Overview

Dataset statistics

Number of variables6
Number of observations24
Missing cells24
Missing cells (%)16.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 KiB
Average record size in memory55.5 B

Variable types

Numeric1
Text2
Categorical1
DateTime1
Unsupported1

Dataset

Description거리가게 허가제를 통해 등록된 합법적인 사업자의 데이터
Author서울특별시 동대문구
URLhttps://www.data.go.kr/data/15064089/fileData.do

Alerts

데이터수집일자 has constant value ""Constant
has 24 (100.0%) missing valuesMissing
연번 has unique valuesUnique
거리가게명 has unique valuesUnique
is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-12 09:11:04.489150
Analysis finished2023-12-12 09:11:05.006468
Duration0.52 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.5
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T18:11:05.082264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.15
Q16.75
median12.5
Q318.25
95-th percentile22.85
Maximum24
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation7.0710678
Coefficient of variation (CV)0.56568542
Kurtosis-1.2
Mean12.5
Median Absolute Deviation (MAD)6
Skewness0
Sum300
Variance50
MonotonicityStrictly increasing
2023-12-12T18:11:05.242826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 1
 
4.2%
14 1
 
4.2%
24 1
 
4.2%
23 1
 
4.2%
22 1
 
4.2%
21 1
 
4.2%
20 1
 
4.2%
19 1
 
4.2%
18 1
 
4.2%
17 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
1 1
4.2%
2 1
4.2%
3 1
4.2%
4 1
4.2%
5 1
4.2%
6 1
4.2%
7 1
4.2%
8 1
4.2%
9 1
4.2%
10 1
4.2%
ValueCountFrequency (%)
24 1
4.2%
23 1
4.2%
22 1
4.2%
21 1
4.2%
20 1
4.2%
19 1
4.2%
18 1
4.2%
17 1
4.2%
16 1
4.2%
15 1
4.2%

거리가게명
Text

UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size324.0 B
2023-12-12T18:11:05.491928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.1666667
Min length3

Characters and Unicode

Total characters100
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)100.0%

Sample

1st row2-002
2nd row3-008
3rd row3-015
4th row3-024
5th row3-027
ValueCountFrequency (%)
2-002 1
 
4.2%
3-008 1
 
4.2%
휘경09 1
 
4.2%
휘경08 1
 
4.2%
휘경07 1
 
4.2%
휘경06 1
 
4.2%
휘경05 1
 
4.2%
휘경04 1
 
4.2%
휘경03 1
 
4.2%
휘경1 1
 
4.2%
Other values (14) 14
58.3%
2023-12-12T18:11:05.925859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 20
20.0%
- 10
10.0%
경 9
9.0%
휘 9
9.0%
5 9
9.0%
3 7
 
7.0%
1 6
 
6.0%
2 5
 
5.0%
4 5
 
5.0%
전 3
 
3.0%
Other values (9) 17
17.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 62
62.0%
Other Letter 28
28.0%
Dash Punctuation 10
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20
32.3%
5 9
14.5%
3 7
 
11.3%
1 6
 
9.7%
2 5
 
8.1%
4 5
 
8.1%
8 3
 
4.8%
9 3
 
4.8%
7 2
 
3.2%
6 2
 
3.2%
Other Letter
ValueCountFrequency (%)
경 9
32.1%
휘 9
32.1%
전 3
 
10.7%
농 3
 
10.7%
신 1
 
3.6%
설 1
 
3.6%
장 1
 
3.6%
안 1
 
3.6%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 72
72.0%
Hangul 28
 
28.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20
27.8%
- 10
13.9%
5 9
12.5%
3 7
 
9.7%
1 6
 
8.3%
2 5
 
6.9%
4 5
 
6.9%
8 3
 
4.2%
9 3
 
4.2%
7 2
 
2.8%
Hangul
ValueCountFrequency (%)
경 9
32.1%
휘 9
32.1%
전 3
 
10.7%
농 3
 
10.7%
신 1
 
3.6%
설 1
 
3.6%
장 1
 
3.6%
안 1
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72
72.0%
Hangul 28
 
28.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20
27.8%
- 10
13.9%
5 9
12.5%
3 7
 
9.7%
1 6
 
8.3%
2 5
 
6.9%
4 5
 
6.9%
8 3
 
4.2%
9 3
 
4.2%
7 2
 
2.8%
Hangul
ValueCountFrequency (%)
경 9
32.1%
휘 9
32.1%
전 3
 
10.7%
농 3
 
10.7%
신 1
 
3.6%
설 1
 
3.6%
장 1
 
3.6%
안 1
 
3.6%

업종(요식업)
Categorical

Distinct9
Distinct (%)37.5%
Missing0
Missing (%)0.0%
Memory size324.0 B
분식
7 
붕어빵
5 
토스트
5 
곱창
2 
분식
1 
Other values (4)
4 

Length

Max length3
Median length3
Mean length2.5833333
Min length2

Unique

Unique5 ?
Unique (%)20.8%

Sample

1st row분식
2nd row붕어빵
3rd row국화빵
4th row분식
5th row토스트

Common Values

ValueCountFrequency (%)
분식 7
29.2%
붕어빵 5
20.8%
토스트 5
20.8%
곱창 2
 
8.3%
분식 1
 
4.2%
국화빵 1
 
4.2%
탕우루 1
 
4.2%
호떡 1
 
4.2%
쌀국수 1
 
4.2%

Length

2023-12-12T18:11:06.159956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:11:06.337988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
분식 8
33.3%
붕어빵 5
20.8%
토스트 5
20.8%
곱창 2
 
8.3%
국화빵 1
 
4.2%
탕우루 1
 
4.2%
호떡 1
 
4.2%
쌀국수 1
 
4.2%

주소
Text

Distinct18
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Memory size324.0 B
2023-12-12T18:11:06.611267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length11.708333
Min length9

Characters and Unicode

Total characters281
Distinct characters31
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)62.5%

Sample

1st row왕산로 109 앞 도로
2nd row왕산로 191 앞 도로
3rd row왕산로 180 앞 도로
4th row왕산로 194 앞 도로
5th row왕산로 40길 4 앞 도로
ValueCountFrequency (%)
앞 24
25.8%
도로 24
25.8%
회기로 8
 
8.6%
왕산로 6
 
6.5%
194 6
 
6.5%
전농로 2
 
2.2%
188 2
 
2.2%
왕산로207 2
 
2.2%
이문로 1
 
1.1%
136 1
 
1.1%
Other values (17) 17
18.3%
2023-12-12T18:11:07.005121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
69
24.6%
로 48
17.1%
앞 24
 
8.5%
도 24
 
8.5%
1 18
 
6.4%
왕 9
 
3.2%
9 9
 
3.2%
4 9
 
3.2%
산 9
 
3.2%
0 8
 
2.8%
Other values (21) 54
19.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 146
52.0%
Space Separator 69
24.6%
Decimal Number 65
23.1%
Dash Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 48
32.9%
앞 24
16.4%
도 24
16.4%
왕 9
 
6.2%
산 9
 
6.2%
기 8
 
5.5%
회 8
 
5.5%
농 2
 
1.4%
전 2
 
1.4%
릉 2
 
1.4%
Other values (9) 10
 
6.8%
Decimal Number
ValueCountFrequency (%)
1 18
27.7%
9 9
13.8%
4 9
13.8%
0 8
12.3%
8 6
 
9.2%
2 5
 
7.7%
3 3
 
4.6%
5 3
 
4.6%
6 2
 
3.1%
7 2
 
3.1%
Space Separator
ValueCountFrequency (%)
69
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 146
52.0%
Common 135
48.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 48
32.9%
앞 24
16.4%
도 24
16.4%
왕 9
 
6.2%
산 9
 
6.2%
기 8
 
5.5%
회 8
 
5.5%
농 2
 
1.4%
전 2
 
1.4%
릉 2
 
1.4%
Other values (9) 10
 
6.8%
Common
ValueCountFrequency (%)
69
51.1%
1 18
 
13.3%
9 9
 
6.7%
4 9
 
6.7%
0 8
 
5.9%
8 6
 
4.4%
2 5
 
3.7%
3 3
 
2.2%
5 3
 
2.2%
6 2
 
1.5%
Other values (2) 3
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 146
52.0%
ASCII 135
48.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
69
51.1%
1 18
 
13.3%
9 9
 
6.7%
4 9
 
6.7%
0 8
 
5.9%
8 6
 
4.4%
2 5
 
3.7%
3 3
 
2.2%
5 3
 
2.2%
6 2
 
1.5%
Other values (2) 3
 
2.2%
Hangul
ValueCountFrequency (%)
로 48
32.9%
앞 24
16.4%
도 24
16.4%
왕 9
 
6.2%
산 9
 
6.2%
기 8
 
5.5%
회 8
 
5.5%
농 2
 
1.4%
전 2
 
1.4%
릉 2
 
1.4%
Other values (9) 10
 
6.8%

데이터수집일자
Date

CONSTANT 

Distinct1
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size324.0 B
Minimum2020-09-01 00:00:00
Maximum2020-09-01 00:00:00
2023-12-12T18:11:07.149109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:07.245191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)


Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing24
Missing (%)100.0%
Memory size348.0 B

Interactions

2023-12-12T18:11:04.691915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:11:07.340623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번거리가게명업종(요식업)주소
연번1.0001.0000.5390.919
거리가게명1.0001.0001.0001.000
업종(요식업)0.5391.0001.0000.000
주소0.9191.0000.0001.000
2023-12-12T18:11:07.438158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종(요식업)
연번1.0000.224
업종(요식업)0.2241.000

Missing values

2023-12-12T18:11:04.821098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:11:04.961635image/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

연번거리가게명업종(요식업)주소데이터수집일자
012-002분식왕산로 109 앞 도로2020.09.01<NA>
123-008붕어빵왕산로 191 앞 도로2020.09.01<NA>
233-015국화빵왕산로 180 앞 도로2020.09.01<NA>
343-024분식왕산로 194 앞 도로2020.09.01<NA>
453-027토스트왕산로 40길 4 앞 도로2020.09.01<NA>
565-039붕어빵홍릉로 5-1 앞 도로2020.09.01<NA>
675-042토스트홍릉로6 앞 도로2020.09.01<NA>
785-046분식왕산로205 앞 도로2020.09.01<NA>
895-049분식왕산로207 앞 도로2020.09.01<NA>
9105-050토스트왕산로207 앞 도로2020.09.01<NA>
연번거리가게명업종(요식업)주소데이터수집일자
1415장안1토스트장한로 3 앞 도로2020.09.01<NA>
1516휘경1토스트회기로 194 앞 도로2020.09.01<NA>
1617휘경03분식회기로 194 앞 도로2020.09.01<NA>
1718휘경04탕우루회기로 194 앞 도로2020.09.01<NA>
1819휘경05호떡회기로 194 앞 도로2020.09.01<NA>
1920휘경06곱창회기로 194 앞 도로2020.09.01<NA>
2021휘경07쌀국수회기로 190 앞 도로2020.09.01<NA>
2122휘경08분식회기로 188 앞 도로2020.09.01<NA>
2223휘경09붕어빵회기로 188 앞 도로2020.09.01<NA>
2324휘경11곱창이문로 30 앞 도로2020.09.01<NA>