Overview

Dataset statistics

Number of variables9
Number of observations10000
Missing cells4215
Missing cells (%)4.7%
Duplicate rows181
Duplicate rows (%)1.8%
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 181 (1.8%) duplicate rowsDuplicates
재배면적 is highly overall correlated with 조사물량High correlation
조사물량 is highly overall correlated with 재배면적High correlation
재배양식 is highly imbalanced (74.7%)Imbalance
분석결과 is highly imbalanced (96.9%)Imbalance
재배면적 has 2503 (25.0%) missing valuesMissing
조사물량 has 1709 (17.1%) missing valuesMissing
재배면적 is highly skewed (γ1 = 86.58520512)Skewed
조사물량 is highly skewed (γ1 = 21.50368795)Skewed

Reproduction

Analysis started2023-12-22 22:18:32.639315
Analysis finished2023-12-22 22:18:39.893957
Duration7.25 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

품목명
Text

Distinct141
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-22T22:18:41.077166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length16
Mean length3.8175
Min length1

Characters and Unicode

Total characters38175
Distinct characters189
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)0.3%

Sample

1st row멥쌀(일반)
2nd row멥쌀(일반)
3rd row배추
4th row멥쌀(일반)
5th row멥쌀(일반)
ValueCountFrequency (%)
멥쌀(일반 2455
24.6%
풋고추 783
 
7.8%
옥수수 505
 
5.1%
홍고추(붉은고추 406
 
4.1%
배추 385
 
3.9%
감자 368
 
3.7%
마늘 324
 
3.2%
딸기 297
 
3.0%
사과 292
 
2.9%
무 271
 
2.7%
Other values (131) 3914
39.1%
2023-12-22T22:18:43.223352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 3340
 
8.7%
) 3340
 
8.7%
쌀 2544
 
6.7%
일 2498
 
6.5%
반 2494
 
6.5%
멥 2455
 
6.4%
추 2351
 
6.2%
고 1949
 
5.1%
수 1099
 
2.9%
풋 784
 
2.1%
Other values (179) 15321
40.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 31472
82.4%
Open Punctuation 3340
 
8.7%
Close Punctuation 3340
 
8.7%
Other Punctuation 20
 
0.1%
Decimal Number 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
쌀 2544
 
8.1%
일 2498
 
7.9%
반 2494
 
7.9%
멥 2455
 
7.8%
추 2351
 
7.5%
고 1949
 
6.2%
수 1099
 
3.5%
풋 784
 
2.5%
마 660
 
2.1%
배 578
 
1.8%
Other values (174) 14060
44.7%
Decimal Number
ValueCountFrequency (%)
4 2
66.7%
3 1
33.3%
Open Punctuation
ValueCountFrequency (%)
( 3340
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3340
100.0%
Other Punctuation
ValueCountFrequency (%)
, 20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 31472
82.4%
Common 6703
 
17.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
쌀 2544
 
8.1%
일 2498
 
7.9%
반 2494
 
7.9%
멥 2455
 
7.8%
추 2351
 
7.5%
고 1949
 
6.2%
수 1099
 
3.5%
풋 784
 
2.5%
마 660
 
2.1%
배 578
 
1.8%
Other values (174) 14060
44.7%
Common
ValueCountFrequency (%)
( 3340
49.8%
) 3340
49.8%
, 20
 
0.3%
4 2
 
< 0.1%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 31472
82.4%
ASCII 6703
 
17.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 3340
49.8%
) 3340
49.8%
, 20
 
0.3%
4 2
 
< 0.1%
3 1
 
< 0.1%
Hangul
ValueCountFrequency (%)
쌀 2544
 
8.1%
일 2498
 
7.9%
반 2494
 
7.9%
멥 2455
 
7.8%
추 2351
 
7.5%
고 1949
 
6.2%
수 1099
 
3.5%
풋 784
 
2.5%
마 660
 
2.1%
배 578
 
1.8%
Other values (174) 14060
44.7%

수거단계
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
생산
7499 
유통/판매
2501 

Length

Max length5
Median length2
Mean length2.7503
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
생산 7499
75.0%
유통/판매 2501
 
25.0%

Length

2023-12-22T22:18:43.815183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-22T22:18:44.373420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
생산 7499
75.0%
유통/판매 2501
 
25.0%

재배양식
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
일반
9013 
친환경(인증) 무농약
 
649
GAP(인증)
 
244
친환경(인증) 유기
 
89
지리적표시
 
5

Length

Max length11
Median length3
Mean length3.705
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반 9013
90.1%
친환경(인증) 무농약 649
 
6.5%
GAP(인증) 244
 
2.4%
친환경(인증) 유기 89
 
0.9%
지리적표시 5
 
0.1%

Length

2023-12-22T22:18:44.874651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-22T22:18:45.649055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반 9013
83.9%
친환경(인증 738
 
6.9%
무농약 649
 
6.0%
gap(인증 244
 
2.3%
유기 89
 
0.8%
지리적표시 5
 
< 0.1%

생산자
Text

Distinct719
Distinct (%)7.2%
Missing3
Missing (%)< 0.1%
Memory size156.2 KiB
2023-12-22T22:18:46.709568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length3
Mean length3.4562369
Min length3

Characters and Unicode

Total characters34552
Distinct characters329
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

Unique434 ?
Unique (%)4.3%

Sample

1st row하**
2nd row이**
3rd row이**
4th row신**협
5th row문**
ValueCountFrequency (%)
김 1797
18.0%
이 1228
 
12.3%
박 747
 
7.5%
정 488
 
4.9%
최 429
 
4.3%
조 267
 
2.7%
강 222
 
2.2%
임 216
 
2.2%
황 175
 
1.8%
유 174
 
1.7%
Other values (693) 4254
42.6%
2023-12-22T22:18:48.826760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 20069
58.1%
김 1807
 
5.2%
이 1278
 
3.7%
박 750
 
2.2%
협 541
 
1.6%
조 512
 
1.5%
정 512
 
1.5%
최 439
 
1.3%
농 434
 
1.3%
합 310
 
0.9%
Other values (319) 7900
 
22.9%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 20071
58.1%
Other Letter 14275
41.3%
Uppercase Letter 46
 
0.1%
Close Punctuation 43
 
0.1%
Open Punctuation 42
 
0.1%
Decimal Number 32
 
0.1%
Dash Punctuation 21
 
0.1%
Control 14
 
< 0.1%
Lowercase Letter 8
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 1807
 
12.7%
이 1278
 
9.0%
박 750
 
5.3%
협 541
 
3.8%
조 512
 
3.6%
정 512
 
3.6%
최 439
 
3.1%
농 434
 
3.0%
합 310
 
2.2%
인 271
 
1.9%
Other values (287) 7421
52.0%
Uppercase Letter
ValueCountFrequency (%)
C 13
28.3%
P 10
21.7%
R 8
17.4%
O 5
 
10.9%
G 3
 
6.5%
B 2
 
4.3%
A 2
 
4.3%
K 1
 
2.2%
H 1
 
2.2%
F 1
 
2.2%
Decimal Number
ValueCountFrequency (%)
2 7
21.9%
1 7
21.9%
8 4
12.5%
0 4
12.5%
7 3
9.4%
3 3
9.4%
6 2
 
6.2%
4 1
 
3.1%
5 1
 
3.1%
Lowercase Letter
ValueCountFrequency (%)
r 2
25.0%
p 2
25.0%
c 2
25.0%
o 1
12.5%
g 1
12.5%
Other Punctuation
ValueCountFrequency (%)
* 20069
> 99.9%
& 1
 
< 0.1%
, 1
 
< 0.1%
Control
ValueCountFrequency (%)
7
50.0%
7
50.0%
Close Punctuation
ValueCountFrequency (%)
) 43
100.0%
Open Punctuation
ValueCountFrequency (%)
( 42
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 20223
58.5%
Hangul 14275
41.3%
Latin 54
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 1807
 
12.7%
이 1278
 
9.0%
박 750
 
5.3%
협 541
 
3.8%
조 512
 
3.6%
정 512
 
3.6%
최 439
 
3.1%
농 434
 
3.0%
합 310
 
2.2%
인 271
 
1.9%
Other values (287) 7421
52.0%
Common
ValueCountFrequency (%)
* 20069
99.2%
) 43
 
0.2%
( 42
 
0.2%
- 21
 
0.1%
7
 
< 0.1%
7
 
< 0.1%
2 7
 
< 0.1%
1 7
 
< 0.1%
8 4
 
< 0.1%
0 4
 
< 0.1%
Other values (7) 12
 
0.1%
Latin
ValueCountFrequency (%)
C 13
24.1%
P 10
18.5%
R 8
14.8%
O 5
 
9.3%
G 3
 
5.6%
r 2
 
3.7%
p 2
 
3.7%
c 2
 
3.7%
B 2
 
3.7%
A 2
 
3.7%
Other values (5) 5
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20277
58.7%
Hangul 14274
41.3%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 20069
99.0%
) 43
 
0.2%
( 42
 
0.2%
- 21
 
0.1%
C 13
 
0.1%
P 10
 
< 0.1%
R 8
 
< 0.1%
7
 
< 0.1%
7
 
< 0.1%
2 7
 
< 0.1%
Other values (22) 50
 
0.2%
Hangul
ValueCountFrequency (%)
김 1807
 
12.7%
이 1278
 
9.0%
박 750
 
5.3%
협 541
 
3.8%
조 512
 
3.6%
정 512
 
3.6%
최 439
 
3.1%
농 434
 
3.0%
합 310
 
2.2%
인 271
 
1.9%
Other values (286) 7420
52.0%
Compat Jamo
ValueCountFrequency (%)
ㅏ 1
100.0%

주소
Text

Distinct191
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-22T22:18:50.451932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length11
Mean length10.8502
Min length10

Characters and Unicode

Total characters108502
Distinct characters128
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

Unique16 ?
Unique (%)0.2%

Sample

1st row경상남도 고성군
2nd row충청남도 청양군
3rd row경기도 양평군
4th row충청남도 당진시
5th row전라남도 화순군
ValueCountFrequency (%)
경상북도 1766
 
8.8%
충청남도 1524
 
7.6%
경상남도 1394
 
7.0%
강원도 1282
 
6.4%
경기도 1067
 
5.3%
충청북도 1018
 
5.1%
전라남도 900
 
4.5%
전라북도 461
 
2.3%
청도군 379
 
1.9%
정선군 338
 
1.7%
Other values (187) 9870
49.4%
2023-12-22T22:18:52.438082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
40000
36.9%
도 9913
 
9.1%
군 5654
 
5.2%
시 4642
 
4.3%
경 4401
 
4.1%
남 4103
 
3.8%
북 3309
 
3.0%
청 3261
 
3.0%
상 3252
 
3.0%
충 2652
 
2.4%
Other values (118) 27315
25.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 68502
63.1%
Space Separator 40000
36.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 9913
 
14.5%
군 5654
 
8.3%
시 4642
 
6.8%
경 4401
 
6.4%
남 4103
 
6.0%
북 3309
 
4.8%
청 3261
 
4.8%
상 3252
 
4.7%
충 2652
 
3.9%
주 1756
 
2.6%
Other values (117) 25559
37.3%
Space Separator
ValueCountFrequency (%)
40000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 68502
63.1%
Common 40000
36.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 9913
 
14.5%
군 5654
 
8.3%
시 4642
 
6.8%
경 4401
 
6.4%
남 4103
 
6.0%
북 3309
 
4.8%
청 3261
 
4.8%
상 3252
 
4.7%
충 2652
 
3.9%
주 1756
 
2.6%
Other values (117) 25559
37.3%
Common
ValueCountFrequency (%)
40000
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 68502
63.1%
ASCII 40000
36.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
40000
100.0%
Hangul
ValueCountFrequency (%)
도 9913
 
14.5%
군 5654
 
8.3%
시 4642
 
6.8%
경 4401
 
6.4%
남 4103
 
6.0%
북 3309
 
4.8%
청 3261
 
4.8%
상 3252
 
4.7%
충 2652
 
3.9%
주 1756
 
2.6%
Other values (117) 25559
37.3%

재배면적
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct2586
Distinct (%)34.5%
Missing2503
Missing (%)25.0%
Infinite0
Infinite (%)0.0%
Mean135512.44
Minimum2
Maximum1.000015 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-22T22:18:53.939350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile200
Q1600
median1289
Q32592
95-th percentile6486.6
Maximum1.000015 × 109
Range1.000015 × 109
Interquartile range (IQR)1992

Descriptive statistics

Standard deviation11549465
Coefficient of variation (CV)85.228076
Kurtosis7496.9985
Mean135512.44
Median Absolute Deviation (MAD)869
Skewness86.585205
Sum1.0159368 × 109
Variance1.3339014 × 1014
MonotonicityNot monotonic
2023-12-22T22:18:54.968422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
330.0 536
 
5.4%
1000.0 223
 
2.2%
660.0 170
 
1.7%
200.0 147
 
1.5%
300.0 139
 
1.4%
500.0 132
 
1.3%
10000.0 132
 
1.3%
600.0 106
 
1.1%
3000.0 98
 
1.0%
890.0 83
 
0.8%
Other values (2576) 5731
57.3%
(Missing) 2503
25.0%
ValueCountFrequency (%)
2.0 1
 
< 0.1%
15.0 1
 
< 0.1%
20.0 2
 
< 0.1%
24.0 2
 
< 0.1%
25.0 2
 
< 0.1%
30.0 5
0.1%
33.0 3
 
< 0.1%
50.0 10
0.1%
60.0 2
 
< 0.1%
63.0 1
 
< 0.1%
ValueCountFrequency (%)
1000015010.0 1
< 0.1%
117722.0 1
< 0.1%
112000.0 1
< 0.1%
99000.0 1
< 0.1%
90000.0 1
< 0.1%
59400.0 1
< 0.1%
53130.0 1
< 0.1%
39744.0 1
< 0.1%
39672.0 1
< 0.1%
37000.0 1
< 0.1%

조사물량
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct869
Distinct (%)10.5%
Missing1709
Missing (%)17.1%
Infinite0
Infinite (%)0.0%
Mean1964.1738
Minimum0
Maximum300000
Zeros12
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-22T22:18:56.119291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q1100
median500
Q31500
95-th percentile8000
Maximum300000
Range300000
Interquartile range (IQR)1400

Descriptive statistics

Standard deviation7900.6125
Coefficient of variation (CV)4.0223593
Kurtosis654.95809
Mean1964.1738
Median Absolute Deviation (MAD)470
Skewness21.503688
Sum16284965
Variance62419679
MonotonicityNot monotonic
2023-12-22T22:18:57.528498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.0 587
 
5.9%
1000.0 441
 
4.4%
500.0 359
 
3.6%
200.0 339
 
3.4%
2000.0 309
 
3.1%
300.0 303
 
3.0%
30.0 272
 
2.7%
50.0 253
 
2.5%
10.0 201
 
2.0%
400.0 201
 
2.0%
Other values (859) 5026
50.3%
(Missing) 1709
 
17.1%
ValueCountFrequency (%)
0.0 12
 
0.1%
0.2 1
 
< 0.1%
0.5 2
 
< 0.1%
0.7 1
 
< 0.1%
1.0 48
 
0.5%
1.5 6
 
0.1%
1.6 1
 
< 0.1%
2.0 192
1.9%
3.0 61
 
0.6%
3.2 1
 
< 0.1%
ValueCountFrequency (%)
300000.0 2
< 0.1%
200000.0 2
< 0.1%
190000.0 1
 
< 0.1%
130000.0 1
 
< 0.1%
120000.0 1
 
< 0.1%
110000.0 1
 
< 0.1%
100000.0 3
< 0.1%
90000.0 1
 
< 0.1%
70000.0 1
 
< 0.1%
64800.0 1
 
< 0.1%

등록일자
Date

Distinct846
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2017-05-01 00:00:00
Maximum2021-09-27 00:00:00
2023-12-22T22:18:58.362308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-22T22:18:59.127678image/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
9946 
부적합 (폐기)
 
53
부적합(회수폐기 및 생산 단계 재조사)
 
1

Length

Max length21
Median length2
Mean length2.0337
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
적합 9946
99.5%
부적합 (폐기) 53
 
0.5%
부적합(회수폐기 및 생산 단계 재조사) 1
 
< 0.1%

Length

2023-12-22T22:18:59.903172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-22T22:19:00.540030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
적합 9946
98.9%
부적합 53
 
0.5%
폐기 53
 
0.5%
부적합(회수폐기 1
 
< 0.1%
및 1
 
< 0.1%
생산 1
 
< 0.1%
단계 1
 
< 0.1%
재조사 1
 
< 0.1%

Interactions

2023-12-22T22:18:36.786877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-22T22:18:35.690394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-22T22:18:37.193716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-22T22:18:36.254113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-22T22:19:00.878720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수거단계재배양식재배면적조사물량분석결과
수거단계1.0000.1310.0000.0150.026
재배양식0.1311.0000.0250.0570.000
재배면적0.0000.0251.0000.0000.000
조사물량0.0150.0570.0001.0000.000
분석결과0.0260.0000.0000.0001.000
2023-12-22T22:19:01.414881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
재배양식수거단계분석결과
재배양식1.0000.1610.000
수거단계0.1611.0000.043
분석결과0.0000.0431.000
2023-12-22T22:19:01.793960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
재배면적조사물량수거단계재배양식분석결과
재배면적1.0000.6640.0000.0300.000
조사물량0.6641.0000.0160.0360.000
수거단계0.0000.0161.0000.1610.043
재배양식0.0300.0360.1611.0000.000
분석결과0.0000.0000.0430.0001.000

Missing values

2023-12-22T22:18:37.875402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-22T22:18:38.991691image/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.
2023-12-22T22:18:39.603731image/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
660멥쌀(일반)생산일반하**경상남도 고성군973.0500.02020-09-24적합
1612멥쌀(일반)생산일반이**충청남도 청양군1234.0600.02020-09-24적합
11236배추생산일반이**경기도 양평군330.0100.02018-09-07적합
6903멥쌀(일반)유통/판매일반신**협충청남도 당진시<NA><NA>2019-08-06적합
4124멥쌀(일반)생산일반문**전라남도 화순군2341.01224.02019-09-25적합
14788멥쌀(일반)생산일반정**충청남도 서산시3000.01000.02017-09-13적합
2870부추유통/판매일반박**울산광역시 북구<NA><NA>2019-04-17적합
4087감자유통/판매일반이**경상북도 청송군<NA>30.02019-04-17적합
11993시금치유통/판매일반일**협경기도 포천시<NA><NA>2018-07-05적합
4847풋고추생산일반김**충청남도 홍성군500.0200.02019-07-29적합
품목명수거단계재배양식생산자주소재배면적조사물량등록일자분석결과
8298멥쌀(일반)생산일반설**경상북도 영양군1438.0970.02018-10-12적합
3561무생산일반박**경상북도 봉화군4500.02100.02019-07-18적합
11134배추유통/판매일반이**강원도 횡성군<NA><NA>2018-10-04적합
3269당근유통/판매일반농**인동아농산주식회사충청남도 공주시<NA><NA>2019-05-10적합
6150대파유통/판매일반신**강원도 강릉시<NA>3.02019-07-16적합
4003고구마유통/판매일반임**대전광역시 대덕구<NA>10.02019-06-25적합
872멥쌀(일반)생산일반송**경상북도 울진군1133.01000.02020-09-22적합
5906옥수수생산일반이**강원도 홍천군3000.01500.02019-09-16적합
11937마늘유통/판매일반함**협제주특별자치도 제주시<NA><NA>2018-08-07적합
10321풋고추생산일반최**충청북도 진천군50.030.02018-07-16적합

Duplicate rows

Most frequently occurring

품목명수거단계재배양식생산자주소재배면적조사물량등록일자분석결과# duplicates
90멥쌀(일반)유통/판매일반-***강원도 철원군<NA><NA>2017-11-03적합12
91멥쌀(일반)유통/판매일반-***강원도 철원군<NA><NA>2017-11-09적합9
82멥쌀(일반)생산일반최**강원도 정선군300.0300.02019-10-08적합8
81멥쌀(일반)생산일반최**강원도 정선군300.0300.02019-10-04적합7
84멥쌀(일반)생산일반최**강원도 정선군2000.0700.02020-10-09적합7
85멥쌀(일반)생산일반최**강원도 정선군3000.01000.02020-10-09적합7
87멥쌀(일반)생산일반최**순강원도 정선군300.030.02019-10-02적합7
80멥쌀(일반)생산일반최**강원도 정선군300.0100.02019-10-04적합5
98배추생산일반이**경기도 양평군100.060.02017-10-13적합5
100배추생산일반진**강원도 정선군300.0300.02019-10-18적합5