Overview

Dataset statistics

Number of variables9
Number of observations10000
Missing cells4612
Missing cells (%)5.1%
Duplicate rows66
Duplicate rows (%)0.7%
Total size in memory800.8 KiB
Average record size in memory82.0 B

Variable types

Text3
Categorical3
Numeric2
DateTime1

Dataset

Description생산 또는 유통 중인 농산물에 대해 시군, 생산자(판매자), 작물별로 중금속 여부를 분석한 결과
Author국립농산물품질관리원
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20220204000000001677

Alerts

Dataset has 66 (0.7%) duplicate rowsDuplicates
재배면적 is highly overall correlated with 조사물량 and 1 other fieldsHigh correlation
조사물량 is highly overall correlated with 재배면적High correlation
수거단계 is highly overall correlated with 재배면적High correlation
수거단계 is highly imbalanced (56.6%)Imbalance
재배양식 is highly imbalanced (87.7%)Imbalance
분석결과 is highly imbalanced (94.7%)Imbalance
재배면적 has 2803 (28.0%) missing valuesMissing
조사물량 has 1808 (18.1%) missing valuesMissing
조사물량 is highly skewed (γ1 = 89.69777119)Skewed

Reproduction

Analysis started2023-12-22 22:19:07.217089
Analysis finished2023-12-22 22:19:15.151044
Duration7.93 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

품목명
Text

Distinct337
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-22T22:19:16.049124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length23
Mean length4.7453
Min length1

Characters and Unicode

Total characters47453
Distinct characters329
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)0.9%

Sample

1st row포도
2nd row밤고구마
3rd row백태
4th row현미
5th row멥쌀(일반)
ValueCountFrequency (%)
멥쌀(일반 2418
24.1%
현미 1651
 
16.5%
홍고추(붉은고추 297
 
3.0%
수미(슈페리어 215
 
2.1%
일반부추(조선부추 192
 
1.9%
풋고추 188
 
1.9%
백태 186
 
1.9%
대파 183
 
1.8%
시금치 179
 
1.8%
밤고구마 176
 
1.8%
Other values (329) 4338
43.3%
2023-12-22T22:19:17.984835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 4480
 
9.4%
) 4480
 
9.4%
반 2900
 
6.1%
일 2896
 
6.1%
쌀 2475
 
5.2%
멥 2418
 
5.1%
미 1999
 
4.2%
추 1752
 
3.7%
현 1686
 
3.6%
고 1325
 
2.8%
Other values (319) 21042
44.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 37944
80.0%
Open Punctuation 4480
 
9.4%
Close Punctuation 4480
 
9.4%
Other Punctuation 424
 
0.9%
Decimal Number 90
 
0.2%
Space Separator 23
 
< 0.1%
Uppercase Letter 12
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
반 2900
 
7.6%
일 2896
 
7.6%
쌀 2475
 
6.5%
멥 2418
 
6.4%
미 1999
 
5.3%
추 1752
 
4.6%
현 1686
 
4.4%
고 1325
 
3.5%
수 1196
 
3.2%
마 619
 
1.6%
Other values (305) 18678
49.2%
Decimal Number
ValueCountFrequency (%)
1 26
28.9%
5 22
24.4%
4 14
15.6%
0 13
14.4%
3 7
 
7.8%
6 5
 
5.6%
7 3
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
B 4
33.3%
M 4
33.3%
A 4
33.3%
Open Punctuation
ValueCountFrequency (%)
( 4480
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4480
100.0%
Other Punctuation
ValueCountFrequency (%)
. 424
100.0%
Space Separator
ValueCountFrequency (%)
23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 37944
80.0%
Common 9497
 
20.0%
Latin 12
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
반 2900
 
7.6%
일 2896
 
7.6%
쌀 2475
 
6.5%
멥 2418
 
6.4%
미 1999
 
5.3%
추 1752
 
4.6%
현 1686
 
4.4%
고 1325
 
3.5%
수 1196
 
3.2%
마 619
 
1.6%
Other values (305) 18678
49.2%
Common
ValueCountFrequency (%)
( 4480
47.2%
) 4480
47.2%
. 424
 
4.5%
1 26
 
0.3%
23
 
0.2%
5 22
 
0.2%
4 14
 
0.1%
0 13
 
0.1%
3 7
 
0.1%
6 5
 
0.1%
Latin
ValueCountFrequency (%)
B 4
33.3%
M 4
33.3%
A 4
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 37944
80.0%
ASCII 9509
 
20.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 4480
47.1%
) 4480
47.1%
. 424
 
4.5%
1 26
 
0.3%
23
 
0.2%
5 22
 
0.2%
4 14
 
0.1%
0 13
 
0.1%
3 7
 
0.1%
6 5
 
0.1%
Other values (4) 15
 
0.2%
Hangul
ValueCountFrequency (%)
반 2900
 
7.6%
일 2896
 
7.6%
쌀 2475
 
6.5%
멥 2418
 
6.4%
미 1999
 
5.3%
추 1752
 
4.6%
현 1686
 
4.4%
고 1325
 
3.5%
수 1196
 
3.2%
마 619
 
1.6%
Other values (305) 18678
49.2%

수거단계
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
생산
7199 
유통/판매
2788 
출하
 
7
저장
 
6

Length

Max length5
Median length2
Mean length2.8364
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row생산
2nd row유통/판매
3rd row유통/판매
4th row생산
5th row유통/판매

Common Values

ValueCountFrequency (%)
생산 7199
72.0%
유통/판매 2788
 
27.9%
출하 7
 
0.1%
저장 6
 
0.1%

Length

2023-12-22T22:19:18.833565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-22T22:19:19.472858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
생산 7199
72.0%
유통/판매 2788
 
27.9%
출하 7
 
0.1%
저장 6
 
0.1%

재배양식
Categorical

IMBALANCE 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
일반
9519 
친환경(인증) 무농약
 
255
직불제(쌀소득)
 
86
GAP(인증)
 
76
친환경(인증) 저농약
 
30
Other values (3)
 
34

Length

Max length11
Median length3
Mean length3.3396
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반 9519
95.2%
친환경(인증) 무농약 255
 
2.5%
직불제(쌀소득) 86
 
0.9%
GAP(인증) 76
 
0.8%
친환경(인증) 저농약 30
 
0.3%
친환경(인증) 유기 30
 
0.3%
지리적표시 2
 
< 0.1%
우수식품 2
 
< 0.1%

Length

2023-12-22T22:19:20.122989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-22T22:19:20.685496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반 9519
92.3%
친환경(인증 315
 
3.1%
무농약 255
 
2.5%
직불제(쌀소득 86
 
0.8%
gap(인증 76
 
0.7%
저농약 30
 
0.3%
유기 30
 
0.3%
지리적표시 2
 
< 0.1%
우수식품 2
 
< 0.1%

생산자
Text

Distinct761
Distinct (%)7.6%
Missing1
Missing (%)< 0.1%
Memory size156.2 KiB
2023-12-22T22:19:21.460524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length3
Mean length3.379938
Min length3

Characters and Unicode

Total characters33796
Distinct characters341
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique503 ?
Unique (%)5.0%

Sample

1st row정**
2nd row생**불명
3rd row생**불명
4th row김**
5th row이**
ValueCountFrequency (%)
김 1783
17.8%
이 1221
 
12.2%
박 717
 
7.2%
정 435
 
4.4%
최 399
 
4.0%
강 282
 
2.8%
조 268
 
2.7%
장 202
 
2.0%
권 200
 
2.0%
임 182
 
1.8%
Other values (747) 4310
43.1%
2023-12-22T22:19:23.061620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 20054
59.3%
김 1795
 
5.3%
이 1260
 
3.7%
박 717
 
2.1%
조 467
 
1.4%
정 450
 
1.3%
협 423
 
1.3%
최 402
 
1.2%
농 356
 
1.1%
강 290
 
0.9%
Other values (331) 7582
 
22.4%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 20060
59.4%
Other Letter 13597
40.2%
Uppercase Letter 51
 
0.2%
Open Punctuation 46
 
0.1%
Decimal Number 22
 
0.1%
Dash Punctuation 7
 
< 0.1%
Close Punctuation 7
 
< 0.1%
Lowercase Letter 5
 
< 0.1%
Modifier Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 1795
 
13.2%
이 1260
 
9.3%
박 717
 
5.3%
조 467
 
3.4%
정 450
 
3.3%
협 423
 
3.1%
최 402
 
3.0%
농 356
 
2.6%
강 290
 
2.1%
합 244
 
1.8%
Other values (301) 7193
52.9%
Decimal Number
ValueCountFrequency (%)
1 4
18.2%
5 4
18.2%
6 4
18.2%
2 3
13.6%
0 2
9.1%
4 1
 
4.5%
3 1
 
4.5%
8 1
 
4.5%
7 1
 
4.5%
9 1
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
P 15
29.4%
C 15
29.4%
R 13
25.5%
A 2
 
3.9%
B 2
 
3.9%
S 1
 
2.0%
O 1
 
2.0%
G 1
 
2.0%
D 1
 
2.0%
Lowercase Letter
ValueCountFrequency (%)
o 2
40.0%
r 1
20.0%
p 1
20.0%
c 1
20.0%
Other Punctuation
ValueCountFrequency (%)
* 20054
> 99.9%
/ 4
 
< 0.1%
: 2
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 46
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 20143
59.6%
Hangul 13597
40.2%
Latin 56
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 1795
 
13.2%
이 1260
 
9.3%
박 717
 
5.3%
조 467
 
3.4%
정 450
 
3.3%
협 423
 
3.1%
최 402
 
3.0%
농 356
 
2.6%
강 290
 
2.1%
합 244
 
1.8%
Other values (301) 7193
52.9%
Common
ValueCountFrequency (%)
* 20054
99.6%
( 46
 
0.2%
- 7
 
< 0.1%
) 7
 
< 0.1%
/ 4
 
< 0.1%
1 4
 
< 0.1%
5 4
 
< 0.1%
6 4
 
< 0.1%
2 3
 
< 0.1%
: 2
 
< 0.1%
Other values (7) 8
 
< 0.1%
Latin
ValueCountFrequency (%)
P 15
26.8%
C 15
26.8%
R 13
23.2%
A 2
 
3.6%
B 2
 
3.6%
o 2
 
3.6%
S 1
 
1.8%
O 1
 
1.8%
r 1
 
1.8%
p 1
 
1.8%
Other values (3) 3
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20199
59.8%
Hangul 13596
40.2%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 20054
99.3%
( 46
 
0.2%
P 15
 
0.1%
C 15
 
0.1%
R 13
 
0.1%
- 7
 
< 0.1%
) 7
 
< 0.1%
/ 4
 
< 0.1%
1 4
 
< 0.1%
5 4
 
< 0.1%
Other values (20) 30
 
0.1%
Hangul
ValueCountFrequency (%)
김 1795
 
13.2%
이 1260
 
9.3%
박 717
 
5.3%
조 467
 
3.4%
정 450
 
3.3%
협 423
 
3.1%
최 402
 
3.0%
농 356
 
2.6%
강 290
 
2.1%
합 244
 
1.8%
Other values (300) 7192
52.9%
Compat Jamo
ValueCountFrequency (%)
ㄴ 1
100.0%

주소
Text

Distinct384
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-22T22:19:24.481814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length14
Mean length9.9517
Min length8

Characters and Unicode

Total characters99517
Distinct characters131
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)0.3%

Sample

1st row충청남도 천안시
2nd row강원 원주시
3rd row충청북도 괴산군
4th row경북 상주시
5th row전라북도 임실군
ValueCountFrequency (%)
충남 862
 
4.3%
경북 852
 
4.3%
경상북도 827
 
4.1%
충청남도 816
 
4.1%
경상남도 628
 
3.1%
경남 613
 
3.1%
강원도 582
 
2.9%
경기도 558
 
2.8%
전남 558
 
2.8%
강원 535
 
2.7%
Other values (219) 13169
65.8%
2023-12-22T22:19:26.728522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
40000
40.2%
군 5682
 
5.7%
도 4928
 
5.0%
시 4380
 
4.4%
남 4292
 
4.3%
경 4114
 
4.1%
북 3225
 
3.2%
충 2651
 
2.7%
청 1881
 
1.9%
전 1709
 
1.7%
Other values (121) 26655
26.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 59517
59.8%
Space Separator 40000
40.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 5682
 
9.5%
도 4928
 
8.3%
시 4380
 
7.4%
남 4292
 
7.2%
경 4114
 
6.9%
북 3225
 
5.4%
충 2651
 
4.5%
청 1881
 
3.2%
전 1709
 
2.9%
주 1589
 
2.7%
Other values (120) 25066
42.1%
Space Separator
ValueCountFrequency (%)
40000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 59517
59.8%
Common 40000
40.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 5682
 
9.5%
도 4928
 
8.3%
시 4380
 
7.4%
남 4292
 
7.2%
경 4114
 
6.9%
북 3225
 
5.4%
충 2651
 
4.5%
청 1881
 
3.2%
전 1709
 
2.9%
주 1589
 
2.7%
Other values (120) 25066
42.1%
Common
ValueCountFrequency (%)
40000
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 59517
59.8%
ASCII 40000
40.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
40000
100.0%
Hangul
ValueCountFrequency (%)
군 5682
 
9.5%
도 4928
 
8.3%
시 4380
 
7.4%
남 4292
 
7.2%
경 4114
 
6.9%
북 3225
 
5.4%
충 2651
 
4.5%
청 1881
 
3.2%
전 1709
 
2.9%
주 1589
 
2.7%
Other values (120) 25066
42.1%

재배면적
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2843
Distinct (%)39.5%
Missing2803
Missing (%)28.0%
Infinite0
Infinite (%)0.0%
Mean1991.2118
Minimum5
Maximum40320
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-22T22:19:27.688504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile195
Q1660
median1418
Q32572
95-th percentile5207
Maximum40320
Range40315
Interquartile range (IQR)1912

Descriptive statistics

Standard deviation2474.3478
Coefficient of variation (CV)1.2426341
Kurtosis72.914557
Mean1991.2118
Median Absolute Deviation (MAD)888
Skewness6.4745945
Sum14330751
Variance6122396.8
MonotonicityNot monotonic
2023-12-22T22:19:28.522798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
330.0 293
 
2.9%
1000.0 168
 
1.7%
660.0 142
 
1.4%
300.0 103
 
1.0%
100.0 95
 
0.9%
500.0 95
 
0.9%
200.0 93
 
0.9%
165.0 65
 
0.7%
600.0 65
 
0.7%
2000.0 55
 
0.5%
Other values (2833) 6023
60.2%
(Missing) 2803
28.0%
ValueCountFrequency (%)
5.0 1
 
< 0.1%
6.0 1
 
< 0.1%
15.0 1
 
< 0.1%
20.0 4
< 0.1%
24.0 2
 
< 0.1%
25.0 2
 
< 0.1%
30.0 5
0.1%
33.0 7
0.1%
38.0 1
 
< 0.1%
40.0 2
 
< 0.1%
ValueCountFrequency (%)
40320.0 1
 
< 0.1%
40000.0 3
< 0.1%
39625.0 1
 
< 0.1%
39000.0 1
 
< 0.1%
34711.0 1
 
< 0.1%
33000.0 2
< 0.1%
30177.0 1
 
< 0.1%
30000.0 2
< 0.1%
27594.0 1
 
< 0.1%
27000.0 1
 
< 0.1%

조사물량
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct1169
Distinct (%)14.3%
Missing1808
Missing (%)18.1%
Infinite0
Infinite (%)0.0%
Mean3496.3426
Minimum0
Maximum15002016
Zeros43
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-22T22:19:29.260448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.11
Q1100
median520
Q31375.5
95-th percentile5000
Maximum15002016
Range15002016
Interquartile range (IQR)1275.5

Descriptive statistics

Standard deviation166249.89
Coefficient of variation (CV)47.549657
Kurtosis8090.5529
Mean3496.3426
Median Absolute Deviation (MAD)480
Skewness89.697771
Sum28642039
Variance2.7639027 × 1010
MonotonicityNot monotonic
2023-12-22T22:19:30.368741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.0 448
 
4.5%
1000.0 374
 
3.7%
300.0 294
 
2.9%
500.0 288
 
2.9%
200.0 281
 
2.8%
50.0 242
 
2.4%
2000.0 226
 
2.3%
10.0 201
 
2.0%
20.0 199
 
2.0%
2.0 176
 
1.8%
Other values (1159) 5463
54.6%
(Missing) 1808
 
18.1%
ValueCountFrequency (%)
0.0 43
 
0.4%
0.1 15
 
0.1%
0.5 31
 
0.3%
1.0 88
0.9%
1.4 1
 
< 0.1%
1.5 2
 
< 0.1%
2.0 176
1.8%
2.4 2
 
< 0.1%
2.5 2
 
< 0.1%
3.0 50
 
0.5%
ValueCountFrequency (%)
15002016.0 1
< 0.1%
1036449.0 1
< 0.1%
400100.0 1
< 0.1%
190000.0 1
< 0.1%
113855.0 1
< 0.1%
100000.0 1
< 0.1%
98000.0 1
< 0.1%
97200.0 1
< 0.1%
66000.0 1
< 0.1%
56350.0 1
< 0.1%

등록일자
Date

Distinct1912
Distinct (%)19.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2007-07-06 00:00:00
Maximum2022-06-03 00:00:00
2023-12-22T22:19:30.886040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-22T22:19:31.439949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

분석결과
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
적합
9898 
부적합 (폐기)
 
100
부적합(회수폐기 및 생산 단계 재조사)
 
2

Length

Max length21
Median length2
Mean length2.0638
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
적합 9898
99.0%
부적합 (폐기) 100
 
1.0%
부적합(회수폐기 및 생산 단계 재조사) 2
 
< 0.1%

Length

2023-12-22T22:19:32.183040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-22T22:19:32.897502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
적합 9898
97.9%
부적합 100
 
1.0%
폐기 100
 
1.0%
부적합(회수폐기 2
 
< 0.1%
및 2
 
< 0.1%
생산 2
 
< 0.1%
단계 2
 
< 0.1%
재조사 2
 
< 0.1%

Interactions

2023-12-22T22:19:11.231991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-22T22:19:10.041402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-22T22:19:12.038599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-22T22:19:10.568176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-22T22:19:33.289651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수거단계재배양식재배면적조사물량분석결과
수거단계1.0000.212NaN0.0000.047
재배양식0.2121.0000.1960.0000.000
재배면적NaN0.1961.0000.0000.000
조사물량0.0000.0000.0001.0000.000
분석결과0.0470.0000.0000.0001.000
2023-12-22T22:19:33.720076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
재배양식수거단계분석결과
재배양식1.0000.0970.000
수거단계0.0971.0000.044
분석결과0.0000.0441.000
2023-12-22T22:19:34.237885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
재배면적조사물량수거단계재배양식분석결과
재배면적1.0000.7041.0000.1040.000
조사물량0.7041.0000.0000.0000.000
수거단계1.0000.0001.0000.0970.044
재배양식0.1040.0000.0971.0000.000
분석결과0.0000.0000.0440.0001.000

Missing values

2023-12-22T22:19:12.752213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-22T22:19:13.958317image/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.
2023-12-22T22:19:14.791834image/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

품목명수거단계재배양식생산자주소재배면적조사물량등록일자분석결과
10821포도생산일반정**충청남도 천안시1944.01000.02018-08-09적합
54132밤고구마유통/판매일반생**불명강원 원주시<NA>30.02011-09-05적합
25539백태유통/판매일반생**불명충청북도 괴산군<NA>50.02015-07-01적합
66087현미생산일반김**경북 상주시698.0200.02007-10-09적합
26633멥쌀(일반)유통/판매일반이**전라북도 임실군<NA><NA>2015-05-22적합
37909멥쌀(일반)생산일반김**경북 구미시477.010000.02013-10-10적합
23679수미(슈페리어)유통/판매일반미**서울특별시 은평구<NA>1.02016-06-30적합
35740시금치유통/판매일반이**경기도 남양주시<NA><NA>2014-06-23적합
42946고냉지배추유통/판매일반임**강원 홍천군<NA><NA>2013-10-18적합
12991대파유통/판매일반임**협전라남도 신안군<NA><NA>2018-04-17적합
품목명수거단계재배양식생산자주소재배면적조사물량등록일자분석결과
12427무유통/판매일반정**전라북도 고창군<NA><NA>2018-06-14적합
45621밤고구마유통/판매일반대**협경기 여주군<NA><NA>2012-09-04적합
53550멥쌀(일반)생산일반박**충북 보은군758.0100.02011-11-22적합
42583멥쌀(일반)생산일반이**경기 화성시760.0500.02013-09-30적합
14278복숭아생산일반정**경상북도 청도군1216.02100.02017-07-19적합
31762멥쌀(일반)생산일반정**경상북도 경주시1000.0100.02014-10-01적합
32402현미생산일반손**전라남도 보성군2228.0110.02014-10-07적합
37346청초(일반)생산일반반**경북 울진군600.0200.02013-08-09적합
13469딸기생산일반정**경상남도 산청군1200.050.02017-12-21적합
60566멥쌀(일반)생산일반조**충북 제천시1547.0720.02009-10-06적합

Duplicate rows

Most frequently occurring

품목명수거단계재배양식생산자주소재배면적조사물량등록일자분석결과# duplicates
14멥쌀(일반)생산일반김**경상북도 경주시1000.0100.02014-10-01적합5
13멥쌀(일반)생산일반김**경북 영덕군330.0300.02012-09-28적합4
21멥쌀(일반)유통/판매일반-***강원도 철원군<NA><NA>2017-11-03적합4
40신고생산친환경(인증) 저농약김**울산 울주군100.050.02012-12-18적합4
2기타가공품유통/판매일반소**원충청북도 음성군<NA><NA>2016-11-22적합3
17멥쌀(일반)생산일반정**충북 보은군2200.01100.02008-10-09적합3
19멥쌀(일반)생산일반최**강원도 정선군2000.0700.02020-10-09적합3
22멥쌀(일반)유통/판매일반-***강원도 철원군<NA><NA>2017-11-09적합3
27멥쌀(일반)유통/판매일반이**충청남도 당진시<NA>0.02015-06-11적합3
42양송이버섯505호생산일반이**충남 부여군198.030.02011-08-09적합3