Overview

Dataset statistics

Number of variables11
Number of observations86
Missing cells31
Missing cells (%)3.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.9 KiB
Average record size in memory94.5 B

Variable types

Text2
Numeric4
Categorical5

Dataset

Description화성시 포도 스마트 팜 현황 데이터 입니다. 농가 code, 재배 면적, 재배 품종, 상세 위치 등의 정보를 제공합니다.
Author경기도 화성시
URLhttps://www.data.go.kr/data/15097760/fileData.do

Alerts

농장번호 is highly overall correlated with CLSSHigh correlation
법정리 is highly overall correlated with X and 3 other fieldsHigh correlation
품종 is highly overall correlated with Y and 1 other fieldsHigh correlation
행정동명 is highly overall correlated with X and 3 other fieldsHigh correlation
CLSS is highly overall correlated with 농가번호 and 7 other fieldsHigh correlation
농가번호 is highly overall correlated with CLSSHigh correlation
면적 is highly overall correlated with CLSSHigh correlation
X is highly overall correlated with 행정동명 and 2 other fieldsHigh correlation
Y is highly overall correlated with 품종 and 3 other fieldsHigh correlation
CLSS is highly imbalanced (68.0%)Imbalance
면적 has 16 (18.6%) missing valuesMissing
지번 has 5 (5.8%) missing valuesMissing
X has 5 (5.8%) missing valuesMissing
Y has 5 (5.8%) missing valuesMissing
code has unique valuesUnique

Reproduction

Analysis started2023-12-12 01:55:34.015595
Analysis finished2023-12-12 01:55:37.186481
Duration3.17 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

code
Text

UNIQUE 

Distinct86
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size820.0 B
2023-12-12T10:55:37.468873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.8604651
Min length3

Characters and Unicode

Total characters332
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique86 ?
Unique (%)100.0%

Sample

1st row1_1
2nd row1_2
3rd row2_1
4th row2_2
5th row3_0
ValueCountFrequency (%)
1_1 1
 
1.2%
48_1 1
 
1.2%
55_0 1
 
1.2%
54_0 1
 
1.2%
53_0 1
 
1.2%
52_0 1
 
1.2%
51_0 1
 
1.2%
50_0 1
 
1.2%
49_0 1
 
1.2%
61_0 1
 
1.2%
Other values (76) 76
88.4%
2023-12-12T10:55:38.044156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 86
25.9%
0 79
23.8%
1 29
 
8.7%
2 26
 
7.8%
3 20
 
6.0%
4 20
 
6.0%
5 19
 
5.7%
6 18
 
5.4%
7 18
 
5.4%
8 9
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 246
74.1%
Connector Punctuation 86
 
25.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 79
32.1%
1 29
 
11.8%
2 26
 
10.6%
3 20
 
8.1%
4 20
 
8.1%
5 19
 
7.7%
6 18
 
7.3%
7 18
 
7.3%
8 9
 
3.7%
9 8
 
3.3%
Connector Punctuation
ValueCountFrequency (%)
_ 86
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 332
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
_ 86
25.9%
0 79
23.8%
1 29
 
8.7%
2 26
 
7.8%
3 20
 
6.0%
4 20
 
6.0%
5 19
 
5.7%
6 18
 
5.4%
7 18
 
5.4%
8 9
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 332
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 86
25.9%
0 79
23.8%
1 29
 
8.7%
2 26
 
7.8%
3 20
 
6.0%
4 20
 
6.0%
5 19
 
5.7%
6 18
 
5.4%
7 18
 
5.4%
8 9
 
2.7%

농가번호
Real number (ℝ)

HIGH CORRELATION 

Distinct79
Distinct (%)91.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.069767
Minimum1
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size906.0 B
2023-12-12T10:55:38.244256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.25
Q117.25
median37.5
Q357.75
95-th percentile74.75
Maximum79
Range78
Interquartile range (IQR)40.5

Descriptive statistics

Standard deviation23.409295
Coefficient of variation (CV)0.61490511
Kurtosis-1.2160252
Mean38.069767
Median Absolute Deviation (MAD)20.5
Skewness0.067119497
Sum3274
Variance547.99508
MonotonicityIncreasing
2023-12-12T10:55:38.443264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2
 
2.3%
2 2
 
2.3%
5 2
 
2.3%
48 2
 
2.3%
11 2
 
2.3%
33 2
 
2.3%
14 2
 
2.3%
53 1
 
1.2%
59 1
 
1.2%
58 1
 
1.2%
Other values (69) 69
80.2%
ValueCountFrequency (%)
1 2
2.3%
2 2
2.3%
3 1
1.2%
4 1
1.2%
5 2
2.3%
6 1
1.2%
7 1
1.2%
8 1
1.2%
9 1
1.2%
10 1
1.2%
ValueCountFrequency (%)
79 1
1.2%
78 1
1.2%
77 1
1.2%
76 1
1.2%
75 1
1.2%
74 1
1.2%
73 1
1.2%
72 1
1.2%
71 1
1.2%
70 1
1.2%

농장번호
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size820.0 B
0
72 
1
 
7
2
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row2
5th row0

Common Values

ValueCountFrequency (%)
0 72
83.7%
1 7
 
8.1%
2 7
 
8.1%

Length

2023-12-12T10:55:38.619991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:55:38.750310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 72
83.7%
1 7
 
8.1%
2 7
 
8.1%

면적
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct60
Distinct (%)85.7%
Missing16
Missing (%)18.6%
Infinite0
Infinite (%)0.0%
Mean2664.4857
Minimum690
Maximum6877
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size906.0 B
2023-12-12T10:55:38.876403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum690
5-th percentile1000
Q11793.5
median2510
Q33387.5
95-th percentile5022.5
Maximum6877
Range6187
Interquartile range (IQR)1594

Descriptive statistics

Standard deviation1251.3035
Coefficient of variation (CV)0.46962288
Kurtosis0.96516953
Mean2664.4857
Median Absolute Deviation (MAD)840
Skewness0.86427454
Sum186514
Variance1565760.3
MonotonicityNot monotonic
2023-12-12T10:55:39.403669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 5
 
5.8%
3370 2
 
2.3%
2510 2
 
2.3%
1960 2
 
2.3%
1320 2
 
2.3%
2000 2
 
2.3%
1553 2
 
2.3%
1780 1
 
1.2%
3720 1
 
1.2%
2050 1
 
1.2%
Other values (50) 50
58.1%
(Missing) 16
 
18.6%
ValueCountFrequency (%)
690 1
 
1.2%
950 1
 
1.2%
1000 5
5.8%
1260 1
 
1.2%
1280 1
 
1.2%
1300 1
 
1.2%
1320 2
 
2.3%
1553 2
 
2.3%
1690 1
 
1.2%
1707 1
 
1.2%
ValueCountFrequency (%)
6877 1
1.2%
5622 1
1.2%
5420 1
1.2%
5360 1
1.2%
4610 1
1.2%
4250 1
1.2%
4221 1
1.2%
4210 1
1.2%
3960 1
1.2%
3870 1
1.2%

품종
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Memory size820.0 B
캠벨얼리
60 
샤인머스켓
16 
<NA>
 
5
샤인머스메켓
 
4
샤인머스메켓,자옥
 
1

Length

Max length9
Median length4
Mean length4.3372093
Min length4

Unique

Unique1 ?
Unique (%)1.2%

Sample

1st row캠벨얼리
2nd row캠벨얼리
3rd row캠벨얼리
4th row캠벨얼리
5th row샤인머스켓

Common Values

ValueCountFrequency (%)
캠벨얼리 60
69.8%
샤인머스켓 16
 
18.6%
<NA> 5
 
5.8%
샤인머스메켓 4
 
4.7%
샤인머스메켓,자옥 1
 
1.2%

Length

2023-12-12T10:55:39.597498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:55:39.733984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
캠벨얼리 60
69.8%
샤인머스켓 16
 
18.6%
na 5
 
5.8%
샤인머스메켓 4
 
4.7%
샤인머스메켓,자옥 1
 
1.2%

행정동명
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Memory size820.0 B
송산면
46 
서신면
17 
마도면
8 
남양읍
6 
<NA>
5 
Other values (3)
 
4

Length

Max length4
Median length3
Mean length3.0581395
Min length3

Unique

Unique2 ?
Unique (%)2.3%

Sample

1st row송산면
2nd row송산면
3rd row서신면
4th row서신면
5th row송산면

Common Values

ValueCountFrequency (%)
송산면 46
53.5%
서신면 17
 
19.8%
마도면 8
 
9.3%
남양읍 6
 
7.0%
<NA> 5
 
5.8%
우정읍 2
 
2.3%
매송면 1
 
1.2%
비봉면 1
 
1.2%

Length

2023-12-12T10:55:39.891335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:55:40.077735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
송산면 46
53.5%
서신면 17
 
19.8%
마도면 8
 
9.3%
남양읍 6
 
7.0%
na 5
 
5.8%
우정읍 2
 
2.3%
매송면 1
 
1.2%
비봉면 1
 
1.2%

법정리
Categorical

HIGH CORRELATION 

Distinct33
Distinct (%)38.4%
Missing0
Missing (%)0.0%
Memory size820.0 B
고정리
9 
고포리
7 
삼존리
 
5
금당리
 
5
광평리
 
5
Other values (28)
55 

Length

Max length4
Median length3
Mean length3.0581395
Min length3

Unique

Unique13 ?
Unique (%)15.1%

Sample

1st row고정리
2nd row고정리
3rd row광평리
4th row광평리
5th row쌍정리

Common Values

ValueCountFrequency (%)
고정리 9
 
10.5%
고포리 7
 
8.1%
삼존리 5
 
5.8%
금당리 5
 
5.8%
광평리 5
 
5.8%
<NA> 5
 
5.8%
마산리 4
 
4.7%
육일리 3
 
3.5%
독지리 3
 
3.5%
장외리 3
 
3.5%
Other values (23) 37
43.0%

Length

2023-12-12T10:55:40.255702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
고정리 9
 
10.5%
고포리 7
 
8.1%
삼존리 5
 
5.8%
금당리 5
 
5.8%
광평리 5
 
5.8%
na 5
 
5.8%
마산리 4
 
4.7%
육일리 3
 
3.5%
장외리 3
 
3.5%
용포리 3
 
3.5%
Other values (23) 37
43.0%

지번
Text

MISSING 

Distinct79
Distinct (%)97.5%
Missing5
Missing (%)5.8%
Memory size820.0 B
2023-12-12T10:55:40.581460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length15
Mean length12.111111
Min length10

Characters and Unicode

Total characters981
Distinct characters70
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

Unique77 ?
Unique (%)95.1%

Sample

1st row송산면 고정리 67
2nd row송산면 고정리 749
3rd row서신면 광평리 271-1
4th row서신면 광평리 464
5th row송산면 쌍정리 719-3
ValueCountFrequency (%)
송산면 46
 
18.8%
서신면 17
 
6.9%
고정리 9
 
3.7%
마도면 8
 
3.3%
고포리 7
 
2.9%
남양읍 6
 
2.4%
광평리 5
 
2.0%
삼존리 5
 
2.0%
금당리 5
 
2.0%
마산리 4
 
1.6%
Other values (108) 133
54.3%
2023-12-12T10:55:41.117476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
164
16.7%
리 81
 
8.3%
면 73
 
7.4%
산 52
 
5.3%
송 48
 
4.9%
1 47
 
4.8%
2 45
 
4.6%
- 36
 
3.7%
3 34
 
3.5%
9 27
 
2.8%
Other values (60) 374
38.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 502
51.2%
Decimal Number 278
28.3%
Space Separator 164
 
16.7%
Dash Punctuation 36
 
3.7%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 81
16.1%
면 73
14.5%
산 52
 
10.4%
송 48
 
9.6%
신 24
 
4.8%
서 17
 
3.4%
고 16
 
3.2%
정 14
 
2.8%
마 12
 
2.4%
지 11
 
2.2%
Other values (47) 154
30.7%
Decimal Number
ValueCountFrequency (%)
1 47
16.9%
2 45
16.2%
3 34
12.2%
9 27
9.7%
6 25
9.0%
4 25
9.0%
7 19
6.8%
8 19
6.8%
0 19
6.8%
5 18
 
6.5%
Space Separator
ValueCountFrequency (%)
164
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 36
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 502
51.2%
Common 479
48.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 81
16.1%
면 73
14.5%
산 52
 
10.4%
송 48
 
9.6%
신 24
 
4.8%
서 17
 
3.4%
고 16
 
3.2%
정 14
 
2.8%
마 12
 
2.4%
지 11
 
2.2%
Other values (47) 154
30.7%
Common
ValueCountFrequency (%)
164
34.2%
1 47
 
9.8%
2 45
 
9.4%
- 36
 
7.5%
3 34
 
7.1%
9 27
 
5.6%
6 25
 
5.2%
4 25
 
5.2%
7 19
 
4.0%
8 19
 
4.0%
Other values (3) 38
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 502
51.2%
ASCII 479
48.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
164
34.2%
1 47
 
9.8%
2 45
 
9.4%
- 36
 
7.5%
3 34
 
7.1%
9 27
 
5.6%
6 25
 
5.2%
4 25
 
5.2%
7 19
 
4.0%
8 19
 
4.0%
Other values (3) 38
 
7.9%
Hangul
ValueCountFrequency (%)
리 81
16.1%
면 73
14.5%
산 52
 
10.4%
송 48
 
9.6%
신 24
 
4.8%
서 17
 
3.4%
고 16
 
3.2%
정 14
 
2.8%
마 12
 
2.4%
지 11
 
2.2%
Other values (47) 154
30.7%

X
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct79
Distinct (%)97.5%
Missing5
Missing (%)5.8%
Infinite0
Infinite (%)0.0%
Mean931719.23
Minimum926052
Maximum944324
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size906.0 B
2023-12-12T10:55:41.312186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum926052
5-th percentile927296
Q1928872
median931639
Q3933379
95-th percentile938740
Maximum944324
Range18272
Interquartile range (IQR)4507

Descriptive statistics

Standard deviation3681.9419
Coefficient of variation (CV)0.0039517719
Kurtosis1.013043
Mean931719.23
Median Absolute Deviation (MAD)2215
Skewness1.0045845
Sum75469258
Variance13556696
MonotonicityNot monotonic
2023-12-12T10:55:41.495084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
933762 2
 
2.3%
933322 2
 
2.3%
939057 1
 
1.2%
927223 1
 
1.2%
927734 1
 
1.2%
938699 1
 
1.2%
933836 1
 
1.2%
932691 1
 
1.2%
933258 1
 
1.2%
926901 1
 
1.2%
Other values (69) 69
80.2%
(Missing) 5
 
5.8%
ValueCountFrequency (%)
926052 1
1.2%
926901 1
1.2%
927086 1
1.2%
927223 1
1.2%
927296 1
1.2%
927302 1
1.2%
927368 1
1.2%
927667 1
1.2%
927696 1
1.2%
927707 1
1.2%
ValueCountFrequency (%)
944324 1
1.2%
940379 1
1.2%
940355 1
1.2%
939057 1
1.2%
938740 1
1.2%
938699 1
1.2%
938149 1
1.2%
937806 1
1.2%
937628 1
1.2%
937625 1
1.2%

Y
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct79
Distinct (%)97.5%
Missing5
Missing (%)5.8%
Infinite0
Infinite (%)0.0%
Mean1912759.6
Minimum1893837
Maximum1919454
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size906.0 B
2023-12-12T10:55:41.713829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1893837
5-th percentile1906102
Q11909536
median1914214
Q31916098
95-th percentile1917810
Maximum1919454
Range25617
Interquartile range (IQR)6562

Descriptive statistics

Standard deviation4622.3479
Coefficient of variation (CV)0.0024165859
Kurtosis3.0067974
Mean1912759.6
Median Absolute Deviation (MAD)2320
Skewness-1.3862253
Sum1.5493352 × 108
Variance21366100
MonotonicityNot monotonic
2023-12-12T10:55:41.921552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1909536 2
 
2.3%
1914991 2
 
2.3%
1919329 1
 
1.2%
1908474 1
 
1.2%
1917810 1
 
1.2%
1919448 1
 
1.2%
1916073 1
 
1.2%
1916467 1
 
1.2%
1916186 1
 
1.2%
1916054 1
 
1.2%
Other values (69) 69
80.2%
(Missing) 5
 
5.8%
ValueCountFrequency (%)
1893837 1
1.2%
1898204 1
1.2%
1904023 1
1.2%
1904194 1
1.2%
1906102 1
1.2%
1906335 1
1.2%
1906930 1
1.2%
1907199 1
1.2%
1907260 1
1.2%
1907367 1
1.2%
ValueCountFrequency (%)
1919454 1
1.2%
1919448 1
1.2%
1919329 1
1.2%
1919048 1
1.2%
1917810 1
1.2%
1917255 1
1.2%
1917023 1
1.2%
1916756 1
1.2%
1916742 1
1.2%
1916723 1
1.2%

CLSS
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size820.0 B
정좌표
81 
<NA>
 
5

Length

Max length4
Median length3
Mean length3.0581395
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정좌표 81
94.2%
<NA> 5
 
5.8%

Length

2023-12-12T10:55:42.103170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:55:42.234327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정좌표 81
94.2%
na 5
 
5.8%

Interactions

2023-12-12T10:55:36.065765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:34.693692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:35.202609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:35.616013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:36.256644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:34.823313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:35.330696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:35.720504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:36.370649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:34.979923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:35.423479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:35.819786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:36.473612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:35.106900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:35.524925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:35.940385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T10:55:42.310481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
code농가번호농장번호면적품종행정동명법정리지번XY
code1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
농가번호1.0001.0000.3460.2630.4250.3560.5380.9720.2280.312
농장번호1.0000.3461.0000.1220.2270.0000.0000.0000.0000.000
면적1.0000.2630.1221.0000.2630.0000.5610.9630.2610.000
품종1.0000.4250.2270.2631.0000.2330.3590.0000.1350.896
행정동명1.0000.3560.0000.0000.2331.0001.0001.0000.8300.846
법정리1.0000.5380.0000.5610.3591.0001.0001.0000.9490.980
지번1.0000.9720.0000.9630.0001.0001.0001.0001.0001.000
X1.0000.2280.0000.2610.1350.8300.9491.0001.0000.432
Y1.0000.3120.0000.0000.8960.8460.9801.0000.4321.000
2023-12-12T10:55:42.451941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
농장번호법정리품종행정동명CLSS
농장번호1.0000.0000.2140.0001.000
법정리0.0001.0000.1240.8141.000
품종0.2140.1241.0000.1561.000
행정동명0.0000.8140.1561.0001.000
CLSS1.0001.0001.0001.0001.000
2023-12-12T10:55:42.573272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
농가번호면적XY농장번호품종행정동명법정리CLSS
농가번호1.000-0.0370.120-0.0550.2090.2540.1810.1691.000
면적-0.0371.0000.0640.0660.1430.1700.0000.1741.000
X0.1200.0641.0000.1010.0000.0710.6320.6201.000
Y-0.0550.0660.1011.0000.0000.6310.6480.7341.000
농장번호0.2090.1430.0000.0001.0000.2140.0000.0001.000
품종0.2540.1700.0710.6310.2141.0000.1560.1241.000
행정동명0.1810.0000.6320.6480.0000.1561.0000.8141.000
법정리0.1690.1740.6200.7340.0000.1240.8141.0001.000
CLSS1.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-12-12T10:55:36.654500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T10:55:36.880388image/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.
2023-12-12T10:55:37.071289image/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

code농가번호농장번호면적품종행정동명법정리지번XYCLSS
01_1112560캠벨얼리송산면고정리송산면 고정리 679334101916594정좌표
11_2122127캠벨얼리송산면고정리송산면 고정리 7499322871916365정좌표
22_1211780캠벨얼리서신면광평리서신면 광평리 271-19281081908559정좌표
32_2222028캠벨얼리서신면광평리서신면 광평리 4649288511908578정좌표
43_0301880샤인머스켓송산면쌍정리송산면 쌍정리 719-39305371915224정좌표
54_0401730캠벨얼리송산면신천리송산면 신천리 6539306621916284정좌표
65_1515420캠벨얼리송산면고포리송산면 고포리 627번지9273681915562정좌표
75_2523238샤인머스메켓송산면고포리송산면 고포리 4599276671915993정좌표
86_0603370샤인머스켓송산면고정리송산면 고정리 201-59330921916410정좌표
97_0703020캠벨얼리서신면장외리서신면 장외리 359277071909084정좌표
code농가번호농장번호면적품종행정동명법정리지번XYCLSS
7670_0700<NA>샤인머스켓송산면용포리송산면 용포리 7129333221914991정좌표
7771_07101553샤인머스켓송산면고포리송산면 고포리 835, 8369270861915484정좌표
7872_0720<NA>샤인머스켓마도면금당리마도면 금당리 10289325211910580정좌표
7973_07301000샤인머스켓송산면고포리송산면 고포리 507-29273021915671정좌표
8074_07401000샤인머스메켓우정읍이화리우정읍 이화리 7079376251893837정좌표
8175_0750<NA><NA><NA><NA><NA><NA><NA><NA>
8276_0760<NA><NA><NA><NA><NA><NA><NA><NA>
8377_0770<NA><NA><NA><NA><NA><NA><NA><NA>
8478_0780<NA><NA><NA><NA><NA><NA><NA><NA>
8579_0790<NA><NA><NA><NA><NA><NA><NA><NA>