Overview

Dataset statistics

Number of variables9
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory830.1 KiB
Average record size in memory85.0 B

Variable types

Categorical4
Numeric4
Text1

Dataset

Description정부보급종 정선 입고종자 종자규격 내역으로 년산,지원명,부서명,종자규격번호,작물명,품종명,길이,폭,두께 등의 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15066323/fileData.do

Alerts

부서명 is highly overall correlated with 지원명High correlation
지원명 is highly overall correlated with 부서명High correlation
폭 is highly overall correlated with 두께High correlation
두께 is highly overall correlated with 폭 and 1 other fieldsHigh correlation
작물명 is highly overall correlated with 두께High correlation

Reproduction

Analysis started2023-12-12 06:44:42.649115
Analysis finished2023-12-12 06:44:45.796237
Duration3.15 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년산
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2020
3387 
2021
3362 
2022
3251 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2022
4th row2021
5th row2022

Common Values

ValueCountFrequency (%)
2020 3387
33.9%
2021 3362
33.6%
2022 3251
32.5%

Length

2023-12-12T15:44:45.867588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:44:45.976011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 3387
33.9%
2021 3362
33.6%
2022 3251
32.5%

지원명
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
전북지원
2070 
경남지원
1626 
전남지원
1456 
경기종자관리소
1143 
충남지원
1036 
Other values (3)
2669 

Length

Max length7
Median length4
Mean length4.3429
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전북지원 2070
20.7%
경남지원 1626
16.3%
전남지원 1456
14.6%
경기종자관리소 1143
11.4%
충남지원 1036
10.4%
경북지원 1035
10.3%
강원지원 911
9.1%
충북지원 723
 
7.2%

Length

2023-12-12T15:44:46.081804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:44:46.238166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전북지원 2070
20.7%
경남지원 1626
16.3%
전남지원 1456
14.6%
경기종자관리소 1143
11.4%
충남지원 1036
10.4%
경북지원 1035
10.3%
강원지원 911
9.1%
충북지원 723
 
7.2%

부서명
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
6474 
정읍
1226 
영암
1103 
익산
844 
함평
 
353

Length

Max length4
Median length4
Mean length3.2948
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row영암
2nd row<NA>
3rd row<NA>
4th row함평
5th row익산

Common Values

ValueCountFrequency (%)
<NA> 6474
64.7%
정읍 1226
 
12.3%
영암 1103
 
11.0%
익산 844
 
8.4%
함평 353
 
3.5%

Length

2023-12-12T15:44:46.420421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:44:46.557607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 6474
64.7%
정읍 1226
 
12.3%
영암 1103
 
11.0%
익산 844
 
8.4%
함평 353
 
3.5%

종자규격번호
Real number (ℝ)

Distinct100
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.6843
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T15:44:46.677971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q126
median51
Q375
95-th percentile95
Maximum100
Range99
Interquartile range (IQR)49

Descriptive statistics

Standard deviation28.776648
Coefficient of variation (CV)0.56776255
Kurtosis-1.1906824
Mean50.6843
Median Absolute Deviation (MAD)25
Skewness-0.017124126
Sum506843
Variance828.09544
MonotonicityNot monotonic
2023-12-12T15:44:46.831640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 116
 
1.2%
22 115
 
1.1%
38 113
 
1.1%
12 113
 
1.1%
66 111
 
1.1%
90 111
 
1.1%
70 111
 
1.1%
32 111
 
1.1%
73 109
 
1.1%
25 109
 
1.1%
Other values (90) 8881
88.8%
ValueCountFrequency (%)
1 108
1.1%
2 98
1.0%
3 103
1.0%
4 104
1.0%
5 106
1.1%
6 100
1.0%
7 105
1.1%
8 90
0.9%
9 92
0.9%
10 91
0.9%
ValueCountFrequency (%)
100 98
1.0%
99 100
1.0%
98 102
1.0%
97 89
0.9%
96 106
1.1%
95 92
0.9%
94 98
1.0%
93 96
1.0%
92 92
0.9%
91 101
1.0%

작물명
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
벼
5404 
콩
2053 
보리
1438 
밀
639 
팥
 
249

Length

Max length2
Median length1
Mean length1.1655
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row보리
2nd row보리
3rd row벼
4th row밀
5th row콩

Common Values

ValueCountFrequency (%)
벼 5404
54.0%
콩 2053
 
20.5%
보리 1438
 
14.4%
밀 639
 
6.4%
팥 249
 
2.5%
호밀 217
 
2.2%

Length

2023-12-12T15:44:46.993064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:44:47.100908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
벼 5404
54.0%
콩 2053
 
20.5%
보리 1438
 
14.4%
밀 639
 
6.4%
팥 249
 
2.5%
호밀 217
 
2.2%

품종명
Text

Distinct58
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T15:44:47.367109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.5286
Min length2

Characters and Unicode

Total characters35286
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

Unique0 ?
Unique (%)0.0%

Sample

1st row새쌀보리
2nd row영양보리
3rd row삼광벼
4th row새금강밀
5th row선풍콩
ValueCountFrequency (%)
삼광벼 651
 
6.5%
대원콩 605
 
6.0%
해담쌀 384
 
3.8%
추청벼 375
 
3.8%
대찬콩 347
 
3.5%
신동진벼 332
 
3.3%
새일미벼 289
 
2.9%
선풍콩 287
 
2.9%
흰찰쌀보리 286
 
2.9%
백옥찰벼 270
 
2.7%
Other values (48) 6174
61.7%
2023-12-12T15:44:47.851382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
벼 3889
 
11.0%
리 2197
 
6.2%
콩 2053
 
5.8%
보 1521
 
4.3%
대 1293
 
3.7%
쌀 1187
 
3.4%
찰 1160
 
3.3%
새 1103
 
3.1%
광 1019
 
2.9%
미 893
 
2.5%
Other values (64) 18971
53.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 34917
99.0%
Decimal Number 369
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
벼 3889
 
11.1%
리 2197
 
6.3%
콩 2053
 
5.9%
보 1521
 
4.4%
대 1293
 
3.7%
쌀 1187
 
3.4%
찰 1160
 
3.3%
새 1103
 
3.2%
광 1019
 
2.9%
미 893
 
2.6%
Other values (63) 18602
53.3%
Decimal Number
ValueCountFrequency (%)
1 369
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 34917
99.0%
Common 369
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
벼 3889
 
11.1%
리 2197
 
6.3%
콩 2053
 
5.9%
보 1521
 
4.4%
대 1293
 
3.7%
쌀 1187
 
3.4%
찰 1160
 
3.3%
새 1103
 
3.2%
광 1019
 
2.9%
미 893
 
2.6%
Other values (63) 18602
53.3%
Common
ValueCountFrequency (%)
1 369
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 34917
99.0%
ASCII 369
 
1.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
벼 3889
 
11.1%
리 2197
 
6.3%
콩 2053
 
5.9%
보 1521
 
4.4%
대 1293
 
3.7%
쌀 1187
 
3.4%
찰 1160
 
3.3%
새 1103
 
3.2%
광 1019
 
2.9%
미 893
 
2.6%
Other values (63) 18602
53.3%
ASCII
ValueCountFrequency (%)
1 369
100.0%

길이
Real number (ℝ)

Distinct485
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.164665
Minimum2.53
Maximum9.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T15:44:48.029876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.53
5-th percentile5.86
Q16.73
median7.13
Q37.6325
95-th percentile8.48
Maximum9.96
Range7.43
Interquartile range (IQR)0.9025

Descriptive statistics

Standard deviation0.77528807
Coefficient of variation (CV)0.10820995
Kurtosis0.6552613
Mean7.164665
Median Absolute Deviation (MAD)0.45
Skewness-0.033925203
Sum71646.65
Variance0.60107159
MonotonicityNot monotonic
2023-12-12T15:44:48.216894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.1 119
 
1.2%
6.87 94
 
0.9%
7.2 94
 
0.9%
7.11 91
 
0.9%
7.14 88
 
0.9%
6.88 87
 
0.9%
6.8 82
 
0.8%
6.98 82
 
0.8%
7.13 80
 
0.8%
6.83 74
 
0.7%
Other values (475) 9109
91.1%
ValueCountFrequency (%)
2.53 1
< 0.1%
3.88 1
< 0.1%
4.02 1
< 0.1%
4.03 1
< 0.1%
4.1 1
< 0.1%
4.13 1
< 0.1%
4.16 1
< 0.1%
4.22 1
< 0.1%
4.31 1
< 0.1%
4.44 1
< 0.1%
ValueCountFrequency (%)
9.96 2
< 0.1%
9.92 1
 
< 0.1%
9.86 3
< 0.1%
9.82 1
 
< 0.1%
9.73 1
 
< 0.1%
9.7 1
 
< 0.1%
9.66 1
 
< 0.1%
9.65 1
 
< 0.1%
9.57 2
< 0.1%
9.54 1
 
< 0.1%

폭
Real number (ℝ)

HIGH CORRELATION 

Distinct633
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.079742
Minimum0.1
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T15:44:48.371557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile2.66
Q13.09
median3.3
Q33.7
95-th percentile7.89
Maximum42
Range41.9
Interquartile range (IQR)0.61

Descriptive statistics

Standard deviation1.7904609
Coefficient of variation (CV)0.4388662
Kurtosis21.4378
Mean4.079742
Median Absolute Deviation (MAD)0.26
Skewness2.347353
Sum40797.42
Variance3.2057502
MonotonicityNot monotonic
2023-12-12T15:44:48.498267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.2 189
 
1.9%
3.3 173
 
1.7%
3.1 150
 
1.5%
3.25 140
 
1.4%
3.26 137
 
1.4%
3.32 133
 
1.3%
3.12 130
 
1.3%
3.14 129
 
1.3%
3.21 127
 
1.3%
3.18 126
 
1.3%
Other values (623) 8566
85.7%
ValueCountFrequency (%)
0.1 1
< 0.1%
1.53 2
< 0.1%
1.54 1
< 0.1%
1.76 1
< 0.1%
1.77 1
< 0.1%
1.79 2
< 0.1%
1.8 1
< 0.1%
1.86 1
< 0.1%
1.87 1
< 0.1%
1.89 1
< 0.1%
ValueCountFrequency (%)
42.0 1
< 0.1%
23.3 1
< 0.1%
9.19 1
< 0.1%
9.04 1
< 0.1%
9.02 1
< 0.1%
8.98 2
< 0.1%
8.96 1
< 0.1%
8.95 1
< 0.1%
8.94 1
< 0.1%
8.93 1
< 0.1%

두께
Real number (ℝ)

HIGH CORRELATION 

Distinct616
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.264636
Minimum0
Maximum8.97
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T15:44:48.913341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.01
Q12.2
median2.33
Q33.14
95-th percentile7.05
Maximum8.97
Range8.97
Interquartile range (IQR)0.94

Descriptive statistics

Standard deviation1.7801278
Coefficient of variation (CV)0.54527605
Kurtosis0.34625098
Mean3.264636
Median Absolute Deviation (MAD)0.21
Skewness1.4014222
Sum32646.36
Variance3.1688551
MonotonicityNot monotonic
2023-12-12T15:44:49.070258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.3 283
 
2.8%
2.21 220
 
2.2%
2.2 210
 
2.1%
2.25 202
 
2.0%
2.24 194
 
1.9%
2.28 194
 
1.9%
2.31 183
 
1.8%
2.26 180
 
1.8%
2.27 179
 
1.8%
2.23 179
 
1.8%
Other values (606) 7976
79.8%
ValueCountFrequency (%)
0.0 1
 
< 0.1%
1.28 1
 
< 0.1%
1.32 1
 
< 0.1%
1.35 4
< 0.1%
1.36 1
 
< 0.1%
1.39 1
 
< 0.1%
1.41 1
 
< 0.1%
1.44 1
 
< 0.1%
1.45 2
< 0.1%
1.46 2
< 0.1%
ValueCountFrequency (%)
8.97 2
< 0.1%
8.86 1
 
< 0.1%
8.77 1
 
< 0.1%
8.72 1
 
< 0.1%
8.71 1
 
< 0.1%
8.7 1
 
< 0.1%
8.67 2
< 0.1%
8.65 3
< 0.1%
8.63 1
 
< 0.1%
8.61 1
 
< 0.1%

Interactions

2023-12-12T15:44:45.123439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:44:43.790298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:44:44.263437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:44:44.705108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:44:45.210997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:44:43.890054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:44:44.358594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:44:44.801836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:44:45.308171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:44:44.025795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:44:44.480777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:44:44.919953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:44:45.413553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:44:44.168038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:44:44.589008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:44:45.017133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:44:49.196356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년산지원명부서명종자규격번호작물명품종명길이폭두께
년산1.0000.1630.1910.0000.1270.4990.1370.0700.186
지원명0.1631.0001.0000.0000.3790.9390.3310.2080.389
부서명0.1911.0001.0000.0000.4940.8900.3050.3910.522
종자규격번호0.0000.0000.0001.0000.0000.0000.0320.0340.000
작물명0.1270.3790.4940.0001.0001.0000.5050.6400.801
품종명0.4990.9390.8900.0001.0001.0000.7640.7990.885
길이0.1370.3310.3050.0320.5050.7641.0000.4780.608
폭0.0700.2080.3910.0340.6400.7990.4781.0000.838
두께0.1860.3890.5220.0000.8010.8850.6080.8381.000
2023-12-12T15:44:49.317322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부서명지원명년산작물명
부서명1.0001.0000.1820.340
지원명1.0001.0000.1040.221
년산0.1820.1041.0000.052
작물명0.3400.2210.0521.000
2023-12-12T15:44:49.418794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
종자규격번호길이폭두께년산지원명부서명작물명
종자규격번호1.000-0.002-0.008-0.0030.0000.0000.0000.000
길이-0.0021.0000.4000.3830.0820.1650.1990.295
폭-0.0080.4001.0000.7400.0530.1290.1610.500
두께-0.0030.3830.7401.0000.1120.1970.2550.588
년산0.0000.0820.0530.1121.0000.1040.1820.052
지원명0.0000.1650.1290.1970.1041.0001.0000.221
부서명0.0000.1990.1610.2550.1821.0001.0000.340
작물명0.0000.2950.5000.5880.0520.2210.3401.000

Missing values

2023-12-12T15:44:45.542332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:44:45.707146image/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

년산지원명부서명종자규격번호작물명품종명길이폭두께
126562021전남지원영암56보리새쌀보리5.23.141.86
138052021경북지원<NA>6보리영양보리8.643.542.83
214102022경북지원<NA>87벼삼광벼7.113.312.31
133052021전남지원함평6밀새금강밀6.423.682.92
196712022전북지원익산72콩선풍콩8.07.56.3
147672021경남지원<NA>68벼현품벼6.893.112.16
53062020경북지원<NA>10벼삼광벼6.812.952.04
217142022경북지원<NA>15콩대원콩7.717.186.75
54812020경북지원<NA>82벼백옥찰벼6.412.451.78
170162022충북지원<NA>17벼삼광벼6.593.082.2
년산지원명부서명종자규격번호작물명품종명길이폭두께
208402022전남지원영암41콩태광콩7.46.695.72
70402020경남지원<NA>41밀조경밀6.873.563.14
100292021충남지원<NA>41벼미품벼6.872.832.3
92232021충북지원<NA>24벼오대벼7.483.42.65
20162020전북지원익산17벼해담쌀6.83.12.2
53022020경북지원<NA>6벼삼광벼6.683.112.32
190532022전북지원익산54벼신동진벼8.63.42.1
106452021전북지원익산6벼해담쌀7.273.112.32
79032020강원지원<NA>4콩청아콩8.27.826.56
209112022전남지원영암12콩선풍콩8.577.546.38