Overview

Dataset statistics

Number of variables7
Number of observations408
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory24.4 KiB
Average record size in memory61.3 B

Variable types

Numeric5
Categorical1
Text1

Dataset

Description일반농산어촌지역개발사업 지역별 사업 현황 통계
Author농림수산식품교육문화정보원
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20220209000000001738

Alerts

SIDO_CD is highly overall correlated with SGG_CD and 1 other fieldsHigh correlation
SGG_CD is highly overall correlated with SIDO_CD and 1 other fieldsHigh correlation
BIZPLAN_YEAR is highly overall correlated with CNTHigh correlation
CNT is highly overall correlated with BIZPLAN_YEAR and 1 other fieldsHigh correlation
BUDGET is highly overall correlated with CNTHigh correlation
SIDO_NM is highly overall correlated with SIDO_CD and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-12-11 03:32:11.575848
Analysis finished2023-12-11 03:32:14.587361
Duration3.01 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

SIDO_CD
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.656863
Minimum27
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-12-11T12:32:14.645605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile41
Q143
median45
Q347
95-th percentile48
Maximum50
Range23
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.1349503
Coefficient of variation (CV)0.070200863
Kurtosis10.804981
Mean44.656863
Median Absolute Deviation (MAD)2
Skewness-2.4173268
Sum18220
Variance9.8279135
MonotonicityDecreasing
2023-12-11T12:32:14.764313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
46 62
15.2%
47 58
14.2%
45 57
14.0%
48 51
12.5%
43 50
12.3%
44 48
11.8%
42 40
9.8%
41 26
6.4%
50 7
 
1.7%
36 2
 
0.5%
Other values (4) 7
 
1.7%
ValueCountFrequency (%)
27 2
 
0.5%
28 2
 
0.5%
29 2
 
0.5%
31 1
 
0.2%
36 2
 
0.5%
41 26
6.4%
42 40
9.8%
43 50
12.3%
44 48
11.8%
45 57
14.0%
ValueCountFrequency (%)
50 7
 
1.7%
48 51
12.5%
47 58
14.2%
46 62
15.2%
45 57
14.0%
44 48
11.8%
43 50
12.3%
42 40
9.8%
41 26
6.4%
36 2
 
0.5%

SIDO_NM
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
전라남도
62 
경상북도
58 
전라북도
57 
경상남도
51 
충청북도
50 
Other values (9)
130 

Length

Max length7
Median length4
Mean length3.9215686
Min length3

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row제주특별자치도
2nd row제주특별자치도
3rd row제주특별자치도
4th row제주특별자치도
5th row제주특별자치도

Common Values

ValueCountFrequency (%)
전라남도 62
15.2%
경상북도 58
14.2%
전라북도 57
14.0%
경상남도 51
12.5%
충청북도 50
12.3%
충청남도 48
11.8%
강원도 40
9.8%
경기도 26
6.4%
제주특별자치도 7
 
1.7%
세종특별자치시 2
 
0.5%
Other values (4) 7
 
1.7%

Length

2023-12-11T12:32:14.905822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전라남도 62
15.2%
경상북도 58
14.2%
전라북도 57
14.0%
경상남도 51
12.5%
충청북도 50
12.3%
충청남도 48
11.8%
강원도 40
9.8%
경기도 26
6.4%
제주특별자치도 7
 
1.7%
세종특별자치시 2
 
0.5%
Other values (4) 7
 
1.7%

SGG_CD
Real number (ℝ)

HIGH CORRELATION 

Distinct137
Distinct (%)33.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45231.696
Minimum27260
Maximum50130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-12-11T12:32:15.066046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27260
5-th percentile41550
Q143745
median45730
Q347210
95-th percentile48860
Maximum50130
Range22870
Interquartile range (IQR)3465

Descriptive statistics

Standard deviation3163.0237
Coefficient of variation (CV)0.069929364
Kurtosis10.658146
Mean45231.696
Median Absolute Deviation (MAD)1960
Skewness-2.412358
Sum18454532
Variance10004719
MonotonicityNot monotonic
2023-12-11T12:32:15.252046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45210 8
 
2.0%
45790 8
 
2.0%
42750 8
 
2.0%
43800 7
 
1.7%
43110 6
 
1.5%
43730 6
 
1.5%
42770 6
 
1.5%
44230 6
 
1.5%
45710 6
 
1.5%
45190 6
 
1.5%
Other values (127) 341
83.6%
ValueCountFrequency (%)
27260 1
0.2%
27710 1
0.2%
28710 2
0.5%
29155 2
0.5%
31710 1
0.2%
36110 2
0.5%
41220 2
0.5%
41280 1
0.2%
41360 1
0.2%
41390 1
0.2%
ValueCountFrequency (%)
50130 2
 
0.5%
50110 5
1.2%
48890 3
0.7%
48880 6
1.5%
48870 2
 
0.5%
48860 5
1.2%
48850 5
1.2%
48840 2
 
0.5%
48820 5
1.2%
48740 2
 
0.5%

SGG_NM
Text

Distinct136
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
2023-12-11T12:32:15.682205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.0612745
Min length2

Characters and Unicode

Total characters1249
Distinct characters104
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

Unique23 ?
Unique (%)5.6%

Sample

1st row서귀포시
2nd row제주시
3rd row제주시
4th row제주시
5th row제주시
ValueCountFrequency (%)
김제시 8
 
1.9%
청주시 8
 
1.9%
고창군 8
 
1.9%
영월군 8
 
1.9%
단양군 7
 
1.7%
논산시 6
 
1.4%
완주군 6
 
1.4%
거창군 6
 
1.4%
정선군 6
 
1.4%
고성군 6
 
1.4%
Other values (127) 345
83.3%
2023-12-11T12:32:16.138711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 259
20.7%
시 153
 
12.2%
주 57
 
4.6%
천 47
 
3.8%
산 41
 
3.3%
양 36
 
2.9%
성 36
 
2.9%
영 29
 
2.3%
안 24
 
1.9%
창 22
 
1.8%
Other values (94) 545
43.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1243
99.5%
Space Separator 6
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 259
20.8%
시 153
 
12.3%
주 57
 
4.6%
천 47
 
3.8%
산 41
 
3.3%
양 36
 
2.9%
성 36
 
2.9%
영 29
 
2.3%
안 24
 
1.9%
창 22
 
1.8%
Other values (93) 539
43.4%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1243
99.5%
Common 6
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 259
20.8%
시 153
 
12.3%
주 57
 
4.6%
천 47
 
3.8%
산 41
 
3.3%
양 36
 
2.9%
성 36
 
2.9%
영 29
 
2.3%
안 24
 
1.9%
창 22
 
1.8%
Other values (93) 539
43.4%
Common
ValueCountFrequency (%)
6
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1243
99.5%
ASCII 6
 
0.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 259
20.8%
시 153
 
12.3%
주 57
 
4.6%
천 47
 
3.8%
산 41
 
3.3%
양 36
 
2.9%
성 36
 
2.9%
영 29
 
2.3%
안 24
 
1.9%
창 22
 
1.8%
Other values (93) 539
43.4%
ASCII
ValueCountFrequency (%)
6
100.0%

BIZPLAN_YEAR
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.4706
Minimum2003
Maximum2017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-12-11T12:32:16.262486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2003
5-th percentile2007
Q12010.75
median2015
Q32017
95-th percentile2017
Maximum2017
Range14
Interquartile range (IQR)6.25

Descriptive statistics

Standard deviation3.8432579
Coefficient of variation (CV)0.0019087728
Kurtosis-0.43273462
Mean2013.4706
Median Absolute Deviation (MAD)2
Skewness-0.95713534
Sum821496
Variance14.770632
MonotonicityNot monotonic
2023-12-11T12:32:16.409646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2017 115
28.2%
2015 96
23.5%
2016 39
 
9.6%
2008 32
 
7.8%
2007 30
 
7.4%
2014 22
 
5.4%
2013 17
 
4.2%
2009 17
 
4.2%
2011 11
 
2.7%
2004 10
 
2.5%
Other values (5) 19
 
4.7%
ValueCountFrequency (%)
2003 1
 
0.2%
2004 10
 
2.5%
2005 3
 
0.7%
2006 3
 
0.7%
2007 30
7.4%
2008 32
7.8%
2009 17
4.2%
2010 6
 
1.5%
2011 11
 
2.7%
2012 6
 
1.5%
ValueCountFrequency (%)
2017 115
28.2%
2016 39
 
9.6%
2015 96
23.5%
2014 22
 
5.4%
2013 17
 
4.2%
2012 6
 
1.5%
2011 11
 
2.7%
2010 6
 
1.5%
2009 17
 
4.2%
2008 32
 
7.8%

CNT
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3259804
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-12-11T12:32:16.543464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile7
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9812664
Coefficient of variation (CV)0.85179843
Kurtosis1.6019964
Mean2.3259804
Median Absolute Deviation (MAD)0
Skewness1.5722892
Sum949
Variance3.9254167
MonotonicityNot monotonic
2023-12-11T12:32:16.653978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 225
55.1%
2 57
 
14.0%
3 41
 
10.0%
4 25
 
6.1%
6 19
 
4.7%
5 15
 
3.7%
7 14
 
3.4%
8 9
 
2.2%
9 2
 
0.5%
10 1
 
0.2%
ValueCountFrequency (%)
1 225
55.1%
2 57
 
14.0%
3 41
 
10.0%
4 25
 
6.1%
5 15
 
3.7%
6 19
 
4.7%
7 14
 
3.4%
8 9
 
2.2%
9 2
 
0.5%
10 1
 
0.2%
ValueCountFrequency (%)
10 1
 
0.2%
9 2
 
0.5%
8 9
 
2.2%
7 14
 
3.4%
6 19
 
4.7%
5 15
 
3.7%
4 25
 
6.1%
3 41
 
10.0%
2 57
 
14.0%
1 225
55.1%

BUDGET
Real number (ℝ)

HIGH CORRELATION 

Distinct351
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7673921 × 109
Minimum50000000
Maximum5.2743 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-12-11T12:32:16.793741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50000000
5-th percentile3 × 108
Q11.8476072 × 109
median5.012421 × 109
Q37.309734 × 109
95-th percentile1.5631959 × 1010
Maximum5.2743 × 1010
Range5.2693 × 1010
Interquartile range (IQR)5.4621268 × 109

Descriptive statistics

Standard deviation5.4269943 × 109
Coefficient of variation (CV)0.9409789
Kurtosis16.04244
Mean5.7673921 × 109
Median Absolute Deviation (MAD)2.727997 × 109
Skewness2.8288247
Sum2.353096 × 1012
Variance2.9452267 × 1019
MonotonicityNot monotonic
2023-12-11T12:32:16.961375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200000000.0 7
 
1.7%
6000000000.0 6
 
1.5%
600000000.0 5
 
1.2%
50000000.0 5
 
1.2%
7000000000.0 5
 
1.2%
1200000000.0 4
 
1.0%
1000000000.0 4
 
1.0%
6700000000.0 4
 
1.0%
8000000000.0 3
 
0.7%
4700000000.0 3
 
0.7%
Other values (341) 362
88.7%
ValueCountFrequency (%)
50000000.0 5
1.2%
90000000.0 1
 
0.2%
100000000.0 1
 
0.2%
130000000.0 1
 
0.2%
157000000.0 1
 
0.2%
199600000.0 1
 
0.2%
200000000.0 7
1.7%
214000000.0 1
 
0.2%
262500000.0 1
 
0.2%
292206000.0 1
 
0.2%
ValueCountFrequency (%)
52743000000.0 1
0.2%
34576000000.0 1
0.2%
26109000000.0 1
0.2%
25300000000.0 1
0.2%
24400000000.0 2
0.5%
21289000000.0 1
0.2%
20366000000.0 1
0.2%
20281606000.0 1
0.2%
19318999000.0 1
0.2%
17200000000.0 1
0.2%

Interactions

2023-12-11T12:32:13.913339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:11.926105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:12.408684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:12.927320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:13.457120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:14.010533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:12.022333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:12.491361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:13.026531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:13.541613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:14.090938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:12.109915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:12.578386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:13.123937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:13.627255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:14.182202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:12.228080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:12.685284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:13.253127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:13.715238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:14.288384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:12.320701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:12.799867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:13.353137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:13.794829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T12:32:17.065661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SIDO_CDSIDO_NMSGG_CDBIZPLAN_YEARCNTBUDGET
SIDO_CD1.0001.0000.9960.0000.0000.182
SIDO_NM1.0001.0000.9990.0340.0000.000
SGG_CD0.9960.9991.0000.0000.0000.162
BIZPLAN_YEAR0.0000.0340.0001.0000.5090.226
CNT0.0000.0000.0000.5091.0000.456
BUDGET0.1820.0000.1620.2260.4561.000
2023-12-11T12:32:17.535466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SIDO_CDSGG_CDBIZPLAN_YEARCNTBUDGETSIDO_NM
SIDO_CD1.0000.9920.0070.1640.2210.991
SGG_CD0.9921.0000.0010.1680.2260.936
BIZPLAN_YEAR0.0070.0011.0000.6090.0940.000
CNT0.1640.1680.6091.0000.5400.000
BUDGET0.2210.2260.0940.5401.0000.000
SIDO_NM0.9910.9360.0000.0000.0001.000

Missing values

2023-12-11T12:32:14.422567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T12:32:14.537637image/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

SIDO_CDSIDO_NMSGG_CDSGG_NMBIZPLAN_YEARCNTBUDGET
050제주특별자치도50130서귀포시2015515688000000.0
150제주특별자치도50110제주시20131157000000.0
250제주특별자치도50110제주시201741700000000.0
350제주특별자치도50110제주시200716023927000.0
450제주특별자치도50110제주시20141292206000.0
550제주특별자치도50110제주시201523211750000.0
650제주특별자치도50130서귀포시2017610192000000.0
748경상남도48860산청군201313731000000.0
848경상남도48730함안군2017913201057000.0
948경상남도48330양산시200716999604170.0
SIDO_CDSIDO_NMSGG_CDSGG_NMBIZPLAN_YEARCNTBUDGET
39841경기도41670여주시201116999480000.0
39936세종특별자치시36110세종시201726100140000.0
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