Overview

Dataset statistics

Number of variables4
Number of observations10000
Missing cells59
Missing cells (%)0.1%
Duplicate rows620
Duplicate rows (%)6.2%
Total size in memory390.6 KiB
Average record size in memory40.0 B

Variable types

Text2
Categorical1
Boolean1

Dataset

Description가축분뇨 전자인계관리시스템 에서 운영관리하는 정보중 액비 배출자 인계서 살포지 에 대한 내용입니다.(액비살포지번호, 주소 등)
Author한국환경공단
URLhttps://www.data.go.kr/data/15041901/fileData.do

Alerts

허가살포지확인동의 has constant value ""Constant
Dataset has 620 (6.2%) duplicate rowsDuplicates

Reproduction

Analysis started2023-12-12 12:12:52.376500
Analysis finished2023-12-12 12:12:53.196730
Duration0.82 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

액비살포지번호
Text

Distinct3059
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T21:12:53.588019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.9435
Min length1

Characters and Unicode

Total characters59435
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1971 ?
Unique (%)19.7%

Sample

1st rowN00006
2nd rowN02934
3rd rowN00001
4th rowN00004
5th rowN01882
ValueCountFrequency (%)
n00001 2770
27.7%
n00002 628
 
6.3%
n00003 334
 
3.3%
n00004 240
 
2.4%
n00005 149
 
1.5%
n00006 134
 
1.3%
1 113
 
1.1%
n00007 106
 
1.1%
n00009 66
 
0.7%
n00008 59
 
0.6%
Other values (3049) 5401
54.0%
2023-12-12T21:12:54.240167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 27725
46.6%
N 9856
 
16.6%
1 5759
 
9.7%
2 3170
 
5.3%
3 2472
 
4.2%
6 1970
 
3.3%
4 1808
 
3.0%
9 1758
 
3.0%
5 1667
 
2.8%
8 1626
 
2.7%
Other values (2) 1624
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49548
83.4%
Uppercase Letter 9887
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 27725
56.0%
1 5759
 
11.6%
2 3170
 
6.4%
3 2472
 
5.0%
6 1970
 
4.0%
4 1808
 
3.6%
9 1758
 
3.5%
5 1667
 
3.4%
8 1626
 
3.3%
7 1593
 
3.2%
Uppercase Letter
ValueCountFrequency (%)
N 9856
99.7%
E 31
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 49548
83.4%
Latin 9887
 
16.6%

Most frequent character per script

Common
ValueCountFrequency (%)
0 27725
56.0%
1 5759
 
11.6%
2 3170
 
6.4%
3 2472
 
5.0%
6 1970
 
4.0%
4 1808
 
3.6%
9 1758
 
3.5%
5 1667
 
3.4%
8 1626
 
3.3%
7 1593
 
3.2%
Latin
ValueCountFrequency (%)
N 9856
99.7%
E 31
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 59435
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 27725
46.6%
N 9856
 
16.6%
1 5759
 
9.7%
2 3170
 
5.3%
3 2472
 
4.2%
6 1970
 
3.3%
4 1808
 
3.0%
9 1758
 
3.0%
5 1667
 
2.8%
8 1626
 
2.7%
Other values (2) 1624
 
2.7%

액비살포지주소1
Categorical

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
전라남도
1659 
경기도
1456 
경상남도
1429 
전라북도
1395 
충청남도
1289 
Other values (9)
2772 

Length

Max length7
Median length4
Mean length4.1653
Min length3

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row전라남도
2nd row경기도
3rd row전라남도
4th row전라남도
5th row경상북도

Common Values

ValueCountFrequency (%)
전라남도 1659
16.6%
경기도 1456
14.6%
경상남도 1429
14.3%
전라북도 1395
14.0%
충청남도 1289
12.9%
제주특별자치도 1074
10.7%
경상북도 1054
10.5%
강원도 335
 
3.4%
충청북도 205
 
2.1%
세종특별자치시 59
 
0.6%
Other values (4) 45
 
0.4%

Length

2023-12-12T21:12:54.480029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전라남도 1659
16.6%
경기도 1456
14.6%
경상남도 1429
14.3%
전라북도 1395
14.0%
충청남도 1289
12.9%
제주특별자치도 1074
10.7%
경상북도 1054
10.5%
강원도 335
 
3.4%
충청북도 205
 
2.1%
세종특별자치시 59
 
0.6%
Other values (4) 45
 
0.4%

액비살포지주소2
Text

Distinct111
Distinct (%)1.1%
Missing59
Missing (%)0.6%
Memory size156.2 KiB
2023-12-12T21:12:54.851321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.0967709
Min length2

Characters and Unicode

Total characters30785
Distinct characters91
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row무안군
2nd row화성시
3rd row장성군
4th row무안군
5th row안동시
ValueCountFrequency (%)
제주시 731
 
7.2%
포천시 613
 
6.1%
화성시 550
 
5.4%
정읍시 420
 
4.2%
고창군 374
 
3.7%
서귀포시 343
 
3.4%
나주시 333
 
3.3%
무안군 258
 
2.6%
논산시 246
 
2.4%
경산시 242
 
2.4%
Other values (106) 5987
59.3%
2023-12-12T21:12:55.345044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 5902
19.2%
군 4097
 
13.3%
주 1744
 
5.7%
천 1324
 
4.3%
산 1198
 
3.9%
성 1015
 
3.3%
포 996
 
3.2%
제 994
 
3.2%
화 695
 
2.3%
안 693
 
2.3%
Other values (81) 12127
39.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 30629
99.5%
Space Separator 156
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 5902
19.3%
군 4097
 
13.4%
주 1744
 
5.7%
천 1324
 
4.3%
산 1198
 
3.9%
성 1015
 
3.3%
포 996
 
3.3%
제 994
 
3.2%
화 695
 
2.3%
안 693
 
2.3%
Other values (80) 11971
39.1%
Space Separator
ValueCountFrequency (%)
156
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 30629
99.5%
Common 156
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 5902
19.3%
군 4097
 
13.4%
주 1744
 
5.7%
천 1324
 
4.3%
산 1198
 
3.9%
성 1015
 
3.3%
포 996
 
3.3%
제 994
 
3.2%
화 695
 
2.3%
안 693
 
2.3%
Other values (80) 11971
39.1%
Common
ValueCountFrequency (%)
156
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 30629
99.5%
ASCII 156
 
0.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 5902
19.3%
군 4097
 
13.4%
주 1744
 
5.7%
천 1324
 
4.3%
산 1198
 
3.9%
성 1015
 
3.3%
포 996
 
3.3%
제 994
 
3.2%
화 695
 
2.3%
안 693
 
2.3%
Other values (80) 11971
39.1%
ASCII
ValueCountFrequency (%)
156
100.0%

허가살포지확인동의
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size87.9 KiB
True
10000 
ValueCountFrequency (%)
True 10000
100.0%
2023-12-12T21:12:55.469205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Missing values

2023-12-12T21:12:52.998508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:12:53.127684image/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

액비살포지번호액비살포지주소1액비살포지주소2허가살포지확인동의
65832N00006전라남도무안군Y
5740N02934경기도화성시Y
23871N00001전라남도장성군Y
2426N00004전라남도무안군Y
35574N01882경상북도안동시Y
17541N03028경기도화성시Y
70056N00007경기도포천시Y
74563N00001제주특별자치도서귀포시Y
87510N01319전라북도김제시Y
93281N00003경상남도산청군Y
액비살포지번호액비살포지주소1액비살포지주소2허가살포지확인동의
29046N00343경상남도진주시Y
38008N00003충청북도제천시Y
31694N00685충청남도부여군Y
47681N00001전라남도나주시Y
5313N00471강원도철원군Y
23231N00001경상남도합천군Y
69195N00974전라북도김제시Y
58553N00001전라남도영암군Y
10568N19031충청남도논산시Y
52556N01101경상남도함안군Y

Duplicate rows

Most frequently occurring

액비살포지번호액비살포지주소1액비살포지주소2허가살포지확인동의# duplicates
63N00001전라북도정읍시Y277
54N00001전라북도고창군Y233
66N00001제주특별자치도제주시Y190
18N00001경기도포천시Y158
14N00001경기도여주시Y117
88N00002경기도포천시Y108
110N00002전라북도고창군Y87
46N00001전라남도무안군Y85
73N00001충청남도아산시Y82
65N00001제주특별자치도서귀포시Y81