Overview

Dataset statistics

Number of variables11
Number of observations10000
Missing cells6
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory996.1 KiB
Average record size in memory102.0 B

Variable types

Categorical3
Text3
Numeric5

Dataset

Description농림축산식품부 국립종자원에서 생산한 식량종자(벼, 보리, 콩 등) 보급종의 연도별, 지역별, 품종별 공급 현황 자료
Author농림축산식품부 국립종자원
URLhttps://www.data.go.kr/data/3055532/fileData.do

Alerts

행정기관을 통한 신청공급량 is highly overall correlated with 공급량합계High correlation
공급량합계 is highly overall correlated with 행정기관을 통한 신청공급량High correlation
미곡종합처리장 (RPC)공급량 is highly skewed (γ1 = 56.65769962)Skewed
우선신청경영체 공급량 is highly skewed (γ1 = 22.48484953)Skewed
행정기관을 통한 신청공급량 is highly skewed (γ1 = 20.14773184)Skewed
개인 신청 공급량 is highly skewed (γ1 = 65.05817015)Skewed
미곡종합처리장 (RPC)공급량 has 9976 (99.8%) zerosZeros
우선신청경영체 공급량 has 9646 (96.5%) zerosZeros
행정기관을 통한 신청공급량 has 547 (5.5%) zerosZeros
개인 신청 공급량 has 9062 (90.6%) zerosZeros

Reproduction

Analysis started2023-12-12 12:49:16.731806
Analysis finished2023-12-12 12:49:21.701226
Duration4.97 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

공급년도
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2009
2201 
2008
2017 
2006
1996 
2010
1896 
2007
1890 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2007
2nd row2007
3rd row2008
4th row2006
5th row2008

Common Values

ValueCountFrequency (%)
2009 2201
22.0%
2008 2017
20.2%
2006 1996
20.0%
2010 1896
19.0%
2007 1890
18.9%

Length

2023-12-12T21:49:21.772702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:49:21.923081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2009 2201
22.0%
2008 2017
20.2%
2006 1996
20.0%
2010 1896
19.0%
2007 1890
18.9%

작 물
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
벼
5168 
콩
1864 
보리
1132 
봄감자
900 
옥수수
652 
Other values (2)
 
284

Length

Max length4
Median length1
Mean length1.4962
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
벼 5168
51.7%
콩 1864
 
18.6%
보리 1132
 
11.3%
봄감자 900
 
9.0%
옥수수 652
 
6.5%
가을감자 242
 
2.4%
밀 42
 
0.4%

Length

2023-12-12T21:49:22.078367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:49:22.220933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
벼 5168
51.7%
콩 1864
 
18.6%
보리 1132
 
11.3%
봄감자 900
 
9.0%
옥수수 652
 
6.5%
가을감자 242
 
2.4%
밀 42
 
0.4%

품 종
Text

Distinct93
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T21:49:22.492827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.283
Min length2

Characters and Unicode

Total characters32830
Distinct characters95
Distinct categories4 ?
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 (%)0.1%

Sample

1st row영양보리
2nd row운광벼
3rd row청자콩3호
4th row오대벼
5th row영양보리
ValueCountFrequency (%)
수미 875
 
8.8%
태광콩 658
 
6.6%
대원콩 590
 
5.9%
동진1호 565
 
5.7%
남평벼 483
 
4.8%
일미벼 387
 
3.9%
운광벼 386
 
3.9%
주남벼 368
 
3.7%
추청벼 343
 
3.4%
찰옥1호 293
 
2.9%
Other values (83) 5052
50.5%
2023-12-12T21:49:22.988878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
벼 4224
 
12.9%
콩 1864
 
5.7%
호 1481
 
4.5%
미 1454
 
4.4%
광 1337
 
4.1%
대 1329
 
4.0%
리 1296
 
3.9%
보 1171
 
3.6%
수 1111
 
3.4%
찰 1075
 
3.3%
Other values (85) 16488
50.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 31398
95.6%
Decimal Number 1352
 
4.1%
Close Punctuation 40
 
0.1%
Open Punctuation 40
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
벼 4224
 
13.5%
콩 1864
 
5.9%
호 1481
 
4.7%
미 1454
 
4.6%
광 1337
 
4.3%
대 1329
 
4.2%
리 1296
 
4.1%
보 1171
 
3.7%
수 1111
 
3.5%
찰 1075
 
3.4%
Other values (79) 15056
48.0%
Decimal Number
ValueCountFrequency (%)
1 1060
78.4%
2 135
 
10.0%
9 91
 
6.7%
3 66
 
4.9%
Close Punctuation
ValueCountFrequency (%)
) 40
100.0%
Open Punctuation
ValueCountFrequency (%)
( 40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 31398
95.6%
Common 1432
 
4.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
벼 4224
 
13.5%
콩 1864
 
5.9%
호 1481
 
4.7%
미 1454
 
4.6%
광 1337
 
4.3%
대 1329
 
4.2%
리 1296
 
4.1%
보 1171
 
3.7%
수 1111
 
3.5%
찰 1075
 
3.4%
Other values (79) 15056
48.0%
Common
ValueCountFrequency (%)
1 1060
74.0%
2 135
 
9.4%
9 91
 
6.4%
3 66
 
4.6%
) 40
 
2.8%
( 40
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 31398
95.6%
ASCII 1432
 
4.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
벼 4224
 
13.5%
콩 1864
 
5.9%
호 1481
 
4.7%
미 1454
 
4.6%
광 1337
 
4.3%
대 1329
 
4.2%
리 1296
 
4.1%
보 1171
 
3.7%
수 1111
 
3.5%
찰 1075
 
3.4%
Other values (79) 15056
48.0%
ASCII
ValueCountFrequency (%)
1 1060
74.0%
2 135
 
9.4%
9 91
 
6.4%
3 66
 
4.6%
) 40
 
2.8%
( 40
 
2.8%

시 도
Categorical

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
경북
1762 
전남
1542 
경남
1337 
충남
1286 
전북
1154 
Other values (11)
2919 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전남
2nd row전남
3rd row경남
4th row강원
5th row강원

Common Values

ValueCountFrequency (%)
경북 1762
17.6%
전남 1542
15.4%
경남 1337
13.4%
충남 1286
12.9%
전북 1154
11.5%
경기 1029
10.3%
강원 688
 
6.9%
충북 665
 
6.7%
대구 121
 
1.2%
인천 102
 
1.0%
Other values (6) 314
 
3.1%

Length

2023-12-12T21:49:23.148081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경북 1762
17.6%
전남 1542
15.4%
경남 1337
13.4%
충남 1286
12.9%
전북 1154
11.5%
경기 1029
10.3%
강원 688
 
6.9%
충북 665
 
6.7%
대구 121
 
1.2%
인천 102
 
1.0%
Other values (6) 314
 
3.1%

시 군
Text

Distinct195
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T21:49:23.559344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length3.0115
Min length2

Characters and Unicode

Total characters30115
Distinct characters129
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 (%)0.1%

Sample

1st row영광군
2nd row화순군
3rd row함양군
4th row영월군
5th row강릉시
ValueCountFrequency (%)
김제시 143
 
1.4%
익산시 141
 
1.4%
경주시 138
 
1.4%
상주시 121
 
1.2%
정읍시 121
 
1.2%
안동시 119
 
1.2%
천안시 119
 
1.2%
진주시 118
 
1.2%
영주시 117
 
1.2%
충주시 115
 
1.1%
Other values (185) 8763
87.5%
2023-12-12T21:49:24.151815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 5093
 
16.9%
시 4736
 
15.7%
주 1543
 
5.1%
천 1260
 
4.2%
산 964
 
3.2%
성 899
 
3.0%
안 788
 
2.6%
양 739
 
2.5%
영 612
 
2.0%
진 507
 
1.7%
Other values (119) 12974
43.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 30100
> 99.9%
Space Separator 15
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 5093
 
16.9%
시 4736
 
15.7%
주 1543
 
5.1%
천 1260
 
4.2%
산 964
 
3.2%
성 899
 
3.0%
안 788
 
2.6%
양 739
 
2.5%
영 612
 
2.0%
진 507
 
1.7%
Other values (118) 12959
43.1%
Space Separator
ValueCountFrequency (%)
15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 30100
> 99.9%
Common 15
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 5093
 
16.9%
시 4736
 
15.7%
주 1543
 
5.1%
천 1260
 
4.2%
산 964
 
3.2%
성 899
 
3.0%
안 788
 
2.6%
양 739
 
2.5%
영 612
 
2.0%
진 507
 
1.7%
Other values (118) 12959
43.1%
Common
ValueCountFrequency (%)
15
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 30100
> 99.9%
ASCII 15
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 5093
 
16.9%
시 4736
 
15.7%
주 1543
 
5.1%
천 1260
 
4.2%
산 964
 
3.2%
성 899
 
3.0%
안 788
 
2.6%
양 739
 
2.5%
영 612
 
2.0%
진 507
 
1.7%
Other values (118) 12959
43.1%
ASCII
ValueCountFrequency (%)
15
100.0%

읍 면 동
Text

Distinct1801
Distinct (%)18.0%
Missing6
Missing (%)0.1%
Memory size156.2 KiB
2023-12-12T21:49:24.624057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length3
Mean length3.0793476
Min length1

Characters and Unicode

Total characters30775
Distinct characters312
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique235 ?
Unique (%)2.4%

Sample

1st row백수읍
2nd row이양면
3rd row병곡면
4th row주천면
5th row구정면
ValueCountFrequency (%)
남면 85
 
0.8%
서면 58
 
0.6%
북면 57
 
0.6%
동면 35
 
0.3%
중앙동 33
 
0.3%
대산면 33
 
0.3%
마산합포구 29
 
0.3%
의창구 28
 
0.3%
성산면 26
 
0.3%
옥산면 25
 
0.2%
Other values (1792) 9660
95.9%
2023-12-12T21:49:25.228647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
면 6925
22.5%
동 2395
 
7.8%
읍 1240
 
4.0%
산 959
 
3.1%
천 537
 
1.7%
성 521
 
1.7%
남 517
 
1.7%
북 383
 
1.2%
서 381
 
1.2%
양 371
 
1.2%
Other values (302) 16546
53.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 30210
98.2%
Decimal Number 458
 
1.5%
Space Separator 75
 
0.2%
Other Punctuation 32
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
면 6925
22.9%
동 2395
 
7.9%
읍 1240
 
4.1%
산 959
 
3.2%
천 537
 
1.8%
성 521
 
1.7%
남 517
 
1.7%
북 383
 
1.3%
서 381
 
1.3%
양 371
 
1.2%
Other values (291) 15981
52.9%
Decimal Number
ValueCountFrequency (%)
1 198
43.2%
2 140
30.6%
3 45
 
9.8%
0 22
 
4.8%
4 20
 
4.4%
5 20
 
4.4%
6 11
 
2.4%
9 1
 
0.2%
7 1
 
0.2%
Space Separator
ValueCountFrequency (%)
75
100.0%
Other Punctuation
ValueCountFrequency (%)
. 32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 30210
98.2%
Common 565
 
1.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
면 6925
22.9%
동 2395
 
7.9%
읍 1240
 
4.1%
산 959
 
3.2%
천 537
 
1.8%
성 521
 
1.7%
남 517
 
1.7%
북 383
 
1.3%
서 381
 
1.3%
양 371
 
1.2%
Other values (291) 15981
52.9%
Common
ValueCountFrequency (%)
1 198
35.0%
2 140
24.8%
75
 
13.3%
3 45
 
8.0%
. 32
 
5.7%
0 22
 
3.9%
4 20
 
3.5%
5 20
 
3.5%
6 11
 
1.9%
9 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 30210
98.2%
ASCII 565
 
1.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
면 6925
22.9%
동 2395
 
7.9%
읍 1240
 
4.1%
산 959
 
3.2%
천 537
 
1.8%
성 521
 
1.7%
남 517
 
1.7%
북 383
 
1.3%
서 381
 
1.3%
양 371
 
1.2%
Other values (291) 15981
52.9%
ASCII
ValueCountFrequency (%)
1 198
35.0%
2 140
24.8%
75
 
13.3%
3 45
 
8.0%
. 32
 
5.7%
0 22
 
3.9%
4 20
 
3.5%
5 20
 
3.5%
6 11
 
1.9%
9 1
 
0.2%

미곡종합처리장 (RPC)공급량
Real number (ℝ)

SKEWED  ZEROS 

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.528
Minimum0
Maximum90000
Zeros9976
Zeros (%)99.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T21:49:25.387427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum90000
Range90000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1337.2761
Coefficient of variation (CV)38.730194
Kurtosis3525.6548
Mean34.528
Median Absolute Deviation (MAD)0
Skewness56.6577
Sum345280
Variance1788307.4
MonotonicityNot monotonic
2023-12-12T21:49:25.529733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 9976
99.8%
3000 2
 
< 0.1%
420 2
 
< 0.1%
8000 2
 
< 0.1%
16000 1
 
< 0.1%
1800 1
 
< 0.1%
4000 1
 
< 0.1%
35000 1
 
< 0.1%
2620 1
 
< 0.1%
26000 1
 
< 0.1%
Other values (12) 12
 
0.1%
ValueCountFrequency (%)
0 9976
99.8%
420 2
 
< 0.1%
840 1
 
< 0.1%
900 1
 
< 0.1%
1060 1
 
< 0.1%
1800 1
 
< 0.1%
2620 1
 
< 0.1%
3000 2
 
< 0.1%
3900 1
 
< 0.1%
4000 1
 
< 0.1%
ValueCountFrequency (%)
90000 1
< 0.1%
81960 1
< 0.1%
35000 1
< 0.1%
26000 1
< 0.1%
20000 1
< 0.1%
16000 1
< 0.1%
11280 1
< 0.1%
10000 1
< 0.1%
8000 2
< 0.1%
7080 1
< 0.1%

우선신청경영체 공급량
Real number (ℝ)

SKEWED  ZEROS 

Distinct193
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174.9058
Minimum0
Maximum80000
Zeros9646
Zeros (%)96.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T21:49:25.691587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum80000
Range80000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2132.3861
Coefficient of variation (CV)12.191626
Kurtosis627.24847
Mean174.9058
Median Absolute Deviation (MAD)0
Skewness22.48485
Sum1749058
Variance4547070.4
MonotonicityNot monotonic
2023-12-12T21:49:25.934550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9646
96.5%
1000 13
 
0.1%
400 13
 
0.1%
200 11
 
0.1%
100 9
 
0.1%
600 7
 
0.1%
4000 7
 
0.1%
3000 7
 
0.1%
2000 7
 
0.1%
40 7
 
0.1%
Other values (183) 273
 
2.7%
ValueCountFrequency (%)
0 9646
96.5%
6 2
 
< 0.1%
16 1
 
< 0.1%
20 5
 
0.1%
36 1
 
< 0.1%
40 7
 
0.1%
54 1
 
< 0.1%
60 3
 
< 0.1%
80 7
 
0.1%
100 9
 
0.1%
ValueCountFrequency (%)
80000 1
< 0.1%
74760 1
< 0.1%
65080 1
< 0.1%
55200 1
< 0.1%
54000 1
< 0.1%
50000 1
< 0.1%
46000 1
< 0.1%
40000 2
< 0.1%
32360 1
< 0.1%
31100 1
< 0.1%

행정기관을 통한 신청공급량
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct910
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1494.7857
Minimum0
Maximum248520
Zeros547
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T21:49:26.177395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q140
median200
Q31040
95-th percentile6601
Maximum248520
Range248520
Interquartile range (IQR)1000

Descriptive statistics

Standard deviation5439.6271
Coefficient of variation (CV)3.6390682
Kurtosis683.4624
Mean1494.7857
Median Absolute Deviation (MAD)190
Skewness20.147732
Sum14947857
Variance29589543
MonotonicityNot monotonic
2023-12-12T21:49:26.356015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 627
 
6.3%
40 572
 
5.7%
0 547
 
5.5%
100 340
 
3.4%
60 334
 
3.3%
80 302
 
3.0%
120 243
 
2.4%
200 230
 
2.3%
140 186
 
1.9%
10 157
 
1.6%
Other values (900) 6462
64.6%
ValueCountFrequency (%)
0 547
5.5%
2 98
 
1.0%
4 81
 
0.8%
5 147
 
1.5%
6 44
 
0.4%
8 33
 
0.3%
10 157
 
1.6%
12 17
 
0.2%
14 18
 
0.2%
15 71
 
0.7%
ValueCountFrequency (%)
248520 1
< 0.1%
192300 1
< 0.1%
138140 1
< 0.1%
126280 1
< 0.1%
103980 1
< 0.1%
91500 1
< 0.1%
85000 1
< 0.1%
70000 1
< 0.1%
61600 1
< 0.1%
59340 1
< 0.1%

개인 신청 공급량
Real number (ℝ)

SKEWED  ZEROS 

Distinct141
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.8362
Minimum0
Maximum54200
Zeros9062
Zeros (%)90.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T21:49:26.524538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile40
Maximum54200
Range54200
Interquartile range (IQR)0

Descriptive statistics

Standard deviation638.55908
Coefficient of variation (CV)18.872068
Kurtosis5258.7954
Mean33.8362
Median Absolute Deviation (MAD)0
Skewness65.05817
Sum338362
Variance407757.69
MonotonicityNot monotonic
2023-12-12T21:49:26.711419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9062
90.6%
20 145
 
1.5%
40 108
 
1.1%
10 58
 
0.6%
5 50
 
0.5%
80 47
 
0.5%
60 45
 
0.4%
2 41
 
0.4%
200 34
 
0.3%
100 33
 
0.3%
Other values (131) 377
 
3.8%
ValueCountFrequency (%)
0 9062
90.6%
1 1
 
< 0.1%
2 41
 
0.4%
4 10
 
0.1%
5 50
 
0.5%
6 6
 
0.1%
8 4
 
< 0.1%
10 58
 
0.6%
12 3
 
< 0.1%
13 1
 
< 0.1%
ValueCountFrequency (%)
54200 1
< 0.1%
17740 1
< 0.1%
9920 1
< 0.1%
9540 1
< 0.1%
9420 1
< 0.1%
7000 1
< 0.1%
6000 1
< 0.1%
5520 1
< 0.1%
5200 1
< 0.1%
5080 1
< 0.1%

공급량합계
Real number (ℝ)

HIGH CORRELATION 

Distinct988
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1738.0557
Minimum0
Maximum248520
Zeros78
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T21:49:26.886189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q160
median235
Q31178.5
95-th percentile7401
Maximum248520
Range248520
Interquartile range (IQR)1118.5

Descriptive statistics

Standard deviation6185.4717
Coefficient of variation (CV)3.5588455
Kurtosis434.32547
Mean1738.0557
Median Absolute Deviation (MAD)215
Skewness15.924691
Sum17380557
Variance38260060
MonotonicityNot monotonic
2023-12-12T21:49:27.019178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 681
 
6.8%
40 600
 
6.0%
100 347
 
3.5%
60 343
 
3.4%
80 318
 
3.2%
120 249
 
2.5%
200 246
 
2.5%
140 191
 
1.9%
10 175
 
1.8%
5 172
 
1.7%
Other values (978) 6678
66.8%
ValueCountFrequency (%)
0 78
0.8%
2 117
1.2%
4 80
0.8%
5 172
1.7%
6 50
 
0.5%
8 35
 
0.4%
10 175
1.8%
12 21
 
0.2%
14 18
 
0.2%
15 78
0.8%
ValueCountFrequency (%)
248520 1
< 0.1%
192300 1
< 0.1%
138140 1
< 0.1%
126280 1
< 0.1%
108760 1
< 0.1%
103980 1
< 0.1%
91500 1
< 0.1%
90000 1
< 0.1%
89860 1
< 0.1%
86760 1
< 0.1%

Interactions

2023-12-12T21:49:20.824095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:49:18.261856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:49:18.876207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:49:19.761538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:49:20.303317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:49:20.928442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:49:18.359012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:49:18.990021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:49:19.852511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:49:20.406790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:49:21.042600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:49:18.503880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:49:19.118629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:49:19.972678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:49:20.513546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:49:21.161599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:49:18.605296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:49:19.544450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:49:20.077072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:49:20.605503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:49:21.283732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:49:18.749896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:49:19.655201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:49:20.177937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:49:20.713120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T21:49:27.138561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공급년도작 물품 종시 도미곡종합처리장 (RPC)공급량우선신청경영체 공급량행정기관을 통한 신청공급량개인 신청 공급량공급량합계
공급년도1.0000.1380.6080.0710.0290.0690.0260.0000.022
작 물0.1381.0001.0000.2050.0000.0150.0730.0000.058
품 종0.6081.0001.0000.7450.0000.0850.0000.0000.000
시 도0.0710.2050.7451.0000.0000.0000.0000.0000.000
미곡종합처리장 (RPC)공급량0.0290.0000.0000.0001.0000.0000.0560.0000.475
우선신청경영체 공급량0.0690.0150.0850.0000.0001.0000.1110.0000.688
행정기관을 통한 신청공급량0.0260.0730.0000.0000.0560.1111.0000.0000.993
개인 신청 공급량0.0000.0000.0000.0000.0000.0000.0001.0000.354
공급량합계0.0220.0580.0000.0000.4750.6880.9930.3541.000
2023-12-12T21:49:27.266050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공급년도시 도작 물
공급년도1.0000.0360.088
시 도0.0361.0000.095
작 물0.0880.0951.000
2023-12-12T21:49:27.642421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
미곡종합처리장 (RPC)공급량우선신청경영체 공급량행정기관을 통한 신청공급량개인 신청 공급량공급량합계공급년도작 물시 도
미곡종합처리장 (RPC)공급량1.000-0.0090.0490.0060.0740.0110.0000.000
우선신청경영체 공급량-0.0091.0000.0410.0300.2070.0390.0080.000
행정기관을 통한 신청공급량0.0490.0411.000-0.1370.9410.0160.0390.000
개인 신청 공급량0.0060.030-0.1371.0000.0030.0000.0000.000
공급량합계0.0740.2070.9410.0031.0000.0130.0310.000
공급년도0.0110.0390.0160.0000.0131.0000.0880.036
작 물0.0000.0080.0390.0000.0310.0881.0000.095
시 도0.0000.0000.0000.0000.0000.0360.0951.000

Missing values

2023-12-12T21:49:21.430557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:49:21.628706image/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

공급년도작 물품 종시 도시 군읍 면 동미곡종합처리장 (RPC)공급량우선신청경영체 공급량행정기관을 통한 신청공급량개인 신청 공급량공급량합계
218872007보리영양보리전남영광군백수읍00408004080
339282007벼운광벼전남화순군이양면0040040
529302008콩청자콩3호경남함양군병곡면0035035
13222006벼오대벼강원영월군주천면009460209480
463852008보리영양보리강원강릉시구정면003000300
747672009벼풍미1호경남합천군가회면0020020
751452009옥수수광평옥경기남양주시조안면0020020
862662010벼삼광벼강원원주시단계동003200320
270292007벼호평벼전남영암군미암면004200420
557522008콩청자콩3호경남창원시마산회원구 내서읍00505
공급년도작 물품 종시 도시 군읍 면 동미곡종합처리장 (RPC)공급량우선신청경영체 공급량행정기관을 통한 신청공급량개인 신청 공급량공급량합계
430102008벼운광벼강원양양군손양면00100001000
177312006콩대원콩경북영주시가흥2동0015015
620252009벼일품벼경북군위군의흥면00136001360
710772009벼보석찰벼전남순천시외서면0080080
637722009봄감자수미경북영천시대창면007400740
920802010콩대원콩충남논산시가야곡면0050050
550622008옥수수찰옥1호경북예천군용궁면0010010
540062008벼주남벼경남남해군미조면0020020
875412010봄감자수미전북군산시나포면002200220
163352006가을감자대지경남산청군시천면0020020