Overview

Dataset statistics

Number of variables5
Number of observations923
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory38.0 KiB
Average record size in memory42.1 B

Variable types

Numeric2
Text2
Categorical1

Dataset

Description전라북도 임실군의 축산업 현황입니다. 데이터 세부내역에는 행정동명, 사업장명칭, 주사육업종, 사육두수, 사업장소재지(지번), 사업장소재지(도로명)를 포함하여 제공하고 있습니다.
Author전라북도 임실군
URLhttps://www.data.go.kr/data/15034248/fileData.do

Alerts

사육두수 is highly overall correlated with 주사육업종High correlation
주사육업종 is highly overall correlated with 사육두수High correlation
주사육업종 is highly imbalanced (57.4%)Imbalance
순번 has unique valuesUnique
사육두수 has 31 (3.4%) zerosZeros

Reproduction

Analysis started2023-12-16 15:26:03.206706
Analysis finished2023-12-16 15:26:08.306413
Duration5.1 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct923
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean462
Minimum1
Maximum923
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2023-12-16T15:26:08.723681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile47.1
Q1231.5
median462
Q3692.5
95-th percentile876.9
Maximum923
Range922
Interquartile range (IQR)461

Descriptive statistics

Standard deviation266.59145
Coefficient of variation (CV)0.57703777
Kurtosis-1.2
Mean462
Median Absolute Deviation (MAD)231
Skewness0
Sum426426
Variance71071
MonotonicityNot monotonic
2023-12-16T15:26:09.655759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
325 1
 
0.1%
502 1
 
0.1%
34 1
 
0.1%
274 1
 
0.1%
81 1
 
0.1%
127 1
 
0.1%
513 1
 
0.1%
647 1
 
0.1%
86 1
 
0.1%
190 1
 
0.1%
Other values (913) 913
98.9%
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 (%)
923 1
0.1%
922 1
0.1%
921 1
0.1%
920 1
0.1%
919 1
0.1%
918 1
0.1%
917 1
0.1%
916 1
0.1%
915 1
0.1%
914 1
0.1%

사업장명칭
Text

Distinct800
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
2023-12-16T15:26:10.654706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length4
Mean length4.2166847
Min length1

Characters and Unicode

Total characters3892
Distinct characters317
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

Unique692 ?
Unique (%)75.0%

Sample

1st row기성목장
2nd row상철농장
3rd row송학농장
4th row동산목장
5th row숲골목장
ValueCountFrequency (%)
대성농장 4
 
0.4%
농장 4
 
0.4%
행복농장 3
 
0.3%
전북축산 3
 
0.3%
운암농장 3
 
0.3%
영환농장 3
 
0.3%
원농장 3
 
0.3%
우리목장 3
 
0.3%
백련농장 3
 
0.3%
상현농장 3
 
0.3%
Other values (800) 905
96.6%
2023-12-16T15:26:12.621337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 843
21.7%
농 744
 
19.1%
목 114
 
2.9%
우 60
 
1.5%
산 52
 
1.3%
수 46
 
1.2%
영 39
 
1.0%
진 38
 
1.0%
한 37
 
1.0%
성 36
 
0.9%
Other values (307) 1883
48.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3854
99.0%
Space Separator 14
 
0.4%
Open Punctuation 5
 
0.1%
Close Punctuation 5
 
0.1%
Uppercase Letter 5
 
0.1%
Decimal Number 4
 
0.1%
Lowercase Letter 4
 
0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 843
21.9%
농 744
19.3%
목 114
 
3.0%
우 60
 
1.6%
산 52
 
1.3%
수 46
 
1.2%
영 39
 
1.0%
진 38
 
1.0%
한 37
 
1.0%
성 36
 
0.9%
Other values (293) 1845
47.9%
Uppercase Letter
ValueCountFrequency (%)
K 2
40.0%
L 1
20.0%
O 1
20.0%
D 1
20.0%
Lowercase Letter
ValueCountFrequency (%)
m 1
25.0%
a 1
25.0%
e 1
25.0%
r 1
25.0%
Decimal Number
ValueCountFrequency (%)
2 3
75.0%
3 1
 
25.0%
Space Separator
ValueCountFrequency (%)
14
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3854
99.0%
Common 29
 
0.7%
Latin 9
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 843
21.9%
농 744
19.3%
목 114
 
3.0%
우 60
 
1.6%
산 52
 
1.3%
수 46
 
1.2%
영 39
 
1.0%
진 38
 
1.0%
한 37
 
1.0%
성 36
 
0.9%
Other values (293) 1845
47.9%
Latin
ValueCountFrequency (%)
K 2
22.2%
L 1
11.1%
O 1
11.1%
m 1
11.1%
a 1
11.1%
e 1
11.1%
r 1
11.1%
D 1
11.1%
Common
ValueCountFrequency (%)
14
48.3%
( 5
 
17.2%
) 5
 
17.2%
2 3
 
10.3%
3 1
 
3.4%
. 1
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3854
99.0%
ASCII 38
 
1.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 843
21.9%
농 744
19.3%
목 114
 
3.0%
우 60
 
1.6%
산 52
 
1.3%
수 46
 
1.2%
영 39
 
1.0%
진 38
 
1.0%
한 37
 
1.0%
성 36
 
0.9%
Other values (293) 1845
47.9%
ASCII
ValueCountFrequency (%)
14
36.8%
( 5
 
13.2%
) 5
 
13.2%
2 3
 
7.9%
K 2
 
5.3%
3 1
 
2.6%
L 1
 
2.6%
. 1
 
2.6%
O 1
 
2.6%
m 1
 
2.6%
Other values (4) 4
 
10.5%

주사육업종
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct10
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
한우
690 
염소
 
63
육계
 
56
젖소
 
49
돼지
 
46
Other values (5)
 
19

Length

Max length3
Median length2
Mean length2.0032503
Min length2

Unique

Unique2 ?
Unique (%)0.2%

Sample

1st row한우
2nd row한우
3rd row돼지
4th row젖소
5th row젖소

Common Values

ValueCountFrequency (%)
한우 690
74.8%
염소 63
 
6.8%
육계 56
 
6.1%
젖소 49
 
5.3%
돼지 46
 
5.0%
산양 9
 
1.0%
육우 6
 
0.7%
메추리 2
 
0.2%
사슴 1
 
0.1%
기러기 1
 
0.1%

Length

2023-12-16T15:26:13.290854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T15:26:13.976698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한우 690
74.8%
염소 63
 
6.8%
육계 56
 
6.1%
젖소 49
 
5.3%
돼지 46
 
5.0%
산양 9
 
1.0%
육우 6
 
0.7%
메추리 2
 
0.2%
사슴 1
 
0.1%
기러기 1
 
0.1%

사육두수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct151
Distinct (%)16.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3083.2384
Minimum0
Maximum500000
Zeros31
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2023-12-16T15:26:14.710904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median18
Q353
95-th percentile2018.9
Maximum500000
Range500000
Interquartile range (IQR)47

Descriptive statistics

Standard deviation20411.284
Coefficient of variation (CV)6.6200798
Kurtosis384.25861
Mean3083.2384
Median Absolute Deviation (MAD)14
Skewness16.771967
Sum2845829
Variance4.1662051 × 108
MonotonicityNot monotonic
2023-12-16T15:26:15.692472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 51
 
5.5%
2 42
 
4.6%
4 42
 
4.6%
10 39
 
4.2%
20 34
 
3.7%
6 34
 
3.7%
3 32
 
3.5%
0 31
 
3.4%
30 31
 
3.4%
8 25
 
2.7%
Other values (141) 562
60.9%
ValueCountFrequency (%)
0 31
3.4%
1 24
2.6%
2 42
4.6%
3 32
3.5%
4 42
4.6%
5 51
5.5%
6 34
3.7%
7 22
2.4%
8 25
2.7%
9 16
 
1.7%
ValueCountFrequency (%)
500000 1
 
0.1%
100000 1
 
0.1%
95000 1
 
0.1%
90000 1
 
0.1%
85000 1
 
0.1%
80000 1
 
0.1%
75000 2
 
0.2%
70000 2
 
0.2%
65000 5
0.5%
62000 1
 
0.1%

사업장소재지(지번)
Text

Distinct906
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
2023-12-16T15:26:17.695887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length34
Mean length25.455038
Min length18

Characters and Unicode

Total characters23495
Distinct characters129
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique890 ?
Unique (%)96.4%

Sample

1st row전라북도 임실군 청웅면 구고리 466-1
2nd row전라북도 임실군 강진면 학석리 171-1
3rd row전라북도 임실군 신덕면 금정리 306-1
4th row전라북도 임실군 임실읍 장재리 80-2
5th row전라북도 임실군 임실읍 금성리 166
ValueCountFrequency (%)
전라북도 922
 
18.0%
임실군 922
 
18.0%
1호 209
 
4.1%
관촌면 129
 
2.5%
오수면 125
 
2.4%
삼계면 114
 
2.2%
임실읍 111
 
2.2%
2호 77
 
1.5%
신덕면 71
 
1.4%
신평면 63
 
1.2%
Other values (836) 2393
46.6%
2023-12-16T15:26:20.433693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5889
25.1%
임 1038
 
4.4%
실 1034
 
4.4%
도 963
 
4.1%
지 947
 
4.0%
전 945
 
4.0%
군 927
 
3.9%
북 923
 
3.9%
라 923
 
3.9%
리 898
 
3.8%
Other values (119) 9008
38.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 14233
60.6%
Space Separator 5889
25.1%
Decimal Number 3310
 
14.1%
Dash Punctuation 50
 
0.2%
Other Punctuation 8
 
< 0.1%
Open Punctuation 2
 
< 0.1%
Close Punctuation 2
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
임 1038
 
7.3%
실 1034
 
7.3%
도 963
 
6.8%
지 947
 
6.7%
전 945
 
6.6%
군 927
 
6.5%
북 923
 
6.5%
라 923
 
6.5%
리 898
 
6.3%
번 867
 
6.1%
Other values (102) 4768
33.5%
Decimal Number
ValueCountFrequency (%)
1 656
19.8%
2 377
11.4%
4 357
10.8%
3 349
10.5%
5 298
9.0%
6 285
8.6%
7 275
8.3%
8 250
 
7.6%
0 241
 
7.3%
9 222
 
6.7%
Other Punctuation
ValueCountFrequency (%)
, 5
62.5%
/ 3
37.5%
Space Separator
ValueCountFrequency (%)
5889
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 50
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 14233
60.6%
Common 9262
39.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
임 1038
 
7.3%
실 1034
 
7.3%
도 963
 
6.8%
지 947
 
6.7%
전 945
 
6.6%
군 927
 
6.5%
북 923
 
6.5%
라 923
 
6.5%
리 898
 
6.3%
번 867
 
6.1%
Other values (102) 4768
33.5%
Common
ValueCountFrequency (%)
5889
63.6%
1 656
 
7.1%
2 377
 
4.1%
4 357
 
3.9%
3 349
 
3.8%
5 298
 
3.2%
6 285
 
3.1%
7 275
 
3.0%
8 250
 
2.7%
0 241
 
2.6%
Other values (7) 285
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 14233
60.6%
ASCII 9262
39.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5889
63.6%
1 656
 
7.1%
2 377
 
4.1%
4 357
 
3.9%
3 349
 
3.8%
5 298
 
3.2%
6 285
 
3.1%
7 275
 
3.0%
8 250
 
2.7%
0 241
 
2.6%
Other values (7) 285
 
3.1%
Hangul
ValueCountFrequency (%)
임 1038
 
7.3%
실 1034
 
7.3%
도 963
 
6.8%
지 947
 
6.7%
전 945
 
6.6%
군 927
 
6.5%
북 923
 
6.5%
라 923
 
6.5%
리 898
 
6.3%
번 867
 
6.1%
Other values (102) 4768
33.5%

Interactions

2023-12-16T15:26:06.080799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:26:04.371250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:26:06.713697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:26:05.313714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-16T15:26:21.597938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번주사육업종사육두수
순번1.0000.4920.327
주사육업종0.4921.0000.788
사육두수0.3270.7881.000
2023-12-16T15:26:22.137599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번사육두수주사육업종
순번1.000-0.1660.170
사육두수-0.1661.0000.670
주사육업종0.1700.6701.000

Missing values

2023-12-16T15:26:07.476403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-16T15:26:08.121281image/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

순번사업장명칭주사육업종사육두수사업장소재지(지번)
0325기성목장한우3전라북도 임실군 청웅면 구고리 466-1
1376상철농장한우150전라북도 임실군 강진면 학석리 171-1
2378송학농장돼지2000전라북도 임실군 신덕면 금정리 306-1
3382동산목장젖소60전라북도 임실군 임실읍 장재리 80-2
4383숲골목장젖소140전라북도 임실군 임실읍 금성리 166
5388신선농장육계55000전라북도 임실군 덕치면 인덕로 1446-21
6391행여나목장젖소60전라북도 임실군 덕치면 사곡리 529
7393대성농장한우455전라북도 임실군 오수면 대정길 107-100
8395성광목장젖소73전라북도 임실군 성수면 삼봉리 803-1
9397창주농장한우1전라북도임실군 강진면 학석리 404-2
순번사업장명칭주사육업종사육두수사업장소재지(지번)
913583지우농장한우34전라북도 임실군 청웅면 옥전리 512번지 1호
914154상만농장한우6전라북도 임실군 청웅면 옥전리 638번지 1호
91591덕의농장한우10전라북도 임실군 청웅면 옥전리 724번지
916581청계목장한우11전라북도 임실군 청웅면 청계리 307번지 3호
917584성진농장젖소120전라북도 임실군 청웅면 청계리 310번지 1호
918740시우농장육계60전라북도 임실군 청웅면 향교리 365번지 1호
91997참사랑한우농장한우25전라북도 임실군 청웅면 향교리 465번지
920404순남농장한우1전라북도 임실군 청웅면 향교리 522번지
921665문수농장육계90전라북도 임실군 청웅면 향교리 602번지
922580미래농장한우59전라북도 임실군 청웅면 향교리 931번지 1호