Overview

Dataset statistics

Number of variables10
Number of observations116
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.1 KiB
Average record size in memory89.1 B

Variable types

Categorical2
Numeric8

Dataset

Description국가기술자격의 질적 수준 향상 및 현장성 반영 등을 위해 해당 자격종목을 전문기관에서 수행토록하는 정부방침에 따라 광업자원분야(광해, 자원) 검정업무를 한국광해광업공단에서 실시합니다. 광산피해의 방지 및 복구에 관한 법률상 전문사업자 등록 시 광업자원분야 자격자 고용은 필수요건으로, 공단은 실무인재를 배출하고 자격취득자 대상 지속적인 사후관리 교육을 실시하고 있습니다. 이와 관련, 국가기술자격 검정 현황을 개방 및 공개합니다.
URLhttps://www.data.go.kr/data/15067757/fileData.do

Alerts

필기 접수(명) is highly overall correlated with 필기 응시(명) and 4 other fieldsHigh correlation
필기 응시(명) is highly overall correlated with 필기 접수(명) and 4 other fieldsHigh correlation
필기 합격(명) is highly overall correlated with 필기 접수(명) and 5 other fieldsHigh correlation
필기 합격률(퍼센트) is highly overall correlated with 필기 합격(명) and 3 other fieldsHigh correlation
실기 접수(명) is highly overall correlated with 필기 접수(명) and 5 other fieldsHigh correlation
실기 응시(명) is highly overall correlated with 필기 접수(명) and 5 other fieldsHigh correlation
실기 합격(명) is highly overall correlated with 필기 접수(명) and 6 other fieldsHigh correlation
실기 합격률(퍼센트) is highly overall correlated with 실기 합격(명)High correlation

Reproduction

Analysis started2023-12-12 15:16:12.926754
Analysis finished2023-12-12 15:16:20.146287
Duration7.22 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

Distinct7
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
광해방지기사
18 
광산보안기사
17 
광산보안산업기사
17 
광산보안기능사
17 
시추기능사
17 
Other values (2)
30 

Length

Max length8
Median length7
Mean length6.5517241
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
광해방지기사 18
15.5%
광산보안기사 17
14.7%
광산보안산업기사 17
14.7%
광산보안기능사 17
14.7%
시추기능사 17
14.7%
광해방지기술사 15
12.9%
자원관리기술사 15
12.9%

Length

2023-12-13T00:16:20.200439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:16:20.298404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
광해방지기사 18
15.5%
광산보안기사 17
14.7%
광산보안산업기사 17
14.7%
광산보안기능사 17
14.7%
시추기능사 17
14.7%
광해방지기술사 15
12.9%
자원관리기술사 15
12.9%

연도
Categorical

Distinct21
Distinct (%)18.1%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2018
 
7
2017
 
7
2022
 
7
2021
 
7
2020
 
7
Other values (16)
81 

Length

Max length9
Median length4
Mean length4.2586207
Min length4

Unique

Unique4 ?
Unique (%)3.4%

Sample

1st row2005-2008
2nd row2009
3rd row2010
4th row2011
5th row2012

Common Values

ValueCountFrequency (%)
2018 7
 
6.0%
2017 7
 
6.0%
2022 7
 
6.0%
2021 7
 
6.0%
2020 7
 
6.0%
2010 7
 
6.0%
2009 7
 
6.0%
2019 7
 
6.0%
2016 7
 
6.0%
2015 7
 
6.0%
Other values (11) 46
39.7%

Length

2023-12-13T00:16:20.406584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2018 7
 
6.0%
2016 7
 
6.0%
2011 7
 
6.0%
2012 7
 
6.0%
2013 7
 
6.0%
2017 7
 
6.0%
2015 7
 
6.0%
2014 7
 
6.0%
2019 7
 
6.0%
2009 7
 
6.0%
Other values (11) 46
39.7%

필기 접수(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct94
Distinct (%)81.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean718.7069
Minimum8
Maximum55076
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T00:16:20.507000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile10.75
Q135.75
median90
Q3145.75
95-th percentile268.5
Maximum55076
Range55068
Interquartile range (IQR)110

Descriptive statistics

Standard deviation5188.3284
Coefficient of variation (CV)7.2189768
Kurtosis107.36858
Mean718.7069
Median Absolute Deviation (MAD)55
Skewness10.218676
Sum83370
Variance26918752
MonotonicityNot monotonic
2023-12-13T00:16:20.617059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
87 3
 
2.6%
17 3
 
2.6%
9 3
 
2.6%
86 2
 
1.7%
108 2
 
1.7%
84 2
 
1.7%
145 2
 
1.7%
46 2
 
1.7%
64 2
 
1.7%
118 2
 
1.7%
Other values (84) 93
80.2%
ValueCountFrequency (%)
8 1
 
0.9%
9 3
2.6%
10 2
1.7%
11 2
1.7%
14 2
1.7%
17 3
2.6%
18 1
 
0.9%
20 1
 
0.9%
21 1
 
0.9%
22 1
 
0.9%
ValueCountFrequency (%)
55076 1
0.9%
9093 1
0.9%
5659 1
0.9%
2406 1
0.9%
403 1
0.9%
315 1
0.9%
253 1
0.9%
244 1
0.9%
241 1
0.9%
237 1
0.9%

필기 응시(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct86
Distinct (%)74.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean592.56034
Minimum4
Maximum46437
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T00:16:20.744052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile9
Q124.75
median70
Q3116.25
95-th percentile217.75
Maximum46437
Range46433
Interquartile range (IQR)91.5

Descriptive statistics

Standard deviation4365.6467
Coefficient of variation (CV)7.3674298
Kurtosis108.37821
Mean592.56034
Median Absolute Deviation (MAD)46
Skewness10.280525
Sum68737
Variance19058871
MonotonicityNot monotonic
2023-12-13T00:16:20.869379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 4
 
3.4%
22 4
 
3.4%
110 3
 
2.6%
14 3
 
2.6%
10 3
 
2.6%
6 3
 
2.6%
99 2
 
1.7%
28 2
 
1.7%
123 2
 
1.7%
116 2
 
1.7%
Other values (76) 88
75.9%
ValueCountFrequency (%)
4 1
 
0.9%
6 3
2.6%
8 1
 
0.9%
9 2
1.7%
10 3
2.6%
11 1
 
0.9%
13 1
 
0.9%
14 3
2.6%
15 1
 
0.9%
16 1
 
0.9%
ValueCountFrequency (%)
46437 1
0.9%
7009 1
0.9%
4731 1
0.9%
1879 1
0.9%
292 1
0.9%
238 1
0.9%
211 1
0.9%
196 1
0.9%
188 1
0.9%
187 1
0.9%

필기 합격(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct59
Distinct (%)50.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean237.74138
Minimum1
Maximum20964
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T00:16:20.990410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q17
median25
Q346.25
95-th percentile111.75
Maximum20964
Range20963
Interquartile range (IQR)39.25

Descriptive statistics

Standard deviation1949.947
Coefficient of variation (CV)8.2019671
Kurtosis113.86098
Mean237.74138
Median Absolute Deviation (MAD)20
Skewness10.628275
Sum27578
Variance3802293.1
MonotonicityNot monotonic
2023-12-13T00:16:21.127980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 9
 
7.8%
1 7
 
6.0%
3 6
 
5.2%
8 6
 
5.2%
5 4
 
3.4%
7 4
 
3.4%
23 4
 
3.4%
26 4
 
3.4%
42 3
 
2.6%
25 3
 
2.6%
Other values (49) 66
56.9%
ValueCountFrequency (%)
1 7
6.0%
2 9
7.8%
3 6
5.2%
4 2
 
1.7%
5 4
3.4%
7 4
3.4%
8 6
5.2%
10 1
 
0.9%
11 1
 
0.9%
13 3
 
2.6%
ValueCountFrequency (%)
20964 1
0.9%
1668 1
0.9%
964 1
0.9%
655 1
0.9%
133 1
0.9%
120 1
0.9%
109 1
0.9%
106 1
0.9%
103 1
0.9%
94 1
0.9%

필기 합격률(퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct54
Distinct (%)46.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.810345
Minimum5
Maximum77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T00:16:21.294242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile11.75
Q119.75
median31
Q345.25
95-th percentile67
Maximum77
Range72
Interquartile range (IQR)25.5

Descriptive statistics

Standard deviation16.757043
Coefficient of variation (CV)0.49561882
Kurtosis-0.29767274
Mean33.810345
Median Absolute Deviation (MAD)12.5
Skewness0.59860121
Sum3922
Variance280.7985
MonotonicityNot monotonic
2023-12-13T00:16:21.503886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 7
 
6.0%
50 6
 
5.2%
18 5
 
4.3%
31 5
 
4.3%
29 4
 
3.4%
20 4
 
3.4%
15 4
 
3.4%
32 4
 
3.4%
41 3
 
2.6%
40 3
 
2.6%
Other values (44) 71
61.2%
ValueCountFrequency (%)
5 1
 
0.9%
9 1
 
0.9%
10 1
 
0.9%
11 3
2.6%
12 2
1.7%
13 2
1.7%
14 3
2.6%
15 4
3.4%
16 1
 
0.9%
17 3
2.6%
ValueCountFrequency (%)
77 1
0.9%
76 1
0.9%
71 2
1.7%
70 1
0.9%
67 2
1.7%
65 1
0.9%
62 1
0.9%
60 1
0.9%
58 1
0.9%
57 1
0.9%

실기 접수(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct73
Distinct (%)62.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean384.78448
Minimum2
Maximum33580
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T00:16:21.662131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q19
median36
Q368.25
95-th percentile168.5
Maximum33580
Range33578
Interquartile range (IQR)59.25

Descriptive statistics

Standard deviation3135.8487
Coefficient of variation (CV)8.1496235
Kurtosis111.97828
Mean384.78448
Median Absolute Deviation (MAD)28
Skewness10.510296
Sum44635
Variance9833546.8
MonotonicityNot monotonic
2023-12-13T00:16:21.810592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 6
 
5.2%
7 6
 
5.2%
2 4
 
3.4%
8 4
 
3.4%
72 3
 
2.6%
52 3
 
2.6%
10 3
 
2.6%
6 3
 
2.6%
4 3
 
2.6%
11 3
 
2.6%
Other values (63) 78
67.2%
ValueCountFrequency (%)
2 4
3.4%
3 6
5.2%
4 3
2.6%
5 2
 
1.7%
6 3
2.6%
7 6
5.2%
8 4
3.4%
9 2
 
1.7%
10 3
2.6%
11 3
2.6%
ValueCountFrequency (%)
33580 1
0.9%
4148 1
0.9%
1601 1
0.9%
510 1
0.9%
197 1
0.9%
170 1
0.9%
168 1
0.9%
157 1
0.9%
134 1
0.9%
113 1
0.9%

실기 응시(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct70
Distinct (%)60.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean354.96552
Minimum2
Maximum30876
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T00:16:21.963756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q19
median34
Q361.5
95-th percentile152.75
Maximum30876
Range30874
Interquartile range (IQR)52.5

Descriptive statistics

Standard deviation2885.4274
Coefficient of variation (CV)8.128754
Kurtosis111.63212
Mean354.96552
Median Absolute Deviation (MAD)26
Skewness10.488856
Sum41176
Variance8325691
MonotonicityNot monotonic
2023-12-13T00:16:22.135055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 6
 
5.2%
7 5
 
4.3%
6 5
 
4.3%
2 4
 
3.4%
9 4
 
3.4%
34 3
 
2.6%
47 3
 
2.6%
4 3
 
2.6%
90 3
 
2.6%
8 3
 
2.6%
Other values (60) 77
66.4%
ValueCountFrequency (%)
2 4
3.4%
3 6
5.2%
4 3
2.6%
5 2
 
1.7%
6 5
4.3%
7 5
4.3%
8 3
2.6%
9 4
3.4%
10 2
 
1.7%
11 2
 
1.7%
ValueCountFrequency (%)
30876 1
0.9%
3999 1
0.9%
1499 1
0.9%
441 1
0.9%
155 2
1.7%
152 1
0.9%
124 1
0.9%
121 1
0.9%
110 1
0.9%
93 1
0.9%

실기 합격(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct51
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean164.59483
Minimum0
Maximum12473
Zeros1
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T00:16:22.322844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median12.5
Q335.25
95-th percentile71.75
Maximum12473
Range12473
Interquartile range (IQR)32.25

Descriptive statistics

Standard deviation1201.0447
Coefficient of variation (CV)7.2969775
Kurtosis98.578148
Mean164.59483
Median Absolute Deviation (MAD)10.5
Skewness9.7278083
Sum19093
Variance1442508.5
MonotonicityNot monotonic
2023-12-13T00:16:22.486808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 11
 
9.5%
2 10
 
8.6%
3 8
 
6.9%
4 5
 
4.3%
6 4
 
3.4%
5 4
 
3.4%
33 4
 
3.4%
7 4
 
3.4%
10 4
 
3.4%
38 3
 
2.6%
Other values (41) 59
50.9%
ValueCountFrequency (%)
0 1
 
0.9%
1 11
9.5%
2 10
8.6%
3 8
6.9%
4 5
4.3%
5 4
 
3.4%
6 4
 
3.4%
7 4
 
3.4%
8 1
 
0.9%
9 3
 
2.6%
ValueCountFrequency (%)
12473 1
0.9%
3593 1
0.9%
662 1
0.9%
125 1
0.9%
85 1
0.9%
74 1
0.9%
71 1
0.9%
63 2
1.7%
61 1
0.9%
60 1
0.9%

실기 합격률(퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct63
Distinct (%)54.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.482759
Minimum0
Maximum96
Zeros1
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T00:16:22.660607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13
Q132.5
median50
Q371.25
95-th percentile88.25
Maximum96
Range96
Interquartile range (IQR)38.75

Descriptive statistics

Standard deviation24.897523
Coefficient of variation (CV)0.49318864
Kurtosis-1.0978696
Mean50.482759
Median Absolute Deviation (MAD)20
Skewness0.03034956
Sum5856
Variance619.88666
MonotonicityNot monotonic
2023-12-13T00:16:22.980517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33 12
 
10.3%
50 7
 
6.0%
63 5
 
4.3%
81 3
 
2.6%
29 3
 
2.6%
30 3
 
2.6%
85 3
 
2.6%
67 3
 
2.6%
17 3
 
2.6%
86 3
 
2.6%
Other values (53) 71
61.2%
ValueCountFrequency (%)
0 1
 
0.9%
3 1
 
0.9%
7 1
 
0.9%
9 1
 
0.9%
13 3
2.6%
14 2
1.7%
16 2
1.7%
17 3
2.6%
20 2
1.7%
23 1
 
0.9%
ValueCountFrequency (%)
96 1
 
0.9%
95 2
1.7%
93 1
 
0.9%
90 1
 
0.9%
89 1
 
0.9%
88 1
 
0.9%
86 3
2.6%
85 3
2.6%
83 1
 
0.9%
82 2
1.7%

Interactions

2023-12-13T00:16:19.373951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:13.430180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:14.323377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:15.096733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:15.932400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:16.761184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:17.565874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:18.479614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:19.444482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:13.538736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:14.430571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:15.210731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:16.037190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:16.859527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:17.671724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:18.570899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:19.517748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:13.647062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:14.513013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:15.305984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:16.120770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:16.939428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:17.783794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:18.643669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:19.587775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:13.749529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:14.592598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:15.386797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:16.212706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:17.011733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:17.917077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:18.721797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:19.669780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:13.874246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:14.696862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:15.484430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:16.344242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:17.108979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:18.045656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:18.814411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:19.743418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:13.990599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:14.788806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:15.624737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:16.492609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:17.203497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:18.189616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:18.906391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:19.816059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:14.096802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:14.893852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:15.744642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:16.585667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:17.309680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:18.296106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:19.226905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:19.893457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:14.211533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:14.985108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:15.851125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:16.670620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:17.443764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:18.392986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:19.296510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:16:23.144843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분연도필기 접수(명)필기 응시(명)필기 합격(명)필기 합격률(퍼센트)실기 접수(명)실기 응시(명)실기 합격(명)실기 합격률(퍼센트)
구분1.0000.0000.0000.0000.0000.5780.0000.0000.0000.445
연도0.0001.0000.7560.7560.3340.2610.5410.5410.5410.189
필기 접수(명)0.0000.7561.0001.0001.0000.3450.9880.9880.9880.212
필기 응시(명)0.0000.7561.0001.0001.0000.3450.9880.9880.9880.212
필기 합격(명)0.0000.3341.0001.0001.0000.2711.0001.0001.0000.100
필기 합격률(퍼센트)0.5780.2610.3450.3450.2711.0000.1940.1940.1940.549
실기 접수(명)0.0000.5410.9880.9881.0000.1941.0001.0001.0000.295
실기 응시(명)0.0000.5410.9880.9881.0000.1941.0001.0001.0000.295
실기 합격(명)0.0000.5410.9880.9881.0000.1941.0001.0001.0000.295
실기 합격률(퍼센트)0.4450.1890.2120.2120.1000.5490.2950.2950.2951.000
2023-12-13T00:16:23.298806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분연도
구분1.0000.000
연도0.0001.000
2023-12-13T00:16:23.762424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
필기 접수(명)필기 응시(명)필기 합격(명)필기 합격률(퍼센트)실기 접수(명)실기 응시(명)실기 합격(명)실기 합격률(퍼센트)구분연도
필기 접수(명)1.0000.9920.9240.4190.9070.9110.8420.1480.0000.449
필기 응시(명)0.9921.0000.9230.4080.9070.9120.8530.1750.0000.449
필기 합격(명)0.9240.9231.0000.6860.9540.9580.8650.1300.0000.265
필기 합격률(퍼센트)0.4190.4080.6861.0000.6140.6130.513-0.0020.3340.085
실기 접수(명)0.9070.9070.9540.6141.0000.9970.8850.0900.0000.270
실기 응시(명)0.9110.9120.9580.6130.9971.0000.8930.1020.0000.270
실기 합격(명)0.8420.8530.8650.5130.8850.8931.0000.5010.0000.270
실기 합격률(퍼센트)0.1480.1750.130-0.0020.0900.1020.5011.0000.2390.053
구분0.0000.0000.0000.3340.0000.0000.0000.2391.0000.000
연도0.4490.4490.2650.0850.2700.2700.2700.0530.0001.000

Missing values

2023-12-13T00:16:19.987382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:16:20.100920image/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

구분연도필기 접수(명)필기 응시(명)필기 합격(명)필기 합격률(퍼센트)실기 접수(명)실기 응시(명)실기 합격(명)실기 합격률(퍼센트)
0광해방지기술사2005-2008403292602193933841
1광해방지기술사2009101698122019737
2광해방지기술사201055387181414429
3광해방지기술사201143253121212433
4광해방지기술사201230204201010330
5광해방지기술사2013322042077114
6광해방지기술사2014282031577229
7광해방지기술사2015171421466233
8광해방지기술사2016141033077229
9광해방지기술사2017201421455120
구분연도필기 접수(명)필기 응시(명)필기 합격(명)필기 합격률(퍼센트)실기 접수(명)실기 응시(명)실기 합격(명)실기 합격률(퍼센트)
106시추기능사2013158114464074742534
107시추기능사201411182394855533668
108시추기능사20154632103124231461
109시추기능사201610885141617161063
110시추기능사20178969243525241042
111시추기능사201810286455259543870
112시추기능사20198670223134322681
113시추기능사202011077334337362981
114시추기능사20218157254426262077
115시추기능사20228470263727201365