Overview

Dataset statistics

Number of variables6
Number of observations160
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.8 KiB
Average record size in memory49.8 B

Variable types

Numeric1
Text3
DateTime1
Categorical1

Dataset

Description후원방문판매등록현황20148
Author전라북도
URLhttps://www.bigdatahub.go.kr/opendata/dataSet/detail.nm?contentId=37&rlik=49451aebf056b486&serviceId=202146

Alerts

연번 has unique valuesUnique
등록번호 has unique valuesUnique

Reproduction

Analysis started2024-03-14 01:22:52.261478
Analysis finished2024-03-14 01:22:52.753042
Duration0.49 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct160
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.5
Minimum1
Maximum160
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-03-14T10:22:52.814088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8.95
Q140.75
median80.5
Q3120.25
95-th percentile152.05
Maximum160
Range159
Interquartile range (IQR)79.5

Descriptive statistics

Standard deviation46.332134
Coefficient of variation (CV)0.57555446
Kurtosis-1.2
Mean80.5
Median Absolute Deviation (MAD)40
Skewness0
Sum12880
Variance2146.6667
MonotonicityStrictly increasing
2024-03-14T10:22:52.934775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.6%
82 1
 
0.6%
104 1
 
0.6%
105 1
 
0.6%
106 1
 
0.6%
107 1
 
0.6%
108 1
 
0.6%
109 1
 
0.6%
110 1
 
0.6%
111 1
 
0.6%
Other values (150) 150
93.8%
ValueCountFrequency (%)
1 1
0.6%
2 1
0.6%
3 1
0.6%
4 1
0.6%
5 1
0.6%
6 1
0.6%
7 1
0.6%
8 1
0.6%
9 1
0.6%
10 1
0.6%
ValueCountFrequency (%)
160 1
0.6%
159 1
0.6%
158 1
0.6%
157 1
0.6%
156 1
0.6%
155 1
0.6%
154 1
0.6%
153 1
0.6%
152 1
0.6%
151 1
0.6%

상 호
Text

Distinct155
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-03-14T10:22:53.175881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length14
Mean length8.425
Min length2

Characters and Unicode

Total characters1348
Distinct characters159
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique152 ?
Unique (%)95.0%

Sample

1st row한국화장품 전북대리점
2nd row전주 진북지사
3rd row녹십초 전주지사
4th row유니베라남전주대리점
5th row유니베라신전주
ValueCountFrequency (%)
마임 18
 
8.0%
김정문알로에 8
 
3.6%
뉴랜드알로에 7
 
3.1%
오휘 5
 
2.2%
아모레 4
 
1.8%
유니베라 3
 
1.3%
알로에 3
 
1.3%
이든네이처 2
 
0.9%
아모레퍼시픽 2
 
0.9%
전주 2
 
0.9%
Other values (168) 170
75.9%
2024-03-14T10:22:53.515241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 79
 
5.9%
지 79
 
5.9%
64
 
4.7%
임 51
 
3.8%
점 51
 
3.8%
마 48
 
3.6%
에 34
 
2.5%
알 31
 
2.3%
로 31
 
2.3%
주 30
 
2.2%
Other values (149) 850
63.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1253
93.0%
Space Separator 64
 
4.7%
Close Punctuation 12
 
0.9%
Open Punctuation 10
 
0.7%
Other Punctuation 5
 
0.4%
Uppercase Letter 4
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 79
 
6.3%
지 79
 
6.3%
임 51
 
4.1%
점 51
 
4.1%
마 48
 
3.8%
에 34
 
2.7%
알 31
 
2.5%
로 31
 
2.5%
주 30
 
2.4%
산 29
 
2.3%
Other values (142) 790
63.0%
Other Punctuation
ValueCountFrequency (%)
. 4
80.0%
: 1
 
20.0%
Uppercase Letter
ValueCountFrequency (%)
G 2
50.0%
L 2
50.0%
Space Separator
ValueCountFrequency (%)
64
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1253
93.0%
Common 91
 
6.8%
Latin 4
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 79
 
6.3%
지 79
 
6.3%
임 51
 
4.1%
점 51
 
4.1%
마 48
 
3.8%
에 34
 
2.7%
알 31
 
2.5%
로 31
 
2.5%
주 30
 
2.4%
산 29
 
2.3%
Other values (142) 790
63.0%
Common
ValueCountFrequency (%)
64
70.3%
) 12
 
13.2%
( 10
 
11.0%
. 4
 
4.4%
: 1
 
1.1%
Latin
ValueCountFrequency (%)
G 2
50.0%
L 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1253
93.0%
ASCII 95
 
7.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 79
 
6.3%
지 79
 
6.3%
임 51
 
4.1%
점 51
 
4.1%
마 48
 
3.8%
에 34
 
2.7%
알 31
 
2.5%
로 31
 
2.5%
주 30
 
2.4%
산 29
 
2.3%
Other values (142) 790
63.0%
ASCII
ValueCountFrequency (%)
64
67.4%
) 12
 
12.6%
( 10
 
10.5%
. 4
 
4.2%
G 2
 
2.1%
L 2
 
2.1%
: 1
 
1.1%

등록일
Date

Distinct22
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
Minimum2013-06-27 00:00:00
Maximum2014-08-14 00:00:00
2024-03-14T10:22:53.605688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:22:53.689696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)

등록번호
Text

UNIQUE 

Distinct160
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-03-14T10:22:53.876341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.25625
Min length11

Characters and Unicode

Total characters1961
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique160 ?
Unique (%)100.0%

Sample

1st row전북 2013-제1호
2nd row전북 2013-제2호
3rd row전북 2013-제3호
4th row전북 2013-제4호
5th row전북 2013-제5호
ValueCountFrequency (%)
전북 160
50.0%
2013-제87호 1
 
0.3%
2013-제118호 1
 
0.3%
2013-제110호 1
 
0.3%
2013-제111호 1
 
0.3%
2013-제112호 1
 
0.3%
2013-제113호 1
 
0.3%
2013-제114호 1
 
0.3%
2013-제115호 1
 
0.3%
2013-제117호 1
 
0.3%
Other values (151) 151
47.2%
2024-03-14T10:22:54.192317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 253
12.9%
2 195
9.9%
3 185
9.4%
0 184
9.4%
전 160
8.2%
북 160
8.2%
160
8.2%
- 160
8.2%
제 160
8.2%
호 160
8.2%
Other values (6) 184
9.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001
51.0%
Other Letter 640
32.6%
Space Separator 160
 
8.2%
Dash Punctuation 160
 
8.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 253
25.3%
2 195
19.5%
3 185
18.5%
0 184
18.4%
4 41
 
4.1%
5 36
 
3.6%
6 34
 
3.4%
7 25
 
2.5%
9 24
 
2.4%
8 24
 
2.4%
Other Letter
ValueCountFrequency (%)
전 160
25.0%
북 160
25.0%
제 160
25.0%
호 160
25.0%
Space Separator
ValueCountFrequency (%)
160
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 160
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1321
67.4%
Hangul 640
32.6%

Most frequent character per script

Common
ValueCountFrequency (%)
1 253
19.2%
2 195
14.8%
3 185
14.0%
0 184
13.9%
160
12.1%
- 160
12.1%
4 41
 
3.1%
5 36
 
2.7%
6 34
 
2.6%
7 25
 
1.9%
Other values (2) 48
 
3.6%
Hangul
ValueCountFrequency (%)
전 160
25.0%
북 160
25.0%
제 160
25.0%
호 160
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1321
67.4%
Hangul 640
32.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 253
19.2%
2 195
14.8%
3 185
14.0%
0 184
13.9%
160
12.1%
- 160
12.1%
4 41
 
3.1%
5 36
 
2.7%
6 34
 
2.6%
7 25
 
1.9%
Other values (2) 48
 
3.6%
Hangul
ValueCountFrequency (%)
전 160
25.0%
북 160
25.0%
제 160
25.0%
호 160
25.0%

사업장 소재지
Text

Distinct159
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-03-14T10:22:54.469425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length40
Mean length31.65
Min length18

Characters and Unicode

Total characters5064
Distinct characters166
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

Unique158 ?
Unique (%)98.8%

Sample

1st row전라북도 김제시 요촌동 288번지 38호
2nd row전라북도 전주시 완산구 서노송동 642번지 13호
3rd row전라북도 전주시 완산구 서노송동 568번지 88호 용호빌딩
4th row전라북도 전주시 완산구 중화산동2가 744번지 6호
5th row전라북도 전주시 완산구 팔달로 244(서노송동. 2층)
ValueCountFrequency (%)
전라북도 157
 
16.7%
전주시 71
 
7.6%
완산구 48
 
5.1%
덕진구 24
 
2.6%
군산시 23
 
2.5%
익산시 22
 
2.3%
1호 19
 
2.0%
2호 13
 
1.4%
정읍시 12
 
1.3%
김제시 12
 
1.3%
Other values (341) 537
57.2%
2024-03-14T10:22:54.868487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1627
32.1%
전 235
 
4.6%
라 163
 
3.2%
북 162
 
3.2%
도 158
 
3.1%
동 155
 
3.1%
시 150
 
3.0%
1 144
 
2.8%
지 137
 
2.7%
2 134
 
2.6%
Other values (156) 1999
39.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2586
51.1%
Space Separator 1627
32.1%
Decimal Number 765
 
15.1%
Close Punctuation 27
 
0.5%
Open Punctuation 27
 
0.5%
Other Punctuation 25
 
0.5%
Uppercase Letter 4
 
0.1%
Dash Punctuation 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 235
 
9.1%
라 163
 
6.3%
북 162
 
6.3%
도 158
 
6.1%
동 155
 
6.0%
시 150
 
5.8%
지 137
 
5.3%
호 134
 
5.2%
번 130
 
5.0%
산 108
 
4.2%
Other values (136) 1054
40.8%
Decimal Number
ValueCountFrequency (%)
1 144
18.8%
2 134
17.5%
3 91
11.9%
4 79
10.3%
5 69
9.0%
6 57
 
7.5%
0 56
 
7.3%
8 51
 
6.7%
7 46
 
6.0%
9 38
 
5.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
25.0%
Y 1
25.0%
C 1
25.0%
K 1
25.0%
Other Punctuation
ValueCountFrequency (%)
. 24
96.0%
@ 1
 
4.0%
Space Separator
ValueCountFrequency (%)
1627
100.0%
Close Punctuation
ValueCountFrequency (%)
) 27
100.0%
Open Punctuation
ValueCountFrequency (%)
( 27
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2586
51.1%
Common 2474
48.9%
Latin 4
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 235
 
9.1%
라 163
 
6.3%
북 162
 
6.3%
도 158
 
6.1%
동 155
 
6.0%
시 150
 
5.8%
지 137
 
5.3%
호 134
 
5.2%
번 130
 
5.0%
산 108
 
4.2%
Other values (136) 1054
40.8%
Common
ValueCountFrequency (%)
1627
65.8%
1 144
 
5.8%
2 134
 
5.4%
3 91
 
3.7%
4 79
 
3.2%
5 69
 
2.8%
6 57
 
2.3%
0 56
 
2.3%
8 51
 
2.1%
7 46
 
1.9%
Other values (6) 120
 
4.9%
Latin
ValueCountFrequency (%)
B 1
25.0%
Y 1
25.0%
C 1
25.0%
K 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2586
51.1%
ASCII 2478
48.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1627
65.7%
1 144
 
5.8%
2 134
 
5.4%
3 91
 
3.7%
4 79
 
3.2%
5 69
 
2.8%
6 57
 
2.3%
0 56
 
2.3%
8 51
 
2.1%
7 46
 
1.9%
Other values (10) 124
 
5.0%
Hangul
ValueCountFrequency (%)
전 235
 
9.1%
라 163
 
6.3%
북 162
 
6.3%
도 158
 
6.1%
동 155
 
6.0%
시 150
 
5.8%
지 137
 
5.3%
호 134
 
5.2%
번 130
 
5.0%
산 108
 
4.2%
Other values (136) 1054
40.8%

취급품목
Categorical

Distinct12
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
건강식품. 화장품/미용용품
83 
건강식품. 화장품/미용용품. 기타
29 
건강식품. 화장품/미용용품. 생활용품/세제류. 기타
23 
건강식품. 화장품/미용용품. 생활용품/세제류
 
8
기타
 
6
Other values (7)
11 

Length

Max length28
Median length14
Mean length16.7875
Min length2

Unique

Unique5 ?
Unique (%)3.1%

Sample

1st row화장품/미용용품
2nd row건강식품. 화장품/미용용품
3rd row건강식품. 화장품/미용용품. 생활용품/세제류
4th row건강식품. 화장품/미용용품. 생활용품/세제류
5th row화장품/미용용품. 교육/도서. 회원권/상품권

Common Values

ValueCountFrequency (%)
건강식품. 화장품/미용용품 83
51.9%
건강식품. 화장품/미용용품. 기타 29
 
18.1%
건강식품. 화장품/미용용품. 생활용품/세제류. 기타 23
 
14.4%
건강식품. 화장품/미용용품. 생활용품/세제류 8
 
5.0%
기타 6
 
3.8%
의류/패션. 기타 4
 
2.5%
화장품/미용용품. 교육/도서. 회원권/상품권 2
 
1.2%
화장품/미용용품 1
 
0.6%
건강식품. 화장품/미용용품. 기타(이온수기) 1
 
0.6%
기타(상조) 1
 
0.6%
Other values (2) 2
 
1.2%

Length

2024-03-14T10:22:54.975700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
화장품/미용용품 147
36.9%
건강식품 145
36.4%
기타 62
15.6%
생활용품/세제류 33
 
8.3%
의류/패션 4
 
1.0%
교육/도서 2
 
0.5%
회원권/상품권 2
 
0.5%
기타(이온수기 1
 
0.3%
기타(상조 1
 
0.3%
화장품/미용요품 1
 
0.3%

Interactions

2024-03-14T10:22:52.523867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T10:22:55.029296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번등록일취급품목
연번1.0000.9090.719
등록일0.9091.0000.941
취급품목0.7190.9411.000
2024-03-14T10:22:55.100009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번취급품목
연번1.0000.401
취급품목0.4011.000

Missing values

2024-03-14T10:22:52.638300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T10:22:52.717385image/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

연번상 호등록일등록번호사업장 소재지취급품목
01한국화장품 전북대리점2013.06.27전북 2013-제1호전라북도 김제시 요촌동 288번지 38호화장품/미용용품
12전주 진북지사2013.07.19전북 2013-제2호전라북도 전주시 완산구 서노송동 642번지 13호건강식품. 화장품/미용용품
23녹십초 전주지사2013.07.26전북 2013-제3호전라북도 전주시 완산구 서노송동 568번지 88호 용호빌딩건강식품. 화장품/미용용품. 생활용품/세제류
34유니베라남전주대리점2013.08.05전북 2013-제4호전라북도 전주시 완산구 중화산동2가 744번지 6호건강식품. 화장품/미용용품. 생활용품/세제류
45유니베라신전주2013.08.06전북 2013-제5호전라북도 전주시 완산구 팔달로 244(서노송동. 2층)화장품/미용용품. 교육/도서. 회원권/상품권
56유니베라 효자대리점2013.08.06전북 2013-제6호전라북도 전주시 완산구 효자동1가 157번지 76호화장품/미용용품. 교육/도서. 회원권/상품권
67유니베라남양알로에전주중부대리점2013.08.06전북 2013-제7호전라북도 전주시 덕진구 금암동 1589번지 2호 2층건강식품. 화장품/미용용품. 생활용품/세제류
78유니베라익산대리점2013.08.06전북 2013-제9호전라북도 익산시 모현동1가 186번지 19호건강식품. 화장품/미용용품. 생활용품/세제류
89유니베라익산부송대리점2013.08.06전북 2013-제10호전라북도 익산시 부송동 1035번지 2호 3층건강식품. 화장품/미용용품. 생활용품/세제류
910유니베라 정읍대리점2013.08.06전북 2013-제11호전라북도 정읍시 수성동 699번지 2호건강식품. 화장품/미용용품. 생활용품/세제류
연번상 호등록일등록번호사업장 소재지취급품목
150151봄빛2014.01.24.전북 2014-제1호전라북도 정읍시 태평7길 71 (시기동)기타
151152상록수2014.02.12.전북 2014-제2호전라북도 전주시 덕진구 송천2길 25. 2-801(송천동1가. 라이프@)기타
152153유니베라 군산제일대리점2014.03.06전북 2014-제3호전라북도 군산시 나운로 4 (문화동. 현대코아307동)건강식품. 화장품/미용요품. 생활용품/세제류
153154아모레퍼시픽리리코스완산지사2014.03.11.전북 2014-제4호전라북도 전주시 완산구 홍산남로 29-7(효자동2가. 1동 301호)건강식품. 화장품/미용용품. 생활용품/세제류. 기타
154155전북지사(아모레퍼시픽. 리리코스)2014.03.11.전북 2014-제5호전라북도 전주시 완산구 유연로 271(서신동. 우담빌딩)건강식품. 화장품/미용용품. 생활용품/세제류. 기타
155156뷰티애비뉴 전주점2014.05.12.전북 2014-제6호전라북도 전주시 덕진구 아중로 205(우아동2가)건강식품. 화장품/미용용품
156157오휘송천지사2014.06.27.전북 2014-제7호전북 전주시 덕진구 두간로 24(송천동1가)건강식품. 화장품/미용용품
157158은하수2014.06.27.전북 2014-제8호전북 전주시 덕진구 솔내로 120 402동 1103호(송천동1가. 현대4차아파트)생활용품/세제류
158159완주대리점2014.07.17.전북 2014-제9호전북 완산구 봉동읍 원둔산2길 17. 303건강식품. 화장품/미용용품
159160뷰티에비뉴 익산점2014.08.14.전북 2014-제10호전라북도 익산시 무왕로 1134 (어양동)건강식품. 화장품/미용용품