Overview

Dataset statistics

Number of variables20
Number of observations270
Missing cells2050
Missing cells (%)38.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory46.5 KiB
Average record size in memory176.5 B

Variable types

Numeric15
Categorical5

Dataset

Description주요 재배지별 주요 산림약용자원인 2작물(참당귀, 천궁)를 가해하는 병해충 피해에 대해 2020년 5월 22일부터 2020년 10월 12일 기간동안 육안으로 조사한 정보
Author산림청 국립산림과학원
URLhttps://www.data.go.kr/data/15125604/fileData.do

Alerts

2020-06-12 is highly overall correlated with 번호 and 10 other fieldsHigh correlation
조사지역 is highly overall correlated with 번호 and 7 other fieldsHigh correlation
병해충명 is highly overall correlated with 2020-08-31 and 2 other fieldsHigh correlation
구분 is highly overall correlated with 2020-06-12High correlation
작물명 is highly overall correlated with 번호 and 13 other fieldsHigh correlation
번호 is highly overall correlated with 작물명 and 2 other fieldsHigh correlation
2020-05-22 is highly overall correlated with 작물명High correlation
2020-06-02 is highly overall correlated with 작물명High correlation
2020-06-18 is highly overall correlated with 2020-08-05 and 4 other fieldsHigh correlation
2020-06-26 is highly overall correlated with 2020-08-04 and 7 other fieldsHigh correlation
2020-08-04 is highly overall correlated with 2020-06-26 and 7 other fieldsHigh correlation
2020-08-05 is highly overall correlated with 2020-06-18 and 5 other fieldsHigh correlation
2020-08-19 is highly overall correlated with 2020-06-26 and 7 other fieldsHigh correlation
2020-08-20 is highly overall correlated with 2020-06-18 and 5 other fieldsHigh correlation
2020-08-31 is highly overall correlated with 2020-06-26 and 8 other fieldsHigh correlation
2020-09-01 is highly overall correlated with 2020-08-05 and 3 other fieldsHigh correlation
2020-09-18 is highly overall correlated with 2020-06-26 and 7 other fieldsHigh correlation
2020-09-23 is highly overall correlated with 2020-06-18 and 4 other fieldsHigh correlation
2020-10-08 is highly overall correlated with 2020-06-18 and 4 other fieldsHigh correlation
2020-10-12 is highly overall correlated with 2020-06-26 and 7 other fieldsHigh correlation
2020-05-22 has 170 (63.0%) missing valuesMissing
2020-06-02 has 170 (63.0%) missing valuesMissing
2020-06-18 has 170 (63.0%) missing valuesMissing
2020-06-26 has 130 (48.1%) missing valuesMissing
2020-08-04 has 130 (48.1%) missing valuesMissing
2020-08-05 has 170 (63.0%) missing valuesMissing
2020-08-19 has 130 (48.1%) missing valuesMissing
2020-08-20 has 170 (63.0%) missing valuesMissing
2020-08-31 has 130 (48.1%) missing valuesMissing
2020-09-01 has 140 (51.9%) missing valuesMissing
2020-09-18 has 130 (48.1%) missing valuesMissing
2020-09-23 has 140 (51.9%) missing valuesMissing
2020-10-08 has 140 (51.9%) missing valuesMissing
2020-10-12 has 130 (48.1%) missing valuesMissing
번호 has unique valuesUnique
2020-05-22 has 71 (26.3%) zerosZeros
2020-06-02 has 39 (14.4%) zerosZeros
2020-06-18 has 44 (16.3%) zerosZeros
2020-06-26 has 53 (19.6%) zerosZeros
2020-08-04 has 44 (16.3%) zerosZeros
2020-08-05 has 35 (13.0%) zerosZeros
2020-08-19 has 54 (20.0%) zerosZeros
2020-08-20 has 38 (14.1%) zerosZeros
2020-08-31 has 45 (16.7%) zerosZeros
2020-09-01 has 28 (10.4%) zerosZeros
2020-09-18 has 36 (13.3%) zerosZeros
2020-09-23 has 55 (20.4%) zerosZeros
2020-10-08 has 53 (19.6%) zerosZeros
2020-10-12 has 27 (10.0%) zerosZeros

Reproduction

Analysis started2023-12-16 15:27:59.518121
Analysis finished2023-12-16 15:29:54.449428
Duration1 minute and 54.93 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct270
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean135.5
Minimum1
Maximum270
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-16T15:29:54.913755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14.45
Q168.25
median135.5
Q3202.75
95-th percentile256.55
Maximum270
Range269
Interquartile range (IQR)134.5

Descriptive statistics

Standard deviation78.086491
Coefficient of variation (CV)0.57628406
Kurtosis-1.2
Mean135.5
Median Absolute Deviation (MAD)67.5
Skewness0
Sum36585
Variance6097.5
MonotonicityStrictly increasing
2023-12-16T15:29:55.705747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.4%
187 1
 
0.4%
173 1
 
0.4%
174 1
 
0.4%
175 1
 
0.4%
176 1
 
0.4%
177 1
 
0.4%
178 1
 
0.4%
179 1
 
0.4%
180 1
 
0.4%
Other values (260) 260
96.3%
ValueCountFrequency (%)
1 1
0.4%
2 1
0.4%
3 1
0.4%
4 1
0.4%
5 1
0.4%
6 1
0.4%
7 1
0.4%
8 1
0.4%
9 1
0.4%
10 1
0.4%
ValueCountFrequency (%)
270 1
0.4%
269 1
0.4%
268 1
0.4%
267 1
0.4%
266 1
0.4%
265 1
0.4%
264 1
0.4%
263 1
0.4%
262 1
0.4%
261 1
0.4%

작물명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
참당귀
240 
천궁
30 

Length

Max length3
Median length3
Mean length2.8888889
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
참당귀 240
88.9%
천궁 30
 
11.1%

Length

2023-12-16T15:29:56.679651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T15:29:57.224642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
참당귀 240
88.9%
천궁 30
 
11.1%

조사지역
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
경상북도 영주시 봉현면 한천리 11
140 
경상북도 영양군 수비면 낙동정맥로 4372 (수비초등학교신암분교)
65 
경상북도 봉화군 소천면 고선리 산39
65 

Length

Max length36
Median length19
Mean length23.333333
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경상북도 영양군 수비면 낙동정맥로 4372 (수비초등학교신암분교)
2nd row경상북도 영양군 수비면 낙동정맥로 4372 (수비초등학교신암분교)
3rd row경상북도 영양군 수비면 낙동정맥로 4372 (수비초등학교신암분교)
4th row경상북도 영양군 수비면 낙동정맥로 4372 (수비초등학교신암분교)
5th row경상북도 영양군 수비면 낙동정맥로 4372 (수비초등학교신암분교)

Common Values

ValueCountFrequency (%)
경상북도 영주시 봉현면 한천리 11 140
51.9%
경상북도 영양군 수비면 낙동정맥로 4372 (수비초등학교신암분교) 65
24.1%
경상북도 봉화군 소천면 고선리 산39 65
24.1%

Length

2023-12-16T15:29:57.693051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T15:29:58.155114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상북도 270
19.1%
영주시 140
9.9%
봉현면 140
9.9%
한천리 140
9.9%
11 140
9.9%
영양군 65
 
4.6%
수비면 65
 
4.6%
낙동정맥로 65
 
4.6%
4372 65
 
4.6%
수비초등학교신암분교 65
 
4.6%
Other values (4) 260
18.4%

병해충명
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
시들음병
34 
총채벌레류
24 
응애류
24 
곤충 식엽피해
24 
굴파리류
24 
Other values (11)
140 

Length

Max length31
Median length8
Mean length5.6740741
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
시들음병 34
12.6%
총채벌레류 24
8.9%
응애류 24
8.9%
곤충 식엽피해 24
8.9%
굴파리류 24
8.9%
산호랑나비 유충 24
8.9%
진딧물류 24
8.9%
점무늬병 24
8.9%
줄기썩음병 24
8.9%
생리장애 24
8.9%
Other values (6) 20
7.4%

Length

2023-12-16T15:29:58.820386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
시들음병 34
 
9.8%
유충 24
 
6.9%
생리장애 24
 
6.9%
총채벌레류 24
 
6.9%
점무늬병 24
 
6.9%
진딧물류 24
 
6.9%
줄기썩음병 24
 
6.9%
산호랑나비 24
 
6.9%
굴파리류 24
 
6.9%
식엽피해 24
 
6.9%
Other values (11) 98
28.2%

구분
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
발생률1
36 
발생률2
36 
발생률3
36 
평균
36 
표준오차
36 
Other values (9)
90 

Length

Max length5
Median length4
Mean length3.8444444
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row발생률1
2nd row발생률2
3rd row발생률3
4th row평균
5th row표준오차

Common Values

ValueCountFrequency (%)
발생률1 36
13.3%
발생률2 36
13.3%
발생률3 36
13.3%
평균 36
13.3%
표준오차 36
13.3%
발생률4 10
 
3.7%
발생률5 10
 
3.7%
발생률6 10
 
3.7%
발생률7 10
 
3.7%
발생률8 10
 
3.7%
Other values (4) 40
14.8%

Length

2023-12-16T15:29:59.670738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
발생률1 36
13.3%
발생률2 36
13.3%
발생률3 36
13.3%
평균 36
13.3%
표준오차 36
13.3%
발생률4 10
 
3.7%
발생률5 10
 
3.7%
발생률6 10
 
3.7%
발생률7 10
 
3.7%
발생률8 10
 
3.7%
Other values (4) 40
14.8%

2020-05-22
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct25
Distinct (%)25.0%
Missing170
Missing (%)63.0%
Infinite0
Infinite (%)0.0%
Mean1.2640218
Minimum0
Maximum10.71
Zeros71
Zeros (%)26.3%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-16T15:30:00.420453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.5151515
95-th percentile7.69
Maximum10.71
Range10.71
Interquartile range (IQR)1.5151515

Descriptive statistics

Standard deviation2.5353955
Coefficient of variation (CV)2.0058163
Kurtosis4.2963666
Mean1.2640218
Median Absolute Deviation (MAD)0
Skewness2.2320092
Sum126.40218
Variance6.4282306
MonotonicityNot monotonic
2023-12-16T15:30:01.199508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0.0 71
26.3%
7.69 2
 
0.7%
1.515151515 2
 
0.7%
1.52 2
 
0.7%
4.55 2
 
0.7%
3.85 2
 
0.7%
6.67 1
 
0.4%
1.111111111 1
 
0.4%
2.564102564 1
 
0.4%
2.56 1
 
0.4%
Other values (15) 15
 
5.6%
(Missing) 170
63.0%
ValueCountFrequency (%)
0.0 71
26.3%
0.911896651 1
 
0.4%
1.11 1
 
0.4%
1.111111111 1
 
0.4%
1.515151515 2
 
0.7%
1.52 2
 
0.7%
1.932077702 1
 
0.4%
2.063997742 1
 
0.4%
2.56 1
 
0.4%
2.564102564 1
 
0.4%
ValueCountFrequency (%)
10.71 1
0.4%
10.0 1
0.4%
9.47 1
0.4%
9.09 1
0.4%
7.69 2
0.7%
7.14 1
0.4%
6.67 1
0.4%
5.0 1
0.4%
4.7 1
0.4%
4.55 2
0.7%

2020-06-02
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct41
Distinct (%)41.0%
Missing170
Missing (%)63.0%
Infinite0
Infinite (%)0.0%
Mean4.2705814
Minimum0
Maximum19.23
Zeros39
Zeros (%)14.4%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-16T15:30:01.928226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.5103988
Q36.875
95-th percentile14.29
Maximum19.23
Range19.23
Interquartile range (IQR)6.875

Descriptive statistics

Standard deviation5.0706502
Coefficient of variation (CV)1.1873442
Kurtosis0.0089241854
Mean4.2705814
Median Absolute Deviation (MAD)2.5103988
Skewness1.058088
Sum427.05814
Variance25.711493
MonotonicityNot monotonic
2023-12-16T15:30:03.217849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0.0 39
 
14.4%
4.55 5
 
1.9%
5.0 4
 
1.5%
3.85 4
 
1.5%
13.64 3
 
1.1%
11.54 3
 
1.1%
10.71 2
 
0.7%
0.335575293 2
 
0.7%
4.46 2
 
0.7%
9.98 2
 
0.7%
Other values (31) 34
 
12.6%
(Missing) 170
63.0%
ValueCountFrequency (%)
0.0 39
14.4%
0.335575293 2
 
0.7%
1.006725878 1
 
0.4%
1.19 1
 
0.4%
1.19047619 1
 
0.4%
1.515151515 1
 
0.4%
1.52 1
 
0.4%
1.591197238 1
 
0.4%
1.862026862 1
 
0.4%
1.926136682 1
 
0.4%
ValueCountFrequency (%)
19.23 1
 
0.4%
16.67 1
 
0.4%
15.38 1
 
0.4%
15.0 1
 
0.4%
14.29 2
0.7%
13.64 3
1.1%
13.39 1
 
0.4%
12.97 1
 
0.4%
11.54 3
1.1%
10.71 2
0.7%

2020-06-12
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
0
140 
<NA>
130 

Length

Max length4
Median length1
Mean length2.4444444
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
0 140
51.9%
<NA> 130
48.1%

Length

2023-12-16T15:30:04.755736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T15:30:05.802138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 140
51.9%
na 130
48.1%

2020-06-18
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct53
Distinct (%)53.0%
Missing170
Missing (%)63.0%
Infinite0
Infinite (%)0.0%
Mean13.390686
Minimum0
Maximum84.62
Zeros44
Zeros (%)16.3%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-16T15:30:06.931940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3.6825897
Q318.4425
95-th percentile63.378
Maximum84.62
Range84.62
Interquartile range (IQR)18.4425

Descriptive statistics

Standard deviation19.683247
Coefficient of variation (CV)1.4699207
Kurtosis2.4702195
Mean13.390686
Median Absolute Deviation (MAD)3.6825897
Skewness1.7689757
Sum1339.0686
Variance387.43021
MonotonicityNot monotonic
2023-12-16T15:30:08.353133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 44
 
16.3%
26.92 2
 
0.7%
15.0 2
 
0.7%
19.23 2
 
0.7%
17.86 2
 
0.7%
21.43 1
 
0.4%
43.33 1
 
0.4%
25.89 1
 
0.4%
5.704779973 1
 
0.4%
3.85 1
 
0.4%
Other values (43) 43
 
15.9%
(Missing) 170
63.0%
ValueCountFrequency (%)
0.0 44
16.3%
1.28 1
 
0.4%
1.282051282 1
 
0.4%
1.827875939 1
 
0.4%
2.980162341 1
 
0.4%
3.307252033 1
 
0.4%
3.515179339 1
 
0.4%
3.85 1
 
0.4%
4.330601475 1
 
0.4%
5.257379265 1
 
0.4%
ValueCountFrequency (%)
84.62 1
0.4%
69.63 1
0.4%
65.38 1
0.4%
65.0 1
0.4%
64.29 1
0.4%
63.33 1
0.4%
60.0 1
0.4%
53.72 1
0.4%
50.0 1
0.4%
48.86 1
0.4%

2020-06-26
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct55
Distinct (%)39.3%
Missing130
Missing (%)48.1%
Infinite0
Infinite (%)0.0%
Mean12.673997
Minimum0
Maximum90
Zeros53
Zeros (%)19.6%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-16T15:30:09.105318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4.76
Q320
95-th percentile47.62
Maximum90
Range90
Interquartile range (IQR)20

Descriptive statistics

Standard deviation17.665522
Coefficient of variation (CV)1.3938399
Kurtosis3.3968749
Mean12.673997
Median Absolute Deviation (MAD)4.76
Skewness1.8141251
Sum1774.3595
Variance312.07068
MonotonicityNot monotonic
2023-12-16T15:30:09.755913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 53
19.6%
4.76 7
 
2.6%
5.0 5
 
1.9%
15.0 4
 
1.5%
9.52 4
 
1.5%
35.0 4
 
1.5%
4.0 3
 
1.1%
47.62 3
 
1.1%
28.57 3
 
1.1%
20.0 3
 
1.1%
Other values (45) 51
 
18.9%
(Missing) 130
48.1%
ValueCountFrequency (%)
0.0 53
19.6%
1.031226771 1
 
0.4%
1.297495344 1
 
0.4%
1.584468294 1
 
0.4%
2.787993299 1
 
0.4%
3.42 1
 
0.4%
3.45 1
 
0.4%
3.671146941 1
 
0.4%
4.0 3
 
1.1%
4.17 2
 
0.7%
ValueCountFrequency (%)
90.0 1
 
0.4%
76.19 1
 
0.4%
66.67 1
 
0.4%
61.9 1
 
0.4%
54.55 1
 
0.4%
50.0 1
 
0.4%
47.62 3
1.1%
44.83 1
 
0.4%
44.51 1
 
0.4%
42.86 1
 
0.4%

2020-08-04
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct64
Distinct (%)45.7%
Missing130
Missing (%)48.1%
Infinite0
Infinite (%)0.0%
Mean28.930188
Minimum0
Maximum100
Zeros44
Zeros (%)16.3%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-16T15:30:10.716338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median23.445
Q342.11
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)42.11

Descriptive statistics

Standard deviation30.829489
Coefficient of variation (CV)1.0656512
Kurtosis0.027187813
Mean28.930188
Median Absolute Deviation (MAD)23.445
Skewness0.98973393
Sum4050.2263
Variance950.45741
MonotonicityNot monotonic
2023-12-16T15:30:11.610172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 44
 
16.3%
100.0 8
 
3.0%
31.58 4
 
1.5%
21.05 3
 
1.1%
41.67 3
 
1.1%
33.33 3
 
1.1%
36.84 3
 
1.1%
14.29 3
 
1.1%
40.0 3
 
1.1%
37.5 2
 
0.7%
Other values (54) 64
23.7%
(Missing) 130
48.1%
ValueCountFrequency (%)
0.0 44
16.3%
0.996149753 1
 
0.4%
2.46249991 1
 
0.4%
2.523574555 1
 
0.4%
2.981666836 1
 
0.4%
4.189081128 1
 
0.4%
4.3224096 1
 
0.4%
5.26 1
 
0.4%
5.800879739 1
 
0.4%
8.0 1
 
0.4%
ValueCountFrequency (%)
100.0 8
3.0%
97.89 1
 
0.4%
96.0 1
 
0.4%
95.83 1
 
0.4%
92.86 1
 
0.4%
90.0 2
 
0.7%
76.19 1
 
0.4%
73.68 1
 
0.4%
69.23 1
 
0.4%
68.42 2
 
0.7%

2020-08-05
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct55
Distinct (%)55.0%
Missing170
Missing (%)63.0%
Infinite0
Infinite (%)0.0%
Mean23.732164
Minimum0
Maximum100
Zeros35
Zeros (%)13.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-16T15:30:12.536524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median13.485
Q342.35
95-th percentile86.68
Maximum100
Range100
Interquartile range (IQR)42.35

Descriptive statistics

Standard deviation29.343712
Coefficient of variation (CV)1.2364532
Kurtosis0.20457635
Mean23.732164
Median Absolute Deviation (MAD)13.485
Skewness1.1630411
Sum2373.2164
Variance861.05341
MonotonicityNot monotonic
2023-12-16T15:30:13.334396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 35
 
13.0%
50.0 4
 
1.5%
100.0 2
 
0.7%
83.33 2
 
0.7%
36.36 2
 
0.7%
59.09 2
 
0.7%
18.18 2
 
0.7%
19.23 2
 
0.7%
16.67 2
 
0.7%
13.64 2
 
0.7%
Other values (45) 45
 
16.7%
(Missing) 170
63.0%
ValueCountFrequency (%)
0.0 35
13.0%
0.252525253 1
 
0.4%
1.111111111 1
 
0.4%
1.336238036 1
 
0.4%
1.790380866 1
 
0.4%
2.518483363 1
 
0.4%
3.072102288 1
 
0.4%
3.75979169 1
 
0.4%
3.984885781 1
 
0.4%
5.954457638 1
 
0.4%
ValueCountFrequency (%)
100.0 2
0.7%
96.43 1
0.4%
94.37 1
0.4%
86.87 1
0.4%
86.67 1
0.4%
83.33 2
0.7%
77.27 1
0.4%
76.92 1
0.4%
71.28 1
0.4%
66.67 1
0.4%

2020-08-19
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct56
Distinct (%)40.0%
Missing130
Missing (%)48.1%
Infinite0
Infinite (%)0.0%
Mean30.886957
Minimum0
Maximum100
Zeros54
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-16T15:30:14.379806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median8.33
Q366.67
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)66.67

Descriptive statistics

Standard deviation36.83186
Coefficient of variation (CV)1.1924729
Kurtosis-1.085344
Mean30.886957
Median Absolute Deviation (MAD)8.33
Skewness0.74084217
Sum4324.174
Variance1356.5859
MonotonicityNot monotonic
2023-12-16T15:30:15.129942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 54
20.0%
100.0 10
 
3.7%
83.33 5
 
1.9%
8.33 3
 
1.1%
52.63 3
 
1.1%
5.26 3
 
1.1%
66.67 3
 
1.1%
21.05 2
 
0.7%
22.22 2
 
0.7%
14.29 2
 
0.7%
Other values (46) 53
19.6%
(Missing) 130
48.1%
ValueCountFrequency (%)
0.0 54
20.0%
0.552875217 1
 
0.4%
1.393199956 1
 
0.4%
2.077208918 1
 
0.4%
2.891493028 1
 
0.4%
3.213683199 1
 
0.4%
3.272137967 1
 
0.4%
3.62 1
 
0.4%
4.17 1
 
0.4%
4.35 1
 
0.4%
ValueCountFrequency (%)
100.0 10
3.7%
99.19 1
 
0.4%
95.83 1
 
0.4%
94.44 1
 
0.4%
91.67 1
 
0.4%
89.47 2
 
0.7%
88.89 1
 
0.4%
84.21 1
 
0.4%
83.33 5
1.9%
82.61 1
 
0.4%

2020-08-20
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct56
Distinct (%)56.0%
Missing170
Missing (%)63.0%
Infinite0
Infinite (%)0.0%
Mean23.14284
Minimum0
Maximum100
Zeros38
Zeros (%)14.1%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-16T15:30:15.920194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4.55
Q338.825
95-th percentile95.485
Maximum100
Range100
Interquartile range (IQR)38.825

Descriptive statistics

Standard deviation32.650896
Coefficient of variation (CV)1.4108423
Kurtosis0.16295757
Mean23.14284
Median Absolute Deviation (MAD)4.55
Skewness1.2777425
Sum2314.284
Variance1066.081
MonotonicityNot monotonic
2023-12-16T15:30:16.774694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 38
 
14.1%
4.55 4
 
1.5%
58.33 2
 
0.7%
50.0 2
 
0.7%
16.67 2
 
0.7%
100.0 2
 
0.7%
12.24942711 1
 
0.4%
5.125702811 1
 
0.4%
10.1 1
 
0.4%
13.64 1
 
0.4%
Other values (46) 46
 
17.0%
(Missing) 170
63.0%
ValueCountFrequency (%)
0.0 38
14.1%
1.20570393 1
 
0.4%
1.515151515 1
 
0.4%
1.750593867 1
 
0.4%
2.063492063 1
 
0.4%
2.735689953 1
 
0.4%
3.03 1
 
0.4%
3.164132345 1
 
0.4%
3.33 1
 
0.4%
3.49 1
 
0.4%
ValueCountFrequency (%)
100.0 2
0.7%
97.61 1
0.4%
96.67 1
0.4%
96.15 1
0.4%
95.45 1
0.4%
92.93 1
0.4%
91.67 1
0.4%
90.91 1
0.4%
85.71 1
0.4%
84.62 1
0.4%

2020-08-31
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct70
Distinct (%)50.0%
Missing130
Missing (%)48.1%
Infinite0
Infinite (%)0.0%
Mean26.645953
Minimum0
Maximum100
Zeros45
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-16T15:30:17.865907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median10.265
Q340.4175
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)40.4175

Descriptive statistics

Standard deviation34.112095
Coefficient of variation (CV)1.280198
Kurtosis-0.074674168
Mean26.645953
Median Absolute Deviation (MAD)10.265
Skewness1.1663982
Sum3730.4335
Variance1163.635
MonotonicityNot monotonic
2023-12-16T15:30:18.595521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 45
 
16.7%
100.0 12
 
4.4%
16.67 5
 
1.9%
20.0 3
 
1.1%
91.67 2
 
0.7%
21.05 2
 
0.7%
4.0 2
 
0.7%
42.11 2
 
0.7%
5.26 2
 
0.7%
22.22 2
 
0.7%
Other values (60) 63
23.3%
(Missing) 130
48.1%
ValueCountFrequency (%)
0.0 45
16.7%
0.33 1
 
0.4%
0.333333333 1
 
0.4%
0.694444417 1
 
0.4%
0.963046466 1
 
0.4%
1.73 1
 
0.4%
2.111657826 1
 
0.4%
2.195183072 1
 
0.4%
2.351093412 1
 
0.4%
2.836358863 1
 
0.4%
ValueCountFrequency (%)
100.0 12
4.4%
99.31 1
 
0.4%
94.12 1
 
0.4%
91.67 2
 
0.7%
85.0 1
 
0.4%
83.33 1
 
0.4%
81.82 1
 
0.4%
80.0 1
 
0.4%
78.95 1
 
0.4%
78.26 1
 
0.4%

2020-09-01
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct91
Distinct (%)70.0%
Missing140
Missing (%)51.9%
Infinite0
Infinite (%)0.0%
Mean22.748933
Minimum0
Maximum100
Zeros28
Zeros (%)10.4%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-16T15:30:19.375319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.1206946
median10.62
Q335.5325
95-th percentile92.1805
Maximum100
Range100
Interquartile range (IQR)33.411805

Descriptive statistics

Standard deviation28.721128
Coefficient of variation (CV)1.2625264
Kurtosis0.76594505
Mean22.748933
Median Absolute Deviation (MAD)10.62
Skewness1.3875254
Sum2957.3612
Variance824.90321
MonotonicityNot monotonic
2023-12-16T15:30:20.199836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 28
 
10.4%
11.76 4
 
1.5%
20.0 3
 
1.1%
10.71 2
 
0.7%
7.14 2
 
0.7%
15.0 2
 
0.7%
17.86 2
 
0.7%
5.26 2
 
0.7%
100.0 2
 
0.7%
3.57 2
 
0.7%
Other values (81) 81
30.0%
(Missing) 140
51.9%
ValueCountFrequency (%)
0.0 28
10.4%
1.19047619 1
 
0.4%
1.210344342 1
 
0.4%
1.666666667 1
 
0.4%
1.67 1
 
0.4%
2.053701929 1
 
0.4%
2.321672601 1
 
0.4%
2.572750983 1
 
0.4%
3.149703942 1
 
0.4%
3.33 1
 
0.4%
ValueCountFrequency (%)
100.0 2
0.7%
98.81 1
0.4%
96.43 1
0.4%
95.0 1
0.4%
93.75 1
0.4%
93.22 1
0.4%
90.91 1
0.4%
88.0 1
0.4%
77.27 1
0.4%
73.33 1
0.4%

2020-09-18
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct63
Distinct (%)45.0%
Missing130
Missing (%)48.1%
Infinite0
Infinite (%)0.0%
Mean27.587166
Minimum0
Maximum100
Zeros36
Zeros (%)13.3%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-16T15:30:20.787119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median21.43
Q339.13
95-th percentile95.65
Maximum100
Range100
Interquartile range (IQR)39.13

Descriptive statistics

Standard deviation29.88641
Coefficient of variation (CV)1.0833447
Kurtosis0.24518555
Mean27.587166
Median Absolute Deviation (MAD)19.606655
Skewness1.1203568
Sum3862.2032
Variance893.19749
MonotonicityNot monotonic
2023-12-16T15:30:21.771385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 36
 
13.3%
33.33 6
 
2.2%
100.0 5
 
1.9%
50.0 4
 
1.5%
25.0 4
 
1.5%
17.39 3
 
1.1%
21.74 3
 
1.1%
27.78 3
 
1.1%
40.0 3
 
1.1%
21.43 3
 
1.1%
Other values (53) 70
25.9%
(Missing) 130
48.1%
ValueCountFrequency (%)
0.0 36
13.3%
0.32 1
 
0.4%
0.320516667 1
 
0.4%
1.785643033 1
 
0.4%
1.861046734 1
 
0.4%
2.210600194 1
 
0.4%
2.63288056 1
 
0.4%
3.109258348 1
 
0.4%
3.366048047 1
 
0.4%
3.43 1
 
0.4%
ValueCountFrequency (%)
100.0 5
1.9%
96.15 1
 
0.4%
95.65 2
 
0.7%
94.44 1
 
0.4%
94.29 1
 
0.4%
88.89 1
 
0.4%
88.2 1
 
0.4%
85.71 1
 
0.4%
80.0 2
 
0.7%
75.0 2
 
0.7%

2020-09-23
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct62
Distinct (%)47.7%
Missing140
Missing (%)51.9%
Infinite0
Infinite (%)0.0%
Mean21.845353
Minimum0
Maximum100
Zeros55
Zeros (%)20.4%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-16T15:30:22.572254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7.3167692
Q340
95-th percentile92.472
Maximum100
Range100
Interquartile range (IQR)40

Descriptive statistics

Standard deviation29.042718
Coefficient of variation (CV)1.3294689
Kurtosis0.7286535
Mean21.845353
Median Absolute Deviation (MAD)7.3167692
Skewness1.3088545
Sum2839.8959
Variance843.47947
MonotonicityNot monotonic
2023-12-16T15:30:23.234910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 55
 
20.4%
100.0 5
 
1.9%
50.0 4
 
1.5%
9.09 2
 
0.7%
40.0 2
 
0.7%
30.77 2
 
0.7%
25.0 2
 
0.7%
57.69 2
 
0.7%
57.14 2
 
0.7%
46.43 2
 
0.7%
Other values (52) 52
 
19.3%
(Missing) 140
51.9%
ValueCountFrequency (%)
0.0 55
20.4%
0.183150183 1
 
0.4%
1.515151515 1
 
0.4%
2.222465403 1
 
0.4%
2.657190895 1
 
0.4%
2.685122515 1
 
0.4%
3.734422803 1
 
0.4%
4.55 1
 
0.4%
4.642894984 1
 
0.4%
4.772459422 1
 
0.4%
ValueCountFrequency (%)
100.0 5
1.9%
94.89 1
 
0.4%
93.75 1
 
0.4%
90.91 1
 
0.4%
81.82 1
 
0.4%
80.77 1
 
0.4%
64.29 1
 
0.4%
63.83 1
 
0.4%
63.64 1
 
0.4%
62.5 1
 
0.4%

2020-10-08
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct59
Distinct (%)45.4%
Missing140
Missing (%)51.9%
Infinite0
Infinite (%)0.0%
Mean19.154861
Minimum0
Maximum100
Zeros53
Zeros (%)19.6%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-16T15:30:23.953879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6.6458272
Q332.4925
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)32.4925

Descriptive statistics

Standard deviation27.461956
Coefficient of variation (CV)1.4336808
Kurtosis2.2738948
Mean19.154861
Median Absolute Deviation (MAD)6.6458272
Skewness1.7044354
Sum2490.1319
Variance754.15905
MonotonicityNot monotonic
2023-12-16T15:30:24.651257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 53
 
19.6%
100.0 8
 
3.0%
14.29 3
 
1.1%
25.0 3
 
1.1%
4.35 2
 
0.7%
9.09 2
 
0.7%
15.0 2
 
0.7%
45.0 2
 
0.7%
38.46 2
 
0.7%
10.71 2
 
0.7%
Other values (49) 51
 
18.9%
(Missing) 140
51.9%
ValueCountFrequency (%)
0.0 53
19.6%
1.079654407 1
 
0.4%
1.166888015 1
 
0.4%
1.409780615 1
 
0.4%
1.471111172 1
 
0.4%
1.712523417 1
 
0.4%
3.244042889 1
 
0.4%
4.35 2
 
0.7%
4.376484669 1
 
0.4%
4.849627891 1
 
0.4%
ValueCountFrequency (%)
100.0 8
3.0%
63.64 2
 
0.7%
62.5 1
 
0.4%
57.14 1
 
0.4%
57.05 1
 
0.4%
54.55 1
 
0.4%
53.33 1
 
0.4%
50.0 2
 
0.7%
47.53 1
 
0.4%
46.15 1
 
0.4%

2020-10-12
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct81
Distinct (%)57.9%
Missing130
Missing (%)48.1%
Infinite0
Infinite (%)0.0%
Mean31.32852
Minimum0
Maximum100
Zeros27
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-16T15:30:25.731238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.8678647
median28.64
Q348.215
95-th percentile83.3705
Maximum100
Range100
Interquartile range (IQR)44.347135

Descriptive statistics

Standard deviation27.036856
Coefficient of variation (CV)0.86301096
Kurtosis-0.39519486
Mean31.32852
Median Absolute Deviation (MAD)21.36
Skewness0.62859079
Sum4385.9928
Variance730.99159
MonotonicityNot monotonic
2023-12-16T15:30:26.549112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 27
 
10.0%
25.0 7
 
2.6%
50.0 5
 
1.9%
38.46 5
 
1.9%
36.84 4
 
1.5%
21.05 4
 
1.5%
38.89 3
 
1.1%
28.57 3
 
1.1%
10.53 2
 
0.7%
36.0 2
 
0.7%
Other values (71) 78
28.9%
(Missing) 130
48.1%
ValueCountFrequency (%)
0.0 27
10.0%
0.971520536 1
 
0.4%
1.76 1
 
0.4%
2.263007859 1
 
0.4%
2.594374476 1
 
0.4%
2.890349175 1
 
0.4%
3.278461237 1
 
0.4%
3.301293951 1
 
0.4%
3.650758988 1
 
0.4%
3.940233216 1
 
0.4%
ValueCountFrequency (%)
100.0 2
0.7%
96.0 1
0.4%
94.74 1
0.4%
84.62 1
0.4%
84.21 1
0.4%
84.14 1
0.4%
83.33 2
0.7%
80.95 1
0.4%
77.8 1
0.4%
77.78 1
0.4%

Interactions

2023-12-16T15:29:42.320571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:06.032695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:13.652645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:19.847056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:27.177081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:32.961993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:39.871174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:46.939644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:54.325467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:01.324740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:08.630656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:15.442886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:21.893125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:28.151955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:35.304317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:42.632861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:06.444587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:14.007022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:20.365835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:27.602240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:33.295546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:40.252761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:47.261370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:54.814022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:02.036508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:09.086557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:15.856759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:22.290614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:28.499496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:36.323015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:42.982746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:06.824099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:14.633807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:20.856996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:27.973819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:33.538893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:40.815207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:47.813128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:55.193750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:02.719341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:09.373580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:16.268073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:22.743535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:28.896600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:36.682118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:43.819986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:07.297417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:15.072878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:21.501454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:28.400618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:33.817795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:41.227191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:48.261359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:55.598735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:03.167902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:09.598715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:16.917615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:23.343109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:29.300059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:37.106082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:44.252486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:07.940298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:15.313396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:22.072992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:28.647644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:34.092077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:41.635564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:48.549668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:56.040107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:03.631806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:09.924008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:17.246654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:23.780218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:29.587870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:37.571074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:44.729441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:08.599445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:15.679293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:22.632120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:29.180670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:34.642800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:42.241914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:48.960828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:56.346454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:04.630886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:10.543970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:17.615601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:24.164255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:29.966858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:37.921376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:45.219032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:09.416350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:16.201782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:22.968218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:29.591824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:35.229993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:42.633377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:49.214744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:56.829097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:04.986396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:11.241804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:17.976339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:24.721678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:30.504534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:38.425739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:45.528090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:09.780895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:16.639224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:23.744902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:30.138616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:35.905567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:43.207421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:49.676875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:57.111367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:05.257617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:11.690522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:18.404679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:25.091496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:31.033321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:38.949013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:46.182725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:10.200049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:17.044053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:24.269257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:30.515713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:36.514193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:43.828219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:50.422619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:57.694675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:05.759511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:12.408772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:18.724705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:25.489058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:31.572703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:39.287998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:46.666440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:10.626250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:17.400428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:24.826692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:30.789001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-16T15:28:44.248716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-16T15:29:06.724409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-16T15:29:19.662023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:26.266087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:32.510129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-16T15:29:47.708502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-16T15:28:25.506855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-16T15:28:45.053850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:52.203967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:59.355305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:07.121374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:13.910377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:20.160410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:26.595429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-16T15:28:52.824077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:59.992810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:07.518186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:14.226906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:20.800905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:26.990077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:34.022084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:41.029358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:48.628507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:12.536530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:18.970535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:26.451280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:32.430743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:38.955668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:45.989875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:53.248001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:00.272834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:07.973083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:14.579022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:21.167277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:27.409463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:34.484184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:41.666706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:49.149326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:13.098103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:19.381485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:26.879414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:32.717893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:39.342147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:46.417300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:28:53.879926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:00.852962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:08.343562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:14.976662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:21.512759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:27.752941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:34.978374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:29:41.920979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-16T15:30:27.273301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호작물명조사지역병해충명구분2020-05-222020-06-022020-06-182020-06-262020-08-042020-08-052020-08-192020-08-202020-08-312020-09-012020-09-182020-09-232020-10-082020-10-12
번호1.0000.9960.9050.8190.0600.3130.3360.0000.5510.6980.5190.6400.3690.7240.4220.6430.5280.4810.611
작물명0.9961.0000.2190.9640.157NaNNaNNaNNaNNaNNaNNaNNaNNaN0.000NaN0.0000.365NaN
조사지역0.9050.2191.0000.0000.6260.2960.4160.000NaNNaN0.000NaN0.000NaN0.069NaN0.0000.000NaN
병해충명0.8190.9640.0001.0000.0000.4620.6070.7080.6720.8880.8430.8980.8450.9050.7410.8900.7610.8770.879
구분0.0600.1570.6260.0001.0000.2400.2680.3380.0000.1420.4570.0000.1750.0000.4420.0000.1180.1460.167
2020-05-220.313NaN0.2960.4620.2401.0000.4580.471NaNNaN0.578NaN0.622NaN0.680NaN0.5420.465NaN
2020-06-020.336NaN0.4160.6070.2680.4581.0000.358NaNNaN0.660NaN0.794NaN0.698NaN0.5300.267NaN
2020-06-180.000NaN0.0000.7080.3380.4710.3581.000NaNNaN0.774NaN0.694NaN0.714NaN0.5560.612NaN
2020-06-260.551NaNNaN0.6720.000NaNNaNNaN1.0000.847NaN0.731NaN0.856NaN0.664NaNNaN0.686
2020-08-040.698NaNNaN0.8880.142NaNNaNNaN0.8471.000NaN0.876NaN0.860NaN0.893NaNNaN0.849
2020-08-050.519NaN0.0000.8430.4570.5780.6600.774NaNNaN1.000NaN0.888NaN0.898NaN0.7990.790NaN
2020-08-190.640NaNNaN0.8980.000NaNNaNNaN0.7310.876NaN1.000NaN0.832NaN0.830NaNNaN0.839
2020-08-200.369NaN0.0000.8450.1750.6220.7940.694NaNNaN0.888NaN1.000NaN0.895NaN0.7610.799NaN
2020-08-310.724NaNNaN0.9050.000NaNNaNNaN0.8560.860NaN0.832NaN1.000NaN0.878NaNNaN0.826
2020-09-010.4220.0000.0690.7410.4420.6800.6980.714NaNNaN0.898NaN0.895NaN1.000NaN0.7600.798NaN
2020-09-180.643NaNNaN0.8900.000NaNNaNNaN0.6640.893NaN0.830NaN0.878NaN1.000NaNNaN0.882
2020-09-230.5280.0000.0000.7610.1180.5420.5300.556NaNNaN0.799NaN0.761NaN0.760NaN1.0000.842NaN
2020-10-080.4810.3650.0000.8770.1460.4650.2670.612NaNNaN0.790NaN0.799NaN0.798NaN0.8421.000NaN
2020-10-120.611NaNNaN0.8790.167NaNNaNNaN0.6860.849NaN0.839NaN0.826NaN0.882NaNNaN1.000
2023-12-16T15:30:28.024544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2020-06-12조사지역병해충명구분작물명
2020-06-121.0001.0001.0001.0001.000
조사지역1.0001.0000.0000.4300.357
병해충명1.0000.0001.0000.0000.826
구분1.0000.4300.0001.0000.119
작물명1.0000.3570.8260.1191.000
2023-12-16T15:30:28.827322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호2020-05-222020-06-022020-06-182020-06-262020-08-042020-08-052020-08-192020-08-202020-08-312020-09-012020-09-182020-09-232020-10-082020-10-12작물명조사지역병해충명구분2020-06-12
번호1.000-0.318-0.017-0.063-0.231-0.1100.087-0.2180.008-0.011-0.0470.041-0.0630.014-0.0430.9330.8510.4940.0201.000
2020-05-22-0.3181.0000.1800.333NaNNaN0.272NaN0.327NaN0.333NaN0.2570.241NaN1.0000.2840.2270.1350.000
2020-06-02-0.0170.1801.0000.163NaNNaN0.324NaN0.399NaN0.385NaN0.1880.177NaN1.0000.3050.2230.1070.000
2020-06-18-0.0630.3330.1631.000NaNNaN0.521NaN0.540NaN0.444NaN0.5430.532NaN1.0000.0000.2860.1400.000
2020-06-26-0.231NaNNaNNaN1.0000.862NaN0.825NaN0.686NaN0.724NaNNaN0.6901.0001.0000.2630.0001.000
2020-08-04-0.110NaNNaNNaN0.8621.000NaN0.874NaN0.789NaN0.865NaNNaN0.8071.0001.0000.4660.0491.000
2020-08-050.0870.2720.3240.521NaNNaN1.000NaN0.938NaN0.856NaN0.9170.899NaN1.0000.0000.4170.1940.000
2020-08-19-0.218NaNNaNNaN0.8250.874NaN1.000NaN0.783NaN0.781NaNNaN0.7861.0001.0000.4950.0001.000
2020-08-200.0080.3270.3990.540NaNNaN0.938NaN1.000NaN0.823NaN0.8500.830NaN1.0000.0000.4220.1020.000
2020-08-31-0.011NaNNaNNaN0.6860.789NaN0.783NaN1.000NaN0.818NaNNaN0.7941.0001.0000.5100.0001.000
2020-09-01-0.0470.3330.3850.444NaNNaN0.856NaN0.823NaN1.000NaN0.7950.819NaN0.0000.0430.3850.1690.000
2020-09-180.041NaNNaNNaN0.7240.865NaN0.781NaN0.818NaN1.000NaNNaN0.8371.0001.0000.4780.0001.000
2020-09-23-0.0630.2570.1880.543NaNNaN0.917NaN0.850NaN0.795NaN1.0000.947NaN0.0820.0000.4240.1130.000
2020-10-080.0140.2410.1770.532NaNNaN0.899NaN0.830NaN0.819NaN0.9471.000NaN0.2700.0000.4920.0680.000
2020-10-12-0.043NaNNaNNaN0.6900.807NaN0.786NaN0.794NaN0.837NaNNaN1.0001.0001.0000.4690.0271.000
작물명0.9331.0001.0001.0001.0001.0001.0001.0001.0001.0000.0001.0000.0820.2701.0001.0000.3570.8260.1191.000
조사지역0.8510.2840.3050.0001.0001.0000.0001.0000.0001.0000.0431.0000.0000.0001.0000.3571.0000.0000.4301.000
병해충명0.4940.2270.2230.2860.2630.4660.4170.4950.4220.5100.3850.4780.4240.4920.4690.8260.0001.0000.0001.000
구분0.0200.1350.1070.1400.0000.0490.1940.0000.1020.0000.1690.0000.1130.0680.0270.1190.4300.0001.0001.000
2020-06-121.0000.0000.0000.0001.0001.0000.0001.0000.0001.0000.0001.0000.0000.0001.0001.0001.0001.0001.0001.000

Missing values

2023-12-16T15:29:50.238116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-16T15:29:51.957474image/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.
2023-12-16T15:29:53.337176image/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

번호작물명조사지역병해충명구분2020-05-222020-06-022020-06-122020-06-182020-06-262020-08-042020-08-052020-08-192020-08-202020-08-312020-09-012020-09-182020-09-232020-10-082020-10-12
01참당귀경상북도 영양군 수비면 낙동정맥로 4372 (수비초등학교신암분교)총채벌레류발생률10.03.57<NA>17.86<NA><NA>53.57<NA>75.0<NA>50.0<NA>64.2950.0<NA>
12참당귀경상북도 영양군 수비면 낙동정맥로 4372 (수비초등학교신암분교)총채벌레류발생률23.850.0<NA>65.38<NA><NA>76.92<NA>50.0<NA>40.0<NA>80.7723.08<NA>
23참당귀경상북도 영양군 수비면 낙동정맥로 4372 (수비초등학교신암분교)총채벌레류발생률36.676.67<NA>63.33<NA><NA>83.33<NA>50.0<NA>28.57<NA>46.4314.29<NA>
34참당귀경상북도 영양군 수비면 낙동정맥로 4372 (수비초등학교신암분교)총채벌레류평균3.53.41<NA>48.86<NA><NA>71.28<NA>58.33<NA>39.52<NA>63.8329.12<NA>
45참당귀경상북도 영양군 수비면 낙동정맥로 4372 (수비초등학교신암분교)총채벌레류표준오차1.9320781.926137<NA>15.511917<NA><NA>9.043604<NA>8.333333<NA>6.190476<NA>9.91593810.743597<NA>
56참당귀경상북도 영양군 수비면 낙동정맥로 4372 (수비초등학교신암분교)응애류발생률110.7114.29<NA>21.43<NA><NA>50.0<NA>85.71<NA>57.14<NA>57.1457.14<NA>
67참당귀경상북도 영양군 수비면 낙동정맥로 4372 (수비초등학교신암분교)응애류발생률27.697.69<NA>26.92<NA><NA>50.0<NA>84.62<NA>88.0<NA>57.6946.15<NA>
78참당귀경상북도 영양군 수비면 낙동정맥로 4372 (수비초등학교신암분교)응애류발생률310.010.0<NA>43.33<NA><NA>46.67<NA>63.33<NA>67.86<NA>57.1439.29<NA>
89참당귀경상북도 영양군 수비면 낙동정맥로 4372 (수비초등학교신암분교)응애류평균9.4710.66<NA>30.56<NA><NA>48.89<NA>77.89<NA>71.0<NA>57.3347.53<NA>
910참당귀경상북도 영양군 수비면 낙동정맥로 4372 (수비초등학교신암분교)응애류표준오차0.9118971.931692<NA>6.579871<NA><NA>1.111111<NA>7.284078<NA>9.045238<NA>0.183155.200466<NA>
번호작물명조사지역병해충명구분2020-05-222020-06-022020-06-122020-06-182020-06-262020-08-042020-08-052020-08-192020-08-202020-08-312020-09-012020-09-182020-09-232020-10-082020-10-12
260261천궁경상북도 봉화군 소천면 고선리 산39시들음병발생률1<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>22.73<NA>22.734.35<NA>
261262천궁경상북도 봉화군 소천면 고선리 산39시들음병발생률2<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>15.38<NA>0.08.33<NA>
262263천궁경상북도 봉화군 소천면 고선리 산39시들음병발생률3<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>0.0<NA>0.09.09<NA>
263264천궁경상북도 봉화군 소천면 고선리 산39시들음병평균<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>12.7<NA>7.587.26<NA>
264265천궁경상북도 봉화군 소천면 고선리 산39시들음병표준오차<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>6.696309<NA>7.5757581.471111<NA>
265266천궁경상북도 봉화군 소천면 고선리 산39잎맥 황화 바이러스 1,2(CnVYV-1,CnVYV-2)발생률1<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>4.55<NA>4.554.35<NA>
266267천궁경상북도 봉화군 소천면 고선리 산39잎맥 황화 바이러스 1,2(CnVYV-1,CnVYV-3)발생률2<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>7.69<NA>23.0825.0<NA>
267268천궁경상북도 봉화군 소천면 고선리 산39잎맥 황화 바이러스 1,2(CnVYV-1,CnVYV-4)발생률3<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>20.0<NA>0.00.0<NA>
268269천궁경상북도 봉화군 소천면 고선리 산39잎맥 황화 바이러스 1,2(CnVYV-1,CnVYV-5)평균<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>10.75<NA>9.219.78<NA>
269270천궁경상북도 봉화군 소천면 고선리 산39잎맥 황화 바이러스 1,2(CnVYV-1,CnVYV-6)표준오차<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>4.715371<NA>7.0577817.711521<NA>