Overview

Dataset statistics

Number of variables14
Number of observations88
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.6 KiB
Average record size in memory123.5 B

Variable types

Numeric10
Categorical3
DateTime1

Dataset

Description국립종자원 정부보급종 공매 내역에 대한 데이터로 년도,지원명,작물명,계약일자,공급잔량,부산물_상품물량,부산물_중품물량,부산물_기타물량,단가_공급잔량,단가_상품,단가_중품,단가_기타,계약금액,계약업체 등의 항목을 제공합니다.
Author농림축산식품부 국립종자원
URLhttps://www.data.go.kr/data/15066233/fileData.do

Alerts

년도 is highly overall correlated with 계약업체High correlation
공급잔량 is highly overall correlated with 계약금액 and 1 other fieldsHigh correlation
부산물_상품물량 is highly overall correlated with 부산물_중품물량 and 2 other fieldsHigh correlation
부산물_중품물량 is highly overall correlated with 부산물_상품물량 and 2 other fieldsHigh correlation
부산물_기타물량 is highly overall correlated with 단가_기타 and 1 other fieldsHigh correlation
단가_공급잔량 is highly overall correlated with 부산물_상품물량 and 1 other fieldsHigh correlation
단가_상품 is highly overall correlated with 부산물_상품물량 and 1 other fieldsHigh correlation
단가_중품 is highly overall correlated with 부산물_중품물량 and 1 other fieldsHigh correlation
단가_기타 is highly overall correlated with 부산물_기타물량 and 1 other fieldsHigh correlation
계약금액 is highly overall correlated with 공급잔량 and 1 other fieldsHigh correlation
작물명 is highly overall correlated with 단가_공급잔량High correlation
계약업체 is highly overall correlated with 년도 and 3 other fieldsHigh correlation
계약금액 has unique valuesUnique
공급잔량 has 13 (14.8%) zerosZeros
부산물_상품물량 has 39 (44.3%) zerosZeros
부산물_중품물량 has 25 (28.4%) zerosZeros
부산물_기타물량 has 36 (40.9%) zerosZeros
단가_공급잔량 has 13 (14.8%) zerosZeros
단가_상품 has 42 (47.7%) zerosZeros
단가_중품 has 26 (29.5%) zerosZeros
단가_기타 has 40 (45.5%) zerosZeros

Reproduction

Analysis started2023-12-12 13:14:43.322384
Analysis finished2023-12-12 13:14:53.662169
Duration10.34 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년도
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.0682
Minimum2015
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size924.0 B
2023-12-12T22:14:53.706864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2015
5-th percentile2017
Q12017
median2018
Q32019
95-th percentile2019
Maximum2020
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.014781
Coefficient of variation (CV)0.00050284775
Kurtosis0.17233893
Mean2018.0682
Median Absolute Deviation (MAD)1
Skewness-0.61174988
Sum177590
Variance1.0297806
MonotonicityNot monotonic
2023-12-12T22:14:53.814317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2019 35
39.8%
2017 25
28.4%
2018 23
26.1%
2015 2
 
2.3%
2020 2
 
2.3%
2016 1
 
1.1%
ValueCountFrequency (%)
2015 2
 
2.3%
2016 1
 
1.1%
2017 25
28.4%
2018 23
26.1%
2019 35
39.8%
2020 2
 
2.3%
ValueCountFrequency (%)
2020 2
 
2.3%
2019 35
39.8%
2018 23
26.1%
2017 25
28.4%
2016 1
 
1.1%
2015 2
 
2.3%

지원명
Categorical

Distinct6
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Memory size836.0 B
전남지원
29 
전북지원
23 
충남지원
12 
경북지원
10 
충북지원
7 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전남지원
2nd row전남지원
3rd row경북지원
4th row충남지원
5th row전남지원

Common Values

ValueCountFrequency (%)
전남지원 29
33.0%
전북지원 23
26.1%
충남지원 12
13.6%
경북지원 10
 
11.4%
충북지원 7
 
8.0%
강원지원 7
 
8.0%

Length

2023-12-12T22:14:53.939215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:14:54.296251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전남지원 29
33.0%
전북지원 23
26.1%
충남지원 12
13.6%
경북지원 10
 
11.4%
충북지원 7
 
8.0%
강원지원 7
 
8.0%

작물명
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size836.0 B
벼
38 
콩
24 
보리
15 
호밀
6 
밀
5 

Length

Max length2
Median length1
Mean length1.2386364
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row밀
2nd row보리
3rd row호밀
4th row밀
5th row밀

Common Values

ValueCountFrequency (%)
벼 38
43.2%
콩 24
27.3%
보리 15
 
17.0%
호밀 6
 
6.8%
밀 5
 
5.7%

Length

2023-12-12T22:14:54.428665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:14:54.546218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
벼 38
43.2%
콩 24
27.3%
보리 15
 
17.0%
호밀 6
 
6.8%
밀 5
 
5.7%

계약일자
Date

Distinct61
Distinct (%)69.3%
Missing0
Missing (%)0.0%
Memory size836.0 B
Minimum2018-01-09 00:00:00
Maximum2020-10-20 00:00:00
2023-12-12T22:14:54.686136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:54.824168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

공급잔량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct75
Distinct (%)85.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93840.784
Minimum0
Maximum798642
Zeros13
Zeros (%)14.8%
Negative0
Negative (%)0.0%
Memory size924.0 B
2023-12-12T22:14:54.977379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1625
median17074.5
Q3114398.5
95-th percentile527618.8
Maximum798642
Range798642
Interquartile range (IQR)113773.5

Descriptive statistics

Standard deviation168663.39
Coefficient of variation (CV)1.7973357
Kurtosis7.6270183
Mean93840.784
Median Absolute Deviation (MAD)17074.5
Skewness2.7430542
Sum8257989
Variance2.844734 × 1010
MonotonicityNot monotonic
2023-12-12T22:14:55.117164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13
 
14.8%
100 2
 
2.3%
22 1
 
1.1%
88505 1
 
1.1%
13240 1
 
1.1%
783 1
 
1.1%
1740 1
 
1.1%
2181 1
 
1.1%
56950 1
 
1.1%
176655 1
 
1.1%
Other values (65) 65
73.9%
ValueCountFrequency (%)
0 13
14.8%
10 1
 
1.1%
20 1
 
1.1%
22 1
 
1.1%
100 2
 
2.3%
110 1
 
1.1%
210 1
 
1.1%
400 1
 
1.1%
610 1
 
1.1%
630 1
 
1.1%
ValueCountFrequency (%)
798642 1
1.1%
794314 1
1.1%
648000 1
1.1%
553755 1
1.1%
548350 1
1.1%
489118 1
1.1%
375475 1
1.1%
286070 1
1.1%
221329 1
1.1%
210779 1
1.1%

부산물_상품물량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct50
Distinct (%)56.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7568.0909
Minimum0
Maximum78872
Zeros39
Zeros (%)44.3%
Negative0
Negative (%)0.0%
Memory size924.0 B
2023-12-12T22:14:55.267881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median216.5
Q35058.5
95-th percentile39092
Maximum78872
Range78872
Interquartile range (IQR)5058.5

Descriptive statistics

Standard deviation15306.438
Coefficient of variation (CV)2.0224966
Kurtosis6.3983736
Mean7568.0909
Median Absolute Deviation (MAD)216.5
Skewness2.4545637
Sum665992
Variance2.3428705 × 108
MonotonicityNot monotonic
2023-12-12T22:14:55.425361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 39
44.3%
9433 1
 
1.1%
1688 1
 
1.1%
1594 1
 
1.1%
107 1
 
1.1%
468 1
 
1.1%
558 1
 
1.1%
661 1
 
1.1%
183 1
 
1.1%
1697 1
 
1.1%
Other values (40) 40
45.5%
ValueCountFrequency (%)
0 39
44.3%
20 1
 
1.1%
28 1
 
1.1%
107 1
 
1.1%
110 1
 
1.1%
183 1
 
1.1%
250 1
 
1.1%
280 1
 
1.1%
385 1
 
1.1%
419 1
 
1.1%
ValueCountFrequency (%)
78872 1
1.1%
60590 1
1.1%
47510 1
1.1%
46571 1
1.1%
39260 1
1.1%
38780 1
1.1%
32714 1
1.1%
30320 1
1.1%
28602 1
1.1%
28423 1
1.1%

부산물_중품물량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct64
Distinct (%)72.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36103.045
Minimum0
Maximum224454
Zeros25
Zeros (%)28.4%
Negative0
Negative (%)0.0%
Memory size924.0 B
2023-12-12T22:14:55.594362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2534.5
Q360493.75
95-th percentile150127.1
Maximum224454
Range224454
Interquartile range (IQR)60493.75

Descriptive statistics

Standard deviation58351.233
Coefficient of variation (CV)1.6162413
Kurtosis1.6153458
Mean36103.045
Median Absolute Deviation (MAD)2534.5
Skewness1.6262896
Sum3177068
Variance3.4048664 × 109
MonotonicityNot monotonic
2023-12-12T22:14:55.754381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 25
28.4%
2204 1
 
1.1%
710 1
 
1.1%
3105 1
 
1.1%
597 1
 
1.1%
37668 1
 
1.1%
225 1
 
1.1%
31374 1
 
1.1%
20649 1
 
1.1%
3502 1
 
1.1%
Other values (54) 54
61.4%
ValueCountFrequency (%)
0 25
28.4%
202 1
 
1.1%
225 1
 
1.1%
245 1
 
1.1%
270 1
 
1.1%
450 1
 
1.1%
597 1
 
1.1%
631 1
 
1.1%
710 1
 
1.1%
1213 1
 
1.1%
ValueCountFrequency (%)
224454 1
1.1%
206350 1
1.1%
199854 1
1.1%
184973 1
1.1%
150545 1
1.1%
149351 1
1.1%
145054 1
1.1%
142640 1
1.1%
141639 1
1.1%
139278 1
1.1%

부산물_기타물량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct52
Distinct (%)59.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1094.9318
Minimum0
Maximum68000
Zeros36
Zeros (%)40.9%
Negative0
Negative (%)0.0%
Memory size924.0 B
2023-12-12T22:14:55.920706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median89
Q3462
95-th percentile1451.15
Maximum68000
Range68000
Interquartile range (IQR)462

Descriptive statistics

Standard deviation7232.6345
Coefficient of variation (CV)6.6055569
Kurtosis87.069079
Mean1094.9318
Median Absolute Deviation (MAD)89
Skewness9.3080418
Sum96354
Variance52311001
MonotonicityNot monotonic
2023-12-12T22:14:56.112954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 36
40.9%
1270 2
 
2.3%
12 1
 
1.1%
235 1
 
1.1%
289 1
 
1.1%
215 1
 
1.1%
152 1
 
1.1%
7 1
 
1.1%
270 1
 
1.1%
212 1
 
1.1%
Other values (42) 42
47.7%
ValueCountFrequency (%)
0 36
40.9%
7 1
 
1.1%
12 1
 
1.1%
35 1
 
1.1%
44 1
 
1.1%
59 1
 
1.1%
70 1
 
1.1%
85 1
 
1.1%
88 1
 
1.1%
90 1
 
1.1%
ValueCountFrequency (%)
68000 1
1.1%
2960 1
1.1%
1705 1
1.1%
1539 1
1.1%
1511 1
1.1%
1340 1
1.1%
1275 1
1.1%
1270 2
2.3%
1181 1
1.1%
1099 1
1.1%

단가_공급잔량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct69
Distinct (%)78.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1920.2386
Minimum0
Maximum5379
Zeros13
Zeros (%)14.8%
Negative0
Negative (%)0.0%
Memory size924.0 B
2023-12-12T22:14:56.268549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1549
median1511.5
Q34210.5
95-th percentile4899.65
Maximum5379
Range5379
Interquartile range (IQR)3661.5

Descriptive statistics

Standard deviation1785.644
Coefficient of variation (CV)0.92990732
Kurtosis-0.91802487
Mean1920.2386
Median Absolute Deviation (MAD)986
Skewness0.79896852
Sum168981
Variance3188524.3
MonotonicityNot monotonic
2023-12-12T22:14:56.453437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13
 
14.8%
1168 2
 
2.3%
660 2
 
2.3%
1606 2
 
2.3%
4850 2
 
2.3%
5045 2
 
2.3%
555 2
 
2.3%
4824 2
 
2.3%
630 1
 
1.1%
4318 1
 
1.1%
Other values (59) 59
67.0%
ValueCountFrequency (%)
0 13
14.8%
337 1
 
1.1%
375 1
 
1.1%
410 1
 
1.1%
415 1
 
1.1%
481 1
 
1.1%
501 1
 
1.1%
509 1
 
1.1%
520 1
 
1.1%
531 1
 
1.1%
ValueCountFrequency (%)
5379 1
1.1%
5082 1
1.1%
5045 2
2.3%
4900 1
1.1%
4899 1
1.1%
4870 1
1.1%
4860 1
1.1%
4850 2
2.3%
4833 1
1.1%
4825 1
1.1%

단가_상품
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct44
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean641.01136
Minimum0
Maximum4500
Zeros42
Zeros (%)47.7%
Negative0
Negative (%)0.0%
Memory size924.0 B
2023-12-12T22:14:56.663023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median343.5
Q31290.25
95-th percentile1501.95
Maximum4500
Range4500
Interquartile range (IQR)1290.25

Descriptive statistics

Standard deviation948.04071
Coefficient of variation (CV)1.4789764
Kurtosis6.1758194
Mean641.01136
Median Absolute Deviation (MAD)343.5
Skewness2.2916898
Sum56409
Variance898781.18
MonotonicityNot monotonic
2023-12-12T22:14:56.802734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0 42
47.7%
440 3
 
3.4%
1415 2
 
2.3%
337 1
 
1.1%
545 1
 
1.1%
375 1
 
1.1%
588 1
 
1.1%
420 1
 
1.1%
400 1
 
1.1%
500 1
 
1.1%
Other values (34) 34
38.6%
ValueCountFrequency (%)
0 42
47.7%
250 1
 
1.1%
337 1
 
1.1%
350 1
 
1.1%
360 1
 
1.1%
375 1
 
1.1%
400 1
 
1.1%
420 1
 
1.1%
440 3
 
3.4%
500 1
 
1.1%
ValueCountFrequency (%)
4500 1
1.1%
4341 1
1.1%
3765 1
1.1%
3720 1
1.1%
1503 1
1.1%
1500 1
1.1%
1495 1
1.1%
1485 1
1.1%
1480 1
1.1%
1463 1
1.1%

단가_중품
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct58
Distinct (%)65.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1285.5682
Minimum0
Maximum4850
Zeros26
Zeros (%)29.5%
Negative0
Negative (%)0.0%
Memory size924.0 B
2023-12-12T22:14:56.947811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1046
Q31503.75
95-th percentile4369.8
Maximum4850
Range4850
Interquartile range (IQR)1503.75

Descriptive statistics

Standard deviation1456.7043
Coefficient of variation (CV)1.133121
Kurtosis-0.10047443
Mean1285.5682
Median Absolute Deviation (MAD)1046
Skewness1.0702892
Sum113130
Variance2121987.5
MonotonicityNot monotonic
2023-12-12T22:14:57.074672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 26
29.5%
3040 4
 
4.5%
1235 2
 
2.3%
4000 2
 
2.3%
1215 1
 
1.1%
2720 1
 
1.1%
485 1
 
1.1%
275 1
 
1.1%
483 1
 
1.1%
215 1
 
1.1%
Other values (48) 48
54.5%
ValueCountFrequency (%)
0 26
29.5%
180 1
 
1.1%
185 1
 
1.1%
202 1
 
1.1%
215 1
 
1.1%
216 1
 
1.1%
275 1
 
1.1%
340 1
 
1.1%
360 1
 
1.1%
415 1
 
1.1%
ValueCountFrequency (%)
4850 1
1.1%
4611 1
1.1%
4600 1
1.1%
4400 1
1.1%
4374 1
1.1%
4362 1
1.1%
4000 2
2.3%
3980 1
1.1%
3977 1
1.1%
3572 1
1.1%

단가_기타
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)53.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean979.65909
Minimum0
Maximum4889
Zeros40
Zeros (%)45.5%
Negative0
Negative (%)0.0%
Memory size924.0 B
2023-12-12T22:14:57.215068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median219.5
Q31275.25
95-th percentile4362.8
Maximum4889
Range4889
Interquartile range (IQR)1275.25

Descriptive statistics

Standard deviation1467.7929
Coefficient of variation (CV)1.498269
Kurtosis1.1165135
Mean979.65909
Median Absolute Deviation (MAD)219.5
Skewness1.5534577
Sum86210
Variance2154416
MonotonicityNot monotonic
2023-12-12T22:14:57.375205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 40
45.5%
3040 3
 
3.4%
420 1
 
1.1%
4400 1
 
1.1%
485 1
 
1.1%
240 1
 
1.1%
275 1
 
1.1%
483 1
 
1.1%
223 1
 
1.1%
216 1
 
1.1%
Other values (37) 37
42.0%
ValueCountFrequency (%)
0 40
45.5%
180 1
 
1.1%
185 1
 
1.1%
200 1
 
1.1%
216 1
 
1.1%
223 1
 
1.1%
240 1
 
1.1%
275 1
 
1.1%
280 1
 
1.1%
360 1
 
1.1%
ValueCountFrequency (%)
4889 1
1.1%
4870 1
1.1%
4600 1
1.1%
4400 1
1.1%
4374 1
1.1%
4342 1
1.1%
4341 1
1.1%
4328 1
1.1%
4123 1
1.1%
3980 1
1.1%

계약금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct88
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4366337 × 108
Minimum11680
Maximum1.4389559 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size924.0 B
2023-12-12T22:14:57.512199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11680
5-th percentile523548.5
Q111863552
median87199600
Q33.5666968 × 108
95-th percentile8.5839643 × 108
Maximum1.4389559 × 109
Range1.4389442 × 109
Interquartile range (IQR)3.4480613 × 108

Descriptive statistics

Standard deviation3.2519229 × 108
Coefficient of variation (CV)1.3345965
Kurtosis3.6376547
Mean2.4366337 × 108
Median Absolute Deviation (MAD)86013460
Skewness1.8795848
Sum2.1442377 × 1010
Variance1.0575002 × 1017
MonotonicityNot monotonic
2023-12-12T22:14:57.657192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3182180 1
 
1.1%
86099120 1
 
1.1%
329180730 1
 
1.1%
11819625 1
 
1.1%
1116310 1
 
1.1%
1020370 1
 
1.1%
7543175 1
 
1.1%
4616740 1
 
1.1%
28835742 1
 
1.1%
93128050 1
 
1.1%
Other values (78) 78
88.6%
ValueCountFrequency (%)
11680 1
1.1%
47580 1
1.1%
115920 1
1.1%
359100 1
1.1%
506640 1
1.1%
554950 1
1.1%
1020370 1
1.1%
1116310 1
1.1%
1255970 1
1.1%
1568070 1
1.1%
ValueCountFrequency (%)
1438955930 1
1.1%
1432419060 1
1.1%
1282575600 1
1.1%
866381880 1
1.1%
862750290 1
1.1%
850310680 1
1.1%
836558125 1
1.1%
785523508 1
1.1%
784190988 1
1.1%
640377384 1
1.1%

계약업체
Categorical

HIGH CORRELATION 

Distinct27
Distinct (%)30.7%
Missing0
Missing (%)0.0%
Memory size836.0 B
주식회사 영남농산[김미란]
11 
피엔라이스[나준수]
9 
한길물류(김박영)[]
8 
청주농산[김공배]
7 
오곡식품(김경순)[]
7 
Other values (22)
46 

Length

Max length18
Median length14
Mean length11.272727
Min length7

Unique

Unique10 ?
Unique (%)11.4%

Sample

1st row청주농산[김공배]
2nd row청주농산[김공배]
3rd row청주농산[김공배]
4th row대성양곡[채종우]
5th row대성양곡[채종우]

Common Values

ValueCountFrequency (%)
주식회사 영남농산[김미란] 11
12.5%
피엔라이스[나준수] 9
 
10.2%
한길물류(김박영)[] 8
 
9.1%
청주농산[김공배] 7
 
8.0%
오곡식품(김경순)[] 7
 
8.0%
대성양곡[채종우] 6
 
6.8%
(주) 대우미곡[조준호] 4
 
4.5%
(주)명품마켓[] 4
 
4.5%
영화안성미곡(주)[] 3
 
3.4%
이택영농조합법인[] 3
 
3.4%
Other values (17) 26
29.5%

Length

2023-12-12T22:14:57.820870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
주식회사 12
 
11.2%
영남농산[김미란 11
 
10.3%
피엔라이스[나준수 9
 
8.4%
한길물류(김박영 8
 
7.5%
청주농산[김공배 7
 
6.5%
오곡식품(김경순 7
 
6.5%
대성양곡[채종우 6
 
5.6%
주)명품마켓 4
 
3.7%
대우미곡[조준호 4
 
3.7%
주 4
 
3.7%
Other values (21) 35
32.7%

Interactions

2023-12-12T22:14:52.459056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:43.906246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:44.836578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:45.906832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:47.100730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:48.032536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:49.186624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:49.930702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:50.609297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:51.554299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:52.543070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:44.006419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:44.947618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:46.008209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:47.189693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:48.126859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:49.257844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:50.008655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:50.681593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:51.630069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:52.633575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:44.116720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:45.075203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:46.135422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:47.307496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:48.215261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:49.340291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:50.089908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:50.764526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:51.721972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:52.734636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:44.215707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:45.186879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:46.287717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:47.419505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:48.305230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:49.427646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:50.159556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:50.878190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:51.832446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:52.837479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:44.309227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:45.287405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:46.377694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:47.512561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:48.400434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:49.490659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:50.219859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:50.959427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:51.920460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:52.936772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:44.405357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:45.400571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:46.511323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:47.612669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:48.498670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:49.562315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:50.287992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:51.049965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:52.014896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:53.024282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:44.498889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:45.505702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:46.625827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:47.699696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:48.603047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:49.627134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:50.347364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:51.145864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:52.102009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:53.114527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:44.573737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:45.597076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:46.740336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:47.771217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:48.964567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:49.697847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:50.406413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:51.237174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:52.192049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:53.196289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:44.669130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:45.688040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:46.862215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:47.851350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:49.034282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:49.778728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:50.468954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:51.350959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:52.278144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:53.280521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:44.747355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:45.790841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:46.970079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:47.938909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:49.104328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:49.844033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:50.537933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:51.423441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:52.358179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:14:57.899073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도지원명작물명계약일자공급잔량부산물_상품물량부산물_중품물량부산물_기타물량단가_공급잔량단가_상품단가_중품단가_기타계약금액계약업체
년도1.0000.0000.0000.9380.0000.0000.0000.0000.2150.1330.1670.0000.0000.887
지원명0.0001.0000.1970.0000.2600.0000.0000.3950.0000.0000.1760.0000.5550.687
작물명0.0000.1971.0000.8840.0000.0000.3830.0000.7770.5110.7030.5630.1980.814
계약일자0.9380.0000.8841.0000.9080.8800.7700.0000.9160.7610.7050.5840.0000.940
공급잔량0.0000.2600.0000.9081.0000.7710.5600.0000.3100.2430.0000.0000.8700.897
부산물_상품물량0.0000.0000.0000.8800.7711.0000.8950.0000.0000.5300.3000.1710.8410.878
부산물_중품물량0.0000.0000.3830.7700.5600.8951.0000.0000.1490.7360.6680.5500.7840.869
부산물_기타물량0.0000.3950.0000.0000.0000.0000.0001.0000.1400.8710.1630.6790.1591.000
단가_공급잔량0.2150.0000.7770.9160.3100.0000.1490.1401.0000.5080.6360.7820.4070.680
단가_상품0.1330.0000.5110.7610.2430.5300.7360.8710.5081.0000.7230.6540.6310.615
단가_중품0.1670.1760.7030.7050.0000.3000.6680.1630.6360.7231.0000.8500.7910.776
단가_기타0.0000.0000.5630.5840.0000.1710.5500.6790.7820.6540.8501.0000.3760.699
계약금액0.0000.5550.1980.0000.8700.8410.7840.1590.4070.6310.7910.3761.0000.925
계약업체0.8870.6870.8140.9400.8970.8780.8691.0000.6800.6150.7760.6990.9251.000
2023-12-12T22:14:58.051080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
작물명계약업체지원명
작물명1.0000.4790.131
계약업체0.4791.0000.327
지원명0.1310.3271.000
2023-12-12T22:14:58.147781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도공급잔량부산물_상품물량부산물_중품물량부산물_기타물량단가_공급잔량단가_상품단가_중품단가_기타계약금액지원명작물명계약업체
년도1.000-0.0150.0370.1370.0890.0330.1420.1010.156-0.1360.0000.0810.519
공급잔량-0.0151.000-0.1340.0720.3580.463-0.0360.0940.2680.7500.1420.0000.557
부산물_상품물량0.037-0.1341.0000.6890.142-0.5070.8010.107-0.0010.1260.0000.0000.451
부산물_중품물량0.1370.0720.6891.0000.339-0.2490.5800.5330.2640.3960.0000.2250.438
부산물_기타물량0.0890.3580.1420.3391.0000.1680.2300.2230.7630.3580.2760.0000.842
단가_공급잔량0.0330.463-0.507-0.2490.1681.000-0.3160.3260.2590.3690.0000.6140.301
단가_상품0.142-0.0360.8010.5800.230-0.3161.0000.2230.1670.1570.0000.3750.274
단가_중품0.1010.0940.1070.5330.2230.3260.2231.0000.5030.4260.0800.4910.330
단가_기타0.1560.268-0.0010.2640.7630.2590.1670.5031.0000.3130.0000.3810.316
계약금액-0.1360.7500.1260.3960.3580.3690.1570.4260.3131.0000.2890.0940.534
지원명0.0000.1420.0000.0000.2760.0000.0000.0800.0000.2891.0000.1310.327
작물명0.0810.0000.0000.2250.0000.6140.3750.4910.3810.0940.1311.0000.479
계약업체0.5190.5570.4510.4380.8420.3010.2740.3300.3160.5340.3270.4791.000

Missing values

2023-12-12T22:14:53.430006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:14:53.601893image/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

년도지원명작물명계약일자공급잔량부산물_상품물량부산물_중품물량부산물_기타물량단가_공급잔량단가_상품단가_중품단가_기타계약금액계약업체
02015전남지원밀2018-01-0922943300337337003182180청주농산[김공배]
12015전남지원보리2018-01-0940025316006756750017354950청주농산[김공배]
22017경북지원호밀2018-01-09167348200583582001255970청주농산[김공배]
32017충남지원밀2018-02-0629066108124230509509509016593340대성양곡[채종우]
42017전남지원밀2018-02-237781200053100041318170대성양곡[채종우]
52017전남지원보리2018-02-23675000751000506640대성양곡[채종우]
62017전남지원벼2018-04-050284237219000140711270121389550피엔라이스[나준수]
72017전남지원벼2018-05-100605903997000148510210130828560옥강정미소[유상권]
82017전북지원벼2018-05-100190517210500016770120934820피엔라이스[나준수]
92017전북지원벼2018-05-1103032014935100015060224912150옥강정미소[]
년도지원명작물명계약일자공급잔량부산물_상품물량부산물_중품물량부산물_기타물량단가_공급잔량단가_상품단가_중품단가_기타계약금액계약업체
782019충북지원콩2020-09-08700700180004900048500352073000청주농산[김공배]
792020경북지원콩2020-09-0868700086217424850039803980370459740대성양곡[채종우]
802019전남지원콩2020-09-239140049930504504000066083300주식회사 영남농산[김미란]
812019전남지원콩2020-09-246295012130486004611036186843주식회사 영남농산[김미란]
822019전남지원콩2020-09-251100005045000554950주식회사 영남농산[김미란]
832019강원지원콩2020-09-2844502014642174850046004600223567300주식회사 영남농산[김미란]
842019전남지원콩2020-09-285225000475000024818750주식회사 영남농산[김미란]
852019전북지원콩2020-09-281492403546162483304362432888300080주식회사 영남농산[김미란]
862019충남지원콩2020-09-30626202013055004870450044004400312991400주식회사 영남농산[김미란]
872019전북지원호밀2020-10-201004267616448105404204203528240(주)홀그레인호밀농장[]