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 4460 (44.6%) zerosZeros
형사처벌건수 has 6400 (64.0%) zerosZeros
고발건수 has 9604 (96.0%) zerosZeros
과태료부과건수 has 5671 (56.7%) zerosZeros

Reproduction

Analysis started2024-03-23 07:52:00.913602
Analysis finished2024-03-23 07:52:09.597003
Duration8.68 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

처분년월
Date

Distinct298
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum1998-01-01 00:00:00
Maximum2023-09-01 00:00:00
2024-03-23T07:52:09.790787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:52:10.242393image/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
3321 
양곡표시
2234 
축산물이력
1894 
미검사품
1216 
GMO
1034 

Length

Max length5
Median length5
Mean length4.4482
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
원산지단속 3321
33.2%
양곡표시 2234
22.3%
축산물이력 1894
18.9%
미검사품 1216
 
12.2%
GMO 1034
 
10.3%
재사용화환 301
 
3.0%

Length

2024-03-23T07:52:10.860994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T07:52:11.159701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
원산지단속 3321
33.2%
양곡표시 2234
22.3%
축산물이력 1894
18.9%
미검사품 1216
 
12.2%
gmo 1034
 
10.3%
재사용화환 301
 
3.0%

시도명
Categorical

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
경기도
 
658
전라남도
 
646
경상남도
 
640
충청북도
 
624
경상북도
 
613
Other values (12)
6819 

Length

Max length7
Median length5
Mean length4.8328
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경기도
2nd row전라북도
3rd row광주광역시
4th row경기도
5th row경기도

Common Values

ValueCountFrequency (%)
경기도 658
 
6.6%
전라남도 646
 
6.5%
경상남도 640
 
6.4%
충청북도 624
 
6.2%
경상북도 613
 
6.1%
전라북도 611
 
6.1%
강원특별자치도 605
 
6.0%
충청남도 604
 
6.0%
제주특별자치도 600
 
6.0%
서울특별시 579
 
5.8%
Other values (7) 3820
38.2%

Length

2024-03-23T07:52:11.628081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 658
 
6.6%
전라남도 646
 
6.5%
경상남도 640
 
6.4%
충청북도 624
 
6.2%
경상북도 613
 
6.1%
전라북도 611
 
6.1%
강원특별자치도 605
 
6.0%
충청남도 604
 
6.0%
제주특별자치도 600
 
6.0%
서울특별시 579
 
5.8%
Other values (7) 3820
38.2%

조사장소수
Real number (ℝ)

HIGH CORRELATION 

Distinct1890
Distinct (%)18.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean443.4922
Minimum0
Maximum13387
Zeros22
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-23T07:52:12.009743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q129
median131
Q3442
95-th percentile2136.1
Maximum13387
Range13387
Interquartile range (IQR)413

Descriptive statistics

Standard deviation822.0994
Coefficient of variation (CV)1.8536953
Kurtosis23.782085
Mean443.4922
Median Absolute Deviation (MAD)120
Skewness3.8701756
Sum4434922
Variance675847.42
MonotonicityNot monotonic
2024-03-23T07:52:12.379408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 250
 
2.5%
2 155
 
1.6%
3 149
 
1.5%
4 120
 
1.2%
7 110
 
1.1%
5 107
 
1.1%
6 105
 
1.1%
10 95
 
0.9%
8 88
 
0.9%
20 88
 
0.9%
Other values (1880) 8733
87.3%
ValueCountFrequency (%)
0 22
 
0.2%
1 250
2.5%
2 155
1.6%
3 149
1.5%
4 120
1.2%
5 107
1.1%
6 105
1.1%
7 110
1.1%
8 88
 
0.9%
9 84
 
0.8%
ValueCountFrequency (%)
13387 1
< 0.1%
11496 1
< 0.1%
10401 1
< 0.1%
8803 1
< 0.1%
7855 1
< 0.1%
7360 1
< 0.1%
6723 1
< 0.1%
6666 1
< 0.1%
6663 1
< 0.1%
6607 1
< 0.1%

위반업소수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct93
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9589
Minimum0
Maximum167
Zeros4460
Zeros (%)44.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-23T07:52:12.711554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q38
95-th percentile35
Maximum167
Range167
Interquartile range (IQR)8

Descriptive statistics

Standard deviation12.932298
Coefficient of variation (CV)1.8583825
Kurtosis12.372445
Mean6.9589
Median Absolute Deviation (MAD)1
Skewness2.9707898
Sum69589
Variance167.24434
MonotonicityNot monotonic
2024-03-23T07:52:12.978700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4460
44.6%
1 974
 
9.7%
2 603
 
6.0%
3 446
 
4.5%
4 314
 
3.1%
5 265
 
2.6%
6 236
 
2.4%
7 175
 
1.8%
8 160
 
1.6%
11 153
 
1.5%
Other values (83) 2214
22.1%
ValueCountFrequency (%)
0 4460
44.6%
1 974
 
9.7%
2 603
 
6.0%
3 446
 
4.5%
4 314
 
3.1%
5 265
 
2.6%
6 236
 
2.4%
7 175
 
1.8%
8 160
 
1.6%
9 138
 
1.4%
ValueCountFrequency (%)
167 1
< 0.1%
138 1
< 0.1%
132 1
< 0.1%
128 1
< 0.1%
102 1
< 0.1%
101 1
< 0.1%
98 1
< 0.1%
92 1
< 0.1%
91 1
< 0.1%
90 1
< 0.1%

형사처벌건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0631
Minimum0
Maximum86
Zeros6400
Zeros (%)64.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-23T07:52:13.544161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile23
Maximum86
Range86
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.6948651
Coefficient of variation (CV)2.1399584
Kurtosis10.88695
Mean4.0631
Median Absolute Deviation (MAD)0
Skewness3.0031222
Sum40631
Variance75.600678
MonotonicityNot monotonic
2024-03-23T07:52:13.886997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6400
64.0%
1 567
 
5.7%
2 289
 
2.9%
3 239
 
2.4%
4 218
 
2.2%
5 186
 
1.9%
6 156
 
1.6%
7 150
 
1.5%
10 130
 
1.3%
8 123
 
1.2%
Other values (61) 1542
 
15.4%
ValueCountFrequency (%)
0 6400
64.0%
1 567
 
5.7%
2 289
 
2.9%
3 239
 
2.4%
4 218
 
2.2%
5 186
 
1.9%
6 156
 
1.6%
7 150
 
1.5%
8 123
 
1.2%
9 122
 
1.2%
ValueCountFrequency (%)
86 1
< 0.1%
74 1
< 0.1%
73 1
< 0.1%
69 1
< 0.1%
68 1
< 0.1%
67 1
< 0.1%
64 1
< 0.1%
63 2
< 0.1%
62 1
< 0.1%
61 2
< 0.1%

고발건수
Real number (ℝ)

ZEROS 

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.101
Minimum0
Maximum25
Zeros9604
Zeros (%)96.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-23T07:52:14.120393image/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 deviation0.76841268
Coefficient of variation (CV)7.6080463
Kurtosis289.80683
Mean0.101
Median Absolute Deviation (MAD)0
Skewness14.526408
Sum1010
Variance0.59045805
MonotonicityNot monotonic
2024-03-23T07:52:14.464152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 9604
96.0%
1 195
 
1.9%
2 97
 
1.0%
3 39
 
0.4%
4 15
 
0.1%
5 12
 
0.1%
6 8
 
0.1%
9 7
 
0.1%
10 4
 
< 0.1%
7 4
 
< 0.1%
Other values (8) 15
 
0.1%
ValueCountFrequency (%)
0 9604
96.0%
1 195
 
1.9%
2 97
 
1.0%
3 39
 
0.4%
4 15
 
0.1%
5 12
 
0.1%
6 8
 
0.1%
7 4
 
< 0.1%
8 2
 
< 0.1%
9 7
 
0.1%
ValueCountFrequency (%)
25 1
 
< 0.1%
19 1
 
< 0.1%
17 2
 
< 0.1%
16 1
 
< 0.1%
13 2
 
< 0.1%
12 3
< 0.1%
11 3
< 0.1%
10 4
< 0.1%
9 7
0.1%
8 2
 
< 0.1%

과태료부과건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct62
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7948
Minimum0
Maximum157
Zeros5671
Zeros (%)56.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-23T07:52:14.851724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile14
Maximum157
Range157
Interquartile range (IQR)3

Descriptive statistics

Standard deviation6.2766578
Coefficient of variation (CV)2.2458343
Kurtosis70.934701
Mean2.7948
Median Absolute Deviation (MAD)0
Skewness5.7852548
Sum27948
Variance39.396433
MonotonicityNot monotonic
2024-03-23T07:52:15.307417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5671
56.7%
1 991
 
9.9%
2 656
 
6.6%
3 471
 
4.7%
4 368
 
3.7%
5 249
 
2.5%
6 216
 
2.2%
7 182
 
1.8%
8 167
 
1.7%
9 124
 
1.2%
Other values (52) 905
 
9.0%
ValueCountFrequency (%)
0 5671
56.7%
1 991
 
9.9%
2 656
 
6.6%
3 471
 
4.7%
4 368
 
3.7%
5 249
 
2.5%
6 216
 
2.2%
7 182
 
1.8%
8 167
 
1.7%
9 124
 
1.2%
ValueCountFrequency (%)
157 1
< 0.1%
120 1
< 0.1%
89 1
< 0.1%
87 1
< 0.1%
72 1
< 0.1%
68 1
< 0.1%
66 1
< 0.1%
63 1
< 0.1%
57 1
< 0.1%
56 1
< 0.1%

Interactions

2024-03-23T07:52:07.682073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:52:02.419506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:52:03.744435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:52:05.003235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:52:06.226142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:52:07.907208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:52:02.705434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:52:03.930276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:52:05.283106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:52:06.509129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:52:08.133700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:52:03.010849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:52:04.202079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:52:05.562126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:52:06.780579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:52:08.396044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:52:03.268712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:52:04.456733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:52:05.733539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:52:07.035545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:52:08.670535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:52:03.532549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:52:04.720763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:52:05.949681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:52:07.289739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T07:52:15.589399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업무구분명시도명조사장소수위반업소수형사처벌건수고발건수과태료부과건수
업무구분명1.0000.1120.3490.4420.4880.1120.198
시도명0.1121.0000.2780.2350.2230.1070.156
조사장소수0.3490.2781.0000.5260.5010.1560.255
위반업소수0.4420.2350.5261.0000.8280.2540.907
형사처벌건수0.4880.2230.5010.8281.0000.1500.295
고발건수0.1120.1070.1560.2540.1501.0000.133
과태료부과건수0.1980.1560.2550.9070.2950.1331.000
2024-03-23T07:52:15.871243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업무구분명시도명
업무구분명1.0000.053
시도명0.0531.000
2024-03-23T07:52:16.113291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
조사장소수위반업소수형사처벌건수고발건수과태료부과건수업무구분명시도명
조사장소수1.0000.5350.4320.1760.6000.1920.112
위반업소수0.5351.0000.8340.2600.7910.2510.093
형사처벌건수0.4320.8341.0000.2520.4570.2830.088
고발건수0.1760.2600.2521.0000.1850.0560.039
과태료부과건수0.6000.7910.4570.1851.0000.1110.066
업무구분명0.1920.2510.2830.0560.1111.0000.053
시도명0.1120.0930.0880.0390.0660.0531.000

Missing values

2024-03-23T07:52:09.045816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T07:52:09.419529image/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
97402011-10-01양곡표시경기도15547205
141712002-05-01원산지단속전라북도47151500
49282017-08-01양곡표시광주광역시480000
2142023-06-01양곡표시경기도3040000
74262014-05-01원산지단속경기도34194723024
109092010-06-01GMO경상남도8750000
10662022-07-01원산지단속세종특별자치시1072200
43722018-05-01미검사품세종특별자치시10000
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