Overview

Dataset statistics

Number of variables3
Number of observations794
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.5 KiB
Average record size in memory25.2 B

Variable types

Numeric1
Text2

Dataset

Description안전한 외식문화 실천을 위한 창원시 안심식당 지정 현황입니다[지정조건]- 덜어먹기 도구 비치- 위생적 수저관리- 조리종사자 마스크 착용
Author경상남도 창원시
URLhttps://www.data.go.kr/data/15075704/fileData.do

Alerts

연번 has unique valuesUnique

Reproduction

Analysis started2024-04-06 08:16:28.943355
Analysis finished2024-04-06 08:16:30.272461
Duration1.33 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct794
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean397.5
Minimum1
Maximum794
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2024-04-06T17:16:30.414842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile40.65
Q1199.25
median397.5
Q3595.75
95-th percentile754.35
Maximum794
Range793
Interquartile range (IQR)396.5

Descriptive statistics

Standard deviation229.35235
Coefficient of variation (CV)0.57698704
Kurtosis-1.2
Mean397.5
Median Absolute Deviation (MAD)198.5
Skewness0
Sum315615
Variance52602.5
MonotonicityStrictly increasing
2024-04-06T17:16:30.714169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
523 1
 
0.1%
525 1
 
0.1%
526 1
 
0.1%
527 1
 
0.1%
528 1
 
0.1%
529 1
 
0.1%
530 1
 
0.1%
531 1
 
0.1%
532 1
 
0.1%
Other values (784) 784
98.7%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
794 1
0.1%
793 1
0.1%
792 1
0.1%
791 1
0.1%
790 1
0.1%
789 1
0.1%
788 1
0.1%
787 1
0.1%
786 1
0.1%
785 1
0.1%

업소명
Text

Distinct790
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size6.3 KiB
2024-04-06T17:16:31.358515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length14
Mean length5.709068
Min length2

Characters and Unicode

Total characters4533
Distinct characters496
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique786 ?
Unique (%)99.0%

Sample

1st row백제령삼계탕
2nd row모티키친
3rd row함흥집
4th row오즈마법사
5th row판문점
ValueCountFrequency (%)
진고려삼계탕 3
 
0.4%
요와라와라삼겹살 2
 
0.2%
목포세발낙지 2
 
0.2%
소담 2
 
0.2%
착한꽃대패 2
 
0.2%
저도식당 1
 
0.1%
삼구통닭 1
 
0.1%
등대횟집 1
 
0.1%
등대해물짬뽕 1
 
0.1%
맛있는고기에솜씨를더하다 1
 
0.1%
Other values (817) 817
98.1%
2024-04-06T17:16:32.827646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
점 128
 
2.8%
집 100
 
2.2%
식 84
 
1.9%
원 74
 
1.6%
가 66
 
1.5%
산 63
 
1.4%
당 63
 
1.4%
장 61
 
1.3%
이 59
 
1.3%
고 58
 
1.3%
Other values (486) 3777
83.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4404
97.2%
Decimal Number 42
 
0.9%
Space Separator 40
 
0.9%
Uppercase Letter 15
 
0.3%
Other Punctuation 10
 
0.2%
Close Punctuation 9
 
0.2%
Open Punctuation 9
 
0.2%
Lowercase Letter 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 128
 
2.9%
집 100
 
2.3%
식 84
 
1.9%
원 74
 
1.7%
가 66
 
1.5%
산 63
 
1.4%
당 63
 
1.4%
장 61
 
1.4%
이 59
 
1.3%
고 58
 
1.3%
Other values (454) 3648
82.8%
Uppercase Letter
ValueCountFrequency (%)
O 4
26.7%
N 2
13.3%
A 1
 
6.7%
J 1
 
6.7%
L 1
 
6.7%
D 1
 
6.7%
E 1
 
6.7%
Y 1
 
6.7%
G 1
 
6.7%
K 1
 
6.7%
Decimal Number
ValueCountFrequency (%)
1 10
23.8%
9 5
11.9%
2 5
11.9%
0 5
11.9%
5 4
 
9.5%
7 4
 
9.5%
3 3
 
7.1%
8 2
 
4.8%
6 2
 
4.8%
4 2
 
4.8%
Other Punctuation
ValueCountFrequency (%)
& 6
60.0%
. 2
 
20.0%
' 1
 
10.0%
/ 1
 
10.0%
Lowercase Letter
ValueCountFrequency (%)
n 1
25.0%
i 1
25.0%
t 1
25.0%
s 1
25.0%
Space Separator
ValueCountFrequency (%)
40
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4403
97.1%
Common 110
 
2.4%
Latin 19
 
0.4%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 128
 
2.9%
집 100
 
2.3%
식 84
 
1.9%
원 74
 
1.7%
가 66
 
1.5%
산 63
 
1.4%
당 63
 
1.4%
장 61
 
1.4%
이 59
 
1.3%
고 58
 
1.3%
Other values (453) 3647
82.8%
Common
ValueCountFrequency (%)
40
36.4%
1 10
 
9.1%
) 9
 
8.2%
( 9
 
8.2%
& 6
 
5.5%
9 5
 
4.5%
2 5
 
4.5%
0 5
 
4.5%
5 4
 
3.6%
7 4
 
3.6%
Other values (7) 13
 
11.8%
Latin
ValueCountFrequency (%)
O 4
21.1%
N 2
 
10.5%
A 1
 
5.3%
J 1
 
5.3%
L 1
 
5.3%
n 1
 
5.3%
i 1
 
5.3%
D 1
 
5.3%
E 1
 
5.3%
Y 1
 
5.3%
Other values (5) 5
26.3%
Han
ValueCountFrequency (%)
新 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4403
97.1%
ASCII 129
 
2.8%
CJK 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
점 128
 
2.9%
집 100
 
2.3%
식 84
 
1.9%
원 74
 
1.7%
가 66
 
1.5%
산 63
 
1.4%
당 63
 
1.4%
장 61
 
1.4%
이 59
 
1.3%
고 58
 
1.3%
Other values (453) 3647
82.8%
ASCII
ValueCountFrequency (%)
40
31.0%
1 10
 
7.8%
) 9
 
7.0%
( 9
 
7.0%
& 6
 
4.7%
9 5
 
3.9%
2 5
 
3.9%
0 5
 
3.9%
O 4
 
3.1%
5 4
 
3.1%
Other values (22) 32
24.8%
CJK
ValueCountFrequency (%)
新 1
100.0%

소재지
Text

Distinct783
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Memory size6.3 KiB
2024-04-06T17:16:33.543709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length58
Median length45
Mean length25.20529
Min length13

Characters and Unicode

Total characters20013
Distinct characters269
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique774 ?
Unique (%)97.5%

Sample

1st row창원시 마산합포구 3.15대로 383-4
2nd row창원시 마산합포구 동서북10길 62
3rd row창원시 마산합포구 문화북1길 42
4th row창원시 마산합포구 문화의길 19
5th row창원시 마산합포구 문화의길 8(오동동)
ValueCountFrequency (%)
창원시 794
 
19.3%
성산구 195
 
4.7%
의창구 185
 
4.5%
진해구 144
 
3.5%
마산합포구 138
 
3.3%
마산회원구 132
 
3.2%
1층 91
 
2.2%
2층 55
 
1.3%
상남동 45
 
1.1%
용호동 31
 
0.8%
Other values (1118) 2314
56.1%
2024-04-06T17:16:34.707207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3331
 
16.6%
1 1050
 
5.2%
원 1021
 
5.1%
창 1000
 
5.0%
구 830
 
4.1%
시 823
 
4.1%
동 647
 
3.2%
, 621
 
3.1%
로 594
 
3.0%
산 560
 
2.8%
Other values (259) 9536
47.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11365
56.8%
Decimal Number 3636
 
18.2%
Space Separator 3331
 
16.6%
Other Punctuation 637
 
3.2%
Close Punctuation 403
 
2.0%
Open Punctuation 403
 
2.0%
Dash Punctuation 179
 
0.9%
Uppercase Letter 50
 
0.2%
Math Symbol 9
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 1021
 
9.0%
창 1000
 
8.8%
구 830
 
7.3%
시 823
 
7.2%
동 647
 
5.7%
로 594
 
5.2%
산 560
 
4.9%
길 451
 
4.0%
마 342
 
3.0%
층 325
 
2.9%
Other values (226) 4772
42.0%
Uppercase Letter
ValueCountFrequency (%)
C 14
28.0%
D 6
12.0%
E 4
 
8.0%
A 3
 
6.0%
S 3
 
6.0%
T 3
 
6.0%
L 3
 
6.0%
M 2
 
4.0%
B 2
 
4.0%
N 2
 
4.0%
Other values (5) 8
16.0%
Decimal Number
ValueCountFrequency (%)
1 1050
28.9%
2 506
13.9%
3 392
 
10.8%
5 290
 
8.0%
0 285
 
7.8%
4 267
 
7.3%
8 223
 
6.1%
6 222
 
6.1%
7 216
 
5.9%
9 185
 
5.1%
Other Punctuation
ValueCountFrequency (%)
, 621
97.5%
. 12
 
1.9%
· 4
 
0.6%
Space Separator
ValueCountFrequency (%)
3331
100.0%
Close Punctuation
ValueCountFrequency (%)
) 403
100.0%
Open Punctuation
ValueCountFrequency (%)
( 403
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 179
100.0%
Math Symbol
ValueCountFrequency (%)
~ 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11365
56.8%
Common 8598
43.0%
Latin 50
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 1021
 
9.0%
창 1000
 
8.8%
구 830
 
7.3%
시 823
 
7.2%
동 647
 
5.7%
로 594
 
5.2%
산 560
 
4.9%
길 451
 
4.0%
마 342
 
3.0%
층 325
 
2.9%
Other values (226) 4772
42.0%
Common
ValueCountFrequency (%)
3331
38.7%
1 1050
 
12.2%
, 621
 
7.2%
2 506
 
5.9%
) 403
 
4.7%
( 403
 
4.7%
3 392
 
4.6%
5 290
 
3.4%
0 285
 
3.3%
4 267
 
3.1%
Other values (8) 1050
 
12.2%
Latin
ValueCountFrequency (%)
C 14
28.0%
D 6
12.0%
E 4
 
8.0%
A 3
 
6.0%
S 3
 
6.0%
T 3
 
6.0%
L 3
 
6.0%
M 2
 
4.0%
B 2
 
4.0%
N 2
 
4.0%
Other values (5) 8
16.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11365
56.8%
ASCII 8644
43.2%
None 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3331
38.5%
1 1050
 
12.1%
, 621
 
7.2%
2 506
 
5.9%
) 403
 
4.7%
( 403
 
4.7%
3 392
 
4.5%
5 290
 
3.4%
0 285
 
3.3%
4 267
 
3.1%
Other values (22) 1096
 
12.7%
Hangul
ValueCountFrequency (%)
원 1021
 
9.0%
창 1000
 
8.8%
구 830
 
7.3%
시 823
 
7.2%
동 647
 
5.7%
로 594
 
5.2%
산 560
 
4.9%
길 451
 
4.0%
마 342
 
3.0%
층 325
 
2.9%
Other values (226) 4772
42.0%
None
ValueCountFrequency (%)
· 4
100.0%

Interactions

2024-04-06T17:16:29.721649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Missing values

2024-04-06T17:16:30.082036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:16:30.220264image/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

연번업소명소재지
01백제령삼계탕창원시 마산합포구 3.15대로 383-4
12모티키친창원시 마산합포구 동서북10길 62
23함흥집창원시 마산합포구 문화북1길 42
34오즈마법사창원시 마산합포구 문화의길 19
45판문점창원시 마산합포구 문화의길 8(오동동)
56아다지오창원시 마산합포구 사궁두미길 92
67노렌스시창원시 마산합포구 해안대로 288 (신포동1가)
78삼대초밥창원시 마산합포구 합포로 11, 2층 (신포동2가)
89LA바닷가재창원시 마산합포구 어시장8길 55(남성동)
910둥지활어횟집창원시 마산합포구 어시장8길 56(남성동)
연번업소명소재지
784785민아네뒷통구이창원시 진해구 중원로43번길 3,
785786투고샐러드창원시 진해구 진해대로 752,104호
786787청마루양곱창창원시 진해구 진해대로 999,
787788삼굽살진해점창원시 진해구 진희로 17,
788789이모네손칼국수창원시 진해구 충무동 21-2,
789790정성담은아구찜창원시 진해구 충무로 68,
790791원트피자진해장천점창원시 진해구 충장로657번길 35,
791792거제식당창원시 진해구 충장로657번길 6,
792793준석이네뒷고기풍호점창원시 진해구 풍호동 737-7,
793794다복정창원시 진해구 풍호동 814-1,