Overview

Dataset statistics

Number of variables16
Number of observations102
Missing cells550
Missing cells (%)33.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.4 KiB
Average record size in memory144.3 B

Variable types

Numeric10
Categorical6

Dataset

Description구미시의 축종별 가축분뇨처리현황 자료입니다.2018년도부터 2023년도까지의 데이터가 존재합니다.데이터 항목 : 연도, 축종, 사육두수, 발생량, 퇴비화, 액비화, 정화
Author경상북도 구미시
URLhttps://www.data.go.kr/data/15126859/fileData.do

Alerts

액비화 공동자원화(농축협_영농법인) is highly overall correlated with 연도 and 11 other fieldsHigh correlation
액비유통센터 is highly overall correlated with 연도 and 12 other fieldsHigh correlation
정화 공공처리장(환경부) is highly overall correlated with 연도 and 12 other fieldsHigh correlation
축종별 is highly overall correlated with 사육두수(두_ 수) and 8 other fieldsHigh correlation
액비화 개별시설(농가 액비저장조) is highly overall correlated with 연도 and 8 other fieldsHigh correlation
연도 is highly overall correlated with 퇴비화 공동자원화(농축협_영농법인) and 5 other fieldsHigh correlation
사육두수(두_ 수) is highly overall correlated with 발생량(톤_연) and 10 other fieldsHigh correlation
발생량(톤_연) is highly overall correlated with 사육두수(두_ 수) and 10 other fieldsHigh correlation
퇴비화(계) is highly overall correlated with 사육두수(두_ 수) and 10 other fieldsHigh correlation
퇴비화 개별시설(농가 퇴비사) is highly overall correlated with 사육두수(두_ 수) and 11 other fieldsHigh correlation
퇴비화 공동자원화(농축협_영농법인) is highly overall correlated with 연도 and 6 other fieldsHigh correlation
퇴비화 기타1(농경지살포) is highly overall correlated with 사육두수(두_ 수) and 9 other fieldsHigh correlation
액비화(계) is highly overall correlated with 발생량(톤_연) and 5 other fieldsHigh correlation
정화(계) is highly overall correlated with 연도 and 8 other fieldsHigh correlation
비고(기타_농지살포) is highly overall correlated with 사육두수(두_ 수) and 10 other fieldsHigh correlation
액비화 개별시설(농가 액비저장조) is highly imbalanced (82.4%)Imbalance
액비화 공동자원화(농축협_영농법인) is highly imbalanced (83.8%)Imbalance
액비유통센터 is highly imbalanced (84.7%)Imbalance
정화 공동자원화(농축협_영농법인) is highly imbalanced (92.1%)Imbalance
정화 공공처리장(환경부) is highly imbalanced (84.7%)Imbalance
사육두수(두_ 수) has 24 (23.5%) missing valuesMissing
발생량(톤_연) has 25 (24.5%) missing valuesMissing
퇴비화(계) has 32 (31.4%) missing valuesMissing
퇴비화 개별시설(농가 퇴비사) has 49 (48.0%) missing valuesMissing
퇴비화 공동자원화(농축협_영농법인) has 89 (87.3%) missing valuesMissing
퇴비화 기타1(농경지살포) has 58 (56.9%) missing valuesMissing
액비화(계) has 96 (94.1%) missing valuesMissing
정화(계) has 96 (94.1%) missing valuesMissing
비고(기타_농지살포) has 81 (79.4%) missing valuesMissing
발생량(톤_연) has 7 (6.9%) zerosZeros

Reproduction

Analysis started2024-03-14 19:26:09.850298
Analysis finished2024-03-14 19:26:38.175788
Duration28.33 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2020.5
Minimum2018
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-03-15T04:26:38.348089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2018
5-th percentile2018
Q12019
median2020.5
Q32022
95-th percentile2023
Maximum2023
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7162589
Coefficient of variation (CV)0.00084942286
Kurtosis-1.271813
Mean2020.5
Median Absolute Deviation (MAD)1.5
Skewness0
Sum206091
Variance2.9455446
MonotonicityDecreasing
2024-03-15T04:26:38.651912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2023 17
16.7%
2022 17
16.7%
2021 17
16.7%
2020 17
16.7%
2019 17
16.7%
2018 17
16.7%
ValueCountFrequency (%)
2018 17
16.7%
2019 17
16.7%
2020 17
16.7%
2021 17
16.7%
2022 17
16.7%
2023 17
16.7%
ValueCountFrequency (%)
2023 17
16.7%
2022 17
16.7%
2021 17
16.7%
2020 17
16.7%
2019 17
16.7%
2018 17
16.7%

축종별
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size944.0 B
한육우
 
6
젖소
 
6
말
 
6
돼지
 
6
육계
 
6
Other values (12)
72 

Length

Max length3
Median length2
Mean length2.1176471
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row한육우
2nd row젖소
3rd row말
4th row돼지
5th row육계

Common Values

ValueCountFrequency (%)
한육우 6
 
5.9%
젖소 6
 
5.9%
말 6
 
5.9%
돼지 6
 
5.9%
육계 6
 
5.9%
산란계 6
 
5.9%
오리 6
 
5.9%
거위 6
 
5.9%
산양 6
 
5.9%
면양 6
 
5.9%
Other values (7) 42
41.2%

Length

2024-03-15T04:26:38.999288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
한육우 6
 
5.9%
면양 6
 
5.9%
타조 6
 
5.9%
메추리 6
 
5.9%
토끼 6
 
5.9%
개 6
 
5.9%
사슴 6
 
5.9%
염소 6
 
5.9%
산양 6
 
5.9%
젖소 6
 
5.9%
Other values (7) 42
41.2%

사육두수(두_ 수)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct70
Distinct (%)89.7%
Missing24
Missing (%)23.5%
Infinite0
Infinite (%)0.0%
Mean42529.205
Minimum2
Maximum421000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-03-15T04:26:39.410145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2.85
Q138
median3437.5
Q314370.5
95-th percentile392517.8
Maximum421000
Range420998
Interquartile range (IQR)14332.5

Descriptive statistics

Standard deviation105510.01
Coefficient of variation (CV)2.4808836
Kurtosis7.8737885
Mean42529.205
Median Absolute Deviation (MAD)3430.5
Skewness3.0298947
Sum3317278
Variance1.1132362 × 1010
MonotonicityNot monotonic
2024-03-15T04:26:39.799035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 4
 
3.9%
7000 2
 
2.0%
12 2
 
2.0%
5 2
 
2.0%
3 2
 
2.0%
38 2
 
2.0%
5987 1
 
1.0%
160 1
 
1.0%
952 1
 
1.0%
47413 1
 
1.0%
Other values (60) 60
58.8%
(Missing) 24
 
23.5%
ValueCountFrequency (%)
2 4
3.9%
3 2
2.0%
5 2
2.0%
6 1
 
1.0%
8 1
 
1.0%
10 1
 
1.0%
11 1
 
1.0%
12 2
2.0%
13 1
 
1.0%
17 1
 
1.0%
ValueCountFrequency (%)
421000 1
1.0%
417000 1
1.0%
396000 1
1.0%
395282 1
1.0%
392030 1
1.0%
364100 1
1.0%
70268 1
1.0%
69863 1
1.0%
65550 1
1.0%
65106 1
1.0%

발생량(톤_연)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct60
Distinct (%)77.9%
Missing25
Missing (%)24.5%
Infinite0
Infinite (%)0.0%
Mean33573.649
Minimum0
Maximum329574
Zeros7
Zeros (%)6.9%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-03-15T04:26:40.059811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median790
Q315145
95-th percentile243485.6
Maximum329574
Range329574
Interquartile range (IQR)15138

Descriptive statistics

Standard deviation79542.455
Coefficient of variation (CV)2.369193
Kurtosis6.9836816
Mean33573.649
Median Absolute Deviation (MAD)789
Skewness2.7830894
Sum2585171
Variance6.3270021 × 109
MonotonicityNot monotonic
2024-03-15T04:26:40.310674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 9
 
8.8%
0 7
 
6.9%
307 3
 
2.9%
3 2
 
2.0%
18265 1
 
1.0%
106009 1
 
1.0%
18440 1
 
1.0%
645 1
 
1.0%
1214 1
 
1.0%
13 1
 
1.0%
Other values (50) 50
49.0%
(Missing) 25
24.5%
ValueCountFrequency (%)
0 7
6.9%
1 9
8.8%
3 2
 
2.0%
6 1
 
1.0%
7 1
 
1.0%
8 1
 
1.0%
9 1
 
1.0%
13 1
 
1.0%
15 1
 
1.0%
17 1
 
1.0%
ValueCountFrequency (%)
329574 1
1.0%
327675 1
1.0%
325563 1
1.0%
269072 1
1.0%
237089 1
1.0%
208911 1
1.0%
112569 1
1.0%
110138 1
1.0%
107841 1
1.0%
106009 1
1.0%

퇴비화(계)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct58
Distinct (%)82.9%
Missing32
Missing (%)31.4%
Infinite0
Infinite (%)0.0%
Mean30389.729
Minimum0
Maximum329574
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-03-15T04:26:40.576247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q175.25
median982
Q317337
95-th percentile250838
Maximum329574
Range329574
Interquartile range (IQR)17261.75

Descriptive statistics

Standard deviation79683.27
Coefficient of variation (CV)2.6220461
Kurtosis8.338541
Mean30389.729
Median Absolute Deviation (MAD)981
Skewness3.0877742
Sum2127281
Variance6.3494236 × 109
MonotonicityNot monotonic
2024-03-15T04:26:40.944608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 9
 
8.8%
307 3
 
2.9%
800 2
 
2.0%
9 2
 
2.0%
582 1
 
1.0%
30621 1
 
1.0%
18440 1
 
1.0%
645 1
 
1.0%
1214 1
 
1.0%
2404 1
 
1.0%
Other values (48) 48
47.1%
(Missing) 32
31.4%
ValueCountFrequency (%)
0 1
 
1.0%
1 9
8.8%
3 1
 
1.0%
6 1
 
1.0%
7 1
 
1.0%
8 1
 
1.0%
9 2
 
2.0%
15 1
 
1.0%
17 1
 
1.0%
250 1
 
1.0%
ValueCountFrequency (%)
329574 1
1.0%
327675 1
1.0%
325563 1
1.0%
269072 1
1.0%
228552 1
1.0%
208911 1
1.0%
76869 1
1.0%
32078 1
1.0%
30621 1
1.0%
21042 1
1.0%

퇴비화 개별시설(농가 퇴비사)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct44
Distinct (%)83.0%
Missing49
Missing (%)48.0%
Infinite0
Infinite (%)0.0%
Mean19726.094
Minimum0
Maximum235000
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-03-15T04:26:41.223402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q190
median971
Q34251
95-th percentile149586
Maximum235000
Range235000
Interquartile range (IQR)4161

Descriptive statistics

Standard deviation50630.192
Coefficient of variation (CV)2.5666607
Kurtosis9.0272153
Mean19726.094
Median Absolute Deviation (MAD)970
Skewness3.0770343
Sum1045483
Variance2.5634163 × 109
MonotonicityNot monotonic
2024-03-15T04:26:41.659341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
1 5
 
4.9%
307 3
 
2.9%
9 2
 
2.0%
3 2
 
2.0%
800 2
 
2.0%
2010 1
 
1.0%
11991 1
 
1.0%
0 1
 
1.0%
1214 1
 
1.0%
2317 1
 
1.0%
Other values (34) 34
33.3%
(Missing) 49
48.0%
ValueCountFrequency (%)
0 1
 
1.0%
1 5
4.9%
3 2
 
2.0%
6 1
 
1.0%
9 2
 
2.0%
15 1
 
1.0%
57 1
 
1.0%
90 1
 
1.0%
98 1
 
1.0%
100 1
 
1.0%
ValueCountFrequency (%)
235000 1
1.0%
195694 1
1.0%
154608 1
1.0%
146238 1
1.0%
76869 1
1.0%
63772 1
1.0%
63173 1
1.0%
19433 1
1.0%
17702 1
1.0%
12141 1
1.0%

퇴비화 공동자원화(농축협_영농법인)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)84.6%
Missing89
Missing (%)87.3%
Infinite0
Infinite (%)0.0%
Mean4302.7692
Minimum694
Maximum12410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-03-15T04:26:41.855688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum694
5-th percentile789.4
Q1853
median1814
Q37500
95-th percentile12050
Maximum12410
Range11716
Interquartile range (IQR)6647

Descriptive statistics

Standard deviation4393.9493
Coefficient of variation (CV)1.021191
Kurtosis-0.72535327
Mean4302.7692
Median Absolute Deviation (MAD)961
Skewness0.91579902
Sum55936
Variance19306790
MonotonicityNot monotonic
2024-03-15T04:26:42.173363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
853 3
 
2.9%
12410 1
 
1.0%
2114 1
 
1.0%
11810 1
 
1.0%
1814 1
 
1.0%
7500 1
 
1.0%
1115 1
 
1.0%
7118 1
 
1.0%
1118 1
 
1.0%
7684 1
 
1.0%
(Missing) 89
87.3%
ValueCountFrequency (%)
694 1
 
1.0%
853 3
2.9%
1115 1
 
1.0%
1118 1
 
1.0%
1814 1
 
1.0%
2114 1
 
1.0%
7118 1
 
1.0%
7500 1
 
1.0%
7684 1
 
1.0%
11810 1
 
1.0%
ValueCountFrequency (%)
12410 1
 
1.0%
11810 1
 
1.0%
7684 1
 
1.0%
7500 1
 
1.0%
7118 1
 
1.0%
2114 1
 
1.0%
1814 1
 
1.0%
1118 1
 
1.0%
1115 1
 
1.0%
853 3
2.9%

퇴비화 기타1(농경지살포)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct38
Distinct (%)86.4%
Missing58
Missing (%)56.9%
Infinite0
Infinite (%)0.0%
Mean23315.045
Minimum1
Maximum253392
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-03-15T04:26:42.552204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q1256
median1883.5
Q316479
95-th percentile80542.55
Maximum253392
Range253391
Interquartile range (IQR)16223

Descriptive statistics

Standard deviation54602.563
Coefficient of variation (CV)2.3419454
Kurtosis13.621848
Mean23315.045
Median Absolute Deviation (MAD)1882.5
Skewness3.6489326
Sum1025862
Variance2.9814398 × 109
MonotonicityNot monotonic
2024-03-15T04:26:43.034214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
1 4
 
3.9%
15138 2
 
2.0%
66260 2
 
2.0%
256 2
 
2.0%
21042 1
 
1.0%
250 1
 
1.0%
7 1
 
1.0%
30621 1
 
1.0%
17322 1
 
1.0%
645 1
 
1.0%
Other values (28) 28
27.5%
(Missing) 58
56.9%
ValueCountFrequency (%)
1 4
3.9%
7 1
 
1.0%
8 1
 
1.0%
14 1
 
1.0%
228 1
 
1.0%
242 1
 
1.0%
250 1
 
1.0%
256 2
2.0%
300 1
 
1.0%
391 1
 
1.0%
ValueCountFrequency (%)
253392 1
1.0%
252692 1
1.0%
83063 1
1.0%
66260 2
2.0%
62673 1
1.0%
32078 1
1.0%
30621 1
1.0%
21042 1
1.0%
17571 1
1.0%
17322 1
1.0%

액비화(계)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)100.0%
Missing96
Missing (%)94.1%
Infinite0
Infinite (%)0.0%
Mean43786
Minimum35700
Maximum53847
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-03-15T04:26:43.385782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35700
5-th percentile35950.25
Q138242.5
median43267
Q348367.25
95-th percentile52868.75
Maximum53847
Range18147
Interquartile range (IQR)10124.75

Descriptive statistics

Standard deviation7147.8804
Coefficient of variation (CV)0.16324579
Kurtosis-1.2970388
Mean43786
Median Absolute Deviation (MAD)6616.5
Skewness0.30039198
Sum262716
Variance51092194
MonotonicityNot monotonic
2024-03-15T04:26:43.738584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
42867 1
 
1.0%
43667 1
 
1.0%
36701 1
 
1.0%
49934 1
 
1.0%
53847 1
 
1.0%
35700 1
 
1.0%
(Missing) 96
94.1%
ValueCountFrequency (%)
35700 1
1.0%
36701 1
1.0%
42867 1
1.0%
43667 1
1.0%
49934 1
1.0%
53847 1
1.0%
ValueCountFrequency (%)
53847 1
1.0%
49934 1
1.0%
43667 1
1.0%
42867 1
1.0%
36701 1
1.0%
35700 1
1.0%

액비화 개별시설(농가 액비저장조)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size944.0 B
<NA>
96 
30440
 
2
23526
 
1
24126
 
1
30000
 
1

Length

Max length5
Median length4
Mean length4.0588235
Min length4

Unique

Unique4 ?
Unique (%)3.9%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row23526
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 96
94.1%
30440 2
 
2.0%
23526 1
 
1.0%
24126 1
 
1.0%
30000 1
 
1.0%
35700 1
 
1.0%

Length

2024-03-15T04:26:44.206242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T04:26:44.550187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 96
94.1%
30440 2
 
2.0%
23526 1
 
1.0%
24126 1
 
1.0%
30000 1
 
1.0%
35700 1
 
1.0%

액비화 공동자원화(농축협_영농법인)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Memory size944.0 B
<NA>
97 
7178
 
2
1392
 
1
5486
 
1
6011
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique3 ?
Unique (%)2.9%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row7178
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 97
95.1%
7178 2
 
2.0%
1392 1
 
1.0%
5486 1
 
1.0%
6011 1
 
1.0%

Length

2024-03-15T04:26:44.925997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T04:26:45.197554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 97
95.1%
7178 2
 
2.0%
1392 1
 
1.0%
5486 1
 
1.0%
6011 1
 
1.0%

액비유통센터
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size944.0 B
<NA>
97 
12163
 
1
12363
 
1
5309
 
1
14008
 
1

Length

Max length5
Median length4
Mean length4.0392157
Min length4

Unique

Unique5 ?
Unique (%)4.9%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row12163
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 97
95.1%
12163 1
 
1.0%
12363 1
 
1.0%
5309 1
 
1.0%
14008 1
 
1.0%
17396 1
 
1.0%

Length

2024-03-15T04:26:45.579795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T04:26:45.865061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 97
95.1%
12163 1
 
1.0%
12363 1
 
1.0%
5309 1
 
1.0%
14008 1
 
1.0%
17396 1
 
1.0%

정화(계)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)100.0%
Missing96
Missing (%)94.1%
Infinite0
Infinite (%)0.0%
Mean32529.5
Minimum8537
Maximum45251
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-03-15T04:26:46.230229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8537
5-th percentile12766.25
Q126671.75
median35842
Q343528
95-th percentile45001
Maximum45251
Range36714
Interquartile range (IQR)16856.25

Descriptive statistics

Standard deviation14196.85
Coefficient of variation (CV)0.43643001
Kurtosis0.38487491
Mean32529.5
Median Absolute Deviation (MAD)8909
Skewness-1.0294007
Sum195177
Variance2.0155055 × 108
MonotonicityNot monotonic
2024-03-15T04:26:46.566630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
44251 1
 
1.0%
45251 1
 
1.0%
41359 1
 
1.0%
25454 1
 
1.0%
8537 1
 
1.0%
30325 1
 
1.0%
(Missing) 96
94.1%
ValueCountFrequency (%)
8537 1
1.0%
25454 1
1.0%
30325 1
1.0%
41359 1
1.0%
44251 1
1.0%
45251 1
1.0%
ValueCountFrequency (%)
45251 1
1.0%
44251 1
1.0%
41359 1
1.0%
30325 1
1.0%
25454 1
1.0%
8537 1
1.0%

정화 공동자원화(농축협_영농법인)
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size944.0 B
<NA>
101 
8537
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique1 ?
Unique (%)1.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 101
99.0%
8537 1
 
1.0%

Length

2024-03-15T04:26:46.967098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T04:26:47.271143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 101
99.0%
8537 1
 
1.0%

정화 공공처리장(환경부)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size944.0 B
<NA>
97 
44251
 
1
45251
 
1
41359
 
1
25454
 
1

Length

Max length5
Median length4
Mean length4.0490196
Min length4

Unique

Unique5 ?
Unique (%)4.9%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row44251
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 97
95.1%
44251 1
 
1.0%
45251 1
 
1.0%
41359 1
 
1.0%
25454 1
 
1.0%
30325 1
 
1.0%

Length

2024-03-15T04:26:47.601554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T04:26:47.954195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 97
95.1%
44251 1
 
1.0%
45251 1
 
1.0%
41359 1
 
1.0%
25454 1
 
1.0%
30325 1
 
1.0%

비고(기타_농지살포)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)85.7%
Missing81
Missing (%)79.4%
Infinite0
Infinite (%)0.0%
Mean28704.333
Minimum1
Maximum253392
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-03-15T04:26:48.289880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q1391
median749
Q312116
95-th percentile252692
Maximum253392
Range253391
Interquartile range (IQR)11725

Descriptive statistics

Standard deviation74805.253
Coefficient of variation (CV)2.6060613
Kurtosis7.4354205
Mean28704.333
Median Absolute Deviation (MAD)748
Skewness2.9421719
Sum602791
Variance5.5958258 × 109
MonotonicityNot monotonic
2024-03-15T04:26:48.623103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
15138 2
 
2.0%
9071 2
 
2.0%
1 2
 
2.0%
392 1
 
1.0%
8 1
 
1.0%
14 1
 
1.0%
619 1
 
1.0%
402 1
 
1.0%
228 1
 
1.0%
8755 1
 
1.0%
Other values (8) 8
 
7.8%
(Missing) 81
79.4%
ValueCountFrequency (%)
1 2
2.0%
8 1
1.0%
14 1
1.0%
228 1
1.0%
391 1
1.0%
392 1
1.0%
402 1
1.0%
442 1
1.0%
619 1
1.0%
749 1
1.0%
ValueCountFrequency (%)
253392 1
1.0%
252692 1
1.0%
15138 2
2.0%
15116 1
1.0%
12116 1
1.0%
9071 2
2.0%
9055 1
1.0%
8755 1
1.0%
749 1
1.0%
619 1
1.0%

Interactions

2024-03-15T04:26:34.052092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:11.077267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:13.384247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:15.652991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:18.274848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:20.869609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:23.468645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:25.959701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:28.532424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:31.187476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:34.308067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:11.327031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:13.542466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:15.892090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:18.493433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:21.128005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:23.736255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:26.282779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:28.803138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:31.659137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:34.610953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:11.565678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:13.853214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:16.142830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:18.635836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:21.400307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:23.971117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:26.540204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:29.079429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:31.928581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:34.859476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:11.817233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:14.102377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:16.462156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:18.931192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:21.672624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:24.224472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:26.828229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:29.461383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:32.188882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:35.109051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:12.075273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:14.352832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:16.715246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:19.200319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:21.962563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:24.478789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:27.079974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:29.743534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:32.424082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:35.366484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:12.346023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:14.602157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:16.958929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:19.445580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:22.242777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:24.714611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:27.325463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:30.031259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:32.770365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:35.610437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:12.579663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:14.848228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:17.159151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:19.749532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:22.607593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:24.956599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:27.475767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:30.293327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:33.035056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:35.854402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:12.764668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:15.039451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:17.414138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:20.007027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:22.809310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:25.199041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:27.722825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:30.502059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:33.284486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:36.185126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:13.006001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:15.196956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:17.685821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:20.277476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:22.987120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:25.433893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:28.009450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:30.660674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:33.541325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:36.371895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:13.239331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:15.380134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:18.127419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:20.534281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:23.223777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:25.683936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:28.273251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:30.917785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:26:33.798676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T04:26:48.880517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도축종별사육두수(두_ 수)발생량(톤_연)퇴비화(계)퇴비화 개별시설(농가 퇴비사)퇴비화 공동자원화(농축협_영농법인)퇴비화 기타1(농경지살포)액비화(계)액비화 개별시설(농가 액비저장조)액비화 공동자원화(농축협_영농법인)액비유통센터정화(계)정화 공공처리장(환경부)비고(기타_농지살포)
연도1.0000.0000.0000.0000.0000.0000.0000.1971.0001.0001.0001.0001.0001.0000.000
축종별0.0001.0000.8800.7890.6450.0000.6260.558NaNNaNNaNNaNNaNNaN1.000
사육두수(두_ 수)0.0000.8801.0000.6730.3440.4670.8150.339NaNNaNNaNNaNNaNNaN0.804
발생량(톤_연)0.0000.7890.6731.0000.9200.9810.8830.833NaNNaNNaNNaNNaNNaN1.000
퇴비화(계)0.0000.6450.3440.9201.0001.0000.8830.827NaN1.000NaNNaNNaNNaN0.896
퇴비화 개별시설(농가 퇴비사)0.0000.0000.4670.9811.0001.0000.7261.000NaN1.000NaNNaNNaNNaN0.885
퇴비화 공동자원화(농축협_영농법인)0.0000.6260.8150.8830.8830.7261.0000.964NaNNaNNaNNaNNaNNaN1.000
퇴비화 기타1(농경지살포)0.1970.5580.3390.8330.8271.0000.9641.0001.0000.0001.0001.0001.0001.0000.896
액비화(계)1.000NaNNaNNaNNaNNaNNaN1.0001.0001.0001.0001.0001.0001.000NaN
액비화 개별시설(농가 액비저장조)1.000NaNNaNNaN1.0001.000NaN0.0001.0001.0000.7711.0000.7711.000NaN
액비화 공동자원화(농축협_영농법인)1.000NaNNaNNaNNaNNaNNaN1.0001.0000.7711.0001.0001.0001.000NaN
액비유통센터1.000NaNNaNNaNNaNNaNNaN1.0001.0001.0001.0001.0001.0001.000NaN
정화(계)1.000NaNNaNNaNNaNNaNNaN1.0001.0000.7711.0001.0001.0001.000NaN
정화 공공처리장(환경부)1.000NaNNaNNaNNaNNaNNaN1.0001.0001.0001.0001.0001.0001.000NaN
비고(기타_농지살포)0.0001.0000.8041.0000.8960.8851.0000.896NaNNaNNaNNaNNaNNaN1.000
2024-03-15T04:26:49.269372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
액비화 공동자원화(농축협_영농법인)액비유통센터정화 공공처리장(환경부)축종별정화 공동자원화(농축협_영농법인)액비화 개별시설(농가 액비저장조)
액비화 공동자원화(농축협_영농법인)1.0001.0001.0001.000NaN0.000
액비유통센터1.0001.0001.0001.000NaN1.000
정화 공공처리장(환경부)1.0001.0001.0001.000NaN1.000
축종별1.0001.0001.0001.000NaN1.000
정화 공동자원화(농축협_영농법인)NaNNaNNaNNaN1.000NaN
액비화 개별시설(농가 액비저장조)0.0001.0001.0001.000NaN1.000
2024-03-15T04:26:49.597193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도사육두수(두_ 수)발생량(톤_연)퇴비화(계)퇴비화 개별시설(농가 퇴비사)퇴비화 공동자원화(농축협_영농법인)퇴비화 기타1(농경지살포)액비화(계)정화(계)비고(기타_농지살포)축종별액비화 개별시설(농가 액비저장조)액비화 공동자원화(농축협_영농법인)액비유통센터정화 공동자원화(농축협_영농법인)정화 공공처리장(환경부)
연도1.0000.0540.0270.0760.0490.501-0.0770.0290.8410.1990.0001.0001.0001.000NaN1.000
사육두수(두_ 수)0.0541.0000.8480.7990.7290.0330.795-0.4860.9430.7210.6691.0001.0001.000NaN1.000
발생량(톤_연)0.0270.8481.0000.9990.9740.6520.902-0.829-0.2570.9890.4811.0001.0001.000NaN1.000
퇴비화(계)0.0760.7990.9991.0000.9760.6520.896-0.486-0.7710.9850.3010.5001.0001.000NaN1.000
퇴비화 개별시설(농가 퇴비사)0.0490.7290.9740.9761.0000.5830.689-0.500-0.5000.9900.0001.0001.0001.000NaN1.000
퇴비화 공동자원화(농축협_영농법인)0.5010.0330.6520.6520.5831.0000.787NaNNaN1.0000.5930.0000.0000.000NaN0.000
퇴비화 기타1(농경지살포)-0.0770.7950.9020.8960.6890.7871.000-0.154-0.8120.9940.3080.0000.5771.000NaN1.000
액비화(계)0.029-0.486-0.829-0.486-0.500NaN-0.1541.000-0.500NaN1.0000.0001.0001.0000.0001.000
정화(계)0.8410.943-0.257-0.771-0.500NaN-0.812-0.5001.000NaN0.5000.0001.0001.000NaN1.000
비고(기타_농지살포)0.1990.7210.9890.9850.9901.0000.994NaNNaN1.0000.6881.0001.0001.0000.0001.000
축종별0.0000.6690.4810.3010.0000.5930.3081.0000.5000.6881.0001.0001.0001.000NaN1.000
액비화 개별시설(농가 액비저장조)1.0001.0001.0000.5001.0000.0000.0000.0000.0001.0001.0001.0000.0001.0000.0001.000
액비화 공동자원화(농축협_영농법인)1.0001.0001.0001.0001.0000.0000.5771.0001.0001.0001.0000.0001.0001.0000.0001.000
액비유통센터1.0001.0001.0001.0001.0000.0001.0001.0001.0001.0001.0001.0001.0001.0000.0001.000
정화 공동자원화(농축협_영농법인)NaNNaNNaNNaNNaNNaNNaN0.000NaN0.000NaN0.0000.0000.0001.0000.000
정화 공공처리장(환경부)1.0001.0001.0001.0001.0000.0001.0001.0001.0001.0001.0001.0001.0001.0000.0001.000

Missing values

2024-03-15T04:26:36.742034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T04:26:37.402842image/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.
2024-03-15T04:26:37.773672image/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

연도축종별사육두수(두_ 수)발생량(톤_연)퇴비화(계)퇴비화 개별시설(농가 퇴비사)퇴비화 공동자원화(농축협_영농법인)퇴비화 기타1(농경지살포)액비화(계)액비화 개별시설(농가 액비저장조)액비화 공동자원화(농축협_영농법인)액비유통센터정화(계)정화 공동자원화(농축협_영농법인)정화 공공처리장(환경부)비고(기타_농지살포)
02023한육우702683295743295746377212410253392<NA><NA><NA><NA><NA><NA><NA>253392
12023젖소190819367193674251<NA>15116<NA><NA><NA><NA><NA><NA><NA>15116
22023말138559559117<NA>442<NA><NA><NA><NA><NA><NA><NA>442
32023돼지61298105828187103572<NA>15138428672352671781216344251<NA>4425115138
42023육계3920301373713737256821149055<NA><NA><NA><NA><NA><NA><NA>9055
52023산란계<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
62023오리690048148190<NA>391<NA><NA><NA><NA><NA><NA><NA>391
72023거위<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
82023산양<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
92023면양<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
연도축종별사육두수(두_ 수)발생량(톤_연)퇴비화(계)퇴비화 개별시설(농가 퇴비사)퇴비화 공동자원화(농축협_영농법인)퇴비화 기타1(농경지살포)액비화(계)액비화 개별시설(농가 액비저장조)액비화 공동자원화(농축협_영농법인)액비유통센터정화(계)정화 공동자원화(농축협_영농법인)정화 공공처리장(환경부)비고(기타_농지살포)
922018거위17111<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
932018산양<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
942018면양<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
952018염소4891125012501250<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
962018사슴25666<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
972018개5770231723172317<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
982018토끼38333<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
992018메추리<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
1002018타조3111<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
1012018칠면조2000<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>