Overview

Dataset statistics

Number of variables9
Number of observations51
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.1 KiB
Average record size in memory82.5 B

Variable types

Text1
Numeric8

Dataset

Description농작물 중에서 재해보험에 가입된 농작물을 사고원인별로 호우, 태풍, 우박 조수해, 기타 자연재해 등에 대한 데이터를 제공합니다.
Author농업정책보험금융원
URLhttps://www.data.go.kr/data/15126085/fileData.do

Alerts

냉해_동해_동상해 is highly overall correlated with 우박 and 1 other fieldsHigh correlation
호우 is highly overall correlated with 태풍_강풍 and 4 other fieldsHigh correlation
태풍_강풍 is highly overall correlated with 호우 and 3 other fieldsHigh correlation
우박 is highly overall correlated with 냉해_동해_동상해 and 2 other fieldsHigh correlation
한해(가뭄)_폭염 is highly overall correlated with 호우 and 3 other fieldsHigh correlation
조수해 is highly overall correlated with 호우 and 1 other fieldsHigh correlation
기타자연재해 is highly overall correlated with 냉해_동해_동상해 and 3 other fieldsHigh correlation
품목 has unique valuesUnique
냉해_동해_동상해 has 7 (13.7%) zerosZeros
호우 has 11 (21.6%) zerosZeros
태풍_강풍 has 6 (11.8%) zerosZeros
우박 has 34 (66.7%) zerosZeros
한해(가뭄)_폭염 has 2 (3.9%) zerosZeros
조수해 has 16 (31.4%) zerosZeros
폭설_설해 has 35 (68.6%) zerosZeros
기타자연재해 has 4 (7.8%) zerosZeros

Reproduction

Analysis started2024-03-14 11:00:34.953731
Analysis finished2024-03-14 11:00:51.490947
Duration16.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

품목
Text

UNIQUE 

Distinct51
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size536.0 B
2024-03-14T20:00:52.438401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length13
Mean length3.1176471
Min length1

Characters and Unicode

Total characters159
Distinct characters82
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

Unique51 ?
Unique (%)100.0%

Sample

1st row사과
2nd row배
3rd row단감
4th row떫은감
5th row감귤
ValueCountFrequency (%)
포함 2
 
3.7%
벼 2
 
3.7%
사과 1
 
1.9%
양파 1
 
1.9%
마늘 1
 
1.9%
양배추 1
 
1.9%
브로콜리 1
 
1.9%
고랭지무 1
 
1.9%
월동무 1
 
1.9%
고랭지배추 1
 
1.9%
Other values (42) 42
77.8%
2024-03-14T20:00:53.995846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
자 7
 
4.4%
감 6
 
3.8%
추 6
 
3.8%
배 6
 
3.8%
고 5
 
3.1%
사 4
 
2.5%
파 4
 
2.5%
수 4
 
2.5%
무 3
 
1.9%
시 3
 
1.9%
Other values (72) 111
69.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 150
94.3%
Close Punctuation 3
 
1.9%
Open Punctuation 3
 
1.9%
Space Separator 3
 
1.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
자 7
 
4.7%
감 6
 
4.0%
추 6
 
4.0%
배 6
 
4.0%
고 5
 
3.3%
사 4
 
2.7%
파 4
 
2.7%
수 4
 
2.7%
무 3
 
2.0%
시 3
 
2.0%
Other values (69) 102
68.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 150
94.3%
Common 9
 
5.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
자 7
 
4.7%
감 6
 
4.0%
추 6
 
4.0%
배 6
 
4.0%
고 5
 
3.3%
사 4
 
2.7%
파 4
 
2.7%
수 4
 
2.7%
무 3
 
2.0%
시 3
 
2.0%
Other values (69) 102
68.0%
Common
ValueCountFrequency (%)
) 3
33.3%
( 3
33.3%
3
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 150
94.3%
ASCII 9
 
5.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
자 7
 
4.7%
감 6
 
4.0%
추 6
 
4.0%
배 6
 
4.0%
고 5
 
3.3%
사 4
 
2.7%
파 4
 
2.7%
수 4
 
2.7%
무 3
 
2.0%
시 3
 
2.0%
Other values (69) 102
68.0%
ASCII
ValueCountFrequency (%)
) 3
33.3%
( 3
33.3%
3
33.3%

냉해_동해_동상해
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct41
Distinct (%)80.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1290.4314
Minimum0
Maximum34618
Zeros7
Zeros (%)13.7%
Negative0
Negative (%)0.0%
Memory size587.0 B
2024-03-14T20:00:54.380505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.5
median69
Q3382
95-th percentile4345.5
Maximum34618
Range34618
Interquartile range (IQR)376.5

Descriptive statistics

Standard deviation4951.3385
Coefficient of variation (CV)3.8369638
Kurtosis43.14572
Mean1290.4314
Median Absolute Deviation (MAD)69
Skewness6.3753269
Sum65812
Variance24515753
MonotonicityNot monotonic
2024-03-14T20:00:54.802173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0 7
 
13.7%
3 3
 
5.9%
1 2
 
3.9%
6 2
 
3.9%
34 1
 
2.0%
373 1
 
2.0%
4475 1
 
2.0%
7 1
 
2.0%
69 1
 
2.0%
4216 1
 
2.0%
Other values (31) 31
60.8%
ValueCountFrequency (%)
0 7
13.7%
1 2
 
3.9%
3 3
5.9%
5 1
 
2.0%
6 2
 
3.9%
7 1
 
2.0%
8 1
 
2.0%
10 1
 
2.0%
11 1
 
2.0%
18 1
 
2.0%
ValueCountFrequency (%)
34618 1
2.0%
6752 1
2.0%
4475 1
2.0%
4216 1
2.0%
3261 1
2.0%
2812 1
2.0%
1993 1
2.0%
1506 1
2.0%
1381 1
2.0%
1023 1
2.0%

호우
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)72.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean999.54902
Minimum0
Maximum27598
Zeros11
Zeros (%)21.6%
Negative0
Negative (%)0.0%
Memory size587.0 B
2024-03-14T20:00:55.134020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.5
median56
Q3240.5
95-th percentile3932
Maximum27598
Range27598
Interquartile range (IQR)237

Descriptive statistics

Standard deviation4005.3156
Coefficient of variation (CV)4.0071228
Kurtosis40.817466
Mean999.54902
Median Absolute Deviation (MAD)56
Skewness6.182201
Sum50977
Variance16042553
MonotonicityNot monotonic
2024-03-14T20:00:55.441826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 11
21.6%
4 3
 
5.9%
64 2
 
3.9%
56 2
 
3.9%
215 1
 
2.0%
154 1
 
2.0%
36 1
 
2.0%
63 1
 
2.0%
175 1
 
2.0%
595 1
 
2.0%
Other values (27) 27
52.9%
ValueCountFrequency (%)
0 11
21.6%
1 1
 
2.0%
3 1
 
2.0%
4 3
 
5.9%
6 1
 
2.0%
8 1
 
2.0%
18 1
 
2.0%
19 1
 
2.0%
20 1
 
2.0%
32 1
 
2.0%
ValueCountFrequency (%)
27598 1
2.0%
7138 1
2.0%
4879 1
2.0%
2985 1
2.0%
2244 1
2.0%
800 1
2.0%
735 1
2.0%
595 1
2.0%
580 1
2.0%
508 1
2.0%

태풍_강풍
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)78.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4786.5294
Minimum0
Maximum137387
Zeros6
Zeros (%)11.8%
Negative0
Negative (%)0.0%
Memory size587.0 B
2024-03-14T20:00:55.686860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19
median89
Q31790.5
95-th percentile15152.5
Maximum137387
Range137387
Interquartile range (IQR)1781.5

Descriptive statistics

Standard deviation19632.458
Coefficient of variation (CV)4.101606
Kurtosis43.795316
Mean4786.5294
Median Absolute Deviation (MAD)89
Skewness6.4481447
Sum244113
Variance3.8543339 × 108
MonotonicityNot monotonic
2024-03-14T20:00:55.914190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0 6
 
11.8%
2 3
 
5.9%
9 2
 
3.9%
24 2
 
3.9%
21 2
 
3.9%
1 2
 
3.9%
988 1
 
2.0%
7 1
 
2.0%
2792 1
 
2.0%
1348 1
 
2.0%
Other values (30) 30
58.8%
ValueCountFrequency (%)
0 6
11.8%
1 2
 
3.9%
2 3
5.9%
7 1
 
2.0%
9 2
 
3.9%
13 1
 
2.0%
15 1
 
2.0%
21 2
 
3.9%
24 2
 
3.9%
28 1
 
2.0%
ValueCountFrequency (%)
137387 1
2.0%
28373 1
2.0%
16220 1
2.0%
14085 1
2.0%
13981 1
2.0%
7011 1
2.0%
3248 1
2.0%
2803 1
2.0%
2792 1
2.0%
2004 1
2.0%

우박
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)29.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.921569
Minimum0
Maximum3255
Zeros34
Zeros (%)66.7%
Negative0
Negative (%)0.0%
Memory size587.0 B
2024-03-14T20:00:56.123096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile129
Maximum3255
Range3255
Interquartile range (IQR)2

Descriptive statistics

Standard deviation456.36875
Coefficient of variation (CV)5.7102076
Kurtosis49.648414
Mean79.921569
Median Absolute Deviation (MAD)0
Skewness7.0080396
Sum4076
Variance208272.43
MonotonicityNot monotonic
2024-03-14T20:00:56.316099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 34
66.7%
1 3
 
5.9%
2 2
 
3.9%
3255 1
 
2.0%
132 1
 
2.0%
14 1
 
2.0%
100 1
 
2.0%
18 1
 
2.0%
59 1
 
2.0%
310 1
 
2.0%
Other values (5) 5
 
9.8%
ValueCountFrequency (%)
0 34
66.7%
1 3
 
5.9%
2 2
 
3.9%
4 1
 
2.0%
12 1
 
2.0%
14 1
 
2.0%
16 1
 
2.0%
18 1
 
2.0%
23 1
 
2.0%
59 1
 
2.0%
ValueCountFrequency (%)
3255 1
2.0%
310 1
2.0%
132 1
2.0%
126 1
2.0%
100 1
2.0%
59 1
2.0%
23 1
2.0%
18 1
2.0%
16 1
2.0%
14 1
2.0%

한해(가뭄)_폭염
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)92.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1003.3137
Minimum0
Maximum11538
Zeros2
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size587.0 B
2024-03-14T20:00:56.591800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q124.5
median146
Q3914
95-th percentile4831.5
Maximum11538
Range11538
Interquartile range (IQR)889.5

Descriptive statistics

Standard deviation2063.2492
Coefficient of variation (CV)2.0564348
Kurtosis13.906266
Mean1003.3137
Median Absolute Deviation (MAD)141
Skewness3.4224316
Sum51169
Variance4256997.4
MonotonicityNot monotonic
2024-03-14T20:00:57.082433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
2 2
 
3.9%
70 2
 
3.9%
0 2
 
3.9%
1 2
 
3.9%
44 1
 
2.0%
3271 1
 
2.0%
41 1
 
2.0%
19 1
 
2.0%
4641 1
 
2.0%
73 1
 
2.0%
Other values (37) 37
72.5%
ValueCountFrequency (%)
0 2
3.9%
1 2
3.9%
2 2
3.9%
4 1
2.0%
5 1
2.0%
7 1
2.0%
8 1
2.0%
12 1
2.0%
19 1
2.0%
20 1
2.0%
ValueCountFrequency (%)
11538 1
2.0%
6507 1
2.0%
5022 1
2.0%
4641 1
2.0%
3271 1
2.0%
2775 1
2.0%
2326 1
2.0%
2149 1
2.0%
1897 1
2.0%
1822 1
2.0%

조수해
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)45.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean165.09804
Minimum0
Maximum4582
Zeros16
Zeros (%)31.4%
Negative0
Negative (%)0.0%
Memory size587.0 B
2024-03-14T20:00:57.303098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q338.5
95-th percentile400
Maximum4582
Range4582
Interquartile range (IQR)38.5

Descriptive statistics

Standard deviation683.53699
Coefficient of variation (CV)4.1401884
Kurtosis36.872909
Mean165.09804
Median Absolute Deviation (MAD)3
Skewness5.894106
Sum8420
Variance467222.81
MonotonicityNot monotonic
2024-03-14T20:00:57.508491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 16
31.4%
1 5
 
9.8%
9 3
 
5.9%
3 3
 
5.9%
54 2
 
3.9%
37 2
 
3.9%
14 2
 
3.9%
2 2
 
3.9%
5 2
 
3.9%
298 1
 
2.0%
Other values (13) 13
25.5%
ValueCountFrequency (%)
0 16
31.4%
1 5
 
9.8%
2 2
 
3.9%
3 3
 
5.9%
5 2
 
3.9%
9 3
 
5.9%
13 1
 
2.0%
14 2
 
3.9%
22 1
 
2.0%
25 1
 
2.0%
ValueCountFrequency (%)
4582 1
2.0%
1801 1
2.0%
502 1
2.0%
298 1
2.0%
269 1
2.0%
222 1
2.0%
132 1
2.0%
91 1
2.0%
87 1
2.0%
71 1
2.0%

폭설_설해
Real number (ℝ)

ZEROS 

Distinct13
Distinct (%)25.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.196078
Minimum0
Maximum504
Zeros35
Zeros (%)68.6%
Negative0
Negative (%)0.0%
Memory size587.0 B
2024-03-14T20:00:57.727575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.5
95-th percentile63
Maximum504
Range504
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation92.308184
Coefficient of variation (CV)4.1587609
Kurtosis22.485816
Mean22.196078
Median Absolute Deviation (MAD)0
Skewness4.7998561
Sum1132
Variance8520.8008
MonotonicityNot monotonic
2024-03-14T20:00:58.087732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 35
68.6%
1 3
 
5.9%
2 3
 
5.9%
6 1
 
2.0%
5 1
 
2.0%
3 1
 
2.0%
60 1
 
2.0%
13 1
 
2.0%
22 1
 
2.0%
66 1
 
2.0%
Other values (3) 3
 
5.9%
ValueCountFrequency (%)
0 35
68.6%
1 3
 
5.9%
2 3
 
5.9%
3 1
 
2.0%
5 1
 
2.0%
6 1
 
2.0%
10 1
 
2.0%
13 1
 
2.0%
22 1
 
2.0%
60 1
 
2.0%
ValueCountFrequency (%)
504 1
2.0%
434 1
2.0%
66 1
2.0%
60 1
2.0%
22 1
2.0%
13 1
2.0%
10 1
2.0%
6 1
2.0%
5 1
2.0%
3 1
2.0%

기타자연재해
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct41
Distinct (%)80.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3864.6863
Minimum0
Maximum129054
Zeros4
Zeros (%)7.8%
Negative0
Negative (%)0.0%
Memory size587.0 B
2024-03-14T20:00:58.463820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.5
median51
Q3318
95-th percentile3742.5
Maximum129054
Range129054
Interquartile range (IQR)313.5

Descriptive statistics

Standard deviation19147.088
Coefficient of variation (CV)4.9543706
Kurtosis38.714087
Mean3864.6863
Median Absolute Deviation (MAD)50
Skewness6.0819923
Sum197099
Variance3.6661099 × 108
MonotonicityNot monotonic
2024-03-14T20:00:58.895918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0 4
 
7.8%
3 3
 
5.9%
1 3
 
5.9%
6 2
 
3.9%
112 2
 
3.9%
4 2
 
3.9%
14 1
 
2.0%
401 1
 
2.0%
196 1
 
2.0%
347 1
 
2.0%
Other values (31) 31
60.8%
ValueCountFrequency (%)
0 4
7.8%
1 3
5.9%
2 1
 
2.0%
3 3
5.9%
4 2
3.9%
5 1
 
2.0%
6 2
3.9%
14 1
 
2.0%
15 1
 
2.0%
18 1
 
2.0%
ValueCountFrequency (%)
129054 1
2.0%
48886 1
2.0%
5199 1
2.0%
2286 1
2.0%
2242 1
2.0%
1857 1
2.0%
1700 1
2.0%
1575 1
2.0%
616 1
2.0%
474 1
2.0%

Interactions

2024-03-14T20:00:48.495823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:35.295872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:37.315191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:38.857744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:40.861007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:42.843303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:45.032253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:46.931382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:48.754896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:35.558751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:37.564754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:39.104173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:41.116047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:43.099260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:45.277752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:47.184339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:49.004024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:35.805123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:37.801494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:39.352689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:41.359420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:43.344458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:45.511155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:47.420760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:49.262537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:36.065459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:38.053534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:39.610215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:41.614314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:43.803207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:45.756709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:47.675717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:49.528713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:36.318341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:38.297332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:39.867348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:41.860579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:44.051919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:45.996871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:47.839843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:49.853625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:36.572405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:38.441811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:40.119687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:42.109114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:44.299635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:46.237197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:47.985422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:50.096309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:36.815070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:38.573369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:40.361737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:42.344666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:44.536246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:46.461387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:48.115419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:50.341806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:37.060340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:38.711560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:40.606771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:42.588337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:44.779835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:46.692939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:00:48.254154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T20:00:59.121995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
품목냉해_동해_동상해호우태풍_강풍우박한해(가뭄)_폭염조수해폭설_설해기타자연재해
품목1.0001.0001.0001.0001.0001.0001.0001.0001.000
냉해_동해_동상해1.0001.0000.1300.6601.0000.6680.3410.0000.000
호우1.0000.1301.0000.9250.0000.9450.9950.0000.793
태풍_강풍1.0000.6600.9251.0001.0000.8350.9250.5300.659
우박1.0001.0000.0001.0001.0000.4280.0000.0000.000
한해(가뭄)_폭염1.0000.6680.9450.8350.4281.0000.9090.4220.984
조수해1.0000.3410.9950.9250.0000.9091.0000.0000.658
폭설_설해1.0000.0000.0000.5300.0000.4220.0001.0000.000
기타자연재해1.0000.0000.7930.6590.0000.9840.6580.0001.000
2024-03-14T20:00:59.331615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
냉해_동해_동상해호우태풍_강풍우박한해(가뭄)_폭염조수해폭설_설해기타자연재해
냉해_동해_동상해1.0000.2230.3700.5620.4960.0790.3560.548
호우0.2231.0000.6610.5380.6030.6040.2130.668
태풍_강풍0.3700.6611.0000.5580.5370.2840.2780.669
우박0.5620.5380.5581.0000.4300.2500.0380.486
한해(가뭄)_폭염0.4960.6030.5370.4301.0000.5220.3090.776
조수해0.0790.6040.2840.2500.5221.0000.2370.436
폭설_설해0.3560.2130.2780.0380.3090.2371.0000.461
기타자연재해0.5480.6680.6690.4860.7760.4360.4611.000

Missing values

2024-03-14T20:00:50.767814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T20:00:51.305837image/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

품목냉해_동해_동상해호우태풍_강풍우박한해(가뭄)_폭염조수해폭설_설해기타자연재해
0사과346182152837332551296002286
1배1506194140851328100153
2단감68519188521350125
3떫은감19932497011141420065
4감귤33863248021494065199
5복숭아67524879200410080150201575
6포도13818001601814654590
7자두2812284337597591080
8매실1023015020802112
9참다래2961330290040
품목냉해_동해_동상해호우태풍_강풍우박한해(가뭄)_폭염조수해폭설_설해기타자연재해
41차16400082006
42오디1380707104
43인삼102580988018975504616
44대추10842613024182210289
45밤67351928024913051
46복분자1940001013
47오미자12511012103
48호두1002400000
49원예시설(시설작물 포함)3912244162201623262224341700
50버섯재배사(버섯작물 포함)3558907001021