3.1%
Other values (3) 9
 
5.7%
Decimal Number
ValueCountFrequency (%)
1 7345
17.1%
2 6000
14.0%
3 5106
11.9%
4 5077
11.8%
5 3815
8.9%
0 3526
8.2%
6 3369
7.9%
7 3090
7.2%
8 3008
7.0%
9 2551
 
5.9%
Other Punctuation
ValueCountFrequency (%)
. 10025
98.7%
, 65
 
0.6%
& 34
 
0.3%
· 17
 
0.2%
? 11
 
0.1%
Math Symbol
ValueCountFrequency (%)
~ 9
64.3%
+ 5
35.7%
Other Number
ValueCountFrequency (%)
② 3
60.0%
① 2
40.0%
Space Separator
ValueCountFrequency (%)
19440
100.0%
Close Punctuation
ValueCountFrequency (%)
) 766
100.0%
Open Punctuation
ValueCountFrequency (%)
( 766
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 50
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 6
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 80532
51.6%
Common 74086
47.4%
Latin 1540
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
앞 3136
 
3.9%
구 3037
 
3.8%
역 2227
 
2.8%
동 2219
 
2.8%
번 1930
 
2.4%
출 1885
 
2.3%
교 1651
 
2.1%
파 1569
 
1.9%
아 1527
 
1.9%
트 1432
 
1.8%
Other values (517) 59919
74.4%
Latin
ValueCountFrequency (%)
A 145
 
9.4%
K 145
 
9.4%
S 138
 
9.0%
C 124
 
8.1%
T 118
 
7.7%
B 102
 
6.6%
G 92
 
6.0%
D 75
 
4.9%
M 72
 
4.7%
L 65
 
4.2%
Other values (27) 464
30.1%
Common
ValueCountFrequency (%)
19440
26.2%
. 10025
13.5%
1 7345
 
9.9%
2 6000
 
8.1%
3 5106
 
6.9%
4 5077
 
6.9%
5 3815
 
5.1%
0 3526
 
4.8%
6 3369
 
4.5%
7 3090
 
4.2%
Other values (14) 7293
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 80531
51.6%
ASCII 75604
48.4%
None 18
 
< 0.1%
Enclosed Alphanum 5
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
19440
25.7%
. 10025
13.3%
1 7345
 
9.7%
2 6000
 
7.9%
3 5106
 
6.8%
4 5077
 
6.7%
5 3815
 
5.0%
0 3526
 
4.7%
6 3369
 
4.5%
7 3090
 
4.1%
Other values (48) 8811
11.7%
Hangul
ValueCountFrequency (%)
앞 3136
 
3.9%
구 3037
 
3.8%
역 2227
 
2.8%
동 2219
 
2.8%
번 1930
 
2.4%
출 1885
 
2.3%
교 1651
 
2.1%
파 1569
 
1.9%
아 1527
 
1.9%
트 1432
 
1.8%
Other values (516) 59918
74.4%
None
ValueCountFrequency (%)
· 17
94.4%
㈜ 1
 
5.6%
Enclosed Alphanum
ValueCountFrequency (%)
② 3
60.0%
① 2
40.0%

기준년월
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean202309.49
Minimum202307
Maximum202312
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T18:54:58.488025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum202307
5-th percentile202307
Q1202308
median202310
Q3202311
95-th percentile202312
Maximum202312
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7038879
Coefficient of variation (CV)8.4221848 × 10-6
Kurtosis-1.258136
Mean202309.49
Median Absolute Deviation (MAD)1
Skewness0.0017272266
Sum2.0230949 × 109
Variance2.9032341
MonotonicityNot monotonic
2024-03-13T18:54:58.593701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
202310 1719
17.2%
202307 1681
16.8%
202309 1670
16.7%
202312 1646
16.5%
202308 1646
16.5%
202311 1638
16.4%
ValueCountFrequency (%)
202307 1681
16.8%
202308 1646
16.5%
202309 1670
16.7%
202310 1719
17.2%
202311 1638
16.4%
202312 1646
16.5%
ValueCountFrequency (%)
202312 1646
16.5%
202311 1638
16.4%
202310 1719
17.2%
202309 1670
16.7%
202308 1646
16.5%
202307 1681
16.8%

대여건수
Real number (ℝ)

HIGH CORRELATION 

Distinct3468
Distinct (%)34.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1429.5438
Minimum1
Maximum17134
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T18:54:58.717442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile174
Q1529.75
median1030
Q31881
95-th percentile3993.1
Maximum17134
Range17133
Interquartile range (IQR)1351.25

Descriptive statistics

Standard deviation1379.4886
Coefficient of variation (CV)0.96498516
Kurtosis15.843259
Mean1429.5438
Median Absolute Deviation (MAD)609
Skewness2.8989969
Sum14295438
Variance1902988.7
MonotonicityNot monotonic
2024-03-13T18:54:58.841042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
682 18
 
0.2%
421 14
 
0.1%
272 14
 
0.1%
165 13
 
0.1%
352 13
 
0.1%
867 12
 
0.1%
317 12
 
0.1%
571 12
 
0.1%
590 12
 
0.1%
501 12
 
0.1%
Other values (3458) 9868
98.7%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 2
< 0.1%
6 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 2
< 0.1%
11 1
< 0.1%
12 2
< 0.1%
ValueCountFrequency (%)
17134 1
< 0.1%
16723 1
< 0.1%
16588 1
< 0.1%
15901 1
< 0.1%
15551 1
< 0.1%
14278 1
< 0.1%
13370 1
< 0.1%
13287 1
< 0.1%
12609 1
< 0.1%
12594 1
< 0.1%

반납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct3517
Distinct (%)35.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1424.2743
Minimum1
Maximum18386
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T18:54:58.960303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile115
Q1473
median1012
Q31890.5
95-th percentile4118.15
Maximum18386
Range18385
Interquartile range (IQR)1417.5

Descriptive statistics

Standard deviation1437.1481
Coefficient of variation (CV)1.0090389
Kurtosis15.250769
Mean1424.2743
Median Absolute Deviation (MAD)648
Skewness2.84406
Sum14242743
Variance2065394.7
MonotonicityNot monotonic
2024-03-13T18:54:59.082912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
285 15
 
0.1%
172 12
 
0.1%
774 12
 
0.1%
178 11
 
0.1%
294 11
 
0.1%
287 11
 
0.1%
813 11
 
0.1%
374 11
 
0.1%
132 11
 
0.1%
404 11
 
0.1%
Other values (3507) 9884
98.8%
ValueCountFrequency (%)
1 2
 
< 0.1%
2 2
 
< 0.1%
3 3
< 0.1%
4 4
< 0.1%
5 5
0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 6
0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
18386 1
< 0.1%
17425 1
< 0.1%
17257 1
< 0.1%
16721 1
< 0.1%
15991 1
< 0.1%
13944 1
< 0.1%
13359 1
< 0.1%
13004 1
< 0.1%
12750 1
< 0.1%
12238 1
< 0.1%

Interactions

2024-03-13T18:54:57.204332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T18:54:56.613277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T18:54:56.904601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T18:54:57.297307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T18:54:56.703532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T18:54:57.000512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T18:54:57.387276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T18:54:56.805511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T18:54:57.105106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T18:54:59.183372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
자치구기준년월대여건수반납건수
자치구1.0000.0000.3320.322
기준년월0.0001.0000.3150.303
대여건수0.3320.3151.0000.984
반납건수0.3220.3030.9841.000
2024-03-13T18:54:59.265386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년월대여건수반납건수자치구
기준년월1.000-0.177-0.1630.000
대여건수-0.1771.0000.9840.123
반납건수-0.1630.9841.0000.119
자치구0.0000.1230.1191.000

Missing values

2024-03-13T18:54:57.508029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T18:54:57.594584image/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
757강동구1077.강동역 1번출구 앞20230725692519
10752강남구2330. 역삼월드메르디앙아파트 앞202310927920
12691강서구5064. 양천향교역8번출구20231120942293
6147중구442.서울역 서부202309502501
12604강서구3758. 서울남부출입국관리소20231126881876
13103동대문구634. 외국어대 정문 앞20231127502759
9786양천구4522. 신곡시장20231027303671
12877은평구4687. 역촌센트레빌아파트 101동 앞202311398116
13286노원구1636. 상계주공2단지 버스정류장 옆20231122862255
10027송파구4892. 성내유수지 체육공원202310670459
자치구대여소명기준년월대여건수반납건수
12656강동구3690. 강일역 4번출구202311359269
967노원구2902.공릉풍림아파트 108동20230714531398
10507송파구1215. 올림픽공원역 1번출구 앞20231026262627
9421서초구2539.대한무역투자진흥공사 KOTRA 앞20231013711386
14775서대문구171. 임광빌딩 앞202312363374
8817송파구2613. 잠실나들목20231035223901
7631동대문구623. 서울시립대 정문 앞 B20230926171774
15926강동구1022. 길동 사거리(초소앞)202312845838
156강남구2368. 도곡동 경남아파트 건너편202307309248
1278용산구4612. 한남 준 J.FSS 앞2023079890