Overview

Dataset statistics

Number of variables8
Number of observations10000
Missing cells1946
Missing cells (%)2.4%
Duplicate rows266
Duplicate rows (%)2.7%
Total size in memory732.4 KiB
Average record size in memory75.0 B

Variable types

Text3
Categorical2
Numeric3

Dataset

Description국립농산물품질관리원에서 관리하는 생산, 유통단계에서의 농산물 잔류농약 분석결과 누계(품목, 수거단계, 재배양식, 생산 지역, 재배면적, 조사물량, 등록일자, 분석결과)
Author국립농산물품질관리원
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20170912000000000791

Alerts

Dataset has 266 (2.7%) duplicate rowsDuplicates
재배면적 is highly overall correlated with 조사물량High correlation
조사물량 is highly overall correlated with 재배면적High correlation
재배면적 has 1384 (13.8%) missing valuesMissing
조사물량 has 562 (5.6%) missing valuesMissing
재배면적 is highly skewed (γ1 = 23.6838442)Skewed
조사물량 is highly skewed (γ1 = 96.95880564)Skewed

Reproduction

Analysis started2023-12-11 03:01:37.735448
Analysis finished2023-12-11 03:01:40.130570
Duration2.4 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

품목명
Text

Distinct239
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T12:01:40.385767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length17
Mean length3.2898
Min length1

Characters and Unicode

Total characters32898
Distinct characters263
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique65 ?
Unique (%)0.7%

Sample

1st row수박
2nd row차(전체)
3rd row애호박(인큐애호박,진주애호박)
4th row부추
5th row마늘
ValueCountFrequency (%)
양파 535
 
5.3%
감자 463
 
4.6%
복숭아 428
 
4.3%
오이 389
 
3.9%
포도 377
 
3.8%
풋고추 352
 
3.5%
토마토 347
 
3.5%
블루베리 326
 
3.3%
딸기 319
 
3.2%
마늘 310
 
3.1%
Other values (230) 6155
61.5%
2023-12-11T12:01:41.209682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
추 1658
 
5.0%
토 1223
 
3.7%
고 1201
 
3.7%
마 1063
 
3.2%
파 1029
 
3.1%
박 1007
 
3.1%
리 964
 
2.9%
) 945
 
2.9%
( 945
 
2.9%
호 870
 
2.6%
Other values (253) 21993
66.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 30741
93.4%
Close Punctuation 945
 
2.9%
Open Punctuation 945
 
2.9%
Other Punctuation 215
 
0.7%
Decimal Number 44
 
0.1%
Math Symbol 7
 
< 0.1%
Space Separator 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
추 1658
 
5.4%
토 1223
 
4.0%
고 1201
 
3.9%
마 1063
 
3.5%
파 1029
 
3.3%
박 1007
 
3.3%
리 964
 
3.1%
호 870
 
2.8%
양 749
 
2.4%
이 669
 
2.2%
Other values (242) 20308
66.1%
Decimal Number
ValueCountFrequency (%)
6 20
45.5%
5 9
20.5%
1 7
 
15.9%
2 7
 
15.9%
3 1
 
2.3%
Other Punctuation
ValueCountFrequency (%)
, 213
99.1%
/ 2
 
0.9%
Close Punctuation
ValueCountFrequency (%)
) 945
100.0%
Open Punctuation
ValueCountFrequency (%)
( 945
100.0%
Math Symbol
ValueCountFrequency (%)
~ 7
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 30741
93.4%
Common 2157
 
6.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
추 1658
 
5.4%
토 1223
 
4.0%
고 1201
 
3.9%
마 1063
 
3.5%
파 1029
 
3.3%
박 1007
 
3.3%
리 964
 
3.1%
호 870
 
2.8%
양 749
 
2.4%
이 669
 
2.2%
Other values (242) 20308
66.1%
Common
ValueCountFrequency (%)
) 945
43.8%
( 945
43.8%
, 213
 
9.9%
6 20
 
0.9%
5 9
 
0.4%
1 7
 
0.3%
2 7
 
0.3%
~ 7
 
0.3%
/ 2
 
0.1%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 30741
93.4%
ASCII 2157
 
6.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
추 1658
 
5.4%
토 1223
 
4.0%
고 1201
 
3.9%
마 1063
 
3.5%
파 1029
 
3.3%
박 1007
 
3.3%
리 964
 
3.1%
호 870
 
2.8%
양 749
 
2.4%
이 669
 
2.2%
Other values (242) 20308
66.1%
ASCII
ValueCountFrequency (%)
) 945
43.8%
( 945
43.8%
, 213
 
9.9%
6 20
 
0.9%
5 9
 
0.4%
1 7
 
0.3%
2 7
 
0.3%
~ 7
 
0.3%
/ 2
 
0.1%
3 1
 
< 0.1%

수거단계
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
생산
8619 
유통/판매
1381 

Length

Max length5
Median length2
Mean length2.4143
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
생산 8619
86.2%
유통/판매 1381
 
13.8%

Length

2023-12-11T12:01:41.397534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:01:41.516102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
생산 8619
86.2%
유통/판매 1381
 
13.8%

재배양식
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
일반
5804 
친환경(인증) 무농약
1822 
GAP(인증)
1361 
친환경(인증) 유기
875 
친환경(인증) 유기가공품
 
51
Other values (2)
 
87

Length

Max length13
Median length3
Mean length5.8559
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row친환경(인증) 무농약
2nd row친환경(인증) 유기가공품
3rd row일반
4th row일반
5th row일반

Common Values

ValueCountFrequency (%)
일반 5804
58.0%
친환경(인증) 무농약 1822
 
18.2%
GAP(인증) 1361
 
13.6%
친환경(인증) 유기 875
 
8.8%
친환경(인증) 유기가공품 51
 
0.5%
친환경(인증) 51
 
0.5%
친환경(인증) 취급자 36
 
0.4%

Length

2023-12-11T12:01:41.618544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:01:41.723628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반 5804
45.4%
친환경(인증 2835
22.2%
무농약 1822
 
14.3%
gap(인증 1361
 
10.6%
유기 875
 
6.8%
유기가공품 51
 
0.4%
취급자 36
 
0.3%

주소
Text

Distinct193
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T12:01:42.054049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length11
Mean length10.9197
Min length6

Characters and Unicode

Total characters109197
Distinct characters132
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)0.1%

Sample

1st row충청북도 음성군
2nd row전라남도 보성군
3rd row강원도 화천군
4th row충청남도 서천군
5th row경상북도 영천시
ValueCountFrequency (%)
경상북도 1706
 
8.5%
경기도 1421
 
7.1%
전라남도 1259
 
6.3%
충청남도 1142
 
5.7%
경상남도 1073
 
5.4%
전라북도 950
 
4.8%
강원도 813
 
4.1%
충청북도 807
 
4.0%
제주특별자치도 232
 
1.2%
청주시 226
 
1.1%
Other values (192) 10371
51.9%
2023-12-11T12:01:42.556622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
40000
36.6%
도 9572
 
8.8%
시 5479
 
5.0%
군 4804
 
4.4%
경 4480
 
4.1%
남 3814
 
3.5%
북 3509
 
3.2%
상 2943
 
2.7%
청 2425
 
2.2%
전 2315
 
2.1%
Other values (122) 29856
27.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 69191
63.4%
Space Separator 40000
36.6%
Math Symbol 3
 
< 0.1%
Dash Punctuation 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 9572
 
13.8%
시 5479
 
7.9%
군 4804
 
6.9%
경 4480
 
6.5%
남 3814
 
5.5%
북 3509
 
5.1%
상 2943
 
4.3%
청 2425
 
3.5%
전 2315
 
3.3%
주 2264
 
3.3%
Other values (119) 27586
39.9%
Space Separator
ValueCountFrequency (%)
40000
100.0%
Math Symbol
ValueCountFrequency (%)
| 3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 69191
63.4%
Common 40006
36.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 9572
 
13.8%
시 5479
 
7.9%
군 4804
 
6.9%
경 4480
 
6.5%
남 3814
 
5.5%
북 3509
 
5.1%
상 2943
 
4.3%
청 2425
 
3.5%
전 2315
 
3.3%
주 2264
 
3.3%
Other values (119) 27586
39.9%
Common
ValueCountFrequency (%)
40000
> 99.9%
| 3
 
< 0.1%
- 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 69191
63.4%
ASCII 40006
36.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
40000
> 99.9%
| 3
 
< 0.1%
- 3
 
< 0.1%
Hangul
ValueCountFrequency (%)
도 9572
 
13.8%
시 5479
 
7.9%
군 4804
 
6.9%
경 4480
 
6.5%
남 3814
 
5.5%
북 3509
 
5.1%
상 2943
 
4.3%
청 2425
 
3.5%
전 2315
 
3.3%
주 2264
 
3.3%
Other values (119) 27586
39.9%

재배면적
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct2322
Distinct (%)26.9%
Missing1384
Missing (%)13.8%
Infinite0
Infinite (%)0.0%
Mean1970.9191
Minimum13
Maximum250000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T12:01:42.709126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile165
Q1446
median951.5
Q32000
95-th percentile5938.5
Maximum250000
Range249987
Interquartile range (IQR)1554

Descriptive statistics

Standard deviation6219.1337
Coefficient of variation (CV)3.1554485
Kurtosis758.9635
Mean1970.9191
Median Absolute Deviation (MAD)621.5
Skewness23.683844
Sum16981439
Variance38677623
MonotonicityNot monotonic
2023-12-11T12:01:42.918646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
330 924
 
9.2%
660 597
 
6.0%
1000 410
 
4.1%
500 239
 
2.4%
600 195
 
1.9%
2000 146
 
1.5%
400 129
 
1.3%
165 128
 
1.3%
1500 114
 
1.1%
3000 113
 
1.1%
Other values (2312) 5621
56.2%
(Missing) 1384
 
13.8%
ValueCountFrequency (%)
13 1
 
< 0.1%
15 3
 
< 0.1%
17 1
 
< 0.1%
20 5
 
0.1%
21 1
 
< 0.1%
25 1
 
< 0.1%
30 6
0.1%
33 13
0.1%
35 3
 
< 0.1%
36 1
 
< 0.1%
ValueCountFrequency (%)
250000 1
< 0.1%
240000 1
< 0.1%
188618 1
< 0.1%
141074 1
< 0.1%
140356 1
< 0.1%
138742 1
< 0.1%
130342 1
< 0.1%
104177 1
< 0.1%
77100 1
< 0.1%
69800 1
< 0.1%

조사물량
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct652
Distinct (%)6.9%
Missing562
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean22280.269
Minimum0
Maximum1.7 × 108
Zeros23
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T12:01:43.081375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q150
median300
Q31500
95-th percentile10000
Maximum1.7 × 108
Range1.7 × 108
Interquartile range (IQR)1450

Descriptive statistics

Standard deviation1750997.8
Coefficient of variation (CV)78.58962
Kurtosis9413.031
Mean22280.269
Median Absolute Deviation (MAD)290
Skewness96.958806
Sum2.1028117 × 108
Variance3.0659934 × 1012
MonotonicityNot monotonic
2023-12-11T12:01:43.232003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.0 756
 
7.6%
1000.0 561
 
5.6%
500.0 547
 
5.5%
50.0 484
 
4.8%
200.0 467
 
4.7%
300.0 451
 
4.5%
2000.0 342
 
3.4%
10.0 321
 
3.2%
20.0 290
 
2.9%
30.0 290
 
2.9%
Other values (642) 4929
49.3%
(Missing) 562
 
5.6%
ValueCountFrequency (%)
0.0 23
 
0.2%
0.3 1
 
< 0.1%
0.5 4
 
< 0.1%
0.6 3
 
< 0.1%
0.8 3
 
< 0.1%
0.9 2
 
< 0.1%
1.0 246
2.5%
1.1 1
 
< 0.1%
1.2 21
 
0.2%
1.4 6
 
0.1%
ValueCountFrequency (%)
170000000.0 1
< 0.1%
3500000.0 2
< 0.1%
3000000.0 1
< 0.1%
1000000.0 1
< 0.1%
995710.0 1
< 0.1%
600000.0 1
< 0.1%
500000.0 1
< 0.1%
434000.0 1
< 0.1%
400000.0 2
< 0.1%
392300.0 2
< 0.1%

등록일자
Real number (ℝ)

Distinct167
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20200619
Minimum20200122
Maximum20200831
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T12:01:43.407617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20200122
5-th percentile20200226
Q120200525
median20200622
Q320200722
95-th percentile20200824
Maximum20200831
Range709
Interquartile range (IQR)197

Descriptive statistics

Standard deviation157.46831
Coefficient of variation (CV)7.7952218 × 10-6
Kurtosis0.74187632
Mean20200619
Median Absolute Deviation (MAD)99
Skewness-0.94335193
Sum2.0200619 × 1011
Variance24796.268
MonotonicityNot monotonic
2023-12-11T12:01:43.580212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20200715 198
 
2.0%
20200615 194
 
1.9%
20200616 188
 
1.9%
20200610 175
 
1.8%
20200609 172
 
1.7%
20200611 170
 
1.7%
20200623 161
 
1.6%
20200625 161
 
1.6%
20200702 161
 
1.6%
20200716 157
 
1.6%
Other values (157) 8263
82.6%
ValueCountFrequency (%)
20200122 1
 
< 0.1%
20200123 2
 
< 0.1%
20200128 28
0.3%
20200129 28
0.3%
20200130 25
0.2%
20200131 6
 
0.1%
20200203 23
0.2%
20200204 27
0.3%
20200205 40
0.4%
20200206 23
0.2%
ValueCountFrequency (%)
20200831 81
0.8%
20200829 1
 
< 0.1%
20200828 26
 
0.3%
20200827 92
0.9%
20200826 102
1.0%
20200825 140
1.4%
20200824 84
0.8%
20200822 2
 
< 0.1%
20200821 28
 
0.3%
20200820 122
1.2%

분석결과
Text

Distinct102
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T12:01:43.846696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length2
Mean length2.3059
Min length2

Characters and Unicode

Total characters23059
Distinct characters32
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique60 ?
Unique (%)0.6%

Sample

1st row적합
2nd row적합
3rd row적합
4th row적합
5th row적합
ValueCountFrequency (%)
적합 9839
95.3%
출하연기 158
 
1.5%
부적합 158
 
1.5%
2020/07/04 4
 
< 0.1%
단계 3
 
< 0.1%
2020/07/16 3
 
< 0.1%
2020/07/17 3
 
< 0.1%
2020/07/18 3
 
< 0.1%
2020/04/30 3
 
< 0.1%
2020/08/28 3
 
< 0.1%
Other values (98) 151
 
1.5%
2023-12-11T12:01:44.315740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
적 10000
43.4%
합 10000
43.4%
0 540
 
2.3%
2 402
 
1.7%
328
 
1.4%
/ 316
 
1.4%
부 161
 
0.7%
( 161
 
0.7%
기 161
 
0.7%
) 161
 
0.7%
Other values (22) 829
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 20829
90.3%
Decimal Number 1264
 
5.5%
Space Separator 328
 
1.4%
Other Punctuation 316
 
1.4%
Open Punctuation 161
 
0.7%
Close Punctuation 161
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
적 10000
48.0%
합 10000
48.0%
부 161
 
0.8%
기 161
 
0.8%
출 158
 
0.8%
하 158
 
0.8%
연 158
 
0.8%
회 3
 
< 0.1%
수 3
 
< 0.1%
폐 3
 
< 0.1%
Other values (8) 24
 
0.1%
Decimal Number
ValueCountFrequency (%)
0 540
42.7%
2 402
31.8%
1 56
 
4.4%
7 52
 
4.1%
8 43
 
3.4%
3 41
 
3.2%
6 41
 
3.2%
5 37
 
2.9%
4 32
 
2.5%
9 20
 
1.6%
Space Separator
ValueCountFrequency (%)
328
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 316
100.0%
Open Punctuation
ValueCountFrequency (%)
( 161
100.0%
Close Punctuation
ValueCountFrequency (%)
) 161
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 20829
90.3%
Common 2230
 
9.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
적 10000
48.0%
합 10000
48.0%
부 161
 
0.8%
기 161
 
0.8%
출 158
 
0.8%
하 158
 
0.8%
연 158
 
0.8%
회 3
 
< 0.1%
수 3
 
< 0.1%
폐 3
 
< 0.1%
Other values (8) 24
 
0.1%
Common
ValueCountFrequency (%)
0 540
24.2%
2 402
18.0%
328
14.7%
/ 316
14.2%
( 161
 
7.2%
) 161
 
7.2%
1 56
 
2.5%
7 52
 
2.3%
8 43
 
1.9%
3 41
 
1.8%
Other values (4) 130
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 20829
90.3%
ASCII 2230
 
9.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
적 10000
48.0%
합 10000
48.0%
부 161
 
0.8%
기 161
 
0.8%
출 158
 
0.8%
하 158
 
0.8%
연 158
 
0.8%
회 3
 
< 0.1%
수 3
 
< 0.1%
폐 3
 
< 0.1%
Other values (8) 24
 
0.1%
ASCII
ValueCountFrequency (%)
0 540
24.2%
2 402
18.0%
328
14.7%
/ 316
14.2%
( 161
 
7.2%
) 161
 
7.2%
1 56
 
2.5%
7 52
 
2.3%
8 43
 
1.9%
3 41
 
1.8%
Other values (4) 130
 
5.8%

Interactions

2023-12-11T12:01:39.371598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:01:38.619216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:01:38.989869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:01:39.478577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:01:38.743443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:01:39.101696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:01:39.602133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:01:38.877765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:01:39.237971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T12:01:44.430384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수거단계재배양식재배면적조사물량등록일자
수거단계1.0000.4490.0000.0000.191
재배양식0.4491.0000.0930.0000.167
재배면적0.0000.0931.0000.0000.000
조사물량0.0000.0000.0001.0000.000
등록일자0.1910.1670.0000.0001.000
2023-12-11T12:01:44.543441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수거단계재배양식
수거단계1.0000.481
재배양식0.4811.000
2023-12-11T12:01:44.657303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
재배면적조사물량등록일자수거단계재배양식
재배면적1.0000.5450.0370.0000.050
조사물량0.5451.0000.0680.0000.000
등록일자0.0370.0681.0000.1470.085
수거단계0.0000.0000.1471.0000.481
재배양식0.0500.0000.0850.4811.000

Missing values

2023-12-11T12:01:39.791212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T12:01:39.941080image/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.
2023-12-11T12:01:40.062584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

품목명수거단계재배양식주소재배면적조사물량등록일자분석결과
7094수박생산친환경(인증) 무농약충청북도 음성군46666000.020200708적합
14587차(전체)생산친환경(인증) 유기가공품전라남도 보성군35710.020200526적합
8942애호박(인큐애호박,진주애호박)생산일반강원도 화천군31312000.020200625적합
11576부추생산일반충청남도 서천군530100.020200611적합
13443마늘생산일반경상북도 영천시600600.020200602적합
9000부추유통/판매친환경(인증) 무농약전라남도 나주시<NA><NA>20200625적합
3799감자유통/판매친환경(인증) 무농약경기도 파주시<NA><NA>20200729적합
19526딸기생산일반충청남도 서산시89390.020200205적합
8420파프리카생산GAP(인증)경기도 고양시287180000.020200630적합
4780만가닥버섯유통/판매친환경(인증) 무농약경상북도 성주군<NA>5.020200722적합
품목명수거단계재배양식주소재배면적조사물량등록일자분석결과
1015대파생산일반경기도 포천시500300.020200824적합
3773얼갈이배추생산일반경기도 평택시1502000.020200730적합
19795상추유통/판매일반대전광역시 유성구<NA>4.020200128적합
3697배생산GAP(인증)충청남도 아산시1000250.020200730적합
12891블루베리생산친환경(인증) 무농약전라남도 화순군19831000.020200604적합
16917아스파라거스생산일반강원도 양구군9900180.020200506적합
17399가지생산일반경상남도 창녕군3903350.020200423적합
4102배생산일반전라남도 곡성군1000100.020200728적합
14033고사리생산친환경(인증) 유기전라북도 진안군13200150.020200528적합
716참나물생산일반전라북도 완주군33050.020200825적합

Duplicate rows

Most frequently occurring

품목명수거단계재배양식주소재배면적조사물량등록일자분석결과# duplicates
172양파생산일반경상북도 안동시1000500.020200625적합17
108방울토마토생산GAP(인증)충청북도 충주시6003000.020200618적합9
117배생산일반전라남도 곡성군1000100.020200728적합9
122복숭아생산일반경상북도 영천시33050.020200708적합6
198자두생산GAP(인증)경상북도 군위군10002.020200702적합6
248풋고추생산일반강원도 정선군300300.020200721적합6
259홍고추(붉은고추)생산일반경상북도 포항시330100.020200728적합6
20곤달비생산일반경상북도 경주시750150.020200324적합5
114배생산GAP(인증)경상남도 진주시1500500.020200727적합5
126복숭아생산일반충청북도 제천시10002000.020200804적합5