Overview

Dataset statistics

Number of variables10
Number of observations522
Missing cells72
Missing cells (%)1.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory42.4 KiB
Average record size in memory83.3 B

Variable types

Categorical3
Text5
Numeric2

Dataset

Description경관직불 사업지구현황(마을별 경관작물 현황)
Author농림축산식품부
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20220215000000001902

Alerts

사업년도 has constant value ""Constant
참여자수(농가수) is highly overall correlated with 대상면적High correlation
대상면적 is highly overall correlated with 참여자수(농가수)High correlation
시도 is highly overall correlated with 절기(동/하계)High correlation
절기(동/하계) is highly overall correlated with 시도High correlation
절기(동/하계) is highly imbalanced (56.3%)Imbalance
법정리명 has 72 (13.8%) missing valuesMissing

Reproduction

Analysis started2023-12-11 03:10:48.706998
Analysis finished2023-12-11 03:10:50.322256
Duration1.62 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

사업년도
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
2013
522 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2013
2nd row2013
3rd row2013
4th row2013
5th row2013

Common Values

ValueCountFrequency (%)
2013 522
100.0%

Length

2023-12-11T12:10:50.428923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:10:50.558596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2013 522
100.0%

시도
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
전라남도
267 
전라북도
123 
경상남도
42 
충청남도
37 
경상북도
 
18
Other values (8)
35 

Length

Max length7
Median length4
Mean length4.0613027
Min length3

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row부산광역시
2nd row인천광역시
3rd row인천광역시
4th row인천광역시
5th row인천광역시

Common Values

ValueCountFrequency (%)
전라남도 267
51.1%
전라북도 123
23.6%
경상남도 42
 
8.0%
충청남도 37
 
7.1%
경상북도 18
 
3.4%
인천광역시 7
 
1.3%
광주광역시 6
 
1.1%
강원도 6
 
1.1%
제주특별자치도 6
 
1.1%
대전광역시 4
 
0.8%
Other values (3) 6
 
1.1%

Length

2023-12-11T12:10:50.721465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전라남도 267
51.1%
전라북도 123
23.6%
경상남도 42
 
8.0%
충청남도 37
 
7.1%
경상북도 18
 
3.4%
인천광역시 7
 
1.3%
광주광역시 6
 
1.1%
강원도 6
 
1.1%
제주특별자치도 6
 
1.1%
대전광역시 4
 
0.8%
Other values (3) 6
 
1.1%

시군구
Text

Distinct64
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
2023-12-11T12:10:51.015092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0038314
Min length3

Characters and Unicode

Total characters1568
Distinct characters70
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)2.1%

Sample

1st row기장군
2nd row옹진군
3rd row옹진군
4th row옹진군
5th row옹진군
ValueCountFrequency (%)
곡성군 53
 
10.2%
부안군 47
 
9.0%
영광군 41
 
7.9%
서천군 31
 
5.9%
김제시 24
 
4.6%
해남군 22
 
4.2%
화순군 20
 
3.8%
장성군 20
 
3.8%
영암군 19
 
3.6%
고창군 19
 
3.6%
Other values (54) 226
43.3%
2023-12-11T12:10:51.479501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 413
26.3%
시 101
 
6.4%
성 87
 
5.5%
영 65
 
4.1%
안 55
 
3.5%
곡 53
 
3.4%
부 48
 
3.1%
천 48
 
3.1%
광 47
 
3.0%
서 33
 
2.1%
Other values (60) 618
39.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1568
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 413
26.3%
시 101
 
6.4%
성 87
 
5.5%
영 65
 
4.1%
안 55
 
3.5%
곡 53
 
3.4%
부 48
 
3.1%
천 48
 
3.1%
광 47
 
3.0%
서 33
 
2.1%
Other values (60) 618
39.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1568
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 413
26.3%
시 101
 
6.4%
성 87
 
5.5%
영 65
 
4.1%
안 55
 
3.5%
곡 53
 
3.4%
부 48
 
3.1%
천 48
 
3.1%
광 47
 
3.0%
서 33
 
2.1%
Other values (60) 618
39.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1568
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 413
26.3%
시 101
 
6.4%
성 87
 
5.5%
영 65
 
4.1%
안 55
 
3.5%
곡 53
 
3.4%
부 48
 
3.1%
천 48
 
3.1%
광 47
 
3.0%
서 33
 
2.1%
Other values (60) 618
39.4%

읍면동
Text

Distinct180
Distinct (%)34.5%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
2023-12-11T12:10:51.936861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.98659
Min length2

Characters and Unicode

Total characters1559
Distinct characters145
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique85 ?
Unique (%)16.3%

Sample

1st row철마면
2nd row백령면
3rd row백령면
4th row백령면
5th row백령면
ValueCountFrequency (%)
진봉면 18
 
3.4%
군서면 14
 
2.7%
염산면 12
 
2.3%
계화면 11
 
2.1%
백수읍 10
 
1.9%
청산면 10
 
1.9%
해남읍 10
 
1.9%
석곡면 9
 
1.7%
삼기면 9
 
1.7%
하서면 9
 
1.7%
Other values (170) 410
78.5%
2023-12-11T12:10:52.601251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
면 427
27.4%
읍 79
 
5.1%
산 72
 
4.6%
서 55
 
3.5%
진 42
 
2.7%
동 40
 
2.6%
도 36
 
2.3%
곡 28
 
1.8%
천 26
 
1.7%
계 26
 
1.7%
Other values (135) 728
46.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1559
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
면 427
27.4%
읍 79
 
5.1%
산 72
 
4.6%
서 55
 
3.5%
진 42
 
2.7%
동 40
 
2.6%
도 36
 
2.3%
곡 28
 
1.8%
천 26
 
1.7%
계 26
 
1.7%
Other values (135) 728
46.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1559
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
면 427
27.4%
읍 79
 
5.1%
산 72
 
4.6%
서 55
 
3.5%
진 42
 
2.7%
동 40
 
2.6%
도 36
 
2.3%
곡 28
 
1.8%
천 26
 
1.7%
계 26
 
1.7%
Other values (135) 728
46.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1559
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
면 427
27.4%
읍 79
 
5.1%
산 72
 
4.6%
서 55
 
3.5%
진 42
 
2.7%
동 40
 
2.6%
도 36
 
2.3%
곡 28
 
1.8%
천 26
 
1.7%
계 26
 
1.7%
Other values (135) 728
46.7%

대상지구명
Text

Distinct501
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
2023-12-11T12:10:52.982124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length3.2337165
Min length1

Characters and Unicode

Total characters1688
Distinct characters230
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

Unique484 ?
Unique (%)92.7%

Sample

1st row중리마을
2nd row진촌1리
3rd row진촌2리
4th row진촌4리
5th row진촌6리
ValueCountFrequency (%)
신흥 4
 
0.8%
신기 3
 
0.6%
월평 3
 
0.6%
우산 2
 
0.4%
청보리밭지구 2
 
0.4%
강변지구 2
 
0.4%
두모마을 2
 
0.4%
장동 2
 
0.4%
신오 2
 
0.4%
송산마을 2
 
0.4%
Other values (495) 502
95.4%
2023-12-11T12:10:53.611145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 78
 
4.6%
마 76
 
4.5%
을 72
 
4.3%
구 62
 
3.7%
동 62
 
3.7%
지 59
 
3.5%
산 52
 
3.1%
신 33
 
2.0%
1 24
 
1.4%
정 23
 
1.4%
Other values (220) 1147
68.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1562
92.5%
Decimal Number 73
 
4.3%
Open Punctuation 17
 
1.0%
Close Punctuation 17
 
1.0%
Other Punctuation 12
 
0.7%
Space Separator 5
 
0.3%
Uppercase Letter 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 78
 
5.0%
마 76
 
4.9%
을 72
 
4.6%
구 62
 
4.0%
동 62
 
4.0%
지 59
 
3.8%
산 52
 
3.3%
신 33
 
2.1%
정 23
 
1.5%
봉 20
 
1.3%
Other values (207) 1025
65.6%
Decimal Number
ValueCountFrequency (%)
1 24
32.9%
2 23
31.5%
3 15
20.5%
4 7
 
9.6%
5 3
 
4.1%
6 1
 
1.4%
Other Punctuation
ValueCountFrequency (%)
, 7
58.3%
. 5
41.7%
Uppercase Letter
ValueCountFrequency (%)
B 1
50.0%
A 1
50.0%
Open Punctuation
ValueCountFrequency (%)
( 17
100.0%
Close Punctuation
ValueCountFrequency (%)
) 17
100.0%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1562
92.5%
Common 124
 
7.3%
Latin 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 78
 
5.0%
마 76
 
4.9%
을 72
 
4.6%
구 62
 
4.0%
동 62
 
4.0%
지 59
 
3.8%
산 52
 
3.3%
신 33
 
2.1%
정 23
 
1.5%
봉 20
 
1.3%
Other values (207) 1025
65.6%
Common
ValueCountFrequency (%)
1 24
19.4%
2 23
18.5%
( 17
13.7%
) 17
13.7%
3 15
12.1%
, 7
 
5.6%
4 7
 
5.6%
5
 
4.0%
. 5
 
4.0%
5 3
 
2.4%
Latin
ValueCountFrequency (%)
B 1
50.0%
A 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1562
92.5%
ASCII 126
 
7.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 78
 
5.0%
마 76
 
4.9%
을 72
 
4.6%
구 62
 
4.0%
동 62
 
4.0%
지 59
 
3.8%
산 52
 
3.3%
신 33
 
2.1%
정 23
 
1.5%
봉 20
 
1.3%
Other values (207) 1025
65.6%
ASCII
ValueCountFrequency (%)
1 24
19.0%
2 23
18.3%
( 17
13.5%
) 17
13.5%
3 15
11.9%
, 7
 
5.6%
4 7
 
5.6%
5
 
4.0%
. 5
 
4.0%
5 3
 
2.4%
Other values (3) 3
 
2.4%

법정리명
Text

MISSING 

Distinct327
Distinct (%)72.7%
Missing72
Missing (%)13.8%
Memory size4.2 KiB
2023-12-11T12:10:54.024662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length3
Mean length2.8955556
Min length1

Characters and Unicode

Total characters1303
Distinct characters182
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique248 ?
Unique (%)55.1%

Sample

1st row웅천리
2nd row진촌리
3rd row진촌리
4th row진촌리
5th row진촌리
ValueCountFrequency (%)
창북리 6
 
1.3%
심포리 6
 
1.3%
월산리 5
 
1.1%
고사리 5
 
1.1%
신호리 5
 
1.1%
청호리 4
 
0.9%
삼산리 4
 
0.9%
상궐리 4
 
0.9%
월평 4
 
0.9%
연평리 4
 
0.9%
Other values (316) 403
89.6%
2023-12-11T12:10:54.651049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 337
25.9%
산 57
 
4.4%
동 41
 
3.1%
신 24
 
1.8%
월 20
 
1.5%
사 20
 
1.5%
상 19
 
1.5%
대 18
 
1.4%
포 16
 
1.2%
남 15
 
1.2%
Other values (172) 736
56.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1272
97.6%
Other Punctuation 15
 
1.2%
Decimal Number 15
 
1.2%
Space Separator 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 337
26.5%
산 57
 
4.5%
동 41
 
3.2%
신 24
 
1.9%
월 20
 
1.6%
사 20
 
1.6%
상 19
 
1.5%
대 18
 
1.4%
포 16
 
1.3%
남 15
 
1.2%
Other values (166) 705
55.4%
Decimal Number
ValueCountFrequency (%)
1 8
53.3%
2 4
26.7%
3 3
 
20.0%
Other Punctuation
ValueCountFrequency (%)
, 14
93.3%
. 1
 
6.7%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1272
97.6%
Common 31
 
2.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 337
26.5%
산 57
 
4.5%
동 41
 
3.2%
신 24
 
1.9%
월 20
 
1.6%
사 20
 
1.6%
상 19
 
1.5%
대 18
 
1.4%
포 16
 
1.3%
남 15
 
1.2%
Other values (166) 705
55.4%
Common
ValueCountFrequency (%)
, 14
45.2%
1 8
25.8%
2 4
 
12.9%
3 3
 
9.7%
1
 
3.2%
. 1
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1272
97.6%
ASCII 31
 
2.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 337
26.5%
산 57
 
4.5%
동 41
 
3.2%
신 24
 
1.9%
월 20
 
1.6%
사 20
 
1.6%
상 19
 
1.5%
대 18
 
1.4%
포 16
 
1.3%
남 15
 
1.2%
Other values (166) 705
55.4%
ASCII
ValueCountFrequency (%)
, 14
45.2%
1 8
25.8%
2 4
 
12.9%
3 3
 
9.7%
1
 
3.2%
. 1
 
3.2%

절기(동/하계)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
동계
475 
하계
 
47

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
동계 475
91.0%
하계 47
 
9.0%

Length

2023-12-11T12:10:54.804564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:10:54.920937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동계 475
91.0%
하계 47
 
9.0%

경관작물명
Text

Distinct54
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
2023-12-11T12:10:55.133580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length37
Mean length9.6245211
Min length1

Characters and Unicode

Total characters5024
Distinct characters50
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)5.2%

Sample

1st row연꽃
2nd row메밀
3rd row메밀
4th row메밀
5th row메밀
ValueCountFrequency (%)
보리(겉보리,쌀보리,맥주보리,청보리 120
18.1%
헤어리베치 108
16.3%
유채 105
15.8%
등 97
14.6%
이탈리안라이그라스 33
 
5.0%
메밀 32
 
4.8%
등),유채 24
 
3.6%
밀 23
 
3.5%
밀,보리(겉보리,쌀보리,맥주보리,청보리 18
 
2.7%
자운영 16
 
2.4%
Other values (40) 88
13.3%
2023-12-11T12:10:55.897165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 888
17.7%
보 710
 
14.1%
, 540
 
10.7%
) 154
 
3.1%
( 154
 
3.1%
유 151
 
3.0%
채 151
 
3.0%
맥 142
 
2.8%
등 142
 
2.8%
142
 
2.8%
Other values (40) 1850
36.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4034
80.3%
Other Punctuation 540
 
10.7%
Close Punctuation 154
 
3.1%
Open Punctuation 154
 
3.1%
Space Separator 142
 
2.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 888
22.0%
보 710
17.6%
유 151
 
3.7%
채 151
 
3.7%
맥 142
 
3.5%
등 142
 
3.5%
청 142
 
3.5%
주 142
 
3.5%
겉 142
 
3.5%
쌀 142
 
3.5%
Other values (36) 1282
31.8%
Other Punctuation
ValueCountFrequency (%)
, 540
100.0%
Close Punctuation
ValueCountFrequency (%)
) 154
100.0%
Open Punctuation
ValueCountFrequency (%)
( 154
100.0%
Space Separator
ValueCountFrequency (%)
142
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4034
80.3%
Common 990
 
19.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 888
22.0%
보 710
17.6%
유 151
 
3.7%
채 151
 
3.7%
맥 142
 
3.5%
등 142
 
3.5%
청 142
 
3.5%
주 142
 
3.5%
겉 142
 
3.5%
쌀 142
 
3.5%
Other values (36) 1282
31.8%
Common
ValueCountFrequency (%)
, 540
54.5%
) 154
 
15.6%
( 154
 
15.6%
142
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4034
80.3%
ASCII 990
 
19.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 888
22.0%
보 710
17.6%
유 151
 
3.7%
채 151
 
3.7%
맥 142
 
3.5%
등 142
 
3.5%
청 142
 
3.5%
주 142
 
3.5%
겉 142
 
3.5%
쌀 142
 
3.5%
Other values (36) 1282
31.8%
ASCII
ValueCountFrequency (%)
, 540
54.5%
) 154
 
15.6%
( 154
 
15.6%
142
 
14.3%

참여자수(농가수)
Real number (ℝ)

HIGH CORRELATION 

Distinct65
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.091954
Minimum1
Maximum179
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-12-11T12:10:56.061937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.05
Q19
median15
Q326
95-th percentile48
Maximum179
Range178
Interquartile range (IQR)17

Descriptive statistics

Standard deviation18.451138
Coefficient of variation (CV)0.91833468
Kurtosis22.413823
Mean20.091954
Median Absolute Deviation (MAD)8
Skewness3.5176456
Sum10488
Variance340.4445
MonotonicityNot monotonic
2023-12-11T12:10:56.233762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 26
 
5.0%
16 24
 
4.6%
10 24
 
4.6%
12 22
 
4.2%
1 21
 
4.0%
15 20
 
3.8%
11 20
 
3.8%
8 19
 
3.6%
17 19
 
3.6%
6 19
 
3.6%
Other values (55) 308
59.0%
ValueCountFrequency (%)
1 21
4.0%
2 6
 
1.1%
3 14
2.7%
4 13
2.5%
5 10
1.9%
6 19
3.6%
7 16
3.1%
8 19
3.6%
9 17
3.3%
10 24
4.6%
ValueCountFrequency (%)
179 2
0.4%
113 1
0.2%
97 1
0.2%
87 1
0.2%
84 1
0.2%
76 2
0.4%
74 1
0.2%
73 1
0.2%
72 1
0.2%
66 1
0.2%

대상면적
Real number (ℝ)

HIGH CORRELATION 

Distinct517
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean233904.67
Minimum15320
Maximum1946393
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-12-11T12:10:56.384827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15320
5-th percentile38627.2
Q1100000.52
median149739.55
Q3274454.25
95-th percentile662060.54
Maximum1946393
Range1931073
Interquartile range (IQR)174453.73

Descriptive statistics

Standard deviation255442.28
Coefficient of variation (CV)1.0920786
Kurtosis14.7923
Mean233904.67
Median Absolute Deviation (MAD)71012.55
Skewness3.3517606
Sum1.2209824 × 108
Variance6.5250759 × 1010
MonotonicityNot monotonic
2023-12-11T12:10:56.550007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000.0 3
 
0.6%
57137.0 2
 
0.4%
50000.0 2
 
0.4%
41829.0 2
 
0.4%
179654.0 1
 
0.2%
235483.9 1
 
0.2%
260709.9 1
 
0.2%
209108.0 1
 
0.2%
120077.0 1
 
0.2%
19695.0 1
 
0.2%
Other values (507) 507
97.1%
ValueCountFrequency (%)
15320.0 1
0.2%
19695.0 1
0.2%
20086.0 1
0.2%
21250.0 1
0.2%
22261.0 1
0.2%
23000.0 1
0.2%
23366.0 1
0.2%
23922.0 1
0.2%
25124.0 1
0.2%
25644.0 1
0.2%
ValueCountFrequency (%)
1946393.0 1
0.2%
1899401.0 1
0.2%
1764771.2 1
0.2%
1743835.1 1
0.2%
1470059.9 1
0.2%
1411462.8 1
0.2%
1267760.2 1
0.2%
1252127.3 1
0.2%
1235218.0 1
0.2%
1218517.0 1
0.2%

Interactions

2023-12-11T12:10:49.765748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:10:49.525356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:10:49.896004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:10:49.636849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T12:10:56.685625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도시군구절기(동/하계)경관작물명참여자수(농가수)대상면적
시도1.0001.0000.5890.8300.4180.000
시군구1.0001.0000.8480.9460.7670.184
절기(동/하계)0.5890.8481.0001.0000.0850.090
경관작물명0.8300.9461.0001.0000.7150.720
참여자수(농가수)0.4180.7670.0850.7151.0000.737
대상면적0.0000.1840.0900.7200.7371.000
2023-12-11T12:10:56.816226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도절기(동/하계)
시도1.0000.547
절기(동/하계)0.5471.000
2023-12-11T12:10:56.926775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
참여자수(농가수)대상면적시도절기(동/하계)
참여자수(농가수)1.0000.6510.2020.063
대상면적0.6511.0000.0000.068
시도0.2020.0001.0000.547
절기(동/하계)0.0630.0680.5471.000

Missing values

2023-12-11T12:10:50.071703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T12:10:50.246314image/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

사업년도시도시군구읍면동대상지구명법정리명절기(동/하계)경관작물명참여자수(농가수)대상면적
02013부산광역시기장군철마면중리마을웅천리하계연꽃419695.0
12013인천광역시옹진군백령면진촌1리진촌리하계메밀23108358.0
22013인천광역시옹진군백령면진촌2리진촌리하계메밀849624.0
32013인천광역시옹진군백령면진촌4리진촌리하계메밀161179.0
42013인천광역시옹진군백령면진촌6리진촌리하계메밀840540.0
52013인천광역시옹진군백령면가을2리가을리하계메밀15113757.0
62013인천광역시옹진군백령면남포2리남포리하계메밀1686865.0
72013인천광역시옹진군자월면자월2리자월리하계메밀1462200.0
82013광주광역시광산구어룡동박산마을<NA>동계밀24398546.0
92013광주광역시광산구삼도동송산마을<NA>동계밀34593217.0
사업년도시도시군구읍면동대상지구명법정리명절기(동/하계)경관작물명참여자수(농가수)대상면적
5122013경상남도합천군적중면적중1<NA>동계밀10138365.0
5132013경상남도합천군적중면적중2<NA>동계밀10138391.0
5142013경상남도창원시동읍합산마을합산마을동계보리(겉보리,쌀보리,맥주보리,청보리 등)13112872.0
5152013경상남도창원시진북면금산마을금산동계유채1179599.0
5162013제주특별자치도제주시우도면서광리연평리동계유채1270100.0
5172013제주특별자치도제주시우도면천진리연평리동계유채41232417.0
5182013제주특별자치도제주시우도면조일리연평리동계유채1378792.0
5192013제주특별자치도제주시우도면오봉리연평리동계유채46226800.0
5202013제주특별자치도서귀포시대정읍가파리<NA>동계보리(겉보리,쌀보리,맥주보리,청보리 등)24256353.0
5212013제주특별자치도서귀포시안덕면서광동리<NA>하계메밀328601.0