Overview

Dataset statistics

Number of variables16
Number of observations482
Missing cells3346
Missing cells (%)43.4%
Duplicate rows2
Duplicate rows (%)0.4%
Total size in memory67.0 KiB
Average record size in memory142.3 B

Variable types

Categorical2
Text1
Numeric13

Dataset

Description시군별 기능성 양잠에 대한 재배농가, 뽕밭면적, 누에사육량, 누에생산량, 뽕잎생산량, 오디생산량에 대한 총괄적인 통계 정보
Author농림축산식품부
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20181023000000001005

Alerts

Dataset has 2 (0.4%) duplicate rowsDuplicates
재배농가수(호)_누에 is highly overall correlated with 재배농가수(호)_오디 and 5 other fieldsHigh correlation
재배농가수(호)_오디 is highly overall correlated with 재배농가수(호)_누에 and 2 other fieldsHigh correlation
뽕밭면적(ha)_누에사육용 is highly overall correlated with 재배농가수(호)_누에 and 5 other fieldsHigh correlation
뽕밭면적(ha)_오디생산용 is highly overall correlated with 재배농가수(호)_오디 and 3 other fieldsHigh correlation
누에 사육량(상자) is highly overall correlated with 재배농가수(호)_누에 and 4 other fieldsHigh correlation
생산량(kg)_누에고치 is highly overall correlated with 재배농가수(호)_누에 and 5 other fieldsHigh correlation
생산량(kg)_건조누에 is highly overall correlated with 재배농가수(호)_누에 and 3 other fieldsHigh correlation
생산량(kg)_생누에 is highly overall correlated with 재배농가수(호)_누에 and 3 other fieldsHigh correlation
생산량(kg)_동중하초 is highly overall correlated with 생산량(kg)_누에고치High correlation
생산량(kg)_수번데기 is highly overall correlated with 생산량(kg)_누에고치 and 1 other fieldsHigh correlation
생산량(kg)_잠분 is highly overall correlated with 생산량(kg)_생누에 and 1 other fieldsHigh correlation
생산량(kg)_뽕잎 is highly overall correlated with 뽕밭면적(ha)_오디생산용 and 3 other fieldsHigh correlation
생산량(kg)_오디 is highly overall correlated with 재배농가수(호)_오디 and 2 other fieldsHigh correlation
재배농가수(호)_누에 has 147 (30.5%) missing valuesMissing
재배농가수(호)_오디 has 91 (18.9%) missing valuesMissing
뽕밭면적(ha)_누에사육용 has 162 (33.6%) missing valuesMissing
뽕밭면적(ha)_오디생산용 has 99 (20.5%) missing valuesMissing
누에 사육량(상자) has 154 (32.0%) missing valuesMissing
생산량(kg)_누에고치 has 404 (83.8%) missing valuesMissing
생산량(kg)_건조누에 has 187 (38.8%) missing valuesMissing
생산량(kg)_생누에 has 336 (69.7%) missing valuesMissing
생산량(kg)_동중하초 has 397 (82.4%) missing valuesMissing
생산량(kg)_수번데기 has 433 (89.8%) missing valuesMissing
생산량(kg)_잠분 has 427 (88.6%) missing valuesMissing
생산량(kg)_뽕잎 has 345 (71.6%) missing valuesMissing
생산량(kg)_오디 has 164 (34.0%) missing valuesMissing
재배농가수(호)_누에 has 6 (1.2%) zerosZeros
뽕밭면적(ha)_누에사육용 has 12 (2.5%) zerosZeros
생산량(kg)_건조누에 has 47 (9.8%) zerosZeros
생산량(kg)_잠분 has 5 (1.0%) zerosZeros

Reproduction

Analysis started2023-12-11 03:42:39.159429
Analysis finished2023-12-11 03:43:05.171398
Duration26.01 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Categorical

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
2014
228 
2013
127 
2012
127 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2014
2nd row2014
3rd row2014
4th row2014
5th row2014

Common Values

ValueCountFrequency (%)
2014 228
47.3%
2013 127
26.3%
2012 127
26.3%

Length

2023-12-11T12:43:05.271517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:43:05.409688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2014 228
47.3%
2013 127
26.3%
2012 127
26.3%

시도
Categorical

Distinct14
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
경상북도
80 
전라남도
76 
충청남도
58 
전라북도
56 
경상남도
56 
Other values (9)
156 

Length

Max length7
Median length4
Mean length3.8609959
Min length3

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
경상북도 80
16.6%
전라남도 76
15.8%
충청남도 58
12.0%
전라북도 56
11.6%
경상남도 56
11.6%
강원도 44
9.1%
충청북도 44
9.1%
경기도 33
6.8%
경기도 11
 
2.3%
제주도 8
 
1.7%
Other values (4) 16
 
3.3%

Length

2023-12-11T12:43:05.602625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경상북도 80
16.6%
전라남도 76
15.8%
충청남도 58
12.0%
전라북도 56
11.6%
경상남도 56
11.6%
강원도 44
9.1%
충청북도 44
9.1%
경기도 44
9.1%
제주도 8
 
1.7%
광주광역시 8
 
1.7%
Other values (3) 8
 
1.7%

시군
Text

Distinct218
Distinct (%)45.2%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
2023-12-11T12:43:06.106255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.6618257
Min length2

Characters and Unicode

Total characters1283
Distinct characters102
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

Unique58 ?
Unique (%)12.0%

Sample

1st row부여군
2nd row서천군
3rd row청양군
4th row홍성군
5th row예산군
ValueCountFrequency (%)
고성 5
 
1.0%
단양군 4
 
0.8%
공주시 4
 
0.8%
괴산군 4
 
0.8%
증평군 4
 
0.8%
영동군 4
 
0.8%
보은군 4
 
0.8%
충주시 4
 
0.8%
광산구 4
 
0.8%
남양주 4
 
0.8%
Other values (208) 441
91.5%
2023-12-11T12:43:06.819759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
194
 
15.1%
121
 
9.4%
70
 
5.5%
58
 
4.5%
48
 
3.7%
44
 
3.4%
38
 
3.0%
32
 
2.5%
28
 
2.2%
28
 
2.2%
Other values (92) 622
48.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1283
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
194
 
15.1%
121
 
9.4%
70
 
5.5%
58
 
4.5%
48
 
3.7%
44
 
3.4%
38
 
3.0%
32
 
2.5%
28
 
2.2%
28
 
2.2%
Other values (92) 622
48.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1283
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
194
 
15.1%
121
 
9.4%
70
 
5.5%
58
 
4.5%
48
 
3.7%
44
 
3.4%
38
 
3.0%
32
 
2.5%
28
 
2.2%
28
 
2.2%
Other values (92) 622
48.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1283
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
194
 
15.1%
121
 
9.4%
70
 
5.5%
58
 
4.5%
48
 
3.7%
44
 
3.4%
38
 
3.0%
32
 
2.5%
28
 
2.2%
28
 
2.2%
Other values (92) 622
48.5%

재배농가수(호)_누에
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct45
Distinct (%)13.4%
Missing147
Missing (%)30.5%
Infinite0
Infinite (%)0.0%
Mean12.262687
Minimum0
Maximum202
Zeros6
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-12-11T12:43:06.985397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q314
95-th percentile39.3
Maximum202
Range202
Interquartile range (IQR)12

Descriptive statistics

Standard deviation23.778852
Coefficient of variation (CV)1.9391225
Kurtosis29.756415
Mean12.262687
Median Absolute Deviation (MAD)3
Skewness4.9559407
Sum4108
Variance565.43378
MonotonicityNot monotonic
2023-12-11T12:43:07.153312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
1 76
15.8%
2 49
 
10.2%
3 29
 
6.0%
4 14
 
2.9%
5 14
 
2.9%
6 12
 
2.5%
15 9
 
1.9%
8 8
 
1.7%
9 8
 
1.7%
10 7
 
1.5%
Other values (35) 109
22.6%
(Missing) 147
30.5%
ValueCountFrequency (%)
0 6
 
1.2%
1 76
15.8%
2 49
10.2%
3 29
 
6.0%
4 14
 
2.9%
5 14
 
2.9%
6 12
 
2.5%
7 7
 
1.5%
8 8
 
1.7%
9 8
 
1.7%
ValueCountFrequency (%)
202 1
 
0.2%
170 2
 
0.4%
168 1
 
0.2%
103 3
0.6%
102 1
 
0.2%
82 1
 
0.2%
73 1
 
0.2%
44 2
 
0.4%
40 5
1.0%
39 1
 
0.2%

재배농가수(호)_오디
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct94
Distinct (%)24.0%
Missing91
Missing (%)18.9%
Infinite0
Infinite (%)0.0%
Mean57.212276
Minimum0
Maximum1006
Zeros4
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-12-11T12:43:07.360561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median10
Q345.5
95-th percentile195.5
Maximum1006
Range1006
Interquartile range (IQR)43.5

Descriptive statistics

Standard deviation150.88212
Coefficient of variation (CV)2.6372333
Kurtosis22.266933
Mean57.212276
Median Absolute Deviation (MAD)9
Skewness4.6017673
Sum22370
Variance22765.414
MonotonicityNot monotonic
2023-12-11T12:43:07.544100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 58
 
12.0%
2 37
 
7.7%
3 21
 
4.4%
5 19
 
3.9%
4 19
 
3.9%
15 14
 
2.9%
6 11
 
2.3%
10 10
 
2.1%
8 9
 
1.9%
14 7
 
1.5%
Other values (84) 186
38.6%
(Missing) 91
18.9%
ValueCountFrequency (%)
0 4
 
0.8%
1 58
12.0%
2 37
7.7%
3 21
 
4.4%
4 19
 
3.9%
5 19
 
3.9%
6 11
 
2.3%
7 6
 
1.2%
8 9
 
1.9%
9 6
 
1.2%
ValueCountFrequency (%)
1006 4
0.8%
833 1
 
0.2%
793 1
 
0.2%
744 1
 
0.2%
717 1
 
0.2%
683 4
0.8%
490 1
 
0.2%
449 1
 
0.2%
448 2
0.4%
215 1
 
0.2%

뽕밭면적(ha)_누에사육용
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct95
Distinct (%)29.7%
Missing162
Missing (%)33.6%
Infinite0
Infinite (%)0.0%
Mean7.164375
Minimum0
Maximum118
Zeros12
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-12-11T12:43:07.687463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q11
median2
Q36.2
95-th percentile36.8
Maximum118
Range118
Interquartile range (IQR)5.2

Descriptive statistics

Standard deviation15.638797
Coefficient of variation (CV)2.1828558
Kurtosis27.629824
Mean7.164375
Median Absolute Deviation (MAD)1.6
Skewness4.7761495
Sum2292.6
Variance244.57198
MonotonicityNot monotonic
2023-12-11T12:43:07.859176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0 37
 
7.7%
2.0 19
 
3.9%
0.0 12
 
2.5%
3.0 11
 
2.3%
0.4 10
 
2.1%
5.0 10
 
2.1%
0.5 9
 
1.9%
0.1 9
 
1.9%
0.2 8
 
1.7%
1.5 7
 
1.5%
Other values (85) 188
39.0%
(Missing) 162
33.6%
ValueCountFrequency (%)
0.0 12
2.5%
0.1 9
1.9%
0.15 2
 
0.4%
0.2 8
1.7%
0.23 2
 
0.4%
0.27 2
 
0.4%
0.3 2
 
0.4%
0.4 10
2.1%
0.49 2
 
0.4%
0.5 9
1.9%
ValueCountFrequency (%)
118.0 3
0.6%
105.0 1
 
0.2%
57.0 1
 
0.2%
48.0 2
0.4%
46.0 1
 
0.2%
45.0 2
0.4%
44.0 1
 
0.2%
40.0 2
0.4%
38.0 2
0.4%
36.8 2
0.4%

뽕밭면적(ha)_오디생산용
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct123
Distinct (%)32.1%
Missing99
Missing (%)20.5%
Infinite0
Infinite (%)0.0%
Mean18.414256
Minimum0.1
Maximum360
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-12-11T12:43:08.004907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.3
Q11.2
median4
Q313.35
95-th percentile84.2
Maximum360
Range359.9
Interquartile range (IQR)12.15

Descriptive statistics

Standard deviation47.769313
Coefficient of variation (CV)2.5941485
Kurtosis29.226005
Mean18.414256
Median Absolute Deviation (MAD)3.3
Skewness5.0669986
Sum7052.66
Variance2281.9073
MonotonicityNot monotonic
2023-12-11T12:43:08.189749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0 26
 
5.4%
2.0 19
 
3.9%
4.0 14
 
2.9%
0.3 11
 
2.3%
5.0 10
 
2.1%
0.7 9
 
1.9%
0.8 8
 
1.7%
12.0 8
 
1.7%
0.5 7
 
1.5%
10.0 7
 
1.5%
Other values (113) 264
54.8%
(Missing) 99
 
20.5%
ValueCountFrequency (%)
0.1 5
1.0%
0.15 2
 
0.4%
0.19 2
 
0.4%
0.2 5
1.0%
0.3 11
2.3%
0.33 2
 
0.4%
0.4 3
 
0.6%
0.45 2
 
0.4%
0.5 7
1.5%
0.59 2
 
0.4%
ValueCountFrequency (%)
360.0 4
0.8%
219.0 1
 
0.2%
209.0 1
 
0.2%
208.2 1
 
0.2%
191.0 2
0.4%
186.0 1
 
0.2%
178.0 2
0.4%
129.0 1
 
0.2%
123.5 2
0.4%
103.2 1
 
0.2%

누에 사육량(상자)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct132
Distinct (%)40.2%
Missing154
Missing (%)32.0%
Infinite0
Infinite (%)0.0%
Mean191.91768
Minimum0
Maximum2626
Zeros2
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-12-11T12:43:08.363310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q120
median50
Q3148.5
95-th percentile928.6
Maximum2626
Range2626
Interquartile range (IQR)128.5

Descriptive statistics

Standard deviation417.7539
Coefficient of variation (CV)2.1767348
Kurtosis17.202107
Mean191.91768
Median Absolute Deviation (MAD)44.5
Skewness3.9774
Sum62949
Variance174518.32
MonotonicityNot monotonic
2023-12-11T12:43:08.544703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 15
 
3.1%
40 12
 
2.5%
50 11
 
2.3%
5 10
 
2.1%
1 9
 
1.9%
2 8
 
1.7%
4 7
 
1.5%
23 7
 
1.5%
43 7
 
1.5%
22 6
 
1.2%
Other values (122) 236
49.0%
(Missing) 154
32.0%
ValueCountFrequency (%)
0 2
 
0.4%
1 9
1.9%
2 8
1.7%
3 5
1.0%
4 7
1.5%
5 10
2.1%
6 1
 
0.2%
7 3
 
0.6%
8 3
 
0.6%
9 4
 
0.8%
ValueCountFrequency (%)
2626 1
 
0.2%
2500 3
0.6%
2395 1
 
0.2%
2225 1
 
0.2%
2116 2
0.4%
1297 2
0.4%
1200 3
0.6%
1050 1
 
0.2%
1000 1
 
0.2%
981 1
 
0.2%

생산량(kg)_누에고치
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct41
Distinct (%)52.6%
Missing404
Missing (%)83.8%
Infinite0
Infinite (%)0.0%
Mean550.55897
Minimum0
Maximum5400
Zeros4
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-12-11T12:43:08.758338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.8
Q190
median204
Q3660
95-th percentile1567.25
Maximum5400
Range5400
Interquartile range (IQR)570

Descriptive statistics

Standard deviation944.86716
Coefficient of variation (CV)1.7161961
Kurtosis17.118045
Mean550.55897
Median Absolute Deviation (MAD)169
Skewness3.8494364
Sum42943.6
Variance892773.96
MonotonicityNot monotonic
2023-12-11T12:43:08.948722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
100.0 4
 
0.8%
600.0 4
 
0.8%
161.0 4
 
0.8%
50.0 4
 
0.8%
0.0 4
 
0.8%
450.0 3
 
0.6%
40.0 3
 
0.6%
1000.0 3
 
0.6%
200.0 3
 
0.6%
90.0 3
 
0.6%
Other values (31) 43
 
8.9%
(Missing) 404
83.8%
ValueCountFrequency (%)
0.0 4
0.8%
8.0 2
0.4%
12.0 1
 
0.2%
30.0 2
0.4%
40.0 3
0.6%
50.0 4
0.8%
70.0 1
 
0.2%
80.0 2
0.4%
90.0 3
0.6%
100.0 4
0.8%
ValueCountFrequency (%)
5400.0 2
0.4%
3000.0 1
 
0.2%
1750.0 1
 
0.2%
1535.0 1
 
0.2%
1400.0 1
 
0.2%
1354.0 2
0.4%
1200.0 1
 
0.2%
1051.0 1
 
0.2%
1000.0 3
0.6%
935.0 2
0.4%

생산량(kg)_건조누에
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct155
Distinct (%)52.5%
Missing187
Missing (%)38.8%
Infinite0
Infinite (%)0.0%
Mean841.68305
Minimum0
Maximum14348
Zeros47
Zeros (%)9.8%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-12-11T12:43:09.150405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q140
median215
Q3650
95-th percentile4447.5
Maximum14348
Range14348
Interquartile range (IQR)610

Descriptive statistics

Standard deviation2131.14
Coefficient of variation (CV)2.5319982
Kurtosis22.133891
Mean841.68305
Median Absolute Deviation (MAD)205
Skewness4.5461576
Sum248296.5
Variance4541757.7
MonotonicityNot monotonic
2023-12-11T12:43:09.384227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 47
 
9.8%
700.0 11
 
2.3%
300.0 9
 
1.9%
100.0 7
 
1.5%
200.0 7
 
1.5%
50.0 6
 
1.2%
120.0 5
 
1.0%
70.0 4
 
0.8%
420.0 4
 
0.8%
400.0 4
 
0.8%
Other values (145) 191
39.6%
(Missing) 187
38.8%
ValueCountFrequency (%)
0.0 47
9.8%
5.0 1
 
0.2%
6.0 1
 
0.2%
8.0 3
 
0.6%
9.0 1
 
0.2%
10.0 1
 
0.2%
13.0 1
 
0.2%
14.0 2
 
0.4%
15.0 3
 
0.6%
16.0 1
 
0.2%
ValueCountFrequency (%)
14348.0 1
0.2%
14109.0 1
0.2%
13024.0 1
0.2%
13015.0 1
0.2%
12002.0 1
0.2%
11708.0 1
0.2%
9587.0 1
0.2%
6732.0 1
0.2%
6553.0 1
0.2%
6199.0 2
0.4%

생산량(kg)_생누에
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct102
Distinct (%)69.9%
Missing336
Missing (%)69.7%
Infinite0
Infinite (%)0.0%
Mean3725.5685
Minimum0
Maximum79087
Zeros3
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-12-11T12:43:09.590718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.5
Q1142.5
median400
Q31650
95-th percentile16650
Maximum79087
Range79087
Interquartile range (IQR)1507.5

Descriptive statistics

Standard deviation11272.458
Coefficient of variation (CV)3.0257016
Kurtosis27.798172
Mean3725.5685
Median Absolute Deviation (MAD)334
Skewness5.0687785
Sum543933
Variance1.2706832 × 108
MonotonicityNot monotonic
2023-12-11T12:43:09.815325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150 7
 
1.5%
200 6
 
1.2%
500 6
 
1.2%
300 4
 
0.8%
10 4
 
0.8%
80 4
 
0.8%
0 3
 
0.6%
800 3
 
0.6%
400 3
 
0.6%
1000 3
 
0.6%
Other values (92) 103
 
21.4%
(Missing) 336
69.7%
ValueCountFrequency (%)
0 3
0.6%
8 1
 
0.2%
10 4
0.8%
12 1
 
0.2%
15 1
 
0.2%
25 1
 
0.2%
28 1
 
0.2%
29 1
 
0.2%
35 1
 
0.2%
38 1
 
0.2%
ValueCountFrequency (%)
79087 1
0.2%
69483 1
0.2%
67517 1
0.2%
33000 1
0.2%
30780 1
0.2%
29000 1
0.2%
18400 1
0.2%
17500 1
0.2%
14100 1
0.2%
13800 1
0.2%

생산량(kg)_동중하초
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct56
Distinct (%)65.9%
Missing397
Missing (%)82.4%
Infinite0
Infinite (%)0.0%
Mean2326.7882
Minimum0
Maximum69483
Zeros3
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-12-11T12:43:09.977003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q120
median200
Q3800
95-th percentile8968
Maximum69483
Range69483
Interquartile range (IQR)780

Descriptive statistics

Standard deviation8538.61
Coefficient of variation (CV)3.6696979
Kurtosis47.610975
Mean2326.7882
Median Absolute Deviation (MAD)190
Skewness6.4934165
Sum197777
Variance72907860
MonotonicityNot monotonic
2023-12-11T12:43:10.126970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 9
 
1.9%
500 5
 
1.0%
5 3
 
0.6%
15 3
 
0.6%
200 3
 
0.6%
0 3
 
0.6%
50 3
 
0.6%
300 3
 
0.6%
40 2
 
0.4%
150 2
 
0.4%
Other values (46) 49
 
10.2%
(Missing) 397
82.4%
ValueCountFrequency (%)
0 3
 
0.6%
1 1
 
0.2%
5 3
 
0.6%
6 1
 
0.2%
9 1
 
0.2%
10 1
 
0.2%
15 3
 
0.6%
20 9
1.9%
30 1
 
0.2%
32 1
 
0.2%
ValueCountFrequency (%)
69483 1
0.2%
30780 1
0.2%
18400 1
0.2%
14100 1
0.2%
9560 1
0.2%
6600 1
0.2%
6000 1
0.2%
5100 1
0.2%
4500 1
0.2%
3615 1
0.2%

생산량(kg)_수번데기
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct32
Distinct (%)65.3%
Missing433
Missing (%)89.8%
Infinite0
Infinite (%)0.0%
Mean318.44898
Minimum0
Maximum1500
Zeros4
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-12-11T12:43:10.268346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q120
median180
Q3440
95-th percentile1260
Maximum1500
Range1500
Interquartile range (IQR)420

Descriptive statistics

Standard deviation402.38487
Coefficient of variation (CV)1.2635772
Kurtosis2.5925323
Mean318.44898
Median Absolute Deviation (MAD)160
Skewness1.7578891
Sum15604
Variance161913.59
MonotonicityNot monotonic
2023-12-11T12:43:10.389474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
20 5
 
1.0%
0 4
 
0.8%
300 3
 
0.6%
440 3
 
0.6%
1500 3
 
0.6%
190 2
 
0.4%
15 2
 
0.4%
100 2
 
0.4%
80 2
 
0.4%
64 1
 
0.2%
Other values (22) 22
 
4.6%
(Missing) 433
89.8%
ValueCountFrequency (%)
0 4
0.8%
1 1
 
0.2%
5 1
 
0.2%
15 2
 
0.4%
20 5
1.0%
30 1
 
0.2%
50 1
 
0.2%
64 1
 
0.2%
80 2
 
0.4%
100 2
 
0.4%
ValueCountFrequency (%)
1500 3
0.6%
900 1
 
0.2%
859 1
 
0.2%
843 1
 
0.2%
825 1
 
0.2%
795 1
 
0.2%
700 1
 
0.2%
680 1
 
0.2%
440 3
0.6%
304 1
 
0.2%

생산량(kg)_잠분
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct38
Distinct (%)69.1%
Missing427
Missing (%)88.6%
Infinite0
Infinite (%)0.0%
Mean651.47273
Minimum0
Maximum4500
Zeros5
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-12-11T12:43:10.759647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q160
median300
Q3930
95-th percentile2489.5
Maximum4500
Range4500
Interquartile range (IQR)870

Descriptive statistics

Standard deviation896.9375
Coefficient of variation (CV)1.3767844
Kurtosis6.0399986
Mean651.47273
Median Absolute Deviation (MAD)287
Skewness2.2518185
Sum35831
Variance804496.88
MonotonicityNot monotonic
2023-12-11T12:43:10.871001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
600 5
 
1.0%
0 5
 
1.0%
1000 3
 
0.6%
100 3
 
0.6%
1700 3
 
0.6%
13 2
 
0.4%
400 2
 
0.4%
70 2
 
0.4%
4500 1
 
0.2%
1400 1
 
0.2%
Other values (28) 28
 
5.8%
(Missing) 427
88.6%
ValueCountFrequency (%)
0 5
1.0%
6 1
 
0.2%
13 2
 
0.4%
15 1
 
0.2%
16 1
 
0.2%
20 1
 
0.2%
40 1
 
0.2%
45 1
 
0.2%
50 1
 
0.2%
70 2
 
0.4%
ValueCountFrequency (%)
4500 1
 
0.2%
3120 1
 
0.2%
2500 1
 
0.2%
2485 1
 
0.2%
1700 3
0.6%
1600 1
 
0.2%
1500 1
 
0.2%
1400 1
 
0.2%
1124 1
 
0.2%
1000 3
0.6%

생산량(kg)_뽕잎
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct70
Distinct (%)51.1%
Missing345
Missing (%)71.6%
Infinite0
Infinite (%)0.0%
Mean6577.8394
Minimum0
Maximum150000
Zeros3
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-12-11T12:43:10.993356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14.6
Q1300
median1124
Q32700
95-th percentile40000
Maximum150000
Range150000
Interquartile range (IQR)2400

Descriptive statistics

Standard deviation17199.964
Coefficient of variation (CV)2.6148349
Kurtosis36.532397
Mean6577.8394
Median Absolute Deviation (MAD)976
Skewness5.2400856
Sum901164
Variance2.9583877 × 108
MonotonicityNot monotonic
2023-12-11T12:43:11.156715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 11
 
2.3%
200 7
 
1.5%
2000 6
 
1.2%
300 6
 
1.2%
400 5
 
1.0%
100 5
 
1.0%
20000 4
 
0.8%
1500 4
 
0.8%
500 4
 
0.8%
1200 3
 
0.6%
Other values (60) 82
 
17.0%
(Missing) 345
71.6%
ValueCountFrequency (%)
0 3
0.6%
2 1
 
0.2%
7 1
 
0.2%
10 1
 
0.2%
13 1
 
0.2%
15 2
0.4%
30 3
0.6%
40 1
 
0.2%
45 1
 
0.2%
50 2
0.4%
ValueCountFrequency (%)
150000 1
0.2%
56600 1
0.2%
56000 2
0.4%
50000 1
0.2%
43000 1
0.2%
40000 2
0.4%
36580 1
0.2%
34466 1
0.2%
22973 1
0.2%
22476 1
0.2%

생산량(kg)_오디
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct196
Distinct (%)61.6%
Missing164
Missing (%)34.0%
Infinite0
Infinite (%)0.0%
Mean66624.77
Minimum0
Maximum2000000
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-12-11T12:43:11.438242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile200
Q12012.5
median8450
Q332972.25
95-th percentile346705.2
Maximum2000000
Range2000000
Interquartile range (IQR)30959.75

Descriptive statistics

Standard deviation215912.94
Coefficient of variation (CV)3.2407308
Kurtosis37.67325
Mean66624.77
Median Absolute Deviation (MAD)7582.5
Skewness5.7128006
Sum21186677
Variance4.6618399 × 1010
MonotonicityNot monotonic
2023-12-11T12:43:11.661520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3000 10
 
2.1%
1000 8
 
1.7%
1500 7
 
1.5%
50000 6
 
1.2%
300 6
 
1.2%
4000 6
 
1.2%
2000 6
 
1.2%
20000 5
 
1.0%
200 5
 
1.0%
2500 5
 
1.0%
Other values (186) 254
52.7%
(Missing) 164
34.0%
ValueCountFrequency (%)
0 1
 
0.2%
10 3
0.6%
50 2
 
0.4%
55 1
 
0.2%
70 2
 
0.4%
100 2
 
0.4%
150 1
 
0.2%
200 5
1.0%
220 1
 
0.2%
250 1
 
0.2%
ValueCountFrequency (%)
2000000 1
0.2%
1680000 1
0.2%
1340000 1
0.2%
1184000 1
0.2%
1041000 1
0.2%
968000 1
0.2%
915200 1
0.2%
679030 1
0.2%
587500 1
0.2%
578890 1
0.2%

Interactions

2023-12-11T12:43:02.531134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:40.053829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:42.040298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:43.729310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:45.623385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:47.273874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:49.188631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:51.202553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:52.681180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:54.489404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:57.206888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:59.064208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:00.680543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:02.631388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:40.217152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:42.200396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:43.864747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:45.795177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:47.386252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:49.330484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:51.314941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:52.815038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:54.620522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:57.440559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:59.189746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:00.852688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:02.745103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:40.365301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:42.330326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:43.983129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:45.927622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:47.485884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:49.457379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:51.422400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:52.931318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:54.743531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:57.663970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:59.289692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:01.029870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:02.852342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:40.524630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:42.434604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:44.117650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:46.056021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:47.594882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:49.586191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:51.526072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:53.045765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:54.863963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:57.788680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:59.387958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:01.193605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:02.952430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:41.002332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:42.564024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:44.262839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:46.212641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:47.709673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:49.748341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:51.637593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:53.180773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:54.995991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:57.948519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:59.497765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:01.355972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:03.035421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:41.097937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:42.673495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:44.399138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:46.350199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:47.799136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:49.866079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:51.727582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:53.287942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:55.103716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:58.064993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:59.606924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:01.512365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:03.131392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:41.198081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:42.804998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:44.567915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:46.464867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:47.910805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:49.988998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:51.832186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:53.381901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:55.236553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:58.188344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:59.708671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:01.652152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:03.237723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:41.336187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:42.938580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:44.704383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:46.594208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:48.023470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:50.149553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:51.938172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:53.542631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:55.390635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:58.345639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:59.819345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:01.829298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:03.342972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:41.474226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:43.086068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:44.852653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:46.726013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:48.445117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:50.304281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:52.042784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:53.714303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:55.529980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:58.476022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:59.920143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:01.965488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:03.450096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:41.589730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:43.256157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:45.020450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:46.831488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:48.578425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:50.512017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:52.170421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:53.885149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:55.710829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:58.584263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:00.078868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:02.105562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:03.550885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:41.718039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:43.381723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:45.165959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:46.949026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:48.717113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:50.632413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:52.319945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:54.014758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:55.844145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:58.727376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:00.242343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:02.220075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:03.635053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:41.806602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:43.503489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:45.307679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:47.039641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:48.858266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:50.844315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:52.412714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:54.135826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:56.632364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:58.838264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:00.376677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:02.315240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:03.747973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:41.906890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:43.620176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:45.461926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:47.141618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:49.018176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:51.048360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:52.542543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:54.301429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:56.914118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:42:58.941693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:00.526037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:02.412240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T12:43:11.804866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도시도재배농가수(호)_누에재배농가수(호)_오디뽕밭면적(ha)_누에사육용뽕밭면적(ha)_오디생산용누에 사육량(상자)생산량(kg)_누에고치생산량(kg)_건조누에생산량(kg)_생누에생산량(kg)_동중하초생산량(kg)_수번데기생산량(kg)_잠분생산량(kg)_뽕잎생산량(kg)_오디
연도1.0000.0000.0000.1070.0000.0000.0000.1360.0000.2340.0000.5150.2410.0000.000
시도0.0001.0000.0000.3300.3020.3500.2980.0000.2470.0000.0000.0000.0000.4230.251
재배농가수(호)_누에0.0000.0001.0000.2910.8130.3840.8440.6680.9040.7510.8610.7010.5070.5750.000
재배농가수(호)_오디0.1070.3300.2911.0000.4840.9880.2380.0000.0000.0000.0000.0000.0000.6760.845
뽕밭면적(ha)_누에사육용0.0000.3020.8130.4841.0000.5470.9340.5290.7970.7830.7090.5330.7780.4970.283
뽕밭면적(ha)_오디생산용0.0000.3500.3840.9880.5471.0000.3230.3290.0000.3730.0000.0000.0000.6510.884
누에 사육량(상자)0.0000.2980.8440.2380.9340.3231.0000.6150.8080.8770.6880.3690.6640.4850.222
생산량(kg)_누에고치0.1360.0000.6680.0000.5290.3290.6151.0000.7260.2050.0000.7700.2060.0000.000
생산량(kg)_건조누에0.0000.2470.9040.0000.7970.0000.8080.7261.0000.5030.7580.6650.5390.3520.000
생산량(kg)_생누에0.2340.0000.7510.0000.7830.3730.8770.2050.5031.0000.0000.3930.7400.7140.376
생산량(kg)_동중하초0.0000.0000.8610.0000.7090.0000.6880.0000.7580.0001.0000.0000.0000.0000.000
생산량(kg)_수번데기0.5150.0000.7010.0000.5330.0000.3690.7700.6650.3930.0001.0000.2470.5550.000
생산량(kg)_잠분0.2410.0000.5070.0000.7780.0000.6640.2060.5390.7400.0000.2471.0000.6040.000
생산량(kg)_뽕잎0.0000.4230.5750.6760.4970.6510.4850.0000.3520.7140.0000.5550.6041.0000.545
생산량(kg)_오디0.0000.2510.0000.8450.2830.8840.2220.0000.0000.3760.0000.0000.0000.5451.000
2023-12-11T12:43:11.989572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도시도
연도1.0000.000
시도0.0001.000
2023-12-11T12:43:12.097070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
재배농가수(호)_누에재배농가수(호)_오디뽕밭면적(ha)_누에사육용뽕밭면적(ha)_오디생산용누에 사육량(상자)생산량(kg)_누에고치생산량(kg)_건조누에생산량(kg)_생누에생산량(kg)_동중하초생산량(kg)_수번데기생산량(kg)_잠분생산량(kg)_뽕잎생산량(kg)_오디연도시도
재배농가수(호)_누에1.0000.5330.8460.4650.7780.5630.7110.5410.4860.2500.4160.4300.3750.0000.000
재배농가수(호)_오디0.5331.0000.4840.8820.3890.1470.2860.2110.1960.0060.0100.4130.6830.0670.150
뽕밭면적(ha)_누에사육용0.8460.4841.0000.5090.8410.5100.7030.5520.4220.3480.4400.4570.3540.0000.144
뽕밭면적(ha)_오디생산용0.4650.8820.5091.0000.3500.2200.2810.2170.2280.0200.1730.5200.7570.0000.160
누에 사육량(상자)0.7780.3890.8410.3501.0000.5830.7920.6310.4370.3360.4640.3100.2450.0000.141
생산량(kg)_누에고치0.5630.1470.5100.2200.5831.0000.5210.3880.5490.5610.3790.2330.1320.0470.000
생산량(kg)_건조누에0.7110.2860.7030.2810.7920.5211.0000.2950.3700.4220.4330.3840.2260.0000.112
생산량(kg)_생누에0.5410.2110.5520.2170.6310.3880.2951.0000.2530.4470.7760.4480.2970.1580.000
생산량(kg)_동중하초0.4860.1960.4220.2280.4370.5490.3700.2531.0000.4960.4870.3350.1450.0000.000
생산량(kg)_수번데기0.2500.0060.3480.0200.3360.5610.4220.4470.4961.0000.4470.5140.1750.2350.000
생산량(kg)_잠분0.4160.0100.4400.1730.4640.3790.4330.7760.4870.4471.0000.6300.4050.1530.000
생산량(kg)_뽕잎0.4300.4130.4570.5200.3100.2330.3840.4480.3350.5140.6301.0000.5450.0000.236
생산량(kg)_오디0.3750.6830.3540.7570.2450.1320.2260.2970.1450.1750.4050.5451.0000.0000.107
연도0.0000.0670.0000.0000.0000.0470.0000.1580.0000.2350.1530.0000.0001.0000.000
시도0.0000.1500.1440.1600.1410.0000.1120.0000.0000.0000.0000.2360.1070.0001.000

Missing values

2023-12-11T12:43:03.941071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T12:43:04.611977image/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.
2023-12-11T12:43:04.917712image/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

연도시도시군재배농가수(호)_누에재배농가수(호)_오디뽕밭면적(ha)_누에사육용뽕밭면적(ha)_오디생산용누에 사육량(상자)생산량(kg)_누에고치생산량(kg)_건조누에생산량(kg)_생누에생산량(kg)_동중하초생산량(kg)_수번데기생산량(kg)_잠분생산량(kg)_뽕잎생산량(kg)_오디
02014충청남도부여군13172.77.9128<NA>300.0340<NA><NA><NA><NA><NA>
12014충청남도서천군231.50.1918<NA><NA>100500<NA><NA><NA>10
22014충청남도청양군280.90.5110200.0400.0<NA>100015<NA>100300
32014충청남도홍성군31216.41.0169<NA>15.0135215015<NA>13<NA>
42014충청남도예산군250.492.1220<NA>50.0<NA>500<NA><NA><NA><NA>
52014충청남도태안군<NA>15<NA>2.5<NA><NA><NA><NA><NA><NA><NA><NA><NA>
62014전라북도전주시110.41.660<NA>600.0<NA><NA><NA><NA><NA><NA>
72014전라북도군산시120.10.82<NA><NA>120<NA><NA><NA><NA><NA>
82014전라북도익산시1521.90.350<NA><NA>140710<NA><NA><NA><NA>
92014전라북도정읍시126835.9178.094450.0<NA>2901700<NA><NA><NA><NA>
연도시도시군재배농가수(호)_누에재배농가수(호)_오디뽕밭면적(ha)_누에사육용뽕밭면적(ha)_오디생산용누에 사육량(상자)생산량(kg)_누에고치생산량(kg)_건조누에생산량(kg)_생누에생산량(kg)_동중하초생산량(kg)_수번데기생산량(kg)_잠분생산량(kg)_뽕잎생산량(kg)_오디
4722012충청북도청주시<NA>3<NA>1.0<NA><NA><NA><NA><NA><NA><NA><NA><NA>
4732012충청북도충주시20465.015.0230<NA>1380.0<NA><NA><NA><NA>23008600
4742012충청북도제천시241.01.01<NA>8.0<NA><NA><NA><NA><NA><NA>
4752012충청북도청원군4358.018.080<NA>155.0<NA><NA><NA><NA><NA>3500
4762012충청북도보은군33914.04.03411535.01786.0<NA><NA><NA><NA>1000700
4772012충청북도옥천군<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
4782012충청북도영동군632.02.020<NA>100.0<NA><NA><NA><NA><NA><NA>
4792012충청북도증평군3111.08.09<NA>30.0<NA><NA><NA><NA><NA>1700
4802012충청북도진천군<NA>1<NA>1.0<NA><NA><NA><NA><NA><NA><NA><NA>4000
4812012충청북도괴산군1171.04.0<NA><NA><NA><NA><NA><NA><NA><NA>4200

Duplicate rows

Most frequently occurring

연도시도시군재배농가수(호)_누에재배농가수(호)_오디뽕밭면적(ha)_누에사육용뽕밭면적(ha)_오디생산용누에 사육량(상자)생산량(kg)_누에고치생산량(kg)_건조누에생산량(kg)_생누에생산량(kg)_동중하초생산량(kg)_수번데기생산량(kg)_잠분생산량(kg)_뽕잎생산량(kg)_오디# duplicates
02014경상남도창원1<NA>0.5<NA>3<NA>14.0<NA><NA><NA><NA><NA><NA>2
12014경상남도합천6<NA>1.35<NA>17<NA>170.0<NA><NA><NA><NA><NA><NA>2