Overview

Dataset statistics

Number of variables6
Number of observations27
Missing cells87
Missing cells (%)53.7%
Duplicate rows1
Duplicate rows (%)3.7%
Total size in memory1.4 KiB
Average record size in memory53.9 B

Variable types

Unsupported4
Text2

Dataset

Description의뢰기관별유통식품검사현황2017년상반기
Author전라북도
URLhttps://www.bigdatahub.go.kr/opendata/dataSet/detail.nm?contentId=37&rlik=49451aebf056b486&serviceId=202934

Alerts

Dataset has 1 (3.7%) duplicate rowsDuplicates
Unnamed: 0 has 27 (100.0%) missing valuesMissing
의뢰기관별 유통식품 검사현황(2017년 상반기) has 8 (29.6%) missing valuesMissing
Unnamed: 2 has 21 (77.8%) missing valuesMissing
Unnamed: 3 has 4 (14.8%) missing valuesMissing
Unnamed: 4 has 4 (14.8%) missing valuesMissing
Unnamed: 5 has 23 (85.2%) missing valuesMissing
Unnamed: 0 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 3 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 4 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 5 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-03-14 03:08:05.628974
Analysis finished2024-03-14 03:08:05.991754
Duration0.36 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Unnamed: 0
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing27
Missing (%)100.0%
Memory size375.0 B

의뢰기관별 유통식품 검사현황(2017년 상반기)
Text

MISSING 

Distinct19
Distinct (%)100.0%
Missing8
Missing (%)29.6%
Memory size348.0 B
2024-03-14T12:08:06.094149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.3684211
Min length3

Characters and Unicode

Total characters64
Distinct characters38
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)100.0%

Sample

1st row시․군 명
2nd row합 계
3rd row도교육청
4th row전라북도
5th row전주시
ValueCountFrequency (%)
시․군 1
 
4.8%
김제시 1
 
4.8%
고창군 1
 
4.8%
순창군 1
 
4.8%
임실군 1
 
4.8%
장수군 1
 
4.8%
무주군 1
 
4.8%
진안경찰서 1
 
4.8%
진안군 1
 
4.8%
완주군 1
 
4.8%
Other values (11) 11
52.4%
2024-03-14T12:08:06.369727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 10
 
15.6%
시 7
 
10.9%
주 3
 
4.7%
3
 
4.7%
안 3
 
4.7%
진 2
 
3.1%
산 2
 
3.1%
전 2
 
3.1%
도 2
 
3.1%
창 2
 
3.1%
Other values (28) 28
43.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 60
93.8%
Space Separator 3
 
4.7%
Other Punctuation 1
 
1.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 10
 
16.7%
시 7
 
11.7%
주 3
 
5.0%
안 3
 
5.0%
진 2
 
3.3%
산 2
 
3.3%
전 2
 
3.3%
도 2
 
3.3%
창 2
 
3.3%
실 1
 
1.7%
Other values (26) 26
43.3%
Space Separator
ValueCountFrequency (%)
3
100.0%
Other Punctuation
ValueCountFrequency (%)
․ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 60
93.8%
Common 4
 
6.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 10
 
16.7%
시 7
 
11.7%
주 3
 
5.0%
안 3
 
5.0%
진 2
 
3.3%
산 2
 
3.3%
전 2
 
3.3%
도 2
 
3.3%
창 2
 
3.3%
실 1
 
1.7%
Other values (26) 26
43.3%
Common
ValueCountFrequency (%)
3
75.0%
․ 1
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 60
93.8%
ASCII 3
 
4.7%
Punctuation 1
 
1.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 10
 
16.7%
시 7
 
11.7%
주 3
 
5.0%
안 3
 
5.0%
진 2
 
3.3%
산 2
 
3.3%
전 2
 
3.3%
도 2
 
3.3%
창 2
 
3.3%
실 1
 
1.7%
Other values (26) 26
43.3%
ASCII
ValueCountFrequency (%)
3
100.0%
Punctuation
ValueCountFrequency (%)
․ 1
100.0%

Unnamed: 2
Text

MISSING 

Distinct6
Distinct (%)100.0%
Missing21
Missing (%)77.8%
Memory size348.0 B
2024-03-14T12:08:06.502334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length4
Mean length3.5
Min length2

Characters and Unicode

Total characters21
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)100.0%

Sample

1st row소계
2nd row본청
3rd row완산구
4th row덕진구
5th row한옥마을
ValueCountFrequency (%)
소계 1
16.7%
본청 1
16.7%
완산구 1
16.7%
덕진구 1
16.7%
한옥마을 1
16.7%
전주완산경찰서 1
16.7%
2024-03-14T12:08:06.744495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
완 2
 
9.5%
산 2
 
9.5%
구 2
 
9.5%
소 1
 
4.8%
마 1
 
4.8%
찰 1
 
4.8%
경 1
 
4.8%
주 1
 
4.8%
전 1
 
4.8%
을 1
 
4.8%
Other values (8) 8
38.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 21
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
완 2
 
9.5%
산 2
 
9.5%
구 2
 
9.5%
소 1
 
4.8%
마 1
 
4.8%
찰 1
 
4.8%
경 1
 
4.8%
주 1
 
4.8%
전 1
 
4.8%
을 1
 
4.8%
Other values (8) 8
38.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 21
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
완 2
 
9.5%
산 2
 
9.5%
구 2
 
9.5%
소 1
 
4.8%
마 1
 
4.8%
찰 1
 
4.8%
경 1
 
4.8%
주 1
 
4.8%
전 1
 
4.8%
을 1
 
4.8%
Other values (8) 8
38.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 21
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
완 2
 
9.5%
산 2
 
9.5%
구 2
 
9.5%
소 1
 
4.8%
마 1
 
4.8%
찰 1
 
4.8%
경 1
 
4.8%
주 1
 
4.8%
전 1
 
4.8%
을 1
 
4.8%
Other values (8) 8
38.1%

Unnamed: 3
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4
Missing (%)14.8%
Memory size348.0 B

Unnamed: 4
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4
Missing (%)14.8%
Memory size348.0 B

Unnamed: 5
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing23
Missing (%)85.2%
Memory size348.0 B

Correlations

2024-03-14T12:08:06.811417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
의뢰기관별 유통식품 검사현황(2017년 상반기)Unnamed: 2
의뢰기관별 유통식품 검사현황(2017년 상반기)1.000NaN
Unnamed: 2NaN1.000

Missing values

2024-03-14T12:08:05.744387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T12:08:05.836763image/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.
2024-03-14T12:08:05.928857image/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

Unnamed: 0의뢰기관별 유통식품 검사현황(2017년 상반기)Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5
0<NA><NA><NA>NaNNaNNaN
1<NA>시․군 명<NA>NaNNaNNaN
2<NA><NA><NA>의뢰건수적 합부적합
3<NA>합 계<NA>7037021
4<NA><NA><NA>
5<NA>도교육청<NA>4949NaN
6<NA>전라북도<NA>8787NaN
7<NA>전주시소계NaNNaNNaN
8<NA><NA>본청NaNNaNNaN
9<NA><NA>완산구38371
Unnamed: 0의뢰기관별 유통식품 검사현황(2017년 상반기)Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5
17<NA>김제시<NA>2424NaN
18<NA>완주군<NA>2525NaN
19<NA>진안군<NA>2121NaN
20<NA>진안경찰서<NA>11NaN
21<NA>무주군<NA>2020NaN
22<NA>장수군<NA>44NaN
23<NA>임실군<NA>4242NaN
24<NA>순창군<NA>2323NaN
25<NA>고창군<NA>5252NaN
26<NA>부안군<NA>4545NaN

Duplicate rows

Most frequently occurring

의뢰기관별 유통식품 검사현황(2017년 상반기)Unnamed: 2# duplicates
0<NA><NA>3