Overview

Dataset statistics

Number of variables6
Number of observations171
Missing cells3
Missing cells (%)0.3%
Duplicate rows7
Duplicate rows (%)4.1%
Total size in memory8.1 KiB
Average record size in memory48.8 B

Variable types

Unsupported1
Text3
Categorical2

Dataset

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

Alerts

Dataset has 7 (4.1%) duplicate rowsDuplicates
Unnamed: 2 is highly overall correlated with Unnamed: 5High correlation
Unnamed: 5 is highly overall correlated with Unnamed: 2High correlation
전라북도 후원방문판매 등록현황(`14.08.18.기준) is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-03-14 01:22:55.890543
Analysis finished2024-03-14 01:22:56.350280
Duration0.46 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

전라북도 후원방문판매 등록현황(`14.08.18.기준)
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size1.5 KiB

Unnamed: 1
Text

Distinct156
Distinct (%)91.8%
Missing1
Missing (%)0.6%
Memory size1.5 KiB
2024-03-14T10:22:56.526672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length13
Mean length8.3764706
Min length2

Characters and Unicode

Total characters1424
Distinct characters161
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

Unique144 ?
Unique (%)84.7%

Sample

1st row상 호
2nd row한국화장품 전북대리점
3rd row전주 진북지사
4th row녹십초 전주지사
5th row유니베라남전주대리점
ValueCountFrequency (%)
마임 18
 
7.6%
김정문알로에 8
 
3.4%
뉴랜드알로에 7
 
2.9%
오휘 5
 
2.1%
유니베라 4
 
1.7%
아모레 4
 
1.7%
알로에 3
 
1.3%
완주대리점 2
 
0.8%
뉴랜드 2
 
0.8%
전북지사(아모레퍼시픽 2
 
0.8%
Other values (170) 183
76.9%
2024-03-14T10:22:56.958895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 82
 
5.8%
사 82
 
5.8%
69
 
4.8%
점 54
 
3.8%
임 51
 
3.6%
마 48
 
3.4%
에 34
 
2.4%
주 32
 
2.2%
산 31
 
2.2%
알 31
 
2.2%
Other values (151) 910
63.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1321
92.8%
Space Separator 69
 
4.8%
Close Punctuation 13
 
0.9%
Open Punctuation 11
 
0.8%
Other Punctuation 6
 
0.4%
Uppercase Letter 4
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 82
 
6.2%
사 82
 
6.2%
점 54
 
4.1%
임 51
 
3.9%
마 48
 
3.6%
에 34
 
2.6%
주 32
 
2.4%
산 31
 
2.3%
알 31
 
2.3%
로 31
 
2.3%
Other values (143) 845
64.0%
Other Punctuation
ValueCountFrequency (%)
. 3
50.0%
, 2
33.3%
: 1
 
16.7%
Uppercase Letter
ValueCountFrequency (%)
G 2
50.0%
L 2
50.0%
Space Separator
ValueCountFrequency (%)
69
100.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1321
92.8%
Common 99
 
7.0%
Latin 4
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 82
 
6.2%
사 82
 
6.2%
점 54
 
4.1%
임 51
 
3.9%
마 48
 
3.6%
에 34
 
2.6%
주 32
 
2.4%
산 31
 
2.3%
알 31
 
2.3%
로 31
 
2.3%
Other values (143) 845
64.0%
Common
ValueCountFrequency (%)
69
69.7%
) 13
 
13.1%
( 11
 
11.1%
. 3
 
3.0%
, 2
 
2.0%
: 1
 
1.0%
Latin
ValueCountFrequency (%)
G 2
50.0%
L 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1321
92.8%
ASCII 103
 
7.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 82
 
6.2%
사 82
 
6.2%
점 54
 
4.1%
임 51
 
3.9%
마 48
 
3.6%
에 34
 
2.6%
주 32
 
2.4%
산 31
 
2.3%
알 31
 
2.3%
로 31
 
2.3%
Other values (143) 845
64.0%
ASCII
ValueCountFrequency (%)
69
67.0%
) 13
 
12.6%
( 11
 
10.7%
. 3
 
2.9%
, 2
 
1.9%
G 2
 
1.9%
L 2
 
1.9%
: 1
 
1.0%

Unnamed: 2
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2013.08.07
55 
2013.08.13
30 
2013.08.12
21 
2013.08.06
19 
2013.08.16
12 
Other values (21)
34 

Length

Max length11
Median length10
Mean length10.02924
Min length3

Unique

Unique14 ?
Unique (%)8.2%

Sample

1st row등록일
2nd row2013.06.27
3rd row2013.07.19
4th row2013.07.26
5th row2013.08.05

Common Values

ValueCountFrequency (%)
2013.08.07 55
32.2%
2013.08.13 30
17.5%
2013.08.12 21
 
12.3%
2013.08.06 19
 
11.1%
2013.08.16 12
 
7.0%
2014.06.27. 4
 
2.3%
2014.03.11. 4
 
2.3%
2013.08.29 3
 
1.8%
2013.08.27 3
 
1.8%
2014.02.12. 2
 
1.2%
Other values (16) 18
 
10.5%

Length

2024-03-14T10:22:57.076167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2013.08.07 55
32.2%
2013.08.13 30
17.5%
2013.08.12 21
 
12.3%
2013.08.06 19
 
11.1%
2013.08.16 12
 
7.0%
2014.06.27 4
 
2.3%
2014.03.11 4
 
2.3%
2013.08.29 3
 
1.8%
2013.08.27 3
 
1.8%
2014.05.12 2
 
1.2%
Other values (15) 18
 
10.5%

Unnamed: 3
Text

Distinct161
Distinct (%)94.7%
Missing1
Missing (%)0.6%
Memory size1.5 KiB
2024-03-14T10:22:57.273117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12.5
Mean length12.141176
Min length4

Characters and Unicode

Total characters2064
Distinct characters19
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

Unique152 ?
Unique (%)89.4%

Sample

1st row등록번호
2nd row전북 2013-제1호
3rd row전북 2013-제2호
4th row전북 2013-제3호
5th row전북 2013-제4호
ValueCountFrequency (%)
전북 169
49.9%
2014-제1호 2
 
0.6%
2014-제3호 2
 
0.6%
2014-제7호 2
 
0.6%
2014-제6호 2
 
0.6%
2014-제8호 2
 
0.6%
2014-제4호 2
 
0.6%
2014-제2호 2
 
0.6%
2014-제9호 2
 
0.6%
2014-제5호 2
 
0.6%
Other values (152) 152
44.8%
2024-03-14T10:22:57.646357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 263
12.7%
2 205
9.9%
0 193
9.4%
3 186
9.0%
호 170
8.2%
제 169
8.2%
북 169
8.2%
전 169
8.2%
- 169
8.2%
169
8.2%
Other values (9) 202
9.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1046
50.7%
Other Letter 680
32.9%
Dash Punctuation 169
 
8.2%
Space Separator 169
 
8.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 263
25.1%
2 205
19.6%
0 193
18.5%
3 186
17.8%
4 51
 
4.9%
5 37
 
3.5%
6 35
 
3.3%
7 26
 
2.5%
9 25
 
2.4%
8 25
 
2.4%
Other Letter
ValueCountFrequency (%)
호 170
25.0%
제 169
24.9%
북 169
24.9%
전 169
24.9%
등 1
 
0.1%
록 1
 
0.1%
번 1
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 169
100.0%
Space Separator
ValueCountFrequency (%)
169
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1384
67.1%
Hangul 680
32.9%

Most frequent character per script

Common
ValueCountFrequency (%)
1 263
19.0%
2 205
14.8%
0 193
13.9%
3 186
13.4%
- 169
12.2%
169
12.2%
4 51
 
3.7%
5 37
 
2.7%
6 35
 
2.5%
7 26
 
1.9%
Other values (2) 50
 
3.6%
Hangul
ValueCountFrequency (%)
호 170
25.0%
제 169
24.9%
북 169
24.9%
전 169
24.9%
등 1
 
0.1%
록 1
 
0.1%
번 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1384
67.1%
Hangul 680
32.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 263
19.0%
2 205
14.8%
0 193
13.9%
3 186
13.4%
- 169
12.2%
169
12.2%
4 51
 
3.7%
5 37
 
2.7%
6 35
 
2.5%
7 26
 
1.9%
Other values (2) 50
 
3.6%
Hangul
ValueCountFrequency (%)
호 170
25.0%
제 169
24.9%
북 169
24.9%
전 169
24.9%
등 1
 
0.1%
록 1
 
0.1%
번 1
 
0.1%

Unnamed: 4
Text

Distinct160
Distinct (%)94.1%
Missing1
Missing (%)0.6%
Memory size1.5 KiB
2024-03-14T10:22:57.890599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length40
Mean length31.482353
Min length7

Characters and Unicode

Total characters5352
Distinct characters168
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

Unique150 ?
Unique (%)88.2%

Sample

1st row사업장 소재지
2nd row전라북도 김제시 요촌동 288번지 38호
3rd row전라북도 전주시 완산구 서노송동 642번지 13호
4th row전라북도 전주시 완산구 서노송동 568번지 88호 용호빌딩
5th row전라북도 전주시 완산구 중화산동2가 744번지 6호
ValueCountFrequency (%)
전라북도 163
 
16.4%
전주시 77
 
7.7%
완산구 51
 
5.1%
덕진구 28
 
2.8%
군산시 24
 
2.4%
익산시 22
 
2.2%
1호 19
 
1.9%
정읍시 13
 
1.3%
2호 13
 
1.3%
2층 12
 
1.2%
Other values (343) 573
57.6%
2024-03-14T10:22:58.233341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1674
31.3%
전 250
 
4.7%
북 171
 
3.2%
라 170
 
3.2%
동 167
 
3.1%
도 164
 
3.1%
시 159
 
3.0%
1 156
 
2.9%
2 146
 
2.7%
지 138
 
2.6%
Other values (158) 2157
40.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2750
51.4%
Space Separator 1674
31.3%
Decimal Number 816
 
15.2%
Open Punctuation 35
 
0.7%
Close Punctuation 35
 
0.7%
Other Punctuation 33
 
0.6%
Dash Punctuation 5
 
0.1%
Uppercase Letter 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 250
 
9.1%
북 171
 
6.2%
라 170
 
6.2%
동 167
 
6.1%
도 164
 
6.0%
시 159
 
5.8%
지 138
 
5.0%
호 136
 
4.9%
번 130
 
4.7%
산 114
 
4.1%
Other values (138) 1151
41.9%
Decimal Number
ValueCountFrequency (%)
1 156
19.1%
2 146
17.9%
3 96
11.8%
4 83
10.2%
5 71
8.7%
0 64
7.8%
6 57
 
7.0%
7 52
 
6.4%
8 52
 
6.4%
9 39
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
Y 1
25.0%
C 1
25.0%
B 1
25.0%
K 1
25.0%
Other Punctuation
ValueCountFrequency (%)
, 31
93.9%
@ 2
 
6.1%
Space Separator
ValueCountFrequency (%)
1674
100.0%
Open Punctuation
ValueCountFrequency (%)
( 35
100.0%
Close Punctuation
ValueCountFrequency (%)
) 35
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2750
51.4%
Common 2598
48.5%
Latin 4
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 250
 
9.1%
북 171
 
6.2%
라 170
 
6.2%
동 167
 
6.1%
도 164
 
6.0%
시 159
 
5.8%
지 138
 
5.0%
호 136
 
4.9%
번 130
 
4.7%
산 114
 
4.1%
Other values (138) 1151
41.9%
Common
ValueCountFrequency (%)
1674
64.4%
1 156
 
6.0%
2 146
 
5.6%
3 96
 
3.7%
4 83
 
3.2%
5 71
 
2.7%
0 64
 
2.5%
6 57
 
2.2%
7 52
 
2.0%
8 52
 
2.0%
Other values (6) 147
 
5.7%
Latin
ValueCountFrequency (%)
Y 1
25.0%
C 1
25.0%
B 1
25.0%
K 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2750
51.4%
ASCII 2602
48.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1674
64.3%
1 156
 
6.0%
2 146
 
5.6%
3 96
 
3.7%
4 83
 
3.2%
5 71
 
2.7%
0 64
 
2.5%
6 57
 
2.2%
7 52
 
2.0%
8 52
 
2.0%
Other values (10) 151
 
5.8%
Hangul
ValueCountFrequency (%)
전 250
 
9.1%
북 171
 
6.2%
라 170
 
6.2%
동 167
 
6.1%
도 164
 
6.0%
시 159
 
5.8%
지 138
 
5.0%
호 136
 
4.9%
번 130
 
4.7%
산 114
 
4.1%
Other values (138) 1151
41.9%

Unnamed: 5
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
건강식품, 화장품/미용용품
86 
건강식품, 화장품/미용용품, 기타
29 
건강식품, 화장품/미용용품, 생활용품/세제류, 기타
25 
건강식품, 화장품/미용용품, 생활용품/세제류
 
8
기타
 
8
Other values (9)
15 

Length

Max length28
Median length14
Mean length16.538012
Min length2

Unique

Unique5 ?
Unique (%)2.9%

Sample

1st row취급품목
2nd row화장품/미용용품
3rd row건강식품, 화장품/미용용품
4th row건강식품, 화장품/미용용품, 생활용품/세제류
5th row건강식품, 화장품/미용용품, 생활용품/세제류

Common Values

ValueCountFrequency (%)
건강식품, 화장품/미용용품 86
50.3%
건강식품, 화장품/미용용품, 기타 29
 
17.0%
건강식품, 화장품/미용용품, 생활용품/세제류, 기타 25
 
14.6%
건강식품, 화장품/미용용품, 생활용품/세제류 8
 
4.7%
기타 8
 
4.7%
의류/패션, 기타 4
 
2.3%
화장품/미용용품, 교육/도서, 회원권/상품권 2
 
1.2%
건강식품, 화장품/미용요품, 생활용품/세제류 2
 
1.2%
생활용품/세제류 2
 
1.2%
취급품목 1
 
0.6%
Other values (4) 4
 
2.3%

Length

2024-03-14T10:22:58.342244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
화장품/미용용품 152
36.2%
건강식품 151
36.0%
기타 66
15.7%
생활용품/세제류 37
 
8.8%
의류/패션 4
 
1.0%
교육/도서 2
 
0.5%
회원권/상품권 2
 
0.5%
화장품/미용요품 2
 
0.5%
취급품목 1
 
0.2%
기타(이온수기 1
 
0.2%
Other values (2) 2
 
0.5%

Correlations

2024-03-14T10:22:58.401069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 2Unnamed: 5
Unnamed: 21.0000.958
Unnamed: 50.9581.000
2024-03-14T10:22:58.672281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 5Unnamed: 2
Unnamed: 51.0000.710
Unnamed: 20.7101.000
2024-03-14T10:22:58.734976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 2Unnamed: 5
Unnamed: 21.0000.710
Unnamed: 50.7101.000

Missing values

2024-03-14T10:22:56.135579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T10:22:56.213920image/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.
2024-03-14T10:22:56.295737image/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

전라북도 후원방문판매 등록현황(`14.08.18.기준)Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5
0연번상 호등록일등록번호사업장 소재지취급품목
11한국화장품 전북대리점2013.06.27전북 2013-제1호전라북도 김제시 요촌동 288번지 38호화장품/미용용품
22전주 진북지사2013.07.19전북 2013-제2호전라북도 전주시 완산구 서노송동 642번지 13호건강식품, 화장품/미용용품
33녹십초 전주지사2013.07.26전북 2013-제3호전라북도 전주시 완산구 서노송동 568번지 88호 용호빌딩건강식품, 화장품/미용용품, 생활용품/세제류
44유니베라남전주대리점2013.08.05전북 2013-제4호전라북도 전주시 완산구 중화산동2가 744번지 6호건강식품, 화장품/미용용품, 생활용품/세제류
55유니베라신전주2013.08.06전북 2013-제5호전라북도 전주시 완산구 팔달로 244(서노송동, 2층)화장품/미용용품, 교육/도서, 회원권/상품권
66유니베라 효자대리점2013.08.06전북 2013-제6호전라북도 전주시 완산구 효자동1가 157번지 76호화장품/미용용품, 교육/도서, 회원권/상품권
77유니베라남양알로에전주중부대리점2013.08.06전북 2013-제7호전라북도 전주시 덕진구 금암동 1589번지 2호 2층건강식품, 화장품/미용용품, 생활용품/세제류
88유니베라익산대리점2013.08.06전북 2013-제9호전라북도 익산시 모현동1가 186번지 19호건강식품, 화장품/미용용품, 생활용품/세제류
99유니베라익산부송대리점2013.08.06전북 2013-제10호전라북도 익산시 부송동 1035번지 2호 3층건강식품, 화장품/미용용품, 생활용품/세제류
전라북도 후원방문판매 등록현황(`14.08.18.기준)Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5
161161봄빛2014.01.24.전북 2014-제1호전라북도 정읍시 태평7길 71 (시기동)기타
162162상록수2014.02.12.전북 2014-제2호전라북도 전주시 덕진구 송천2길 25, 2-801(송천동1가, 라이프@)기타
163163유니베라 군산제일대리점2014.03.06전북 2014-제3호전라북도 군산시 나운로 4 (문화동, 현대코아307동)건강식품, 화장품/미용요품, 생활용품/세제류
164164아모레퍼시픽리리코스완산지사2014.03.11.전북 2014-제4호전라북도 전주시 완산구 홍산남로 29-7(효자동2가, 1동 301호)건강식품, 화장품/미용용품, 생활용품/세제류, 기타
165165전북지사(아모레퍼시픽, 리리코스)2014.03.11.전북 2014-제5호전라북도 전주시 완산구 유연로 271(서신동, 우담빌딩)건강식품, 화장품/미용용품, 생활용품/세제류, 기타
166166뷰티애비뉴 전주점2014.05.12.전북 2014-제6호전라북도 전주시 덕진구 아중로 205(우아동2가)건강식품, 화장품/미용용품
167167오휘송천지사2014.06.27.전북 2014-제7호전북 전주시 덕진구 두간로 24(송천동1가)건강식품, 화장품/미용용품
168168은하수2014.06.27.전북 2014-제8호전북 전주시 덕진구 솔내로 120 402동 1103호(송천동1가, 현대4차아파트)생활용품/세제류
169169완주대리점2014.07.17.전북 2014-제9호전북 완산구 봉동읍 원둔산2길 17, 303건강식품, 화장품/미용용품
170170뷰티에비뉴 익산점2014.08.14.전북 2014-제10호전라북도 익산시 무왕로 1134 (어양동)건강식품, 화장품/미용용품

Duplicate rows

Most frequently occurring

Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5# duplicates
0봄빛2014.01.24.전북 2014-제1호전라북도 정읍시 태평7길 71 (시기동)기타2
1뷰티애비뉴 전주점2014.05.12.전북 2014-제6호전라북도 전주시 덕진구 아중로 205(우아동2가)건강식품, 화장품/미용용품2
2상록수2014.02.12.전북 2014-제2호전라북도 전주시 덕진구 송천2길 25, 2-801(송천동1가, 라이프@)기타2
3아모레퍼시픽리리코스완산지사2014.03.11.전북 2014-제4호전라북도 전주시 완산구 홍산남로 29-7(효자동2가, 1동 301호)건강식품, 화장품/미용용품, 생활용품/세제류, 기타2
4오휘송천지사2014.06.27.전북 2014-제7호전북 전주시 덕진구 두간로 24(송천동1가)건강식품, 화장품/미용용품2
5은하수2014.06.27.전북 2014-제8호전북 전주시 덕진구 솔내로 120 402동 1103호(송천동1가, 현대4차아파트)생활용품/세제류2
6전북지사(아모레퍼시픽, 리리코스)2014.03.11.전북 2014-제5호전라북도 전주시 완산구 유연로 271(서신동, 우담빌딩)건강식품, 화장품/미용용품, 생활용품/세제류, 기타2