Overview

Dataset statistics

Number of variables4
Number of observations10000
Missing cells31824
Missing cells (%)79.6%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory410.2 KiB
Average record size in memory42.0 B

Variable types

DateTime1
Text1
Numeric2

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15244/S/1/datasetView.do

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
일시 has 7956 (79.6%) missing valuesMissing
대여소 has 7956 (79.6%) missing valuesMissing
대여건수 has 7956 (79.6%) missing valuesMissing
반납건수 has 7956 (79.6%) missing valuesMissing
대여건수 has 727 (7.3%) zerosZeros
반납건수 has 798 (8.0%) zerosZeros

Reproduction

Analysis started2023-12-11 10:02:15.551770
Analysis finished2023-12-11 10:02:16.603892
Duration1.05 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일시
Date

MISSING 

Distinct203
Distinct (%)9.9%
Missing7956
Missing (%)79.6%
Memory size156.2 KiB
Minimum2020-07-01 00:00:00
Maximum2021-01-31 00:00:00
2023-12-11T19:02:16.678192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T19:02:16.813291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

대여소
Text

MISSING 

Distinct726
Distinct (%)35.5%
Missing7956
Missing (%)79.6%
Memory size156.2 KiB
2023-12-11T19:02:17.086147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length29
Mean length15.263699
Min length7

Characters and Unicode

Total characters31199
Distinct characters417
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique340 ?
Unique (%)16.6%

Sample

1st row214. 금융감독원 앞
2nd row755. 목동1단지아파트 상가 앞 (월촌중학교 버스정류소 옆)
3rd row3407.안국동사거리(신)
4th row248. 초원아파트 앞
5th row915. 증산역 4번출구
ValueCountFrequency (%)
앞 675
 
10.3%
옆 142
 
2.2%
1번출구 136
 
2.1%
출구 127
 
1.9%
2번출구 76
 
1.2%
3번출구 71
 
1.1%
뒤 68
 
1.0%
5번출구 66
 
1.0%
8번출구 54
 
0.8%
건너편 46
 
0.7%
Other values (1551) 5097
77.7%
2023-12-11T19:02:17.561363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4535
 
14.5%
. 2052
 
6.6%
1 1699
 
5.4%
2 1431
 
4.6%
구 1007
 
3.2%
3 973
 
3.1%
역 849
 
2.7%
번 789
 
2.5%
앞 788
 
2.5%
5 771
 
2.5%
Other values (407) 16305
52.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 15894
50.9%
Decimal Number 8112
26.0%
Space Separator 4535
 
14.5%
Other Punctuation 2074
 
6.6%
Uppercase Letter 229
 
0.7%
Close Punctuation 153
 
0.5%
Open Punctuation 153
 
0.5%
Dash Punctuation 45
 
0.1%
Math Symbol 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 1007
 
6.3%
역 849
 
5.3%
번 789
 
5.0%
앞 788
 
5.0%
출 768
 
4.8%
동 329
 
2.1%
교 259
 
1.6%
대 258
 
1.6%
사 258
 
1.6%
원 225
 
1.4%
Other values (371) 10364
65.2%
Uppercase Letter
ValueCountFrequency (%)
K 39
17.0%
C 30
13.1%
S 23
10.0%
B 18
 
7.9%
E 15
 
6.6%
M 14
 
6.1%
D 13
 
5.7%
I 13
 
5.7%
T 9
 
3.9%
L 9
 
3.9%
Other values (8) 46
20.1%
Decimal Number
ValueCountFrequency (%)
1 1699
20.9%
2 1431
17.6%
3 973
12.0%
5 771
9.5%
0 694
8.6%
8 637
 
7.9%
4 612
 
7.5%
6 542
 
6.7%
7 414
 
5.1%
9 339
 
4.2%
Other Punctuation
ValueCountFrequency (%)
. 2052
98.9%
, 21
 
1.0%
? 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
4535
100.0%
Close Punctuation
ValueCountFrequency (%)
) 153
100.0%
Open Punctuation
ValueCountFrequency (%)
( 153
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 45
100.0%
Math Symbol
ValueCountFrequency (%)
~ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 15894
50.9%
Common 15076
48.3%
Latin 229
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 1007
 
6.3%
역 849
 
5.3%
번 789
 
5.0%
앞 788
 
5.0%
출 768
 
4.8%
동 329
 
2.1%
교 259
 
1.6%
대 258
 
1.6%
사 258
 
1.6%
원 225
 
1.4%
Other values (371) 10364
65.2%
Common
ValueCountFrequency (%)
4535
30.1%
. 2052
13.6%
1 1699
 
11.3%
2 1431
 
9.5%
3 973
 
6.5%
5 771
 
5.1%
0 694
 
4.6%
8 637
 
4.2%
4 612
 
4.1%
6 542
 
3.6%
Other values (8) 1130
 
7.5%
Latin
ValueCountFrequency (%)
K 39
17.0%
C 30
13.1%
S 23
10.0%
B 18
 
7.9%
E 15
 
6.6%
M 14
 
6.1%
D 13
 
5.7%
I 13
 
5.7%
T 9
 
3.9%
L 9
 
3.9%
Other values (8) 46
20.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 15894
50.9%
ASCII 15305
49.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4535
29.6%
. 2052
13.4%
1 1699
 
11.1%
2 1431
 
9.3%
3 973
 
6.4%
5 771
 
5.0%
0 694
 
4.5%
8 637
 
4.2%
4 612
 
4.0%
6 542
 
3.5%
Other values (26) 1359
 
8.9%
Hangul
ValueCountFrequency (%)
구 1007
 
6.3%
역 849
 
5.3%
번 789
 
5.0%
앞 788
 
5.0%
출 768
 
4.8%
동 329
 
2.1%
교 259
 
1.6%
대 258
 
1.6%
사 258
 
1.6%
원 225
 
1.4%
Other values (371) 10364
65.2%

대여건수
Real number (ℝ)

MISSING  ZEROS 

Distinct7
Distinct (%)0.3%
Missing7956
Missing (%)79.6%
Infinite0
Infinite (%)0.0%
Mean0.79500978
Minimum0
Maximum6
Zeros727
Zeros (%)7.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T19:02:17.673629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.77247975
Coefficient of variation (CV)0.97166068
Kurtosis5.418499
Mean0.79500978
Median Absolute Deviation (MAD)0
Skewness1.5755724
Sum1625
Variance0.59672496
MonotonicityNot monotonic
2023-12-11T19:02:17.768537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 1102
 
11.0%
0 727
 
7.3%
2 154
 
1.5%
3 37
 
0.4%
4 18
 
0.2%
5 4
 
< 0.1%
6 2
 
< 0.1%
(Missing) 7956
79.6%
ValueCountFrequency (%)
0 727
7.3%
1 1102
11.0%
2 154
 
1.5%
3 37
 
0.4%
4 18
 
0.2%
5 4
 
< 0.1%
6 2
 
< 0.1%
ValueCountFrequency (%)
6 2
 
< 0.1%
5 4
 
< 0.1%
4 18
 
0.2%
3 37
 
0.4%
2 154
 
1.5%
1 1102
11.0%
0 727
7.3%

반납건수
Real number (ℝ)

MISSING  ZEROS 

Distinct9
Distinct (%)0.4%
Missing7956
Missing (%)79.6%
Infinite0
Infinite (%)0.0%
Mean0.768591
Minimum0
Maximum8
Zeros798
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T19:02:17.871446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.81275428
Coefficient of variation (CV)1.0574601
Kurtosis8.7001869
Mean0.768591
Median Absolute Deviation (MAD)0
Skewness1.9522444
Sum1571
Variance0.66056953
MonotonicityNot monotonic
2023-12-11T19:02:17.974539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 1024
 
10.2%
0 798
 
8.0%
2 158
 
1.6%
3 40
 
0.4%
4 16
 
0.2%
5 4
 
< 0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
(Missing) 7956
79.6%
ValueCountFrequency (%)
0 798
8.0%
1 1024
10.2%
2 158
 
1.6%
3 40
 
0.4%
4 16
 
0.2%
5 4
 
< 0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 1
 
< 0.1%
6 2
 
< 0.1%
5 4
 
< 0.1%
4 16
 
0.2%
3 40
 
0.4%
2 158
 
1.6%
1 1024
10.2%
0 798
8.0%

Interactions

2023-12-11T19:02:16.063375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T19:02:15.851496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T19:02:16.171012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T19:02:15.954545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T19:02:18.056147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여건수반납건수
대여건수1.0000.704
반납건수0.7041.000
2023-12-11T19:02:18.128674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여건수반납건수
대여건수1.000-0.420
반납건수-0.4201.000

Missing values

2023-12-11T19:02:16.324506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T19:02:16.430098image/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.
2023-12-11T19:02:16.534877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

일시대여소대여건수반납건수
16752<NA><NA><NA><NA>
21182020-09-16214. 금융감독원 앞10
6892<NA><NA><NA><NA>
13374<NA><NA><NA><NA>
19983<NA><NA><NA><NA>
13521<NA><NA><NA><NA>
41192020-12-01755. 목동1단지아파트 상가 앞 (월촌중학교 버스정류소 옆)11
7286<NA><NA><NA><NA>
6692<NA><NA><NA><NA>
15706<NA><NA><NA><NA>
일시대여소대여건수반납건수
10534<NA><NA><NA><NA>
4819<NA><NA><NA><NA>
13475<NA><NA><NA><NA>
28942020-10-042620. 송파나루역 4번 출구옆10
11901<NA><NA><NA><NA>
11210<NA><NA><NA><NA>
20888<NA><NA><NA><NA>
6498<NA><NA><NA><NA>
17437<NA><NA><NA><NA>
5403<NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

일시대여소대여건수반납건수# duplicates
0<NA><NA><NA><NA>7956