Overview

Dataset statistics

Number of variables3
Number of observations556
Missing cells217
Missing cells (%)13.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.2 KiB
Average record size in memory24.2 B

Variable types

Text3

Dataset

Description경상북도 방역소독업체 현황(업소명, 주소, 연락처 등)(포항시 남구, 포항시 북구, 경주시, 김천시, 안동시, 구미시, 구미시 선산, 영주시, 영천시, 상주시, 문경시, 경산시, 의성군, 청송군, 영양군, 영덕군, 청도군, 고령군, 성주군, 칠곡군, 예천군, 봉화군, 울진군, 울릉군)
Author경상북도
URLhttps://www.data.go.kr/data/15069214/fileData.do

Alerts

영업소 연락처 has 217 (39.0%) missing valuesMissing

Reproduction

Analysis started2023-12-12 23:14:35.937826
Analysis finished2023-12-12 23:14:36.372242
Duration0.43 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct544
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
2023-12-13T08:14:36.554351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length17
Mean length6.8147482
Min length2

Characters and Unicode

Total characters3789
Distinct characters391
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique536 ?
Unique (%)96.4%

Sample

1st row(주)금원기업
2nd row청우실업
3rd row(주)신정
4th row청수기업
5th row주식회사 그린에스
ValueCountFrequency (%)
주식회사 40
 
6.0%
제로시스템 6
 
0.9%
5
 
0.8%
방역 4
 
0.6%
주)세스코 4
 
0.6%
하나방역 3
 
0.5%
협동조합 3
 
0.5%
나온시스템 2
 
0.3%
방역시대 2
 
0.3%
환경 2
 
0.3%
Other values (585) 595
89.3%
2023-12-13T08:14:36.967745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
196
 
5.2%
) 144
 
3.8%
( 143
 
3.8%
111
 
2.9%
107
 
2.8%
106
 
2.8%
103
 
2.7%
94
 
2.5%
90
 
2.4%
77
 
2.0%
Other values (381) 2618
69.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3262
86.1%
Close Punctuation 144
 
3.8%
Open Punctuation 143
 
3.8%
Space Separator 111
 
2.9%
Uppercase Letter 75
 
2.0%
Lowercase Letter 18
 
0.5%
Decimal Number 16
 
0.4%
Other Symbol 9
 
0.2%
Other Punctuation 9
 
0.2%
Dash Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
196
 
6.0%
107
 
3.3%
106
 
3.2%
103
 
3.2%
94
 
2.9%
90
 
2.8%
77
 
2.4%
58
 
1.8%
58
 
1.8%
56
 
1.7%
Other values (335) 2317
71.0%
Uppercase Letter
ValueCountFrequency (%)
C 8
 
10.7%
S 7
 
9.3%
N 6
 
8.0%
O 6
 
8.0%
P 6
 
8.0%
K 6
 
8.0%
E 5
 
6.7%
Z 4
 
5.3%
F 4
 
5.3%
B 3
 
4.0%
Other values (9) 20
26.7%
Lowercase Letter
ValueCountFrequency (%)
e 3
16.7%
n 3
16.7%
i 2
11.1%
a 2
11.1%
t 1
 
5.6%
s 1
 
5.6%
l 1
 
5.6%
g 1
 
5.6%
r 1
 
5.6%
y 1
 
5.6%
Other values (2) 2
11.1%
Decimal Number
ValueCountFrequency (%)
5 4
25.0%
1 4
25.0%
9 3
18.8%
6 2
12.5%
3 2
12.5%
2 1
 
6.2%
Other Punctuation
ValueCountFrequency (%)
. 5
55.6%
, 2
 
22.2%
& 1
 
11.1%
1
 
11.1%
Close Punctuation
ValueCountFrequency (%)
) 144
100.0%
Open Punctuation
ValueCountFrequency (%)
( 143
100.0%
Space Separator
ValueCountFrequency (%)
111
100.0%
Other Symbol
ValueCountFrequency (%)
9
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3271
86.3%
Common 425
 
11.2%
Latin 93
 
2.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
196
 
6.0%
107
 
3.3%
106
 
3.2%
103
 
3.1%
94
 
2.9%
90
 
2.8%
77
 
2.4%
58
 
1.8%
58
 
1.8%
56
 
1.7%
Other values (336) 2326
71.1%
Latin
ValueCountFrequency (%)
C 8
 
8.6%
S 7
 
7.5%
N 6
 
6.5%
O 6
 
6.5%
P 6
 
6.5%
K 6
 
6.5%
E 5
 
5.4%
Z 4
 
4.3%
F 4
 
4.3%
e 3
 
3.2%
Other values (21) 38
40.9%
Common
ValueCountFrequency (%)
) 144
33.9%
( 143
33.6%
111
26.1%
. 5
 
1.2%
5 4
 
0.9%
1 4
 
0.9%
9 3
 
0.7%
, 2
 
0.5%
6 2
 
0.5%
- 2
 
0.5%
Other values (4) 5
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3262
86.1%
ASCII 517
 
13.6%
None 10
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
196
 
6.0%
107
 
3.3%
106
 
3.2%
103
 
3.2%
94
 
2.9%
90
 
2.8%
77
 
2.4%
58
 
1.8%
58
 
1.8%
56
 
1.7%
Other values (335) 2317
71.0%
ASCII
ValueCountFrequency (%)
) 144
27.9%
( 143
27.7%
111
21.5%
C 8
 
1.5%
S 7
 
1.4%
N 6
 
1.2%
O 6
 
1.2%
P 6
 
1.2%
K 6
 
1.2%
. 5
 
1.0%
Other values (34) 75
14.5%
None
ValueCountFrequency (%)
9
90.0%
1
 
10.0%
Distinct550
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
2023-12-13T08:14:37.261982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length36
Mean length23.850719
Min length12

Characters and Unicode

Total characters13261
Distinct characters314
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique544 ?
Unique (%)97.8%

Sample

1st row경북 포항시 남구 양학천로 156 (대도동)
2nd row경북 포항시 남구 상도로42번길 8 (상도동)
3rd row경북 포항시 남구 상공로 10, 매일신문사 동부지역본부 지하1층 (대도동)
4th row경북 포항시 남구 중앙로158번길 25 (해도동)
5th row경북 포항시 남구 중앙로 154, 학산타워빌딩 811호 (해도동)
ValueCountFrequency (%)
경북 547
 
17.3%
포항시 106
 
3.3%
구미시 89
 
2.8%
경주시 72
 
2.3%
1층 63
 
2.0%
남구 61
 
1.9%
안동시 52
 
1.6%
2층 46
 
1.5%
북구 45
 
1.4%
경산시 42
 
1.3%
Other values (1103) 2044
64.5%
2023-12-13T08:14:37.722852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2611
19.7%
702
 
5.3%
627
 
4.7%
1 590
 
4.4%
497
 
3.7%
462
 
3.5%
376
 
2.8%
2 356
 
2.7%
335
 
2.5%
( 317
 
2.4%
Other values (304) 6388
48.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7298
55.0%
Space Separator 2611
 
19.7%
Decimal Number 2298
 
17.3%
Open Punctuation 317
 
2.4%
Close Punctuation 317
 
2.4%
Other Punctuation 247
 
1.9%
Dash Punctuation 144
 
1.1%
Uppercase Letter 29
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
702
 
9.6%
627
 
8.6%
497
 
6.8%
462
 
6.3%
376
 
5.2%
335
 
4.6%
221
 
3.0%
142
 
1.9%
139
 
1.9%
124
 
1.7%
Other values (273) 3673
50.3%
Uppercase Letter
ValueCountFrequency (%)
B 6
20.7%
D 3
10.3%
G 2
 
6.9%
C 2
 
6.9%
M 2
 
6.9%
T 2
 
6.9%
E 2
 
6.9%
K 2
 
6.9%
S 2
 
6.9%
A 2
 
6.9%
Other values (4) 4
13.8%
Decimal Number
ValueCountFrequency (%)
1 590
25.7%
2 356
15.5%
3 248
10.8%
0 195
 
8.5%
4 188
 
8.2%
6 170
 
7.4%
5 169
 
7.4%
7 133
 
5.8%
8 131
 
5.7%
9 118
 
5.1%
Other Punctuation
ValueCountFrequency (%)
, 245
99.2%
& 1
 
0.4%
/ 1
 
0.4%
Space Separator
ValueCountFrequency (%)
2611
100.0%
Open Punctuation
ValueCountFrequency (%)
( 317
100.0%
Close Punctuation
ValueCountFrequency (%)
) 317
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 144
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7298
55.0%
Common 5934
44.7%
Latin 29
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
702
 
9.6%
627
 
8.6%
497
 
6.8%
462
 
6.3%
376
 
5.2%
335
 
4.6%
221
 
3.0%
142
 
1.9%
139
 
1.9%
124
 
1.7%
Other values (273) 3673
50.3%
Common
ValueCountFrequency (%)
2611
44.0%
1 590
 
9.9%
2 356
 
6.0%
( 317
 
5.3%
) 317
 
5.3%
3 248
 
4.2%
, 245
 
4.1%
0 195
 
3.3%
4 188
 
3.2%
6 170
 
2.9%
Other values (7) 697
 
11.7%
Latin
ValueCountFrequency (%)
B 6
20.7%
D 3
10.3%
G 2
 
6.9%
C 2
 
6.9%
M 2
 
6.9%
T 2
 
6.9%
E 2
 
6.9%
K 2
 
6.9%
S 2
 
6.9%
A 2
 
6.9%
Other values (4) 4
13.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7298
55.0%
ASCII 5963
45.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2611
43.8%
1 590
 
9.9%
2 356
 
6.0%
( 317
 
5.3%
) 317
 
5.3%
3 248
 
4.2%
, 245
 
4.1%
0 195
 
3.3%
4 188
 
3.2%
6 170
 
2.9%
Other values (21) 726
 
12.2%
Hangul
ValueCountFrequency (%)
702
 
9.6%
627
 
8.6%
497
 
6.8%
462
 
6.3%
376
 
5.2%
335
 
4.6%
221
 
3.0%
142
 
1.9%
139
 
1.9%
124
 
1.7%
Other values (273) 3673
50.3%

영업소 연락처
Text

MISSING 

Distinct331
Distinct (%)97.6%
Missing217
Missing (%)39.0%
Memory size4.5 KiB
2023-12-13T08:14:37.967393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length11.967552
Min length9

Characters and Unicode

Total characters4057
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique323 ?
Unique (%)95.3%

Sample

1st row054-274-5641
2nd row054-281-7447
3rd row054-282-3478
4th row054-282-7287
5th row054-251-8992
ValueCountFrequency (%)
054-859-6244 2
 
0.6%
054-460-2164 2
 
0.6%
054-285-7700 2
 
0.6%
054-748-8822 2
 
0.6%
054-475-9355 2
 
0.6%
054-744-0662 2
 
0.6%
054-536-0946 2
 
0.6%
053-811-7077 2
 
0.6%
054-334-7774 1
 
0.3%
054-534-3386 1
 
0.3%
Other values (321) 321
94.7%
2023-12-13T08:14:38.361921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/