Overview

Dataset statistics

Number of variables4
Number of observations462
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.9 KiB
Average record size in memory35.3 B

Variable types

Categorical1
Numeric3

Dataset

Description임업분야 국가기술자격취득현황을 나타내는 자료입니다. (산림기술사, 산림산업기사, 산림기사, 산림기능사, 임업종묘기사, 임업종묘기능사 등)
Author산림청
URLhttps://www.data.go.kr/data/15091327/fileData.do

Alerts

합격년도 is highly overall correlated with 등록년도High correlation
등록년도 is highly overall correlated with 합격년도High correlation

Reproduction

Analysis started2023-12-12 07:26:16.669283
Analysis finished2023-12-12 07:26:17.687238
Duration1.02 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

산림자격증명
Categorical

Distinct15
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
산림기사
61 
산림산업기사
56 
산림기능사
51 
임업종묘기능사
45 
산림기술사
41 
Other values (10)
208 

Length

Max length8
Median length7
Mean length5.9329004
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
산림기사 61
13.2%
산림산업기사 56
12.1%
산림기능사 51
11.0%
임업종묘기능사 45
9.7%
산림기술사 41
8.9%
임업종묘기사 37
8.0%
임산가공산업기사 31
6.7%
조경산업기사 31
6.7%
임산가공기사 29
6.3%
임업종묘산업기사 25
5.4%
Other values (5) 55
11.9%

Length

2023-12-12T16:26:17.760137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
산림기사 61
13.2%
산림산업기사 56
12.1%
산림기능사 51
11.0%
임업종묘기능사 45
9.7%
산림기술사 41
8.9%
임업종묘기사 37
8.0%
임산가공산업기사 31
6.7%
조경산업기사 31
6.7%
임산가공기사 29
6.3%
임업종묘산업기사 25
5.4%
Other values (5) 55
11.9%

합격년도
Real number (ℝ)

HIGH CORRELATION 

Distinct46
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2003.0887
Minimum1976
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2023-12-12T16:26:17.873938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1976
5-th percentile1981
Q11996
median2005
Q32011
95-th percentile2019
Maximum2021
Range45
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.383986
Coefficient of variation (CV)0.0056832162
Kurtosis-0.54948402
Mean2003.0887
Median Absolute Deviation (MAD)8
Skewness-0.57064415
Sum925427
Variance129.59514
MonotonicityNot monotonic
2023-12-12T16:26:18.064032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
2009 25
 
5.4%
2010 20
 
4.3%
2011 19
 
4.1%
2006 18
 
3.9%
2008 18
 
3.9%
2007 16
 
3.5%
2005 15
 
3.2%
2014 15
 
3.2%
2018 14
 
3.0%
2002 14
 
3.0%
Other values (36) 288
62.3%
ValueCountFrequency (%)
1976 1
 
0.2%
1977 3
0.6%
1978 6
1.3%
1979 6
1.3%
1980 6
1.3%
1981 6
1.3%
1982 6
1.3%
1983 6
1.3%
1984 2
 
0.4%
1985 6
1.3%
ValueCountFrequency (%)
2021 6
 
1.3%
2020 10
2.2%
2019 9
1.9%
2018 14
3.0%
2017 12
2.6%
2016 12
2.6%
2015 11
2.4%
2014 15
3.2%
2013 13
2.8%
2012 12
2.6%

등록년도
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2011.355
Minimum2009
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2023-12-12T16:26:18.176276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2009
5-th percentile2009
Q12009
median2009
Q32012
95-th percentile2021
Maximum2021
Range12
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.6919869
Coefficient of variation (CV)0.001835572
Kurtosis1.0557486
Mean2011.355
Median Absolute Deviation (MAD)0
Skewness1.5290352
Sum929246
Variance13.630767
MonotonicityNot monotonic
2023-12-12T16:26:18.266772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2009 258
55.8%
2010 39
 
8.4%
2021 32
 
6.9%
2011 31
 
6.7%
2012 23
 
5.0%
2018 20
 
4.3%
2013 14
 
3.0%
2015 13
 
2.8%
2016 12
 
2.6%
2017 12
 
2.6%
ValueCountFrequency (%)
2009 258
55.8%
2010 39
 
8.4%
2011 31
 
6.7%
2012 23
 
5.0%
2013 14
 
3.0%
2014 8
 
1.7%
2015 13
 
2.8%
2016 12
 
2.6%
2017 12
 
2.6%
2018 20
 
4.3%
ValueCountFrequency (%)
2021 32
6.9%
2018 20
4.3%
2017 12
 
2.6%
2016 12
 
2.6%
2015 13
 
2.8%
2014 8
 
1.7%
2013 14
 
3.0%
2012 23
5.0%
2011 31
6.7%
2010 39
8.4%

등록인원
Real number (ℝ)

Distinct194
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.59091
Minimum1
Maximum1685
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2023-12-12T16:26:18.375257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median15
Q3120
95-th percentile538.85
Maximum1685
Range1684
Interquartile range (IQR)117

Descriptive statistics

Standard deviation223.22247
Coefficient of variation (CV)1.9311421
Kurtosis16.452005
Mean115.59091
Median Absolute Deviation (MAD)14
Skewness3.5535308
Sum53403
Variance49828.273
MonotonicityNot monotonic
2023-12-12T16:26:18.503667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 66
 
14.3%
2 35
 
7.6%
3 31
 
6.7%
4 20
 
4.3%
5 12
 
2.6%
6 10
 
2.2%
12 9
 
1.9%
7 8
 
1.7%
9 8
 
1.7%
8 7
 
1.5%
Other values (184) 256
55.4%
ValueCountFrequency (%)
1 66
14.3%
2 35
7.6%
3 31
6.7%
4 20
 
4.3%
5 12
 
2.6%
6 10
 
2.2%
7 8
 
1.7%
8 7
 
1.5%
9 8
 
1.7%
10 6
 
1.3%
ValueCountFrequency (%)
1685 1
0.2%
1595 1
0.2%
1517 1
0.2%
1417 1
0.2%
1097 1
0.2%
1070 1
0.2%
989 1
0.2%
800 1
0.2%
760 1
0.2%
755 1
0.2%

Interactions

2023-12-12T16:26:17.277655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:26:16.793875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:26:17.029786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:26:17.364037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:26:16.866889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:26:17.121003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:26:17.449484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:26:16.945357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:26:17.192425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T16:26:18.593380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
산림자격증명합격년도등록년도등록인원
산림자격증명1.0000.3830.0000.406
합격년도0.3831.0000.7530.212
등록년도0.0000.7531.0000.072
등록인원0.4060.2120.0721.000
2023-12-12T16:26:18.680321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
합격년도등록년도등록인원산림자격증명
합격년도1.0000.8640.1050.153
등록년도0.8641.0000.0580.106
등록인원0.1050.0581.0000.176
산림자격증명0.1530.1060.1761.000

Missing values

2023-12-12T16:26:17.574923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:26:17.650554image/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

산림자격증명합격년도등록년도등록인원
0목공예기능사2018201874
1목공예기능사201820213
2목공예기능사20192021138
3목공예기능사2020202188
4목공예기능사2021202113
5목재가공기능사200020091
6목재가공기능사200220091
7목재가공기능사200420091
8목재가공기능사200520092
9목재가공기능사200920091
산림자격증명합격년도등록년도등록인원
452펄프제지기능사2007200935
453펄프제지기능사2008200948
454펄프제지기능사200820101
455펄프제지기능사200820111
456펄프제지기능사2009200957
457펄프제지기능사2009201041
458펄프제지기능사200920111
459펄프제지기능사20102010108
460펄프제지기능사201020119
461펄프제지기능사2011201174