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 4328 (43.3%) zerosZeros
형사처벌건수 has 6288 (62.9%) zerosZeros
고발건수 has 9565 (95.7%) zerosZeros
과태료부과건수 has 5687 (56.9%) zerosZeros

Reproduction

Analysis started2024-03-23 07:50:25.106726
Analysis finished2024-03-23 07:50:34.078204
Duration8.97 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

처분년월
Date

Distinct279
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum1998-01-01 00:00:00
Maximum2022-03-01 00:00:00
2024-03-23T07:50:34.276615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:50:34.767001image/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
3402 
양곡표시
2172 
축산물이력
1898 
미검사품
1201 
GMO
1165 

Length

Max length5
Median length5
Mean length4.4297
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row원산지단속
2nd row미검사품
3rd rowGMO
4th row양곡표시
5th rowGMO

Common Values

ValueCountFrequency (%)
원산지단속 3402
34.0%
양곡표시 2172
21.7%
축산물이력 1898
19.0%
미검사품 1201
 
12.0%
GMO 1165
 
11.7%
재사용화환 162
 
1.6%

Length

2024-03-23T07:50:35.227251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T07:50:35.587933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
원산지단속 3402
34.0%
양곡표시 2172
21.7%
축산물이력 1898
19.0%
미검사품 1201
 
12.0%
gmo 1165
 
11.7%
재사용화환 162
 
1.6%

시도명
Categorical

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
전라북도
 
653
경상남도
 
647
전라남도
 
641
경기도
 
617
경상북도
 
617
Other values (12)
6825 

Length

Max length7
Median length5
Mean length4.5944
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전라북도 653
 
6.5%
경상남도 647
 
6.5%
전라남도 641
 
6.4%
경기도 617
 
6.2%
경상북도 617
 
6.2%
강원도 609
 
6.1%
충청남도 602
 
6.0%
충청북도 598
 
6.0%
서울특별시 596
 
6.0%
인천광역시 591
 
5.9%
Other values (7) 3829
38.3%

Length

2024-03-23T07:50:36.005808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전라북도 653
 
6.5%
경상남도 647
 
6.5%
전라남도 641
 
6.4%
경기도 617
 
6.2%
경상북도 617
 
6.2%
강원도 609
 
6.1%
충청남도 602
 
6.0%
충청북도 598
 
6.0%
서울특별시 596
 
6.0%
인천광역시 591
 
5.9%
Other values (7) 3829
38.3%

조사장소수
Real number (ℝ)

HIGH CORRELATION 

Distinct1910
Distinct (%)19.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean453.8314
Minimum0
Maximum13387
Zeros20
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-23T07:50:36.477736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q130
median139
Q3459
95-th percentile2170.05
Maximum13387
Range13387
Interquartile range (IQR)429

Descriptive statistics

Standard deviation835.54175
Coefficient of variation (CV)1.841084
Kurtosis20.222741
Mean453.8314
Median Absolute Deviation (MAD)127
Skewness3.7219644
Sum4538314
Variance698130.02
MonotonicityNot monotonic
2024-03-23T07:50:36.926489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 232
 
2.3%
2 151
 
1.5%
3 138
 
1.4%
4 111
 
1.1%
6 107
 
1.1%
7 106
 
1.1%
5 98
 
1.0%
8 92
 
0.9%
11 82
 
0.8%
10 81
 
0.8%
Other values (1900) 8802
88.0%
ValueCountFrequency (%)
0 20
 
0.2%
1 232
2.3%
2 151
1.5%
3 138
1.4%
4 111
1.1%
5 98
1.0%
6 107
1.1%
7 106
1.1%
8 92
 
0.9%
9 80
 
0.8%
ValueCountFrequency (%)
13387 1
< 0.1%
8803 1
< 0.1%
7891 1
< 0.1%
7659 1
< 0.1%
7546 1
< 0.1%
7360 1
< 0.1%
7167 1
< 0.1%
6737 1
< 0.1%
6684 1
< 0.1%
6671 1
< 0.1%

위반업소수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct99
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2593
Minimum0
Maximum169
Zeros4328
Zeros (%)43.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-23T07:50:37.346854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q38
95-th percentile36
Maximum169
Range169
Interquartile range (IQR)8

Descriptive statistics

Standard deviation13.453749
Coefficient of variation (CV)1.8533122
Kurtosis13.7418
Mean7.2593
Median Absolute Deviation (MAD)1
Skewness3.0896901
Sum72593
Variance181.00336
MonotonicityNot monotonic
2024-03-23T07:50:37.691267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4328
43.3%
1 965
 
9.7%
2 624
 
6.2%
3 445
 
4.5%
4 326
 
3.3%
5 292
 
2.9%
6 230
 
2.3%
8 183
 
1.8%
7 174
 
1.7%
11 151
 
1.5%
Other values (89) 2282
22.8%
ValueCountFrequency (%)
0 4328
43.3%
1 965
 
9.7%
2 624
 
6.2%
3 445
 
4.5%
4 326
 
3.3%
5 292
 
2.9%
6 230
 
2.3%
7 174
 
1.7%
8 183
 
1.8%
9 146
 
1.5%
ValueCountFrequency (%)
169 1
< 0.1%
147 1
< 0.1%
141 1
< 0.1%
132 1
< 0.1%
128 1
< 0.1%
123 1
< 0.1%
116 1
< 0.1%
108 1
< 0.1%
102 2
< 0.1%
101 2
< 0.1%

형사처벌건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct72
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3373
Minimum0
Maximum91
Zeros6288
Zeros (%)62.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-23T07:50:38.160957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile25
Maximum91
Range91
Interquartile range (IQR)4

Descriptive statistics

Standard deviation9.2218671
Coefficient of variation (CV)2.1261769
Kurtosis12.352316
Mean4.3373
Median Absolute Deviation (MAD)0
Skewness3.0979367
Sum43373
Variance85.042833
MonotonicityNot monotonic
2024-03-23T07:50:38.690711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6288
62.9%
1 567
 
5.7%
2 310
 
3.1%
3 262
 
2.6%
4 212
 
2.1%
5 174
 
1.7%
6 157
 
1.6%
7 151
 
1.5%
8 134
 
1.3%
9 130
 
1.3%
Other values (62) 1615
 
16.2%
ValueCountFrequency (%)
0 6288
62.9%
1 567
 
5.7%
2 310
 
3.1%
3 262
 
2.6%
4 212
 
2.1%
5 174
 
1.7%
6 157
 
1.6%
7 151
 
1.5%
8 134
 
1.3%
9 130
 
1.3%
ValueCountFrequency (%)
91 3
< 0.1%
86 1
 
< 0.1%
81 1
 
< 0.1%
80 1
 
< 0.1%
73 2
< 0.1%
70 2
< 0.1%
67 1
 
< 0.1%
65 1
 
< 0.1%
64 1
 
< 0.1%
63 2
< 0.1%

고발건수
Real number (ℝ)

ZEROS 

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1056
Minimum0
Maximum25
Zeros9565
Zeros (%)95.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-23T07:50:39.095341image/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 deviation0.77233949
Coefficient of variation (CV)7.3138209
Kurtosis279.34207
Mean0.1056
Median Absolute Deviation (MAD)0
Skewness14.265119
Sum1056
Variance0.59650829
MonotonicityNot monotonic
2024-03-23T07:50:39.751286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 9565
95.7%
1 229
 
2.3%
2 101
 
1.0%
3 37
 
0.4%
4 18
 
0.2%
5 13
 
0.1%
6 11
 
0.1%
9 7
 
0.1%
12 3
 
< 0.1%
14 3
 
< 0.1%
Other values (8) 13
 
0.1%
ValueCountFrequency (%)
0 9565
95.7%
1 229
 
2.3%
2 101
 
1.0%
3 37
 
0.4%
4 18
 
0.2%
5 13
 
0.1%
6 11
 
0.1%
7 2
 
< 0.1%
9 7
 
0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
25 1
 
< 0.1%
19 1
 
< 0.1%
16 1
 
< 0.1%
15 1
 
< 0.1%
14 3
< 0.1%
13 3
< 0.1%
12 3
< 0.1%
11 2
 
< 0.1%
10 2
 
< 0.1%
9 7
0.1%

과태료부과건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct64
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8164
Minimum0
Maximum159
Zeros5687
Zeros (%)56.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-23T07:50:40.166295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile14
Maximum159
Range159
Interquartile range (IQR)3

Descriptive statistics

Standard deviation6.5164973
Coefficient of variation (CV)2.3137684
Kurtosis94.017997
Mean2.8164
Median Absolute Deviation (MAD)0
Skewness6.7072418
Sum28164
Variance42.464738
MonotonicityNot monotonic
2024-03-23T07:50:40.648602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5687
56.9%
1 980
 
9.8%
2 658
 
6.6%
3 453
 
4.5%
4 341
 
3.4%
5 280
 
2.8%
6 221
 
2.2%
7 192
 
1.9%
8 164
 
1.6%
9 125
 
1.2%
Other values (54) 899
 
9.0%
ValueCountFrequency (%)
0 5687
56.9%
1 980
 
9.8%
2 658
 
6.6%
3 453
 
4.5%
4 341
 
3.4%
5 280
 
2.8%
6 221
 
2.2%
7 192
 
1.9%
8 164
 
1.6%
9 125
 
1.2%
ValueCountFrequency (%)
159 1
< 0.1%
144 1
< 0.1%
136 1
< 0.1%
89 1
< 0.1%
87 1
< 0.1%
86 1
< 0.1%
72 1
< 0.1%
68 1
< 0.1%
66 1
< 0.1%
63 1
< 0.1%

Interactions

2024-03-23T07:50:32.177950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:50:26.876965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:50:28.122442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:50:29.523409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:50:30.836291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:50:32.463849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:50:27.087269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:50:28.359411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:50:29.798993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:50:31.115490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:50:32.654682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:50:27.308619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:50:28.683255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:50:30.093688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:50:31.395731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:50:32.828864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:50:27.565364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:50:28.950742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:50:30.312426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:50:31.643479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:50:33.056889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:50:27.836520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:50:29.221491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:50:30.557608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:50:31.899800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T07:50:40.910994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업무구분명시도명조사장소수위반업소수형사처벌건수고발건수과태료부과건수
업무구분명1.0000.0950.3290.4430.4730.1120.196
시도명0.0951.0000.3000.2380.2150.1080.159
조사장소수0.3290.3001.0000.3920.3590.1630.376
위반업소수0.4430.2380.3921.0000.8520.2360.830
형사처벌건수0.4730.2150.3590.8521.0000.1550.305
고발건수0.1120.1080.1630.2360.1551.0000.105
과태료부과건수0.1960.1590.3760.8300.3050.1051.000
2024-03-23T07:50:41.204133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업무구분명시도명
업무구분명1.0000.045
시도명0.0451.000
2024-03-23T07:50:41.450555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
조사장소수위반업소수형사처벌건수고발건수과태료부과건수업무구분명시도명
조사장소수1.0000.5060.3910.1770.5950.1900.130
위반업소수0.5061.0000.8350.2660.7660.2520.095
형사처벌건수0.3910.8351.0000.2600.4210.2720.085
고발건수0.1770.2660.2601.0000.1890.0560.039
과태료부과건수0.5950.7660.4210.1891.0000.1100.067
업무구분명0.1900.2520.2720.0560.1101.0000.045
시도명0.1300.0950.0850.0390.0670.0451.000

Missing values

2024-03-23T07:50:33.441158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T07:50:33.898915image/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
11932020-11-01원산지단속충청남도18005203
28862018-08-01미검사품제주특별자치도20000
110542008-04-01GMO경기도2930000
72022-03-01양곡표시경기도730000
119472006-04-01GMO경기도930000
1782022-01-01재사용화환전라남도40000
72762012-12-01미검사품대구광역시10000
85662011-09-01양곡표시제주특별자치도1550000
25612019-01-01축산물이력세종특별자치시60000
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40892017-01-01미검사품전라남도90000
14352020-07-01양곡표시경상북도770000
65212013-11-01미검사품전라남도140000
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