Overview

Dataset statistics

Number of variables16
Number of observations483
Missing cells3346
Missing cells (%)43.3%
Duplicate rows2
Duplicate rows (%)0.4%
Total size in memory67.1 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://www.data.go.kr/data/15050571/fileData.do

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)_누에고치 and 2 other fieldsHigh correlation
생산량(kg)_수번데기 is highly overall correlated with 생산량(kg)_누에고치 and 3 other fieldsHigh correlation
생산량(kg)_잠분 is highly overall correlated with 생산량(kg)_생누에 and 3 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 (30.4%) missing valuesMissing
재배농가수(호)_오디 has 91 (18.8%) missing valuesMissing
뽕밭면적(ha)_누에사육용 has 162 (33.5%) missing valuesMissing
뽕밭면적(ha)_오디생산용 has 99 (20.5%) missing valuesMissing
누에 사육량(상자) has 154 (31.9%) missing valuesMissing
생산량(kg)_누에고치 has 404 (83.6%) missing valuesMissing
생산량(kg)_건조누에 has 187 (38.7%) missing valuesMissing
생산량(kg)_생누에 has 336 (69.6%) missing valuesMissing
생산량(kg)_동중하초 has 397 (82.2%) missing valuesMissing
생산량(kg)_수번데기 has 433 (89.6%) missing valuesMissing
생산량(kg)_잠분 has 427 (88.4%) missing valuesMissing
생산량(kg)_뽕잎 has 345 (71.4%) 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 (9.7%) zerosZeros
생산량(kg)_잠분 has 5 (1.0%) zerosZeros

Reproduction

Analysis started2023-12-12 07:57:04.220545
Analysis finished2023-12-12 07:57:25.110285
Duration20.89 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 
2012
128 
2013
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
47.2%
2012 128
26.5%
2013 127
26.3%

Length

2023-12-12T16:57:25.173672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:57:25.289541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2014 228
47.2%
2012 128
26.5%
2013 127
26.3%

시도
Categorical

Distinct14
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
경상북도
81 
전라남도
76 
충청남도
58 
전라북도
56 
경상남도
56 
Other values (9)
156 

Length

Max length7
Median length4
Mean length3.8612836
Min length3

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
경상북도 81
16.8%
전라남도 76
15.7%
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

2023-12-12T16:57:25.440511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경상북도 81
16.8%
전라남도 76
15.7%
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

Distinct219
Distinct (%)45.3%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
2023-12-12T16:57:25.813549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.6563147
Min length1

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%
청양군 4
 
0.8%
공주시 4
 
0.8%
괴산군 4
 
0.8%
진천군 4
 
0.8%
증평군 4
 
0.8%
보은군 4
 
0.8%
남양주 4
 
0.8%
충주시 4
 
0.8%
청주시 4
 
0.8%
Other values (209) 442
91.5%
2023-12-12T16:57:26.350124image/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 

Distinct46
Distinct (%)13.7%
Missing147
Missing (%)30.4%
Infinite0
Infinite (%)0.0%
Mean13.568452
Minimum0
Maximum451
Zeros6
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-12-12T16:57:26.512303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q314.25
95-th percentile40
Maximum451
Range451
Interquartile range (IQR)12.25

Descriptive statistics

Standard deviation33.714005
Coefficient of variation (CV)2.4847347
Kurtosis90.91966
Mean13.568452
Median Absolute Deviation (MAD)3
Skewness8.2347642
Sum4559
Variance1136.6341
MonotonicityNot monotonic
2023-12-12T16:57:26.656189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
1 76
15.7%
2 49
 
10.1%
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
 
1.4%
Other values (36) 110
22.8%
(Missing) 147
30.4%
ValueCountFrequency (%)
0 6
 
1.2%
1 76
15.7%
2 49
10.1%
3 29
 
6.0%
4 14
 
2.9%
5 14
 
2.9%
6 12
 
2.5%
7 7
 
1.4%
8 8
 
1.7%
9 8
 
1.7%
ValueCountFrequency (%)
451 1
 
0.2%
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%

재배농가수(호)_오디
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct95
Distinct (%)24.2%
Missing91
Missing (%)18.8%
Infinite0
Infinite (%)0.0%
Mean58.045918
Minimum0
Maximum1006
Zeros4
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-12-12T16:57:26.792518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median10
Q346
95-th percentile201
Maximum1006
Range1006
Interquartile range (IQR)44

Descriptive statistics

Standard deviation151.59028
Coefficient of variation (CV)2.611558
Kurtosis21.688378
Mean58.045918
Median Absolute Deviation (MAD)9
Skewness4.5350815
Sum22754
Variance22979.614
MonotonicityNot monotonic
2023-12-12T16:57:26.971797image/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
 
4.3%
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
 
1.4%
Other values (85) 187
38.7%
(Missing) 91
18.8%
ValueCountFrequency (%)
0 4
 
0.8%
1 58
12.0%
2 37
7.7%
3 21
 
4.3%
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%
384 1
 
0.2%

뽕밭면적(ha)_누에사육용
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct96
Distinct (%)29.9%
Missing162
Missing (%)33.5%
Infinite0
Infinite (%)0.0%
Mean7.9956386
Minimum0
Maximum274
Zeros12
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-12-12T16:57:27.107786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q11
median2
Q36.2
95-th percentile36.8
Maximum274
Range274
Interquartile range (IQR)5.2

Descriptive statistics

Standard deviation21.578194
Coefficient of variation (CV)2.6987455
Kurtosis78.659259
Mean7.9956386
Median Absolute Deviation (MAD)1.6
Skewness7.6424858
Sum2566.6
Variance465.61844
MonotonicityNot monotonic
2023-12-12T16:57:27.239438image/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
 
1.4%
Other values (86) 189
39.1%
(Missing) 162
33.5%
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 (%)
274.0 1
 
0.2%
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%

뽕밭면적(ha)_오디생산용
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct124
Distinct (%)32.3%
Missing99
Missing (%)20.5%
Infinite0
Infinite (%)0.0%
Mean18.788177
Minimum0.1
Maximum360
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-12-12T16:57:27.390236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.3
Q11.2
median4
Q313.7
95-th percentile85
Maximum360
Range359.9
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation48.266335
Coefficient of variation (CV)2.5689738
Kurtosis27.888538
Mean18.788177
Median Absolute Deviation (MAD)3.32
Skewness4.9449163
Sum7214.66
Variance2329.6391
MonotonicityNot monotonic
2023-12-12T16:57:27.528751image/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
 
1.4%
10.0 7
 
1.4%
Other values (114) 265
54.9%
(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
1.4%
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%
162.0 1
 
0.2%
129.0 1
 
0.2%
123.5 2
0.4%

누에 사육량(상자)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct133
Distinct (%)40.4%
Missing154
Missing (%)31.9%
Infinite0
Infinite (%)0.0%
Mean215.96657
Minimum0
Maximum8104
Zeros2
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-12-12T16:57:27.679776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q120
median50
Q3150
95-th percentile960.6
Maximum8104
Range8104
Interquartile range (IQR)130

Descriptive statistics

Standard deviation603.54204
Coefficient of variation (CV)2.7946087
Kurtosis91.991273
Mean215.96657
Median Absolute Deviation (MAD)45
Skewness8.1053337
Sum71053
Variance364262.99
MonotonicityNot monotonic
2023-12-12T16:57:27.809043image/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%
23 7
 
1.4%
43 7
 
1.4%
4 7
 
1.4%
22 6
 
1.2%
Other values (123) 237
49.1%
(Missing) 154
31.9%
ValueCountFrequency (%)
0 2
 
0.4%
1 9
1.9%
2 8
1.7%
3 5
1.0%
4 7
1.4%
5 10
2.1%
6 1
 
0.2%
7 3
 
0.6%
8 3
 
0.6%
9 4
 
0.8%
ValueCountFrequency (%)
8104 1
 
0.2%
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%

생산량(kg)_누에고치
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct42
Distinct (%)53.2%
Missing404
Missing (%)83.6%
Infinite0
Infinite (%)0.0%
Mean606.12152
Minimum0
Maximum5400
Zeros4
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-12-12T16:57:27.973985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.2
Q190
median208
Q3680
95-th percentile1875
Maximum5400
Range5400
Interquartile range (IQR)590

Descriptive statistics

Standard deviation1060.7623
Coefficient of variation (CV)1.7500819
Kurtosis12.725513
Mean606.12152
Median Absolute Deviation (MAD)178
Skewness3.4553955
Sum47883.6
Variance1125216.6
MonotonicityNot monotonic
2023-12-12T16:57:28.114137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
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 (32) 44
 
9.1%
(Missing) 404
83.6%
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%
4940.0 1
 
0.2%
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%

생산량(kg)_건조누에
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct156
Distinct (%)52.7%
Missing187
Missing (%)38.7%
Infinite0
Infinite (%)0.0%
Mean986.83277
Minimum0
Maximum43806
Zeros47
Zeros (%)9.7%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-12-12T16:57:28.260936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q140
median217.5
Q3655
95-th percentile4544
Maximum43806
Range43806
Interquartile range (IQR)615

Descriptive statistics

Standard deviation3280.6433
Coefficient of variation (CV)3.3244167
Kurtosis101.61598
Mean986.83277
Median Absolute Deviation (MAD)207.5
Skewness8.7742728
Sum292102.5
Variance10762620
MonotonicityNot monotonic
2023-12-12T16:57:28.395961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 47
 
9.7%
700.0 11
 
2.3%
300.0 9
 
1.9%
100.0 7
 
1.4%
200.0 7
 
1.4%
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 (146) 192
39.8%
(Missing) 187
38.7%
ValueCountFrequency (%)
0.0 47
9.7%
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 (%)
43806.0 1
0.2%
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%

생산량(kg)_생누에
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct103
Distinct (%)70.1%
Missing336
Missing (%)69.6%
Infinite0
Infinite (%)0.0%
Mean4353.1973
Minimum0
Maximum95987
Zeros3
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-12-12T16:57:28.538855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.6
Q1145
median400
Q31800
95-th percentile18130
Maximum95987
Range95987
Interquartile range (IQR)1655

Descriptive statistics

Standard deviation13568.49
Coefficient of variation (CV)3.1169021
Kurtosis25.839761
Mean4353.1973
Median Absolute Deviation (MAD)338
Skewness4.9285422
Sum639920
Variance1.8410392 × 108
MonotonicityNot monotonic
2023-12-12T16:57:29.018789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150 7
 
1.4%
500 6
 
1.2%
200 6
 
1.2%
10 4
 
0.8%
80 4
 
0.8%
300 4
 
0.8%
400 3
 
0.6%
800 3
 
0.6%
0 3
 
0.6%
1000 3
 
0.6%
Other values (93) 104
 
21.5%
(Missing) 336
69.6%
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 (%)
95987 1
0.2%
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%

생산량(kg)_동중하초
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct57
Distinct (%)66.3%
Missing397
Missing (%)82.2%
Infinite0
Infinite (%)0.0%
Mean2319.907
Minimum0
Maximum69483
Zeros3
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-12-12T16:57:29.192599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q122.5
median200
Q3875
95-th percentile8820
Maximum69483
Range69483
Interquartile range (IQR)852.5

Descriptive statistics

Standard deviation8488.4741
Coefficient of variation (CV)3.6589718
Kurtosis48.189403
Mean2319.907
Median Absolute Deviation (MAD)190.5
Skewness6.532056
Sum199512
Variance72054193
MonotonicityNot monotonic
2023-12-12T16:57:29.332496image/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%
200 3
 
0.6%
0 3
 
0.6%
300 3
 
0.6%
50 3
 
0.6%
15 3
 
0.6%
40 2
 
0.4%
150 2
 
0.4%
Other values (47) 50
 
10.4%
(Missing) 397
82.2%
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 

Distinct33
Distinct (%)66.0%
Missing433
Missing (%)89.6%
Infinite0
Infinite (%)0.0%
Mean385.08
Minimum0
Maximum3650
Zeros4
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-12-12T16:57:29.469866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q122.5
median185
Q3440
95-th percentile1500
Maximum3650
Range3650
Interquartile range (IQR)417.5

Descriptive statistics

Standard deviation616.92291
Coefficient of variation (CV)1.6020643
Kurtosis15.735861
Mean385.08
Median Absolute Deviation (MAD)165
Skewness3.486516
Sum19254
Variance380593.87
MonotonicityNot monotonic
2023-12-12T16:57:29.630985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
20 5
 
1.0%
0 4
 
0.8%
440 3
 
0.6%
1500 3
 
0.6%
300 3
 
0.6%
80 2
 
0.4%
190 2
 
0.4%
100 2
 
0.4%
15 2
 
0.4%
1 1
 
0.2%
Other values (23) 23
 
4.8%
(Missing) 433
89.6%
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 (%)
3650 1
 
0.2%
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%

생산량(kg)_잠분
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct39
Distinct (%)69.6%
Missing427
Missing (%)88.4%
Infinite0
Infinite (%)0.0%
Mean734.32143
Minimum0
Maximum5291
Zeros5
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-12-12T16:57:29.818142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q165
median325
Q31000
95-th percentile2655
Maximum5291
Range5291
Interquartile range (IQR)935

Descriptive statistics

Standard deviation1083.6275
Coefficient of variation (CV)1.4756855
Kurtosis7.021397
Mean734.32143
Median Absolute Deviation (MAD)295
Skewness2.4944073
Sum41122
Variance1174248.5
MonotonicityNot monotonic
2023-12-12T16:57:29.989022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
600 5
 
1.0%
0 5
 
1.0%
100 3
 
0.6%
1700 3
 
0.6%
1000 3
 
0.6%
70 2
 
0.4%
13 2
 
0.4%
400 2
 
0.4%
350 1
 
0.2%
860 1
 
0.2%
Other values (29) 29
 
6.0%
(Missing) 427
88.4%
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 (%)
5291 1
 
0.2%
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%

생산량(kg)_뽕잎
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct71
Distinct (%)51.4%
Missing345
Missing (%)71.4%
Infinite0
Infinite (%)0.0%
Mean7208.8406
Minimum0
Maximum150000
Zeros3
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-12-12T16:57:30.132912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14.7
Q1300
median1142
Q32700
95-th percentile40450
Maximum150000
Range150000
Interquartile range (IQR)2400

Descriptive statistics

Standard deviation18671.523
Coefficient of variation (CV)2.5900869
Kurtosis28.175857
Mean7208.8406
Median Absolute Deviation (MAD)990
Skewness4.7142458
Sum994820
Variance3.4862578 × 108
MonotonicityNot monotonic
2023-12-12T16:57:30.301087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 11
 
2.3%
200 7
 
1.4%
2000 6
 
1.2%
300 6
 
1.2%
400 5
 
1.0%
100 5
 
1.0%
500 4
 
0.8%
1500 4
 
0.8%
20000 4
 
0.8%
1300 3
 
0.6%
Other values (61) 83
 
17.2%
(Missing) 345
71.4%
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%
93656 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%

생산량(kg)_오디
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct197
Distinct (%)61.8%
Missing164
Missing (%)34.0%
Infinite0
Infinite (%)0.0%
Mean67472.091
Minimum0
Maximum2000000
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-12-12T16:57:30.446868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile200
Q12025
median8500
Q334284
95-th percentile346321.8
Maximum2000000
Range2000000
Interquartile range (IQR)32259

Descriptive statistics

Standard deviation216103.74
Coefficient of variation (CV)3.2028612
Kurtosis37.317474
Mean67472.091
Median Absolute Deviation (MAD)7640
Skewness5.6740076
Sum21523597
Variance4.6700827 × 1010
MonotonicityNot monotonic
2023-12-12T16:57:30.607047image/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
 
1.4%
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 (187) 255
52.8%
(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

2023-12-12T16:57:22.803035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:05.057740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:06.434507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:07.786830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:09.003219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:10.959480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:12.998238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:14.359968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:15.735778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:16.918962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:18.683071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:20.197941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:21.514067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:22.890223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:05.168020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:06.515968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:07.872174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:09.113810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:11.177306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:13.104927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:14.449335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:15.817644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:16.992635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:18.794769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:20.292010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:21.590147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:22.994230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:05.271922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:06.630768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:07.969208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:09.233643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:11.397321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:13.195446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:14.548094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:15.896527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:17.397730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:18.917955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:20.401780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:21.687474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:23.145021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:05.366102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:06.732460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:08.052308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:09.319412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:11.652134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:13.291045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:14.654749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:15.994019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:17.497552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:19.017861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:20.495496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:21.791449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:23.288117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:05.480224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:06.831673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:08.141177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:09.741958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:11.817705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:13.398359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:14.781840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:16.124199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:17.594651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:19.143285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:20.606728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:21.882222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:23.434216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:05.595220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:06.940758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:08.243742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:09.856945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:11.947266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:13.500187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:14.915484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:16.206520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:17.735065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:19.275523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:20.728367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:21.974765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:23.554468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:05.720432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:07.038192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:08.349336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:09.946718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:12.088354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:13.597404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:15.002314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:16.279979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:17.854721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:19.392507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:20.839458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:22.056294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:23.963388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:05.795326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:07.132317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:08.444860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:10.029594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:12.202214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:13.701651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:15.089685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:16.357363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:17.964382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:19.510319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:20.921295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:22.153773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:24.053946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:05.890562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:07.263983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:08.534203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:10.108534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:12.314118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:13.794794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:15.184994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:16.445855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:18.072610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:19.603626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:21.006008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:22.238204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:24.153137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:06.004292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:07.378617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:08.615850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:10.200261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:12.487618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:13.927698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:15.297414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:16.535253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:18.179866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:19.731225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:21.104153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:22.328285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:24.243758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:06.101720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:07.467308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:08.703805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:10.297658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:12.597684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:14.037869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:15.429007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:16.615154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:18.293416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:19.854871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:21.204080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:22.477910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:24.323958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:06.203169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:07.558648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:08.804544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:10.448633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:12.735656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:14.145298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:15.524305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:16.701942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:18.399282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:19.971436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:21.291824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:22.558046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:24.416567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:06.311851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:07.674762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:08.900712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:10.683118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:12.881587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:14.247585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:15.635851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:16.809428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:18.543721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:20.078544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:21.395535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:22.681152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T16:57:30.744533image/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
연도1.0000.0000.0000.1720.1300.0000.0000.0000.0000.3790.0000.1240.2750.0000.000
시도0.0001.0000.0000.3060.2130.3420.2460.0000.2690.2910.0000.0000.0000.3630.232
재배농가수(호)_누에0.0000.0001.0000.6840.9800.3570.9140.6810.8830.9640.6210.7180.7850.9080.000
재배농가수(호)_오디0.1720.3060.6841.0000.7260.9440.6960.3270.6790.6080.0000.5470.8240.9000.937
뽕밭면적(ha)_누에사육용0.1300.2130.9800.7261.0000.5000.9080.4770.8310.9510.0000.6820.7170.9010.337
뽕밭면적(ha)_오디생산용0.0000.3420.3570.9440.5001.0000.4450.3910.4780.4350.0000.5590.5810.8130.872
누에 사육량(상자)0.0000.2460.9140.6960.9080.4451.0000.4750.9670.7930.8570.9050.7520.7260.289
생산량(kg)_누에고치0.0000.0000.6810.3270.4770.3910.4751.0000.5510.5720.0000.7500.4570.3770.000
생산량(kg)_건조누에0.0000.2690.8830.6790.8310.4780.9670.5511.0000.8500.3960.8760.7910.7030.094
생산량(kg)_생누에0.3790.2910.9640.6080.9510.4350.7930.5720.8501.0000.0000.7690.8750.9390.318
생산량(kg)_동중하초0.0000.0000.6210.0000.0000.0000.8570.0000.3960.0001.0000.0000.0000.0000.000
생산량(kg)_수번데기0.1240.0000.7180.5470.6820.5590.9050.7500.8760.7690.0001.0000.7480.7760.806
생산량(kg)_잠분0.2750.0000.7850.8240.7170.5810.7520.4570.7910.8750.0000.7481.0000.8090.748
생산량(kg)_뽕잎0.0000.3630.9080.9000.9010.8130.7260.3770.7030.9390.0000.7760.8091.0000.765
생산량(kg)_오디0.0000.2320.0000.9370.3370.8720.2890.0000.0940.3180.0000.8060.7480.7651.000
2023-12-12T16:57:30.942478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도시도
연도1.0000.000
시도0.0001.000
2023-12-12T16:57:31.074430image/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)_누에1.0000.5370.8470.4700.7800.5790.7140.5500.4980.2940.4470.4440.3820.0000.000
재배농가수(호)_오디0.5371.0000.4900.8830.3950.1680.2940.2260.2120.0550.0650.4270.6860.0750.133
뽕밭면적(ha)_누에사육용0.8470.4901.0000.5140.8420.5280.7060.5610.4360.3870.4700.4700.3610.0530.105
뽕밭면적(ha)_오디생산용0.4700.8830.5141.0000.3570.2380.2890.2310.2440.0730.2200.5310.7590.0000.156
누에 사육량(상자)0.7800.3950.8420.3571.0000.5980.7950.6380.4510.3750.4930.3270.2530.0000.134
생산량(kg)_누에고치0.5790.1680.5280.2380.5981.0000.5420.4270.5650.5990.4290.2770.1570.0000.000
생산량(kg)_건조누에0.7140.2940.7060.2890.7950.5421.0000.3160.3940.4620.4660.4000.2350.0000.147
생산량(kg)_생누에0.5500.2260.5610.2310.6380.4270.3161.0000.2750.4960.7930.4710.3130.1670.113
생산량(kg)_동중하초0.4980.2120.4360.2440.4510.5650.3940.2751.0000.5350.5010.3660.1700.0000.000
생산량(kg)_수번데기0.2940.0550.3870.0730.3750.5990.4620.4960.5351.0000.5050.5570.2180.0820.000
생산량(kg)_잠분0.4470.0650.4700.2200.4930.4290.4660.7930.5010.5051.0000.6570.4380.1660.000
생산량(kg)_뽕잎0.4440.4270.4700.5310.3270.2770.4000.4710.3660.5570.6571.0000.5560.0000.182
생산량(kg)_오디0.3820.6860.3610.7590.2530.1570.2350.3130.1700.2180.4380.5561.0000.0000.098
연도0.0000.0750.0530.0000.0000.0000.0000.1670.0000.0820.1660.0000.0001.0000.000
시도0.0000.1330.1050.1560.1340.0000.1470.1130.0000.0000.0000.1820.0980.0001.000

Missing values

2023-12-12T16:57:24.566589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:57:24.759645image/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.
2023-12-12T16:57:24.963628image/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
4732012충청북도청주시<NA>3<NA>1.0<NA><NA><NA><NA><NA><NA><NA><NA><NA>
4742012충청북도충주시20465.015.0230<NA>1380.0<NA><NA><NA><NA>23008600
4752012충청북도제천시241.01.01<NA>8.0<NA><NA><NA><NA><NA><NA>
4762012충청북도청원군4358.018.080<NA>155.0<NA><NA><NA><NA><NA>3500
4772012충청북도보은군33914.04.03411535.01786.0<NA><NA><NA><NA>1000700
4782012충청북도옥천군<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
4792012충청북도영동군632.02.020<NA>100.0<NA><NA><NA><NA><NA><NA>
4802012충청북도증평군3111.08.09<NA>30.0<NA><NA><NA><NA><NA>1700
4812012충청북도진천군<NA>1<NA>1.0<NA><NA><NA><NA><NA><NA><NA><NA>4000
4822012충청북도괴산군1171.04.0<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