Overview

Dataset statistics

Number of variables11
Number of observations46
Missing cells43
Missing cells (%)8.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.3 KiB
Average record size in memory95.9 B

Variable types

Categorical3
Text3
Numeric5

Alerts

축산업무구분명 has constant value ""Constant
인허가일자 is highly overall correlated with 폐업일자High correlation
폐업일자 is highly overall correlated with 인허가일자 and 1 other fieldsHigh correlation
소재지우편번호 is highly overall correlated with 시군명High correlation
WGS84위도 is highly overall correlated with 시군명High correlation
WGS84경도 is highly overall correlated with 시군명High correlation
시군명 is highly overall correlated with 소재지우편번호 and 2 other fieldsHigh correlation
영업상태명 is highly overall correlated with 폐업일자High correlation
폐업일자 has 18 (39.1%) missing valuesMissing
소재지도로명주소 has 10 (21.7%) missing valuesMissing
소재지우편번호 has 1 (2.2%) missing valuesMissing
WGS84위도 has 7 (15.2%) missing valuesMissing
WGS84경도 has 7 (15.2%) missing valuesMissing

Reproduction

Analysis started2023-12-10 21:39:20.360335
Analysis finished2023-12-10 21:39:23.451370
Duration3.09 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)28.3%
Missing0
Missing (%)0.0%
Memory size500.0 B
포천시
11 
안성시
10 
용인시
4 
평택시
4 
양평군
3 
Other values (8)
14 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique4 ?
Unique (%)8.7%

Sample

1st row김포시
2nd row안성시
3rd row안성시
4th row안성시
5th row안성시

Common Values

ValueCountFrequency (%)
포천시 11
23.9%
안성시 10
21.7%
용인시 4
 
8.7%
평택시 4
 
8.7%
양평군 3
 
6.5%
이천시 3
 
6.5%
파주시 3
 
6.5%
여주시 2
 
4.3%
화성시 2
 
4.3%
김포시 1
 
2.2%
Other values (3) 3
 
6.5%

Length

2023-12-11T06:39:23.510173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
포천시 11
23.9%
안성시 10
21.7%
용인시 4
 
8.7%
평택시 4
 
8.7%
양평군 3
 
6.5%
이천시 3
 
6.5%
파주시 3
 
6.5%
여주시 2
 
4.3%
화성시 2
 
4.3%
김포시 1
 
2.2%
Other values (3) 3
 
6.5%

사업장명
Text

Distinct44
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Memory size500.0 B
2023-12-11T06:39:23.701160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12.5
Mean length5.8478261
Min length4

Characters and Unicode

Total characters269
Distinct characters102
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)91.3%

Sample

1st row(농)주식회사상원축산
2nd row(주)다비육종 도화농장
3rd row한백용농장
4th row민재농장
5th row구성농장
ValueCountFrequency (%)
경북종돈 2
 
3.8%
신영농장 2
 
3.8%
포천종돈 1
 
1.9%
관인종돈장 1
 
1.9%
농)주식회사상원축산 1
 
1.9%
대월청안gp 1
 
1.9%
만나농장 1
 
1.9%
수향농장 1
 
1.9%
덕영종돈장 1
 
1.9%
우리종돈장 1
 
1.9%
Other values (40) 40
76.9%
2023-12-11T06:39:24.040397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
농 21
 
7.8%
장 20
 
7.4%
종 16
 
5.9%
돈 16
 
5.9%
인 9
 
3.3%
영 8
 
3.0%
산 7
 
2.6%
6
 
2.2%
법 6
 
2.2%
G 5
 
1.9%
Other values (92) 155
57.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 234
87.0%
Uppercase Letter 14
 
5.2%
Space Separator 6
 
2.2%
Decimal Number 4
 
1.5%
Open Punctuation 3
 
1.1%
Close Punctuation 3
 
1.1%
Lowercase Letter 3
 
1.1%
Other Punctuation 2
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
농 21
 
9.0%
장 20
 
8.5%
종 16
 
6.8%
돈 16
 
6.8%
인 9
 
3.8%
영 8
 
3.4%
산 7
 
3.0%
법 6
 
2.6%
합 5
 
2.1%
조 5
 
2.1%
Other values (77) 121
51.7%
Uppercase Letter
ValueCountFrequency (%)
G 5
35.7%
P 4
28.6%
F 2
 
14.3%
I 1
 
7.1%
N 1
 
7.1%
E 1
 
7.1%
Lowercase Letter
ValueCountFrequency (%)
i 1
33.3%
e 1
33.3%
n 1
33.3%
Decimal Number
ValueCountFrequency (%)
0 3
75.0%
2 1
 
25.0%
Space Separator
ValueCountFrequency (%)
6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Other Punctuation
ValueCountFrequency (%)
· 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 234
87.0%
Common 18
 
6.7%
Latin 17
 
6.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
농 21
 
9.0%
장 20
 
8.5%
종 16
 
6.8%
돈 16
 
6.8%
인 9
 
3.8%
영 8
 
3.4%
산 7
 
3.0%
법 6
 
2.6%
합 5
 
2.1%
조 5
 
2.1%
Other values (77) 121
51.7%
Latin
ValueCountFrequency (%)
G 5
29.4%
P 4
23.5%
F 2
 
11.8%
I 1
 
5.9%
i 1
 
5.9%
N 1
 
5.9%
e 1
 
5.9%
n 1
 
5.9%
E 1
 
5.9%
Common
ValueCountFrequency (%)
6
33.3%
0 3
16.7%
( 3
16.7%
) 3
16.7%
· 2
 
11.1%
2 1
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 234
87.0%
ASCII 33
 
12.3%
None 2
 
0.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
농 21
 
9.0%
장 20
 
8.5%
종 16
 
6.8%
돈 16
 
6.8%
인 9
 
3.8%
영 8
 
3.4%
산 7
 
3.0%
법 6
 
2.6%
합 5
 
2.1%
조 5
 
2.1%
Other values (77) 121
51.7%
ASCII
ValueCountFrequency (%)
6
18.2%
G 5
15.2%
P 4
12.1%
0 3
9.1%
( 3
9.1%
) 3
9.1%
F 2
 
6.1%
I 1
 
3.0%
i 1
 
3.0%
N 1
 
3.0%
Other values (4) 4
12.1%
None
ValueCountFrequency (%)
· 2
100.0%

인허가일자
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20036794
Minimum19850501
Maximum20171201
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-11T06:39:24.192389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19850501
5-th percentile19933646
Q120000822
median20040610
Q320090514
95-th percentile20128416
Maximum20171201
Range320700
Interquartile range (IQR)89692

Descriptive statistics

Standard deviation72180.412
Coefficient of variation (CV)0.0036023933
Kurtosis0.34705575
Mean20036794
Median Absolute Deviation (MAD)45000.5
Skewness-0.654166
Sum9.2169251 × 108
Variance5.2100119 × 109
MonotonicityNot monotonic
2023-12-11T06:39:24.334258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
20040609 2
 
4.3%
19851116 1
 
2.2%
20110907 1
 
2.2%
20030422 1
 
2.2%
20131120 1
 
2.2%
19950128 1
 
2.2%
20051104 1
 
2.2%
20080218 1
 
2.2%
20110523 1
 
2.2%
20151028 1
 
2.2%
Other values (35) 35
76.1%
ValueCountFrequency (%)
19850501 1
2.2%
19851116 1
2.2%
19931224 1
2.2%
19940913 1
2.2%
19941004 1
2.2%
19941027 1
2.2%
19950114 1
2.2%
19950128 1
2.2%
19950412 1
2.2%
19970319 1
2.2%
ValueCountFrequency (%)
20171201 1
2.2%
20151028 1
2.2%
20131120 1
2.2%
20120306 1
2.2%
20111209 1
2.2%
20111116 1
2.2%
20110929 1
2.2%
20110907 1
2.2%
20110715 1
2.2%
20110523 1
2.2%

영업상태명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size500.0 B
폐업 등
28 
운영중
17 
휴업 등
 
1

Length

Max length4
Median length4
Mean length3.6304348
Min length3

Unique

Unique1 ?
Unique (%)2.2%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 등 28
60.9%
운영중 17
37.0%
휴업 등 1
 
2.2%

Length

2023-12-11T06:39:24.499441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:39:24.601125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
등 29
38.7%
폐업 28
37.3%
운영중 17
22.7%
휴업 1
 
1.3%

폐업일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct28
Distinct (%)100.0%
Missing18
Missing (%)39.1%
Infinite0
Infinite (%)0.0%
Mean20082425
Minimum19950727
Maximum20170704
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-11T06:39:24.707099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19950727
5-th percentile19963891
Q120050592
median20085662
Q320142756
95-th percentile20160936
Maximum20170704
Range219977
Interquartile range (IQR)92163.5

Descriptive statistics

Standard deviation64678.675
Coefficient of variation (CV)0.0032206607
Kurtosis-0.62850655
Mean20082425
Median Absolute Deviation (MAD)54600.5
Skewness-0.506197
Sum5.6230789 × 108
Variance4.183331 × 109
MonotonicityNot monotonic
2023-12-11T06:39:24.837639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
20101101 1
 
2.2%
20151026 1
 
2.2%
20050503 1
 
2.2%
20090114 1
 
2.2%
20130329 1
 
2.2%
20150626 1
 
2.2%
20081209 1
 
2.2%
20151031 1
 
2.2%
20170704 1
 
2.2%
20160627 1
 
2.2%
Other values (18) 18
39.1%
(Missing) 18
39.1%
ValueCountFrequency (%)
19950727 1
2.2%
19960319 1
2.2%
19970526 1
2.2%
19990624 1
2.2%
20020218 1
2.2%
20020710 1
2.2%
20050503 1
2.2%
20050622 1
2.2%
20050810 1
2.2%
20060630 1
2.2%
ValueCountFrequency (%)
20170704 1
2.2%
20161102 1
2.2%
20160627 1
2.2%
20151031 1
2.2%
20151026 1
2.2%
20150626 1
2.2%
20150114 1
2.2%
20140303 1
2.2%
20140221 1
2.2%
20130329 1
2.2%

축산업무구분명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size500.0 B
종축업
46 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
종축업 46
100.0%

Length

2023-12-11T06:39:24.978592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:39:25.090031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
종축업 46
100.0%

소재지도로명주소
Text

MISSING 

Distinct34
Distinct (%)94.4%
Missing10
Missing (%)21.7%
Memory size500.0 B
2023-12-11T06:39:25.330859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length26
Mean length22.666667
Min length19

Characters and Unicode

Total characters816
Distinct characters108
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

Unique32 ?
Unique (%)88.9%

Sample

1st row경기도 김포시 대곶면 학의동로34번길 189
2nd row경기도 안성시 죽산면 용설로 576 (외 3필지)
3rd row경기도 안성시 양성면 양성로 345
4th row경기도 안성시 삼죽면 서동대로 6362-56
5th row경기도 안성시 일죽면 장암로 127-33
ValueCountFrequency (%)
경기도 36
 
19.4%
포천시 11
 
5.9%
관인면 5
 
2.7%
창수면 4
 
2.2%
안성시 4
 
2.2%
처인구 4
 
2.2%
용인시 4
 
2.2%
백암면 3
 
1.6%
평택시 3
 
1.6%
파주시 3
 
1.6%
Other values (96) 109
58.6%
2023-12-11T06:39:25.703723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
150
 
18.4%
경 36
 
4.4%
도 36
 
4.4%
기 36
 
4.4%
시 33
 
4.0%
면 30
 
3.7%
1 30
 
3.7%
2 28
 
3.4%
로 25
 
3.1%
길 22
 
2.7%
Other values (98) 390
47.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 492
60.3%
Decimal Number 157
 
19.2%
Space Separator 150
 
18.4%
Dash Punctuation 12
 
1.5%
Close Punctuation 2
 
0.2%
Open Punctuation 2
 
0.2%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 36
 
7.3%
도 36
 
7.3%
기 36
 
7.3%
시 33
 
6.7%
면 30
 
6.1%
로 25
 
5.1%
길 22
 
4.5%
천 17
 
3.5%
산 14
 
2.8%
인 13
 
2.6%
Other values (83) 230
46.7%
Decimal Number
ValueCountFrequency (%)
1 30
19.1%
2 28
17.8%
3 21
13.4%
6 18
11.5%
5 16
10.2%
7 13
8.3%
8 11
 
7.0%
9 8
 
5.1%
4 8
 
5.1%
0 4
 
2.5%
Space Separator
ValueCountFrequency (%)
150
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 492
60.3%
Common 324
39.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 36
 
7.3%
도 36
 
7.3%
기 36
 
7.3%
시 33
 
6.7%
면 30
 
6.1%
로 25
 
5.1%
길 22
 
4.5%
천 17
 
3.5%
산 14
 
2.8%
인 13
 
2.6%
Other values (83) 230
46.7%
Common
ValueCountFrequency (%)
150
46.3%
1 30
 
9.3%
2 28
 
8.6%
3 21
 
6.5%
6 18
 
5.6%
5 16
 
4.9%
7 13
 
4.0%
- 12
 
3.7%
8 11
 
3.4%
9 8
 
2.5%
Other values (5) 17
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 492
60.3%
ASCII 324
39.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
150
46.3%
1 30
 
9.3%
2 28
 
8.6%
3 21
 
6.5%
6 18
 
5.6%
5 16
 
4.9%
7 13
 
4.0%
- 12
 
3.7%
8 11
 
3.4%
9 8
 
2.5%
Other values (5) 17
 
5.2%
Hangul
ValueCountFrequency (%)
경 36
 
7.3%
도 36
 
7.3%
기 36
 
7.3%
시 33
 
6.7%
면 30
 
6.1%
로 25
 
5.1%
길 22
 
4.5%
천 17
 
3.5%
산 14
 
2.8%
인 13
 
2.6%
Other values (83) 230
46.7%

소재지지번주소
Text

Distinct44
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Memory size500.0 B
2023-12-11T06:39:25.902599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length51
Median length44
Mean length24.282609
Min length17

Characters and Unicode

Total characters1117
Distinct characters106
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

Unique42 ?
Unique (%)91.3%

Sample

1st row경기도 김포시 대곶면 송마리 828-1번지
2nd row경기도 안성시 죽산면 당목리 580번지 외 3필지
3rd row경기도 안성시 양성면 산정리 48-1 번지
4th row경기도 안성시 삼죽면 진촌리 187번지
5th row경기도 안성시 일죽면 장암리 436번지
ValueCountFrequency (%)
경기도 46
 
18.6%
포천시 11
 
4.5%
안성시 10
 
4.0%
관인면 5
 
2.0%
창수면 4
 
1.6%
평택시 4
 
1.6%
용인시 4
 
1.6%
처인구 4
 
1.6%
산 4
 
1.6%
외 4
 
1.6%
Other values (122) 151
61.1%
2023-12-11T06:39:26.230296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
205
18.4%
지 54
 
4.8%
도 47
 
4.2%
경 46
 
4.1%
기 46
 
4.1%
리 45
 
4.0%
번 43
 
3.8%
시 42
 
3.8%
- 40
 
3.6%
1 40
 
3.6%
Other values (96) 509
45.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 672
60.2%
Space Separator 205
 
18.4%
Decimal Number 187
 
16.7%
Dash Punctuation 40
 
3.6%
Other Punctuation 7
 
0.6%
Close Punctuation 3
 
0.3%
Open Punctuation 3
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 54
 
8.0%
도 47
 
7.0%
경 46
 
6.8%
기 46
 
6.8%
리 45
 
6.7%
번 43
 
6.4%
시 42
 
6.2%
면 38
 
5.7%
성 17
 
2.5%
천 17
 
2.5%
Other values (81) 277
41.2%
Decimal Number
ValueCountFrequency (%)
1 40
21.4%
3 22
11.8%
5 20
10.7%
8 20
10.7%
2 18
9.6%
7 17
9.1%
4 17
9.1%
6 15
 
8.0%
0 14
 
7.5%
9 4
 
2.1%
Space Separator
ValueCountFrequency (%)
205
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 40
100.0%
Other Punctuation
ValueCountFrequency (%)
, 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 672
60.2%
Common 445
39.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 54
 
8.0%
도 47
 
7.0%
경 46
 
6.8%
기 46
 
6.8%
리 45
 
6.7%
번 43
 
6.4%
시 42
 
6.2%
면 38
 
5.7%
성 17
 
2.5%
천 17
 
2.5%
Other values (81) 277
41.2%
Common
ValueCountFrequency (%)
205
46.1%
- 40
 
9.0%
1 40
 
9.0%
3 22
 
4.9%
5 20
 
4.5%
8 20
 
4.5%
2 18
 
4.0%
7 17
 
3.8%
4 17
 
3.8%
6 15
 
3.4%
Other values (5) 31
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 672
60.2%
ASCII 445
39.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
205
46.1%
- 40
 
9.0%
1 40
 
9.0%
3 22
 
4.9%
5 20
 
4.5%
8 20
 
4.5%
2 18
 
4.0%
7 17
 
3.8%
4 17
 
3.8%
6 15
 
3.4%
Other values (5) 31
 
7.0%
Hangul
ValueCountFrequency (%)
지 54
 
8.0%
도 47
 
7.0%
경 46
 
6.8%
기 46
 
6.8%
리 45
 
6.7%
번 43
 
6.4%
시 42
 
6.2%
면 38
 
5.7%
성 17
 
2.5%
천 17
 
2.5%
Other values (81) 277
41.2%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct37
Distinct (%)82.2%
Missing1
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean383143.49
Minimum11101
Maximum487933
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-11T06:39:26.366266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11101
5-th percentile17501.8
Q1415855
median456843
Q3476884
95-th percentile487931
Maximum487933
Range476832
Interquartile range (IQR)61029

Descriptive statistics

Standard deviation173892.85
Coefficient of variation (CV)0.45385828
Kurtosis0.98765024
Mean383143.49
Median Absolute Deviation (MAD)25988
Skewness-1.6887583
Sum17241457
Variance3.0238722 × 1010
MonotonicityNot monotonic
2023-12-11T06:39:26.508457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
487921 3
 
6.5%
476881 2
 
4.3%
487931 2
 
4.3%
487811 2
 
4.3%
449863 2
 
4.3%
487933 2
 
4.3%
469811 2
 
4.3%
415855 1
 
2.2%
413831 1
 
2.2%
413913 1
 
2.2%
Other values (27) 27
58.7%
ValueCountFrequency (%)
11101 1
2.2%
11135 1
2.2%
17501 1
2.2%
17505 1
2.2%
17516 1
2.2%
17526 1
2.2%
17557 1
2.2%
17996 1
2.2%
413831 1
2.2%
413903 1
2.2%
ValueCountFrequency (%)
487933 2
4.3%
487931 2
4.3%
487921 3
6.5%
487811 2
4.3%
486801 1
 
2.2%
482831 1
 
2.2%
476884 1
 
2.2%
476881 2
4.3%
469811 2
4.3%
467881 1
 
2.2%

WGS84위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct37
Distinct (%)94.9%
Missing7
Missing (%)15.2%
Infinite0
Infinite (%)0.0%
Mean37.508558
Minimum36.950431
Maximum38.152648
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-11T06:39:26.620588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.950431
5-th percentile36.972874
Q137.113242
median37.38984
Q337.963334
95-th percentile38.121143
Maximum38.152648
Range1.202217
Interquartile range (IQR)0.85009276

Descriptive statistics

Standard deviation0.4314016
Coefficient of variation (CV)0.011501418
Kurtosis-1.6555984
Mean37.508558
Median Absolute Deviation (MAD)0.35141639
Skewness0.2393902
Sum1462.8338
Variance0.18610734
MonotonicityNot monotonic
2023-12-11T06:39:26.739893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
37.2907902798 2
 
4.3%
38.11832722 2
 
4.3%
36.9504314745 1
 
2.2%
37.0709597456 1
 
2.2%
36.9524931463 1
 
2.2%
38.1105204471 1
 
2.2%
38.0069483312 1
 
2.2%
37.9685939851 1
 
2.2%
37.9810739478 1
 
2.2%
37.7412567551 1
 
2.2%
Other values (27) 27
58.7%
(Missing) 7
 
15.2%
ValueCountFrequency (%)
36.9504314745 1
2.2%
36.9524931463 1
2.2%
36.9751387573 1
2.2%
37.03652704 1
2.2%
37.0618134662 1
2.2%
37.0698439565 1
2.2%
37.0709597456 1
2.2%
37.0838269747 1
2.2%
37.1082910647 1
2.2%
37.11257522 1
2.2%
ValueCountFrequency (%)
38.1526484924 1
2.2%
38.1464848462 1
2.2%
38.11832722 2
4.3%
38.1105204471 1
2.2%
38.0714857718 1
2.2%
38.0084837232 1
2.2%
38.0069483312 1
2.2%
37.9810739478 1
2.2%
37.9685939851 1
2.2%
37.9580747784 1
2.2%

WGS84경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct37
Distinct (%)94.9%
Missing7
Missing (%)15.2%
Infinite0
Infinite (%)0.0%
Mean127.21872
Minimum126.5644
Maximum127.78558
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-11T06:39:27.107710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.5644
5-th percentile126.711
Q1127.08105
median127.21183
Q3127.41337
95-th percentile127.7514
Maximum127.78558
Range1.2211836
Interquartile range (IQR)0.33232699

Descriptive statistics

Standard deviation0.28637935
Coefficient of variation (CV)0.0022510786
Kurtosis0.098676795
Mean127.21872
Median Absolute Deviation (MAD)0.16596683
Skewness-0.11216029
Sum4961.5302
Variance0.082013131
MonotonicityNot monotonic
2023-12-11T06:39:27.224792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
127.5736382018 2
 
4.3%
127.2118286051 2
 
4.3%
127.067367688 1
 
2.2%
127.0045201613 1
 
2.2%
127.0947281905 1
 
2.2%
127.2504376807 1
 
2.2%
127.1742308783 1
 
2.2%
127.217822724 1
 
2.2%
127.1748007705 1
 
2.2%
126.7130467195 1
 
2.2%
Other values (27) 27
58.7%
(Missing) 7
 
15.2%
ValueCountFrequency (%)
126.5643968783 1
2.2%
126.6925686039 1
2.2%
126.7130467195 1
2.2%
126.7566133095 1
2.2%
126.8636144955 1
2.2%
126.9090566496 1
2.2%
126.9821244751 1
2.2%
127.0045201613 1
2.2%
127.045861772 1
2.2%
127.067367688 1
2.2%
ValueCountFrequency (%)
127.7855805084 1
2.2%
127.7776791646 1
2.2%
127.7484754158 1
2.2%
127.5736382018 2
4.3%
127.4818717442 1
2.2%
127.464711055 1
2.2%
127.442057854 1
2.2%
127.4248550465 1
2.2%
127.4174064986 1
2.2%
127.4093433557 1
2.2%

Interactions

2023-12-11T06:39:22.722132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:39:20.937786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:39:21.432713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:39:21.860130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:39:22.289049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:39:22.803368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:39:21.046680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:39:21.521835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:39:21.945583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:39:22.390443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:39:22.878128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:39:21.137956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:39:21.600730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:39:22.034616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:39:22.474429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:39:22.949994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:39:21.244189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:39:21.684012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:39:22.112169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:39:22.552789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:39:23.027744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:39:21.343066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:39:21.774017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:39:22.206371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:39:22.630512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:39:27.311080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명사업장명인허가일자영업상태명폐업일자소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
시군명1.0001.0000.6990.3880.0001.0001.0000.8510.8670.934
사업장명1.0001.0000.9680.9361.0001.0000.9980.8451.0001.000
인허가일자0.6990.9681.0000.0000.7510.9810.9780.8680.5510.669
영업상태명0.3880.9360.0001.000NaN0.9360.9360.6100.0000.617
폐업일자0.0001.0000.751NaN1.0001.0001.0000.7320.3990.682
소재지도로명주소1.0001.0000.9810.9361.0001.0001.0001.0001.0001.000
소재지지번주소1.0000.9980.9780.9361.0001.0001.0001.0001.0001.000
소재지우편번호0.8510.8450.8680.6100.7321.0001.0001.0000.4310.595
WGS84위도0.8671.0000.5510.0000.3991.0001.0000.4311.0000.897
WGS84경도0.9341.0000.6690.6170.6821.0001.0000.5950.8971.000
2023-12-11T06:39:27.436005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명영업상태명
시군명1.0000.195
영업상태명0.1951.000
2023-12-11T06:39:27.523062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인허가일자폐업일자소재지우편번호WGS84위도WGS84경도시군명영업상태명
인허가일자1.0000.6630.0200.182-0.0850.2790.000
폐업일자0.6631.0000.2540.094-0.1570.0001.000
소재지우편번호0.0200.2541.0000.5000.2850.6220.274
WGS84위도0.1820.0940.5001.000-0.0910.5650.000
WGS84경도-0.085-0.1570.285-0.0911.0000.7150.409
시군명0.2790.0000.6220.5650.7151.0000.195
영업상태명0.0001.0000.2740.0000.4090.1951.000

Missing values

2023-12-11T06:39:23.140486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:39:23.272830image/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.
2023-12-11T06:39:23.384673image/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
0김포시(농)주식회사상원축산19851116폐업 등20071031종축업경기도 김포시 대곶면 학의동로34번길 189경기도 김포시 대곶면 송마리 828-1번지41585537.65114126.564397
1안성시(주)다비육종 도화농장20030224운영중<NA>종축업경기도 안성시 죽산면 용설로 576 (외 3필지)경기도 안성시 죽산면 당목리 580번지 외 3필지45689137.036527127.442058
2안성시한백용농장19950114폐업 등19950727종축업경기도 안성시 양성면 양성로 345경기도 안성시 양성면 산정리 48-1 번지1750137.083827127.20616
3안성시민재농장19970319폐업 등19990624종축업경기도 안성시 삼죽면 서동대로 6362-56경기도 안성시 삼죽면 진촌리 187번지1751637.061813127.373853
4안성시구성농장20110929폐업 등20140303종축업경기도 안성시 일죽면 장암로 127-33경기도 안성시 일죽면 장암리 436번지45691337.069844127.464711
5안성시금보종돈20020403폐업 등20060630종축업<NA>경기도 안성시 미양면 보체리 125-1번지45684336.975139127.244162
6안성시광일농장20000219폐업 등20150114종축업<NA>경기도 안성시 삼죽면 진촌리 310번지456882<NA><NA>
7안성시반제농장19950412폐업 등19970526종축업<NA>경기도 안성시 원곡면 반제리 산 9-1 번지17557<NA><NA>
8안성시일죽GP19941027폐업 등20020710종축업<NA>경기도 안성시 일죽면 화곡리 산 28-3번지17526<NA><NA>
9안성시화창농원19941004폐업 등20020218종축업<NA>경기도 안성시 공도읍 만정리 산 50번지<NA><NA><NA>
시군명사업장명인허가일자영업상태명폐업일자축산업무구분명소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
36포천시협동GP20171201운영중<NA>종축업경기도 포천시 창수면 금화봉길 218경기도 포천시 창수면 가양리 37-1번지 외 3필지(가양리 34,35-1,35)1113537.968594127.217823
37포천시호산육종20030217운영중<NA>종축업경기도 포천시 창수면 포천로2721번길 69경기도 포천시 창수면 추동리 340-1번지48792137.981074127.174801
38포천시농업회사법인(주)무림20050516운영중<NA>종축업경기도 포천시 가산면 가산로267번길 34경기도 포천시 가산면 방축리 180-4번지48781137.844926127.17549
39포천시명진농장20041228운영중<NA>종축업경기도 포천시 관인면 윗찬물길 221-43경기도 포천시 관인면 냉정리 805-3번지48793138.146485127.269956
40포천시한비축산20120306폐업 등20150626종축업경기도 포천시 관인면 찬우물길 290경기도 포천시 관인면 냉정리 660-2번지48793138.152648127.27408
41포천시청미원영농조합법인20090615폐업 등20130329종축업경기도 포천시 창수면 옥수로298번길 8경기도 포천시 창수면 주원리 107번지48792138.008484127.188725
42포천시문경FINE20051021폐업 등20090114종축업경기도 포천시 관인면 창동로 1231경기도 포천시 관인면 삼율리 248-5번지48793338.118327127.211829
43포천시세왕농산영농조합법인20000722폐업 등20050503종축업경기도 포천시 가산면 가산로267번길 15경기도 포천시 가산면 방축리 10번지48781137.84362127.179167
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