Overview

Dataset statistics

Number of variables13
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 KiB
Average record size in memory120.4 B

Variable types

Categorical4
Numeric9

Dataset

Description축산물 도축후 지육 잔류검사 집계 현황
Author경기도
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=SV61976S5SS7XN06YRPX11648631&infSeq=1

Alerts

간이정성검사수(건) is highly overall correlated with 누계간이정성검사수(건) and 1 other fieldsHigh correlation
간이정성검사검출수(건) is highly overall correlated with 정밀정량검사수(건) and 5 other fieldsHigh correlation
정밀정량검사수(건) is highly overall correlated with 간이정성검사검출수(건) and 6 other fieldsHigh correlation
정밀정량검출수(건) is highly overall correlated with 간이정성검사검출수(건) and 5 other fieldsHigh correlation
누계간이정성검사수(건) is highly overall correlated with 간이정성검사수(건) and 4 other fieldsHigh correlation
누계간이정성검사검출수(건) is highly overall correlated with 간이정성검사검출수(건) and 6 other fieldsHigh correlation
누계정밀정량검사수(건) is highly overall correlated with 정밀정량검사수(건) and 4 other fieldsHigh correlation
누계정밀정량검사검출수(건) is highly overall correlated with 간이정성검사검출수(건) and 6 other fieldsHigh correlation
누계정밀정량검사위반수(건) is highly overall correlated with 간이정성검사검출수(건) and 7 other fieldsHigh correlation
집계년도 is highly overall correlated with 집계분기High correlation
집계분기 is highly overall correlated with 집계년도High correlation
축종명 is highly overall correlated with 간이정성검사수(건)High correlation
정밀정량검사위반수(건) is highly overall correlated with 간이정성검사검출수(건) and 3 other fieldsHigh correlation
간이정성검사수(건) has unique valuesUnique
누계간이정성검사수(건) has unique valuesUnique
간이정성검사검출수(건) has 13 (43.3%) zerosZeros
정밀정량검사수(건) has 10 (33.3%) zerosZeros
정밀정량검출수(건) has 14 (46.7%) zerosZeros
누계간이정성검사검출수(건) has 10 (33.3%) zerosZeros
누계정밀정량검사수(건) has 6 (20.0%) zerosZeros
누계정밀정량검사검출수(건) has 10 (33.3%) zerosZeros
누계정밀정량검사위반수(건) has 12 (40.0%) zerosZeros

Reproduction

Analysis started2024-04-14 03:15:46.296763
Analysis finished2024-04-14 03:15:54.065257
Duration7.77 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

집계년도
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023
20 
2024
5 
2022
5 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024
2nd row2024
3rd row2024
4th row2024
5th row2024

Common Values

ValueCountFrequency (%)
2023 20
66.7%
2024 5
 
16.7%
2022 5
 
16.7%

Length

2024-04-14T12:15:54.120677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T12:15:54.199034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023 20
66.7%
2024 5
 
16.7%
2022 5
 
16.7%

집계분기
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
1
10 
4
10 
2
5 
3
5 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 10
33.3%
4 10
33.3%
2 5
16.7%
3 5
16.7%

Length

2024-04-14T12:15:54.283927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T12:15:54.360840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 10
33.3%
4 10
33.3%
2 5
16.7%
3 5
16.7%

축종명
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
한우
6 
젖소
6 
육우
6 
돼지
6 
닭
6 

Length

Max length2
Median length2
Mean length1.8
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row한우
2nd row젖소
3rd row육우
4th row돼지
5th row닭

Common Values

ValueCountFrequency (%)
한우 6
20.0%
젖소 6
20.0%
육우 6
20.0%
돼지 6
20.0%
닭 6
20.0%

Length

2024-04-14T12:15:54.448553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T12:15:54.528319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한우 6
20.0%
젖소 6
20.0%
육우 6
20.0%
돼지 6
20.0%
닭 6
20.0%

간이정성검사수(건)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6185.2333
Minimum557
Maximum18957
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-04-14T12:15:54.629814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum557
5-th percentile929.1
Q11476.5
median4589.5
Q37888.5
95-th percentile17788.7
Maximum18957
Range18400
Interquartile range (IQR)6412

Descriptive statistics

Standard deviation5769.1657
Coefficient of variation (CV)0.9327321
Kurtosis-0.032507164
Mean6185.2333
Median Absolute Deviation (MAD)3257.5
Skewness1.064789
Sum185557
Variance33283273
MonotonicityNot monotonic
2024-04-14T12:15:54.723583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
7382 1
 
3.3%
16549 1
 
3.3%
1696 1
 
3.3%
4517 1
 
3.3%
8823 1
 
3.3%
18957 1
 
3.3%
1264 1
 
3.3%
7779 1
 
3.3%
1189 1
 
3.3%
15593 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
557 1
3.3%
786 1
3.3%
1104 1
3.3%
1139 1
3.3%
1189 1
3.3%
1239 1
3.3%
1264 1
3.3%
1475 1
3.3%
1481 1
3.3%
1690 1
3.3%
ValueCountFrequency (%)
18957 1
3.3%
18803 1
3.3%
16549 1
3.3%
15593 1
3.3%
15027 1
3.3%
12484 1
3.3%
8823 1
3.3%
7925 1
3.3%
7779 1
3.3%
7571 1
3.3%

간이정성검사검출수(건)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1
Minimum0
Maximum55
Zeros13
Zeros (%)43.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-04-14T12:15:54.809167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile6.55
Maximum55
Range55
Interquartile range (IQR)2

Descriptive statistics

Standard deviation9.9597466
Coefficient of variation (CV)3.2128215
Kurtosis27.947753
Mean3.1
Median Absolute Deviation (MAD)1
Skewness5.2141401
Sum93
Variance99.196552
MonotonicityNot monotonic
2024-04-14T12:15:54.891350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 13
43.3%
2 6
20.0%
1 6
20.0%
55 1
 
3.3%
3 1
 
3.3%
6 1
 
3.3%
7 1
 
3.3%
4 1
 
3.3%
ValueCountFrequency (%)
0 13
43.3%
1 6
20.0%
2 6
20.0%
3 1
 
3.3%
4 1
 
3.3%
6 1
 
3.3%
7 1
 
3.3%
55 1
 
3.3%
ValueCountFrequency (%)
55 1
 
3.3%
7 1
 
3.3%
6 1
 
3.3%
4 1
 
3.3%
3 1
 
3.3%
2 6
20.0%
1 6
20.0%
0 13
43.3%

정밀정량검사수(건)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.3
Minimum0
Maximum716
Zeros10
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-04-14T12:15:54.975626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q37
95-th percentile94.4
Maximum716
Range716
Interquartile range (IQR)7

Descriptive statistics

Standard deviation131.32774
Coefficient of variation (CV)3.8287971
Kurtosis27.477782
Mean34.3
Median Absolute Deviation (MAD)2
Skewness5.1701938
Sum1029
Variance17246.976
MonotonicityNot monotonic
2024-04-14T12:15:55.068763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 10
33.3%
2 4
 
13.3%
1 4
 
13.3%
7 3
 
10.0%
4 2
 
6.7%
20 1
 
3.3%
134 1
 
3.3%
716 1
 
3.3%
35 1
 
3.3%
46 1
 
3.3%
Other values (2) 2
 
6.7%
ValueCountFrequency (%)
0 10
33.3%
1 4
 
13.3%
2 4
 
13.3%
4 2
 
6.7%
7 3
 
10.0%
8 1
 
3.3%
20 1
 
3.3%
29 1
 
3.3%
35 1
 
3.3%
46 1
 
3.3%
ValueCountFrequency (%)
716 1
 
3.3%
134 1
 
3.3%
46 1
 
3.3%
35 1
 
3.3%
29 1
 
3.3%
20 1
 
3.3%
8 1
 
3.3%
7 3
10.0%
4 2
6.7%
2 4
13.3%

정밀정량검출수(건)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8333333
Minimum0
Maximum52
Zeros14
Zeros (%)46.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-04-14T12:15:55.157495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile6
Maximum52
Range52
Interquartile range (IQR)2

Descriptive statistics

Standard deviation9.4288016
Coefficient of variation (CV)3.3278123
Kurtosis28.032258
Mean2.8333333
Median Absolute Deviation (MAD)1
Skewness5.2253185
Sum85
Variance88.902299
MonotonicityNot monotonic
2024-04-14T12:15:55.240494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 14
46.7%
1 7
23.3%
2 5
 
16.7%
6 2
 
6.7%
52 1
 
3.3%
4 1
 
3.3%
ValueCountFrequency (%)
0 14
46.7%
1 7
23.3%
2 5
 
16.7%
4 1
 
3.3%
6 2
 
6.7%
52 1
 
3.3%
ValueCountFrequency (%)
52 1
 
3.3%
6 2
 
6.7%
4 1
 
3.3%
2 5
 
16.7%
1 7
23.3%
0 14
46.7%

정밀정량검사위반수(건)
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
0
16 
1
8 
2
4 
3
2 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 16
53.3%
1 8
26.7%
2 4
 
13.3%
3 2
 
6.7%

Length

2024-04-14T12:15:55.330348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T12:15:55.412397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 16
53.3%
1 8
26.7%
2 4
 
13.3%
3 2
 
6.7%

누계간이정성검사수(건)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15663.367
Minimum557
Maximum69476
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-04-14T12:15:55.492873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum557
5-th percentile944.85
Q14738.5
median7476.5
Q317835.5
95-th percentile59399.2
Maximum69476
Range68919
Interquartile range (IQR)13097

Descriptive statistics

Standard deviation18495.04
Coefficient of variation (CV)1.1807832
Kurtosis3.0159624
Mean15663.367
Median Absolute Deviation (MAD)5323
Skewness1.8733418
Sum469901
Variance3.4206649 × 108
MonotonicityNot monotonic
2024-04-14T12:15:55.596887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
7382 1
 
3.3%
67537 1
 
3.3%
6189 1
 
3.3%
18417 1
 
3.3%
32459 1
 
3.3%
69476 1
 
3.3%
5634 1
 
3.3%
30753 1
 
3.3%
3469 1
 
3.3%
49453 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
557 1
3.3%
786 1
3.3%
1139 1
3.3%
1923 1
3.3%
2529 1
3.3%
3274 1
3.3%
3469 1
3.3%
4733 1
3.3%
4755 1
3.3%
4759 1
3.3%
ValueCountFrequency (%)
69476 1
3.3%
67537 1
3.3%
49453 1
3.3%
33860 1
3.3%
32459 1
3.3%
30753 1
3.3%
22959 1
3.3%
18417 1
3.3%
16091 1
3.3%
15034 1
3.3%

누계간이정성검사검출수(건)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)36.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5666667
Minimum0
Maximum63
Zeros10
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-04-14T12:15:55.683800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile56.55
Maximum63
Range63
Interquartile range (IQR)4

Descriptive statistics

Standard deviation17.574832
Coefficient of variation (CV)2.0515368
Kurtosis5.3048013
Mean8.5666667
Median Absolute Deviation (MAD)2
Skewness2.5431422
Sum257
Variance308.87471
MonotonicityNot monotonic
2024-04-14T12:15:55.765380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 10
33.3%
3 5
16.7%
2 3
 
10.0%
1 3
 
10.0%
4 2
 
6.7%
12 2
 
6.7%
56 1
 
3.3%
63 1
 
3.3%
5 1
 
3.3%
57 1
 
3.3%
ValueCountFrequency (%)
0 10
33.3%
1 3
 
10.0%
2 3
 
10.0%
3 5
16.7%
4 2
 
6.7%
5 1
 
3.3%
12 2
 
6.7%
20 1
 
3.3%
56 1
 
3.3%
57 1
 
3.3%
ValueCountFrequency (%)
63 1
 
3.3%
57 1
 
3.3%
56 1
 
3.3%
20 1
 
3.3%
12 2
 
6.7%
5 1
 
3.3%
4 2
 
6.7%
3 5
16.7%
2 3
10.0%
1 3
10.0%

누계정밀정량검사수(건)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.6
Minimum0
Maximum716
Zeros6
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-04-14T12:15:55.850768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median10.5
Q348
95-th percentile207.75
Maximum716
Range716
Interquartile range (IQR)47

Descriptive statistics

Standard deviation137.95467
Coefficient of variation (CV)2.3146756
Kurtosis18.55636
Mean59.6
Median Absolute Deviation (MAD)10.5
Skewness4.0458
Sum1788
Variance19031.49
MonotonicityNot monotonic
2024-04-14T12:15:56.091195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 6
20.0%
1 3
 
10.0%
2 3
 
10.0%
4 2
 
6.7%
20 1
 
3.3%
50 1
 
3.3%
12 1
 
3.3%
65 1
 
3.3%
24 1
 
3.3%
205 1
 
3.3%
Other values (10) 10
33.3%
ValueCountFrequency (%)
0 6
20.0%
1 3
10.0%
2 3
10.0%
4 2
 
6.7%
9 1
 
3.3%
12 1
 
3.3%
13 1
 
3.3%
14 1
 
3.3%
20 1
 
3.3%
24 1
 
3.3%
ValueCountFrequency (%)
716 1
3.3%
210 1
3.3%
205 1
3.3%
164 1
3.3%
135 1
3.3%
65 1
3.3%
57 1
3.3%
50 1
3.3%
42 1
3.3%
35 1
3.3%

누계정밀정량검사검출수(건)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)36.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.2666667
Minimum0
Maximum64
Zeros10
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-04-14T12:15:56.174933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.5
Q34
95-th percentile54.75
Maximum64
Range64
Interquartile range (IQR)4

Descriptive statistics

Standard deviation17.364119
Coefficient of variation (CV)2.1004983
Kurtosis5.5444142
Mean8.2666667
Median Absolute Deviation (MAD)1.5
Skewness2.5770841
Sum248
Variance301.51264
MonotonicityNot monotonic
2024-04-14T12:15:56.257377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 10
33.3%
1 5
16.7%
2 4
 
13.3%
4 3
 
10.0%
11 2
 
6.7%
52 1
 
3.3%
64 1
 
3.3%
3 1
 
3.3%
5 1
 
3.3%
57 1
 
3.3%
ValueCountFrequency (%)
0 10
33.3%
1 5
16.7%
2 4
 
13.3%
3 1
 
3.3%
4 3
 
10.0%
5 1
 
3.3%
11 2
 
6.7%
20 1
 
3.3%
52 1
 
3.3%
57 1
 
3.3%
ValueCountFrequency (%)
64 1
 
3.3%
57 1
 
3.3%
52 1
 
3.3%
20 1
 
3.3%
11 2
 
6.7%
5 1
 
3.3%
4 3
10.0%
3 1
 
3.3%
2 4
13.3%
1 5
16.7%

누계정밀정량검사위반수(건)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0333333
Minimum0
Maximum10
Zeros12
Zeros (%)40.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-04-14T12:15:56.349079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile7.55
Maximum10
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.6585365
Coefficient of variation (CV)1.3074769
Kurtosis2.0931442
Mean2.0333333
Median Absolute Deviation (MAD)1
Skewness1.6015099
Sum61
Variance7.0678161
MonotonicityNot monotonic
2024-04-14T12:15:56.435558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 12
40.0%
1 5
16.7%
2 4
 
13.3%
3 3
 
10.0%
4 2
 
6.7%
7 1
 
3.3%
6 1
 
3.3%
10 1
 
3.3%
8 1
 
3.3%
ValueCountFrequency (%)
0 12
40.0%
1 5
16.7%
2 4
 
13.3%
3 3
 
10.0%
4 2
 
6.7%
6 1
 
3.3%
7 1
 
3.3%
8 1
 
3.3%
10 1
 
3.3%
ValueCountFrequency (%)
10 1
 
3.3%
8 1
 
3.3%
7 1
 
3.3%
6 1
 
3.3%
4 2
 
6.7%
3 3
 
10.0%
2 4
 
13.3%
1 5
16.7%
0 12
40.0%

Interactions

2024-04-14T12:15:53.199181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:47.661269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:48.472164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:49.083900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:49.720196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:50.477991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:51.128083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:51.755773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:52.405947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:53.268834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:47.884447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:48.542453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:49.156868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:49.787073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:50.548333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:51.198692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:51.826716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:52.477063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:53.340101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:47.953703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:48.609047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:49.223753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:49.849703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:50.619153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:51.263402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:51.906766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:52.558091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:53.428454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:48.033019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:48.676849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:49.297551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:49.918612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:50.693534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:51.339200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:51.982739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:52.632183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:53.503426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:48.101249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:48.741614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:49.363940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:49.981114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:50.763724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:51.405008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:52.052424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:52.698116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:53.572793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:48.178262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:48.812567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:49.439946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:50.051515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:50.832801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:51.478382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:52.127037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:52.776636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:53.638510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:48.248869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:48.879851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:49.510042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:50.116465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:50.908701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:51.547133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:52.198998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:52.844627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:53.708621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:48.328337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:48.949529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:49.584008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:50.196550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:50.986387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:51.620469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:52.269142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:53.065073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:53.773825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:48.401911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:49.017892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:49.654552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:50.270715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:51.056824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:51.688085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:52.338969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:15:53.132259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-14T12:15:56.508984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
집계년도집계분기축종명간이정성검사수(건)간이정성검사검출수(건)정밀정량검사수(건)정밀정량검출수(건)정밀정량검사위반수(건)누계간이정성검사수(건)누계간이정성검사검출수(건)누계정밀정량검사수(건)누계정밀정량검사검출수(건)누계정밀정량검사위반수(건)
집계년도1.0000.5470.0000.0000.0000.0000.0000.0000.0000.1590.0000.1590.574
집계분기0.5471.0000.0000.0000.2600.1560.2600.0000.2670.2180.2860.1380.000
축종명0.0000.0001.0000.8590.0000.0860.0000.0000.5370.6880.4320.6880.326
간이정성검사수(건)0.0000.0000.8591.0000.8110.0000.8110.3510.7380.8150.6760.6970.719
간이정성검사검출수(건)0.0000.2600.0000.8111.0000.9331.0000.5870.4860.8080.6850.7410.973
정밀정량검사수(건)0.0000.1560.0860.0000.9331.0000.9330.5270.0000.6781.0000.4280.622
정밀정량검출수(건)0.0000.2600.0000.8111.0000.9331.0000.5870.4860.8080.6850.7410.973
정밀정량검사위반수(건)0.0000.0000.0000.3510.5870.5270.5871.0000.3070.5240.7760.4640.837
누계간이정성검사수(건)0.0000.2670.5370.7380.4860.0000.4860.3071.0000.7500.6840.7660.744
누계간이정성검사검출수(건)0.1590.2180.6880.8150.8080.6780.8080.5240.7501.0000.8190.9940.908
누계정밀정량검사수(건)0.0000.2860.4320.6760.6851.0000.6850.7760.6840.8191.0000.6680.672
누계정밀정량검사검출수(건)0.1590.1380.6880.6970.7410.4280.7410.4640.7660.9940.6681.0000.932
누계정밀정량검사위반수(건)0.5740.0000.3260.7190.9730.6220.9730.8370.7440.9080.6720.9321.000
2024-04-14T12:15:56.625969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
집계년도집계분기축종명정밀정량검사위반수(건)
집계년도1.0000.5400.0000.000
집계분기0.5401.0000.0000.000
축종명0.0000.0001.0000.000
정밀정량검사위반수(건)0.0000.0000.0001.000
2024-04-14T12:15:56.718902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
간이정성검사수(건)간이정성검사검출수(건)정밀정량검사수(건)정밀정량검출수(건)누계간이정성검사수(건)누계간이정성검사검출수(건)누계정밀정량검사수(건)누계정밀정량검사검출수(건)누계정밀정량검사위반수(건)집계년도집계분기축종명정밀정량검사위반수(건)
간이정성검사수(건)1.0000.2880.3820.1180.8450.4380.4390.3350.3930.0000.0000.6610.216
간이정성검사검출수(건)0.2881.0000.5270.7860.3660.7440.4720.7250.6710.0000.2370.0000.586
정밀정량검사수(건)0.3820.5271.0000.5420.3950.5800.7850.5830.6080.0000.1320.0000.517
정밀정량검출수(건)0.1180.7860.5421.0000.1510.5030.4670.6230.6000.0000.2370.0000.586
누계간이정성검사수(건)0.8450.3660.3950.1511.0000.6320.6070.5210.5640.0000.1870.3700.210
누계간이정성검사검출수(건)0.4380.7440.5800.5030.6321.0000.6800.9080.7760.0920.1610.3130.438
누계정밀정량검사수(건)0.4390.4720.7850.4670.6070.6801.0000.7230.6830.0000.0980.3520.410
누계정밀정량검사검출수(건)0.3350.7250.5830.6230.5210.9080.7231.0000.7340.0920.0850.3130.381
누계정밀정량검사위반수(건)0.3930.6710.6080.6000.5640.7760.6830.7341.0000.2380.0910.1950.619
집계년도0.0000.0000.0000.0000.0000.0920.0000.0920.2381.0000.5400.0000.000
집계분기0.0000.2370.1320.2370.1870.1610.0980.0850.0910.5401.0000.0000.000
축종명0.6610.0000.0000.0000.3700.3130.3520.3130.1950.0000.0001.0000.000
정밀정량검사위반수(건)0.2160.5860.5170.5860.2100.4380.4100.3810.6190.0000.0000.0001.000

Missing values

2024-04-14T12:15:53.867857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-14T12:15:54.005411image/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

집계년도집계분기축종명간이정성검사수(건)간이정성검사검출수(건)정밀정량검사수(건)정밀정량검출수(건)정밀정량검사위반수(건)누계간이정성검사수(건)누계간이정성검사검출수(건)누계정밀정량검사수(건)누계정밀정량검사검출수(건)누계정밀정량검사위반수(건)
020241한우738202000738202000
120241젖소1923221119232211
220241육우78611107861110
320241돼지124840000124840000
420241닭55700005570000
520232한우73701701150343903
620232젖소35580710790734212
720232육우1690000032740000
820232돼지18803551345233386056135524
920231닭11390716221139071622
집계년도집계분기축종명간이정성검사수(건)간이정성검사검출수(건)정밀정량검사수(건)정밀정량검출수(건)정밀정량검사위반수(건)누계간이정성검사수(건)누계간이정성검사검출수(건)누계정밀정량검사수(건)누계정밀정량검사검출수(건)누계정밀정량검사위반수(건)
2020233젖소466228111256955053
2120233육우1481000047550000
2220233돼지15593129014945357164570
2320233닭1189202034693441
2420234한우777911113075341444
2520224닭1264000056340000
2620224돼지18957444069476202052010
2720224한우882300003245902400
2820224젖소45172201184171265118
2920224육우16960000618901200