Overview

Dataset statistics

Number of variables4
Number of observations22
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory858.0 B
Average record size in memory39.0 B

Variable types

Numeric1
Text1
Categorical2

Dataset

Description파일 다운로드
Author강서구
URLhttps://data.seoul.go.kr/dataList/OA-21789/F/1/datasetView.do

Alerts

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

Reproduction

Analysis started2023-12-11 06:48:26.959837
Analysis finished2023-12-11 06:48:27.335716
Duration0.38 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.5
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-11T15:48:27.402955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.05
Q16.25
median11.5
Q316.75
95-th percentile20.95
Maximum22
Range21
Interquartile range (IQR)10.5

Descriptive statistics

Standard deviation6.4935866
Coefficient of variation (CV)0.5646597
Kurtosis-1.2
Mean11.5
Median Absolute Deviation (MAD)5.5
Skewness0
Sum253
Variance42.166667
MonotonicityStrictly increasing
2023-12-11T15:48:27.543130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1 1
 
4.5%
13 1
 
4.5%
22 1
 
4.5%
21 1
 
4.5%
20 1
 
4.5%
19 1
 
4.5%
18 1
 
4.5%
17 1
 
4.5%
16 1
 
4.5%
15 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
1 1
4.5%
2 1
4.5%
3 1
4.5%
4 1
4.5%
5 1
4.5%
6 1
4.5%
7 1
4.5%
8 1
4.5%
9 1
4.5%
10 1
4.5%
ValueCountFrequency (%)
22 1
4.5%
21 1
4.5%
20 1
4.5%
19 1
4.5%
18 1
4.5%
17 1
4.5%
16 1
4.5%
15 1
4.5%
14 1
4.5%
13 1
4.5%

전동킥보드 주차구역
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-11T15:48:27.758369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length24
Mean length22.272727
Min length17

Characters and Unicode

Total characters490
Distinct characters49
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

Unique22 ?
Unique (%)100.0%

Sample

1st row발산역 8번출구(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 7
 
7.6%
출구(마곡동 7
 
7.6%
발산역 4
 
4.3%
마곡역 4
 
4.3%
사이(등촌동 3
 
3.3%
1번 3
 
3.3%
449-30 3
 
3.3%
가양역 3
 
3.3%
4번 3
 
3.3%
증미역 3
 
3.3%
Other values (44) 52
56.5%
2023-12-11T15:48:28.111531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
70
 
14.3%
7 23
 
4.7%
번 22
 
4.5%
( 22
 
4.5%
구 22
 
4.5%
) 22
 
4.5%
출 21
 
4.3%
곡 21
 
4.3%
역 20
 
4.1%
마 20
 
4.1%
Other values (39) 227
46.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 228
46.5%
Decimal Number 125
25.5%
Space Separator 70
 
14.3%
Open Punctuation 22
 
4.5%
Close Punctuation 22
 
4.5%
Dash Punctuation 17
 
3.5%
Other Punctuation 6
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
번 22
 
9.6%
구 22
 
9.6%
출 21
 
9.2%
곡 21
 
9.2%
역 20
 
8.8%
마 20
 
8.8%
동 18
 
7.9%
양 7
 
3.1%
등 7
 
3.1%
촌 7
 
3.1%
Other values (24) 63
27.6%
Decimal Number
ValueCountFrequency (%)
7 23
18.4%
2 18
14.4%
1 16
12.8%
4 15
12.0%
6 13
10.4%
8 11
8.8%
3 10
8.0%
9 8
 
6.4%
0 7
 
5.6%
5 4
 
3.2%
Space Separator
ValueCountFrequency (%)
70
100.0%
Open Punctuation
ValueCountFrequency (%)
( 22
100.0%
Close Punctuation
ValueCountFrequency (%)
) 22
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 17
100.0%
Other Punctuation
ValueCountFrequency (%)
, 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 262
53.5%
Hangul 228
46.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
번 22
 
9.6%
구 22
 
9.6%
출 21
 
9.2%
곡 21
 
9.2%
역 20
 
8.8%
마 20
 
8.8%
동 18
 
7.9%
양 7
 
3.1%
등 7
 
3.1%
촌 7
 
3.1%
Other values (24) 63
27.6%
Common
ValueCountFrequency (%)
70
26.7%
7 23
 
8.8%
( 22
 
8.4%
) 22
 
8.4%
2 18
 
6.9%
- 17
 
6.5%
1 16
 
6.1%
4 15
 
5.7%
6 13
 
5.0%
8 11
 
4.2%
Other values (5) 35
13.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 262
53.5%
Hangul 228
46.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
70
26.7%
7 23
 
8.8%
( 22
 
8.4%
) 22
 
8.4%
2 18
 
6.9%
- 17
 
6.5%
1 16
 
6.1%
4 15
 
5.7%
6 13
 
5.0%
8 11
 
4.2%
Other values (5) 35
13.4%
Hangul
ValueCountFrequency (%)
번 22
 
9.6%
구 22
 
9.6%
출 21
 
9.2%
곡 21
 
9.2%
역 20
 
8.8%
마 20
 
8.8%
동 18
 
7.9%
양 7
 
3.1%
등 7
 
3.1%
촌 7
 
3.1%
Other values (24) 63
27.6%

법정동
Categorical

Distinct3
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Memory size308.0 B
마곡동
13 
등촌동
8 
방화동
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)4.5%

Sample

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

Common Values

ValueCountFrequency (%)
마곡동 13
59.1%
등촌동 8
36.4%
방화동 1
 
4.5%

Length

2023-12-11T15:48:28.236054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T15:48:28.595046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
마곡동 13
59.1%
등촌동 8
36.4%
방화동 1
 
4.5%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-02-22
22 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-02-22
2nd row2023-02-22
3rd row2023-02-22
4th row2023-02-22
5th row2023-02-22

Common Values

ValueCountFrequency (%)
2023-02-22 22
100.0%

Length

2023-12-11T15:48:28.699885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T15:48:28.798434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-02-22 22
100.0%

Interactions

2023-12-11T15:48:27.091284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T15:48:28.853698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번전동킥보드 주차구역법정동
연번1.0001.0000.737
전동킥보드 주차구역1.0001.0001.000
법정동0.7371.0001.000
2023-12-11T15:48:28.938384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번법정동
연번1.0000.466
법정동0.4661.000

Missing values

2023-12-11T15:48:27.210435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T15:48:27.301814image/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 주차구역법정동데이터기준일자
01발산역 8번출구(마곡동 727-1222)마곡동2023-02-22
12마곡역 1번출구(마곡동 727-1428)마곡동2023-02-22
23마곡역 6번 출구(마곡동 728-163)마곡동2023-02-22
34마곡역 5번 출구(마곡동 721-4)마곡동2023-02-22
45마곡역 4번 출구(마곡동 728-190)마곡동2023-02-22
56마곡나루역1번 출구(마곡동 760-3)마곡동2023-02-22
67마곡나루역 2번 출구(마곡동 760-3)마곡동2023-02-22
78마곡광장 입구(마곡중앙5로 지하9)마곡동2023-02-22
89마곡나루역 4번 출구(마곡동 728-190)마곡동2023-02-22
910발산역 1번 출구(마곡동 727-1221)마곡동2023-02-22
연번전동킥보드 주차구역법정동데이터기준일자
1213발산9번, 마곡3번 출구 사이(공항대로 277 앞)마곡동2023-02-22
1314가양역 7번출구(화곡로 416)등촌동2023-02-22
1415가양역8, 9번 출구 사이(등촌동 78-7)등촌동2023-02-22
1516가양역 8번 출구 앞(등촌동 78-7)등촌동2023-02-22
1617가양역 6, 7번 출구 사이(등촌동 666-2)등촌동2023-02-22
1718등촌역 1번 출구(등촌동 648-4)등촌동2023-02-22
1819증미역 1번 출구(가양동 449-30)등촌동2023-02-22
1920증미역 2번 출구(가양동 449-30)등촌동2023-02-22
2021증미역 1,2번 출구 사이(가양동 449-30)등촌동2023-02-22
2122신방화역5, 6번 출구 사이(마곡중앙5로 87)방화동2023-02-22