Overview

Dataset statistics

Number of variables6
Number of observations108
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.7 KiB
Average record size in memory54.2 B

Variable types

Numeric5
Categorical1

Dataset

Description경기도_벼 보급종 공급량 현황
Author경기도
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=6T98794V0223GQQ9O1P421624027&infSeq=1

Alerts

사전주문량(kg) is highly overall correlated with 일반주문량(kg) and 1 other fieldsHigh correlation
일반주문량(kg) is highly overall correlated with 사전주문량(kg) and 1 other fieldsHigh correlation
기간외개별공급량(kg) is highly overall correlated with 사전주문량(kg) and 1 other fieldsHigh correlation
사전주문량(kg) has 70 (64.8%) zerosZeros
일반주문량(kg) has 57 (52.8%) zerosZeros
기간내개별공급량(kg) has 30 (27.8%) zerosZeros
기간외개별공급량(kg) has 59 (54.6%) zerosZeros

Reproduction

Analysis started2023-12-10 21:59:04.769969
Analysis finished2023-12-10 21:59:07.571978
Duration2.8 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

공급년도
Real number (ℝ)

Distinct6
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2020.6019
Minimum2018
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T06:59:07.617076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2018
5-th percentile2018
Q12019
median2021
Q32022
95-th percentile2023
Maximum2023
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7290255
Coefficient of variation (CV)0.00085569828
Kurtosis-1.3175485
Mean2020.6019
Median Absolute Deviation (MAD)2
Skewness-0.059143097
Sum218225
Variance2.9895292
MonotonicityDecreasing
2023-12-11T06:59:07.733776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2023 20
18.5%
2022 20
18.5%
2019 19
17.6%
2020 17
15.7%
2021 16
14.8%
2018 16
14.8%
ValueCountFrequency (%)
2018 16
14.8%
2019 19
17.6%
2020 17
15.7%
2021 16
14.8%
2022 20
18.5%
2023 20
18.5%
ValueCountFrequency (%)
2023 20
18.5%
2022 20
18.5%
2021 16
14.8%
2020 17
15.7%
2019 19
17.6%
2018 16
14.8%

품종명
Categorical

Distinct31
Distinct (%)28.7%
Missing0
Missing (%)0.0%
Memory size996.0 B
고시히카리
 
6
동진찰벼
 
6
오대벼
 
6
추청벼
 
6
삼광벼
 
6
Other values (26)
78 

Length

Max length5
Median length3
Mean length3.4259259
Min length2

Unique

Unique4 ?
Unique (%)3.7%

Sample

1st row고시히카리
2nd row동진찰벼
3rd row미품벼
4th row백옥찰벼
5th row삼광벼

Common Values

ValueCountFrequency (%)
고시히카리 6
 
5.6%
동진찰벼 6
 
5.6%
오대벼 6
 
5.6%
추청벼 6
 
5.6%
삼광벼 6
 
5.6%
영호진미 5
 
4.6%
미품벼 5
 
4.6%
대안벼 5
 
4.6%
참드림 5
 
4.6%
백옥찰벼 4
 
3.7%
Other values (21) 54
50.0%

Length

2023-12-11T06:59:07.848488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
고시히카리 6
 
5.6%
오대벼 6
 
5.6%
추청벼 6
 
5.6%
삼광벼 6
 
5.6%
동진찰벼 6
 
5.6%
영호진미 5
 
4.6%
미품벼 5
 
4.6%
대안벼 5
 
4.6%
참드림 5
 
4.6%
맛드림 4
 
3.7%
Other values (21) 54
50.0%

사전주문량(kg)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)35.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34247.407
Minimum0
Maximum454980
Zeros70
Zeros (%)64.8%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T06:59:07.962885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q39250
95-th percentile275313
Maximum454980
Range454980
Interquartile range (IQR)9250

Descriptive statistics

Standard deviation86942.024
Coefficient of variation (CV)2.5386454
Kurtosis9.2210787
Mean34247.407
Median Absolute Deviation (MAD)0
Skewness3.0830422
Sum3698720
Variance7.5589155 × 109
MonotonicityNot monotonic
2023-12-11T06:59:08.117491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0 70
64.8%
9000 2
 
1.9%
99460 1
 
0.9%
1400 1
 
0.9%
10000 1
 
0.9%
302900 1
 
0.9%
3860 1
 
0.9%
339520 1
 
0.9%
98200 1
 
0.9%
4540 1
 
0.9%
Other values (28) 28
 
25.9%
ValueCountFrequency (%)
0 70
64.8%
200 1
 
0.9%
1400 1
 
0.9%
3860 1
 
0.9%
4000 1
 
0.9%
4540 1
 
0.9%
5000 1
 
0.9%
5800 1
 
0.9%
6000 1
 
0.9%
6500 1
 
0.9%
ValueCountFrequency (%)
454980 1
0.9%
344400 1
0.9%
339520 1
0.9%
329940 1
0.9%
318700 1
0.9%
302900 1
0.9%
224080 1
0.9%
199240 1
0.9%
140960 1
0.9%
130600 1
0.9%

일반주문량(kg)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct52
Distinct (%)48.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86695.926
Minimum0
Maximum1197980
Zeros57
Zeros (%)52.8%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T06:59:08.243075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q365830
95-th percentile458078
Maximum1197980
Range1197980
Interquartile range (IQR)65830

Descriptive statistics

Standard deviation209258.61
Coefficient of variation (CV)2.4137075
Kurtosis13.651578
Mean86695.926
Median Absolute Deviation (MAD)0
Skewness3.5343063
Sum9363160
Variance4.3789165 × 1010
MonotonicityNot monotonic
2023-12-11T06:59:08.390826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 57
52.8%
190000 1
 
0.9%
260 1
 
0.9%
15240 1
 
0.9%
904460 1
 
0.9%
11100 1
 
0.9%
94780 1
 
0.9%
82260 1
 
0.9%
77020 1
 
0.9%
600 1
 
0.9%
Other values (42) 42
38.9%
ValueCountFrequency (%)
0 57
52.8%
40 1
 
0.9%
240 1
 
0.9%
260 1
 
0.9%
320 1
 
0.9%
600 1
 
0.9%
1160 1
 
0.9%
2560 1
 
0.9%
5180 1
 
0.9%
6000 1
 
0.9%
ValueCountFrequency (%)
1197980 1
0.9%
1084860 1
0.9%
904460 1
0.9%
749880 1
0.9%
505020 1
0.9%
466240 1
0.9%
442920 1
0.9%
409920 1
0.9%
276940 1
0.9%
273680 1
0.9%

기간내개별공급량(kg)
Real number (ℝ)

ZEROS 

Distinct59
Distinct (%)54.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5616.6667
Minimum0
Maximum92600
Zeros30
Zeros (%)27.8%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T06:59:08.520121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median220
Q32240
95-th percentile41801
Maximum92600
Range92600
Interquartile range (IQR)2240

Descriptive statistics

Standard deviation15621.189
Coefficient of variation (CV)2.7812206
Kurtosis17.228579
Mean5616.6667
Median Absolute Deviation (MAD)220
Skewness4.0252012
Sum606600
Variance2.4402154 × 108
MonotonicityNot monotonic
2023-12-11T06:59:08.633007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 30
27.8%
60 5
 
4.6%
20 4
 
3.7%
120 4
 
3.7%
40 3
 
2.8%
100 3
 
2.8%
700 2
 
1.9%
1200 2
 
1.9%
220 2
 
1.9%
440 2
 
1.9%
Other values (49) 51
47.2%
ValueCountFrequency (%)
0 30
27.8%
20 4
 
3.7%
40 3
 
2.8%
60 5
 
4.6%
80 1
 
0.9%
100 3
 
2.8%
120 4
 
3.7%
140 1
 
0.9%
180 1
 
0.9%
200 1
 
0.9%
ValueCountFrequency (%)
92600 1
0.9%
88740 1
0.9%
55480 1
0.9%
53920 1
0.9%
45080 1
0.9%
44440 1
0.9%
36900 1
0.9%
18360 1
0.9%
14920 1
0.9%
12060 1
0.9%

기간외개별공급량(kg)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct48
Distinct (%)44.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2960.5556
Minimum0
Maximum46160
Zeros59
Zeros (%)54.6%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T06:59:08.757347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31215
95-th percentile17352
Maximum46160
Range46160
Interquartile range (IQR)1215

Descriptive statistics

Standard deviation7371.0367
Coefficient of variation (CV)2.4897478
Kurtosis14.010647
Mean2960.5556
Median Absolute Deviation (MAD)0
Skewness3.5119925
Sum319740
Variance54332182
MonotonicityNot monotonic
2023-12-11T06:59:08.881648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0 59
54.6%
180 2
 
1.9%
160 2
 
1.9%
1200 1
 
0.9%
11720 1
 
0.9%
9620 1
 
0.9%
30120 1
 
0.9%
1800 1
 
0.9%
1020 1
 
0.9%
2100 1
 
0.9%
Other values (38) 38
35.2%
ValueCountFrequency (%)
0 59
54.6%
20 1
 
0.9%
50 1
 
0.9%
60 1
 
0.9%
100 1
 
0.9%
120 1
 
0.9%
160 2
 
1.9%
180 2
 
1.9%
240 1
 
0.9%
300 1
 
0.9%
ValueCountFrequency (%)
46160 1
0.9%
30120 1
0.9%
29900 1
0.9%
26780 1
0.9%
20920 1
0.9%
18220 1
0.9%
15740 1
0.9%
14320 1
0.9%
11720 1
0.9%
11400 1
0.9%

Interactions

2023-12-11T06:59:06.981931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:59:04.983372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:59:05.414281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:59:05.856619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:59:06.550493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:59:07.060151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:59:05.069346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:59:05.490957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:59:05.944246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:59:06.650594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:59:07.140022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:59:05.159642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:59:05.575512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:59:06.041169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:59:06.744919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:59:07.240933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:59:05.247472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:59:05.676582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:59:06.149013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:59:06.838930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:59:07.338470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:59:05.330539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:59:05.764568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:59:06.235115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:59:06.909620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:59:08.956412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공급년도품종명사전주문량(kg)일반주문량(kg)기간내개별공급량(kg)기간외개별공급량(kg)
공급년도1.0000.0000.0000.0880.3280.050
품종명0.0001.0000.5370.4250.0000.000
사전주문량(kg)0.0000.5371.0000.9700.7870.954
일반주문량(kg)0.0880.4250.9701.0000.7560.960
기간내개별공급량(kg)0.3280.0000.7870.7561.0000.764
기간외개별공급량(kg)0.0500.0000.9540.9600.7641.000
2023-12-11T06:59:09.050442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공급년도사전주문량(kg)일반주문량(kg)기간내개별공급량(kg)기간외개별공급량(kg)품종명
공급년도1.000-0.056-0.0650.0890.1690.000
사전주문량(kg)-0.0561.0000.8790.1610.7250.205
일반주문량(kg)-0.0650.8791.0000.1590.7340.149
기간내개별공급량(kg)0.0890.1610.1591.0000.1280.000
기간외개별공급량(kg)0.1690.7250.7340.1281.0000.000
품종명0.0000.2050.1490.0000.0001.000

Missing values

2023-12-11T06:59:07.439193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:59:07.535387image/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

공급년도품종명사전주문량(kg)일반주문량(kg)기간내개별공급량(kg)기간외개별공급량(kg)
02023고시히카리5750019000061001200
12023동진찰벼019500720380
22023미품벼00600
32023백옥찰벼00119001060
42023삼광벼131602524801001040
52023새누리벼00240400
62023새청무0001160
72023신동진벼000120
82023안평001160240
92023알찬미66100220201202140
공급년도품종명사전주문량(kg)일반주문량(kg)기간내개별공급량(kg)기간외개별공급량(kg)
982018새누리벼0080200
992018영우벼00600
1002018오대벼14000143605600
1012018오륜벼001000
1022018운광벼0037600
1032018조평벼0043400
1042018참드림000525
1052018추청벼45498011979803690029900
1062018친들벼003400
1072018화선찰벼02442061003560