Overview

Dataset statistics

Number of variables5
Number of observations67
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.0 KiB
Average record size in memory46.0 B

Variable types

Categorical2
Numeric3

Dataset

Description경상남도 벼 보급종(영호진미, 새일미벼, 추청벼, 해담쌀, 백옥찰벼, 해품벼, 현품벼, 동진찰벼, 조평벼, 운광벼, 삼광벼, 조명1호, 친들벼, 수광벼 등) 공급량
Author경상남도
URLhttps://www.data.go.kr/data/15102985/fileData.do

Alerts

총 공급량_kg is highly overall correlated with 신청기간내공급량_kg and 1 other fieldsHigh correlation
신청기간내공급량_kg is highly overall correlated with 총 공급량_kg and 1 other fieldsHigh correlation
신청기간외개별공급량_kg is highly overall correlated with 총 공급량_kg and 1 other fieldsHigh correlation
신청기간내공급량_kg has 13 (19.4%) zerosZeros
신청기간외개별공급량_kg has 9 (13.4%) zerosZeros

Reproduction

Analysis started2023-12-12 17:40:59.110369
Analysis finished2023-12-12 17:41:00.174752
Duration1.06 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

공급년도
Categorical

Distinct3
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size668.0 B
2021
24 
2019
23 
2020
20 

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 (%)
2021 24
35.8%
2019 23
34.3%
2020 20
29.9%

Length

2023-12-13T02:41:00.232207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:41:00.320710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 24
35.8%
2019 23
34.3%
2020 20
29.9%

품종명
Categorical

Distinct31
Distinct (%)46.3%
Missing0
Missing (%)0.0%
Memory size668.0 B
동진찰벼
 
3
미품벼
 
3
오륜벼
 
3
백옥찰벼
 
3
삼광벼
 
3
Other values (26)
52 

Length

Max length5
Median length3
Mean length3.3283582
Min length2

Unique

Unique9 ?
Unique (%)13.4%

Sample

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

Common Values

ValueCountFrequency (%)
동진찰벼 3
 
4.5%
미품벼 3
 
4.5%
오륜벼 3
 
4.5%
백옥찰벼 3
 
4.5%
삼광벼 3
 
4.5%
새일미벼 3
 
4.5%
영호진미 3
 
4.5%
운광벼 3
 
4.5%
조평벼 3
 
4.5%
추청벼 3
 
4.5%
Other values (21) 37
55.2%

Length

2023-12-13T02:41:00.417306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
동진찰벼 3
 
4.5%
조평벼 3
 
4.5%
미품벼 3
 
4.5%
호반벼 3
 
4.5%
현품벼 3
 
4.5%
해품벼 3
 
4.5%
해담쌀 3
 
4.5%
추청벼 3
 
4.5%
운광벼 3
 
4.5%
영호진미 3
 
4.5%
Other values (21) 37
55.2%

총 공급량_kg
Real number (ℝ)

HIGH CORRELATION 

Distinct61
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77253.134
Minimum20
Maximum763680
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-13T02:41:00.536430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile40
Q1420
median4260
Q346150
95-th percentile553236
Maximum763680
Range763660
Interquartile range (IQR)45730

Descriptive statistics

Standard deviation172954.46
Coefficient of variation (CV)2.2388019
Kurtosis7.3264083
Mean77253.134
Median Absolute Deviation (MAD)4220
Skewness2.837861
Sum5175960
Variance2.9913246 × 1010
MonotonicityNot monotonic
2023-12-13T02:41:00.668346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 4
 
6.0%
140 2
 
3.0%
20 2
 
3.0%
100 2
 
3.0%
42680 1
 
1.5%
520 1
 
1.5%
1240 1
 
1.5%
360 1
 
1.5%
41820 1
 
1.5%
280 1
 
1.5%
Other values (51) 51
76.1%
ValueCountFrequency (%)
20 2
3.0%
40 4
6.0%
60 1
 
1.5%
100 2
3.0%
120 1
 
1.5%
140 2
3.0%
160 1
 
1.5%
200 1
 
1.5%
240 1
 
1.5%
280 1
 
1.5%
ValueCountFrequency (%)
763680 1
1.5%
676260 1
1.5%
655860 1
1.5%
573960 1
1.5%
504880 1
1.5%
344100 1
1.5%
215720 1
1.5%
205700 1
1.5%
198460 1
1.5%
134020 1
1.5%

신청기간내공급량_kg
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct53
Distinct (%)79.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73848.955
Minimum0
Maximum762600
Zeros13
Zeros (%)19.4%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-13T02:41:01.249348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q150
median2660
Q344430
95-th percentile534192
Maximum762600
Range762600
Interquartile range (IQR)44380

Descriptive statistics

Standard deviation168442.77
Coefficient of variation (CV)2.2809094
Kurtosis7.7208382
Mean73848.955
Median Absolute Deviation (MAD)2660
Skewness2.8922523
Sum4947880
Variance2.8372968 × 1010
MonotonicityNot monotonic
2023-12-13T02:41:01.433942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13
 
19.4%
20 2
 
3.0%
40 2
 
3.0%
32520 1
 
1.5%
3940 1
 
1.5%
212300 1
 
1.5%
4840 1
 
1.5%
99300 1
 
1.5%
27780 1
 
1.5%
20140 1
 
1.5%
Other values (43) 43
64.2%
ValueCountFrequency (%)
0 13
19.4%
20 2
 
3.0%
40 2
 
3.0%
60 1
 
1.5%
120 1
 
1.5%
140 1
 
1.5%
280 1
 
1.5%
320 1
 
1.5%
340 1
 
1.5%
400 1
 
1.5%
ValueCountFrequency (%)
762600 1
1.5%
664040 1
1.5%
626520 1
1.5%
558360 1
1.5%
477800 1
1.5%
319100 1
1.5%
212300 1
1.5%
192820 1
1.5%
191860 1
1.5%
113800 1
1.5%

신청기간외개별공급량_kg
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)70.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3404.1791
Minimum0
Maximum29340
Zeros9
Zeros (%)13.4%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-13T02:41:01.592045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q180
median380
Q33590
95-th percentile18834
Maximum29340
Range29340
Interquartile range (IQR)3510

Descriptive statistics

Standard deviation6545.1546
Coefficient of variation (CV)1.9226822
Kurtosis6.8722729
Mean3404.1791
Median Absolute Deviation (MAD)380
Skewness2.6713901
Sum228080
Variance42839049
MonotonicityNot monotonic
2023-12-13T02:41:01.719461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 9
 
13.4%
100 4
 
6.0%
40 4
 
6.0%
240 3
 
4.5%
80 2
 
3.0%
380 2
 
3.0%
140 2
 
3.0%
20 2
 
3.0%
6600 1
 
1.5%
1380 1
 
1.5%
Other values (37) 37
55.2%
ValueCountFrequency (%)
0 9
13.4%
20 2
 
3.0%
40 4
6.0%
60 1
 
1.5%
80 2
 
3.0%
100 4
6.0%
120 1
 
1.5%
140 2
 
3.0%
200 1
 
1.5%
220 1
 
1.5%
ValueCountFrequency (%)
29340 1
1.5%
27080 1
1.5%
25000 1
1.5%
20220 1
1.5%
15600 1
1.5%
12880 1
1.5%
12220 1
1.5%
10160 1
1.5%
7480 1
1.5%
6700 1
1.5%

Interactions

2023-12-13T02:40:59.764630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:59.285539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:59.529492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:59.847640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:59.371140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:59.606059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:59.932743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:59.455185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:59.685186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:41:01.800722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공급년도품종명총 공급량_kg신청기간내공급량_kg신청기간외개별공급량_kg
공급년도1.0000.0000.0000.0000.000
품종명0.0001.0000.6530.6530.000
총 공급량_kg0.0000.6531.0001.0000.879
신청기간내공급량_kg0.0000.6531.0001.0000.879
신청기간외개별공급량_kg0.0000.0000.8790.8791.000
2023-12-13T02:41:01.905511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공급년도품종명
공급년도1.0000.000
품종명0.0001.000
2023-12-13T02:41:02.002014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
총 공급량_kg신청기간내공급량_kg신청기간외개별공급량_kg공급년도품종명
총 공급량_kg1.0000.9460.7740.0000.236
신청기간내공급량_kg0.9461.0000.6280.0000.236
신청기간외개별공급량_kg0.7740.6281.0000.0000.000
공급년도0.0000.0000.0001.0000.000
품종명0.2360.2360.0000.0001.000

Missing values

2023-12-13T02:41:00.055868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:41:00.140698image/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

공급년도품종명총 공급량_kg신청기간내공급량_kg신청기간외개별공급량_kg
02021동진찰벼426803252010160
12021미품벼1401400
22021백옥찰벼7424074000240
32021보람찰벼20020
42021삼광벼15480114804000
52021새일미벼50488047780027080
62021수광벼19401700240
72021신동진벼20200
82021영진4800480
92021영호진미65586062652029340
공급년도품종명총 공급량_kg신청기간내공급량_kg신청기간외개별공급량_kg
572019오륜벼444030601380
582019운광벼110840110740100
592019조평벼346034600
602019추청벼1984601918606600
612019친들벼7407400
622019하이아미1540146080
632019해담쌀60060
642019해품벼53100497803320
652019현품벼2400240
662019호반벼8000800