Overview

Dataset statistics

Number of variables8
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory752.0 KiB
Average record size in memory77.0 B

Variable types

DateTime1
Categorical2
Numeric5

Dataset

Description국립농산물품질관리원에서 관리하는 농축산물 유통조사 정보(처분년월, 업무구분명, 시도명, 조사장소수, 위반업소수, 형사처벌건수, 고발건수, 과태료부과건수)
Author국립농산물품질관리원
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20220204000000001683

Alerts

조사장소수 is highly overall correlated with 위반업소수 and 1 other fieldsHigh correlation
위반업소수 is highly overall correlated with 조사장소수 and 2 other fieldsHigh correlation
형사처벌건수 is highly overall correlated with 위반업소수High correlation
과태료부과건수 is highly overall correlated with 조사장소수 and 1 other fieldsHigh correlation
위반업소수 has 4368 (43.7%) zerosZeros
형사처벌건수 has 6315 (63.1%) zerosZeros
고발건수 has 9577 (95.8%) zerosZeros
과태료부과건수 has 5662 (56.6%) zerosZeros

Reproduction

Analysis started2024-03-23 07:51:03.051246
Analysis finished2024-03-23 07:51:11.982758
Duration8.93 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

처분년월
Date

Distinct286
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum1998-01-01 00:00:00
Maximum2022-09-01 00:00:00
2024-03-23T07:51:12.180055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:51:12.691104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업무구분명
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
원산지단속
3394 
양곡표시
2140 
축산물이력
1923 
미검사품
1228 
GMO
1101 

Length

Max length5
Median length5
Mean length4.443
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row미검사품
2nd row축산물이력
3rd row축산물이력
4th row양곡표시
5th row축산물이력

Common Values

ValueCountFrequency (%)
원산지단속 3394
33.9%
양곡표시 2140
21.4%
축산물이력 1923
19.2%
미검사품 1228
 
12.3%
GMO 1101
 
11.0%
재사용화환 214
 
2.1%

Length

2024-03-23T07:51:13.182296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T07:51:13.410159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
원산지단속 3394
33.9%
양곡표시 2140
21.4%
축산물이력 1923
19.2%
미검사품 1228
 
12.3%
gmo 1101
 
11.0%
재사용화환 214
 
2.1%

시도명
Categorical

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
강원도
 
633
경상남도
 
632
경기도
 
630
전라북도
 
624
충청북도
 
620
Other values (12)
6861 

Length

Max length7
Median length5
Mean length4.5934
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대구광역시
2nd row대전광역시
3rd row전라북도
4th row울산광역시
5th row경기도

Common Values

ValueCountFrequency (%)
강원도 633
 
6.3%
경상남도 632
 
6.3%
경기도 630
 
6.3%
전라북도 624
 
6.2%
충청북도 620
 
6.2%
전라남도 619
 
6.2%
경상북도 613
 
6.1%
서울특별시 597
 
6.0%
충청남도 596
 
6.0%
제주특별자치도 595
 
5.9%
Other values (7) 3841
38.4%

Length

2024-03-23T07:51:13.662175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강원도 633
 
6.3%
경상남도 632
 
6.3%
경기도 630
 
6.3%
전라북도 624
 
6.2%
충청북도 620
 
6.2%
전라남도 619
 
6.2%
경상북도 613
 
6.1%
서울특별시 597
 
6.0%
충청남도 596
 
6.0%
제주특별자치도 595
 
5.9%
Other values (7) 3841
38.4%

조사장소수
Real number (ℝ)

HIGH CORRELATION 

Distinct1890
Distinct (%)18.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean451.7913
Minimum0
Maximum13387
Zeros21
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-23T07:51:13.945823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q129
median136
Q3451
95-th percentile2159
Maximum13387
Range13387
Interquartile range (IQR)422

Descriptive statistics

Standard deviation832.88799
Coefficient of variation (CV)1.8435237
Kurtosis21.253784
Mean451.7913
Median Absolute Deviation (MAD)124
Skewness3.7543585
Sum4517913
Variance693702.41
MonotonicityNot monotonic
2024-03-23T07:51:14.382315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 232
 
2.3%
3 152
 
1.5%
2 149
 
1.5%
4 120
 
1.2%
7 112
 
1.1%
6 103
 
1.0%
5 95
 
0.9%
10 93
 
0.9%
19 85
 
0.9%
11 85
 
0.9%
Other values (1880) 8774
87.7%
ValueCountFrequency (%)
0 21
 
0.2%
1 232
2.3%
2 149
1.5%
3 152
1.5%
4 120
1.2%
5 95
0.9%
6 103
1.0%
7 112
1.1%
8 85
 
0.9%
9 84
 
0.8%
ValueCountFrequency (%)
13387 1
< 0.1%
10421 1
< 0.1%
8006 1
< 0.1%
7893 1
< 0.1%
7659 1
< 0.1%
7547 1
< 0.1%
7360 1
< 0.1%
7167 1
< 0.1%
6738 1
< 0.1%
6684 1
< 0.1%

위반업소수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct100
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2601
Minimum0
Maximum189
Zeros4368
Zeros (%)43.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-23T07:51:14.747067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q38
95-th percentile36
Maximum189
Range189
Interquartile range (IQR)8

Descriptive statistics

Standard deviation13.559699
Coefficient of variation (CV)1.8677014
Kurtosis16.961295
Mean7.2601
Median Absolute Deviation (MAD)1
Skewness3.2829317
Sum72601
Variance183.86543
MonotonicityNot monotonic
2024-03-23T07:51:15.133265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4368
43.7%
1 931
 
9.3%
2 615
 
6.2%
3 430
 
4.3%
4 336
 
3.4%
5 300
 
3.0%
6 228
 
2.3%
7 178
 
1.8%
8 169
 
1.7%
11 151
 
1.5%
Other values (90) 2294
22.9%
ValueCountFrequency (%)
0 4368
43.7%
1 931
 
9.3%
2 615
 
6.2%
3 430
 
4.3%
4 336
 
3.4%
5 300
 
3.0%
6 228
 
2.3%
7 178
 
1.8%
8 169
 
1.7%
9 150
 
1.5%
ValueCountFrequency (%)
189 1
< 0.1%
169 1
< 0.1%
147 1
< 0.1%
141 1
< 0.1%
138 1
< 0.1%
132 1
< 0.1%
131 1
< 0.1%
128 1
< 0.1%
116 1
< 0.1%
108 1
< 0.1%

형사처벌건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct75
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3066
Minimum0
Maximum131
Zeros6315
Zeros (%)63.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-23T07:51:15.738321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile24
Maximum131
Range131
Interquartile range (IQR)4

Descriptive statistics

Standard deviation9.2217732
Coefficient of variation (CV)2.1413117
Kurtosis15.172557
Mean4.3066
Median Absolute Deviation (MAD)0
Skewness3.2744029
Sum43066
Variance85.041101
MonotonicityNot monotonic
2024-03-23T07:51:16.288603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6315
63.1%
1 548
 
5.5%
2 286
 
2.9%
3 239
 
2.4%
4 219
 
2.2%
5 199
 
2.0%
6 166
 
1.7%
7 156
 
1.6%
8 136
 
1.4%
10 128
 
1.3%
Other values (65) 1608
 
16.1%
ValueCountFrequency (%)
0 6315
63.1%
1 548
 
5.5%
2 286
 
2.9%
3 239
 
2.4%
4 219
 
2.2%
5 199
 
2.0%
6 166
 
1.7%
7 156
 
1.6%
8 136
 
1.4%
9 124
 
1.2%
ValueCountFrequency (%)
131 1
< 0.1%
91 1
< 0.1%
86 1
< 0.1%
84 1
< 0.1%
80 1
< 0.1%
74 1
< 0.1%
73 1
< 0.1%
70 2
< 0.1%
69 1
< 0.1%
68 1
< 0.1%

고발건수
Real number (ℝ)

ZEROS 

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1014
Minimum0
Maximum25
Zeros9577
Zeros (%)95.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-23T07:51:16.700508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum25
Range25
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.75430427
Coefficient of variation (CV)7.4388982
Kurtosis291.65901
Mean0.1014
Median Absolute Deviation (MAD)0
Skewness14.539263
Sum1014
Variance0.56897494
MonotonicityNot monotonic
2024-03-23T07:51:17.181759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 9577
95.8%
1 225
 
2.2%
2 101
 
1.0%
3 36
 
0.4%
4 14
 
0.1%
5 11
 
0.1%
6 8
 
0.1%
9 7
 
0.1%
10 4
 
< 0.1%
12 3
 
< 0.1%
Other values (9) 14
 
0.1%
ValueCountFrequency (%)
0 9577
95.8%
1 225
 
2.2%
2 101
 
1.0%
3 36
 
0.4%
4 14
 
0.1%
5 11
 
0.1%
6 8
 
0.1%
7 1
 
< 0.1%
8 3
 
< 0.1%
9 7
 
0.1%
ValueCountFrequency (%)
25 1
 
< 0.1%
19 1
 
< 0.1%
16 1
 
< 0.1%
15 1
 
< 0.1%
14 1
 
< 0.1%
13 3
< 0.1%
12 3
< 0.1%
11 2
 
< 0.1%
10 4
< 0.1%
9 7
0.1%

과태료부과건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct65
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8521
Minimum0
Maximum182
Zeros5662
Zeros (%)56.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-23T07:51:17.703300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile14
Maximum182
Range182
Interquartile range (IQR)3

Descriptive statistics

Standard deviation6.8161625
Coefficient of variation (CV)2.389875
Kurtosis133.3122
Mean2.8521
Median Absolute Deviation (MAD)0
Skewness8.0634681
Sum28521
Variance46.460072
MonotonicityNot monotonic
2024-03-23T07:51:18.221972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5662
56.6%
1 945
 
9.4%
2 663
 
6.6%
3 480
 
4.8%
4 368
 
3.7%
5 278
 
2.8%
6 218
 
2.2%
7 190
 
1.9%
8 161
 
1.6%
9 133
 
1.3%
Other values (55) 902
 
9.0%
ValueCountFrequency (%)
0 5662
56.6%
1 945
 
9.4%
2 663
 
6.6%
3 480
 
4.8%
4 368
 
3.7%
5 278
 
2.8%
6 218
 
2.2%
7 190
 
1.9%
8 161
 
1.6%
9 133
 
1.3%
ValueCountFrequency (%)
182 1
< 0.1%
159 1
< 0.1%
144 1
< 0.1%
136 1
< 0.1%
120 1
< 0.1%
89 1
< 0.1%
87 1
< 0.1%
86 1
< 0.1%
69 1
< 0.1%
66 1
< 0.1%

Interactions

2024-03-23T07:51:09.984063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:51:04.793665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:51:06.095495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:51:07.393156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:51:08.808890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:51:10.191335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:51:05.072661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:51:06.391738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:51:07.659370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:51:09.048947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:51:10.400145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:51:05.359696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:51:06.736537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:51:07.948386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:51:09.448601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:51:10.673789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:51:05.586760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:51:07.028637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:51:08.280426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:51:09.635071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:51:10.845120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:51:05.843602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:51:07.202302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:51:08.562277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:51:09.806398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T07:51:18.516049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업무구분명시도명조사장소수위반업소수형사처벌건수고발건수과태료부과건수
업무구분명1.0000.1070.3350.4230.3860.1040.185
시도명0.1071.0000.2980.2330.2250.1160.147
조사장소수0.3350.2981.0000.3820.4540.1560.237
위반업소수0.4230.2330.3821.0000.7410.1780.918
형사처벌건수0.3860.2250.4540.7411.0000.1290.245
고발건수0.1040.1160.1560.1780.1291.0000.065
과태료부과건수0.1850.1470.2370.9180.2450.0651.000
2024-03-23T07:51:18.813235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업무구분명시도명
업무구분명1.0000.050
시도명0.0501.000
2024-03-23T07:51:19.058433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
조사장소수위반업소수형사처벌건수고발건수과태료부과건수업무구분명시도명
조사장소수1.0000.5280.4130.1800.6080.1930.129
위반업소수0.5281.0000.8370.2650.7760.2380.092
형사처벌건수0.4130.8371.0000.2620.4380.2260.096
고발건수0.1800.2650.2621.0000.1880.0520.041
과태료부과건수0.6080.7760.4380.1881.0000.0930.059
업무구분명0.1930.2380.2260.0520.0931.0000.050
시도명0.1290.0920.0960.0410.0590.0501.000

Missing values

2024-03-23T07:51:11.319665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T07:51:11.749946image/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

처분년월업무구분명시도명조사장소수위반업소수형사처벌건수고발건수과태료부과건수
36622018-02-01미검사품대구광역시10000
41152017-07-01축산물이력대전광역시5159009
74212013-04-01축산물이력전라북도2211001
101202010-05-01양곡표시울산광역시41001
96762010-11-01축산물이력경기도16703003
28082019-04-01축산물이력울산광역시502002
17012020-10-01원산지단속충청남도30016402
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