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 4418 (44.2%) zerosZeros
형사처벌건수 has 6400 (64.0%) zerosZeros
고발건수 has 9606 (96.1%) zerosZeros
과태료부과건수 has 5690 (56.9%) zerosZeros

Reproduction

Analysis started2024-03-23 07:51:40.107420
Analysis finished2024-03-23 07:51:49.984312
Duration9.88 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

처분년월
Date

Distinct293
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum1998-01-01 00:00:00
Maximum2023-03-01 00:00:00
2024-03-23T07:51:50.268091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:51:50.823161image/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
3319 
양곡표시
2205 
축산물이력
1930 
미검사품
1238 
GMO
1062 

Length

Max length5
Median length5
Mean length4.4433
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
원산지단속 3319
33.2%
양곡표시 2205
22.1%
축산물이력 1930
19.3%
미검사품 1238
 
12.4%
GMO 1062
 
10.6%
재사용화환 246
 
2.5%

Length

2024-03-23T07:51:51.330198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T07:51:51.703985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
원산지단속 3319
33.2%
양곡표시 2205
22.1%
축산물이력 1930
19.3%
미검사품 1238
 
12.4%
gmo 1062
 
10.6%
재사용화환 246
 
2.5%

시도명
Categorical

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
전라북도
 
645
전라남도
 
644
경상북도
 
636
경상남도
 
629
경기도
 
621
Other values (12)
6825 

Length

Max length7
Median length5
Mean length4.5955
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전라북도 645
 
6.5%
전라남도 644
 
6.4%
경상북도 636
 
6.4%
경상남도 629
 
6.3%
경기도 621
 
6.2%
충청북도 617
 
6.2%
대구광역시 597
 
6.0%
충청남도 594
 
5.9%
인천광역시 590
 
5.9%
강원도 589
 
5.9%
Other values (7) 3838
38.4%

Length

2024-03-23T07:51:52.143733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전라북도 645
 
6.5%
전라남도 644
 
6.4%
경상북도 636
 
6.4%
경상남도 629
 
6.3%
경기도 621
 
6.2%
충청북도 617
 
6.2%
대구광역시 597
 
6.0%
충청남도 594
 
5.9%
인천광역시 590
 
5.9%
강원도 589
 
5.9%
Other values (7) 3838
38.4%

조사장소수
Real number (ℝ)

HIGH CORRELATION 

Distinct1908
Distinct (%)19.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean441.9473
Minimum0
Maximum13387
Zeros20
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-23T07:51:52.598924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q128
median132.5
Q3447.25
95-th percentile2115.1
Maximum13387
Range13387
Interquartile range (IQR)419.25

Descriptive statistics

Standard deviation819.31346
Coefficient of variation (CV)1.8538714
Kurtosis22.697451
Mean441.9473
Median Absolute Deviation (MAD)121.5
Skewness3.8560375
Sum4419473
Variance671274.54
MonotonicityNot monotonic
2024-03-23T07:51:53.101721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 243
 
2.4%
3 165
 
1.7%
2 160
 
1.6%
4 118
 
1.2%
7 113
 
1.1%
5 104
 
1.0%
6 98
 
1.0%
8 92
 
0.9%
10 91
 
0.9%
17 84
 
0.8%
Other values (1898) 8732
87.3%
ValueCountFrequency (%)
0 20
 
0.2%
1 243
2.4%
2 160
1.6%
3 165
1.7%
4 118
1.2%
5 104
1.0%
6 98
1.0%
7 113
1.1%
8 92
 
0.9%
9 74
 
0.7%
ValueCountFrequency (%)
13387 1
< 0.1%
10401 1
< 0.1%
8803 1
< 0.1%
8006 1
< 0.1%
7855 1
< 0.1%
7485 1
< 0.1%
7360 1
< 0.1%
7167 1
< 0.1%
6639 1
< 0.1%
6620 1
< 0.1%

위반업소수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct98
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0085
Minimum0
Maximum187
Zeros4418
Zeros (%)44.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-23T07:51:53.637757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q38
95-th percentile35
Maximum187
Range187
Interquartile range (IQR)8

Descriptive statistics

Standard deviation13.112461
Coefficient of variation (CV)1.8709368
Kurtosis15.685135
Mean7.0085
Median Absolute Deviation (MAD)1
Skewness3.2015376
Sum70085
Variance171.93662
MonotonicityNot monotonic
2024-03-23T07:51:54.153440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4418
44.2%
1 973
 
9.7%
2 620
 
6.2%
3 418
 
4.2%
4 331
 
3.3%
5 289
 
2.9%
6 223
 
2.2%
8 166
 
1.7%
7 163
 
1.6%
11 154
 
1.5%
Other values (88) 2245
22.4%
ValueCountFrequency (%)
0 4418
44.2%
1 973
 
9.7%
2 620
 
6.2%
3 418
 
4.2%
4 331
 
3.3%
5 289
 
2.9%
6 223
 
2.2%
7 163
 
1.6%
8 166
 
1.7%
9 149
 
1.5%
ValueCountFrequency (%)
187 1
< 0.1%
147 1
< 0.1%
141 1
< 0.1%
131 1
< 0.1%
128 1
< 0.1%
123 1
< 0.1%
116 1
< 0.1%
108 1
< 0.1%
102 1
< 0.1%
101 1
< 0.1%

형사처벌건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct76
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1269
Minimum0
Maximum131
Zeros6400
Zeros (%)64.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-23T07:51:54.694388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile23
Maximum131
Range131
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.9346169
Coefficient of variation (CV)2.1649705
Kurtosis16.516562
Mean4.1269
Median Absolute Deviation (MAD)0
Skewness3.3505915
Sum41269
Variance79.827379
MonotonicityNot monotonic
2024-03-23T07:51:55.141709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6400
64.0%
1 556
 
5.6%
2 278
 
2.8%
3 236
 
2.4%
4 217
 
2.2%
5 181
 
1.8%
7 161
 
1.6%
6 154
 
1.5%
9 136
 
1.4%
8 135
 
1.4%
Other values (66) 1546
 
15.5%
ValueCountFrequency (%)
0 6400
64.0%
1 556
 
5.6%
2 278
 
2.8%
3 236
 
2.4%
4 217
 
2.2%
5 181
 
1.8%
6 154
 
1.5%
7 161
 
1.6%
8 135
 
1.4%
9 136
 
1.4%
ValueCountFrequency (%)
131 1
< 0.1%
91 2
< 0.1%
86 1
< 0.1%
81 1
< 0.1%
80 1
< 0.1%
74 1
< 0.1%
73 1
< 0.1%
70 1
< 0.1%
69 1
< 0.1%
68 1
< 0.1%

고발건수
Real number (ℝ)

ZEROS 

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0922
Minimum0
Maximum25
Zeros9606
Zeros (%)96.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-23T07:51:55.524111image/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 deviation0.70281474
Coefficient of variation (CV)7.6227195
Kurtosis348.00615
Mean0.0922
Median Absolute Deviation (MAD)0
Skewness15.567788
Sum922
Variance0.49394855
MonotonicityNot monotonic
2024-03-23T07:51:55.893462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 9606
96.1%
1 208
 
2.1%
2 95
 
0.9%
3 37
 
0.4%
5 11
 
0.1%
4 11
 
0.1%
6 8
 
0.1%
9 7
 
0.1%
7 3
 
< 0.1%
10 3
 
< 0.1%
Other values (8) 11
 
0.1%
ValueCountFrequency (%)
0 9606
96.1%
1 208
 
2.1%
2 95
 
0.9%
3 37
 
0.4%
4 11
 
0.1%
5 11
 
0.1%
6 8
 
0.1%
7 3
 
< 0.1%
8 2
 
< 0.1%
9 7
 
0.1%
ValueCountFrequency (%)
25 1
 
< 0.1%
19 1
 
< 0.1%
17 1
 
< 0.1%
14 2
 
< 0.1%
13 1
 
< 0.1%
12 1
 
< 0.1%
11 2
 
< 0.1%
10 3
< 0.1%
9 7
0.1%
8 2
 
< 0.1%

과태료부과건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct61
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7894
Minimum0
Maximum180
Zeros5690
Zeros (%)56.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-23T07:51:56.331407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile14
Maximum180
Range180
Interquartile range (IQR)3

Descriptive statistics

Standard deviation6.4527987
Coefficient of variation (CV)2.3133286
Kurtosis116.49029
Mean2.7894
Median Absolute Deviation (MAD)0
Skewness7.206777
Sum27894
Variance41.638612
MonotonicityNot monotonic
2024-03-23T07:51:56.667441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5690
56.9%
1 979
 
9.8%
2 649
 
6.5%
3 460
 
4.6%
4 356
 
3.6%
5 277
 
2.8%
6 215
 
2.1%
7 190
 
1.9%
8 169
 
1.7%
9 127
 
1.3%
Other values (51) 888
 
8.9%
ValueCountFrequency (%)
0 5690
56.9%
1 979
 
9.8%
2 649
 
6.5%
3 460
 
4.6%
4 356
 
3.6%
5 277
 
2.8%
6 215
 
2.1%
7 190
 
1.9%
8 169
 
1.7%
9 127
 
1.3%
ValueCountFrequency (%)
180 1
< 0.1%
144 1
< 0.1%
136 1
< 0.1%
89 1
< 0.1%
86 1
< 0.1%
72 1
< 0.1%
68 1
< 0.1%
66 1
< 0.1%
62 1
< 0.1%
58 1
< 0.1%

Interactions

2024-03-23T07:51:47.394554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:51:41.683135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:51:42.898278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:51:44.473371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:51:45.838079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:51:47.687576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:51:41.972054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:51:43.177777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:51:44.753398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:51:46.124697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:51:47.975273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:51:42.164007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:51:43.462103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:51:45.030123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:51:46.393450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:51:48.325013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:51:42.360521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:51:43.935883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:51:45.290749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:51:46.704349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:51:48.640473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:51:42.618074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:51:44.196864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:51:45.556134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:51:46.977136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T07:51:57.204753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업무구분명시도명조사장소수위반업소수형사처벌건수고발건수과태료부과건수
업무구분명1.0000.1030.3670.4420.3880.1020.165
시도명0.1031.0000.2870.2230.2150.1000.147
조사장소수0.3670.2871.0000.4890.3150.1890.235
위반업소수0.4420.2230.4891.0000.7420.2760.859
형사처벌건수0.3880.2150.3150.7421.0000.1450.256
고발건수0.1020.1000.1890.2760.1451.0000.077
과태료부과건수0.1650.1470.2350.8590.2560.0771.000
2024-03-23T07:51:57.533473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업무구분명시도명
업무구분명1.0000.048
시도명0.0481.000
2024-03-23T07:51:57.802974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
조사장소수위반업소수형사처벌건수고발건수과태료부과건수업무구분명시도명
조사장소수1.0000.5280.4140.1680.6030.1920.118
위반업소수0.5281.0000.8310.2530.7800.2370.090
형사처벌건수0.4140.8311.0000.2470.4360.2270.092
고발건수0.1680.2530.2471.0000.1760.0510.040
과태료부과건수0.6030.7800.4360.1761.0000.0910.059
업무구분명0.1920.2370.2270.0510.0911.0000.048
시도명0.1180.0900.0920.0400.0590.0481.000

Missing values

2024-03-23T07:51:49.019552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T07:51:49.605146image/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
50522016-12-01원산지단속충청북도25325401
43502017-10-01양곡표시경기도5973102
141252000-06-01원산지단속제주특별자치도64400
67102014-10-01축산물이력전라북도4134004
122822007-08-01원산지단속광주광역시29610505
33012019-03-01양곡표시광주광역시220000
141632000-03-01원산지단속경기도30131300
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