Overview

Dataset statistics

Number of variables9
Number of observations10000
Missing cells4570
Missing cells (%)5.1%
Duplicate rows155
Duplicate rows (%)1.6%
Total size in memory800.8 KiB
Average record size in memory82.0 B

Variable types

Text3
Categorical3
Unsupported1
Numeric2

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 155 (1.6%) duplicate rowsDuplicates
수거단계 is highly overall correlated with 재배양식High correlation
재배양식 is highly overall correlated with 수거단계High correlation
수거단계 is highly imbalanced (66.4%)Imbalance
재배양식 is highly imbalanced (89.9%)Imbalance
분석결과 is highly imbalanced (95.2%)Imbalance
재배면적 has 2752 (27.5%) missing valuesMissing
조사물량 has 1804 (18.0%) missing valuesMissing
조사물량 is highly skewed (γ1 = 28.0993152)Skewed
재배면적 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-22 22:20:16.718967
Analysis finished2023-12-22 22:20:24.959587
Duration8.24 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

품목명
Text

Distinct340
Distinct (%)3.4%
Missing4
Missing (%)< 0.1%
Memory size156.2 KiB
2023-12-22T22:20:26.005180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length28
Mean length4.6854742
Min length1

Characters and Unicode

Total characters46836
Distinct characters326
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

Unique104 ?
Unique (%)1.0%

Sample

1st row백태
2nd row흙당근
3rd row풋고추
4th row현미
5th row복숭아
ValueCountFrequency (%)
멥쌀(일반 2463
24.6%
현미 1688
 
16.8%
홍고추(붉은고추 300
 
3.0%
수미(슈페리어 198
 
2.0%
풋고추 189
 
1.9%
일반부추(조선부추 179
 
1.8%
밤고구마 178
 
1.8%
백태 163
 
1.6%
시금치 160
 
1.6%
대파 155
 
1.5%
Other values (336) 4348
43.4%
2023-12-22T22:20:28.080533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 4440
 
9.5%
) 4440
 
9.5%
반 2989
 
6.4%
일 2977
 
6.4%
쌀 2575
 
5.5%
멥 2463
 
5.3%
미 2013
 
4.3%
현 1719
 
3.7%
추 1708
 
3.6%
고 1315
 
2.8%
Other values (316) 20197
43.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 37413
79.9%
Open Punctuation 4440
 
9.5%
Close Punctuation 4440
 
9.5%
Other Punctuation 447
 
1.0%
Decimal Number 65
 
0.1%
Space Separator 25
 
0.1%
Uppercase Letter 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
반 2989
 
8.0%
일 2977
 
8.0%
쌀 2575
 
6.9%
멥 2463
 
6.6%
미 2013
 
5.4%
현 1719
 
4.6%
추 1708
 
4.6%
고 1315
 
3.5%
수 1147
 
3.1%
마 642
 
1.7%
Other values (302) 17865
47.8%
Decimal Number
ValueCountFrequency (%)
4 15
23.1%
1 15
23.1%
5 12
18.5%
0 8
12.3%
6 6
 
9.2%
3 6
 
9.2%
7 3
 
4.6%
Uppercase Letter
ValueCountFrequency (%)
A 2
33.3%
M 2
33.3%
B 2
33.3%
Open Punctuation
ValueCountFrequency (%)
( 4440
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4440
100.0%
Other Punctuation
ValueCountFrequency (%)
. 447
100.0%
Space Separator
ValueCountFrequency (%)
25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 37413
79.9%
Common 9417
 
20.1%
Latin 6
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
반 2989
 
8.0%
일 2977
 
8.0%
쌀 2575
 
6.9%
멥 2463
 
6.6%
미 2013
 
5.4%
현 1719
 
4.6%
추 1708
 
4.6%
고 1315
 
3.5%
수 1147
 
3.1%
마 642
 
1.7%
Other values (302) 17865
47.8%
Common
ValueCountFrequency (%)
( 4440
47.1%
) 4440
47.1%
. 447
 
4.7%
25
 
0.3%
4 15
 
0.2%
1 15
 
0.2%
5 12
 
0.1%
0 8
 
0.1%
6 6
 
0.1%
3 6
 
0.1%
Latin
ValueCountFrequency (%)
A 2
33.3%
M 2
33.3%
B 2
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 37413
79.9%
ASCII 9423
 
20.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 4440
47.1%
) 4440
47.1%
. 447
 
4.7%
25
 
0.3%
4 15
 
0.2%
1 15
 
0.2%
5 12
 
0.1%
0 8
 
0.1%
6 6
 
0.1%
3 6
 
0.1%
Other values (4) 9
 
0.1%
Hangul
ValueCountFrequency (%)
반 2989
 
8.0%
일 2977
 
8.0%
쌀 2575
 
6.9%
멥 2463
 
6.6%
미 2013
 
5.4%
현 1719
 
4.6%
추 1708
 
4.6%
고 1315
 
3.5%
수 1147
 
3.1%
마 642
 
1.7%
Other values (302) 17865
47.8%

수거단계
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
생산
7252 
유통/판매
2729 
출하
 
11
저장
 
4
충청남도 서산시
 
3

Length

Max length11
Median length2
Mean length2.8223
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
생산 7252
72.5%
유통/판매 2729
 
27.3%
출하 11
 
0.1%
저장 4
 
< 0.1%
충청남도 서산시 3
 
< 0.1%
충청남도 태안군 1
 
< 0.1%

Length

2023-12-22T22:20:28.856534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-22T22:20:29.346990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
생산 7252
72.5%
유통/판매 2729
 
27.3%
출하 11
 
0.1%
저장 4
 
< 0.1%
충청남도 4
 
< 0.1%
서산시 3
 
< 0.1%
태안군 1
 
< 0.1%

재배양식
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
일반
9535 
친환경(인증) 무농약
 
235
GAP(인증)
 
87
직불제(쌀소득)
 
81
친환경(인증) 유기
 
34
Other values (7)
 
28

Length

Max length11
Median length3
Mean length3.3224
Min length3

Unique

Unique6 ?
Unique (%)0.1%

Sample

1st row일반
2nd row일반
3rd row일반
4th row일반
5th rowGAP(인증)

Common Values

ValueCountFrequency (%)
일반 9535
95.3%
친환경(인증) 무농약 235
 
2.4%
GAP(인증) 87
 
0.9%
직불제(쌀소득) 81
 
0.8%
친환경(인증) 유기 34
 
0.3%
친환경(인증) 저농약 22
 
0.2%
우수식품 1
 
< 0.1%
2630 1
 
< 0.1%
1626 1
 
< 0.1%
1261 1
 
< 0.1%
Other values (2) 2
 
< 0.1%

Length

2023-12-22T22:20:29.861767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반 9536
92.7%
친환경(인증 291
 
2.8%
무농약 236
 
2.3%
gap(인증 87
 
0.8%
직불제(쌀소득 81
 
0.8%
유기 34
 
0.3%
저농약 22
 
0.2%
우수식품 1
 
< 0.1%
2630 1
 
< 0.1%
1626 1
 
< 0.1%
Other values (2) 2
 
< 0.1%

생산자
Text

Distinct720
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-22T22:20:30.676465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length3
Mean length3.3601
Min length3

Characters and Unicode

Total characters33601
Distinct characters321
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique473 ?
Unique (%)4.7%

Sample

1st row김**
2nd row해**영농조합
3rd row장**
4th row이**
5th row심**
ValueCountFrequency (%)
김 1751
17.5%
이 1272
 
12.7%
박 720
 
7.2%
정 476
 
4.8%
최 459
 
4.6%
강 266
 
2.7%
조 243
 
2.4%
장 199
 
2.0%
권 177
 
1.8%
신 172
 
1.7%
Other values (704) 4265
42.6%
2023-12-22T22:20:32.279148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 20037
59.6%
김 1763
 
5.2%
이 1326
 
3.9%
박 724
 
2.2%
정 490
 
1.5%
최 460
 
1.4%
조 443
 
1.3%
협 428
 
1.3%
농 334
 
1.0%
강 277
 
0.8%
Other values (311) 7319
 
21.8%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 20043
59.7%
Other Letter 13412
39.9%
Uppercase Letter 48
 
0.1%
Open Punctuation 35
 
0.1%
Decimal Number 26
 
0.1%
Close Punctuation 15
 
< 0.1%
Dash Punctuation 10
 
< 0.1%
Lowercase Letter 8
 
< 0.1%
Modifier Symbol 3
 
< 0.1%
Connector Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 1763
 
13.1%
이 1326
 
9.9%
박 724
 
5.4%
정 490
 
3.7%
최 460
 
3.4%
조 443
 
3.3%
협 428
 
3.2%
농 334
 
2.5%
강 277
 
2.1%
합 247
 
1.8%
Other values (284) 6920
51.6%
Uppercase Letter
ValueCountFrequency (%)
P 14
29.2%
C 14
29.2%
R 12
25.0%
O 3
 
6.2%
A 3
 
6.2%
B 1
 
2.1%
S 1
 
2.1%
Lowercase Letter
ValueCountFrequency (%)
o 2
25.0%
g 1
12.5%
n 1
12.5%
h 1
12.5%
c 1
12.5%
p 1
12.5%
r 1
12.5%
Decimal Number
ValueCountFrequency (%)
0 11
42.3%
1 5
19.2%
2 4
 
15.4%
4 3
 
11.5%
5 2
 
7.7%
9 1
 
3.8%
Other Punctuation
ValueCountFrequency (%)
* 20037
> 99.9%
/ 6
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 35
100.0%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 3
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 20133
59.9%
Hangul 13412
39.9%
Latin 56
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 1763
 
13.1%
이 1326
 
9.9%
박 724
 
5.4%
정 490
 
3.7%
최 460
 
3.4%
조 443
 
3.3%
협 428
 
3.2%
농 334
 
2.5%
강 277
 
2.1%
합 247
 
1.8%
Other values (284) 6920
51.6%
Latin
ValueCountFrequency (%)
P 14
25.0%
C 14
25.0%
R 12
21.4%
O 3
 
5.4%
A 3
 
5.4%
o 2
 
3.6%
B 1
 
1.8%
S 1
 
1.8%
g 1
 
1.8%
n 1
 
1.8%
Other values (4) 4
 
7.1%
Common
ValueCountFrequency (%)
* 20037
99.5%
( 35
 
0.2%
) 15
 
0.1%
0 11
 
0.1%
- 10
 
< 0.1%
/ 6
 
< 0.1%
1 5
 
< 0.1%
2 4
 
< 0.1%
` 3
 
< 0.1%
4 3
 
< 0.1%
Other values (3) 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20189
60.1%
Hangul 13412
39.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 20037
99.2%
( 35
 
0.2%
) 15
 
0.1%
P 14
 
0.1%
C 14
 
0.1%
R 12
 
0.1%
0 11
 
0.1%
- 10
 
< 0.1%
/ 6
 
< 0.1%
1 5
 
< 0.1%
Other values (17) 30
 
0.1%
Hangul
ValueCountFrequency (%)
김 1763
 
13.1%
이 1326
 
9.9%
박 724
 
5.4%
정 490
 
3.7%
최 460
 
3.4%
조 443
 
3.3%
협 428
 
3.2%
농 334
 
2.5%
강 277
 
2.1%
합 247
 
1.8%
Other values (284) 6920
51.6%

주소
Text

Distinct397
Distinct (%)4.0%
Missing3
Missing (%)< 0.1%
Memory size156.2 KiB
2023-12-22T22:20:33.349007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length14
Mean length9.9685906
Min length8

Characters and Unicode

Total characters99656
Distinct characters142
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

Unique47 ?
Unique (%)0.5%

Sample

1st row전남 나주시
2nd row경북 상주시
3rd row경상남도 진주시
4th row충청남도 당진시
5th row충청북도 음성군
ValueCountFrequency (%)
경상북도 872
 
4.4%
충청남도 861
 
4.3%
경북 847
 
4.2%
충남 826
 
4.1%
경상남도 650
 
3.3%
강원도 592
 
3.0%
전남 587
 
2.9%
경기도 573
 
2.9%
경남 560
 
2.8%
강원 510
 
2.6%
Other values (226) 13110
65.6%
2023-12-22T22:20:35.306390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
39972
40.1%
군 5695
 
5.7%
도 4997
 
5.0%
시 4383
 
4.4%
남 4283
 
4.3%
경 4173
 
4.2%
북 3202
 
3.2%
충 2611
 
2.6%
청 1837
 
1.8%
전 1714
 
1.7%
Other values (132) 26789
26.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 59649
59.9%
Space Separator 39972
40.1%
Decimal Number 32
 
< 0.1%
Math Symbol 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 5695
 
9.5%
도 4997
 
8.4%
시 4383
 
7.3%
남 4283
 
7.2%
경 4173
 
7.0%
북 3202
 
5.4%
충 2611
 
4.4%
청 1837
 
3.1%
전 1714
 
2.9%
상 1637
 
2.7%
Other values (125) 25117
42.1%
Decimal Number
ValueCountFrequency (%)
0 12
37.5%
9 7
21.9%
1 6
18.8%
2 4
 
12.5%
3 3
 
9.4%
Space Separator
ValueCountFrequency (%)
39972
100.0%
Math Symbol
ValueCountFrequency (%)
| 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 59649
59.9%
Common 40007
40.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 5695
 
9.5%
도 4997
 
8.4%
시 4383
 
7.3%
남 4283
 
7.2%
경 4173
 
7.0%
북 3202
 
5.4%
충 2611
 
4.4%
청 1837
 
3.1%
전 1714
 
2.9%
상 1637
 
2.7%
Other values (125) 25117
42.1%
Common
ValueCountFrequency (%)
39972
99.9%
0 12
 
< 0.1%
9 7
 
< 0.1%
1 6
 
< 0.1%
2 4
 
< 0.1%
3 3
 
< 0.1%
| 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 59649
59.9%
ASCII 40007
40.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
39972
99.9%
0 12
 
< 0.1%
9 7
 
< 0.1%
1 6
 
< 0.1%
2 4
 
< 0.1%
3 3
 
< 0.1%
| 3
 
< 0.1%
Hangul
ValueCountFrequency (%)
군 5695
 
9.5%
도 4997
 
8.4%
시 4383
 
7.3%
남 4283
 
7.2%
경 4173
 
7.0%
북 3202
 
5.4%
충 2611
 
4.4%
청 1837
 
3.1%
전 1714
 
2.9%
상 1637
 
2.7%
Other values (125) 25117
42.1%

재배면적
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2752
Missing (%)27.5%
Memory size156.2 KiB

조사물량
Real number (ℝ)

MISSING  SKEWED 

Distinct1169
Distinct (%)14.3%
Missing1804
Missing (%)18.0%
Infinite0
Infinite (%)0.0%
Mean1657.7111
Minimum0
Maximum400100
Zeros48
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-22T22:20:36.198407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q1100
median540
Q31400
95-th percentile5000
Maximum400100
Range400100
Interquartile range (IQR)1300

Descriptive statistics

Standard deviation8246.4585
Coefficient of variation (CV)4.9746053
Kurtosis1059.4721
Mean1657.7111
Median Absolute Deviation (MAD)490
Skewness28.099315
Sum13586600
Variance68004078
MonotonicityNot monotonic
2023-12-22T22:20:37.062665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.0 436
 
4.4%
1000.0 381
 
3.8%
200.0 312
 
3.1%
500.0 296
 
3.0%
300.0 234
 
2.3%
50.0 228
 
2.3%
2000.0 214
 
2.1%
2.0 202
 
2.0%
20.0 195
 
1.9%
1500.0 187
 
1.9%
Other values (1159) 5511
55.1%
(Missing) 1804
 
18.0%
ValueCountFrequency (%)
0.0 48
 
0.5%
0.1 18
 
0.2%
0.2 1
 
< 0.1%
0.3 1
 
< 0.1%
0.5 22
 
0.2%
0.6 2
 
< 0.1%
1.0 89
0.9%
1.5 4
 
< 0.1%
1.6 1
 
< 0.1%
2.0 202
2.0%
ValueCountFrequency (%)
400100.0 1
< 0.1%
300000.0 1
< 0.1%
240000.0 1
< 0.1%
200000.0 1
< 0.1%
190000.0 1
< 0.1%
130000.0 2
< 0.1%
120000.0 1
< 0.1%
100000.0 1
< 0.1%
80000.0 1
< 0.1%
79380.0 1
< 0.1%

등록일자
Real number (ℝ)

Distinct1965
Distinct (%)19.7%
Missing7
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean20138310
Minimum20061008
Maximum20230601
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-22T22:20:38.195648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20061008
5-th percentile20071008
Q120110906
median20140630
Q320170629
95-th percentile20200924
Maximum20230601
Range169593
Interquartile range (IQR)59723

Descriptive statistics

Standard deviation39445.993
Coefficient of variation (CV)0.0019587538
Kurtosis-0.76979457
Mean20138310
Median Absolute Deviation (MAD)29813
Skewness-0.00095635002
Sum2.0124214 × 1011
Variance1.5559863 × 109
MonotonicityNot monotonic
2023-12-22T22:20:39.232450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20071009 64
 
0.6%
20071004 58
 
0.6%
20071015 55
 
0.5%
20141014 54
 
0.5%
20070928 52
 
0.5%
20131010 46
 
0.5%
20071001 46
 
0.5%
20071016 44
 
0.4%
20070927 43
 
0.4%
20121009 43
 
0.4%
Other values (1955) 9488
94.9%
ValueCountFrequency (%)
20061008 1
 
< 0.1%
20070627 1
 
< 0.1%
20070712 3
< 0.1%
20070716 1
 
< 0.1%
20070718 4
< 0.1%
20070719 5
0.1%
20070723 1
 
< 0.1%
20070724 3
< 0.1%
20070726 1
 
< 0.1%
20070727 1
 
< 0.1%
ValueCountFrequency (%)
20230601 1
< 0.1%
20230518 1
< 0.1%
20230510 2
< 0.1%
20230502 1
< 0.1%
20230328 1
< 0.1%
20230327 1
< 0.1%
20230321 1
< 0.1%
20230320 1
< 0.1%
20230314 1
< 0.1%
20230227 2
< 0.1%

분석결과
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
적합
9883 
부적합 (폐기)
 
109
<NA>
 
7
부적합(회수폐기 및 생산 단계 재조사)
 
1

Length

Max length21
Median length2
Mean length2.0687
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
적합 9883
98.8%
부적합 (폐기) 109
 
1.1%
<NA> 7
 
0.1%
부적합(회수폐기 및 생산 단계 재조사) 1
 
< 0.1%

Length

2023-12-22T22:20:40.377641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-22T22:20:41.032163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
적합 9883
97.7%
부적합 109
 
1.1%
폐기 109
 
1.1%
na 7
 
0.1%
부적합(회수폐기 1
 
< 0.1%
및 1
 
< 0.1%
생산 1
 
< 0.1%
단계 1
 
< 0.1%
재조사 1
 
< 0.1%

Interactions

2023-12-22T22:20:20.695878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-22T22:20:19.635988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-22T22:20:21.575837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-22T22:20:20.156904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-22T22:20:41.598573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수거단계재배양식조사물량등록일자분석결과
수거단계1.0000.9300.0000.3480.047
재배양식0.9301.0000.0280.2240.000
조사물량0.0000.0281.0000.0480.000
등록일자0.3480.2240.0481.0000.060
분석결과0.0470.0000.0000.0601.000
2023-12-22T22:20:42.128621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
재배양식수거단계분석결과
재배양식1.0000.6360.000
수거단계0.6361.0000.044
분석결과0.0000.0441.000
2023-12-22T22:20:42.685745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
조사물량등록일자수거단계재배양식분석결과
조사물량1.000-0.0180.0000.0150.000
등록일자-0.0181.0000.2150.1080.037
수거단계0.0000.2151.0000.6360.044
재배양식0.0150.1080.6361.0000.000
분석결과0.0000.0370.0440.0001.000

Missing values

2023-12-22T22:20:22.575072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-22T22:20:23.626229image/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.
2023-12-22T22:20:24.216502image/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
40175백태생산일반김**전남 나주시72030.020131028적합
49818흙당근유통/판매일반해**영농조합경북 상주시NaN<NA>20120426적합
9535풋고추유통/판매일반장**경상남도 진주시NaN<NA>20181108적합
35711현미유통/판매일반이**충청남도 당진시NaN1.020140621적합
2476복숭아생산GAP(인증)심**충청북도 음성군14001408.020210824적합
2427풋고추생산일반박**충청북도 제천시696400.020210715적합
33841현미생산일반이**전라남도 광양시973300.020141014적합
31472부유생산일반하**경상남도 창원시727700.020140918적합
40851일반부추(조선부추)유통/판매일반서**전북 완주군NaN<NA>20131008적합
48022밤고구마생산일반김**전남 순천시300500.020121009적합
품목명수거단계재배양식생산자주소재배면적조사물량등록일자분석결과
45973햇마늘난지생산일반김**경남 남해군583800.020120522적합
11793멥쌀(일반)생산일반신**충청남도 서천군34502300.020180929적합
28191현미생산일반김**충청남도 서천군39042350.020150930적합
33633현미생산일반김**전라남도 나주시29351928.020141004적합
17839포도생산일반김**충청북도 영동군20001500.020170816적합
46660멥쌀(일반)생산일반손**경북 고령군20201000.020121011적합
18131멥쌀(일반)유통/판매일반-***강원도 철원군NaN<NA>20171109적합
36302멥쌀(일반)유통/판매일반중**미소인천광역시 강화군NaN<NA>20140806적합
42212현미생산일반윤**충남 공주시700420.020130927적합
14126감자유통/판매일반박**경상남도 밀양시NaN<NA>20180828적합

Duplicate rows

Most frequently occurring

품목명수거단계재배양식생산자주소조사물량등록일자분석결과# duplicates
72멥쌀(일반)유통/판매일반-***강원도 철원군<NA>20171109적합7
61멥쌀(일반)생산일반최**강원도 정선군30.020181005적합5
106침출차유통/판매일반소**원서울 강남구<NA>20120917적합5
23멥쌀(일반)생산일반김**경북 구미시10000.020131010적합4
109캠벨얼리(다크)유통/판매일반김**충남 보령시<NA>20130830적합4
130현미생산일반이**경남 창원시1000.020121008적합4
132현미생산일반이**경북 의성군1100.020071018적합4
4깐잣(알잣)생산일반이**경기도 가평군150.020141017적합3
12멥쌀(일반)생산일반권**전라북도 장수군1000.020141006적합3
17멥쌀(일반)생산일반김**경기 평택시1462.020070912적합3