Overview

Dataset statistics

Number of variables18
Number of observations68
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.8 KiB
Average record size in memory162.9 B

Variable types

Categorical4
Numeric14

Dataset

Description기능성 양잠 현황은 국가 승인통계로 더욱 자세한 정보는 통계청 kosis.kr에서 제공 (위치 : 주제별 통계 > 농림어업 > 농업 > 기능성 양잠산업 현황)
Author농림축산식품부
URLhttps://www.data.go.kr/data/15050572/fileData.do

Alerts

생산량 홍잠 is highly overall correlated with 재배농가수 오디 and 5 other fieldsHigh correlation
생산량 면역누에 is highly overall correlated with 뽕밭면적 누에 and 6 other fieldsHigh correlation
재배농가수 양잠 is highly overall correlated with 재배농가수 오디 and 13 other fieldsHigh correlation
재배농가수 오디 is highly overall correlated with 재배농가수 양잠 and 12 other fieldsHigh correlation
뽕밭면적 누에 is highly overall correlated with 재배농가수 양잠 and 14 other fieldsHigh correlation
뽕밭면적 오디 is highly overall correlated with 재배농가수 양잠 and 13 other fieldsHigh correlation
누에사육량 is highly overall correlated with 재배농가수 양잠 and 13 other fieldsHigh correlation
생산량 누에고치 is highly overall correlated with 재배농가수 양잠 and 13 other fieldsHigh correlation
생산량 냉동건조누에 is highly overall correlated with 재배농가수 양잠 and 13 other fieldsHigh correlation
생산량 열풍건조누에 is highly overall correlated with 재배농가수 양잠 and 12 other fieldsHigh correlation
생산량 생누에 is highly overall correlated with 재배농가수 양잠 and 14 other fieldsHigh correlation
생산량 동충하초 is highly overall correlated with 재배농가수 양잠 and 12 other fieldsHigh correlation
생산량 수번데기 is highly overall correlated with 재배농가수 양잠 and 11 other fieldsHigh correlation
생산량 잠분 is highly overall correlated with 재배농가수 양잠 and 12 other fieldsHigh correlation
생산량 뽕잎 is highly overall correlated with 재배농가수 양잠 and 12 other fieldsHigh correlation
생산량 오디 is highly overall correlated with 재배농가수 양잠 and 12 other fieldsHigh correlation
시군별 is highly overall correlated with 재배농가수 양잠High correlation
생산량 홍잠 is highly imbalanced (86.1%)Imbalance
생산량 면역누에 is highly imbalanced (88.9%)Imbalance
재배농가수 양잠 has 29 (42.6%) zerosZeros
재배농가수 오디 has 20 (29.4%) zerosZeros
뽕밭면적 누에 has 36 (52.9%) zerosZeros
뽕밭면적 오디 has 20 (29.4%) zerosZeros
누에사육량 has 30 (44.1%) zerosZeros
생산량 누에고치 has 42 (61.8%) zerosZeros
생산량 냉동건조누에 has 38 (55.9%) zerosZeros
생산량 열풍건조누에 has 29 (42.6%) zerosZeros
생산량 생누에 has 37 (54.4%) zerosZeros
생산량 동충하초 has 44 (64.7%) zerosZeros
생산량 수번데기 has 54 (79.4%) zerosZeros
생산량 잠분 has 43 (63.2%) zerosZeros
생산량 뽕잎 has 29 (42.6%) zerosZeros
생산량 오디 has 21 (30.9%) zerosZeros

Reproduction

Analysis started2023-12-12 06:17:18.765096
Analysis finished2023-12-12 06:17:40.824909
Duration22.06 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년도
Categorical

Distinct4
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size676.0 B
2015
17 
2016
17 
2017
17 
2018
17 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015
2nd row2015
3rd row2015
4th row2015
5th row2015

Common Values

ValueCountFrequency (%)
2015 17
25.0%
2016 17
25.0%
2017 17
25.0%
2018 17
25.0%

Length

2023-12-12T15:17:41.187056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:17:41.313621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2015 17
25.0%
2016 17
25.0%
2017 17
25.0%
2018 17
25.0%

시군별
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size676.0 B
서울특별시
 
4
부산광역시
 
4
대구광역시
 
4
인천광역시
 
4
광주광역시
 
4
Other values (12)
48 

Length

Max length7
Median length5
Mean length4.6470588
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울특별시
2nd row부산광역시
3rd row대구광역시
4th row인천광역시
5th row광주광역시

Common Values

ValueCountFrequency (%)
서울특별시 4
 
5.9%
부산광역시 4
 
5.9%
대구광역시 4
 
5.9%
인천광역시 4
 
5.9%
광주광역시 4
 
5.9%
대전광역시 4
 
5.9%
울산광역시 4
 
5.9%
세종특별자치시 4
 
5.9%
경기도 4
 
5.9%
강원도 4
 
5.9%
Other values (7) 28
41.2%

Length

2023-12-12T15:17:41.460664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울특별시 4
 
5.9%
강원도 4
 
5.9%
경상남도 4
 
5.9%
경상북도 4
 
5.9%
전라남도 4
 
5.9%
전라북도 4
 
5.9%
충청남도 4
 
5.9%
충청북도 4
 
5.9%
경기도 4
 
5.9%
부산광역시 4
 
5.9%
Other values (7) 28
41.2%

재배농가수 양잠
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)41.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.588235
Minimum0
Maximum343
Zeros29
Zeros (%)42.6%
Negative0
Negative (%)0.0%
Memory size744.0 B
2023-12-12T15:17:41.593087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q362.5
95-th percentile250.6
Maximum343
Range343
Interquartile range (IQR)62.5

Descriptive statistics

Standard deviation80.443219
Coefficient of variation (CV)1.8041355
Kurtosis7.7339821
Mean44.588235
Median Absolute Deviation (MAD)2
Skewness2.7856126
Sum3032
Variance6471.1115
MonotonicityNot monotonic
2023-12-12T15:17:41.731501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 29
42.6%
1 4
 
5.9%
2 3
 
4.4%
31 2
 
2.9%
67 2
 
2.9%
69 2
 
2.9%
21 2
 
2.9%
70 2
 
2.9%
29 2
 
2.9%
61 2
 
2.9%
Other values (18) 18
26.5%
ValueCountFrequency (%)
0 29
42.6%
1 4
 
5.9%
2 3
 
4.4%
21 2
 
2.9%
27 1
 
1.5%
29 2
 
2.9%
31 2
 
2.9%
32 1
 
1.5%
36 1
 
1.5%
40 1
 
1.5%
ValueCountFrequency (%)
343 1
1.5%
339 1
1.5%
332 1
1.5%
315 1
1.5%
131 1
1.5%
124 1
1.5%
121 1
1.5%
92 1
1.5%
78 1
1.5%
73 1
1.5%

재배농가수 오디
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)55.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean257.61765
Minimum0
Maximum3045
Zeros20
Zeros (%)29.4%
Negative0
Negative (%)0.0%
Memory size744.0 B
2023-12-12T15:17:41.866023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median48
Q3233.25
95-th percentile1512.85
Maximum3045
Range3045
Interquartile range (IQR)233.25

Descriptive statistics

Standard deviation614.91371
Coefficient of variation (CV)2.3869239
Kurtosis12.786426
Mean257.61765
Median Absolute Deviation (MAD)48
Skewness3.6256355
Sum17518
Variance378118.87
MonotonicityNot monotonic
2023-12-12T15:17:42.035593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0 20
29.4%
3 5
 
7.4%
48 4
 
5.9%
16 4
 
5.9%
5 2
 
2.9%
276 1
 
1.5%
2517 1
 
1.5%
421 1
 
1.5%
233 1
 
1.5%
210 1
 
1.5%
Other values (28) 28
41.2%
ValueCountFrequency (%)
0 20
29.4%
2 1
 
1.5%
3 5
 
7.4%
5 2
 
2.9%
16 4
 
5.9%
48 4
 
5.9%
67 1
 
1.5%
70 1
 
1.5%
73 1
 
1.5%
75 1
 
1.5%
ValueCountFrequency (%)
3045 1
1.5%
2848 1
1.5%
2517 1
1.5%
1929 1
1.5%
740 1
1.5%
661 1
1.5%
421 1
1.5%
377 1
1.5%
359 1
1.5%
345 1
1.5%

뽕밭면적 누에
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)45.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.361765
Minimum0
Maximum276
Zeros36
Zeros (%)52.9%
Negative0
Negative (%)0.0%
Memory size744.0 B
2023-12-12T15:17:42.188537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q335.5
95-th percentile169.5
Maximum276
Range276
Interquartile range (IQR)35.5

Descriptive statistics

Standard deviation60.878538
Coefficient of variation (CV)2.0051054
Kurtosis9.5813288
Mean30.361765
Median Absolute Deviation (MAD)0
Skewness3.109706
Sum2064.6
Variance3706.1964
MonotonicityNot monotonic
2023-12-12T15:17:42.312250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0.0 36
52.9%
40.0 2
 
2.9%
18.0 2
 
2.9%
13.0 1
 
1.5%
208.0 1
 
1.5%
32.0 1
 
1.5%
98.0 1
 
1.5%
17.0 1
 
1.5%
20.0 1
 
1.5%
41.8 1
 
1.5%
Other values (21) 21
30.9%
ValueCountFrequency (%)
0.0 36
52.9%
12.0 1
 
1.5%
13.0 1
 
1.5%
16.2 1
 
1.5%
17.0 1
 
1.5%
18.0 2
 
2.9%
20.0 1
 
1.5%
23.5 1
 
1.5%
24.0 1
 
1.5%
24.2 1
 
1.5%
ValueCountFrequency (%)
276.0 1
1.5%
270.0 1
1.5%
258.4 1
1.5%
208.0 1
1.5%
98.0 1
1.5%
82.0 1
1.5%
78.0 1
1.5%
57.0 1
1.5%
55.0 1
1.5%
49.3 1
1.5%

뽕밭면적 오디
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct41
Distinct (%)60.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.416176
Minimum0
Maximum935
Zeros20
Zeros (%)29.4%
Negative0
Negative (%)0.0%
Memory size744.0 B
2023-12-12T15:17:42.455012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median13
Q372.075
95-th percentile440.335
Maximum935
Range935
Interquartile range (IQR)72.075

Descriptive statistics

Standard deviation187.11239
Coefficient of variation (CV)2.3560993
Kurtosis13.386854
Mean79.416176
Median Absolute Deviation (MAD)13
Skewness3.6805473
Sum5400.3
Variance35011.048
MonotonicityNot monotonic
2023-12-12T15:17:42.620603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0.0 20
29.4%
3.0 4
 
5.9%
13.0 4
 
5.9%
1.5 2
 
2.9%
1.0 2
 
2.9%
72.0 1
 
1.5%
54.4 1
 
1.5%
732.3 1
 
1.5%
125.7 1
 
1.5%
100.2 1
 
1.5%
Other values (31) 31
45.6%
ValueCountFrequency (%)
0.0 20
29.4%
1.0 2
 
2.9%
1.5 2
 
2.9%
2.5 1
 
1.5%
3.0 4
 
5.9%
3.1 1
 
1.5%
3.5 1
 
1.5%
4.0 1
 
1.5%
13.0 4
 
5.9%
23.5 1
 
1.5%
ValueCountFrequency (%)
935.0 1
1.5%
907.0 1
1.5%
732.3 1
1.5%
557.9 1
1.5%
222.0 1
1.5%
187.0 1
1.5%
130.3 1
1.5%
125.7 1
1.5%
116.0 1
1.5%
108.0 1
1.5%

누에사육량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean710.85294
Minimum0
Maximum7897
Zeros30
Zeros (%)44.1%
Negative0
Negative (%)0.0%
Memory size744.0 B
2023-12-12T15:17:42.768825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median30
Q3731.75
95-th percentile4508.7
Maximum7897
Range7897
Interquartile range (IQR)731.75

Descriptive statistics

Standard deviation1569.3991
Coefficient of variation (CV)2.207769
Kurtosis11.497868
Mean710.85294
Median Absolute Deviation (MAD)30
Skewness3.4225437
Sum48338
Variance2463013.6
MonotonicityNot monotonic
2023-12-12T15:17:42.964051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 30
44.1%
30 4
 
5.9%
20 2
 
2.9%
332 2
 
2.9%
1910 1
 
1.5%
522 1
 
1.5%
916 1
 
1.5%
967 1
 
1.5%
725 1
 
1.5%
5908 1
 
1.5%
Other values (24) 24
35.3%
ValueCountFrequency (%)
0 30
44.1%
20 2
 
2.9%
30 4
 
5.9%
118 1
 
1.5%
266 1
 
1.5%
272 1
 
1.5%
332 2
 
2.9%
477 1
 
1.5%
481 1
 
1.5%
507 1
 
1.5%
ValueCountFrequency (%)
7897 1
1.5%
6532 1
1.5%
6162 1
1.5%
5908 1
1.5%
1910 1
1.5%
1377 1
1.5%
1376 1
1.5%
1367 1
1.5%
1152 1
1.5%
1127 1
1.5%

생산량 누에고치
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)39.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean809.95588
Minimum0
Maximum10634
Zeros42
Zeros (%)61.8%
Negative0
Negative (%)0.0%
Memory size744.0 B
2023-12-12T15:17:43.127186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3405
95-th percentile6191.55
Maximum10634
Range10634
Interquartile range (IQR)405

Descriptive statistics

Standard deviation2347.3414
Coefficient of variation (CV)2.8981102
Kurtosis12.511512
Mean809.95588
Median Absolute Deviation (MAD)0
Skewness3.6716743
Sum55077
Variance5510011.8
MonotonicityNot monotonic
2023-12-12T15:17:43.257864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 42
61.8%
240 1
 
1.5%
2300 1
 
1.5%
10349 1
 
1.5%
80 1
 
1.5%
450 1
 
1.5%
550 1
 
1.5%
1100 1
 
1.5%
350 1
 
1.5%
1130 1
 
1.5%
Other values (17) 17
25.0%
ValueCountFrequency (%)
0 42
61.8%
23 1
 
1.5%
80 1
 
1.5%
200 1
 
1.5%
209 1
 
1.5%
210 1
 
1.5%
240 1
 
1.5%
300 1
 
1.5%
350 1
 
1.5%
390 1
 
1.5%
ValueCountFrequency (%)
10634 1
1.5%
10373 1
1.5%
10349 1
1.5%
8287 1
1.5%
2300 1
1.5%
1554 1
1.5%
1285 1
1.5%
1130 1
1.5%
1100 1
1.5%
1030 1
1.5%

생산량 냉동건조누에
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)44.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1145.7353
Minimum0
Maximum11210
Zeros38
Zeros (%)55.9%
Negative0
Negative (%)0.0%
Memory size744.0 B
2023-12-12T15:17:43.401868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3812.5
95-th percentile8253.3
Maximum11210
Range11210
Interquartile range (IQR)812.5

Descriptive statistics

Standard deviation2553.6933
Coefficient of variation (CV)2.2288685
Kurtosis7.2298877
Mean1145.7353
Median Absolute Deviation (MAD)0
Skewness2.8073349
Sum77910
Variance6521349.5
MonotonicityNot monotonic
2023-12-12T15:17:43.537643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 38
55.9%
20 2
 
2.9%
850 1
 
1.5%
11210 1
 
1.5%
10124 1
 
1.5%
403 1
 
1.5%
1400 1
 
1.5%
2545 1
 
1.5%
417 1
 
1.5%
800 1
 
1.5%
Other values (20) 20
29.4%
ValueCountFrequency (%)
0 38
55.9%
20 2
 
2.9%
120 1
 
1.5%
235 1
 
1.5%
240 1
 
1.5%
266 1
 
1.5%
350 1
 
1.5%
385 1
 
1.5%
403 1
 
1.5%
417 1
 
1.5%
ValueCountFrequency (%)
11210 1
1.5%
10124 1
1.5%
9580 1
1.5%
8394 1
1.5%
7992 1
1.5%
4122 1
1.5%
4095 1
1.5%
2615 1
1.5%
2545 1
1.5%
2235 1
1.5%

생산량 열풍건조누에
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct35
Distinct (%)51.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1290.8824
Minimum0
Maximum8353
Zeros29
Zeros (%)42.6%
Negative0
Negative (%)0.0%
Memory size744.0 B
2023-12-12T15:17:43.664124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median84
Q31647.75
95-th percentile6297.1
Maximum8353
Range8353
Interquartile range (IQR)1647.75

Descriptive statistics

Standard deviation2192.5186
Coefficient of variation (CV)1.6984651
Kurtosis2.6245998
Mean1290.8824
Median Absolute Deviation (MAD)84
Skewness1.8899749
Sum87780
Variance4807137.8
MonotonicityNot monotonic
2023-12-12T15:17:43.775524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0 29
42.6%
200 4
 
5.9%
80 3
 
4.4%
1645 1
 
1.5%
100 1
 
1.5%
1913 1
 
1.5%
1632 1
 
1.5%
2292 1
 
1.5%
4173 1
 
1.5%
7288 1
 
1.5%
Other values (25) 25
36.8%
ValueCountFrequency (%)
0 29
42.6%
10 1
 
1.5%
15 1
 
1.5%
80 3
 
4.4%
88 1
 
1.5%
100 1
 
1.5%
200 4
 
5.9%
310 1
 
1.5%
375 1
 
1.5%
520 1
 
1.5%
ValueCountFrequency (%)
8353 1
1.5%
8060 1
1.5%
7288 1
1.5%
6379 1
1.5%
6145 1
1.5%
5444 1
1.5%
5438 1
1.5%
5097 1
1.5%
4173 1
1.5%
4108 1
1.5%

생산량 생누에
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)47.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8784.0588
Minimum0
Maximum118233
Zeros37
Zeros (%)54.4%
Negative0
Negative (%)0.0%
Memory size744.0 B
2023-12-12T15:17:43.904438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36108.75
95-th percentile58512.85
Maximum118233
Range118233
Interquartile range (IQR)6108.75

Descriptive statistics

Standard deviation21875.521
Coefficient of variation (CV)2.490366
Kurtosis11.946234
Mean8784.0588
Median Absolute Deviation (MAD)0
Skewness3.394367
Sum597316
Variance4.7853842 × 108
MonotonicityNot monotonic
2023-12-12T15:17:44.051892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 37
54.4%
2930 1
 
1.5%
9820 1
 
1.5%
64004 1
 
1.5%
368 1
 
1.5%
12930 1
 
1.5%
3292 1
 
1.5%
6480 1
 
1.5%
3062 1
 
1.5%
10300 1
 
1.5%
Other values (22) 22
32.4%
ValueCountFrequency (%)
0 37
54.4%
65 1
 
1.5%
368 1
 
1.5%
490 1
 
1.5%
650 1
 
1.5%
660 1
 
1.5%
1150 1
 
1.5%
2090 1
 
1.5%
2930 1
 
1.5%
3062 1
 
1.5%
ValueCountFrequency (%)
118233 1
1.5%
83546 1
1.5%
78618 1
1.5%
64004 1
1.5%
48315 1
1.5%
36290 1
1.5%
35820 1
1.5%
12930 1
1.5%
10903 1
1.5%
10530 1
1.5%

생산량 동충하초
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)32.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean131.54412
Minimum0
Maximum1904
Zeros44
Zeros (%)64.7%
Negative0
Negative (%)0.0%
Memory size744.0 B
2023-12-12T15:17:44.156423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q326.5
95-th percentile552.6
Maximum1904
Range1904
Interquartile range (IQR)26.5

Descriptive statistics

Standard deviation373.92618
Coefficient of variation (CV)2.8425914
Kurtosis14.131548
Mean131.54412
Median Absolute Deviation (MAD)0
Skewness3.7504058
Sum8945
Variance139820.79
MonotonicityNot monotonic
2023-12-12T15:17:44.289493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 44
64.7%
20 3
 
4.4%
115 2
 
2.9%
1712 1
 
1.5%
170 1
 
1.5%
465 1
 
1.5%
70 1
 
1.5%
550 1
 
1.5%
1904 1
 
1.5%
13 1
 
1.5%
Other values (12) 12
 
17.6%
ValueCountFrequency (%)
0 44
64.7%
12 1
 
1.5%
13 1
 
1.5%
15 1
 
1.5%
20 3
 
4.4%
22 1
 
1.5%
40 1
 
1.5%
70 1
 
1.5%
80 1
 
1.5%
90 1
 
1.5%
ValueCountFrequency (%)
1904 1
1.5%
1712 1
1.5%
1584 1
1.5%
554 1
1.5%
550 1
1.5%
535 1
1.5%
465 1
1.5%
424 1
1.5%
315 1
1.5%
170 1
1.5%

생산량 수번데기
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)22.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean348.57353
Minimum0
Maximum9570
Zeros54
Zeros (%)79.4%
Negative0
Negative (%)0.0%
Memory size744.0 B
2023-12-12T15:17:44.420466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2383.5
Maximum9570
Range9570
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1331.5404
Coefficient of variation (CV)3.8199698
Kurtosis35.490066
Mean348.57353
Median Absolute Deviation (MAD)0
Skewness5.5669872
Sum23703
Variance1772999.7
MonotonicityNot monotonic
2023-12-12T15:17:44.527538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 54
79.4%
1 1
 
1.5%
500 1
 
1.5%
3330 1
 
1.5%
180 1
 
1.5%
9570 1
 
1.5%
560 1
 
1.5%
3459 1
 
1.5%
1080 1
 
1.5%
35 1
 
1.5%
Other values (5) 5
 
7.4%
ValueCountFrequency (%)
0 54
79.4%
1 1
 
1.5%
35 1
 
1.5%
80 1
 
1.5%
100 1
 
1.5%
180 1
 
1.5%
290 1
 
1.5%
500 1
 
1.5%
560 1
 
1.5%
1080 1
 
1.5%
ValueCountFrequency (%)
9570 1
1.5%
3459 1
1.5%
3330 1
1.5%
2674 1
1.5%
1844 1
1.5%
1080 1
1.5%
560 1
1.5%
500 1
1.5%
290 1
1.5%
180 1
1.5%

생산량 잠분
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)35.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1313.1176
Minimum0
Maximum70000
Zeros43
Zeros (%)63.2%
Negative0
Negative (%)0.0%
Memory size744.0 B
2023-12-12T15:17:44.644949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3111
95-th percentile3186.7
Maximum70000
Range70000
Interquartile range (IQR)111

Descriptive statistics

Standard deviation8492.2948
Coefficient of variation (CV)6.4672764
Kurtosis66.72033
Mean1313.1176
Median Absolute Deviation (MAD)0
Skewness8.1342035
Sum89292
Variance72119071
MonotonicityNot monotonic
2023-12-12T15:17:44.761753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 43
63.2%
40 3
 
4.4%
1272 1
 
1.5%
580 1
 
1.5%
3200 1
 
1.5%
132 1
 
1.5%
225 1
 
1.5%
114 1
 
1.5%
3162 1
 
1.5%
300 1
 
1.5%
Other values (14) 14
 
20.6%
ValueCountFrequency (%)
0 43
63.2%
7 1
 
1.5%
14 1
 
1.5%
20 1
 
1.5%
40 3
 
4.4%
105 1
 
1.5%
110 1
 
1.5%
114 1
 
1.5%
132 1
 
1.5%
140 1
 
1.5%
ValueCountFrequency (%)
70000 1
1.5%
3695 1
1.5%
3292 1
1.5%
3200 1
1.5%
3162 1
1.5%
1294 1
1.5%
1272 1
1.5%
600 1
1.5%
580 1
1.5%
360 1
1.5%

생산량 뽕잎
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8593.8824
Minimum0
Maximum119000
Zeros29
Zeros (%)42.6%
Negative0
Negative (%)0.0%
Memory size744.0 B
2023-12-12T15:17:44.874731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median500
Q36690
95-th percentile48719.25
Maximum119000
Range119000
Interquartile range (IQR)6690

Descriptive statistics

Standard deviation19940.465
Coefficient of variation (CV)2.3203093
Kurtosis15.608614
Mean8593.8824
Median Absolute Deviation (MAD)500
Skewness3.6934214
Sum584384
Variance3.9762215 × 108
MonotonicityNot monotonic
2023-12-12T15:17:44.986764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 29
42.6%
300 4
 
5.9%
1600 3
 
4.4%
500 2
 
2.9%
5350 1
 
1.5%
14000 1
 
1.5%
33961 1
 
1.5%
20100 1
 
1.5%
1500 1
 
1.5%
3000 1
 
1.5%
Other values (24) 24
35.3%
ValueCountFrequency (%)
0 29
42.6%
300 4
 
5.9%
500 2
 
2.9%
882 1
 
1.5%
1152 1
 
1.5%
1500 1
 
1.5%
1600 3
 
4.4%
1650 1
 
1.5%
1942 1
 
1.5%
2385 1
 
1.5%
ValueCountFrequency (%)
119000 1
1.5%
73000 1
1.5%
64142 1
1.5%
56666 1
1.5%
33961 1
1.5%
27586 1
1.5%
27130 1
1.5%
20900 1
1.5%
20100 1
1.5%
19000 1
1.5%

생산량 오디
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct44
Distinct (%)64.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean362467.37
Minimum0
Maximum4912402
Zeros21
Zeros (%)30.9%
Negative0
Negative (%)0.0%
Memory size744.0 B
2023-12-12T15:17:45.123505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median19000
Q3275404.75
95-th percentile2625446
Maximum4912402
Range4912402
Interquartile range (IQR)275404.75

Descriptive statistics

Standard deviation977265.8
Coefficient of variation (CV)2.6961483
Kurtosis12.970725
Mean362467.37
Median Absolute Deviation (MAD)19000
Skewness3.6904852
Sum24647781
Variance9.5504843 × 1011
MonotonicityNot monotonic
2023-12-12T15:17:45.276628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0 21
30.9%
19000 4
 
5.9%
2900 2
 
2.9%
11600 1
 
1.5%
159870 1
 
1.5%
112025 1
 
1.5%
3875650 1
 
1.5%
543997 1
 
1.5%
297589 1
 
1.5%
317233 1
 
1.5%
Other values (34) 34
50.0%
ValueCountFrequency (%)
0 21
30.9%
500 1
 
1.5%
1000 1
 
1.5%
2300 1
 
1.5%
2900 2
 
2.9%
4800 1
 
1.5%
7500 1
 
1.5%
7720 1
 
1.5%
7750 1
 
1.5%
8000 1
 
1.5%
ValueCountFrequency (%)
4912402 1
1.5%
4269829 1
1.5%
3875650 1
1.5%
3398449 1
1.5%
1189869 1
1.5%
861557 1
1.5%
543997 1
1.5%
399999 1
1.5%
369344 1
1.5%
338940 1
1.5%

생산량 홍잠
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size676.0 B
0
66 
56
 
1
140
 
1

Length

Max length3
Median length1
Mean length1.0441176
Min length1

Unique

Unique2 ?
Unique (%)2.9%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 66
97.1%
56 1
 
1.5%
140 1
 
1.5%

Length

2023-12-12T15:17:45.454507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:17:45.634571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 66
97.1%
56 1
 
1.5%
140 1
 
1.5%

생산량 면역누에
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size676.0 B
0
67 
2100
 
1

Length

Max length4
Median length1
Mean length1.0441176
Min length1

Unique

Unique1 ?
Unique (%)1.5%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 67
98.5%
2100 1
 
1.5%

Length

2023-12-12T15:17:45.756777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:17:45.881305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 67
98.5%
2100 1
 
1.5%

Interactions

2023-12-12T15:17:39.146316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:19.515140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:21.352171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:22.655127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:23.988515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:25.388974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:26.967701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:28.545132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:30.027681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:31.346471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:32.940484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:34.514446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:36.167113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:37.712921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:39.259807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:19.672977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:21.460281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:22.740828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:24.134232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:25.507394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:27.076991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:28.629806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:30.114724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:31.491885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:33.071291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:34.916623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:36.289597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:37.813589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:39.374049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:19.771591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:21.554634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:22.842387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:24.255102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:25.631479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:27.168087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:28.758920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:30.191659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:31.603489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:33.209413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:35.003623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:36.403018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:37.911048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:39.466824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:19.884799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:21.641735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:22.939222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:24.342181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:25.731198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:27.259701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:28.895280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:30.293220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:31.715535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:33.319298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:35.112878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:36.516243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:38.027129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:39.557957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:20.015292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:21.724909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:23.035377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:24.426972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:25.829914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:27.334991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:29.011806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:30.390690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:31.818512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:33.416731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:35.196005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:36.604366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:38.114780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:39.670880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:20.174978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:21.824279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:23.163083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:24.550862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:25.978010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:27.438292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:29.137931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:30.487564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:31.935412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:33.541076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:35.302979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:36.706512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:38.218892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:39.750536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:20.289565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:21.924382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:23.252290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:24.628031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:26.089962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:27.517427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:29.267887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:30.564531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:32.045295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:33.660333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:35.400518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:36.814193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:38.300550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:39.837004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:20.383253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:22.009666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:23.328147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:24.717195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:26.213914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:27.896331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:29.357679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:30.635263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:32.169199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:33.778670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:35.501666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:36.910199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:38.384008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:39.930950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:20.471022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:22.095377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:23.402741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:24.815879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:26.329046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:27.977294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:29.436229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:30.716311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:32.274476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:33.875764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:35.578854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:37.005937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:38.501388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:40.042648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:20.562682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:22.191539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:23.494004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:24.911708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:26.432698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:28.064085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:29.531411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:30.817108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:32.390692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:33.988287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:35.659245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:37.139490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:38.622483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:40.132110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:20.652829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:22.276562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:23.583161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:25.018300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:26.553434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:28.170061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:29.649473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:30.906376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:32.522693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:34.091987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:35.754906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:37.274159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:38.715026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:40.211579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:20.747283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:22.370801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:23.668981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:25.110414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:26.647011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:28.263299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:29.740165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:30.993692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:32.627337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:34.195359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:35.835714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:37.390345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:38.822088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:40.306579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:20.870564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:22.466244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:23.763197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:25.201339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:26.752039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:28.370298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:29.876737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:31.125943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:32.732398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:34.305141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:35.944775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:37.497298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:38.937753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:40.387380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:21.267265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:22.562443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:23.866214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:25.302156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:26.842386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:28.462283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:29.950446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:31.231649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:32.829474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:34.401682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:36.052840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:37.605218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:39.040670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:17:45.978397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도시군별재배농가수 양잠재배농가수 오디뽕밭면적 누에뽕밭면적 오디누에사육량생산량 누에고치생산량 냉동건조누에생산량 열풍건조누에생산량 생누에생산량 동충하초생산량 수번데기생산량 잠분생산량 뽕잎생산량 오디생산량 홍잠생산량 면역누에
년도1.0000.0000.0290.0000.0000.0000.0000.0000.1400.0000.0000.0000.0000.0400.0000.0000.0400.040
시군별0.0001.0000.8570.7350.7790.7130.6800.7780.7650.7030.5190.6810.5280.0760.6070.5220.1240.076
재배농가수 양잠0.0290.8571.0000.6180.7770.7290.7550.8590.7480.8500.6760.8490.8230.1400.7160.7070.4010.359
재배농가수 오디0.0000.7350.6181.0000.9110.9890.2990.0000.3570.7560.5370.0000.0000.0000.6040.9340.9290.000
뽕밭면적 누에0.0000.7790.7770.9111.0000.9350.9060.6220.7750.8210.9020.6890.7110.0000.6660.7311.0001.000
뽕밭면적 오디0.0000.7130.7290.9890.9351.0000.6210.0930.5680.8180.6630.2630.2560.0000.6890.9560.9290.000
누에사육량0.0000.6800.7550.2990.9060.6211.0000.8360.8250.8540.9610.7820.7580.0000.8820.0000.7560.864
생산량 누에고치0.0000.7780.8590.0000.6220.0930.8361.0000.8390.7220.8360.8430.8570.0000.7660.0000.4020.436
생산량 냉동건조누에0.1400.7650.7480.3570.7750.5680.8250.8391.0000.7830.8310.7280.7080.0000.8730.4060.6120.618
생산량 열풍건조누에0.0000.7030.8500.7560.8210.8180.8540.7220.7831.0000.8080.8860.8520.0000.8380.7400.6560.482
생산량 생누에0.0000.5190.6760.5370.9020.6630.9610.8360.8310.8081.0000.8920.8880.0000.8640.4641.0001.000
생산량 동충하초0.0000.6810.8490.0000.6890.2630.7820.8430.7280.8860.8921.0000.9710.0000.7590.0000.2520.304
생산량 수번데기0.0000.5280.8230.0000.7110.2560.7580.8570.7080.8520.8880.9711.0001.0000.7300.0000.5100.561
생산량 잠분0.0400.0760.1400.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.0000.0000.0000.0000.000
생산량 뽕잎0.0000.6070.7160.6040.6660.6890.8820.7660.8730.8380.8640.7590.7300.0001.0000.8420.3910.474
생산량 오디0.0000.5220.7070.9340.7310.9560.0000.0000.4060.7400.4640.0000.0000.0000.8421.0000.7440.000
생산량 홍잠0.0400.1240.4010.9291.0000.9290.7560.4020.6120.6561.0000.2520.5100.0000.3910.7441.0001.000
생산량 면역누에0.0400.0760.3590.0001.0000.0000.8640.4360.6180.4821.0000.3040.5610.0000.4740.0001.0001.000
2023-12-12T15:17:46.514907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도생산량 홍잠생산량 면역누에시군별
년도1.0000.0260.0000.000
생산량 홍잠0.0261.0000.9920.000
생산량 면역누에0.0000.9921.0000.000
시군별0.0000.0000.0001.000
2023-12-12T15:17:46.682933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
재배농가수 양잠재배농가수 오디뽕밭면적 누에뽕밭면적 오디누에사육량생산량 누에고치생산량 냉동건조누에생산량 열풍건조누에생산량 생누에생산량 동충하초생산량 수번데기생산량 잠분생산량 뽕잎생산량 오디년도시군별생산량 홍잠생산량 면역누에
재배농가수 양잠1.0000.9090.9370.9290.9740.8060.8840.9720.8750.7470.6120.8270.8700.8880.0000.5920.3260.426
재배농가수 오디0.9091.0000.8740.9870.8940.6390.7960.9080.8370.6020.4880.6630.7940.9570.0000.4130.6630.000
뽕밭면적 누에0.9370.8741.0000.8920.9320.8090.8770.8940.9360.6650.6150.7860.8120.8540.0000.4600.9770.969
뽕밭면적 오디0.9290.9870.8921.0000.9150.7000.8300.9170.8540.6520.5620.7070.8390.9560.0000.3910.6630.000
누에사육량0.9740.8940.9320.9151.0000.8130.8880.9610.9190.7850.6380.8170.8500.8890.0000.3610.4230.651
생산량 누에고치0.8060.6390.8090.7000.8131.0000.8860.7550.8130.7090.6720.7660.7620.6880.0000.4860.3260.517
생산량 냉동건조누에0.8840.7960.8770.8300.8880.8861.0000.8550.8990.7010.5810.7890.7950.8150.0860.4350.4910.640
생산량 열풍건조누에0.9720.9080.8940.9170.9610.7550.8551.0000.8600.7780.5700.7910.8650.8980.0000.3410.3510.455
생산량 생누에0.8750.8370.9360.8540.9190.8130.8990.8601.0000.6380.6050.7100.7780.8560.0000.2200.9610.953
생산량 동충하초0.7470.6020.6650.6520.7850.7090.7010.7780.6381.0000.7120.7920.6460.6030.0000.3880.1910.361
생산량 수번데기0.6120.4880.6150.5620.6380.6720.5810.5700.6050.7121.0000.6620.5190.4710.0000.2680.4390.663
생산량 잠분0.8270.6630.7860.7070.8170.7660.7890.7910.7100.7920.6621.0000.7420.6430.0000.0000.0000.000
생산량 뽕잎0.8700.7940.8120.8390.8500.7620.7950.8650.7780.6460.5190.7421.0000.8440.0000.2940.2750.487
생산량 오디0.8880.9570.8540.9560.8890.6880.8150.8980.8560.6030.4710.6430.8441.0000.0000.2360.6510.000
년도0.0000.0000.0000.0000.0000.0000.0860.0000.0000.0000.0000.0000.0000.0001.0000.0000.0260.000
시군별0.5920.4130.4600.3910.3610.4860.4350.3410.2200.3880.2680.0000.2940.2360.0001.0000.0000.000
생산량 홍잠0.3260.6630.9770.6630.4230.3260.4910.3510.9610.1910.4390.0000.2750.6510.0260.0001.0000.992
생산량 면역누에0.4260.0000.9690.0000.6510.5170.6400.4550.9530.3610.6630.0000.4870.0000.0000.0000.9921.000

Missing values

2023-12-12T15:17:40.514759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:17:40.736632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

년도시군별재배농가수 양잠재배농가수 오디뽕밭면적 누에뽕밭면적 오디누에사육량생산량 누에고치생산량 냉동건조누에생산량 열풍건조누에생산량 생누에생산량 동충하초생산량 수번데기생산량 잠분생산량 뽕잎생산량 오디생산량 홍잠생산량 면역누에
02015서울특별시000.00.0000000000000
12015부산광역시000.00.0000000000000
22015대구광역시1160.03.0300020002000300800000
32015인천광역시000.00.0000000000000
42015광주광역시2480.013.0200080000016001900000
52015대전광역시000.00.0000000000000
62015울산광역시000.00.0000000000000
72015세종특별자치시030.01.0000000000290000
82015경기도2934512.072.048101203758730000029819000
92015강원도2711918.037.0266240950886512120675017240000
년도시군별재배농가수 양잠재배농가수 오디뽕밭면적 누에뽕밭면적 오디누에사육량생산량 누에고치생산량 냉동건조누에생산량 열풍건조누에생산량 생누에생산량 동충하초생산량 수번데기생산량 잠분생산량 뽕잎생산량 오디생산량 홍잠생산량 면역누에
582018세종특별자치시030.01.50000000001160000
592018경기도2924140.054.45260070910300000300026098000
602018강원도216720.041.0332350800103062000114652425000
612018충청북도217317.023.527211004173106480000582014900400
622018충청남도5723418.072.35075502545614532921151002251184323837600
632018전라북도64192998.0557.9752450140014401293000063003398449560
642018전라남도6737732.096.97028040341083687001321900036934400
652018경상북도315222208.091.16162103491012454446400446518443200275863292241402100
662018경상남도3616513.053.9962230011210104098201700580165039999900
672018제주특별자치도020.02.500000000050000