Overview

Dataset statistics

Number of variables11
Number of observations52
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.1 KiB
Average record size in memory99.5 B

Variable types

Numeric9
Text1
DateTime1

Dataset

Description전라남도 광양시의 농특산물 재배 현황(작물명, 재배면적, 생산량, 조수입, 소득)에 대한 데이터를 무료로 전 국민에게 공유합니다.
Author전라남도 광양시
URLhttps://www.data.go.kr/data/3079787/fileData.do

Alerts

데이터기준일 has constant value ""Constant
2023년 재배면적(헥타르) is highly overall correlated with 2023년 재배농가수(호) and 3 other fieldsHigh correlation
2023년 재배농가수(호) is highly overall correlated with 2023년 재배면적(헥타르) and 3 other fieldsHigh correlation
2023년 조수입(천원_10아르당) is highly overall correlated with 2023년 소득(천원_10아르당) and 1 other fieldsHigh correlation
2023년 조수입 전체(백만원) is highly overall correlated with 2023년 재배면적(헥타르) and 3 other fieldsHigh correlation
2023년 소득(천원_10아르당) is highly overall correlated with 2023년 조수입(천원_10아르당) and 1 other fieldsHigh correlation
2023년 소득 전체(백만원) is highly overall correlated with 2023년 재배면적(헥타르) and 3 other fieldsHigh correlation
2023년 생산량(킬로그램_10아르) is highly overall correlated with 2023년 조수입(천원_10아르당) and 1 other fieldsHigh correlation
2023년 생산량 전체(톤)(고로쇠 단위는 리터) is highly overall correlated with 2023년 재배면적(헥타르) and 3 other fieldsHigh correlation
번호 has unique valuesUnique
작물명 has unique valuesUnique
2023년 조수입(천원_10아르당) has unique valuesUnique
2023년 생산량(킬로그램_10아르) has unique valuesUnique

Reproduction

Analysis started2024-04-06 08:06:33.039122
Analysis finished2024-04-06 08:06:49.921854
Duration16.88 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.5
Minimum1
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2024-04-06T17:06:50.080204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.55
Q113.75
median26.5
Q339.25
95-th percentile49.45
Maximum52
Range51
Interquartile range (IQR)25.5

Descriptive statistics

Standard deviation15.154757
Coefficient of variation (CV)0.57187763
Kurtosis-1.2
Mean26.5
Median Absolute Deviation (MAD)13
Skewness0
Sum1378
Variance229.66667
MonotonicityStrictly increasing
2024-04-06T17:06:50.357043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.9%
28 1
 
1.9%
30 1
 
1.9%
31 1
 
1.9%
32 1
 
1.9%
33 1
 
1.9%
34 1
 
1.9%
35 1
 
1.9%
36 1
 
1.9%
37 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
1 1
1.9%
2 1
1.9%
3 1
1.9%
4 1
1.9%
5 1
1.9%
6 1
1.9%
7 1
1.9%
8 1
1.9%
9 1
1.9%
10 1
1.9%
ValueCountFrequency (%)
52 1
1.9%
51 1
1.9%
50 1
1.9%
49 1
1.9%
48 1
1.9%
47 1
1.9%
46 1
1.9%
45 1
1.9%
44 1
1.9%
43 1
1.9%

작물명
Text

UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size548.0 B
2024-04-06T17:06:50.787564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length2.9230769
Min length1

Characters and Unicode

Total characters152
Distinct characters97
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

Unique52 ?
Unique (%)100.0%

Sample

1st row쌀(정곡)
2nd row쌀보리
3rd row맥주보리
4th row
5th row
ValueCountFrequency (%)
쌀(정곡 1
 
1.9%
대파 1
 
1.9%
천혜향 1
 
1.9%
생강 1
 
1.9%
단감 1
 
1.9%
매실 1
 
1.9%
떫은감(곶감포함 1
 
1.9%
1
 
1.9%
참다래 1
 
1.9%
복숭아 1
 
1.9%
Other values (43) 43
81.1%
2024-04-06T17:06:51.459652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6
 
3.9%
5
 
3.3%
5
 
3.3%
5
 
3.3%
) 4
 
2.6%
4
 
2.6%
( 4
 
2.6%
3
 
2.0%
3
 
2.0%
3
 
2.0%
Other values (87) 110
72.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 143
94.1%
Close Punctuation 4
 
2.6%
Open Punctuation 4
 
2.6%
Space Separator 1
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
6
 
4.2%
5
 
3.5%
5
 
3.5%
5
 
3.5%
4
 
2.8%
3
 
2.1%
3
 
2.1%
3
 
2.1%
3
 
2.1%
3
 
2.1%
Other values (84) 103
72.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 143
94.1%
Common 9
 
5.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
6
 
4.2%
5
 
3.5%
5
 
3.5%
5
 
3.5%
4
 
2.8%
3
 
2.1%
3
 
2.1%
3
 
2.1%
3
 
2.1%
3
 
2.1%
Other values (84) 103
72.0%
Common
ValueCountFrequency (%)
) 4
44.4%
( 4
44.4%
1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 143
94.1%
ASCII 9
 
5.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
6
 
4.2%
5
 
3.5%
5
 
3.5%
5
 
3.5%
4
 
2.8%
3
 
2.1%
3
 
2.1%
3
 
2.1%
3
 
2.1%
3
 
2.1%
Other values (84) 103
72.0%
ASCII
ValueCountFrequency (%)
) 4
44.4%
( 4
44.4%
1
 
11.1%

2023년 재배면적(헥타르)
Real number (ℝ)

HIGH CORRELATION 

Distinct46
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean164.84038
Minimum0.1
Maximum2063
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2024-04-06T17:06:51.735911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.255
Q11.4
median8.65
Q324.375
95-th percentile1258.75
Maximum2063
Range2062.9
Interquartile range (IQR)22.975

Descriptive statistics

Standard deviation438.95463
Coefficient of variation (CV)2.6629071
Kurtosis9.5058067
Mean164.84038
Median Absolute Deviation (MAD)8.05
Skewness3.1476535
Sum8571.7
Variance192681.17
MonotonicityNot monotonic
2024-04-06T17:06:52.018608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
1.4 2
 
3.8%
2.9 2
 
3.8%
1.2 2
 
3.8%
1.3 2
 
3.8%
0.2 2
 
3.8%
0.4 2
 
3.8%
1243.0 1
 
1.9%
1278.0 1
 
1.9%
472.3 1
 
1.9%
14.2 1
 
1.9%
Other values (36) 36
69.2%
ValueCountFrequency (%)
0.1 1
1.9%
0.2 2
3.8%
0.3 1
1.9%
0.4 2
3.8%
0.5 1
1.9%
1.1 1
1.9%
1.2 2
3.8%
1.3 2
3.8%
1.4 2
3.8%
1.6 1
1.9%
ValueCountFrequency (%)
2063.0 1
1.9%
1685.0 1
1.9%
1278.0 1
1.9%
1243.0 1
1.9%
750.0 1
1.9%
472.3 1
1.9%
321.1 1
1.9%
241.4 1
1.9%
96.0 1
1.9%
56.0 1
1.9%

2023년 재배농가수(호)
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)80.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean266.51923
Minimum1
Maximum3468
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2024-04-06T17:06:52.309664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q17.75
median27.5
Q3139
95-th percentile1126.15
Maximum3468
Range3467
Interquartile range (IQR)131.25

Descriptive statistics

Standard deviation661.36441
Coefficient of variation (CV)2.4814885
Kurtosis15.319622
Mean266.51923
Median Absolute Deviation (MAD)25
Skewness3.8106733
Sum13859
Variance437402.88
MonotonicityNot monotonic
2024-04-06T17:06:52.617020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
1 4
 
7.7%
15 2
 
3.8%
5 2
 
3.8%
6 2
 
3.8%
18 2
 
3.8%
7 2
 
3.8%
27 2
 
3.8%
11 2
 
3.8%
2973 1
 
1.9%
3468 1
 
1.9%
Other values (32) 32
61.5%
ValueCountFrequency (%)
1 4
7.7%
2 1
 
1.9%
3 1
 
1.9%
4 1
 
1.9%
5 2
3.8%
6 2
3.8%
7 2
3.8%
8 1
 
1.9%
9 1
 
1.9%
11 2
3.8%
ValueCountFrequency (%)
3468 1
1.9%
2973 1
1.9%
1251 1
1.9%
1024 1
1.9%
1021 1
1.9%
774 1
1.9%
513 1
1.9%
455 1
1.9%
346 1
1.9%
302 1
1.9%

2023년 조수입(천원_10아르당)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8168.8077
Minimum107
Maximum47119
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2024-04-06T17:06:52.931728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum107
5-th percentile212.75
Q11113.75
median2273
Q312148.75
95-th percentile33471.45
Maximum47119
Range47012
Interquartile range (IQR)11035

Descriptive statistics

Standard deviation10853.428
Coefficient of variation (CV)1.3286428
Kurtosis3.3103106
Mean8168.8077
Median Absolute Deviation (MAD)2017.5
Skewness1.8815575
Sum424778
Variance1.1779689 × 108
MonotonicityNot monotonic
2024-04-06T17:06:53.199875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
873 1
 
1.9%
1790 1
 
1.9%
3337 1
 
1.9%
1190 1
 
1.9%
882 1
 
1.9%
3015 1
 
1.9%
7712 1
 
1.9%
7485 1
 
1.9%
1174 1
 
1.9%
8811 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
107 1
1.9%
166 1
1.9%
188 1
1.9%
233 1
1.9%
278 1
1.9%
444 1
1.9%
529 1
1.9%
660 1
1.9%
672 1
1.9%
737 1
1.9%
ValueCountFrequency (%)
47119 1
1.9%
36221 1
1.9%
36079 1
1.9%
31338 1
1.9%
27195 1
1.9%
22857 1
1.9%
17100 1
1.9%
16783 1
1.9%
16591 1
1.9%
14281 1
1.9%

2023년 조수입 전체(백만원)
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1961.2308
Minimum3
Maximum16166
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2024-04-06T17:06:54.054373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6.65
Q140.25
median381
Q31858.25
95-th percentile10776.1
Maximum16166
Range16163
Interquartile range (IQR)1818

Descriptive statistics

Standard deviation3746.8249
Coefficient of variation (CV)1.9104457
Kurtosis6.5569453
Mean1961.2308
Median Absolute Deviation (MAD)370
Skewness2.6458252
Sum101984
Variance14038697
MonotonicityNot monotonic
2024-04-06T17:06:54.331360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 2
 
3.8%
5 2
 
3.8%
10852 1
 
1.9%
13 1
 
1.9%
10714 1
 
1.9%
15207 1
 
1.9%
4167 1
 
1.9%
428 1
 
1.9%
1828 1
 
1.9%
105 1
 
1.9%
Other values (40) 40
76.9%
ValueCountFrequency (%)
3 1
1.9%
5 2
3.8%
8 1
1.9%
9 1
1.9%
13 1
1.9%
15 1
1.9%
16 2
3.8%
22 1
1.9%
28 1
1.9%
32 1
1.9%
ValueCountFrequency (%)
16166 1
1.9%
15207 1
1.9%
10852 1
1.9%
10714 1
1.9%
9291 1
1.9%
5525 1
1.9%
4167 1
1.9%
3510 1
1.9%
2881 1
1.9%
2790 1
1.9%

2023년 소득(천원_10아르당)
Real number (ℝ)

HIGH CORRELATION 

Distinct51
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3967.6442
Minimum52.7
Maximum33057
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2024-04-06T17:06:54.667236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum52.7
5-th percentile87.75
Q1660.725
median1324.5
Q36017.25
95-th percentile14839.1
Maximum33057
Range33004.3
Interquartile range (IQR)5356.525

Descriptive statistics

Standard deviation5777.6919
Coefficient of variation (CV)1.4562021
Kurtosis12.325716
Mean3967.6442
Median Absolute Deviation (MAD)1147.8
Skewness3.0733854
Sum206317.5
Variance33381724
MonotonicityNot monotonic
2024-04-06T17:06:54.987902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
968.0 2
 
3.8%
478.0 1
 
1.9%
5975.0 1
 
1.9%
837.0 1
 
1.9%
562.0 1
 
1.9%
1526.0 1
 
1.9%
4149.0 1
 
1.9%
4252.0 1
 
1.9%
672.0 1
 
1.9%
4097.0 1
 
1.9%
Other values (41) 41
78.8%
ValueCountFrequency (%)
52.7 1
1.9%
82.0 1
1.9%
85.0 1
1.9%
90.0 1
1.9%
147.4 1
1.9%
206.0 1
1.9%
275.2 1
1.9%
470.7 1
1.9%
478.0 1
1.9%
541.4 1
1.9%
ValueCountFrequency (%)
33057.0 1
1.9%
17204.0 1
1.9%
16191.0 1
1.9%
13733.0 1
1.9%
9485.0 1
1.9%
7985.0 1
1.9%
7632.0 1
1.9%
7504.0 1
1.9%
7216.0 1
1.9%
7114.8 1
1.9%

2023년 소득 전체(백만원)
Real number (ℝ)

HIGH CORRELATION 

Distinct51
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1103.0385
Minimum1
Maximum10691
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2024-04-06T17:06:55.291911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q117.75
median199.5
Q3920
95-th percentile6336.05
Maximum10691
Range10690
Interquartile range (IQR)902.25

Descriptive statistics

Standard deviation2184.6039
Coefficient of variation (CV)1.9805329
Kurtosis7.9177173
Mean1103.0385
Median Absolute Deviation (MAD)192
Skewness2.7768981
Sum57358
Variance4772494.4
MonotonicityNot monotonic
2024-04-06T17:06:55.604871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 2
 
3.8%
5936 1
 
1.9%
18 1
 
1.9%
6825 1
 
1.9%
10691 1
 
1.9%
2654 1
 
1.9%
217 1
 
1.9%
983 1
 
1.9%
60 1
 
1.9%
16 1
 
1.9%
Other values (41) 41
78.8%
ValueCountFrequency (%)
1 1
1.9%
2 1
1.9%
3 2
3.8%
4 1
1.9%
6 1
1.9%
7 1
1.9%
8 1
1.9%
10 1
1.9%
13 1
1.9%
15 1
1.9%
ValueCountFrequency (%)
10691 1
1.9%
6838 1
1.9%
6825 1
1.9%
5936 1
1.9%
4702 1
1.9%
4274 1
1.9%
2654 1
1.9%
2483 1
1.9%
1486 1
1.9%
1087 1
1.9%

2023년 생산량(킬로그램_10아르)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2197.1519
Minimum30
Maximum11264
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2024-04-06T17:06:55.878103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile84.415
Q1266.25
median876
Q32636.75
95-th percentile9434.1
Maximum11264
Range11234
Interquartile range (IQR)2370.5

Descriptive statistics

Standard deviation2982.9345
Coefficient of variation (CV)1.3576369
Kurtosis2.5967114
Mean2197.1519
Median Absolute Deviation (MAD)717
Skewness1.8482656
Sum114251.9
Variance8897898.5
MonotonicityNot monotonic
2024-04-06T17:06:56.147882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
407.0 1
 
1.9%
85.0 1
 
1.9%
1483.0 1
 
1.9%
446.0 1
 
1.9%
614.0 1
 
1.9%
872.0 1
 
1.9%
1607.0 1
 
1.9%
1786.0 1
 
1.9%
480.0 1
 
1.9%
1250.0 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
30.0 1
1.9%
55.2 1
1.9%
83.7 1
1.9%
85.0 1
1.9%
100.0 1
1.9%
130.0 1
1.9%
140.0 1
1.9%
154.0 1
1.9%
164.0 1
1.9%
177.0 1
1.9%
ValueCountFrequency (%)
11264.0 1
1.9%
11209.0 1
1.9%
9598.0 1
1.9%
9300.0 1
1.9%
7100.0 1
1.9%
6667.0 1
1.9%
6508.0 1
1.9%
5667.0 1
1.9%
4400.0 1
1.9%
4014.0 1
1.9%
Distinct49
Distinct (%)94.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18863.696
Minimum0.4
Maximum930120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2024-04-06T17:06:56.494255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile1.755
Q115
median53.9
Q3870.75
95-th percentile5429.355
Maximum930120
Range930119.6
Interquartile range (IQR)855.75

Descriptive statistics

Standard deviation128871
Coefficient of variation (CV)6.831694
Kurtosis51.957784
Mean18863.696
Median Absolute Deviation (MAD)52.55
Skewness7.2068288
Sum980912.2
Variance1.6607735 × 1010
MonotonicityNot monotonic
2024-04-06T17:06:56.773129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
200.2 2
 
3.8%
1.8 2
 
3.8%
5.5 2
 
3.8%
162.0 1
 
1.9%
930120.0 1
 
1.9%
28.1 1
 
1.9%
4761.9 1
 
1.9%
5699.9 1
 
1.9%
2899.9 1
 
1.9%
123.8 1
 
1.9%
Other values (39) 39
75.0%
ValueCountFrequency (%)
0.4 1
1.9%
1.0 1
1.9%
1.7 1
1.9%
1.8 2
3.8%
1.9 1
1.9%
2.1 1
1.9%
5.0 1
1.9%
5.5 2
3.8%
7.9 1
1.9%
10.1 1
1.9%
ValueCountFrequency (%)
930120.0 1
1.9%
15710.3 1
1.9%
5699.9 1
1.9%
5208.0 1
1.9%
5064.0 1
1.9%
4761.9 1
1.9%
2899.9 1
1.9%
1727.0 1
1.9%
1498.0 1
1.9%
1359.0 1
1.9%

데이터기준일
Date

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size548.0 B
Minimum2024-01-12 00:00:00
Maximum2024-01-12 00:00:00
2024-04-06T17:06:56.971680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:57.148686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-04-06T17:06:47.776111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:34.096749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:36.131979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:37.527331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:39.376589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:40.847007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:42.825317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:44.760035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:46.336328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:47.950780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:34.504110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:36.294626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:37.676127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:39.546901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:41.008293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:42.997301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:44.910699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:46.496320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:48.102338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:34.764464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:36.420964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:37.894465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:39.692061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:41.192523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:43.146292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:45.050901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:46.640758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:48.291598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:35.000013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:36.563582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:38.100097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:39.839220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:41.549735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:43.326713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:45.260914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:46.796483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:48.462331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:35.205220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:36.710150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:38.340176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:39.983761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:41.915694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:43.882131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:45.439287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:46.931345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:48.637583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:35.440227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:36.902208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:38.539355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:40.176709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:42.101391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:44.096286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:45.619980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:47.119113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:48.814651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:35.653308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:37.058193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:38.749457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:40.350743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:42.282218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:44.249058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:45.793057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:47.284846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:48.974494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:35.817391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:37.219197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:38.947408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:40.509023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:42.444538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:44.395439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:45.992734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:47.428902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:49.133583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:35.982677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:37.389660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:39.167693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:40.691398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:42.632240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:44.578411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:46.161292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:47.601456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:06:57.312996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호작물명2023년 재배면적(헥타르)2023년 재배농가수(호)2023년 조수입(천원_10아르당)2023년 조수입 전체(백만원)2023년 소득(천원_10아르당)2023년 소득 전체(백만원)2023년 생산량(킬로그램_10아르)2023년 생산량 전체(톤)(고로쇠 단위는 리터)
번호1.0001.0000.0000.0000.3900.2000.3960.0000.6010.000
작물명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2023년 재배면적(헥타르)0.0001.0001.0000.8520.0000.9400.0000.9380.0001.000
2023년 재배농가수(호)0.0001.0000.8521.0000.0000.7130.0000.8890.0000.000
2023년 조수입(천원_10아르당)0.3901.0000.0000.0001.0000.0000.9690.0000.6940.000
2023년 조수입 전체(백만원)0.2001.0000.9400.7130.0001.0000.0000.9190.4710.186
2023년 소득(천원_10아르당)0.3961.0000.0000.0000.9690.0001.0000.0000.6680.000
2023년 소득 전체(백만원)0.0001.0000.9380.8890.0000.9190.0001.0000.0000.821
2023년 생산량(킬로그램_10아르)0.6011.0000.0000.0000.6940.4710.6680.0001.0000.000
2023년 생산량 전체(톤)(고로쇠 단위는 리터)0.0001.0001.0000.0000.0000.1860.0000.8210.0001.000
2024-04-06T17:06:57.599792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호2023년 재배면적(헥타르)2023년 재배농가수(호)2023년 조수입(천원_10아르당)2023년 조수입 전체(백만원)2023년 소득(천원_10아르당)2023년 소득 전체(백만원)2023년 생산량(킬로그램_10아르)2023년 생산량 전체(톤)(고로쇠 단위는 리터)
번호1.0000.050-0.220-0.1200.004-0.0630.047-0.331-0.142
2023년 재배면적(헥타르)0.0501.0000.831-0.3250.790-0.3040.806-0.1840.822
2023년 재배농가수(호)-0.2200.8311.000-0.3220.632-0.3020.648-0.1780.721
2023년 조수입(천원_10아르당)-0.120-0.325-0.3221.0000.2490.9800.2110.8300.104
2023년 조수입 전체(백만원)0.0040.7900.6320.2491.0000.2610.9900.3000.924
2023년 소득(천원_10아르당)-0.063-0.304-0.3020.9800.2611.0000.2400.7880.109
2023년 소득 전체(백만원)0.0470.8060.6480.2110.9900.2401.0000.2490.916
2023년 생산량(킬로그램_10아르)-0.331-0.184-0.1780.8300.3000.7880.2491.0000.334
2023년 생산량 전체(톤)(고로쇠 단위는 리터)-0.1420.8220.7210.1040.9240.1090.9160.3341.000

Missing values

2024-04-06T17:06:49.350254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:06:49.784839image/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

번호작물명2023년 재배면적(헥타르)2023년 재배농가수(호)2023년 조수입(천원_10아르당)2023년 조수입 전체(백만원)2023년 소득(천원_10아르당)2023년 소득 전체(백만원)2023년 생산량(킬로그램_10아르)2023년 생산량 전체(톤)(고로쇠 단위는 리터)데이터기준일
01쌀(정곡)1243.0297387310852478.05936407.05064.02024-01-12
12쌀보리9.5282332282.08249.023.72024-01-12
23맥주보리2.95278890.03272.07.92024-01-12
3425.5513933238573.0146177.045.12024-01-12
451.3541196161123.015164.02.12024-01-12
56녹두0.21815643684.01191.00.42024-01-12
67가을감자4.4442228981103.0491248.054.92024-01-12
78고구마16.64552105349968.01611352.0224.42024-01-12
89옥수수8.23021881585.07432.035.42024-01-12
910수박96.0135300128811548.014861560.01498.02024-01-12
번호작물명2023년 재배면적(헥타르)2023년 재배농가수(호)2023년 조수입(천원_10아르당)2023년 조수입 전체(백만원)2023년 소득(천원_10아르당)2023년 소득 전체(백만원)2023년 생산량(킬로그램_10아르)2023년 생산량 전체(톤)(고로쇠 단위는 리터)데이터기준일
4243사과1.214445206.02140.01.72024-01-12
4344고사리750.07747375525626.94702130.0975.02024-01-12
4445두릅11.027911010027114.8783481.052.92024-01-12
4546도라지6.0752932275.21730.01.82024-01-12
4647표고21.012672141470.799205.043.12024-01-12
4748복분자7.8221681131963.075154.012.02024-01-12
48492063.01251107220852.7108783.71727.02024-01-12
4950고로쇠1685.03461662790147.4248355.2930120.02024-01-12
5051돌배24.06013763301058.4254675.0162.02024-01-12
5152토종다래20.011660132541.4108100.020.02024-01-12