Overview

Dataset statistics

Number of variables5
Number of observations190
Missing cells115
Missing cells (%)12.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.6 KiB
Average record size in memory40.7 B

Variable types

Categorical2
Text3

Dataset

Description대구광역시 북구 관내에서 운영중인 식육포장처리업(업종구분, 사업장명칭, 소재지주소, 소재지전화 등) 정보를 제공합니다.
Author대구광역시 북구
URLhttps://www.data.go.kr/data/15030565/fileData.do

Alerts

업종 has constant value ""Constant
데이터기준일자 has constant value ""Constant
소재지전화 has 115 (60.5%) missing valuesMissing

Reproduction

Analysis started2023-12-12 17:47:24.580407
Analysis finished2023-12-12 17:47:25.042456
Duration0.46 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
식육포장처리업
190 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
식육포장처리업 190
100.0%

Length

2023-12-13T02:47:25.139605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:47:25.276963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
식육포장처리업 190
100.0%

사업장명칭
Text

Distinct183
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2023-12-13T02:47:25.617454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length16
Mean length5.8
Min length2

Characters and Unicode

Total characters1102
Distinct characters210
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique177 ?
Unique (%)93.2%

Sample

1st row(주)G.M식품
2nd row(주)가나안식품
3rd row성호식품
4th row(주)대일푸드
5th row호박꽃식품
ValueCountFrequency (%)
주식회사 11
 
4.7%
금미식품 3
 
1.3%
축산 3
 
1.3%
농업회사법인 3
 
1.3%
계풍 2
 
0.8%
축산물 2
 
0.8%
닭고기 2
 
0.8%
푸드 2
 
0.8%
갑이식품 2
 
0.8%
오뚜기식품 2
 
0.8%
Other values (202) 204
86.4%
2023-12-13T02:47:26.204375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
축 46
 
4.2%
46
 
4.2%
산 45
 
4.1%
식 44
 
4.0%
드 36
 
3.3%
주 36
 
3.3%
푸 35
 
3.2%
품 29
 
2.6%
) 24
 
2.2%
성 24
 
2.2%
Other values (200) 737
66.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 955
86.7%
Space Separator 46
 
4.2%
Uppercase Letter 32
 
2.9%
Close Punctuation 24
 
2.2%
Open Punctuation 23
 
2.1%
Lowercase Letter 17
 
1.5%
Other Punctuation 5
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
축 46
 
4.8%
산 45
 
4.7%
식 44
 
4.6%
드 36
 
3.8%
주 36
 
3.8%
푸 35
 
3.7%
품 29
 
3.0%
성 24
 
2.5%
유 19
 
2.0%
이 18
 
1.9%
Other values (176) 623
65.2%
Uppercase Letter
ValueCountFrequency (%)
F 6
18.8%
D 4
12.5%
G 4
12.5%
J 3
9.4%
C 3
9.4%
S 2
 
6.2%
H 2
 
6.2%
O 2
 
6.2%
M 2
 
6.2%
B 2
 
6.2%
Other values (2) 2
 
6.2%
Lowercase Letter
ValueCountFrequency (%)
o 4
23.5%
f 3
17.6%
p 3
17.6%
a 2
11.8%
c 2
11.8%
d 2
11.8%
y 1
 
5.9%
Other Punctuation
ValueCountFrequency (%)
& 4
80.0%
. 1
 
20.0%
Space Separator
ValueCountFrequency (%)
46
100.0%
Close Punctuation
ValueCountFrequency (%)
) 24
100.0%
Open Punctuation
ValueCountFrequency (%)
( 23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 955
86.7%
Common 98
 
8.9%
Latin 49
 
4.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
축 46
 
4.8%
산 45
 
4.7%
식 44
 
4.6%
드 36
 
3.8%
주 36
 
3.8%
푸 35
 
3.7%
품 29
 
3.0%
성 24
 
2.5%
유 19
 
2.0%
이 18
 
1.9%
Other values (176) 623
65.2%
Latin
ValueCountFrequency (%)
F 6
 
12.2%
D 4
 
8.2%
G 4
 
8.2%
o 4
 
8.2%
f 3
 
6.1%
J 3
 
6.1%
p 3
 
6.1%
C 3
 
6.1%
a 2
 
4.1%
S 2
 
4.1%
Other values (9) 15
30.6%
Common
ValueCountFrequency (%)
46
46.9%
) 24
24.5%
( 23
23.5%
& 4
 
4.1%
. 1
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 955
86.7%
ASCII 147
 
13.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
축 46
 
4.8%
산 45
 
4.7%
식 44
 
4.6%
드 36
 
3.8%
주 36
 
3.8%
푸 35
 
3.7%
품 29
 
3.0%
성 24
 
2.5%
유 19
 
2.0%
이 18
 
1.9%
Other values (176) 623
65.2%
ASCII
ValueCountFrequency (%)
46
31.3%
) 24
16.3%
( 23
15.6%
F 6
 
4.1%
D 4
 
2.7%
G 4
 
2.7%
o 4
 
2.7%
& 4
 
2.7%
f 3
 
2.0%
J 3
 
2.0%
Other values (14) 26
17.7%

사업장소재지(지번)
Text

Distinct185
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2023-12-13T02:47:26.725043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length31
Mean length19.878947
Min length13

Characters and Unicode

Total characters3777
Distinct characters71
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

Unique180 ?
Unique (%)94.7%

Sample

1st row대구광역시 북구 침산동 780-4
2nd row대구광역시 북구 검단동 777-25
3rd row대구광역시 북구 노원동3가 198-16
4th row대구광역시 북구 검단동 1313
5th row대구광역시 북구 칠성동1가 68-3 1,2,3
ValueCountFrequency (%)
대구광역시 190
24.5%
북구 190
24.5%
구암동 20
 
2.6%
칠성동1가 20
 
2.6%
국우동 19
 
2.4%
팔달동 14
 
1.8%
서변동 14
 
1.8%
검단동 13
 
1.7%
침산동 13
 
1.7%
노원동3가 12
 
1.5%
Other values (209) 271
34.9%
2023-12-13T02:47:27.361230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
775
20.5%
구 401
 
10.6%
1 226
 
6.0%
동 210
 
5.6%
대 192
 
5.1%
시 190
 
5.0%
북 190
 
5.0%
역 190
 
5.0%
광 190
 
5.0%
- 158
 
4.2%
Other values (61) 1055
27.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1972
52.2%
Decimal Number 867
23.0%
Space Separator 775
 
20.5%
Dash Punctuation 158
 
4.2%
Other Punctuation 2
 
0.1%
Close Punctuation 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 401
20.3%
동 210
10.6%
대 192
9.7%
시 190
9.6%
북 190
9.6%
역 190
9.6%
광 190
9.6%
가 38
 
1.9%
변 26
 
1.3%
산 23
 
1.2%
Other values (45) 322
16.3%
Decimal Number
ValueCountFrequency (%)
1 226
26.1%
2 99
11.4%
3 82
 
9.5%
8 81
 
9.3%
7 76
 
8.8%
4 72
 
8.3%
0 65
 
7.5%
5 57
 
6.6%
6 56
 
6.5%
9 53
 
6.1%
Space Separator
ValueCountFrequency (%)
775
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 158
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1972
52.2%
Common 1804
47.8%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 401
20.3%
동 210
10.6%
대 192
9.7%
시 190
9.6%
북 190
9.6%
역 190
9.6%
광 190
9.6%
가 38
 
1.9%
변 26
 
1.3%
산 23
 
1.2%
Other values (45) 322
16.3%
Common
ValueCountFrequency (%)
775
43.0%
1 226
 
12.5%
- 158
 
8.8%
2 99
 
5.5%
3 82
 
4.5%
8 81
 
4.5%
7 76
 
4.2%
4 72
 
4.0%
0 65
 
3.6%
5 57
 
3.2%
Other values (5) 113
 
6.3%
Latin
ValueCountFrequency (%)
A 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1972
52.2%
ASCII 1805
47.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
775
42.9%
1 226
 
12.5%
- 158
 
8.8%
2 99
 
5.5%
3 82
 
4.5%
8 81
 
4.5%
7 76
 
4.2%
4 72
 
4.0%
0 65
 
3.6%
5 57
 
3.2%
Other values (6) 114
 
6.3%
Hangul
ValueCountFrequency (%)
구 401
20.3%
동 210
10.6%
대 192
9.7%
시 190
9.6%
북 190
9.6%
역 190
9.6%
광 190
9.6%
가 38
 
1.9%
변 26
 
1.3%
산 23
 
1.2%
Other values (45) 322
16.3%

소재지전화
Text

MISSING 

Distinct73
Distinct (%)97.3%
Missing115
Missing (%)60.5%
Memory size1.6 KiB
2023-12-13T02:47:27.634624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.04
Min length12

Characters and Unicode

Total characters903
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique71 ?
Unique (%)94.7%

Sample

1st row053-352-3660
2nd row053-383-1475
3rd row053-428-1269
4th row053-421-1247
5th row053-311-9969
ValueCountFrequency (%)
053-426-4203 2
 
2.7%
053-381-3834 2
 
2.7%
053-943-6623 1
 
1.3%
053-765-9000 1
 
1.3%
053-939-1490 1
 
1.3%
053-381-2233 1
 
1.3%
053-323-9602 1
 
1.3%
053-321-1363 1
 
1.3%
053-324-0054 1
 
1.3%
053-382-5447 1
 
1.3%
Other values (63) 63
84.0%
2023-12-13T02:47:28.066932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 164
18.2%
- 150
16.6%
5 128
14.2%
0 120
13.3%
2 70
7.8%
9 60
 
6.6%
1 56
 
6.2%
4 50
 
5.5%
8 40
 
4.4%
6 39
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 753
83.4%
Dash Punctuation 150
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 164
21.8%
5 128
17.0%
0 120
15.9%
2 70
9.3%
9 60
 
8.0%
1 56
 
7.4%
4 50
 
6.6%
8 40
 
5.3%
6 39
 
5.2%
7 26
 
3.5%
Dash Punctuation
ValueCountFrequency (%)
- 150
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 903
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 164
18.2%
- 150
16.6%
5 128
14.2%
0 120
13.3%
2 70
7.8%
9 60
 
6.6%
1 56
 
6.2%
4 50
 
5.5%
8 40
 
4.4%
6 39
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 903
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 164
18.2%
- 150
16.6%
5 128
14.2%
0 120
13.3%
2 70
7.8%
9 60
 
6.6%
1 56
 
6.2%
4 50
 
5.5%
8 40
 
4.4%
6 39
 
4.3%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2023-07-28
190 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-07-28
2nd row2023-07-28
3rd row2023-07-28
4th row2023-07-28
5th row2023-07-28

Common Values

ValueCountFrequency (%)
2023-07-28 190
100.0%

Length

2023-12-13T02:47:28.239026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:47:28.339496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-07-28 190
100.0%

Missing values

2023-12-13T02:47:24.889386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:47:25.000466image/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

업종사업장명칭사업장소재지(지번)소재지전화데이터기준일자
0식육포장처리업(주)G.M식품대구광역시 북구 침산동 780-4053-352-36602023-07-28
1식육포장처리업(주)가나안식품대구광역시 북구 검단동 777-25053-383-14752023-07-28
2식육포장처리업성호식품대구광역시 북구 노원동3가 198-16<NA>2023-07-28
3식육포장처리업(주)대일푸드대구광역시 북구 검단동 1313<NA>2023-07-28
4식육포장처리업호박꽃식품대구광역시 북구 칠성동1가 68-3 1,2,3<NA>2023-07-28
5식육포장처리업꼬꼬식품대구광역시 북구 검단동 998-4<NA>2023-07-28
6식육포장처리업명자상회대구광역시 북구 칠성동1가 70-1053-428-12692023-07-28
7식육포장처리업이화식품대구광역시 북구 칠성동1가 50-7053-421-12472023-07-28
8식육포장처리업굿푸드대구광역시 북구 노원동3가 1108053-311-99692023-07-28
9식육포장처리업상생축산대구광역시 북구 태전동 206-19<NA>2023-07-28
업종사업장명칭사업장소재지(지번)소재지전화데이터기준일자
180식육포장처리업마루미트대구광역시 북구 서변동 857<NA>2023-07-28
181식육포장처리업싱싱에프엔비대구광역시 북구 관음동 1321 한라타운<NA>2023-07-28
182식육포장처리업대지축산대구광역시 북구 산격동 1141-4<NA>2023-07-28
183식육포장처리업조은미트대구광역시 북구 학정동 938-1<NA>2023-07-28
184식육포장처리업오늘잡은소대구광역시 북구 국우동 1109-5 국우동 행정복지센터<NA>2023-07-28
185식육포장처리업바른푸드대구광역시 북구 침산동 837<NA>2023-07-28
186식육포장처리업행복축산대구광역시 북구 읍내동 1278-13<NA>2023-07-28
187식육포장처리업영프레시대구광역시 북구 팔달동 471-1<NA>2023-07-28
188식육포장처리업지티씨대구광역시 북구 서변동 1127<NA>2023-07-28
189식육포장처리업오복 축산대구광역시 북구 국우동 1082-13<NA>2023-07-28