Overview

Dataset statistics

Number of variables9
Number of observations191
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.5 KiB
Average record size in memory77.7 B

Variable types

Categorical4
Text1
Numeric4

Dataset

Description년산, 지원명, 작물명, 품종명, 수매계획면적, 수매계획량, pp포대수(계획), 총포장량 등의 정보를 제공합니다
URLhttps://www.data.go.kr/data/15119659/fileData.do

Alerts

데이터 추출일자 has constant value ""Constant
수매계획면적 is highly overall correlated with 수매계획량 and 2 other fieldsHigh correlation
수매계획량 is highly overall correlated with 수매계획면적 and 2 other fieldsHigh correlation
pp포대수(계획) is highly overall correlated with 수매계획면적 and 2 other fieldsHigh correlation
총포장량 is highly overall correlated with 수매계획면적 and 2 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 03:37:55.086410
Analysis finished2023-12-12 03:37:57.835648
Duration2.75 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년산
Categorical

Distinct3
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2022
87 
2021
86 
2023
18 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2022 87
45.5%
2021 86
45.0%
2023 18
 
9.4%

Length

2023-12-12T12:37:57.920576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:37:58.046897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 87
45.5%
2021 86
45.0%
2023 18
 
9.4%

지원명
Categorical

Distinct8
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
전북지원
38 
전남지원
30 
경남지원
28 
충남지원
27 
경기종자관리소
21 
Other values (3)
47 

Length

Max length7
Median length4
Mean length4.3298429
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전북지원 38
19.9%
전남지원 30
15.7%
경남지원 28
14.7%
충남지원 27
14.1%
경기종자관리소 21
11.0%
경북지원 16
8.4%
강원지원 16
8.4%
충북지원 15
 
7.9%

Length

2023-12-12T12:37:58.217823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:37:58.399782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전북지원 38
19.9%
전남지원 30
15.7%
경남지원 28
14.7%
충남지원 27
14.1%
경기종자관리소 21
11.0%
경북지원 16
8.4%
강원지원 16
8.4%
충북지원 15
 
7.9%

작물명
Categorical

Distinct6
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
벼
84 
콩
44 
보리
35 
밀
20 
팥
 
4

Length

Max length2
Median length1
Mean length1.2041885
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
벼 84
44.0%
콩 44
23.0%
보리 35
18.3%
밀 20
 
10.5%
팥 4
 
2.1%
호밀 4
 
2.1%

Length

2023-12-12T12:37:58.611101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:37:59.132985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
벼 84
44.0%
콩 44
23.0%
보리 35
18.3%
밀 20
 
10.5%
팥 4
 
2.1%
호밀 4
 
2.1%

품종명
Text

Distinct56
Distinct (%)29.3%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2023-12-12T12:37:59.416988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.591623
Min length2

Characters and Unicode

Total characters686
Distinct characters74
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

Unique9 ?
Unique (%)4.7%

Sample

1st row고시히카리
2nd row맛드림
3rd row대안벼
4th row추청벼
5th row삼광벼
ValueCountFrequency (%)
대원콩 12
 
6.3%
삼광벼 11
 
5.8%
새금강밀 8
 
4.2%
대찬콩 8
 
4.2%
선풍콩 7
 
3.7%
큰알보리1호 6
 
3.1%
해담쌀 6
 
3.1%
재안찰쌀보리 6
 
3.1%
흰찰쌀보리 6
 
3.1%
금강밀 5
 
2.6%
Other values (46) 116
60.7%
2023-12-12T12:37:59.927450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
벼 58
 
8.5%
리 48
 
7.0%
콩 44
 
6.4%
보 36
 
5.2%
대 27
 
3.9%
쌀 26
 
3.8%
찰 24
 
3.5%
새 23
 
3.4%
강 20
 
2.9%
밀 20
 
2.9%
Other values (64) 360
52.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 678
98.8%
Decimal Number 8
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
벼 58
 
8.6%
리 48
 
7.1%
콩 44
 
6.5%
보 36
 
5.3%
대 27
 
4.0%
쌀 26
 
3.8%
찰 24
 
3.5%
새 23
 
3.4%
강 20
 
2.9%
밀 20
 
2.9%
Other values (63) 352
51.9%
Decimal Number
ValueCountFrequency (%)
1 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 678
98.8%
Common 8
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
벼 58
 
8.6%
리 48
 
7.1%
콩 44
 
6.5%
보 36
 
5.3%
대 27
 
4.0%
쌀 26
 
3.8%
찰 24
 
3.5%
새 23
 
3.4%
강 20
 
2.9%
밀 20
 
2.9%
Other values (63) 352
51.9%
Common
ValueCountFrequency (%)
1 8
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 678
98.8%
ASCII 8
 
1.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
벼 58
 
8.6%
리 48
 
7.1%
콩 44
 
6.5%
보 36
 
5.3%
대 27
 
4.0%
쌀 26
 
3.8%
찰 24
 
3.5%
새 23
 
3.4%
강 20
 
2.9%
밀 20
 
2.9%
Other values (63) 352
51.9%
ASCII
ValueCountFrequency (%)
1 8
100.0%

수매계획면적
Real number (ℝ)

HIGH CORRELATION 

Distinct176
Distinct (%)92.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6670.3141
Minimum82
Maximum60516
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-12T12:38:00.166979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum82
5-th percentile785.5
Q11785.5
median3080
Q37765.5
95-th percentile22149
Maximum60516
Range60434
Interquartile range (IQR)5980

Descriptive statistics

Standard deviation9318.7211
Coefficient of variation (CV)1.3970438
Kurtosis11.420507
Mean6670.3141
Median Absolute Deviation (MAD)1880
Skewness3.1505517
Sum1274030
Variance86838563
MonotonicityNot monotonic
2023-12-12T12:38:00.361316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2200 4
 
2.1%
1600 4
 
2.1%
1000 3
 
1.6%
2000 2
 
1.0%
1180 2
 
1.0%
2170 2
 
1.0%
1810 2
 
1.0%
3500 2
 
1.0%
1320 2
 
1.0%
800 2
 
1.0%
Other values (166) 166
86.9%
ValueCountFrequency (%)
82 1
0.5%
200 1
0.5%
399 1
0.5%
486 1
0.5%
500 1
0.5%
648 1
0.5%
660 1
0.5%
662 1
0.5%
743 1
0.5%
771 1
0.5%
ValueCountFrequency (%)
60516 1
0.5%
46862 1
0.5%
45797 1
0.5%
45193 1
0.5%
42740 1
0.5%
39504 1
0.5%
38062 1
0.5%
28153 1
0.5%
25022 1
0.5%
22600 1
0.5%

수매계획량
Real number (ℝ)

HIGH CORRELATION 

Distinct146
Distinct (%)76.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean315258.31
Minimum1000
Maximum3355000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-12T12:38:00.574269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile21000
Q144500
median124000
Q3326500
95-th percentile1183500
Maximum3355000
Range3354000
Interquartile range (IQR)282000

Descriptive statistics

Standard deviation541812.43
Coefficient of variation (CV)1.7186301
Kurtosis13.578473
Mean315258.31
Median Absolute Deviation (MAD)92000
Skewness3.4922567
Sum60214338
Variance2.9356071 × 1011
MonotonicityNot monotonic
2023-12-12T12:38:00.821295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
110000 8
 
4.2%
21000 6
 
3.1%
32000 5
 
2.6%
22000 3
 
1.6%
78000 3
 
1.6%
42000 3
 
1.6%
65000 3
 
1.6%
88000 3
 
1.6%
44000 3
 
1.6%
66000 3
 
1.6%
Other values (136) 151
79.1%
ValueCountFrequency (%)
1000 1
 
0.5%
2800 1
 
0.5%
4640 1
 
0.5%
7000 1
 
0.5%
14000 2
 
1.0%
15500 1
 
0.5%
16800 1
 
0.5%
21000 6
3.1%
22000 3
1.6%
23000 1
 
0.5%
ValueCountFrequency (%)
3355000 1
0.5%
3189000 1
0.5%
2750000 1
0.5%
2530000 2
1.0%
2200000 1
0.5%
2002000 1
0.5%
1444000 1
0.5%
1288000 1
0.5%
1190000 1
0.5%
1177000 1
0.5%

pp포대수(계획)
Real number (ℝ)

HIGH CORRELATION 

Distinct157
Distinct (%)82.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7915.3403
Minimum25
Maximum83880
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-12T12:38:01.042070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile525
Q11113
median3200
Q38162.5
95-th percentile29592
Maximum83880
Range83855
Interquartile range (IQR)7049.5

Descriptive statistics

Standard deviation13585.653
Coefficient of variation (CV)1.71637
Kurtosis13.502743
Mean7915.3403
Median Absolute Deviation (MAD)2399
Skewness3.4830092
Sum1511830
Variance1.8456996 × 108
MonotonicityNot monotonic
2023-12-12T12:38:01.212422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2750 7
 
3.7%
525 6
 
3.1%
800 4
 
2.1%
1650 3
 
1.6%
1950 3
 
1.6%
2200 3
 
1.6%
775 3
 
1.6%
1625 3
 
1.6%
825 2
 
1.0%
650 2
 
1.0%
Other values (147) 155
81.2%
ValueCountFrequency (%)
25 1
 
0.5%
70 1
 
0.5%
116 1
 
0.5%
175 1
 
0.5%
350 2
 
1.0%
388 1
 
0.5%
420 1
 
0.5%
525 6
3.1%
550 2
 
1.0%
551 1
 
0.5%
ValueCountFrequency (%)
83880 1
0.5%
79732 1
0.5%
69731 1
0.5%
63256 1
0.5%
63253 1
0.5%
55003 1
0.5%
50056 1
0.5%
36104 1
0.5%
32204 1
0.5%
29755 1
0.5%

총포장량
Real number (ℝ)

HIGH CORRELATION 

Distinct180
Distinct (%)94.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129360.61
Minimum50
Maximum1675849
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-12T12:38:01.446888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile2202
Q19757.5
median34476
Q398305.5
95-th percentile675940.5
Maximum1675849
Range1675799
Interquartile range (IQR)88548

Descriptive statistics

Standard deviation271199.85
Coefficient of variation (CV)2.0964638
Kurtosis13.854048
Mean129360.61
Median Absolute Deviation (MAD)29676
Skewness3.6131852
Sum24707877
Variance7.3549357 × 1010
MonotonicityNot monotonic
2023-12-12T12:38:01.672310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4800 3
 
1.6%
9750 3
 
1.6%
44000 2
 
1.0%
3200 2
 
1.0%
19250 2
 
1.0%
2750 2
 
1.0%
2475 2
 
1.0%
1750 2
 
1.0%
16500 2
 
1.0%
266800 1
 
0.5%
Other values (170) 170
89.0%
ValueCountFrequency (%)
50 1
0.5%
70 1
0.5%
175 1
0.5%
348 1
0.5%
925 1
0.5%
1161 1
0.5%
1430 1
0.5%
1750 2
1.0%
2200 1
0.5%
2204 1
0.5%
ValueCountFrequency (%)
1675849 1
0.5%
1455191 1
0.5%
1413765 1
0.5%
1269603 1
0.5%
1192465 1
0.5%
1120875 1
0.5%
1058400 1
0.5%
822543 1
0.5%
689288 1
0.5%
680725 1
0.5%

데이터 추출일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2023-08-22
191 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-08-22
2nd row2023-08-22
3rd row2023-08-22
4th row2023-08-22
5th row2023-08-22

Common Values

ValueCountFrequency (%)
2023-08-22 191
100.0%

Length

2023-12-12T12:38:01.927552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:38:02.121541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-08-22 191
100.0%

Interactions

2023-12-12T12:37:56.961784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:37:55.502311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:37:56.012450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:37:56.513579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:37:57.108855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:37:55.621622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:37:56.125912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:37:56.620204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:37:57.281455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:37:55.758378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:37:56.262685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:37:56.741582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:37:57.402486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:37:55.884943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:37:56.376445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:37:56.834702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T12:38:02.244543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년산지원명작물명품종명수매계획면적수매계획량pp포대수(계획)총포장량
년산1.0000.0000.6050.0000.0000.0000.0000.000
지원명0.0001.0000.2180.8890.1840.0000.0000.240
작물명0.6050.2181.0001.0000.1100.2090.2120.197
품종명0.0000.8891.0001.0000.7810.7230.7270.841
수매계획면적0.0000.1840.1100.7811.0000.9290.9290.846
수매계획량0.0000.0000.2090.7230.9291.0001.0000.903
pp포대수(계획)0.0000.0000.2120.7270.9291.0001.0000.903
총포장량0.0000.2400.1970.8410.8460.9030.9031.000
2023-12-12T12:38:02.460709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년산지원명작물명
년산1.0000.0000.305
지원명0.0001.0000.121
작물명0.3050.1211.000
2023-12-12T12:38:02.644059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수매계획면적수매계획량pp포대수(계획)총포장량년산지원명작물명
수매계획면적1.0000.8860.8860.8240.0000.0610.059
수매계획량0.8861.0000.9990.9120.0000.0000.111
pp포대수(계획)0.8860.9991.0000.9110.0000.0000.111
총포장량0.8240.9120.9111.0000.0000.1180.097
년산0.0000.0000.0000.0001.0000.0000.305
지원명0.0610.0000.0000.1180.0001.0000.121
작물명0.0590.1110.1110.0970.3050.1211.000

Missing values

2023-12-12T12:37:57.576359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:37:57.764249image/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

년산지원명작물명품종명수매계획면적수매계획량pp포대수(계획)총포장량데이터 추출일자
02021경기종자관리소벼고시히카리728736800092012668002023-08-22
12021경기종자관리소벼맛드림14721160002900580002023-08-22
22021경기종자관리소벼대안벼537627500068761636542023-08-22
32021경기종자관리소벼추청벼216981190000297556892882023-08-22
42021경기종자관리소벼삼광벼512331000077511948832023-08-22
52021경기종자관리소벼보람찰벼660370009259252023-08-22
62021경기종자관리소벼참드림10348647000161763291562023-08-22
72021경기종자관리소보리새찰쌀보리15563270086852022023-08-22
82021경기종자관리소보리재안찰쌀보리4861550038811612023-08-22
92021경기종자관리소콩대원콩78941330003327746162023-08-22
년산지원명작물명품종명수매계획면적수매계획량pp포대수(계획)총포장량데이터 추출일자
1812023전북지원밀금강밀1053042100010525376002023-08-22
1822023전북지원밀새금강밀1110051000012750584962023-08-22
1832023전북지원호밀곡우3670110000275027502023-08-22
1842023전남지원보리새쌀보리1302880002200220002023-08-22
1852023전남지원보리흰찰쌀보리71943280008200953612023-08-22
1862023전남지원밀백강밀48492600006500151132023-08-22
1872023전남지원밀새금강밀9557651000162751382252023-08-22
1882023경북지원보리영양보리145248000120048002023-08-22
1892023경남지원보리큰알보리1호22751000002500375002023-08-22
1902023경남지원밀백강밀10354420000105001166002023-08-22