Overview

Dataset statistics

Number of variables5
Number of observations103
Missing cells2
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.4 KiB
Average record size in memory43.3 B

Variable types

Numeric2
Text3

Dataset

Description부산광역시 서구 식품제조가공업에 대한 데이터로 식품제조가공업 현황(업종명, 업소명, 소재지주소, 전화번호) 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15029602/fileData.do

Alerts

연번 is highly overall correlated with 인허가번호High correlation
인허가번호 is highly overall correlated with 연번High correlation
소재지전화 has 2 (1.9%) missing valuesMissing
연번 has unique valuesUnique
인허가번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 16:24:24.452165
Analysis finished2023-12-12 16:24:25.199354
Duration0.75 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct103
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52
Minimum1
Maximum103
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-13T01:24:25.283573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.1
Q126.5
median52
Q377.5
95-th percentile97.9
Maximum103
Range102
Interquartile range (IQR)51

Descriptive statistics

Standard deviation29.877528
Coefficient of variation (CV)0.57456784
Kurtosis-1.2
Mean52
Median Absolute Deviation (MAD)26
Skewness0
Sum5356
Variance892.66667
MonotonicityStrictly increasing
2023-12-13T01:24:25.423800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
2 1
 
1.0%
77 1
 
1.0%
76 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
Other values (93) 93
90.3%
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 (%)
103 1
1.0%
102 1
1.0%
101 1
1.0%
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%

인허가번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct103
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0092074 × 1010
Minimum1.9710128 × 1010
Maximum2.0230165 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-13T01:24:25.544722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.9710128 × 1010
5-th percentile1.9772128 × 1010
Q12.0040136 × 1010
median2.0140128 × 1010
Q32.0175128 × 1010
95-th percentile2.0210143 × 1010
Maximum2.0230165 × 1010
Range5.2003708 × 108
Interquartile range (IQR)1.3499181 × 108

Descriptive statistics

Standard deviation1.2260459 × 108
Coefficient of variation (CV)0.0061021372
Kurtosis2.4806525
Mean2.0092074 × 1010
Median Absolute Deviation (MAD)59999965
Skewness-1.6311366
Sum2.0694836 × 1012
Variance1.5031886 × 1016
MonotonicityNot monotonic
2023-12-13T01:24:25.670047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19710128015 1
 
1.0%
19710128016 1
 
1.0%
20170128193 1
 
1.0%
20170128115 1
 
1.0%
20170128095 1
 
1.0%
20170128055 1
 
1.0%
20170128018 1
 
1.0%
20170128011 1
 
1.0%
20170128005 1
 
1.0%
20160128209 1
 
1.0%
Other values (93) 93
90.3%
ValueCountFrequency (%)
19710128015 1
1.0%
19710128016 1
1.0%
19720128002 1
1.0%
19720128023 1
1.0%
19770128007 1
1.0%
19770128018 1
1.0%
19790128013 1
1.0%
19870144115 1
1.0%
19900128072 1
1.0%
19910128008 1
1.0%
ValueCountFrequency (%)
20230165098 1
1.0%
20230165074 1
1.0%
20230165042 1
1.0%
20220157207 1
1.0%
20220157010 1
1.0%
20210144291 1
1.0%
20210128148 1
1.0%
20210128135 1
1.0%
20210128031 1
1.0%
20200128234 1
1.0%

업소명
Text

Distinct102
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size956.0 B
2023-12-13T01:24:25.879158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length12
Mean length7.7669903
Min length2

Characters and Unicode

Total characters800
Distinct characters167
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

Unique101 ?
Unique (%)98.1%

Sample

1st row(주)케이엠
2nd row금양제빙2공장
3rd row(주)사조대림
4th row우양냉장주식회사
5th row동성산업(주)
ValueCountFrequency (%)
주식회사 15
 
12.1%
합천수산 2
 
1.6%
주)청아무역 2
 
1.6%
주)서강 1
 
0.8%
주)해천글로벌 1
 
0.8%
동현푸드시스템 1
 
0.8%
해성넷 1
 
0.8%
오션프렌드 1
 
0.8%
주)락피쉬 1
 
0.8%
엠에스 1
 
0.8%
Other values (98) 98
79.0%
2023-12-13T01:24:26.217153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 73
 
9.1%
) 59
 
7.4%
( 59
 
7.4%
드 31
 
3.9%
푸 29
 
3.6%
사 26
 
3.2%
식 26
 
3.2%
산 23
 
2.9%
회 21
 
2.6%
21
 
2.6%
Other values (157) 432
54.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 646
80.8%
Close Punctuation 59
 
7.4%
Open Punctuation 59
 
7.4%
Space Separator 21
 
2.6%
Uppercase Letter 10
 
1.2%
Decimal Number 4
 
0.5%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 73
 
11.3%
드 31
 
4.8%
푸 29
 
4.5%
사 26
 
4.0%
식 26
 
4.0%
산 23
 
3.6%
회 21
 
3.3%
스 14
 
2.2%
에 14
 
2.2%
수 14
 
2.2%
Other values (142) 375
58.0%
Uppercase Letter
ValueCountFrequency (%)
G 2
20.0%
P 1
10.0%
M 1
10.0%
T 1
10.0%
U 1
10.0%
N 1
10.0%
A 1
10.0%
S 1
10.0%
D 1
10.0%
Decimal Number
ValueCountFrequency (%)
2 3
75.0%
3 1
 
25.0%
Close Punctuation
ValueCountFrequency (%)
) 59
100.0%
Open Punctuation
ValueCountFrequency (%)
( 59
100.0%
Space Separator
ValueCountFrequency (%)
21
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 646
80.8%
Common 144
 
18.0%
Latin 10
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 73
 
11.3%
드 31
 
4.8%
푸 29
 
4.5%
사 26
 
4.0%
식 26
 
4.0%
산 23
 
3.6%
회 21
 
3.3%
스 14
 
2.2%
에 14
 
2.2%
수 14
 
2.2%
Other values (142) 375
58.0%
Latin
ValueCountFrequency (%)
G 2
20.0%
P 1
10.0%
M 1
10.0%
T 1
10.0%
U 1
10.0%
N 1
10.0%
A 1
10.0%
S 1
10.0%
D 1
10.0%
Common
ValueCountFrequency (%)
) 59
41.0%
( 59
41.0%
21
 
14.6%
2 3
 
2.1%
- 1
 
0.7%
3 1
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 646
80.8%
ASCII 154
 
19.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 73
 
11.3%
드 31
 
4.8%
푸 29
 
4.5%
사 26
 
4.0%
식 26
 
4.0%
산 23
 
3.6%
회 21
 
3.3%
스 14
 
2.2%
에 14
 
2.2%
수 14
 
2.2%
Other values (142) 375
58.0%
ASCII
ValueCountFrequency (%)
) 59
38.3%
( 59
38.3%
21
 
13.6%
2 3
 
1.9%
G 2
 
1.3%
- 1
 
0.6%
P 1
 
0.6%
M 1
 
0.6%
3 1
 
0.6%
T 1
 
0.6%
Other values (5) 5
 
3.2%

소재지(도로명)
Text

Distinct101
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Memory size956.0 B
2023-12-13T01:24:26.415517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length58
Median length42
Mean length33.699029
Min length21

Characters and Unicode

Total characters3471
Distinct characters90
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique99 ?
Unique (%)96.1%

Sample

1st row부산광역시 서구 충무대로 214 (남부민동)
2nd row부산광역시 서구 해안새벽시장길 54 (충무동1가)
3rd row부산광역시 서구 충무대로 170 (남부민동)
4th row부산광역시 서구 충무대로 226 (남부민동)
5th row부산광역시 서구 충무대로 166-41 (남부민동)
ValueCountFrequency (%)
부산광역시 103
14.7%
서구 103
14.7%
암남동 72
 
10.3%
원양로 67
 
9.6%
1 46
 
6.6%
수산가공선진화단지 45
 
6.4%
b동 30
 
4.3%
남부민동 20
 
2.9%
a동 17
 
2.4%
충무대로 16
 
2.3%
Other values (133) 181
25.9%
2023-12-13T01:24:26.726861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
597
 
17.2%
동 156
 
4.5%
산 153
 
4.4%
부 127
 
3.7%
1 126
 
3.6%
, 114
 
3.3%
시 111
 
3.2%
서 107
 
3.1%
구 107
 
3.1%
) 105
 
3.0%
Other values (80) 1768
50.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2051
59.1%
Space Separator 597
 
17.2%
Decimal Number 441
 
12.7%
Other Punctuation 114
 
3.3%
Close Punctuation 105
 
3.0%
Open Punctuation 105
 
3.0%
Uppercase Letter 49
 
1.4%
Dash Punctuation 8
 
0.2%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 156
 
7.6%
산 153
 
7.5%
부 127
 
6.2%
시 111
 
5.4%
서 107
 
5.2%
구 107
 
5.2%
광 103
 
5.0%
역 103
 
5.0%
로 101
 
4.9%
남 95
 
4.6%
Other values (61) 888
43.3%
Decimal Number
ValueCountFrequency (%)
1 126
28.6%
0 66
15.0%
3 52
11.8%
2 52
11.8%
4 46
 
10.4%
5 35
 
7.9%
6 25
 
5.7%
7 19
 
4.3%
8 11
 
2.5%
9 9
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
B 31
63.3%
A 17
34.7%
C 1
 
2.0%
Space Separator
ValueCountFrequency (%)
597
100.0%
Other Punctuation
ValueCountFrequency (%)
, 114
100.0%
Close Punctuation
ValueCountFrequency (%)
) 105
100.0%
Open Punctuation
ValueCountFrequency (%)
( 105
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2051
59.1%
Common 1371
39.5%
Latin 49
 
1.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 156
 
7.6%
산 153
 
7.5%
부 127
 
6.2%
시 111
 
5.4%
서 107
 
5.2%
구 107
 
5.2%
광 103
 
5.0%
역 103
 
5.0%
로 101
 
4.9%
남 95
 
4.6%
Other values (61) 888
43.3%
Common
ValueCountFrequency (%)
597
43.5%
1 126
 
9.2%
, 114
 
8.3%
) 105
 
7.7%
( 105
 
7.7%
0 66
 
4.8%
3 52
 
3.8%
2 52
 
3.8%
4 46
 
3.4%
5 35
 
2.6%
Other values (6) 73
 
5.3%
Latin
ValueCountFrequency (%)
B 31
63.3%
A 17
34.7%
C 1
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2051
59.1%
ASCII 1420
40.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
597
42.0%
1 126
 
8.9%
, 114
 
8.0%
) 105
 
7.4%
( 105
 
7.4%
0 66
 
4.6%
3 52
 
3.7%
2 52
 
3.7%
4 46
 
3.2%
5 35
 
2.5%
Other values (9) 122
 
8.6%
Hangul
ValueCountFrequency (%)
동 156
 
7.6%
산 153
 
7.5%
부 127
 
6.2%
시 111
 
5.4%
서 107
 
5.2%
구 107
 
5.2%
광 103
 
5.0%
역 103
 
5.0%
로 101
 
4.9%
남 95
 
4.6%
Other values (61) 888
43.3%

소재지전화
Text

MISSING 

Distinct97
Distinct (%)96.0%
Missing2
Missing (%)1.9%
Memory size956.0 B
2023-12-13T01:24:26.954684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length13.841584
Min length9

Characters and Unicode

Total characters1398
Distinct characters20
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique94 ?
Unique (%)93.1%

Sample

1st row 051-254 -7234
2nd row 051- 242-1231
3rd row 051- 250-2193
4th row051 -248 -7621
5th row051 -254 -8255
ValueCountFrequency (%)
051 93
34.7%
231 9
 
3.4%
253 6
 
2.2%
255 5
 
1.9%
243 4
 
1.5%
246 4
 
1.5%
256 4
 
1.5%
254 4
 
1.5%
replace 3
 
1.1%
241 3
 
1.1%
Other values (116) 133
49.6%
2023-12-13T01:24:27.294563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 196
14.0%
192
13.7%
5 175
12.5%
0 174
12.4%
1 164
11.7%
2 137
9.8%
4 68
 
4.9%
3 66
 
4.7%
7 63
 
4.5%
6 56
 
4.0%
Other values (10) 107
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 983
70.3%
Dash Punctuation 196
 
14.0%
Space Separator 192
 
13.7%
Uppercase Letter 21
 
1.5%
Math Symbol 6
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 175
17.8%
0 174
17.7%
1 164
16.7%
2 137
13.9%
4 68
 
6.9%
3 66
 
6.7%
7 63
 
6.4%
6 56
 
5.7%
8 47
 
4.8%
9 33
 
3.4%
Uppercase Letter
ValueCountFrequency (%)
E 6
28.6%
R 3
14.3%
C 3
14.3%
A 3
14.3%
L 3
14.3%
P 3
14.3%
Math Symbol
ValueCountFrequency (%)
> 3
50.0%
< 3
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 196
100.0%
Space Separator
ValueCountFrequency (%)
192
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1377
98.5%
Latin 21
 
1.5%

Most frequent character per script

Common
ValueCountFrequency (%)
- 196
14.2%
192
13.9%
5 175
12.7%
0 174
12.6%
1 164
11.9%
2 137
9.9%
4 68
 
4.9%
3 66
 
4.8%
7 63
 
4.6%
6 56
 
4.1%
Other values (4) 86
6.2%
Latin
ValueCountFrequency (%)
E 6
28.6%
R 3
14.3%
C 3
14.3%
A 3
14.3%
L 3
14.3%
P 3
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1398
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 196
14.0%
192
13.7%
5 175
12.5%
0 174
12.4%
1 164
11.7%
2 137
9.8%
4 68
 
4.9%
3 66
 
4.7%
7 63
 
4.5%
6 56
 
4.0%
Other values (10) 107
7.7%

Interactions

2023-12-13T01:24:24.881878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:24:24.695463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:24:24.957040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:24:24.777539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:24:27.380291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번인허가번호소재지전화
연번1.0000.8840.684
인허가번호0.8841.0000.967
소재지전화0.6840.9671.000
2023-12-13T01:24:27.454806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번인허가번호
연번1.0001.000
인허가번호1.0001.000

Missing values

2023-12-13T01:24:25.064878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:24:25.159469image/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

연번인허가번호업소명소재지(도로명)소재지전화
0119710128015(주)케이엠부산광역시 서구 충무대로 214 (남부민동)051-254 -7234
1219710128016금양제빙2공장부산광역시 서구 해안새벽시장길 54 (충무동1가)051- 242-1231
2319720128023(주)사조대림부산광역시 서구 충무대로 170 (남부민동)051- 250-2193
3419720128002우양냉장주식회사부산광역시 서구 충무대로 226 (남부민동)051 -248 -7621
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