Overview

Dataset statistics

Number of variables19
Number of observations21
Missing cells212
Missing cells (%)53.1%
Duplicate rows1
Duplicate rows (%)4.8%
Total size in memory3.2 KiB
Average record size in memory158.3 B

Variable types

Text1
Unsupported18

Dataset

Description1902182018년전문임업인현황
Author전라북도
URLhttps://www.bigdatahub.go.kr/opendata/dataSet/detail.nm?contentId=37&rlik=49451aebf056b486&serviceId=203000

Alerts

Dataset has 1 (4.8%) duplicate rowsDuplicates
2018년 전문임업인 선발현황 has 5 (23.8%) missing valuesMissing
Unnamed: 1 has 2 (9.5%) missing valuesMissing
Unnamed: 2 has 4 (19.0%) missing valuesMissing
Unnamed: 3 has 5 (23.8%) missing valuesMissing
Unnamed: 4 has 3 (14.3%) missing valuesMissing
Unnamed: 5 has 5 (23.8%) missing valuesMissing
Unnamed: 6 has 18 (85.7%) missing valuesMissing
Unnamed: 7 has 17 (81.0%) missing valuesMissing
Unnamed: 8 has 18 (85.7%) missing valuesMissing
Unnamed: 9 has 19 (90.5%) missing valuesMissing
Unnamed: 10 has 18 (85.7%) missing valuesMissing
Unnamed: 11 has 16 (76.2%) missing valuesMissing
Unnamed: 12 has 13 (61.9%) missing valuesMissing
Unnamed: 13 has 11 (52.4%) missing valuesMissing
Unnamed: 14 has 4 (19.0%) missing valuesMissing
Unnamed: 15 has 4 (19.0%) missing valuesMissing
Unnamed: 16 has 18 (85.7%) missing valuesMissing
Unnamed: 17 has 13 (61.9%) missing valuesMissing
Unnamed: 18 has 19 (90.5%) missing valuesMissing
Unnamed: 1 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 2 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
Unnamed: 6 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 7 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 8 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 9 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 10 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 11 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 12 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 13 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 14 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 15 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 16 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 17 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 18 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-03-13 23:52:08.295797
Analysis finished2024-03-13 23:52:08.985964
Duration0.69 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

2018년 전문임업인 선발현황
Text

MISSING 

Distinct16
Distinct (%)100.0%
Missing5
Missing (%)23.8%
Memory size300.0 B
2024-03-14T08:52:09.081441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters32
Distinct characters27
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

Unique16 ?
Unique (%)100.0%

Sample

1st row구분
2nd row합계
3rd row전주
4th row군산
5th row익산
ValueCountFrequency (%)
구분 1
 
6.2%
합계 1
 
6.2%
전주 1
 
6.2%
군산 1
 
6.2%
익산 1
 
6.2%
정읍 1
 
6.2%
남원 1
 
6.2%
김제 1
 
6.2%
완주 1
 
6.2%
진안 1
 
6.2%
Other values (6) 6
37.5%
2024-03-14T08:52:09.344025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 3
 
9.4%
창 2
 
6.2%
산 2
 
6.2%
안 2
 
6.2%
구 1
 
3.1%
완 1
 
3.1%
고 1
 
3.1%
순 1
 
3.1%
실 1
 
3.1%
임 1
 
3.1%
Other values (17) 17
53.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 32
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 3
 
9.4%
창 2
 
6.2%
산 2
 
6.2%
안 2
 
6.2%
구 1
 
3.1%
완 1
 
3.1%
고 1
 
3.1%
순 1
 
3.1%
실 1
 
3.1%
임 1
 
3.1%
Other values (17) 17
53.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 32
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 3
 
9.4%
창 2
 
6.2%
산 2
 
6.2%
안 2
 
6.2%
구 1
 
3.1%
완 1
 
3.1%
고 1
 
3.1%
순 1
 
3.1%
실 1
 
3.1%
임 1
 
3.1%
Other values (17) 17
53.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 32
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 3
 
9.4%
창 2
 
6.2%
산 2
 
6.2%
안 2
 
6.2%
구 1
 
3.1%
완 1
 
3.1%
고 1
 
3.1%
순 1
 
3.1%
실 1
 
3.1%
임 1
 
3.1%
Other values (17) 17
53.1%

Unnamed: 1
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2
Missing (%)9.5%
Memory size300.0 B

Unnamed: 2
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4
Missing (%)19.0%
Memory size300.0 B

Unnamed: 3
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing5
Missing (%)23.8%
Memory size300.0 B

Unnamed: 4
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3
Missing (%)14.3%
Memory size300.0 B

Unnamed: 5
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing5
Missing (%)23.8%
Memory size300.0 B

Unnamed: 6
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing18
Missing (%)85.7%
Memory size300.0 B

Unnamed: 7
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing17
Missing (%)81.0%
Memory size300.0 B

Unnamed: 8
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing18
Missing (%)85.7%
Memory size300.0 B

Unnamed: 9
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing19
Missing (%)90.5%
Memory size300.0 B

Unnamed: 10
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing18
Missing (%)85.7%
Memory size300.0 B

Unnamed: 11
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing16
Missing (%)76.2%
Memory size300.0 B

Unnamed: 12
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing13
Missing (%)61.9%
Memory size300.0 B

Unnamed: 13
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing11
Missing (%)52.4%
Memory size300.0 B

Unnamed: 14
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4
Missing (%)19.0%
Memory size300.0 B

Unnamed: 15
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4
Missing (%)19.0%
Memory size300.0 B

Unnamed: 16
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing18
Missing (%)85.7%
Memory size300.0 B

Unnamed: 17
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing13
Missing (%)61.9%
Memory size300.0 B

Unnamed: 18
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing19
Missing (%)90.5%
Memory size300.0 B

Missing values

2024-03-14T08:52:08.401752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T08:52:08.595088image/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.
2024-03-14T08:52:08.790141image/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

2018년 전문임업인 선발현황Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13Unnamed: 14Unnamed: 15Unnamed: 16Unnamed: 17Unnamed: 18
0<NA><기재요령 >NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1<NA>17년 : '17년까지 누적선발 인원수(수식변경 불가)NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2<NA>18년 : '18년 1.1 ~'18년 12. 30까지 선발 인원수, 신지식인: 산림청에서 작성NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN('18년 12월30일 현재)NaNNaNNaN
3구분합계\n(독림가+후계자+신지식인)독림가+\n임업후계자NaN독림가(금년/전년까지)NaNNaNNaNNaNNaNNaNNaNNaNNaN임업후계자NaN신지식인NaN임업후계자\n 나이완화 혜택(명)
4<NA>NaNNaNNaN계NaN법인NaN모범NaN우수NaN자영NaNNaNNaNNaNNaNNaN
5<NA>NaN18년17년18년17년18년17년18년17년18년17년18년17년18년17년18년17년NaN
6합계1974339162815132020006151243241496070
7전주42200NaNNaNNaNNaNNaNNaNNaNNaN22NaNNaNNaN
8군산1821500NaNNaNNaNNaNNaNNaNNaNNaN215NaN1NaN
9익산115407410NaNNaNNaNNaNNaNNaN1NaN3974NaN1NaN
2018년 전문임업인 선발현황Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13Unnamed: 14Unnamed: 15Unnamed: 16Unnamed: 17Unnamed: 18
11남원2133417708NaNNaNNaNNaNNaNNaNNaN834169NaN2NaN
12김제53252800NaNNaNNaNNaNNaNNaNNaNNaN2528NaNNaNNaN
13완주18550135310NaNNaNNaNNaNNaN23847125NaNNaNNaN
14진안22824204843NaNNaNNaNNaNNaNNaN84316161NaNNaNNaN
15무주1466139030NaNNaNNaNNaNNaN2NaN286109NaN1NaN
16장수15624132115NaN1NaNNaNNaNNaN11423117NaNNaNNaN
17임실22444179220NaNNaNNaNNaNNaN221842159NaN1NaN
18순창1491912901NaNNaNNaNNaNNaNNaNNaN119128NaN1NaN
19고창1481513300NaNNaNNaNNaNNaNNaNNaNNaN15133NaNNaNNaN
20부안76136301NaN1NaNNaNNaNNaNNaNNaN1362NaNNaNNaN

Duplicate rows

Most frequently occurring

2018년 전문임업인 선발현황# duplicates
0<NA>5