Overview

Dataset statistics

Number of variables18
Number of observations734
Missing cells734
Missing cells (%)5.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory114.1 KiB
Average record size in memory159.2 B

Variable types

Categorical1
Text2
Numeric14
Unsupported1

Dataset

Description지역별 조건불리지역직불금 지급 농가통계, 지급현황 자료
Author농림축산식품부
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20220216000000001985

Alerts

지급대상자 is highly overall correlated with 지급대상면적(논) and 8 other fieldsHigh correlation
지급대상면적(논) is highly overall correlated with 지급대상자 and 6 other fieldsHigh correlation
지급대상면적(밭) is highly overall correlated with 지급대상자 and 6 other fieldsHigh correlation
지급대상면적(과수) is highly overall correlated with 지급액(국비) and 5 other fieldsHigh correlation
지급대상면적(초지) is highly overall correlated with 지급대상면적(초지).1High correlation
지급액(국비) is highly overall correlated with 지급대상자 and 10 other fieldsHigh correlation
지급액(지방비) is highly overall correlated with 지급대상자 and 10 other fieldsHigh correlation
지급대상면적(논).1 is highly overall correlated with 지급대상자 and 6 other fieldsHigh correlation
지급대상면적(밭).1 is highly overall correlated with 지급대상자 and 6 other fieldsHigh correlation
지급대상면적(과수).1 is highly overall correlated with 지급대상면적(과수) and 5 other fieldsHigh correlation
지급대상면적(초지).1 is highly overall correlated with 지급대상면적(초지)High correlation
REAL_BOJO is highly overall correlated with 지급대상자 and 10 other fieldsHigh correlation
GROUP_REAL_BOJO is highly overall correlated with 지급대상자 and 10 other fieldsHigh correlation
Unnamed: 17 is highly overall correlated with 지급대상자 and 10 other fieldsHigh correlation
Unnamed: 10 has 734 (100.0%) missing valuesMissing
지급대상면적(밭).1 has unique valuesUnique
Unnamed: 10 is an unsupported type, check if it needs cleaning or further analysisUnsupported
지급대상면적(논) has 171 (23.3%) zerosZeros
지급대상면적(과수) has 72 (9.8%) zerosZeros
지급대상면적(초지) has 565 (77.0%) zerosZeros
지급대상면적(논).1 has 169 (23.0%) zerosZeros
지급대상면적(과수).1 has 70 (9.5%) zerosZeros
지급대상면적(초지).1 has 565 (77.0%) zerosZeros

Reproduction

Analysis started2023-12-11 03:48:48.036671
Analysis finished2023-12-11 03:49:18.025411
Duration29.99 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도
Categorical

Distinct13
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
경상북도
163 
경상남도
137 
전라남도
112 
강원도
90 
충청북도
76 
Other values (8)
156 

Length

Max length7
Median length4
Mean length3.9250681
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row세종특별자치시
2nd row세종특별자치시
3rd row대구광역시
4th row대구광역시
5th row인천광역시

Common Values

ValueCountFrequency (%)
경상북도 163
22.2%
경상남도 137
18.7%
전라남도 112
15.3%
강원도 90
12.3%
충청북도 76
10.4%
전라북도 47
 
6.4%
충청남도 44
 
6.0%
경기도 28
 
3.8%
인천광역시 20
 
2.7%
제주특별자치도 11
 
1.5%
Other values (3) 6
 
0.8%

Length

2023-12-11T12:49:18.106024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경상북도 163
22.2%
경상남도 137
18.7%
전라남도 112
15.3%
강원도 90
12.3%
충청북도 76
10.4%
전라북도 47
 
6.4%
충청남도 44
 
6.0%
경기도 28
 
3.8%
인천광역시 20
 
2.7%
제주특별자치도 11
 
1.5%
Other values (3) 6
 
0.8%

시군
Text

Distinct126
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
2023-12-11T12:49:18.411462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0108992
Min length3

Characters and Unicode

Total characters2210
Distinct characters98
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

Unique16 ?
Unique (%)2.2%

Sample

1st row세종시
2nd row세종시
3rd row달성군
4th row달성군
5th row강화군
ValueCountFrequency (%)
안동시 14
 
1.9%
신안군 13
 
1.8%
강화군 13
 
1.8%
합천군 13
 
1.8%
완도군 12
 
1.6%
김천시 11
 
1.5%
의령군 11
 
1.5%
고흥군 10
 
1.4%
봉화군 10
 
1.4%
충주시 10
 
1.4%
Other values (116) 617
84.1%
2023-12-11T12:49:18.840441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
490
22.2%
249
 
11.3%
106
 
4.8%
88
 
4.0%
69
 
3.1%
57
 
2.6%
55
 
2.5%
50
 
2.3%
48
 
2.2%
43
 
1.9%
Other values (88) 955
43.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2210
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
490
22.2%
249
 
11.3%
106
 
4.8%
88
 
4.0%
69
 
3.1%
57
 
2.6%
55
 
2.5%
50
 
2.3%
48
 
2.2%
43
 
1.9%
Other values (88) 955
43.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2210
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
490
22.2%
249
 
11.3%
106
 
4.8%
88
 
4.0%
69
 
3.1%
57
 
2.6%
55
 
2.5%
50
 
2.3%
48
 
2.2%
43
 
1.9%
Other values (88) 955
43.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2210
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
490
22.2%
249
 
11.3%
106
 
4.8%
88
 
4.0%
69
 
3.1%
57
 
2.6%
55
 
2.5%
50
 
2.3%
48
 
2.2%
43
 
1.9%
Other values (88) 955
43.2%
Distinct666
Distinct (%)90.7%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
2023-12-11T12:49:19.175384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.9632153
Min length2

Characters and Unicode

Total characters2175
Distinct characters229
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

Unique625 ?
Unique (%)85.1%

Sample

1st row부강면
2nd row금남면
3rd row가창면
4th row현풍면
5th row강화읍
ValueCountFrequency (%)
남면 10
 
1.4%
북면 8
 
1.1%
서면 7
 
1.0%
동면 5
 
0.7%
산내면 4
 
0.5%
산외면 3
 
0.4%
군북면 3
 
0.4%
성산면 3
 
0.4%
안덕면 2
 
0.3%
삼계면 2
 
0.3%
Other values (656) 687
93.6%
2023-12-11T12:49:19.702033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
640
29.4%
95
 
4.4%
67
 
3.1%
47
 
2.2%
43
 
2.0%
42
 
1.9%
39
 
1.8%
31
 
1.4%
30
 
1.4%
28
 
1.3%
Other values (219) 1113
51.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2175
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
640
29.4%
95
 
4.4%
67
 
3.1%
47
 
2.2%
43
 
2.0%
42
 
1.9%
39
 
1.8%
31
 
1.4%
30
 
1.4%
28
 
1.3%
Other values (219) 1113
51.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2175
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
640
29.4%
95
 
4.4%
67
 
3.1%
47
 
2.2%
43
 
2.0%
42
 
1.9%
39
 
1.8%
31
 
1.4%
30
 
1.4%
28
 
1.3%
Other values (219) 1113
51.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2175
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
640
29.4%
95
 
4.4%
67
 
3.1%
47
 
2.2%
43
 
2.0%
42
 
1.9%
39
 
1.8%
31
 
1.4%
30
 
1.4%
28
 
1.3%
Other values (219) 1113
51.2%

지급대상자
Real number (ℝ)

HIGH CORRELATION 

Distinct345
Distinct (%)47.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean202.6485
Minimum1
Maximum3624
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-12-11T12:49:19.844402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q144.25
median105
Q3239.75
95-th percentile608.1
Maximum3624
Range3623
Interquartile range (IQR)195.5

Descriptive statistics

Standard deviation343.736
Coefficient of variation (CV)1.6962178
Kurtosis42.22102
Mean202.6485
Median Absolute Deviation (MAD)76
Skewness5.6949163
Sum148744
Variance118154.44
MonotonicityNot monotonic
2023-12-11T12:49:19.994094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 12
 
1.6%
24 10
 
1.4%
21 8
 
1.1%
19 8
 
1.1%
26 7
 
1.0%
39 7
 
1.0%
78 7
 
1.0%
46 7
 
1.0%
61 6
 
0.8%
47 6
 
0.8%
Other values (335) 656
89.4%
ValueCountFrequency (%)
1 1
 
0.1%
2 1
 
0.1%
3 5
0.7%
4 4
0.5%
5 4
0.5%
6 3
0.4%
7 2
 
0.3%
8 2
 
0.3%
9 4
0.5%
11 5
0.7%
ValueCountFrequency (%)
3624 1
0.1%
3387 1
0.1%
3083 1
0.1%
2827 1
0.1%
2586 1
0.1%
2369 1
0.1%
2316 1
0.1%
1886 1
0.1%
1810 1
0.1%
1731 1
0.1%

지급대상면적(논)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct310
Distinct (%)42.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.904632
Minimum0
Maximum1033.9
Zeros171
Zeros (%)23.3%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-12-11T12:49:20.177952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.4
median5.55
Q318.1
95-th percentile83.025
Maximum1033.9
Range1033.9
Interquartile range (IQR)17.7

Descriptive statistics

Standard deviation50.705093
Coefficient of variation (CV)2.6821518
Kurtosis222.76559
Mean18.904632
Median Absolute Deviation (MAD)5.55
Skewness12.17775
Sum13876
Variance2571.0065
MonotonicityNot monotonic
2023-12-11T12:49:20.340502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 171
 
23.3%
0.4 11
 
1.5%
3.3 11
 
1.5%
0.8 10
 
1.4%
0.7 8
 
1.1%
8.3 7
 
1.0%
3.8 7
 
1.0%
2.8 7
 
1.0%
1.5 7
 
1.0%
3.2 6
 
0.8%
Other values (300) 489
66.6%
ValueCountFrequency (%)
0.0 171
23.3%
0.1 4
 
0.5%
0.2 3
 
0.4%
0.3 3
 
0.4%
0.4 11
 
1.5%
0.5 4
 
0.5%
0.6 2
 
0.3%
0.7 8
 
1.1%
0.8 10
 
1.4%
0.9 4
 
0.5%
ValueCountFrequency (%)
1033.9 1
0.1%
306.8 1
0.1%
297.7 1
0.1%
235.8 1
0.1%
210.3 1
0.1%
201.0 1
0.1%
199.8 1
0.1%
179.6 1
0.1%
178.8 1
0.1%
167.0 1
0.1%

지급대상면적(밭)
Real number (ℝ)

HIGH CORRELATION 

Distinct703
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean995.49074
Minimum1
Maximum26000.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-12-11T12:49:20.529434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile24.965
Q1108.45
median338.25
Q31005.325
95-th percentile3730.56
Maximum26000.6
Range25999.6
Interquartile range (IQR)896.875

Descriptive statistics

Standard deviation2098.2545
Coefficient of variation (CV)2.1077589
Kurtosis51.331673
Mean995.49074
Median Absolute Deviation (MAD)275.5
Skewness6.0807692
Sum730690.2
Variance4402671.9
MonotonicityNot monotonic
2023-12-11T12:49:20.794249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.4 3
 
0.4%
50.3 3
 
0.4%
8.3 2
 
0.3%
484.9 2
 
0.3%
133.7 2
 
0.3%
69.9 2
 
0.3%
38.5 2
 
0.3%
178.6 2
 
0.3%
166.2 2
 
0.3%
964.2 2
 
0.3%
Other values (693) 712
97.0%
ValueCountFrequency (%)
1.0 1
 
0.1%
1.1 1
 
0.1%
1.4 1
 
0.1%
3.0 1
 
0.1%
4.3 2
0.3%
6.1 1
 
0.1%
7.9 1
 
0.1%
8.3 2
0.3%
9.1 1
 
0.1%
9.4 3
0.4%
ValueCountFrequency (%)
26000.6 1
0.1%
21167.4 1
0.1%
17434.3 1
0.1%
16560.1 1
0.1%
12744.4 1
0.1%
12167.6 1
0.1%
10842.4 1
0.1%
9849.8 1
0.1%
9258.6 1
0.1%
8700.1 1
0.1%

지급대상면적(과수)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct545
Distinct (%)74.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean271.86362
Minimum0
Maximum26679.7
Zeros72
Zeros (%)9.8%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-12-11T12:49:20.988115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18.55
median42.35
Q3147.9
95-th percentile892.1
Maximum26679.7
Range26679.7
Interquartile range (IQR)139.35

Descriptive statistics

Standard deviation1324.8605
Coefficient of variation (CV)4.8732542
Kurtosis233.12789
Mean271.86362
Median Absolute Deviation (MAD)40.6
Skewness13.515561
Sum199547.9
Variance1755255.4
MonotonicityNot monotonic
2023-12-11T12:49:21.152409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 72
 
9.8%
18.7 5
 
0.7%
1.0 4
 
0.5%
8.8 4
 
0.5%
0.3 4
 
0.5%
0.2 4
 
0.5%
23.6 3
 
0.4%
1.7 3
 
0.4%
11.3 3
 
0.4%
2.7 3
 
0.4%
Other values (535) 629
85.7%
ValueCountFrequency (%)
0.0 72
9.8%
0.1 2
 
0.3%
0.2 4
 
0.5%
0.3 4
 
0.5%
0.4 2
 
0.3%
0.5 1
 
0.1%
0.6 1
 
0.1%
0.7 2
 
0.3%
0.8 1
 
0.1%
0.9 1
 
0.1%
ValueCountFrequency (%)
26679.7 1
0.1%
11213.9 1
0.1%
11144.0 1
0.1%
9321.3 1
0.1%
7704.4 1
0.1%
6861.4 1
0.1%
6559.1 1
0.1%
5380.0 1
0.1%
4176.6 1
0.1%
2830.7 1
0.1%

지급대상면적(초지)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct132
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.825341
Minimum0
Maximum9402.8
Zeros565
Zeros (%)77.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-12-11T12:49:21.333237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile82.955
Maximum9402.8
Range9402.8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation462.89391
Coefficient of variation (CV)9.1075417
Kurtosis267.79098
Mean50.825341
Median Absolute Deviation (MAD)0
Skewness15.193978
Sum37305.8
Variance214270.77
MonotonicityNot monotonic
2023-12-11T12:49:21.541583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 565
77.0%
2.9 6
 
0.8%
0.5 5
 
0.7%
0.2 4
 
0.5%
0.4 4
 
0.5%
0.6 4
 
0.5%
1.9 4
 
0.5%
2.1 3
 
0.4%
0.3 3
 
0.4%
0.8 3
 
0.4%
Other values (122) 133
 
18.1%
ValueCountFrequency (%)
0.0 565
77.0%
0.1 2
 
0.3%
0.2 4
 
0.5%
0.3 3
 
0.4%
0.4 4
 
0.5%
0.5 5
 
0.7%
0.6 4
 
0.5%
0.7 1
 
0.1%
0.8 3
 
0.4%
0.9 1
 
0.1%
ValueCountFrequency (%)
9402.8 1
0.1%
5892.4 1
0.1%
3123.4 1
0.1%
2816.2 1
0.1%
2698.5 1
0.1%
2174.3 1
0.1%
1562.5 1
0.1%
1382.4 1
0.1%
489.8 1
0.1%
468.6 1
0.1%

지급액(국비)
Real number (ℝ)

HIGH CORRELATION 

Distinct470
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.470981
Minimum0.1
Maximum1136.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-12-11T12:49:21.781951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile1.6
Q17.125
median19.4
Q350.775
95-th percentile177.04
Maximum1136.9
Range1136.8
Interquartile range (IQR)43.65

Descriptive statistics

Standard deviation117.86958
Coefficient of variation (CV)2.2463765
Kurtosis45.820958
Mean52.470981
Median Absolute Deviation (MAD)15.4
Skewness6.2152176
Sum38513.7
Variance13893.238
MonotonicityNot monotonic
2023-12-11T12:49:21.962342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.1 7
 
1.0%
3.7 6
 
0.8%
5.9 6
 
0.8%
9.8 6
 
0.8%
1.4 6
 
0.8%
2.6 5
 
0.7%
1.7 5
 
0.7%
3.3 5
 
0.7%
3.8 5
 
0.7%
11.2 5
 
0.7%
Other values (460) 678
92.4%
ValueCountFrequency (%)
0.1 1
 
0.1%
0.2 2
0.3%
0.3 1
 
0.1%
0.4 3
0.4%
0.5 3
0.4%
0.6 1
 
0.1%
0.7 2
0.3%
0.8 1
 
0.1%
0.9 3
0.4%
1.0 3
0.4%
ValueCountFrequency (%)
1136.9 1
0.1%
1102.1 1
0.1%
1062.2 1
0.1%
1010.3 1
0.1%
1000.8 1
0.1%
937.7 1
0.1%
856.2 1
0.1%
710.1 1
0.1%
701.7 1
0.1%
548.3 1
0.1%

지급액(지방비)
Real number (ℝ)

HIGH CORRELATION 

Distinct259
Distinct (%)35.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.119346
Minimum0
Maximum284.2
Zeros2
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-12-11T12:49:22.173493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.4
Q11.8
median4.85
Q312.7
95-th percentile44.31
Maximum284.2
Range284.2
Interquartile range (IQR)10.9

Descriptive statistics

Standard deviation29.467838
Coefficient of variation (CV)2.2461362
Kurtosis45.818196
Mean13.119346
Median Absolute Deviation (MAD)3.85
Skewness6.215036
Sum9629.6
Variance868.35349
MonotonicityNot monotonic
2023-12-11T12:49:22.357138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9 17
 
2.3%
0.7 15
 
2.0%
0.4 15
 
2.0%
1.0 15
 
2.0%
1.4 13
 
1.8%
1.1 12
 
1.6%
0.8 12
 
1.6%
3.3 12
 
1.6%
1.5 11
 
1.5%
0.3 11
 
1.5%
Other values (249) 601
81.9%
ValueCountFrequency (%)
0.0 2
 
0.3%
0.1 9
1.2%
0.2 7
1.0%
0.3 11
1.5%
0.4 15
2.0%
0.5 9
1.2%
0.6 11
1.5%
0.7 15
2.0%
0.8 12
1.6%
0.9 17
2.3%
ValueCountFrequency (%)
284.2 1
0.1%
275.5 1
0.1%
265.6 1
0.1%
252.6 1
0.1%
250.2 1
0.1%
234.4 1
0.1%
214.1 1
0.1%
177.5 1
0.1%
175.4 1
0.1%
137.1 1
0.1%

Unnamed: 10
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing734
Missing (%)100.0%
Memory size6.6 KiB

지급대상면적(논).1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct554
Distinct (%)75.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18903.631
Minimum0
Maximum1033926
Zeros169
Zeros (%)23.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-12-11T12:49:22.805585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1406
median5556
Q318129.5
95-th percentile82988.55
Maximum1033926
Range1033926
Interquartile range (IQR)17723.5

Descriptive statistics

Standard deviation50706.137
Coefficient of variation (CV)2.6823491
Kurtosis222.77121
Mean18903.631
Median Absolute Deviation (MAD)5556
Skewness12.17798
Sum13875265
Variance2.5711123 × 109
MonotonicityNot monotonic
2023-12-11T12:49:22.985289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 169
 
23.0%
8208 2
 
0.3%
1481 2
 
0.3%
1544 2
 
0.3%
429 2
 
0.3%
2644 2
 
0.3%
3298 2
 
0.3%
1958 2
 
0.3%
793 2
 
0.3%
450 2
 
0.3%
Other values (544) 547
74.5%
ValueCountFrequency (%)
0 169
23.0%
20 1
 
0.1%
40 1
 
0.1%
73 1
 
0.1%
93 1
 
0.1%
116 1
 
0.1%
120 1
 
0.1%
165 1
 
0.1%
187 1
 
0.1%
195 1
 
0.1%
ValueCountFrequency (%)
1033926 1
0.1%
306847 1
0.1%
297670 1
0.1%
235794 1
0.1%
210343 1
0.1%
201022 1
0.1%
199784 1
0.1%
179629 1
0.1%
178758 1
0.1%
166985 1
0.1%

지급대상면적(밭).1
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct734
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean995490.2
Minimum959
Maximum26000640
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-12-11T12:49:23.160267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum959
5-th percentile24976.5
Q1108454.25
median338269.5
Q31005337
95-th percentile3730562.5
Maximum26000640
Range25999681
Interquartile range (IQR)896882.75

Descriptive statistics

Standard deviation2098254.1
Coefficient of variation (CV)2.1077597
Kurtosis51.331928
Mean995490.2
Median Absolute Deviation (MAD)275530
Skewness6.0807825
Sum7.3068981 × 108
Variance4.4026702 × 1012
MonotonicityNot monotonic
2023-12-11T12:49:23.343161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8331 1
 
0.1%
20726 1
 
0.1%
402106 1
 
0.1%
415750 1
 
0.1%
1220077 1
 
0.1%
1060020 1
 
0.1%
165452 1
 
0.1%
1955490 1
 
0.1%
2528976 1
 
0.1%
822129 1
 
0.1%
Other values (724) 724
98.6%
ValueCountFrequency (%)
959 1
0.1%
1134 1
0.1%
1426 1
0.1%
3011 1
0.1%
4262 1
0.1%
4303 1
0.1%
6117 1
0.1%
7873 1
0.1%
8325 1
0.1%
8331 1
0.1%
ValueCountFrequency (%)
26000640 1
0.1%
21167435 1
0.1%
17434279 1
0.1%
16560102 1
0.1%
12744384 1
0.1%
12167631 1
0.1%
10842383 1
0.1%
9849754 1
0.1%
9258556 1
0.1%
8700084 1
0.1%

지급대상면적(과수).1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct662
Distinct (%)90.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean271864.52
Minimum0
Maximum26679682
Zeros70
Zeros (%)9.5%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-12-11T12:49:23.515950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18570.75
median42334
Q3147912
95-th percentile892083.95
Maximum26679682
Range26679682
Interquartile range (IQR)139341.25

Descriptive statistics

Standard deviation1324859.5
Coefficient of variation (CV)4.8732343
Kurtosis233.128
Mean271864.52
Median Absolute Deviation (MAD)40581.5
Skewness13.515566
Sum1.9954856 × 108
Variance1.7552527 × 1012
MonotonicityNot monotonic
2023-12-11T12:49:23.703340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 70
 
9.5%
18745 2
 
0.3%
1000 2
 
0.3%
22817 2
 
0.3%
628772 1
 
0.1%
196887 1
 
0.1%
179414 1
 
0.1%
391274 1
 
0.1%
408956 1
 
0.1%
353598 1
 
0.1%
Other values (652) 652
88.8%
ValueCountFrequency (%)
0 70
9.5%
5 1
 
0.1%
10 1
 
0.1%
50 1
 
0.1%
138 1
 
0.1%
160 1
 
0.1%
188 1
 
0.1%
200 1
 
0.1%
227 1
 
0.1%
255 1
 
0.1%
ValueCountFrequency (%)
26679682 1
0.1%
11213881 1
0.1%
11144038 1
0.1%
9321275 1
0.1%
7704422 1
0.1%
6861371 1
0.1%
6559084 1
0.1%
5379953 1
0.1%
4176597 1
0.1%
2830699 1
0.1%

지급대상면적(초지).1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct167
Distinct (%)22.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50825.373
Minimum0
Maximum9402835
Zeros565
Zeros (%)77.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-12-11T12:49:23.861255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile82952.6
Maximum9402835
Range9402835
Interquartile range (IQR)0

Descriptive statistics

Standard deviation462894.81
Coefficient of variation (CV)9.1075536
Kurtosis267.79142
Mean50825.373
Median Absolute Deviation (MAD)0
Skewness15.193976
Sum37305824
Variance2.142716 × 1011
MonotonicityNot monotonic
2023-12-11T12:49:24.075983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 565
77.0%
500 2
 
0.3%
2922 2
 
0.3%
159 2
 
0.3%
235721 1
 
0.1%
20854 1
 
0.1%
4394 1
 
0.1%
486 1
 
0.1%
1960 1
 
0.1%
783 1
 
0.1%
Other values (157) 157
 
21.4%
ValueCountFrequency (%)
0 565
77.0%
105 1
 
0.1%
109 1
 
0.1%
159 2
 
0.3%
162 1
 
0.1%
200 1
 
0.1%
294 1
 
0.1%
311 1
 
0.1%
348 1
 
0.1%
361 1
 
0.1%
ValueCountFrequency (%)
9402835 1
0.1%
5892355 1
0.1%
3123448 1
0.1%
2816211 1
0.1%
2698476 1
0.1%
2174335 1
0.1%
1562509 1
0.1%
1382424 1
0.1%
489759 1
0.1%
468603 1
0.1%

REAL_BOJO
Real number (ℝ)

HIGH CORRELATION 

Distinct729
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49881940
Minimum58000
Maximum9.29441 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-12-11T12:49:24.271113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum58000
5-th percentile1609850
Q17083500
median18989500
Q350201750
95-th percentile1.734188 × 108
Maximum9.29441 × 108
Range9.29383 × 108
Interquartile range (IQR)43118250

Descriptive statistics

Standard deviation1.0240251 × 108
Coefficient of variation (CV)2.0528974
Kurtosis37.952709
Mean49881940
Median Absolute Deviation (MAD)15096500
Skewness5.5838697
Sum3.6613344 × 1010
Variance1.0486274 × 1016
MonotonicityNot monotonic
2023-12-11T12:49:24.465727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11706000 2
 
0.3%
14661000 2
 
0.3%
9505000 2
 
0.3%
3343000 2
 
0.3%
3339000 2
 
0.3%
108219000 1
 
0.1%
6398000 1
 
0.1%
18461000 1
 
0.1%
21864000 1
 
0.1%
30816000 1
 
0.1%
Other values (719) 719
98.0%
ValueCountFrequency (%)
58000 1
0.1%
171000 1
0.1%
217000 1
0.1%
334000 1
0.1%
375000 1
0.1%
378000 1
0.1%
385000 1
0.1%
433000 1
0.1%
467000 1
0.1%
470000 1
0.1%
ValueCountFrequency (%)
929441000 1
0.1%
911364000 1
0.1%
898648000 1
0.1%
883981000 1
0.1%
820477000 1
0.1%
736746000 1
0.1%
730143000 1
0.1%
614389000 1
0.1%
606277000 1
0.1%
456604000 1
0.1%

GROUP_REAL_BOJO
Real number (ℝ)

HIGH CORRELATION 

Distinct720
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15709309
Minimum15000
Maximum5.14241 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-12-11T12:49:24.645434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15000
5-th percentile405950
Q11832750
median4957500
Q313069750
95-th percentile46005200
Maximum5.14241 × 108
Range5.14226 × 108
Interquartile range (IQR)11237000

Descriptive statistics

Standard deviation46423491
Coefficient of variation (CV)2.955158
Kurtosis68.462301
Mean15709309
Median Absolute Deviation (MAD)3958000
Skewness7.7892782
Sum1.1530633 × 1010
Variance2.1551406 × 1015
MonotonicityNot monotonic
2023-12-11T12:49:24.845564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2883000 2
 
0.3%
1082000 2
 
0.3%
3387000 2
 
0.3%
647000 2
 
0.3%
6105000 2
 
0.3%
2378000 2
 
0.3%
350000 2
 
0.3%
971000 2
 
0.3%
420000 2
 
0.3%
919000 2
 
0.3%
Other values (710) 714
97.3%
ValueCountFrequency (%)
15000 1
0.1%
44000 1
0.1%
55000 1
0.1%
84000 1
0.1%
94000 1
0.1%
96000 1
0.1%
97000 1
0.1%
117000 1
0.1%
118000 1
0.1%
150000 1
0.1%
ValueCountFrequency (%)
514241000 1
0.1%
509811000 1
0.1%
479035000 1
0.1%
398342000 1
0.1%
378856000 1
0.1%
351640000 1
0.1%
340155000 1
0.1%
273282000 1
0.1%
270845000 1
0.1%
228783000 1
0.1%

Unnamed: 17
Real number (ℝ)

HIGH CORRELATION 

Distinct732
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65591249
Minimum73000
Maximum1.421175 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-12-11T12:49:25.021698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum73000
5-th percentile2012800
Q18921250
median24198000
Q363474500
95-th percentile2.213501 × 108
Maximum1.421175 × 109
Range1.421102 × 109
Interquartile range (IQR)54553250

Descriptive statistics

Standard deviation1.4733895 × 108
Coefficient of variation (CV)2.2463202
Kurtosis45.821476
Mean65591249
Median Absolute Deviation (MAD)19200500
Skewness6.2152488
Sum4.8143977 × 1010
Variance2.1708765 × 1016
MonotonicityNot monotonic
2023-12-11T12:49:25.224268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4174000 2
 
0.3%
32477000 2
 
0.3%
23078000 1
 
0.1%
38522000 1
 
0.1%
167957000 1
 
0.1%
55182000 1
 
0.1%
27714000 1
 
0.1%
141056000 1
 
0.1%
135280000 1
 
0.1%
48235000 1
 
0.1%
Other values (722) 722
98.4%
ValueCountFrequency (%)
73000 1
0.1%
215000 1
0.1%
272000 1
0.1%
418000 1
0.1%
469000 1
0.1%
474000 1
0.1%
482000 1
0.1%
584000 1
0.1%
588000 1
0.1%
619000 1
0.1%
ValueCountFrequency (%)
1421175000 1
0.1%
1377683000 1
0.1%
1327783000 1
0.1%
1262837000 1
0.1%
1250987000 1
0.1%
1172117000 1
0.1%
1070298000 1
0.1%
887671000 1
0.1%
877122000 1
0.1%
685387000 1
0.1%

Interactions

2023-12-11T12:49:15.565813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:49.395340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:51.575490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:53.452823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:55.626718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:57.824798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:59.638500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:02.226561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:03.989033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:06.405405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:08.427573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:10.040445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:12.022755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:13.833803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:15.685920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:49.580007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:51.707746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:53.627142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:55.772076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:57.964021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:59.779446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:02.374384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:04.192730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:06.540667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:08.559807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:10.168607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:12.156402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:13.937116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:15.820550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:49.807912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:51.816642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:53.804187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:55.915434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:58.140162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:59.883376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:02.539288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:04.398378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:06.666296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:08.661689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:10.328239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:12.296759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:14.073827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:15.940931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:49.947518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:51.929427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:53.969599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:56.044195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:58.258090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:00.106800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:02.622423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:04.558998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:06.789914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:08.767141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:10.503890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:12.421719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:14.230348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:16.369091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:50.105501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:52.079127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:54.147395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:56.169658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:58.386518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:00.451193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:02.725246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:04.759545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:06.908348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:08.863632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:10.612932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:12.552967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:14.361519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:16.474097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:50.271766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:52.222517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:54.307526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:56.292245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:58.484112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:00.750226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:02.815503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:04.928450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:07.026168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:08.952511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:10.747868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:12.686347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:14.467977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:16.581714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:50.453948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:52.363899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:54.451056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:56.396964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:58.606722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:01.080880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:02.910342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:05.111786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:07.157324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:09.059201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:10.917704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:12.834220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:14.600875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:16.726884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:50.610011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:52.506263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:54.605020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:56.506251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:58.715613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:01.310033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:03.033742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:05.272337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:07.574949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:09.157123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:11.047662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:12.983627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:14.747281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:16.858304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:50.746184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:52.624092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:54.771327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:56.619840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:58.856104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:01.483955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:03.154614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:05.469543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:07.688622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:09.265515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:11.184321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:13.124451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:14.857535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:17.027729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:50.886926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:52.732605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:54.906429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:57.092438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:59.019682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:01.641190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:03.283006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:05.683726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:07.840793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:09.400021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:11.318995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:13.246706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:14.998852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:17.153291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:51.049756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:52.864999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:55.045752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:57.247556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:59.160917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:01.787057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:03.403906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:05.826803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:07.947289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:09.533463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:11.453407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:13.369462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:15.116984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:17.256290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:51.181614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:53.010815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:55.174464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:57.374940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:59.291991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:01.913294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:03.572379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:05.972762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:08.069427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:09.668292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:11.586157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:13.505700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:15.219282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:17.357052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:51.295993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:53.160584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:55.313645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:57.528243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:59.418746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:02.011435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:03.712934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:06.117869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:08.197957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:09.789224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:11.708183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:13.617463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:15.314852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:17.465317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:51.422497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:53.293831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:55.468209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:57.658560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:48:59.525958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:02.123062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:03.849057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:06.257522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:08.315101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:09.905717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:11.860231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:13.725560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:49:15.429167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T12:49:25.382936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도지급대상자지급대상면적(논)지급대상면적(밭)지급대상면적(과수)지급대상면적(초지)지급액(국비)지급액(지방비)지급대상면적(논).1지급대상면적(밭).1지급대상면적(과수).1지급대상면적(초지).1REAL_BOJOGROUP_REAL_BOJOUnnamed: 17
시도1.0000.6230.0000.5240.6300.5850.6330.6330.0000.5240.6300.5850.6150.6340.633
지급대상자0.6231.0000.1710.8450.9070.8840.8950.8950.1710.8450.9070.8840.8750.8680.895
지급대상면적(논)0.0000.1711.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.1610.0000.000
지급대상면적(밭)0.5240.8450.0001.0000.7300.6680.9000.9000.0001.0000.7300.6680.8460.9350.900
지급대상면적(과수)0.6300.9070.0000.7301.0000.9580.8840.8840.0000.7301.0000.9580.8620.8440.884
지급대상면적(초지)0.5850.8840.0000.6680.9581.0000.9300.9300.0000.6680.9581.0000.8690.8450.930
지급액(국비)0.6330.8950.0000.9000.8840.9301.0001.0000.0000.9000.8840.9300.9840.9251.000
지급액(지방비)0.6330.8950.0000.9000.8840.9301.0001.0000.0000.9000.8840.9300.9840.9251.000
지급대상면적(논).10.0000.1711.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.1610.0000.000
지급대상면적(밭).10.5240.8450.0001.0000.7300.6680.9000.9000.0001.0000.7300.6680.8460.9350.900
지급대상면적(과수).10.6300.9070.0000.7301.0000.9580.8840.8840.0000.7301.0000.9580.8620.8440.884
지급대상면적(초지).10.5850.8840.0000.6680.9581.0000.9300.9300.0000.6680.9581.0000.8690.8450.930
REAL_BOJO0.6150.8750.1610.8460.8620.8690.9840.9840.1610.8460.8620.8691.0000.9030.984
GROUP_REAL_BOJO0.6340.8680.0000.9350.8440.8450.9250.9250.0000.9350.8440.8450.9031.0000.925
Unnamed: 170.6330.8950.0000.9000.8840.9301.0001.0000.0000.9000.8840.9300.9840.9251.000
2023-12-11T12:49:25.559498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지급대상자지급대상면적(논)지급대상면적(밭)지급대상면적(과수)지급대상면적(초지)지급액(국비)지급액(지방비)지급대상면적(논).1지급대상면적(밭).1지급대상면적(과수).1지급대상면적(초지).1REAL_BOJOGROUP_REAL_BOJOUnnamed: 17시도
지급대상자1.0000.5580.9010.4930.3240.9410.9420.5580.9010.4930.3240.9420.9350.9410.315
지급대상면적(논)0.5581.0000.4870.3510.1150.5250.5251.0000.4870.3510.1160.5260.5190.5250.000
지급대상면적(밭)0.9010.4871.0000.2940.3350.9340.9340.4861.0000.2940.3350.9330.9300.9340.268
지급대상면적(과수)0.4930.3510.2941.0000.1300.5090.5090.3510.2941.0000.1300.5120.5040.5100.373
지급대상면적(초지)0.3240.1150.3350.1301.0000.3360.3360.1150.3350.1301.0000.3330.3410.3360.336
지급액(국비)0.9410.5250.9340.5090.3361.0001.0000.5250.9340.5100.3360.9990.9961.0000.333
지급액(지방비)0.9420.5250.9340.5090.3361.0001.0000.5250.9340.5100.3360.9990.9961.0000.333
지급대상면적(논).10.5581.0000.4860.3510.1150.5250.5251.0000.4860.3520.1150.5260.5190.5250.000
지급대상면적(밭).10.9010.4871.0000.2940.3350.9340.9340.4861.0000.2940.3350.9330.9300.9340.268
지급대상면적(과수).10.4930.3510.2941.0000.1300.5100.5100.3520.2941.0000.1300.5120.5040.5100.373
지급대상면적(초지).10.3240.1160.3350.1301.0000.3360.3360.1150.3350.1301.0000.3330.3410.3360.336
REAL_BOJO0.9420.5260.9330.5120.3330.9990.9990.5260.9330.5120.3331.0000.9920.9990.319
GROUP_REAL_BOJO0.9350.5190.9300.5040.3410.9960.9960.5190.9300.5040.3410.9921.0000.9960.350
Unnamed: 170.9410.5250.9340.5100.3361.0001.0000.5250.9340.5100.3360.9990.9961.0000.333
시도0.3150.0000.2680.3730.3360.3330.3330.0000.2680.3730.3360.3190.3500.3331.000

Missing values

2023-12-11T12:49:17.641536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T12:49:17.922086image/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

시도시군읍면동지급대상자지급대상면적(논)지급대상면적(밭)지급대상면적(과수)지급대상면적(초지)지급액(국비)지급액(지방비)Unnamed: 10지급대상면적(논).1지급대상면적(밭).1지급대상면적(과수).1지급대상면적(초지).1REAL_BOJOGROUP_REAL_BOJOUnnamed: 17
0세종특별자치시세종시부강면30.08.30.00.00.30.1<NA>083310033400084000418000
1세종특별자치시세종시금남면260.080.77.10.03.50.9<NA>0806897138035140008790004393000
2대구광역시달성군가창면920.0154.672.20.09.12.3<NA>01545587218809074000227400011348000
3대구광역시달성군현풍면290.040.229.60.02.80.7<NA>04020929635027950006990003494000
4인천광역시강화군강화읍40636.0956.663.10.042.210.6<NA>35990956614631230422380001056500052803000
5인천광역시강화군선원면40431.01006.625.60.042.510.6<NA>310021006616256250425340001063700053171000
6인천광역시강화군불은면58434.31957.944.00.081.520.4<NA>3434519578824404408145500020368000101823000
7인천광역시강화군길상면53558.42022.6147.30.089.122.3<NA>58410202263514729108914300022294000111437000
8인천광역시강화군화도면35811.3937.324.20.038.99.7<NA>1128693726124188038919000973600048655000
9인천광역시강화군양도면44320.81014.7121.30.046.311.6<NA>2084810146511212990462780001157300057851000
시도시군읍면동지급대상자지급대상면적(논)지급대상면적(밭)지급대상면적(과수)지급대상면적(초지)지급액(국비)지급액(지방비)Unnamed: 10지급대상면적(논).1지급대상면적(밭).1지급대상면적(과수).1지급대상면적(초지).1REAL_BOJOGROUP_REAL_BOJOUnnamed: 17
724제주특별자치도제주시애월읍30837.512744.49321.35892.41000.8250.2<NA>748712744384932127558923557367460005142410001250987000
725제주특별자치도제주시구좌읍28270.026000.6465.72174.31102.1275.5<NA>02600064046571321743358986480004790350001377683000
726제주특별자치도제주시조천읍18860.04979.111213.92698.5701.7175.4<NA>04979079112138812698476606277000270845000877122000
727제주특별자치도제주시한경면258612.716560.16861.416.1937.7234.4<NA>12651165601026861371160758204770003516400001172117000
728제주특별자치도제주시우도면1940.01084.40.00.043.410.8<NA>0108440400433780001084600054224000
729제주특별자치도서귀포시대정읍33873.821167.45380.08.51062.2265.6<NA>378521167435537995384879294410003983420001327783000
730제주특별자치도서귀포시남원읍36240.0962.226679.71562.51136.9284.2<NA>09622362667968215625099113640005098110001421175000
731제주특별자치도서귀포시성산읍23690.017434.37704.4235.71010.3252.6<NA>01743427977044222357218839810003788560001262837000
732제주특별자치도서귀포시안덕면17310.06457.26559.11382.4548.3137.1<NA>0645723065590841382424456604000228783000685387000
733제주특별자치도서귀포시표선면18100.08700.111144.03123.4856.2214.1<NA>087000841114403831234487301430003401550001070298000