Overview

Dataset statistics

Number of variables9
Number of observations10000
Missing cells4553
Missing cells (%)5.1%
Duplicate rows61
Duplicate rows (%)0.6%
Total size in memory810.5 KiB
Average record size in memory83.0 B

Variable types

Text3
Categorical3
Numeric3

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 61 (0.6%) duplicate rowsDuplicates
재배면적 is highly overall correlated with 조사물량High correlation
조사물량 is highly overall correlated with 재배면적High correlation
수거단계 is highly imbalanced (56.3%)Imbalance
재배양식 is highly imbalanced (87.2%)Imbalance
분석결과 is highly imbalanced (94.6%)Imbalance
재배면적 has 2772 (27.7%) missing valuesMissing
조사물량 has 1779 (17.8%) missing valuesMissing
재배면적 is highly skewed (γ1 = 79.46800029)Skewed
조사물량 is highly skewed (γ1 = 48.05498915)Skewed

Reproduction

Analysis started2023-12-22 22:19:39.810665
Analysis finished2023-12-22 22:19:52.398395
Duration12.59 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

품목명
Text

Distinct333
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-22T22:19:53.074521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length28
Mean length4.6857
Min length1

Characters and Unicode

Total characters46857
Distinct characters323
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

Unique99 ?
Unique (%)1.0%

Sample

1st row멥쌀(일반)
2nd row현미
3rd row일반부추(조선부추)
4th row현미
5th row청초(일반)
ValueCountFrequency (%)
멥쌀(일반 2539
25.3%
현미 1704
17.0%
홍고추(붉은고추 296
 
3.0%
풋고추 205
 
2.0%
수미(슈페리어 186
 
1.9%
시금치 178
 
1.8%
밤고구마 177
 
1.8%
일반부추(조선부추 170
 
1.7%
백태 162
 
1.6%
대파 145
 
1.4%
Other values (328) 4259
42.5%
2023-12-22T22:19:54.820771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 4476
 
9.6%
) 4476
 
9.6%
일 3024
 
6.5%
반 3019
 
6.4%
쌀 2650
 
5.7%
멥 2539
 
5.4%
미 2016
 
4.3%
현 1735
 
3.7%
추 1712
 
3.7%
고 1322
 
2.8%
Other values (313) 19888
42.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 37373
79.8%
Open Punctuation 4476
 
9.6%
Close Punctuation 4476
 
9.6%
Other Punctuation 433
 
0.9%
Decimal Number 66
 
0.1%
Space Separator 21
 
< 0.1%
Uppercase Letter 12
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
일 3024
 
8.1%
반 3019
 
8.1%
쌀 2650
 
7.1%
멥 2539
 
6.8%
미 2016
 
5.4%
현 1735
 
4.6%
추 1712
 
4.6%
고 1322
 
3.5%
수 1098
 
2.9%
마 621
 
1.7%
Other values (298) 17637
47.2%
Decimal Number
ValueCountFrequency (%)
1 15
22.7%
5 12
18.2%
0 11
16.7%
4 11
16.7%
6 7
10.6%
3 5
 
7.6%
7 4
 
6.1%
2 1
 
1.5%
Uppercase Letter
ValueCountFrequency (%)
B 4
33.3%
A 4
33.3%
M 4
33.3%
Open Punctuation
ValueCountFrequency (%)
( 4476
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4476
100.0%
Other Punctuation
ValueCountFrequency (%)
. 433
100.0%
Space Separator
ValueCountFrequency (%)
21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 37373
79.8%
Common 9472
 
20.2%
Latin 12
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
일 3024
 
8.1%
반 3019
 
8.1%
쌀 2650
 
7.1%
멥 2539
 
6.8%
미 2016
 
5.4%
현 1735
 
4.6%
추 1712
 
4.6%
고 1322
 
3.5%
수 1098
 
2.9%
마 621
 
1.7%
Other values (298) 17637
47.2%
Common
ValueCountFrequency (%)
( 4476
47.3%
) 4476
47.3%
. 433
 
4.6%
21
 
0.2%
1 15
 
0.2%
5 12
 
0.1%
0 11
 
0.1%
4 11
 
0.1%
6 7
 
0.1%
3 5
 
0.1%
Other values (2) 5
 
0.1%
Latin
ValueCountFrequency (%)
B 4
33.3%
A 4
33.3%
M 4
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 37373
79.8%
ASCII 9484
 
20.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 4476
47.2%
) 4476
47.2%
. 433
 
4.6%
21
 
0.2%
1 15
 
0.2%
5 12
 
0.1%
0 11
 
0.1%
4 11
 
0.1%
6 7
 
0.1%
3 5
 
0.1%
Other values (5) 17
 
0.2%
Hangul
ValueCountFrequency (%)
일 3024
 
8.1%
반 3019
 
8.1%
쌀 2650
 
7.1%
멥 2539
 
6.8%
미 2016
 
5.4%
현 1735
 
4.6%
추 1712
 
4.6%
고 1322
 
3.5%
수 1098
 
2.9%
마 621
 
1.7%
Other values (298) 17637
47.2%

수거단계
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
생산
7229 
유통/판매
2747 
출하
 
16
저장
 
8

Length

Max length5
Median length2
Mean length2.8241
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
생산 7229
72.3%
유통/판매 2747
 
27.5%
출하 16
 
0.2%
저장 8
 
0.1%

Length

2023-12-22T22:19:55.551296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-22T22:19:56.176774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
생산 7229
72.3%
유통/판매 2747
 
27.5%
출하 16
 
0.2%
저장 8
 
0.1%

재배양식
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
일반
9535 
친환경(인증) 무농약
 
245
GAP(인증)
 
82
직불제(쌀소득)
 
74
친환경(인증) 유기
 
38
Other values (2)
 
26

Length

Max length11
Median length3
Mean length3.3282
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반 9535
95.3%
친환경(인증) 무농약 245
 
2.5%
GAP(인증) 82
 
0.8%
직불제(쌀소득) 74
 
0.7%
친환경(인증) 유기 38
 
0.4%
친환경(인증) 저농약 24
 
0.2%
친환경(인증) 2
 
< 0.1%

Length

2023-12-22T22:19:57.090906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-22T22:19:57.637008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반 9535
92.5%
친환경(인증 309
 
3.0%
무농약 245
 
2.4%
gap(인증 82
 
0.8%
직불제(쌀소득 74
 
0.7%
유기 38
 
0.4%
저농약 24
 
0.2%

생산자
Text

Distinct723
Distinct (%)7.2%
Missing2
Missing (%)< 0.1%
Memory size156.2 KiB
2023-12-22T22:19:58.611169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length3
Mean length3.3462693
Min length3

Characters and Unicode

Total characters33456
Distinct characters321
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique476 ?
Unique (%)4.8%

Sample

1st row윤**
2nd row하**
3rd row김**
4th row이**
5th row김**
ValueCountFrequency (%)
김 1783
17.8%
이 1310
 
13.1%
박 772
 
7.7%
정 454
 
4.5%
최 415
 
4.2%
강 254
 
2.5%
조 241
 
2.4%
임 183
 
1.8%
권 177
 
1.8%
유 176
 
1.8%
Other values (704) 4233
42.3%
2023-12-22T22:20:00.774027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 20044
59.9%
김 1797
 
5.4%
이 1345
 
4.0%
박 774
 
2.3%
정 470
 
1.4%
조 431
 
1.3%
협 420
 
1.3%
최 415
 
1.2%
농 322
 
1.0%
강 265
 
0.8%
Other values (311) 7173
 
21.4%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 20051
59.9%
Other Letter 13281
39.7%
Uppercase Letter 32
 
0.1%
Open Punctuation 30
 
0.1%
Decimal Number 20
 
0.1%
Close Punctuation 18
 
0.1%
Dash Punctuation 14
 
< 0.1%
Lowercase Letter 6
 
< 0.1%
Control 2
 
< 0.1%
Connector Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 1797
 
13.5%
이 1345
 
10.1%
박 774
 
5.8%
정 470
 
3.5%
조 431
 
3.2%
협 420
 
3.2%
최 415
 
3.1%
농 322
 
2.4%
강 265
 
2.0%
합 241
 
1.8%
Other values (282) 6801
51.2%
Decimal Number
ValueCountFrequency (%)
6 5
25.0%
1 4
20.0%
0 3
15.0%
2 2
 
10.0%
7 2
 
10.0%
5 1
 
5.0%
4 1
 
5.0%
8 1
 
5.0%
3 1
 
5.0%
Uppercase Letter
ValueCountFrequency (%)
C 10
31.2%
P 9
28.1%
R 7
21.9%
B 3
 
9.4%
A 2
 
6.2%
O 1
 
3.1%
Lowercase Letter
ValueCountFrequency (%)
o 2
33.3%
c 1
16.7%
p 1
16.7%
r 1
16.7%
g 1
16.7%
Other Punctuation
ValueCountFrequency (%)
* 20044
> 99.9%
/ 7
 
< 0.1%
Control
ValueCountFrequency (%)
1
50.0%
1
50.0%
Open Punctuation
ValueCountFrequency (%)
( 30
100.0%
Close Punctuation
ValueCountFrequency (%)
) 18
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 20137
60.2%
Hangul 13281
39.7%
Latin 38
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 1797
 
13.5%
이 1345
 
10.1%
박 774
 
5.8%
정 470
 
3.5%
조 431
 
3.2%
협 420
 
3.2%
최 415
 
3.1%
농 322
 
2.4%
강 265
 
2.0%
합 241
 
1.8%
Other values (282) 6801
51.2%
Common
ValueCountFrequency (%)
* 20044
99.5%
( 30
 
0.1%
) 18
 
0.1%
- 14
 
0.1%
/ 7
 
< 0.1%
6 5
 
< 0.1%
1 4
 
< 0.1%
0 3
 
< 0.1%
2 2
 
< 0.1%
7 2
 
< 0.1%
Other values (8) 8
 
< 0.1%
Latin
ValueCountFrequency (%)
C 10
26.3%
P 9
23.7%
R 7
18.4%
B 3
 
7.9%
o 2
 
5.3%
A 2
 
5.3%
c 1
 
2.6%
p 1
 
2.6%
r 1
 
2.6%
g 1
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20175
60.3%
Hangul 13280
39.7%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 20044
99.4%
( 30
 
0.1%
) 18
 
0.1%
- 14
 
0.1%
C 10
 
< 0.1%
P 9
 
< 0.1%
R 7
 
< 0.1%
/ 7
 
< 0.1%
6 5
 
< 0.1%
1 4
 
< 0.1%
Other values (19) 27
 
0.1%
Hangul
ValueCountFrequency (%)
김 1797
 
13.5%
이 1345
 
10.1%
박 774
 
5.8%
정 470
 
3.5%
조 431
 
3.2%
협 420
 
3.2%
최 415
 
3.1%
농 322
 
2.4%
강 265
 
2.0%
합 241
 
1.8%
Other values (281) 6800
51.2%
Compat Jamo
ValueCountFrequency (%)
ㅏ 1
100.0%

주소
Text

Distinct391
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-22T22:20:01.959882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length14
Mean length9.9609
Min length8

Characters and Unicode

Total characters99609
Distinct characters136
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)0.4%

Sample

1st row충청남도 청양군
2nd row경남 거창군
3rd row경기 양평군
4th row충남 서산시
5th row경북 울진군
ValueCountFrequency (%)
경상북도 872
 
4.4%
충청남도 836
 
4.2%
경북 833
 
4.2%
충남 832
 
4.2%
경상남도 645
 
3.2%
강원도 623
 
3.1%
경남 594
 
3.0%
전남 589
 
2.9%
경기도 540
 
2.7%
강원 509
 
2.5%
Other values (223) 13124
65.6%
2023-12-22T22:20:04.241371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
40000
40.2%
군 5770
 
5.8%
도 5010
 
5.0%
남 4308
 
4.3%
시 4298
 
4.3%
경 4157
 
4.2%
북 3231
 
3.2%
충 2617
 
2.6%
청 1843
 
1.9%
전 1724
 
1.7%
Other values (126) 26651
26.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 59606
59.8%
Space Separator 40000
40.2%
Math Symbol 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 5770
 
9.7%
도 5010
 
8.4%
남 4308
 
7.2%
시 4298
 
7.2%
경 4157
 
7.0%
북 3231
 
5.4%
충 2617
 
4.4%
청 1843
 
3.1%
전 1724
 
2.9%
상 1631
 
2.7%
Other values (124) 25017
42.0%
Space Separator
ValueCountFrequency (%)
40000
100.0%
Math Symbol
ValueCountFrequency (%)
| 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 59606
59.8%
Common 40003
40.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 5770
 
9.7%
도 5010
 
8.4%
남 4308
 
7.2%
시 4298
 
7.2%
경 4157
 
7.0%
북 3231
 
5.4%
충 2617
 
4.4%
청 1843
 
3.1%
전 1724
 
2.9%
상 1631
 
2.7%
Other values (124) 25017
42.0%
Common
ValueCountFrequency (%)
40000
> 99.9%
| 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 59606
59.8%
ASCII 40003
40.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
40000
> 99.9%
| 3
 
< 0.1%
Hangul
ValueCountFrequency (%)
군 5770
 
9.7%
도 5010
 
8.4%
남 4308
 
7.2%
시 4298
 
7.2%
경 4157
 
7.0%
북 3231
 
5.4%
충 2617
 
4.4%
청 1843
 
3.1%
전 1724
 
2.9%
상 1631
 
2.7%
Other values (124) 25017
42.0%

재배면적
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct2854
Distinct (%)39.5%
Missing2772
Missing (%)27.7%
Infinite0
Infinite (%)0.0%
Mean2215.692
Minimum5
Maximum1400000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-22T22:20:04.675719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile200
Q1660
median1430.5
Q32516
95-th percentile5053.65
Maximum1400000
Range1399995
Interquartile range (IQR)1856

Descriptive statistics

Standard deviation16834.903
Coefficient of variation (CV)7.598034
Kurtosis6580.7554
Mean2215.692
Median Absolute Deviation (MAD)856
Skewness79.468
Sum16015022
Variance2.8341397 × 108
MonotonicityNot monotonic
2023-12-22T22:20:05.156848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
330.0 280
 
2.8%
1000.0 162
 
1.6%
660.0 134
 
1.3%
100.0 94
 
0.9%
200.0 92
 
0.9%
500.0 90
 
0.9%
300.0 88
 
0.9%
600.0 83
 
0.8%
2000.0 63
 
0.6%
165.0 60
 
0.6%
Other values (2844) 6082
60.8%
(Missing) 2772
27.7%
ValueCountFrequency (%)
5.0 2
 
< 0.1%
6.0 1
 
< 0.1%
15.0 1
 
< 0.1%
20.0 3
 
< 0.1%
24.0 2
 
< 0.1%
30.0 6
0.1%
33.0 9
0.1%
35.0 2
 
< 0.1%
40.0 1
 
< 0.1%
50.0 4
< 0.1%
ValueCountFrequency (%)
1400000.0 1
< 0.1%
189620.0 1
< 0.1%
93000.0 1
< 0.1%
61000.0 1
< 0.1%
55625.0 1
< 0.1%
48635.0 1
< 0.1%
46000.0 1
< 0.1%
40320.0 1
< 0.1%
40000.0 1
< 0.1%
39672.0 1
< 0.1%

조사물량
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct1190
Distinct (%)14.5%
Missing1779
Missing (%)17.8%
Infinite0
Infinite (%)0.0%
Mean1633.2664
Minimum0
Maximum700485
Zeros43
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-22T22:20:05.982708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q1100
median550
Q31380
95-th percentile4840
Maximum700485
Range700485
Interquartile range (IQR)1280

Descriptive statistics

Standard deviation10296.599
Coefficient of variation (CV)6.3042984
Kurtosis2936.2292
Mean1633.2664
Median Absolute Deviation (MAD)500
Skewness48.054989
Sum13427083
Variance1.0601995 × 108
MonotonicityNot monotonic
2023-12-22T22:20:06.841063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.0 438
 
4.4%
1000.0 379
 
3.8%
500.0 301
 
3.0%
200.0 288
 
2.9%
300.0 259
 
2.6%
50.0 253
 
2.5%
2000.0 231
 
2.3%
2.0 218
 
2.2%
600.0 203
 
2.0%
800.0 187
 
1.9%
Other values (1180) 5464
54.6%
(Missing) 1779
 
17.8%
ValueCountFrequency (%)
0.0 43
 
0.4%
0.1 20
 
0.2%
0.5 30
 
0.3%
1.0 90
0.9%
1.5 2
 
< 0.1%
2.0 218
2.2%
2.4 1
 
< 0.1%
2.5 1
 
< 0.1%
3.0 52
 
0.5%
3.7 1
 
< 0.1%
ValueCountFrequency (%)
700485.0 1
< 0.1%
420600.0 1
< 0.1%
190000.0 1
< 0.1%
124560.0 1
< 0.1%
100680.0 1
< 0.1%
100000.0 2
< 0.1%
80000.0 1
< 0.1%
71500.0 1
< 0.1%
70000.0 1
< 0.1%
66000.0 2
< 0.1%

등록일자
Real number (ℝ)

Distinct1924
Distinct (%)19.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20137911
Minimum20070628
Maximum20221129
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-22T22:20:08.122503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20070628
5-th percentile20071007
Q120110906
median20140624
Q320170629
95-th percentile20200827
Maximum20221129
Range150501
Interquartile range (IQR)59723

Descriptive statistics

Standard deviation39319.04
Coefficient of variation (CV)0.0019524885
Kurtosis-0.81397298
Mean20137911
Median Absolute Deviation (MAD)29814.5
Skewness-0.036093791
Sum2.0137911 × 1011
Variance1.5459869 × 109
MonotonicityNot monotonic
2023-12-22T22:20:08.772052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20071015 68
 
0.7%
20071004 65
 
0.7%
20071009 62
 
0.6%
20070927 53
 
0.5%
20071011 52
 
0.5%
20071001 50
 
0.5%
20130114 49
 
0.5%
20141014 47
 
0.5%
20071008 43
 
0.4%
20091007 41
 
0.4%
Other values (1914) 9470
94.7%
ValueCountFrequency (%)
20070628 1
 
< 0.1%
20070710 1
 
< 0.1%
20070712 1
 
< 0.1%
20070718 2
< 0.1%
20070719 4
< 0.1%
20070720 1
 
< 0.1%
20070723 1
 
< 0.1%
20070724 3
< 0.1%
20070725 2
< 0.1%
20070726 1
 
< 0.1%
ValueCountFrequency (%)
20221129 1
 
< 0.1%
20221128 1
 
< 0.1%
20221117 1
 
< 0.1%
20221116 2
 
< 0.1%
20221031 1
 
< 0.1%
20221025 1
 
< 0.1%
20221019 1
 
< 0.1%
20221017 3
< 0.1%
20221014 1
 
< 0.1%
20221013 6
0.1%

분석결과
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
적합
9895 
부적합 (폐기)
 
103
부적합(회수폐기 및 생산 단계 재조사)
 
2

Length

Max length21
Median length2
Mean length2.0656
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부적합 (폐기)
2nd row적합
3rd row적합
4th row적합
5th row부적합 (폐기)

Common Values

ValueCountFrequency (%)
적합 9895
99.0%
부적합 (폐기) 103
 
1.0%
부적합(회수폐기 및 생산 단계 재조사) 2
 
< 0.1%

Length

2023-12-22T22:20:09.498800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-22T22:20:09.965161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
적합 9895
97.9%
부적합 103
 
1.0%
폐기 103
 
1.0%
부적합(회수폐기 2
 
< 0.1%
및 2
 
< 0.1%
생산 2
 
< 0.1%
단계 2
 
< 0.1%
재조사 2
 
< 0.1%

Interactions

2023-12-22T22:19:47.266173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-22T22:19:42.963554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-22T22:19:44.695676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-22T22:19:48.535913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-22T22:19:43.824916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-22T22:19:45.775833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-22T22:19:49.621864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-22T22:19:44.302798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-22T22:19:46.424380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-22T22:20:10.460541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수거단계재배양식재배면적조사물량등록일자분석결과
수거단계1.0000.1300.0000.0000.3990.047
재배양식0.1301.0000.2830.0000.2410.000
재배면적0.0000.2831.0000.3550.0000.000
조사물량0.0000.0000.3551.0000.0000.000
등록일자0.3990.2410.0000.0001.0000.076
분석결과0.0470.0000.0000.0000.0761.000
2023-12-22T22:20:11.021951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
재배양식수거단계분석결과
재배양식1.0000.0890.000
수거단계0.0891.0000.044
분석결과0.0000.0441.000
2023-12-22T22:20:11.451107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
재배면적조사물량등록일자수거단계재배양식분석결과
재배면적1.0000.707-0.0270.0000.1220.000
조사물량0.7071.000-0.0330.0000.0000.000
등록일자-0.027-0.0331.0000.2490.1240.045
수거단계0.0000.0000.2491.0000.0890.044
재배양식0.1220.0000.1240.0891.0000.000
분석결과0.0000.0000.0450.0440.0001.000

Missing values

2023-12-22T22:19:50.564437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-22T22:19:51.354109image/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.
2023-12-22T22:19:52.057845image/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
16798멥쌀(일반)생산일반윤**충청남도 청양군5500.02914.020171011부적합 (폐기)
62708현미생산일반하**경남 거창군2290.01385.020081015적합
50440일반부추(조선부추)생산일반김**경기 양평군330.0200.020120712적합
54297현미생산일반이**충남 서산시4968.62400.020111007적합
38479청초(일반)생산일반김**경북 울진군2014.0600.020130807부적합 (폐기)
71204백태생산일반신**강원 영월군1653.0380.020071102부적합 (폐기)
24225청초(일반)생산일반신**경기도 양평군330.0500.020160727적합
68648현미생산일반이**전남 보성군3534.01069.020071004적합
65834멥쌀(일반)생산일반박**경남 고성군2242.01360.020071018적합
28552현미생산일반김**충청남도 서산시3845.03460.020151009적합
품목명수거단계재배양식생산자주소재배면적조사물량등록일자분석결과
51318흙당근유통/판매일반이**경남 밀양시<NA><NA>20120806적합
28558현미생산일반박**충청남도 서산시4826.04343.020151009적합
69421멥쌀(일반)생산일반김**충남 서천군2715.02400.020071001적합
28673청초(일반)생산일반임**충청남도 금산군2502.0100.020150729적합
29231밤고구마생산일반염**충청북도 옥천군1143.0800.020150825적합
40297여름배추유통/판매일반강**협연합사업단강원 춘천시<NA><NA>20130909적합
16924홍고추(붉은고추)생산일반지**충청남도 부여군1408.02000.020170809적합
17117멥쌀(일반)생산일반조**충청남도 예산군4719.01000.020170928적합
58793피땅콩생산일반인**충남 당진군70.020.020101006적합
23501멥쌀(일반)유통/판매일반육**충청남도 당진시<NA><NA>20160531적합

Duplicate rows

Most frequently occurring

품목명수거단계재배양식생산자주소재배면적조사물량등록일자분석결과# duplicates
18멥쌀(일반)유통/판매일반-***강원도 철원군<NA><NA>20171103적합8
19멥쌀(일반)유통/판매일반-***강원도 철원군<NA><NA>20171109적합6
14멥쌀(일반)생산일반김**경북 영덕군100.0100.020111006적합4
34침출차유통/판매일반소**원서울 강남구<NA><NA>20120917적합4
57현미유통/판매직불제(쌀소득)김**충북 청원군<NA>0.520130106적합4
26신고생산친환경(인증) 저농약박**울산 울주군100.050.020121218적합3
30오미자저장일반이**강원 인제군<NA>11.020090602적합3
35캠벨얼리(다크)유통/판매일반이**충남 보령시<NA><NA>20130830적합3
40현미생산일반감**경남 창원시1950.01000.020121008적합3
41현미생산일반김**경북 의성군2401.01800.020071015적합3