Overview

Dataset statistics

Number of variables11
Number of observations155
Missing cells118
Missing cells (%)6.9%
Duplicate rows1
Duplicate rows (%)0.6%
Total size in memory14.2 KiB
Average record size in memory93.9 B

Variable types

Categorical3
Text3
Numeric5

Dataset

Description사료 제조업체 현황(배합)
Author행정안전부
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=VHBFO6H89O214K80N4M81406098&infSeq=1

Alerts

축산업무구분명 has constant value ""Constant
Dataset has 1 (0.6%) duplicate rowsDuplicates
폐업일자 is highly overall correlated with 소재지우편번호 and 1 other fieldsHigh correlation
소재지우편번호 is highly overall correlated with 폐업일자 and 1 other fieldsHigh correlation
WGS84위도 is highly overall correlated with 소재지우편번호 and 1 other fieldsHigh correlation
WGS84경도 is highly overall correlated with 시군명High correlation
시군명 is highly overall correlated with WGS84위도 and 1 other fieldsHigh correlation
영업상태명 is highly overall correlated with 폐업일자High correlation
폐업일자 has 111 (71.6%) missing valuesMissing
소재지도로명주소 has 2 (1.3%) missing valuesMissing
WGS84위도 has 2 (1.3%) missing valuesMissing
WGS84경도 has 2 (1.3%) missing valuesMissing

Reproduction

Analysis started2023-12-10 22:05:54.416277
Analysis finished2023-12-10 22:05:56.989000
Duration2.57 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct27
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
평택시
16 
안성시
16 
화성시
13 
용인시
13 
이천시
11 
Other values (22)
86 

Length

Max length4
Median length3
Mean length3.0709677
Min length3

Unique

Unique6 ?
Unique (%)3.9%

Sample

1st row가평군
2nd row가평군
3rd row가평군
4th row고양시
5th row고양시

Common Values

ValueCountFrequency (%)
평택시 16
 
10.3%
안성시 16
 
10.3%
화성시 13
 
8.4%
용인시 13
 
8.4%
이천시 11
 
7.1%
남양주시 9
 
5.8%
포천시 8
 
5.2%
김포시 8
 
5.2%
안산시 8
 
5.2%
파주시 7
 
4.5%
Other values (17) 46
29.7%

Length

2023-12-11T07:05:57.043880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
평택시 16
 
10.3%
안성시 16
 
10.3%
화성시 13
 
8.4%
용인시 13
 
8.4%
이천시 11
 
7.1%
남양주시 9
 
5.8%
포천시 8
 
5.2%
김포시 8
 
5.2%
안산시 8
 
5.2%
파주시 7
 
4.5%
Other values (17) 46
29.7%

사업장명
Text

Distinct145
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-11T07:05:57.219518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length14
Mean length8.3806452
Min length2

Characters and Unicode

Total characters1299
Distinct characters207
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique137 ?
Unique (%)88.4%

Sample

1st row신광그린
2nd row가평군TMR영농조합법인
3rd row피드피아
4th row인아웃
5th row(주)버섯연구소
ValueCountFrequency (%)
주식회사 14
 
7.2%
주)카길애그리퓨리나 4
 
2.1%
주)코모텍 3
 
1.5%
평택공장 3
 
1.5%
농업회사법인 3
 
1.5%
주)에이티바이오 3
 
1.5%
dh 2
 
1.0%
제2공장 2
 
1.0%
주)에이치앤씨파트너스 2
 
1.0%
사료공장 2
 
1.0%
Other values (148) 156
80.4%
2023-12-11T07:05:57.512542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 86
 
6.6%
( 70
 
5.4%
) 70
 
5.4%
이 42
 
3.2%
사 40
 
3.1%
39
 
3.0%
농 33
 
2.5%
인 28
 
2.2%
합 27
 
2.1%
스 25
 
1.9%
Other values (197) 839
64.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1094
84.2%
Open Punctuation 70
 
5.4%
Close Punctuation 70
 
5.4%
Space Separator 39
 
3.0%
Uppercase Letter 18
 
1.4%
Lowercase Letter 5
 
0.4%
Decimal Number 3
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 86
 
7.9%
이 42
 
3.8%
사 40
 
3.7%
농 33
 
3.0%
인 28
 
2.6%
합 27
 
2.5%
스 25
 
2.3%
조 24
 
2.2%
산 24
 
2.2%
법 22
 
2.0%
Other values (181) 743
67.9%
Uppercase Letter
ValueCountFrequency (%)
R 4
22.2%
M 4
22.2%
T 4
22.2%
D 3
16.7%
H 2
11.1%
P 1
 
5.6%
Lowercase Letter
ValueCountFrequency (%)
s 1
20.0%
t 1
20.0%
a 1
20.0%
y 1
20.0%
e 1
20.0%
Decimal Number
ValueCountFrequency (%)
2 2
66.7%
3 1
33.3%
Open Punctuation
ValueCountFrequency (%)
( 70
100.0%
Close Punctuation
ValueCountFrequency (%)
) 70
100.0%
Space Separator
ValueCountFrequency (%)
39
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1094
84.2%
Common 182
 
14.0%
Latin 23
 
1.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 86
 
7.9%
이 42
 
3.8%
사 40
 
3.7%
농 33
 
3.0%
인 28
 
2.6%
합 27
 
2.5%
스 25
 
2.3%
조 24
 
2.2%
산 24
 
2.2%
법 22
 
2.0%
Other values (181) 743
67.9%
Latin
ValueCountFrequency (%)
R 4
17.4%
M 4
17.4%
T 4
17.4%
D 3
13.0%
H 2
8.7%
s 1
 
4.3%
t 1
 
4.3%
a 1
 
4.3%
y 1
 
4.3%
e 1
 
4.3%
Common
ValueCountFrequency (%)
( 70
38.5%
) 70
38.5%
39
21.4%
2 2
 
1.1%
3 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1094
84.2%
ASCII 205
 
15.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 86
 
7.9%
이 42
 
3.8%
사 40
 
3.7%
농 33
 
3.0%
인 28
 
2.6%
합 27
 
2.5%
스 25
 
2.3%
조 24
 
2.2%
산 24
 
2.2%
법 22
 
2.0%
Other values (181) 743
67.9%
ASCII
ValueCountFrequency (%)
( 70
34.1%
) 70
34.1%
39
19.0%
R 4
 
2.0%
M 4
 
2.0%
T 4
 
2.0%
D 3
 
1.5%
H 2
 
1.0%
2 2
 
1.0%
s 1
 
0.5%
Other values (6) 6
 
2.9%

인허가일자
Real number (ℝ)

Distinct145
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20085179
Minimum19760714
Maximum20180803
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-11T07:05:57.629395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19760714
5-th percentile19960329
Q120041228
median20090309
Q320141016
95-th percentile20170673
Maximum20180803
Range420089
Interquartile range (IQR)99788.5

Descriptive statistics

Standard deviation71545.666
Coefficient of variation (CV)0.0035621124
Kurtosis3.4346082
Mean20085179
Median Absolute Deviation (MAD)49186
Skewness-1.3189188
Sum3.1132028 × 109
Variance5.1187823 × 109
MonotonicityNot monotonic
2023-12-11T07:05:57.748900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20130403 3
 
1.9%
20160302 3
 
1.9%
20070724 2
 
1.3%
19960329 2
 
1.3%
20140527 2
 
1.3%
20130524 2
 
1.3%
20131022 2
 
1.3%
20170814 2
 
1.3%
20090424 1
 
0.6%
20150324 1
 
0.6%
Other values (135) 135
87.1%
ValueCountFrequency (%)
19760714 1
0.6%
19791112 1
0.6%
19860227 1
0.6%
19960214 1
0.6%
19960223 1
0.6%
19960227 1
0.6%
19960318 1
0.6%
19960329 2
1.3%
19971120 1
0.6%
19980406 1
0.6%
ValueCountFrequency (%)
20180803 1
0.6%
20180112 1
0.6%
20180102 1
0.6%
20171219 1
0.6%
20171115 1
0.6%
20170908 1
0.6%
20170814 2
1.3%
20170613 1
0.6%
20170601 1
0.6%
20170405 1
0.6%

영업상태명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
운영중
95 
폐업 등
44 
휴업 등
16 

Length

Max length4
Median length3
Mean length3.3870968
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row운영중
2nd row운영중
3rd row휴업 등
4th row운영중
5th row운영중

Common Values

ValueCountFrequency (%)
운영중 95
61.3%
폐업 등 44
28.4%
휴업 등 16
 
10.3%

Length

2023-12-11T07:05:57.854202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:05:57.937198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
운영중 95
44.2%
등 60
27.9%
폐업 44
20.5%
휴업 16
 
7.4%

폐업일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct41
Distinct (%)93.2%
Missing111
Missing (%)71.6%
Infinite0
Infinite (%)0.0%
Mean20126824
Minimum20060522
Maximum20180704
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-11T07:05:58.026773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20060522
5-th percentile20070543
Q120100582
median20135668
Q320151071
95-th percentile20171082
Maximum20180704
Range120182
Interquartile range (IQR)50489

Descriptive statistics

Standard deviation35964.202
Coefficient of variation (CV)0.0017868792
Kurtosis-1.0805005
Mean20126824
Median Absolute Deviation (MAD)25063
Skewness-0.42939298
Sum8.8558023 × 108
Variance1.2934238 × 109
MonotonicityNot monotonic
2023-12-11T07:05:58.135712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
20150528 3
 
1.9%
20130322 2
 
1.3%
20160530 1
 
0.6%
20151019 1
 
0.6%
20171130 1
 
0.6%
20070622 1
 
0.6%
20071127 1
 
0.6%
20180704 1
 
0.6%
20130808 1
 
0.6%
20140709 1
 
0.6%
Other values (31) 31
 
20.0%
(Missing) 111
71.6%
ValueCountFrequency (%)
20060522 1
0.6%
20070108 1
0.6%
20070531 1
0.6%
20070614 1
0.6%
20070622 1
0.6%
20071016 1
0.6%
20071127 1
0.6%
20080407 1
0.6%
20080508 1
0.6%
20080813 1
0.6%
ValueCountFrequency (%)
20180704 1
0.6%
20180608 1
0.6%
20171130 1
0.6%
20170808 1
0.6%
20170724 1
0.6%
20170414 1
0.6%
20161202 1
0.6%
20160728 1
0.6%
20160726 1
0.6%
20160530 1
0.6%

축산업무구분명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
사료제조업
155 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
사료제조업 155
100.0%

Length

2023-12-11T07:05:58.240624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:05:58.315924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사료제조업 155
100.0%

소재지도로명주소
Text

MISSING 

Distinct140
Distinct (%)91.5%
Missing2
Missing (%)1.3%
Memory size1.3 KiB
2023-12-11T07:05:58.512921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length35
Mean length23.875817
Min length14

Characters and Unicode

Total characters3653
Distinct characters210
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

Unique130 ?
Unique (%)85.0%

Sample

1st row경기도 가평군 하면 보름골길 26
2nd row경기도 가평군 청군로 1566
3rd row경기도 고양시 일산동구 문봉길62번길 85 (설문동)
4th row경기도 고양시 덕양구 무원로36번길 25 (행신동)
5th row경기도 과천시 별양상가로 2 (별양동, 그레이스호텔)
ValueCountFrequency (%)
경기도 153
 
18.9%
평택시 16
 
2.0%
안성시 15
 
1.8%
용인시 13
 
1.6%
화성시 13
 
1.6%
이천시 11
 
1.4%
처인구 9
 
1.1%
남양주시 9
 
1.1%
포천시 8
 
1.0%
김포시 8
 
1.0%
Other values (392) 556
68.6%
2023-12-11T07:05:58.855070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
658
 
18.0%
기 165
 
4.5%
도 164
 
4.5%
경 160
 
4.4%
시 156
 
4.3%
1 140
 
3.8%
로 113
 
3.1%
길 90
 
2.5%
2 89
 
2.4%
동 76
 
2.1%
Other values (200) 1842
50.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2168
59.3%
Space Separator 658
 
18.0%
Decimal Number 645
 
17.7%
Dash Punctuation 52
 
1.4%
Close Punctuation 49
 
1.3%
Open Punctuation 49
 
1.3%
Other Punctuation 29
 
0.8%
Uppercase Letter 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 165
 
7.6%
도 164
 
7.6%
경 160
 
7.4%
시 156
 
7.2%
로 113
 
5.2%
길 90
 
4.2%
동 76
 
3.5%
면 60
 
2.8%
번 48
 
2.2%
천 43
 
2.0%
Other values (182) 1093
50.4%
Decimal Number
ValueCountFrequency (%)
1 140
21.7%
2 89
13.8%
3 75
11.6%
4 61
9.5%
8 56
 
8.7%
7 52
 
8.1%
9 46
 
7.1%
6 44
 
6.8%
5 44
 
6.8%
0 38
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
I 1
33.3%
A 1
33.3%
T 1
33.3%
Space Separator
ValueCountFrequency (%)
658
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 52
100.0%
Close Punctuation
ValueCountFrequency (%)
) 49
100.0%
Open Punctuation
ValueCountFrequency (%)
( 49
100.0%
Other Punctuation
ValueCountFrequency (%)
, 29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2168
59.3%
Common 1482
40.6%
Latin 3
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 165
 
7.6%
도 164
 
7.6%
경 160
 
7.4%
시 156
 
7.2%
로 113
 
5.2%
길 90
 
4.2%
동 76
 
3.5%
면 60
 
2.8%
번 48
 
2.2%
천 43
 
2.0%
Other values (182) 1093
50.4%
Common
ValueCountFrequency (%)
658
44.4%
1 140
 
9.4%
2 89
 
6.0%
3 75
 
5.1%
4 61
 
4.1%
8 56
 
3.8%
7 52
 
3.5%
- 52
 
3.5%
) 49
 
3.3%
( 49
 
3.3%
Other values (5) 201
 
13.6%
Latin
ValueCountFrequency (%)
I 1
33.3%
A 1
33.3%
T 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2168
59.3%
ASCII 1485
40.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
658
44.3%
1 140
 
9.4%
2 89
 
6.0%
3 75
 
5.1%
4 61
 
4.1%
8 56
 
3.8%
7 52
 
3.5%
- 52
 
3.5%
) 49
 
3.3%
( 49
 
3.3%
Other values (8) 204
 
13.7%
Hangul
ValueCountFrequency (%)
기 165
 
7.6%
도 164
 
7.6%
경 160
 
7.4%
시 156
 
7.2%
로 113
 
5.2%
길 90
 
4.2%
동 76
 
3.5%
면 60
 
2.8%
번 48
 
2.2%
천 43
 
2.0%
Other values (182) 1093
50.4%

소재지지번주소
Text

Distinct146
Distinct (%)94.8%
Missing1
Missing (%)0.6%
Memory size1.3 KiB
2023-12-11T07:05:59.063066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length35
Mean length23.935065
Min length8

Characters and Unicode

Total characters3686
Distinct characters188
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique140 ?
Unique (%)90.9%

Sample

1st row331번지 1호
2nd row경기도 가평군 상면 원흥리 519-7번지
3rd row경기도 가평군 설악면 설곡리 798번지
4th row경기도 고양시 일산동구 설문동 159번지 94호 2층
5th row경기도 고양시 덕양구 행신동 722번지 13호 지하1층
ValueCountFrequency (%)
경기도 152
 
17.6%
1호 25
 
2.9%
안성시 16
 
1.8%
평택시 15
 
1.7%
용인시 13
 
1.5%
화성시 13
 
1.5%
2호 12
 
1.4%
3호 11
 
1.3%
이천시 11
 
1.3%
5호 10
 
1.2%
Other values (385) 588
67.9%
2023-12-11T07:05:59.396614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
757
20.5%
도 163
 
4.4%
지 159
 
4.3%
기 157
 
4.3%
시 154
 
4.2%
경 152
 
4.1%
번 152
 
4.1%
1 140
 
3.8%
호 98
 
2.7%
리 98
 
2.7%
Other values (178) 1656
44.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2257
61.2%
Space Separator 757
 
20.5%
Decimal Number 631
 
17.1%
Dash Punctuation 36
 
1.0%
Uppercase Letter 3
 
0.1%
Other Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 163
 
7.2%
지 159
 
7.0%
기 157
 
7.0%
시 154
 
6.8%
경 152
 
6.7%
번 152
 
6.7%
호 98
 
4.3%
리 98
 
4.3%
동 76
 
3.4%
면 59
 
2.6%
Other values (162) 989
43.8%
Decimal Number
ValueCountFrequency (%)
1 140
22.2%
2 78
12.4%
3 68
10.8%
5 59
9.4%
4 57
9.0%
7 53
 
8.4%
6 48
 
7.6%
9 46
 
7.3%
0 45
 
7.1%
8 37
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
I 1
33.3%
T 1
33.3%
A 1
33.3%
Space Separator
ValueCountFrequency (%)
757
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 36
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2257
61.2%
Common 1426
38.7%
Latin 3
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 163
 
7.2%
지 159
 
7.0%
기 157
 
7.0%
시 154
 
6.8%
경 152
 
6.7%
번 152
 
6.7%
호 98
 
4.3%
리 98
 
4.3%
동 76
 
3.4%
면 59
 
2.6%
Other values (162) 989
43.8%
Common
ValueCountFrequency (%)
757
53.1%
1 140
 
9.8%
2 78
 
5.5%
3 68
 
4.8%
5 59
 
4.1%
4 57
 
4.0%
7 53
 
3.7%
6 48
 
3.4%
9 46
 
3.2%
0 45
 
3.2%
Other values (3) 75
 
5.3%
Latin
ValueCountFrequency (%)
I 1
33.3%
T 1
33.3%
A 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2257
61.2%
ASCII 1429
38.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
757
53.0%
1 140
 
9.8%
2 78
 
5.5%
3 68
 
4.8%
5 59
 
4.1%
4 57
 
4.0%
7 53
 
3.7%
6 48
 
3.4%
9 46
 
3.2%
0 45
 
3.1%
Other values (6) 78
 
5.5%
Hangul
ValueCountFrequency (%)
도 163
 
7.2%
지 159
 
7.0%
기 157
 
7.0%
시 154
 
6.8%
경 152
 
6.7%
번 152
 
6.7%
호 98
 
4.3%
리 98
 
4.3%
동 76
 
3.4%
면 59
 
2.6%
Other values (162) 989
43.8%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct133
Distinct (%)85.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean149313.46
Minimum10009
Maximum487881
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-11T07:05:59.529121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10009
5-th percentile10201.5
Q112502.5
median17407
Q3426327.5
95-th percentile469360
Maximum487881
Range477872
Interquartile range (IQR)413825

Descriptive statistics

Standard deviation202305.37
Coefficient of variation (CV)1.3549038
Kurtosis-1.2951092
Mean149313.46
Median Absolute Deviation (MAD)5663
Skewness0.84065239
Sum23143586
Variance4.0927464 × 1010
MonotonicityNot monotonic
2023-12-11T07:05:59.854500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17407 4
 
2.6%
16945 3
 
1.9%
17784 3
 
1.9%
17961 3
 
1.9%
11191 2
 
1.3%
449935 2
 
1.3%
420806 2
 
1.3%
14453 2
 
1.3%
17957 2
 
1.3%
17542 2
 
1.3%
Other values (123) 130
83.9%
ValueCountFrequency (%)
10009 1
0.6%
10010 1
0.6%
10012 1
0.6%
10014 1
0.6%
10023 1
0.6%
10038 1
0.6%
10072 2
1.3%
10257 1
0.6%
10524 1
0.6%
10839 1
0.6%
ValueCountFrequency (%)
487881 1
0.6%
487832 1
0.6%
487813 1
0.6%
483120 1
0.6%
482863 1
0.6%
477852 1
0.6%
472868 1
0.6%
472811 1
0.6%
467881 1
0.6%
467854 1
0.6%

WGS84위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct138
Distinct (%)90.2%
Missing2
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean37.389278
Minimum36.944549
Maximum38.155409
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-11T07:05:59.961182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.944549
5-th percentile36.98165
Q137.14292
median37.324366
Q337.692805
95-th percentile37.882374
Maximum38.155409
Range1.2108599
Interquartile range (IQR)0.549885

Descriptive statistics

Standard deviation0.30451713
Coefficient of variation (CV)0.008144504
Kurtosis-0.88804545
Mean37.389278
Median Absolute Deviation (MAD)0.24166655
Skewness0.42927217
Sum5720.5596
Variance0.09273068
MonotonicityNot monotonic
2023-12-11T07:06:00.074590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.1766627741 3
 
1.9%
37.0403211937 3
 
1.9%
36.9445487055 3
 
1.9%
37.2903734451 2
 
1.3%
37.5087029778 2
 
1.3%
37.5218062101 2
 
1.3%
36.9816500057 2
 
1.3%
37.3888702531 2
 
1.3%
37.2521480581 2
 
1.3%
37.7692554045 2
 
1.3%
Other values (128) 130
83.9%
ValueCountFrequency (%)
36.9445487055 3
1.9%
36.95696866 1
 
0.6%
36.9598546134 1
 
0.6%
36.9635434038 1
 
0.6%
36.9785904388 1
 
0.6%
36.9816500057 2
1.3%
36.9821804745 1
 
0.6%
36.9918193497 1
 
0.6%
36.9920978084 1
 
0.6%
37.0191252238 1
 
0.6%
ValueCountFrequency (%)
38.1554086163 1
0.6%
38.099152433 1
0.6%
38.0878066089 1
0.6%
37.9877236609 1
0.6%
37.9728818116 1
0.6%
37.9350557052 1
0.6%
37.911092105 1
0.6%
37.9062758993 1
0.6%
37.8664395388 1
0.6%
37.8619333946 1
0.6%

WGS84경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct138
Distinct (%)90.2%
Missing2
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean127.06425
Minimum126.56997
Maximum127.62045
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-11T07:06:00.185445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.56997
5-th percentile126.69362
Q1126.83395
median127.06108
Q3127.24029
95-th percentile127.51001
Maximum127.62045
Range1.0504818
Interquartile range (IQR)0.40634158

Descriptive statistics

Standard deviation0.26580248
Coefficient of variation (CV)0.0020918746
Kurtosis-1.0189072
Mean127.06425
Median Absolute Deviation (MAD)0.21174933
Skewness0.15431728
Sum19440.831
Variance0.070650957
MonotonicityNot monotonic
2023-12-11T07:06:00.299436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.4648980006 3
 
1.9%
127.0669481413 3
 
1.9%
126.8342407987 3
 
1.9%
127.0978644532 2
 
1.3%
126.7864029868 2
 
1.3%
126.7800767363 2
 
1.3%
126.8339513775 2
 
1.3%
126.999329007 2
 
1.3%
127.2135253971 2
 
1.3%
127.1939770459 2
 
1.3%
Other values (128) 130
83.9%
ValueCountFrequency (%)
126.5699725664 1
0.6%
126.5954293021 1
0.6%
126.6029806853 1
0.6%
126.6031186826 1
0.6%
126.6272719943 1
0.6%
126.6530174891 1
0.6%
126.6773115915 1
0.6%
126.6800906916 1
0.6%
126.7026464625 1
0.6%
126.7044273786 1
0.6%
ValueCountFrequency (%)
127.6204543242 1
0.6%
127.5855096655 1
0.6%
127.5521270242 1
0.6%
127.5429492029 1
0.6%
127.5312398653 1
0.6%
127.5150303754 1
0.6%
127.5149199288 1
0.6%
127.5142950535 1
0.6%
127.5071559151 1
0.6%
127.5069208066 1
0.6%

Interactions

2023-12-11T07:05:56.330014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:05:54.967310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:05:55.323313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:05:55.672884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:05:56.001705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:05:56.400672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:05:55.027975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:05:55.406746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:05:55.734632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:05:56.064051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:05:56.475582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:05:55.101223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:05:55.472886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:05:55.804715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:05:56.131326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:05:56.537616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:05:55.160531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:05:55.540826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:05:55.867373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:05:56.196586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:05:56.604325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:05:55.240358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:05:55.605554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:05:55.936592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:05:56.265370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:06:00.373849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명인허가일자영업상태명폐업일자소재지우편번호WGS84위도WGS84경도
시군명1.0000.2100.3190.7000.7920.9320.902
인허가일자0.2101.0000.1210.5340.5220.3670.281
영업상태명0.3190.1211.000NaN0.5950.3600.246
폐업일자0.7000.534NaN1.0000.8000.6110.485
소재지우편번호0.7920.5220.5950.8001.0000.4480.393
WGS84위도0.9320.3670.3600.6110.4481.0000.686
WGS84경도0.9020.2810.2460.4850.3930.6861.000
2023-12-11T07:06:00.459829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명영업상태명
시군명1.0000.139
영업상태명0.1391.000
2023-12-11T07:06:00.527230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인허가일자폐업일자소재지우편번호WGS84위도WGS84경도시군명영업상태명
인허가일자1.0000.496-0.4520.132-0.1060.0690.115
폐업일자0.4961.000-0.6750.131-0.2180.2931.000
소재지우편번호-0.452-0.6751.000-0.5430.1810.4910.261
WGS84위도0.1320.131-0.5431.000-0.1670.6510.225
WGS84경도-0.106-0.2180.181-0.1671.0000.5780.146
시군명0.0690.2930.4910.6510.5781.0000.139
영업상태명0.1151.0000.2610.2250.1460.1391.000

Missing values

2023-12-11T07:05:56.704996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:05:56.822372image/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.
2023-12-11T07:05:56.927915image/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

시군명사업장명인허가일자영업상태명폐업일자축산업무구분명소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
0가평군신광그린20080326운영중<NA>사료제조업경기도 가평군 하면 보름골길 26331번지 1호1243937.810207127.365036
1가평군가평군TMR영농조합법인20051028운영중<NA>사료제조업경기도 가평군 청군로 1566경기도 가평군 상면 원흥리 519-7번지1244237.822497127.32154
2가평군피드피아20050106휴업 등<NA>사료제조업<NA>경기도 가평군 설악면 설곡리 798번지47785237.618702127.506921
3고양시인아웃20160603운영중<NA>사료제조업경기도 고양시 일산동구 문봉길62번길 85 (설문동)경기도 고양시 일산동구 설문동 159번지 94호 2층1025737.708939126.81614
4고양시(주)버섯연구소20160504운영중<NA>사료제조업경기도 고양시 덕양구 무원로36번길 25 (행신동)경기도 고양시 덕양구 행신동 722번지 13호 지하1층1052437.616031126.83358
5과천시(주)인피니티20161026운영중<NA>사료제조업경기도 과천시 별양상가로 2 (별양동, 그레이스호텔)경기도 과천시 별양동 1-15번지 그레이스호텔1383737.427751126.992051
6과천시지바이오20100708운영중<NA>사료제조업경기도 과천시 용마2로 1경기도 과천시 과천동 513-54 번지1381237.447072126.992995
7광명시마미펫20101208폐업 등20140610사료제조업경기도 광명시 광명로798번길 24 (광명동)경기도 광명시 광명동 322번지 8호42381137.471887126.852911
8광주시광주낙우영농조합법인20041231운영중<NA>사료제조업경기도 광주시 도척면 마도로 112-19경기도 광주시 도척면 방도리 237번지46488237.28277127.338097
9광주시(주)케어사이드20131022운영중<NA>사료제조업경기도 광주시 오포읍 양촌길 35경기도 광주시 오포읍 고산리 200번지 1호1279037.371127.238136
시군명사업장명인허가일자영업상태명폐업일자축산업무구분명소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
145화성시네추럴 프로20131204운영중<NA>사료제조업경기도 화성시 향남읍 한두골안길 63경기도 화성시 향남읍 백토리 123번지 1호1858937.120433126.954005
146화성시펫퍼스 바이오캠20170601운영중<NA>사료제조업경기도 화성시 마도면 청원산단5길 114경기도 화성시 마도면 청원리 1250번지 5호1854337.166423126.765452
147화성시주식회사 바이오그린20100624운영중<NA>사료제조업경기도 화성시 우정읍 기아자동차로 82경기도 화성시 우정읍 매향리 780번지 1호44595137.040217126.762576
148화성시대성물산20040708운영중<NA>사료제조업경기도 화성시 장안면 수정로 194경기도 화성시 장안면 독정리 112번지 8호1858337.083154126.860956
149화성시수원화성오산축협 티엠알20081217운영중<NA>사료제조업경기도 화성시 팔탄면 현대기아로 33-6경기도 화성시 팔탄면 노하리 621번지 3호44591337.159486126.849333
150화성시(주)피드텍20020318폐업 등20141216사료제조업경기도 화성시 팔탄면 율암길95번길 15-8경기도 화성시 팔탄면 율암리 443번지44591337.16053126.879293
151화성시(주)이레식품20050602폐업 등20080407사료제조업경기도 화성시 팔탄면 시청로 1204-7경기도 화성시 팔탄면 구장리 70-2번지44591137.170549126.901154
152화성시동방농수산20041214폐업 등20070108사료제조업경기도 화성시 안녕길 47 (안녕동,안녕리)경기도 화성시 안녕동 188번지 351호 안녕리44597637.203491126.991023
153화성시파레스바이오피드(주)화성공장20070702휴업 등<NA>사료제조업경기도 화성시 우정읍 사기말길27번길 87-25경기도 화성시 우정읍 화산리 364번지 6호44595337.075161126.794514
154화성시화성티엠알영농조합법인20041224휴업 등<NA>사료제조업경기도 화성시 양감면 줄터길 39-17경기도 화성시 양감면 용소리 981-2번지44593237.082699126.973818

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