Overview

Dataset statistics

Number of variables5
Number of observations1853
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory74.3 KiB
Average record size in memory41.1 B

Variable types

Numeric1
Categorical2
Text2

Dataset

Description전북특별자치도 김제시 축산업현황입니다.
Author전라북도
URLhttps://www.bigdatahub.go.kr/index.jeonbuk?startPage=7&menuCd=DOM_000000103007001000&pListTypeStr=&pId=15034296

Alerts

연번 is highly overall correlated with 행정동명High correlation
행정동명 is highly overall correlated with 연번High correlation
주사육업종 is highly imbalanced (56.7%)Imbalance
연번 has unique valuesUnique

Reproduction

Analysis started2024-03-14 00:07:54.232181
Analysis finished2024-03-14 00:07:54.876374
Duration0.64 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1853
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean927
Minimum1
Maximum1853
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.4 KiB
2024-03-14T09:07:54.954586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile93.6
Q1464
median927
Q31390
95-th percentile1760.4
Maximum1853
Range1852
Interquartile range (IQR)926

Descriptive statistics

Standard deviation535.05934
Coefficient of variation (CV)0.57719454
Kurtosis-1.2
Mean927
Median Absolute Deviation (MAD)463
Skewness0
Sum1717731
Variance286288.5
MonotonicityStrictly increasing
2024-03-14T09:07:55.069356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
1246 1
 
0.1%
1244 1
 
0.1%
1243 1
 
0.1%
1242 1
 
0.1%
1241 1
 
0.1%
1240 1
 
0.1%
1239 1
 
0.1%
1238 1
 
0.1%
1237 1
 
0.1%
Other values (1843) 1843
99.5%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
1853 1
0.1%
1852 1
0.1%
1851 1
0.1%
1850 1
0.1%
1849 1
0.1%
1848 1
0.1%
1847 1
0.1%
1846 1
0.1%
1845 1
0.1%
1844 1
0.1%

행정동명
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size14.6 KiB
용지면
380 
금산면
242 
백산면
182 
봉남면
137 
황산면
134 
Other values (14)
778 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row검산동
2nd row검산동
3rd row검산동
4th row검산동
5th row검산동

Common Values

ValueCountFrequency (%)
용지면 380
20.5%
금산면 242
13.1%
백산면 182
9.8%
봉남면 137
 
7.4%
황산면 134
 
7.2%
금구면 131
 
7.1%
교월동 111
 
6.0%
공덕면 77
 
4.2%
죽산면 60
 
3.2%
검산동 59
 
3.2%
Other values (9) 340
18.3%

Length

2024-03-14T09:07:55.176562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
용지면 380
20.5%
금산면 242
13.1%
백산면 182
9.8%
봉남면 137
 
7.4%
황산면 134
 
7.2%
금구면 131
 
7.1%
교월동 111
 
6.0%
공덕면 77
 
4.2%
죽산면 60
 
3.2%
검산동 59
 
3.2%
Other values (9) 340
18.3%

사업장명칭
Text

Distinct1417
Distinct (%)76.5%
Missing0
Missing (%)0.0%
Memory size14.6 KiB
2024-03-14T09:07:55.396544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length4
Mean length4.4711279
Min length2

Characters and Unicode

Total characters8285
Distinct characters397
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

Unique1154 ?
Unique (%)62.3%

Sample

1st row은지농장
2nd row태호농장
3rd row수종농장
4th row종기농장
5th row하동농장
ValueCountFrequency (%)
우리농장 12
 
0.6%
대성농장 11
 
0.6%
희망농장 10
 
0.5%
신흥농장 9
 
0.5%
농장 9
 
0.5%
보람농장 8
 
0.4%
형제농장 7
 
0.4%
쌍용농장 6
 
0.3%
행복농장 6
 
0.3%
용암농장 5
 
0.3%
Other values (1419) 1804
95.6%
2024-03-14T09:07:55.781960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
농 1734
20.9%
장 1717
20.7%
성 145
 
1.8%
영 143
 
1.7%
우 125
 
1.5%
산 123
 
1.5%
정 96
 
1.2%
한 91
 
1.1%
원 86
 
1.0%
조 81
 
1.0%
Other values (387) 3944
47.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8160
98.5%
Decimal Number 47
 
0.6%
Space Separator 34
 
0.4%
Uppercase Letter 17
 
0.2%
Open Punctuation 10
 
0.1%
Close Punctuation 10
 
0.1%
Lowercase Letter 4
 
< 0.1%
Other Punctuation 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
농 1734
21.2%
장 1717
21.0%
성 145
 
1.8%
영 143
 
1.8%
우 125
 
1.5%
산 123
 
1.5%
정 96
 
1.2%
한 91
 
1.1%
원 86
 
1.1%
조 81
 
1.0%
Other values (364) 3819
46.8%
Uppercase Letter
ValueCountFrequency (%)
D 3
17.6%
K 3
17.6%
A 2
11.8%
I 2
11.8%
J 2
11.8%
N 1
 
5.9%
S 1
 
5.9%
G 1
 
5.9%
P 1
 
5.9%
C 1
 
5.9%
Decimal Number
ValueCountFrequency (%)
2 32
68.1%
1 10
 
21.3%
3 4
 
8.5%
4 1
 
2.1%
Lowercase Letter
ValueCountFrequency (%)
e 1
25.0%
m 1
25.0%
r 1
25.0%
a 1
25.0%
Other Punctuation
ValueCountFrequency (%)
, 2
66.7%
& 1
33.3%
Space Separator
ValueCountFrequency (%)
34
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8160
98.5%
Common 104
 
1.3%
Latin 21
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
농 1734
21.2%
장 1717
21.0%
성 145
 
1.8%
영 143
 
1.8%
우 125
 
1.5%
산 123
 
1.5%
정 96
 
1.2%
한 91
 
1.1%
원 86
 
1.1%
조 81
 
1.0%
Other values (364) 3819
46.8%
Latin
ValueCountFrequency (%)
D 3
14.3%
K 3
14.3%
A 2
9.5%
I 2
9.5%
J 2
9.5%
N 1
 
4.8%
e 1
 
4.8%
m 1
 
4.8%
r 1
 
4.8%
a 1
 
4.8%
Other values (4) 4
19.0%
Common
ValueCountFrequency (%)
34
32.7%
2 32
30.8%
1 10
 
9.6%
( 10
 
9.6%
) 10
 
9.6%
3 4
 
3.8%
, 2
 
1.9%
4 1
 
1.0%
& 1
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8160
98.5%
ASCII 125
 
1.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
농 1734
21.2%
장 1717
21.0%
성 145
 
1.8%
영 143
 
1.8%
우 125
 
1.5%
산 123
 
1.5%
정 96
 
1.2%
한 91
 
1.1%
원 86
 
1.1%
조 81
 
1.0%
Other values (364) 3819
46.8%
ASCII
ValueCountFrequency (%)
34
27.2%
2 32
25.6%
1 10
 
8.0%
( 10
 
8.0%
) 10
 
8.0%
3 4
 
3.2%
D 3
 
2.4%
K 3
 
2.4%
A 2
 
1.6%
I 2
 
1.6%
Other values (13) 15
12.0%

주사육업종
Categorical

IMBALANCE 

Distinct11
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size14.6 KiB
한우
1330 
돼지
207 
산란계
 
133
육계
 
104
젖소
 
28
Other values (6)
 
51

Length

Max length3
Median length2
Mean length2.0728548
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row육계
2nd row한우
3rd row한우
4th row한우
5th row한우

Common Values

ValueCountFrequency (%)
한우 1330
71.8%
돼지 207
 
11.2%
산란계 133
 
7.2%
육계 104
 
5.6%
젖소 28
 
1.5%
오리 20
 
1.1%
염소 10
 
0.5%
사슴 10
 
0.5%
육우 6
 
0.3%
산양 3
 
0.2%

Length

2024-03-14T09:07:55.943673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
한우 1330
71.8%
돼지 207
 
11.2%
산란계 133
 
7.2%
육계 104
 
5.6%
젖소 28
 
1.5%
오리 20
 
1.1%
염소 10
 
0.5%
사슴 10
 
0.5%
육우 6
 
0.3%
산양 3
 
0.2%

사업장소재지
Text

Distinct1764
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Memory size14.6 KiB
2024-03-14T09:07:56.206872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length105
Median length55
Mean length26.202914
Min length18

Characters and Unicode

Total characters48554
Distinct characters144
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1680 ?
Unique (%)90.7%

Sample

1st row전라북도 김제시 콩쥐팥쥐로 305-83 (상동동)
2nd row전라북도 김제시 검산동 1번지
3rd row전라북도 김제시 검산동 42번지 5호 ,42-6
4th row전라북도 김제시 검산동 740번지 5호
5th row전라북도 김제시 검산동 900번지 2호
ValueCountFrequency (%)
전라북도 1853
 
17.5%
김제시 1853
 
17.5%
용지면 380
 
3.6%
1호 309
 
2.9%
금산면 242
 
2.3%
백산면 182
 
1.7%
봉남면 137
 
1.3%
황산면 134
 
1.3%
금구면 131
 
1.2%
2호 128
 
1.2%
Other values (1195) 5216
49.4%
2024-03-14T09:07:56.839996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12225
25.2%
지 2279
 
4.7%
도 1882
 
3.9%
전 1872
 
3.9%
제 1868
 
3.8%
라 1861
 
3.8%
북 1853
 
3.8%
시 1853
 
3.8%
김 1853
 
3.8%
번 1824
 
3.8%
Other values (134) 19184
39.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 28541
58.8%
Space Separator 12225
25.2%
Decimal Number 7508
 
15.5%
Dash Punctuation 133
 
0.3%
Other Punctuation 131
 
0.3%
Open Punctuation 8
 
< 0.1%
Close Punctuation 8
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 2279
 
8.0%
도 1882
 
6.6%
전 1872
 
6.6%
제 1868
 
6.5%
라 1861
 
6.5%
북 1853
 
6.5%
시 1853
 
6.5%
김 1853
 
6.5%
번 1824
 
6.4%
리 1623
 
5.7%
Other values (117) 9773
34.2%
Decimal Number
ValueCountFrequency (%)
1 1371
18.3%
2 985
13.1%
3 865
11.5%
4 797
10.6%
5 779
10.4%
6 635
8.5%
7 561
7.5%
8 545
 
7.3%
0 518
 
6.9%
9 452
 
6.0%
Other Punctuation
ValueCountFrequency (%)
, 95
72.5%
/ 20
 
15.3%
. 16
 
12.2%
Space Separator
ValueCountFrequency (%)
12225
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 133
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 28541
58.8%
Common 20013
41.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 2279
 
8.0%
도 1882
 
6.6%
전 1872
 
6.6%
제 1868
 
6.5%
라 1861
 
6.5%
북 1853
 
6.5%
시 1853
 
6.5%
김 1853
 
6.5%
번 1824
 
6.4%
리 1623
 
5.7%
Other values (117) 9773
34.2%
Common
ValueCountFrequency (%)
12225
61.1%
1 1371
 
6.9%
2 985
 
4.9%
3 865
 
4.3%
4 797
 
4.0%
5 779
 
3.9%
6 635
 
3.2%
7 561
 
2.8%
8 545
 
2.7%
0 518
 
2.6%
Other values (7) 732
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 28541
58.8%
ASCII 20013
41.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12225
61.1%
1 1371
 
6.9%
2 985
 
4.9%
3 865
 
4.3%
4 797
 
4.0%
5 779
 
3.9%
6 635
 
3.2%
7 561
 
2.8%
8 545
 
2.7%
0 518
 
2.6%
Other values (7) 732
 
3.7%
Hangul
ValueCountFrequency (%)
지 2279
 
8.0%
도 1882
 
6.6%
전 1872
 
6.6%
제 1868
 
6.5%
라 1861
 
6.5%
북 1853
 
6.5%
시 1853
 
6.5%
김 1853
 
6.5%
번 1824
 
6.4%
리 1623
 
5.7%
Other values (117) 9773
34.2%

Interactions

2024-03-14T09:07:54.570219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T09:07:56.964213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번행정동명주사육업종
연번1.0000.9670.396
행정동명0.9671.0000.497
주사육업종0.3960.4971.000
2024-03-14T09:07:57.061077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주사육업종행정동명
주사육업종1.0000.206
행정동명0.2061.000
2024-03-14T09:07:57.133610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번행정동명주사육업종
연번1.0000.8250.180
행정동명0.8251.0000.206
주사육업종0.1800.2061.000

Missing values

2024-03-14T09:07:54.728323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T09:07:54.831751image/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

연번행정동명사업장명칭주사육업종사업장소재지
01검산동은지농장육계전라북도 김제시 콩쥐팥쥐로 305-83 (상동동)
12검산동태호농장한우전라북도 김제시 검산동 1번지
23검산동수종농장한우전라북도 김제시 검산동 42번지 5호 ,42-6
34검산동종기농장한우전라북도 김제시 검산동 740번지 5호
45검산동하동농장한우전라북도 김제시 검산동 900번지 2호
56검산동검산축산한우전라북도 김제시 백학동 1136번지 외 1필지
67검산동상욱농장한우전라북도 김제시 백학동 124번지 1호
78검산동박용만농장한우전라북도 김제시 백학동 128번지 1호
89검산동민서농장한우전라북도 김제시 백학동 12번지
910검산동용주농장한우전라북도 김제시 백학동 137번지
연번행정동명사업장명칭주사육업종사업장소재지
18431844황산면일우농장한우전라북도 김제시 황산면 홍정리 172번지 1호
18441845황산면연화농장한우전라북도 김제시 황산면 홍정리 183번지
18451846황산면홍정축산한우전라북도 김제시 황산면 홍정리 192번지
18461847황산면황산농장한우전라북도 김제시 황산면 홍정리 196번지
18471848황산면명성농장한우전라북도 김제시 황산면 홍정리 201번지
18481849황산면홍정농장한우전라북도 김제시 황산면 홍정리 213번지
18491850황산면신홍농장한우전라북도 김제시 황산면 홍정리 219번지 3호
18501851황산면우리농장한우전라북도 김제시 황산면 홍정리 246번지 1호
18511852황산면장철농장한우전라북도 김제시 황산면 홍정리 302번지 14호
18521853황산면점동농장한우전라북도 김제시 황산면 홍정리 331번지