Overview

Dataset statistics

Number of variables11
Number of observations7892
Missing cells103
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory701.5 KiB
Average record size in memory91.0 B

Variable types

Numeric3
Text4
Categorical4

Dataset

Description검정업무(농산물, 원산지,우수식품, LMO, 술품질인증) 통합 관리 정보(신청일자, 품목, 종류, 생산지, 검정항목, 검정기관 등)
Author국립농산물품질관리원
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20220204000000001680

Alerts

REQST_DE is highly overall correlated with ATHRZ_INSTT_CODEHigh correlation
PRDLC_CODE is highly overall correlated with PRDLCHigh correlation
ATHRZ_INSTT_CODE is highly overall correlated with REQST_DE and 2 other fieldsHigh correlation
KND is highly overall correlated with ATHRZ_IEMHigh correlation
PRDLC is highly overall correlated with PRDLC_CODEHigh correlation
ATHRZ_IEM is highly overall correlated with ATHRZ_INSTT_CODE and 2 other fieldsHigh correlation
ATHRZ_INSTT is highly overall correlated with ATHRZ_INSTT_CODE and 1 other fieldsHigh correlation
ATHRZ_IEM is highly imbalanced (50.5%)Imbalance
ATHRZ_INSTT is highly imbalanced (70.8%)Imbalance
PRDLC_CODE has 103 (1.3%) missing valuesMissing
SPLORE_NO has unique valuesUnique

Reproduction

Analysis started2024-03-23 07:34:43.307261
Analysis finished2024-03-23 07:34:48.843015
Duration5.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

REQST_DE
Real number (ℝ)

HIGH CORRELATION 

Distinct654
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20134543
Minimum20100108
Maximum20220113
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size69.5 KiB
2024-03-23T07:34:49.049958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20100108
5-th percentile20100727
Q120120509
median20130923
Q320150618
95-th percentile20170206
Maximum20220113
Range120005
Interquartile range (IQR)30109

Descriptive statistics

Standard deviation22178.256
Coefficient of variation (CV)0.0011015028
Kurtosis0.64202738
Mean20134543
Median Absolute Deviation (MAD)19281
Skewness0.58749405
Sum1.5890181 × 1011
Variance4.9187504 × 108
MonotonicityNot monotonic
2024-03-23T07:34:49.707276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20120712 85
 
1.1%
20160719 79
 
1.0%
20130715 78
 
1.0%
20110808 71
 
0.9%
20140710 69
 
0.9%
20201211 67
 
0.8%
20150629 67
 
0.8%
20191126 66
 
0.8%
20160725 63
 
0.8%
20160829 62
 
0.8%
Other values (644) 7185
91.0%
ValueCountFrequency (%)
20100108 15
0.2%
20100128 8
 
0.1%
20100129 1
 
< 0.1%
20100204 27
0.3%
20100205 30
0.4%
20100210 18
0.2%
20100219 9
 
0.1%
20100224 6
 
0.1%
20100303 1
 
< 0.1%
20100309 6
 
0.1%
ValueCountFrequency (%)
20220113 19
 
0.2%
20211220 22
 
0.3%
20210422 1
 
< 0.1%
20210107 8
 
0.1%
20201211 67
0.8%
20200214 2
 
< 0.1%
20200213 2
 
< 0.1%
20200212 3
 
< 0.1%
20200211 4
 
0.1%
20200207 5
 
0.1%

PRDLST
Text

Distinct73
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size61.8 KiB
2024-03-23T07:34:50.196951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length4.3848201
Min length1

Characters and Unicode

Total characters34605
Distinct characters131
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

Unique13 ?
Unique (%)0.2%

Sample

1st row호밀
2nd row청보리
3rd row청보리
4th row청보리
5th row수수
ValueCountFrequency (%)
호밀 1914
24.3%
라이그라스(이탈리안 954
12.1%
베치 701
 
8.9%
청보리 660
 
8.4%
수단그라스 614
 
7.8%
콩나물콩 295
 
3.7%
귀리 274
 
3.5%
옥수수 272
 
3.4%
페스큐(톨 254
 
3.2%
금계국 188
 
2.4%
Other values (63) 1766
22.4%
2024-03-23T07:34:51.101891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
라 3124
 
9.0%
스 2500
 
7.2%
이 2461
 
7.1%
리 2107
 
6.1%
그 1988
 
5.7%
밀 1962
 
5.7%
호 1914
 
5.5%
( 1602
 
4.6%
) 1602
 
4.6%
수 1431
 
4.1%
Other values (121) 13914
40.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 31401
90.7%
Open Punctuation 1602
 
4.6%
Close Punctuation 1602
 
4.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
라 3124
 
9.9%
스 2500
 
8.0%
이 2461
 
7.8%
리 2107
 
6.7%
그 1988
 
6.3%
밀 1962
 
6.2%
호 1914
 
6.1%
수 1431
 
4.6%
탈 954
 
3.0%
안 954
 
3.0%
Other values (119) 12006
38.2%
Open Punctuation
ValueCountFrequency (%)
( 1602
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1602
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 31401
90.7%
Common 3204
 
9.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
라 3124
 
9.9%
스 2500
 
8.0%
이 2461
 
7.8%
리 2107
 
6.7%
그 1988
 
6.3%
밀 1962
 
6.2%
호 1914
 
6.1%
수 1431
 
4.6%
탈 954
 
3.0%
안 954
 
3.0%
Other values (119) 12006
38.2%
Common
ValueCountFrequency (%)
( 1602
50.0%
) 1602
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 31401
90.7%
ASCII 3204
 
9.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
라 3124
 
9.9%
스 2500
 
8.0%
이 2461
 
7.8%
리 2107
 
6.7%
그 1988
 
6.3%
밀 1962
 
6.2%
호 1914
 
6.1%
수 1431
 
4.6%
탈 954
 
3.0%
안 954
 
3.0%
Other values (119) 12006
38.2%
ASCII
ValueCountFrequency (%)
( 1602
50.0%
) 1602
50.0%

KND
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size61.8 KiB
사료.목초종자
2247 
맥류
1839 
농산물종자류
1203 
조사료
1190 
화훼종자류
686 
Other values (11)
727 

Length

Max length7
Median length6
Mean length4.5277496
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row맥류
2nd row농산물종자류
3rd row농산물종자류
4th row농산물종자류
5th row잡곡류

Common Values

ValueCountFrequency (%)
사료.목초종자 2247
28.5%
맥류 1839
23.3%
농산물종자류 1203
15.2%
조사료 1190
15.1%
화훼종자류 686
 
8.7%
두류 370
 
4.7%
잡곡류 154
 
2.0%
산림종묘 73
 
0.9%
LMO농산물 60
 
0.8%
미곡류 38
 
0.5%
Other values (6) 32
 
0.4%

Length

2024-03-23T07:34:51.475226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
사료.목초종자 2247
28.5%
맥류 1839
23.3%
농산물종자류 1203
15.2%
조사료 1190
15.1%
화훼종자류 686
 
8.7%
두류 370
 
4.7%
잡곡류 154
 
2.0%
산림종묘 73
 
0.9%
lmo농산물 60
 
0.8%
미곡류 38
 
0.5%
Other values (6) 32
 
0.4%

PRDLC
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size61.8 KiB
미국
3785 
중국
1052 
대한민국(국산)
1038 
캐나다
687 
오스트레일리아(호주)
657 
Other values (17)
673 

Length

Max length11
Median length2
Mean length3.9071211
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row미국
2nd row대한민국(국산)
3rd row대한민국(국산)
4th row대한민국(국산)
5th row오스트레일리아(호주)

Common Values

ValueCountFrequency (%)
미국 3785
48.0%
중국 1052
 
13.3%
대한민국(국산) 1038
 
13.2%
캐나다 687
 
8.7%
오스트레일리아(호주) 657
 
8.3%
남아프리카공화국 271
 
3.4%
<NA> 105
 
1.3%
인도 64
 
0.8%
오리건주 52
 
0.7%
스페인 32
 
0.4%
Other values (12) 149
 
1.9%

Length

2024-03-23T07:34:51.850285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미국 3785
48.0%
중국 1052
 
13.3%
대한민국(국산 1038
 
13.2%
캐나다 687
 
8.7%
오스트레일리아(호주 657
 
8.3%
남아프리카공화국 271
 
3.4%
na 105
 
1.3%
인도 64
 
0.8%
오리건주 52
 
0.7%
스페인 32
 
0.4%
Other values (12) 149
 
1.9%

ATHRZ_IEM
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct40
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size61.8 KiB
이종종자, 이물, 발아율
2225 
이종종자, 이물, 발아율, 수분
1718 
정립, 발아율
1609 
발아율
1241 
정립, 발아율, 수분
607 
Other values (35)
492 

Length

Max length95
Median length66
Mean length10.914597
Min length2

Unique

Unique11 ?
Unique (%)0.1%

Sample

1st row이종종자, 이물, 발아율, 수분
2nd row이종종자, 이물, 발아율, 수분
3rd row이종종자, 이물, 발아율, 수분
4th row이종종자, 이물, 발아율, 수분
5th row이종종자, 이물, 발아율

Common Values

ValueCountFrequency (%)
이종종자, 이물, 발아율 2225
28.2%
이종종자, 이물, 발아율, 수분 1718
21.8%
정립, 발아율 1609
20.4%
발아율 1241
15.7%
정립, 발아율, 수분 607
 
7.7%
정립, 이물, 발아율 141
 
1.8%
정립, 이종종자, 발아율 90
 
1.1%
수분 76
 
1.0%
이물, 발아율 23
 
0.3%
이물, 싸라기, 색택, 수분 18
 
0.2%
Other values (30) 144
 
1.8%

Length

2024-03-23T07:34:52.239807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
발아율 7710
36.2%
이물 4202
19.7%
이종종자 4037
18.9%
수분 2527
 
11.9%
정립 2460
 
11.5%
파쇄립 42
 
0.2%
피해립 28
 
0.1%
순도 26
 
0.1%
다른종피색 24
 
0.1%
변질률 24
 
0.1%
Other values (21) 233
 
1.1%

ATHRZ_INSTT
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size61.8 KiB
국립농산물품질관리원 시험연구소 원산지검정과
5744 
국립농산물품질관리원 시험연구소 품질조사과
2122 
국립농산물품질관리원 전남지원 유통관리과
 
13
국립농산물품질관리원 경남지원 유통관리과
 
6
국립농산물품질관리원 경북지원 유통관리과
 
3
Other values (3)
 
4

Length

Max length23
Median length23
Mean length22.724531
Min length21

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row국립농산물품질관리원 시험연구소 원산지검정과
2nd row국립농산물품질관리원 시험연구소 원산지검정과
3rd row국립농산물품질관리원 시험연구소 원산지검정과
4th row국립농산물품질관리원 시험연구소 원산지검정과
5th row국립농산물품질관리원 시험연구소 원산지검정과

Common Values

ValueCountFrequency (%)
국립농산물품질관리원 시험연구소 원산지검정과 5744
72.8%
국립농산물품질관리원 시험연구소 품질조사과 2122
 
26.9%
국립농산물품질관리원 전남지원 유통관리과 13
 
0.2%
국립농산물품질관리원 경남지원 유통관리과 6
 
0.1%
국립농산물품질관리원 경북지원 유통관리과 3
 
< 0.1%
국립농산물품질관리원 전남지원 품질관리과 2
 
< 0.1%
국립농산물품질관리원 전남지원 영광사무소 1
 
< 0.1%
국립농산물품질관리원 충북지원 유통관리과 1
 
< 0.1%

Length

2024-03-23T07:34:52.664243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T07:34:53.043141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
국립농산물품질관리원 7892
33.3%
시험연구소 7866
33.2%
원산지검정과 5744
24.3%
품질조사과 2122
 
9.0%
유통관리과 23
 
0.1%
전남지원 16
 
0.1%
경남지원 6
 
< 0.1%
경북지원 3
 
< 0.1%
품질관리과 2
 
< 0.1%
영광사무소 1
 
< 0.1%

REQST_SN
Text

Distinct2525
Distinct (%)32.0%
Missing0
Missing (%)0.0%
Memory size61.8 KiB
2024-03-23T07:34:53.562272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters157840
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1479 ?
Unique (%)18.7%

Sample

1st rowUCI00000000001252958
2nd rowUCI00000000001253043
3rd rowUCI00000000001253044
4th rowUCI00000000001253047
5th rowUCI00000000001262573
ValueCountFrequency (%)
uci00000000001376618 66
 
0.8%
uci00000000000004303 40
 
0.5%
uci00000000001397303 40
 
0.5%
uci00000000001267776 37
 
0.5%
uci00000000001263118 30
 
0.4%
uci00000000001278952 29
 
0.4%
uci00000000001279005 29
 
0.4%
uci00000000001392271 27
 
0.3%
uci00000000000005008 26
 
0.3%
uci00000000000005812 24
 
0.3%
Other values (2515) 7544
95.6%
2024-03-23T07:34:54.561918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 85323
54.1%
1 10068
 
6.4%
2 9417
 
6.0%
C 7892
 
5.0%
U 7816
 
5.0%
I 7816
 
5.0%
5 5468
 
3.5%
4 5376
 
3.4%
6 4213
 
2.7%
7 3968
 
2.5%
Other values (5) 10483
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 134164
85.0%
Uppercase Letter 23676
 
15.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 85323
63.6%
1 10068
 
7.5%
2 9417
 
7.0%
5 5468
 
4.1%
4 5376
 
4.0%
6 4213
 
3.1%
7 3968
 
3.0%
8 3835
 
2.9%
9 3494
 
2.6%
3 3002
 
2.2%
Uppercase Letter
ValueCountFrequency (%)
C 7892
33.3%
U 7816
33.0%
I 7816
33.0%
A 76
 
0.3%
P 76
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 134164
85.0%
Latin 23676
 
15.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 85323
63.6%
1 10068
 
7.5%
2 9417
 
7.0%
5 5468
 
4.1%
4 5376
 
4.0%
6 4213
 
3.1%
7 3968
 
3.0%
8 3835
 
2.9%
9 3494
 
2.6%
3 3002
 
2.2%
Latin
ValueCountFrequency (%)
C 7892
33.3%
U 7816
33.0%
I 7816
33.0%
A 76
 
0.3%
P 76
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 157840
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 85323
54.1%
1 10068
 
6.4%
2 9417
 
6.0%
C 7892
 
5.0%
U 7816
 
5.0%
I 7816
 
5.0%
5 5468
 
3.5%
4 5376
 
3.4%
6 4213
 
2.7%
7 3968
 
2.5%
Other values (5) 10483
 
6.6%

SPLORE_NO
Text

UNIQUE 

Distinct7892
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size61.8 KiB
2024-03-23T07:34:55.028299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters157840
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7892 ?
Unique (%)100.0%

Sample

1st rowSMP00000000000056366
2nd rowSMP00000000000056553
3rd rowSMP00000000000056554
4th rowSMP00000000000056557
5th rowSMP00000000000080191
ValueCountFrequency (%)
smp00000000000056366 1
 
< 0.1%
smp00000000000084335 1
 
< 0.1%
smp00000000000100925 1
 
< 0.1%
smp00000000000100924 1
 
< 0.1%
smp00000000000111899 1
 
< 0.1%
smp00000000000111898 1
 
< 0.1%
smp00000000000111897 1
 
< 0.1%
smp00000000000111896 1
 
< 0.1%
smp00000000000111785 1
 
< 0.1%
smp00000000000111784 1
 
< 0.1%
Other values (7882) 7882
99.9%
2024-03-23T07:34:55.891151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 98107
62.2%
S 7892
 
5.0%
M 7892
 
5.0%
P 7892
 
5.0%
1 4431
 
2.8%
7 4300
 
2.7%
9 4193
 
2.7%
4 4113
 
2.6%
5 4071
 
2.6%
2 4008
 
2.5%
Other values (3) 10941
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 134164
85.0%
Uppercase Letter 23676
 
15.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98107
73.1%
1 4431
 
3.3%
7 4300
 
3.2%
9 4193
 
3.1%
4 4113
 
3.1%
5 4071
 
3.0%
2 4008
 
3.0%
3 3751
 
2.8%
8 3639
 
2.7%
6 3551
 
2.6%
Uppercase Letter
ValueCountFrequency (%)
S 7892
33.3%
M 7892
33.3%
P 7892
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 134164
85.0%
Latin 23676
 
15.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 98107
73.1%
1 4431
 
3.3%
7 4300
 
3.2%
9 4193
 
3.1%
4 4113
 
3.1%
5 4071
 
3.0%
2 4008
 
3.0%
3 3751
 
2.8%
8 3639
 
2.7%
6 3551
 
2.6%
Latin
ValueCountFrequency (%)
S 7892
33.3%
M 7892
33.3%
P 7892
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 157840
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 98107
62.2%
S 7892
 
5.0%
M 7892
 
5.0%
P 7892
 
5.0%
1 4431
 
2.8%
7 4300
 
2.7%
9 4193
 
2.7%
4 4113
 
2.6%
5 4071
 
2.6%
2 4008
 
2.5%
Other values (3) 10941
 
6.9%

PRDLST_CODE
Text

Distinct81
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size61.8 KiB
2024-03-23T07:34:56.309404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters47352
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)0.2%

Sample

1st row020500
2nd row153500
3rd row153500
4th row153500
5th row040300
ValueCountFrequency (%)
020500 1616
20.5%
582024 954
12.1%
5602aa 701
 
8.9%
153500 660
 
8.4%
582035 614
 
7.8%
153600 298
 
3.8%
030109 295
 
3.7%
560222 254
 
3.2%
272401 188
 
2.4%
582025 179
 
2.3%
Other values (71) 2133
27.0%
2024-03-23T07:34:57.261287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 16305
34.4%
2 8493
17.9%
5 8012
16.9%
1 3148
 
6.6%
3 2687
 
5.7%
8 2499
 
5.3%
6 1815
 
3.8%
A 1426
 
3.0%
4 1353
 
2.9%
7 944
 
2.0%
Other values (4) 670
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45902
96.9%
Uppercase Letter 1450
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16305
35.5%
2 8493
18.5%
5 8012
17.5%
1 3148
 
6.9%
3 2687
 
5.9%
8 2499
 
5.4%
6 1815
 
4.0%
4 1353
 
2.9%
7 944
 
2.1%
9 646
 
1.4%
Uppercase Letter
ValueCountFrequency (%)
A 1426
98.3%
F 16
 
1.1%
B 7
 
0.5%
G 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 45902
96.9%
Latin 1450
 
3.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16305
35.5%
2 8493
18.5%
5 8012
17.5%
1 3148
 
6.9%
3 2687
 
5.9%
8 2499
 
5.4%
6 1815
 
4.0%
4 1353
 
2.9%
7 944
 
2.1%
9 646
 
1.4%
Latin
ValueCountFrequency (%)
A 1426
98.3%
F 16
 
1.1%
B 7
 
0.5%
G 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47352
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16305
34.4%
2 8493
17.9%
5 8012
16.9%
1 3148
 
6.6%
3 2687
 
5.7%
8 2499
 
5.3%
6 1815
 
3.8%
A 1426
 
3.0%
4 1353
 
2.9%
7 944
 
2.0%
Other values (4) 670
 
1.4%

PRDLC_CODE
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)0.3%
Missing103
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean9296.6989
Minimum0
Maximum22700
Zeros26
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size69.5 KiB
2024-03-23T07:34:57.722304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3000
Q16800
median6800
Q314700
95-th percentile18300
Maximum22700
Range22700
Interquartile range (IQR)7900

Descriptive statistics

Standard deviation5415.8009
Coefficient of variation (CV)0.58255096
Kurtosis-1.1761567
Mean9296.6989
Median Absolute Deviation (MAD)3800
Skewness0.53949041
Sum72411988
Variance29330899
MonotonicityNot monotonic
2024-03-23T07:34:58.361630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
6800 3785
48.0%
16900 1052
 
13.3%
3000 1038
 
13.2%
18300 687
 
8.7%
14700 657
 
8.3%
1800 271
 
3.4%
16200 64
 
0.8%
6807 52
 
0.7%
11800 32
 
0.4%
3100 27
 
0.3%
Other values (12) 124
 
1.6%
(Missing) 103
 
1.3%
ValueCountFrequency (%)
0 26
 
0.3%
36 2
 
< 0.1%
1800 271
 
3.4%
2000 17
 
0.2%
2400 3
 
< 0.1%
3000 1038
 
13.2%
3100 27
 
0.3%
6800 3785
48.0%
6804 13
 
0.2%
6807 52
 
0.7%
ValueCountFrequency (%)
22700 11
 
0.1%
22200 1
 
< 0.1%
18300 687
8.7%
18200 19
 
0.2%
16900 1052
13.3%
16400 5
 
0.1%
16200 64
 
0.8%
16100 10
 
0.1%
15200 2
 
< 0.1%
14700 657
8.3%

ATHRZ_INSTT_CODE
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1541213.5
Minimum1541211
Maximum1541353
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size69.5 KiB
2024-03-23T07:34:59.066355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1541211
5-th percentile1541211
Q11541211
median1541214
Q31541214
95-th percentile1541214
Maximum1541353
Range142
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.5589683
Coefficient of variation (CV)3.6068775 × 10-6
Kurtosis322.16417
Mean1541213.5
Median Absolute Deviation (MAD)0
Skewness17.048393
Sum1.2163257 × 1010
Variance30.902128
MonotonicityNot monotonic
2024-03-23T07:34:59.661726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1541214 5744
72.8%
1541211 2122
 
26.9%
1541290 13
 
0.2%
1541333 6
 
0.1%
1541311 3
 
< 0.1%
1541289 2
 
< 0.1%
1541303 1
 
< 0.1%
1541353 1
 
< 0.1%
ValueCountFrequency (%)
1541211 2122
 
26.9%
1541214 5744
72.8%
1541289 2
 
< 0.1%
1541290 13
 
0.2%
1541303 1
 
< 0.1%
1541311 3
 
< 0.1%
1541333 6
 
0.1%
1541353 1
 
< 0.1%
ValueCountFrequency (%)
1541353 1
 
< 0.1%
1541333 6
 
0.1%
1541311 3
 
< 0.1%
1541303 1
 
< 0.1%
1541290 13
 
0.2%
1541289 2
 
< 0.1%
1541214 5744
72.8%
1541211 2122
 
26.9%

Interactions

2024-03-23T07:34:47.156112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:34:45.551241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:34:46.343500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:34:47.448917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:34:45.826547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:34:46.608421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:34:47.711554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:34:46.089067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:34:46.842003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T07:34:59.964964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
REQST_DEPRDLSTKNDPRDLCATHRZ_IEMATHRZ_INSTTPRDLST_CODEPRDLC_CODEATHRZ_INSTT_CODE
REQST_DE1.0000.7580.6530.5300.6760.6040.7860.4440.075
PRDLST0.7581.0000.9950.9430.9550.8381.0000.8980.887
KND0.6530.9951.0000.8230.9200.7941.0000.8310.761
PRDLC0.5300.9430.8231.0000.7900.2440.9431.0000.115
ATHRZ_IEM0.6760.9550.9200.7901.0000.9890.9600.6610.977
ATHRZ_INSTT0.6040.8380.7940.2440.9891.0000.8410.2821.000
PRDLST_CODE0.7861.0001.0000.9430.9600.8411.0000.9000.900
PRDLC_CODE0.4440.8980.8311.0000.6610.2820.9001.0000.123
ATHRZ_INSTT_CODE0.0750.8870.7610.1150.9771.0000.9000.1231.000
2024-03-23T07:35:00.315277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ATHRZ_INSTTATHRZ_IEMPRDLCKND
ATHRZ_INSTT1.0000.8540.1030.408
ATHRZ_IEM0.8541.0000.3100.540
PRDLC0.1030.3101.0000.418
KND0.4080.5400.4181.000
2024-03-23T07:35:00.678297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
REQST_DEPRDLC_CODEATHRZ_INSTT_CODEKNDPRDLCATHRZ_IEMATHRZ_INSTT
REQST_DE1.000-0.117-0.7380.3320.2350.3180.351
PRDLC_CODE-0.1171.0000.1730.4520.9990.3260.097
ATHRZ_INSTT_CODE-0.7380.1731.0000.4190.0420.8921.000
KND0.3320.4520.4191.0000.4180.5400.408
PRDLC0.2350.9990.0420.4181.0000.3100.103
ATHRZ_IEM0.3180.3260.8920.5400.3101.0000.854
ATHRZ_INSTT0.3510.0971.0000.4080.1030.8541.000

Missing values

2024-03-23T07:34:48.105065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T07:34:48.622304image/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.

Sample

REQST_DEPRDLSTKNDPRDLCATHRZ_IEMATHRZ_INSTTREQST_SNSPLORE_NOPRDLST_CODEPRDLC_CODEATHRZ_INSTT_CODE
020130624호밀맥류미국이종종자, 이물, 발아율, 수분국립농산물품질관리원 시험연구소 원산지검정과UCI00000000001252958SMP0000000000005636602050068001541214
120130626청보리농산물종자류대한민국(국산)이종종자, 이물, 발아율, 수분국립농산물품질관리원 시험연구소 원산지검정과UCI00000000001253043SMP0000000000005655315350030001541214
220130626청보리농산물종자류대한민국(국산)이종종자, 이물, 발아율, 수분국립농산물품질관리원 시험연구소 원산지검정과UCI00000000001253044SMP0000000000005655415350030001541214
320130626청보리농산물종자류대한민국(국산)이종종자, 이물, 발아율, 수분국립농산물품질관리원 시험연구소 원산지검정과UCI00000000001253047SMP0000000000005655715350030001541214
420150224수수잡곡류오스트레일리아(호주)이종종자, 이물, 발아율국립농산물품질관리원 시험연구소 원산지검정과UCI00000000001262573SMP00000000000080191040300147001541214
520130717라이그라스(페레니얼)사료.목초종자미국정립, 발아율국립농산물품질관리원 시험연구소 원산지검정과UCI00000000001253189SMP0000000000005693158202568001541214
620130703호밀맥류캐나다이종종자, 이물, 발아율, 수분국립농산물품질관리원 시험연구소 원산지검정과UCI00000000001253191SMP00000000000056945020500183001541214
720130703호밀맥류캐나다이종종자, 이물, 발아율, 수분국립농산물품질관리원 시험연구소 원산지검정과UCI00000000001253191SMP00000000000056944020500183001541214
820120705베치조사료중국이종종자, 이물, 발아율국립농산물품질관리원 시험연구소 원산지검정과UCI00000000001247104SMP000000000000405435602AA169001541214
920120705베치조사료중국이종종자, 이물, 발아율국립농산물품질관리원 시험연구소 원산지검정과UCI00000000001247104SMP000000000000405445602AA169001541214
REQST_DEPRDLSTKNDPRDLCATHRZ_IEMATHRZ_INSTTREQST_SNSPLORE_NOPRDLST_CODEPRDLC_CODEATHRZ_INSTT_CODE
788220150930호밀맥류미국이종종자, 이물, 발아율, 수분국립농산물품질관리원 시험연구소 품질조사과UCI00000000001265672SMP0000000000008791802050068001541211
788320150930호밀맥류미국이종종자, 이물, 발아율, 수분국립농산물품질관리원 시험연구소 품질조사과UCI00000000001265672SMP0000000000008791902050068001541211
788420150930호밀맥류미국이종종자, 이물, 발아율, 수분국립농산물품질관리원 시험연구소 품질조사과UCI00000000001265672SMP0000000000008792002050068001541211
788520150930호밀맥류미국이종종자, 이물, 발아율, 수분국립농산물품질관리원 시험연구소 품질조사과UCI00000000001265672SMP0000000000008792102050068001541211
788620150818라이그라스(이탈리안)사료.목초종자미국이종종자, 이물, 발아율국립농산물품질관리원 시험연구소 품질조사과UCI00000000001264785SMP0000000000008598458202468001541211
788720160617옥수수잡곡류대한민국(국산)정립, 발아율국립농산물품질관리원 시험연구소 품질조사과UCI00000000001275992SMP0000000000009694704010030001541211
788820160920페스큐(톨)조사료미국정립, 발아율국립농산물품질관리원 시험연구소 품질조사과UCI00000000001290818SMP0000000000009999556022268001541211
788920160920라이그라스(페레니얼)사료.목초종자미국정립, 발아율국립농산물품질관리원 시험연구소 품질조사과UCI00000000001290818SMP0000000000009999658202568001541211
789020160920페스큐(크리핑레드)사료.목초종자미국정립, 발아율국립농산물품질관리원 시험연구소 품질조사과UCI00000000001290818SMP0000000000009999758203968001541211
789120160517패랭이화훼종자류중국정립, 발아율국립농산물품질관리원 시험연구소 품질조사과UCI00000000001275266SMP00000000000095716273601169001541211