Overview

Dataset statistics

Number of variables5
Number of observations32
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 KiB
Average record size in memory45.1 B

Variable types

Numeric1
Text1
Categorical1
DateTime2

Dataset

Description서울특별시 강서구 전동킥보드 주차구역 주소 및 행정동 데이터 입니다. 제공데이터 : 연번 / 전동킥보드 주차구역 / 법정동 / 데이터기준일자
URLhttps://www.data.go.kr/data/15107741/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
연번 has unique valuesUnique
전동킥보드 주차구역 has unique valuesUnique

Reproduction

Analysis started2023-12-12 09:00:56.507618
Analysis finished2023-12-12 09:00:56.906041
Duration0.4 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.5
Minimum1
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T18:00:56.980453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.55
Q18.75
median16.5
Q324.25
95-th percentile30.45
Maximum32
Range31
Interquartile range (IQR)15.5

Descriptive statistics

Standard deviation9.3808315
Coefficient of variation (CV)0.56853524
Kurtosis-1.2
Mean16.5
Median Absolute Deviation (MAD)8
Skewness0
Sum528
Variance88
MonotonicityStrictly increasing
2023-12-12T18:00:57.111580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1 1
 
3.1%
18 1
 
3.1%
32 1
 
3.1%
31 1
 
3.1%
30 1
 
3.1%
29 1
 
3.1%
28 1
 
3.1%
27 1
 
3.1%
26 1
 
3.1%
25 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
1 1
3.1%
2 1
3.1%
3 1
3.1%
4 1
3.1%
5 1
3.1%
6 1
3.1%
7 1
3.1%
8 1
3.1%
9 1
3.1%
10 1
3.1%
ValueCountFrequency (%)
32 1
3.1%
31 1
3.1%
30 1
3.1%
29 1
3.1%
28 1
3.1%
27 1
3.1%
26 1
3.1%
25 1
3.1%
24 1
3.1%
23 1
3.1%

전동킥보드 주차구역
Text

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-12T18:00:57.416453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length26
Mean length22.3125
Min length17

Characters and Unicode

Total characters714
Distinct characters54
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row발산역 8번 출구(마곡동 727-1222)
2nd row마곡역 1번출구(마곡동 727-1428)
3rd row마곡역 6번 출구(마곡동 728-163)
4th row마곡역 5번 출구(마곡동 721-4)
5th row마곡역 4번 출구(마곡동 728-190)
ValueCountFrequency (%)
출구(마곡동 13
 
9.2%
출구 8
 
5.7%
마곡역 6
 
4.3%
1번 6
 
4.3%
8번 5
 
3.5%
4번 4
 
2.8%
신방화역 4
 
2.8%
가양역 4
 
2.8%
마곡나루역 4
 
2.8%
발산역 4
 
2.8%
Other values (60) 83
58.9%
2023-12-12T18:00:57.898181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
109
 
15.3%
) 32
 
4.5%
번 32
 
4.5%
구 32
 
4.5%
( 32
 
4.5%
7 32
 
4.5%
출 31
 
4.3%
역 30
 
4.2%
곡 29
 
4.1%
마 28
 
3.9%
Other values (44) 327
45.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 333
46.6%
Decimal Number 178
24.9%
Space Separator 109
 
15.3%
Close Punctuation 32
 
4.5%
Open Punctuation 32
 
4.5%
Dash Punctuation 24
 
3.4%
Other Punctuation 6
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
번 32
 
9.6%
구 32
 
9.6%
출 31
 
9.3%
역 30
 
9.0%
곡 29
 
8.7%
마 28
 
8.4%
동 26
 
7.8%
촌 10
 
3.0%
등 10
 
3.0%
화 8
 
2.4%
Other values (29) 97
29.1%
Decimal Number
ValueCountFrequency (%)
7 32
18.0%
2 25
14.0%
1 22
12.4%
4 19
10.7%
3 19
10.7%
8 18
10.1%
6 16
9.0%
5 10
 
5.6%
0 9
 
5.1%
9 8
 
4.5%
Space Separator
ValueCountFrequency (%)
109
100.0%
Close Punctuation
ValueCountFrequency (%)
) 32
100.0%
Open Punctuation
ValueCountFrequency (%)
( 32
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 24
100.0%
Other Punctuation
ValueCountFrequency (%)
, 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 381
53.4%
Hangul 333
46.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
번 32
 
9.6%
구 32
 
9.6%
출 31
 
9.3%
역 30
 
9.0%
곡 29
 
8.7%
마 28
 
8.4%
동 26
 
7.8%
촌 10
 
3.0%
등 10
 
3.0%
화 8
 
2.4%
Other values (29) 97
29.1%
Common
ValueCountFrequency (%)
109
28.6%
) 32
 
8.4%
( 32
 
8.4%
7 32
 
8.4%
2 25
 
6.6%
- 24
 
6.3%
1 22
 
5.8%
4 19
 
5.0%
3 19
 
5.0%
8 18
 
4.7%
Other values (5) 49
12.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 381
53.4%
Hangul 333
46.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
109
28.6%
) 32
 
8.4%
( 32
 
8.4%
7 32
 
8.4%
2 25
 
6.6%
- 24
 
6.3%
1 22
 
5.8%
4 19
 
5.0%
3 19
 
5.0%
8 18
 
4.7%
Other values (5) 49
12.9%
Hangul
ValueCountFrequency (%)
번 32
 
9.6%
구 32
 
9.6%
출 31
 
9.3%
역 30
 
9.0%
곡 29
 
8.7%
마 28
 
8.4%
동 26
 
7.8%
촌 10
 
3.0%
등 10
 
3.0%
화 8
 
2.4%
Other values (29) 97
29.1%

법정동
Categorical

Distinct5
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Memory size388.0 B
마곡동
18 
등촌동
9 
방화동
3 
염창동
 
1
가양동
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique2 ?
Unique (%)6.2%

Sample

1st row마곡동
2nd row마곡동
3rd row마곡동
4th row마곡동
5th row마곡동

Common Values

ValueCountFrequency (%)
마곡동 18
56.2%
등촌동 9
28.1%
방화동 3
 
9.4%
염창동 1
 
3.1%
가양동 1
 
3.1%

Length

2023-12-12T18:00:58.074388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:00:58.252227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
마곡동 18
56.2%
등촌동 9
28.1%
방화동 3
 
9.4%
염창동 1
 
3.1%
가양동 1
 
3.1%

설치일자
Date

Distinct3
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Memory size388.0 B
Minimum2022-08-01 00:00:00
Maximum2023-07-01 00:00:00
2023-12-12T18:00:58.377872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:58.502496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=3)

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size388.0 B
Minimum2023-08-11 00:00:00
Maximum2023-08-11 00:00:00
2023-12-12T18:00:58.629806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:58.762406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T18:00:56.647474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:00:58.883175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번전동킥보드 주차구역법정동설치일자
연번1.0001.0000.7540.861
전동킥보드 주차구역1.0001.0001.0001.000
법정동0.7541.0001.0000.225
설치일자0.8611.0000.2251.000
2023-12-12T18:00:59.030306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번법정동
연번1.0000.359
법정동0.3591.000

Missing values

2023-12-12T18:00:56.766562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:00:56.858808image/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

연번전동킥보드 주차구역법정동설치일자데이터기준일자
01발산역 8번 출구(마곡동 727-1222)마곡동2022-082023-08-11
12마곡역 1번출구(마곡동 727-1428)마곡동2022-082023-08-11
23마곡역 6번 출구(마곡동 728-163)마곡동2022-122023-08-11
34마곡역 5번 출구(마곡동 721-4)마곡동2022-122023-08-11
45마곡역 4번 출구(마곡동 728-190)마곡동2022-122023-08-11
56마곡나루역 1번 출구(마곡동 760-3)마곡동2022-122023-08-11
67마곡나루역 2번 출구(마곡동 760-3)마곡동2022-122023-08-11
78마곡광장 입구(마곡중앙5로 지하9)마곡동2022-122023-08-11
89마곡나루역 4번 출구(마곡동 728-190)마곡동2022-122023-08-11
910발산역 1번 출구(마곡동 727-1221)마곡동2022-122023-08-11
연번전동킥보드 주차구역법정동설치일자데이터기준일자
2223마곡역 1번 출구(마곡동 727-1428 부근)마곡동2023-072023-08-11
2324마곡역 2번 출구(공항대로 지하 163)마곡동2023-072023-08-11
2425방화역 1번 출구(방화동 830-8)방화동2023-072023-08-11
2526등촌역 3번 출구 부근(공항대로 571)염창동2023-072023-08-11
2627등촌역 8번 출구(등촌동 505-3)등촌동2023-072023-08-11
2728신방화역 4번 출구(마곡동 736-3)마곡동2023-072023-08-11
2829신방화역 5번 출구(마곡동 743)마곡동2023-072023-08-11
2930신방화역 8번 출구(방화동 247-285)방화동2023-072023-08-11
3031마곡나루역 5번 출구(마곡동 373-12)마곡동2023-072023-08-11
3132양천향교역 8번 출구(마곡동 776-2)가양동2023-072023-08-11