Overview

Dataset statistics

Number of variables7
Number of observations96
Missing cells18
Missing cells (%)2.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.5 KiB
Average record size in memory58.4 B

Variable types

Numeric1
Text6

Dataset

Description경상남도 밀양시의 식품제조가공업 현황에 대해 개방합니다.
Author경상남도 밀양시
URLhttps://www.data.go.kr/data/15022022/fileData.do

Alerts

소재지전화번호 has 17 (17.7%) missing valuesMissing
식품의유형 has 1 (1.0%) missing valuesMissing
연번 has unique valuesUnique
업소명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 13:15:49.095358
Analysis finished2023-12-12 13:15:51.240800
Duration2.15 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct96
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.5
Minimum1
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2023-12-12T22:15:51.322650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.75
Q124.75
median48.5
Q372.25
95-th percentile91.25
Maximum96
Range95
Interquartile range (IQR)47.5

Descriptive statistics

Standard deviation27.856777
Coefficient of variation (CV)0.57436653
Kurtosis-1.2
Mean48.5
Median Absolute Deviation (MAD)24
Skewness0
Sum4656
Variance776
MonotonicityStrictly increasing
2023-12-12T22:15:51.515233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
50 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
67 1
 
1.0%
66 1
 
1.0%
65 1
 
1.0%
Other values (86) 86
89.6%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%
91 1
1.0%
90 1
1.0%
89 1
1.0%
88 1
1.0%
87 1
1.0%

업소명
Text

UNIQUE 

Distinct96
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size900.0 B
2023-12-12T22:15:51.803586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length15
Mean length7.0729167
Min length2

Characters and Unicode

Total characters679
Distinct characters194
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

Unique96 ?
Unique (%)100.0%

Sample

1st row대동국수
2nd row수산국수
3rd row황금제과
4th row은혜식품(주)
5th row한동식품
ValueCountFrequency (%)
농업회사법인 8
 
6.9%
주식회사 2
 
1.7%
영농조합법인 2
 
1.7%
대동국수 1
 
0.9%
캠프힐 1
 
0.9%
천연식초 1
 
0.9%
감익는마을 1
 
0.9%
가야술빵 1
 
0.9%
예촌식품 1
 
0.9%
혜민농산(주 1
 
0.9%
Other values (97) 97
83.6%
2023-12-12T22:15:52.230103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
농 30
 
4.4%
식 27
 
4.0%
주 27
 
4.0%
) 26
 
3.8%
( 26
 
3.8%
품 22
 
3.2%
20
 
2.9%
사 19
 
2.8%
인 19
 
2.8%
법 17
 
2.5%
Other values (184) 446
65.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 596
87.8%
Close Punctuation 26
 
3.8%
Open Punctuation 26
 
3.8%
Space Separator 20
 
2.9%
Lowercase Letter 6
 
0.9%
Uppercase Letter 4
 
0.6%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
농 30
 
5.0%
식 27
 
4.5%
주 27
 
4.5%
품 22
 
3.7%
사 19
 
3.2%
인 19
 
3.2%
법 17
 
2.9%
회 16
 
2.7%
업 14
 
2.3%
양 13
 
2.2%
Other values (171) 392
65.8%
Lowercase Letter
ValueCountFrequency (%)
o 2
33.3%
g 1
16.7%
n 1
16.7%
i 1
16.7%
d 1
16.7%
Uppercase Letter
ValueCountFrequency (%)
K 1
25.0%
F 1
25.0%
S 1
25.0%
M 1
25.0%
Close Punctuation
ValueCountFrequency (%)
) 26
100.0%
Open Punctuation
ValueCountFrequency (%)
( 26
100.0%
Space Separator
ValueCountFrequency (%)
20
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 596
87.8%
Common 73
 
10.8%
Latin 10
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
농 30
 
5.0%
식 27
 
4.5%
주 27
 
4.5%
품 22
 
3.7%
사 19
 
3.2%
인 19
 
3.2%
법 17
 
2.9%
회 16
 
2.7%
업 14
 
2.3%
양 13
 
2.2%
Other values (171) 392
65.8%
Latin
ValueCountFrequency (%)
o 2
20.0%
g 1
10.0%
n 1
10.0%
i 1
10.0%
K 1
10.0%
d 1
10.0%
F 1
10.0%
S 1
10.0%
M 1
10.0%
Common
ValueCountFrequency (%)
) 26
35.6%
( 26
35.6%
20
27.4%
& 1
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 596
87.8%
ASCII 83
 
12.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
농 30
 
5.0%
식 27
 
4.5%
주 27
 
4.5%
품 22
 
3.7%
사 19
 
3.2%
인 19
 
3.2%
법 17
 
2.9%
회 16
 
2.7%
업 14
 
2.3%
양 13
 
2.2%
Other values (171) 392
65.8%
ASCII
ValueCountFrequency (%)
) 26
31.3%
( 26
31.3%
20
24.1%
o 2
 
2.4%
g 1
 
1.2%
n 1
 
1.2%
i 1
 
1.2%
K 1
 
1.2%
d 1
 
1.2%
F 1
 
1.2%
Other values (3) 3
 
3.6%

소재지전화번호
Text

MISSING 

Distinct78
Distinct (%)98.7%
Missing17
Missing (%)17.7%
Memory size900.0 B
2023-12-12T22:15:52.472064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.012658
Min length12

Characters and Unicode

Total characters949
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

Unique77 ?
Unique (%)97.5%

Sample

1st row055-354-2526
2nd row055-391-3400
3rd row055-355-2054
4th row055-354-3118
5th row055-354-2803
ValueCountFrequency (%)
055-354-2526 2
 
2.5%
055-391-6054 1
 
1.3%
055-355-8806 1
 
1.3%
055-355-4811 1
 
1.3%
055-356-2646 1
 
1.3%
055-355-1561 1
 
1.3%
051-413-5646 1
 
1.3%
055-352-6943 1
 
1.3%
055-391-7555 1
 
1.3%
055-352-6201 1
 
1.3%
Other values (68) 68
86.1%
2023-12-12T22:15:52.849910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 261
27.5%
- 158
16.6%
0 118
12.4%
3 110
11.6%
1 74
 
7.8%
2 55
 
5.8%
6 46
 
4.8%
4 44
 
4.6%
9 42
 
4.4%
7 23
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 791
83.4%
Dash Punctuation 158
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 261
33.0%
0 118
14.9%
3 110
13.9%
1 74
 
9.4%
2 55
 
7.0%
6 46
 
5.8%
4 44
 
5.6%
9 42
 
5.3%
7 23
 
2.9%
8 18
 
2.3%
Dash Punctuation
ValueCountFrequency (%)
- 158
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 261
27.5%
- 158
16.6%
0 118
12.4%
3 110
11.6%
1 74
 
7.8%
2 55
 
5.8%
6 46
 
4.8%
4 44
 
4.6%
9 42
 
4.4%
7 23
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 261
27.5%
- 158
16.6%
0 118
12.4%
3 110
11.6%
1 74
 
7.8%
2 55
 
5.8%
6 46
 
4.8%
4 44
 
4.6%
9 42
 
4.4%
7 23
 
2.4%

소재지(도로명)
Text

Distinct94
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Memory size900.0 B
2023-12-12T22:15:53.190463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length20
Mean length16.708333
Min length13

Characters and Unicode

Total characters1604
Distinct characters125
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

Unique92 ?
Unique (%)95.8%

Sample

1st row밀양시 삼문2길 21-4 (삼문동)
2nd row밀양시 하남읍 수산중앙로 19-1
3rd row밀양시 산외면 남기동길 181
4th row밀양시 상남면 예평로 15
5th row밀양시 상남면 상남로 1088-10
ValueCountFrequency (%)
밀양시 96
24.7%
상남면 15
 
3.9%
하남읍 12
 
3.1%
초동면 10
 
2.6%
단장면 9
 
2.3%
산내면 9
 
2.3%
산외면 9
 
2.3%
부북면 7
 
1.8%
가곡동 5
 
1.3%
삼랑진읍 5
 
1.3%
Other values (179) 211
54.4%
2023-12-12T22:15:53.654808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
292
18.2%
양 102
 
6.4%
밀 100
 
6.2%
시 97
 
6.0%
1 84
 
5.2%
면 69
 
4.3%
길 51
 
3.2%
2 46
 
2.9%
로 46
 
2.9%
- 39
 
2.4%
Other values (115) 678
42.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 924
57.6%
Decimal Number 322
 
20.1%
Space Separator 292
 
18.2%
Dash Punctuation 39
 
2.4%
Close Punctuation 12
 
0.7%
Open Punctuation 12
 
0.7%
Other Punctuation 3
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
양 102
 
11.0%
밀 100
 
10.8%
시 97
 
10.5%
면 69
 
7.5%
길 51
 
5.5%
로 46
 
5.0%
동 38
 
4.1%
남 34
 
3.7%
산 31
 
3.4%
상 24
 
2.6%
Other values (100) 332
35.9%
Decimal Number
ValueCountFrequency (%)
1 84
26.1%
2 46
14.3%
3 37
11.5%
5 33
 
10.2%
9 26
 
8.1%
7 24
 
7.5%
8 21
 
6.5%
4 20
 
6.2%
0 16
 
5.0%
6 15
 
4.7%
Space Separator
ValueCountFrequency (%)
292
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 39
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 924
57.6%
Common 680
42.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
양 102
 
11.0%
밀 100
 
10.8%
시 97
 
10.5%
면 69
 
7.5%
길 51
 
5.5%
로 46
 
5.0%
동 38
 
4.1%
남 34
 
3.7%
산 31
 
3.4%
상 24
 
2.6%
Other values (100) 332
35.9%
Common
ValueCountFrequency (%)
292
42.9%
1 84
 
12.4%
2 46
 
6.8%
- 39
 
5.7%
3 37
 
5.4%
5 33
 
4.9%
9 26
 
3.8%
7 24
 
3.5%
8 21
 
3.1%
4 20
 
2.9%
Other values (5) 58
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 924
57.6%
ASCII 680
42.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
292
42.9%
1 84
 
12.4%
2 46
 
6.8%
- 39
 
5.7%
3 37
 
5.4%
5 33
 
4.9%
9 26
 
3.8%
7 24
 
3.5%
8 21
 
3.1%
4 20
 
2.9%
Other values (5) 58
 
8.5%
Hangul
ValueCountFrequency (%)
양 102
 
11.0%
밀 100
 
10.8%
시 97
 
10.5%
면 69
 
7.5%
길 51
 
5.5%
로 46
 
5.0%
동 38
 
4.1%
남 34
 
3.7%
산 31
 
3.4%
상 24
 
2.6%
Other values (100) 332
35.9%

소재지(지번)
Text

Distinct94
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Memory size900.0 B
2023-12-12T22:15:54.015540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length26
Mean length19.75
Min length16

Characters and Unicode

Total characters1896
Distinct characters98
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

Unique92 ?
Unique (%)95.8%

Sample

1st row밀양시 삼문동 355 - 3
2nd row밀양시 하남읍 수산리 814 - 2
3rd row밀양시 산외면 남기리 750 - 3
4th row밀양시 상남면 예림리 843 - 4
5th row밀양시 상남면 기산리 800 - 1
ValueCountFrequency (%)
밀양시 96
 
18.8%
66
 
12.9%
1 22
 
4.3%
상남면 15
 
2.9%
2 13
 
2.5%
하남읍 12
 
2.4%
초동면 10
 
2.0%
단장면 9
 
1.8%
산내면 9
 
1.8%
산외면 9
 
1.8%
Other values (169) 249
48.8%
2023-12-12T22:15:54.474931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
603
31.8%
양 99
 
5.2%
밀 96
 
5.1%
시 96
 
5.1%
리 86
 
4.5%
1 78
 
4.1%
면 69
 
3.6%
- 66
 
3.5%
2 44
 
2.3%
4 40
 
2.1%
Other values (88) 619
32.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 848
44.7%
Space Separator 603
31.8%
Decimal Number 376
19.8%
Dash Punctuation 66
 
3.5%
Open Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
양 99
 
11.7%
밀 96
 
11.3%
시 96
 
11.3%
리 86
 
10.1%
면 69
 
8.1%
산 39
 
4.6%
남 33
 
3.9%
동 28
 
3.3%
상 19
 
2.2%
읍 17
 
2.0%
Other values (73) 266
31.4%
Decimal Number
ValueCountFrequency (%)
1 78
20.7%
2 44
11.7%
4 40
10.6%
8 37
9.8%
7 35
9.3%
6 32
8.5%
0 30
 
8.0%
3 29
 
7.7%
9 27
 
7.2%
5 24
 
6.4%
Space Separator
ValueCountFrequency (%)
603
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 66
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1048
55.3%
Hangul 848
44.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
양 99
 
11.7%
밀 96
 
11.3%
시 96
 
11.3%
리 86
 
10.1%
면 69
 
8.1%
산 39
 
4.6%
남 33
 
3.9%
동 28
 
3.3%
상 19
 
2.2%
읍 17
 
2.0%
Other values (73) 266
31.4%
Common
ValueCountFrequency (%)
603
57.5%
1 78
 
7.4%
- 66
 
6.3%
2 44
 
4.2%
4 40
 
3.8%
8 37
 
3.5%
7 35
 
3.3%
6 32
 
3.1%
0 30
 
2.9%
3 29
 
2.8%
Other values (5) 54
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1048
55.3%
Hangul 848
44.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
603
57.5%
1 78
 
7.4%
- 66
 
6.3%
2 44
 
4.2%
4 40
 
3.8%
8 37
 
3.5%
7 35
 
3.3%
6 32
 
3.1%
0 30
 
2.9%
3 29
 
2.8%
Other values (5) 54
 
5.2%
Hangul
ValueCountFrequency (%)
양 99
 
11.7%
밀 96
 
11.3%
시 96
 
11.3%
리 86
 
10.1%
면 69
 
8.1%
산 39
 
4.6%
남 33
 
3.9%
동 28
 
3.3%
상 19
 
2.2%
읍 17
 
2.0%
Other values (73) 266
31.4%

식품의종류
Text

Distinct73
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Memory size900.0 B
2023-12-12T22:15:54.684718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length238
Median length50.5
Mean length24.34375
Min length4

Characters and Unicode

Total characters2337
Distinct characters92
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

Unique57 ?
Unique (%)59.4%

Sample

1st row 면류, 면류
2nd row 면류
3rd row 과자류
4th row 기타식품류, 규격외일반가공식품, 절임식품, 규격외일반가공식품
5th row 절임식품
ValueCountFrequency (%)
다류 48
14.2%
규격외일반가공식품 40
11.8%
음료류 33
 
9.7%
기타식품류 33
 
9.7%
조미식품 24
 
7.1%
장류 21
 
6.2%
절임식품 16
 
4.7%
면류 13
 
3.8%
두부류또는묵류 10
 
2.9%
빵또는떡류 9
 
2.7%
Other values (41) 92
27.1%
2023-12-12T22:15:55.098130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
678
29.0%
, 243
 
10.4%
류 228
 
9.8%
품 130
 
5.6%
식 120
 
5.1%
가 57
 
2.4%
공 57
 
2.4%
다 48
 
2.1%
기 41
 
1.8%
외 40
 
1.7%
Other values (82) 695
29.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1414
60.5%
Space Separator 678
29.0%
Other Punctuation 245
 
10.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
류 228
 
16.1%
품 130
 
9.2%
식 120
 
8.5%
가 57
 
4.0%
공 57
 
4.0%
다 48
 
3.4%
기 41
 
2.9%
외 40
 
2.8%
일 40
 
2.8%
반 40
 
2.8%
Other values (79) 613
43.4%
Other Punctuation
ValueCountFrequency (%)
, 243
99.2%
. 2
 
0.8%
Space Separator
ValueCountFrequency (%)
678
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1414
60.5%
Common 923
39.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
류 228
 
16.1%
품 130
 
9.2%
식 120
 
8.5%
가 57
 
4.0%
공 57
 
4.0%
다 48
 
3.4%
기 41
 
2.9%
외 40
 
2.8%
일 40
 
2.8%
반 40
 
2.8%
Other values (79) 613
43.4%
Common
ValueCountFrequency (%)
678
73.5%
, 243
 
26.3%
. 2
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1414
60.5%
ASCII 923
39.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
678
73.5%
, 243
 
26.3%
. 2
 
0.2%
Hangul
ValueCountFrequency (%)
류 228
 
16.1%
품 130
 
9.2%
식 120
 
8.5%
가 57
 
4.0%
공 57
 
4.0%
다 48
 
3.4%
기 41
 
2.9%
외 40
 
2.8%
일 40
 
2.8%
반 40
 
2.8%
Other values (79) 613
43.4%

식품의유형
Text

MISSING 

Distinct78
Distinct (%)82.1%
Missing1
Missing (%)1.0%
Memory size900.0 B
2023-12-12T22:15:55.398706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length290
Median length48
Mean length22.410526
Min length4

Characters and Unicode

Total characters2129
Distinct characters119
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique71 ?
Unique (%)74.7%

Sample

1st row 국수
2nd row 국수
3rd row 과자
4th row 과.채가공품, 기타가공품, 당절임
5th row 절임류
ValueCountFrequency (%)
액상차 27
 
8.6%
기타가공품 25
 
7.9%
절임류 14
 
4.4%
국수 12
 
3.8%
한식된장 11
 
3.5%
재래한식간장 11
 
3.5%
침출차 11
 
3.5%
곡류가공품 10
 
3.2%
과.채주스 10
 
3.2%
과.채가공품 8
 
2.5%
Other values (68) 176
55.9%
2023-12-12T22:15:55.907900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
630
29.6%
, 220
 
10.3%
품 76
 
3.6%
가 74
 
3.5%
공 72
 
3.4%
류 63
 
3.0%
기 46
 
2.2%
식 45
 
2.1%
장 45
 
2.1%
타 43
 
2.0%
Other values (109) 815
38.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1239
58.2%
Space Separator 630
29.6%
Other Punctuation 256
 
12.0%
Close Punctuation 2
 
0.1%
Open Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
품 76
 
6.1%
가 74
 
6.0%
공 72
 
5.8%
류 63
 
5.1%
기 46
 
3.7%
식 45
 
3.6%
장 45
 
3.6%
타 43
 
3.5%
차 41
 
3.3%
과 38
 
3.1%
Other values (104) 696
56.2%
Other Punctuation
ValueCountFrequency (%)
, 220
85.9%
. 36
 
14.1%
Space Separator
ValueCountFrequency (%)
630
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1239
58.2%
Common 890
41.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
품 76
 
6.1%
가 74
 
6.0%
공 72
 
5.8%
류 63
 
5.1%
기 46
 
3.7%
식 45
 
3.6%
장 45
 
3.6%
타 43
 
3.5%
차 41
 
3.3%
과 38
 
3.1%
Other values (104) 696
56.2%
Common
ValueCountFrequency (%)
630
70.8%
, 220
 
24.7%
. 36
 
4.0%
) 2
 
0.2%
( 2
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1239
58.2%
ASCII 890
41.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
630
70.8%
, 220
 
24.7%
. 36
 
4.0%
) 2
 
0.2%
( 2
 
0.2%
Hangul
ValueCountFrequency (%)
품 76
 
6.1%
가 74
 
6.0%
공 72
 
5.8%
류 63
 
5.1%
기 46
 
3.7%
식 45
 
3.6%
장 45
 
3.6%
타 43
 
3.5%
차 41
 
3.3%
과 38
 
3.1%
Other values (104) 696
56.2%

Interactions

2023-12-12T22:15:50.466875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:15:56.011290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업소명소재지전화번호소재지(도로명)소재지(지번)식품의종류식품의유형
연번1.0001.0000.0000.9730.9730.6440.479
업소명1.0001.0001.0001.0001.0001.0001.000
소재지전화번호0.0001.0001.0000.9990.9990.9971.000
소재지(도로명)0.9731.0000.9991.0001.0000.9960.990
소재지(지번)0.9731.0000.9991.0001.0000.9980.996
식품의종류0.6441.0000.9970.9960.9981.0000.993
식품의유형0.4791.0001.0000.9900.9960.9931.000

Missing values

2023-12-12T22:15:50.606936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:15:51.055992image/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.
2023-12-12T22:15:51.187213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

연번업소명소재지전화번호소재지(도로명)소재지(지번)식품의종류식품의유형
01대동국수055-354-2526밀양시 삼문2길 21-4 (삼문동)밀양시 삼문동 355 - 3면류, 면류국수
12수산국수055-391-3400밀양시 하남읍 수산중앙로 19-1밀양시 하남읍 수산리 814 - 2면류국수
23황금제과055-355-2054밀양시 산외면 남기동길 181밀양시 산외면 남기리 750 - 3과자류과자
34은혜식품(주)055-354-3118밀양시 상남면 예평로 15밀양시 상남면 예림리 843 - 4기타식품류, 규격외일반가공식품, 절임식품, 규격외일반가공식품과.채가공품, 기타가공품, 당절임
45한동식품055-354-2803밀양시 상남면 상남로 1088-10밀양시 상남면 기산리 800 - 1절임식품절임류
56삼락식품055-391-6666밀양시 초동면 검암1길 15-1밀양시 초동면 검암리 40 - 4면류, 면류국수, 국수
67향진식품055-391-5800밀양시 하남읍 대평2안길 23밀양시 하남읍 수산리 370 - 7빵또는떡류, 면류, 면류국수, 국수
78산동농협대추가공공장055-353-5367밀양시 단장면 표충로 89-62밀양시 단장면 단장리 781 - 1과자류, 다류, 음료류, 음료류, 혼합음료, 조미식품, 규격외일반가공식품, 다류, 액상차, 음료류, 과.채주스, 기타식품류액상차, 과.채주스, 과.채음료, 인삼.홍삼음료, 혼합음료, 액상차, 과.채주스, 과.채음료, 혼합음료, 음료베이스
89새한매실농원055-352-2738밀양시 상동면 상동로 254-13밀양시 상동면 가곡리 786다류, 음료류, 조미식품, 절임식품, 규격외일반가공식품, 음료류침출차, 액상차, 발효식초, 기타식초, 절임류, 기타가공품, 기타발효음료
910아랑농산055-352-7155밀양시 청도면 요고길 179-10밀양시 청도면 고법리 1144음료류, 기타식품류, 규격외일반가공식품인삼.홍삼음료, 추출가공식품, 기타가공품
연번업소명소재지전화번호소재지(도로명)소재지(지번)식품의종류식품의유형
8687농업회사법인 바드리(주)051-972-1222밀양시 단장면 바드리길 435-42밀양시 단장면 고례리 558장류재래한식간장, 한식된장, 청국장
8788King콩푸드<NA>밀양시 상남면 고노실중앙길 37밀양시 상남면 기산리 1449 - 8두부류또는묵류두부
8889새미음식문화예술촌055-353-3599밀양시 상동면 상동로 576밀양시 상동면 금산리 875 - 2과자류, 다류, 기타식품류과자, 캔디류, 침출차, 즉석조리식품
8990햇빛식품055-352-3905밀양시 산내면 인곡안길 11-18밀양시 산내면 가인리 2191음료류과.채음료
9091찌아찌아하우스055-356-1171밀양시 단장면 단장로 536밀양시 단장면 감물리 959 - 1장류혼합간장, 된장
9192미락에프엔에스055-351-4585밀양시 삼랑진읍 사기점길 11-2밀양시 삼랑진읍 용전리 429 - 2규격외일반가공식품곡류가공품, 기타가공품
9293토마토닥터055-391-4306밀양시 초동면 초동로 159밀양시 초동면 오방리 21 - 1잼류, 다류, 음료류, 조미식품, 절임식품잼, 액상차, 기타발효음료, 발효식초, 절임류
9394(주)송림에프앤비055-351-1548밀양시 무안면 판정로 134밀양시 무안면 정곡리 산 16조미식품고춧가루
9495농업회사법인 레드애플팜(주)055-351-4558밀양시 산내면 하양지길 25-40밀양시 산내면 삼양리 2073 - 1음료류, 기타식품류과.채주스, 과.채가공품류
9596핼시아로니아055-355-4952밀양시 수월3길 18 (삼문동)밀양시 삼문동 220 - 5다류<NA>