Overview

Dataset statistics

Number of variables10
Number of observations32
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.9 KiB
Average record size in memory92.1 B

Variable types

Text1
Categorical1
Numeric8

Dataset

Description정보통신기사 등 국가기술자격검정 최종 합격자 필기/실기 합격률 및 통계 입니다.
Author한국방송통신전파진흥원
URLhttps://www.data.go.kr/data/15042406/fileData.do

Alerts

필기접수 is highly overall correlated with 필기응시 and 4 other fieldsHigh correlation
필기응시 is highly overall correlated with 필기접수 and 4 other fieldsHigh correlation
필기합격 is highly overall correlated with 필기접수 and 4 other fieldsHigh correlation
실기접수 is highly overall correlated with 필기접수 and 4 other fieldsHigh correlation
실기응시 is highly overall correlated with 필기접수 and 4 other fieldsHigh correlation
실기합격 is highly overall correlated with 필기접수 and 4 other fieldsHigh correlation
필기합격 has 2 (6.2%) zerosZeros
필기합격률(퍼센트) has 2 (6.2%) zerosZeros
실기접수 has 2 (6.2%) zerosZeros
실기응시 has 2 (6.2%) zerosZeros
실기합격 has 3 (9.4%) zerosZeros
실기합격율(퍼센트) has 3 (9.4%) zerosZeros

Reproduction

Analysis started2023-12-12 03:12:50.253251
Analysis finished2023-12-12 03:12:58.092049
Duration7.84 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

종목
Text

Distinct16
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-12T12:12:58.223938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length7.4375
Min length6

Characters and Unicode

Total characters238
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전파전자통신기사
2nd row전파전자통신기사
3rd row전파전자통신산업기사
4th row전파전자통신산업기사
5th row전파전자통신기능사
ValueCountFrequency (%)
전파전자통신기사 2
 
6.2%
전파전자통신산업기사 2
 
6.2%
전파전자통신기능사 2
 
6.2%
무선설비기사 2
 
6.2%
무선설비산업기사 2
 
6.2%
무선설비기능사 2
 
6.2%
정보통신기술사 2
 
6.2%
정보통신기사 2
 
6.2%
정보통신산업기사 2
 
6.2%
방송통신기사 2
 
6.2%
Other values (6) 12
37.5%
2023-12-12T12:12:58.604332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
기 36
15.1%
사 30
12.6%
통 26
10.9%
신 26
10.9%
전 12
 
5.0%
능 12
 
5.0%
산 10
 
4.2%
업 10
 
4.2%
선 10
 
4.2%
설 8
 
3.4%
Other values (11) 58
24.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 238
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 36
15.1%
사 30
12.6%
통 26
10.9%
신 26
10.9%
전 12
 
5.0%
능 12
 
5.0%
산 10
 
4.2%
업 10
 
4.2%
선 10
 
4.2%
설 8
 
3.4%
Other values (11) 58
24.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 238
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 36
15.1%
사 30
12.6%
통 26
10.9%
신 26
10.9%
전 12
 
5.0%
능 12
 
5.0%
산 10
 
4.2%
업 10
 
4.2%
선 10
 
4.2%
설 8
 
3.4%
Other values (11) 58
24.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 238
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
기 36
15.1%
사 30
12.6%
통 26
10.9%
신 26
10.9%
전 12
 
5.0%
능 12
 
5.0%
산 10
 
4.2%
업 10
 
4.2%
선 10
 
4.2%
설 8
 
3.4%
Other values (11) 58
24.4%

성별
Categorical

Distinct2
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size388.0 B
전체
16 
여성
16 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전체
2nd row여성
3rd row전체
4th row여성
5th row전체

Common Values

ValueCountFrequency (%)
전체 16
50.0%
여성 16
50.0%

Length

2023-12-12T12:12:58.765928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:12:58.885369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전체 16
50.0%
여성 16
50.0%

필기접수
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean403
Minimum1
Maximum3390
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T12:12:59.048215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.75
Q115.5
median69.5
Q3512.5
95-th percentile1680.1
Maximum3390
Range3389
Interquartile range (IQR)497

Descriptive statistics

Standard deviation737.95052
Coefficient of variation (CV)1.8311427
Kurtosis9.2107836
Mean403
Median Absolute Deviation (MAD)62
Skewness2.9002389
Sum12896
Variance544570.97
MonotonicityNot monotonic
2023-12-12T12:12:59.258538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
9 2
 
6.2%
8 2
 
6.2%
208 1
 
3.1%
132 1
 
3.1%
1 1
 
3.1%
27 1
 
3.1%
51 1
 
3.1%
851 1
 
3.1%
16 1
 
3.1%
155 1
 
3.1%
Other values (20) 20
62.5%
ValueCountFrequency (%)
1 1
3.1%
3 1
3.1%
8 2
6.2%
9 2
6.2%
10 1
3.1%
14 1
3.1%
16 1
3.1%
17 1
3.1%
27 1
3.1%
43 1
3.1%
ValueCountFrequency (%)
3390 1
3.1%
2350 1
3.1%
1132 1
3.1%
1030 1
3.1%
851 1
3.1%
805 1
3.1%
753 1
3.1%
529 1
3.1%
507 1
3.1%
322 1
3.1%

필기응시
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean327.1875
Minimum1
Maximum2620
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T12:12:59.468238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.75
Q114.75
median61.5
Q3432.75
95-th percentile1354.05
Maximum2620
Range2619
Interquartile range (IQR)418

Descriptive statistics

Standard deviation582.71624
Coefficient of variation (CV)1.7809857
Kurtosis8.2780135
Mean327.1875
Median Absolute Deviation (MAD)54.5
Skewness2.7634971
Sum10470
Variance339558.22
MonotonicityNot monotonic
2023-12-12T12:12:59.663762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
7 4
 
12.5%
100 2
 
6.2%
175 1
 
3.1%
11 1
 
3.1%
1 1
 
3.1%
22 1
 
3.1%
43 1
 
3.1%
688 1
 
3.1%
16 1
 
3.1%
129 1
 
3.1%
Other values (18) 18
56.2%
ValueCountFrequency (%)
1 1
 
3.1%
2 1
 
3.1%
7 4
12.5%
9 1
 
3.1%
11 1
 
3.1%
16 1
 
3.1%
17 1
 
3.1%
22 1
 
3.1%
31 1
 
3.1%
39 1
 
3.1%
ValueCountFrequency (%)
2620 1
3.1%
1898 1
3.1%
909 1
3.1%
885 1
3.1%
700 1
3.1%
688 1
3.1%
584 1
3.1%
465 1
3.1%
422 1
3.1%
284 1
3.1%

필기합격
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean144.59375
Minimum0
Maximum1269
Zeros2
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T12:12:59.894074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.55
Q16.5
median21
Q3231
95-th percentile656.25
Maximum1269
Range1269
Interquartile range (IQR)224.5

Descriptive statistics

Standard deviation281.77615
Coefficient of variation (CV)1.9487436
Kurtosis9.5313395
Mean144.59375
Median Absolute Deviation (MAD)19.5
Skewness3.0077991
Sum4627
Variance79397.797
MonotonicityNot monotonic
2023-12-12T12:13:00.079575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
2 2
 
6.2%
0 2
 
6.2%
4 2
 
6.2%
14 2
 
6.2%
96 1
 
3.1%
402 1
 
3.1%
10 1
 
3.1%
234 1
 
3.1%
31 1
 
3.1%
13 1
 
3.1%
Other values (18) 18
56.2%
ValueCountFrequency (%)
0 2
6.2%
1 1
3.1%
2 2
6.2%
4 2
6.2%
5 1
3.1%
7 1
3.1%
8 1
3.1%
10 1
3.1%
13 1
3.1%
14 2
6.2%
ValueCountFrequency (%)
1269 1
3.1%
967 1
3.1%
402 1
3.1%
289 1
3.1%
286 1
3.1%
267 1
3.1%
244 1
3.1%
234 1
3.1%
230 1
3.1%
96 1
3.1%

필기합격률(퍼센트)
Real number (ℝ)

ZEROS 

Distinct30
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.77125
Minimum0
Maximum82.35
Zeros2
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T12:13:00.243777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.904
Q128.51
median41.77
Q351.33
95-th percentile60.9395
Maximum82.35
Range82.35
Interquartile range (IQR)22.82

Descriptive statistics

Standard deviation18.40798
Coefficient of variation (CV)0.47478428
Kurtosis0.38298352
Mean38.77125
Median Absolute Deviation (MAD)12.91
Skewness-0.26612346
Sum1240.68
Variance338.85373
MonotonicityNot monotonic
2023-12-12T12:13:00.429840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
28.57 2
 
6.2%
0.0 2
 
6.2%
54.86 1
 
3.1%
44.22 1
 
3.1%
45.45 1
 
3.1%
32.56 1
 
3.1%
34.01 1
 
3.1%
25.0 1
 
3.1%
24.03 1
 
3.1%
33.33 1
 
3.1%
Other values (20) 20
62.5%
ValueCountFrequency (%)
0.0 2
6.2%
5.28 1
3.1%
14.29 1
3.1%
24.03 1
3.1%
25.0 1
3.1%
25.81 1
3.1%
28.33 1
3.1%
28.57 2
6.2%
32.32 1
3.1%
32.56 1
3.1%
ValueCountFrequency (%)
82.35 1
3.1%
63.64 1
3.1%
58.73 1
3.1%
57.14 1
3.1%
55.56 1
3.1%
54.86 1
3.1%
54.5 1
3.1%
52.47 1
3.1%
50.95 1
3.1%
49.49 1
3.1%

실기접수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean179.4375
Minimum0
Maximum1134
Zeros2
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T12:13:00.608302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.55
Q16.5
median21.5
Q3218.5
95-th percentile970.65
Maximum1134
Range1134
Interquartile range (IQR)212

Descriptive statistics

Standard deviation313.43322
Coefficient of variation (CV)1.7467543
Kurtosis3.6440065
Mean179.4375
Median Absolute Deviation (MAD)20
Skewness2.1431156
Sum5742
Variance98240.383
MonotonicityNot monotonic
2023-12-12T12:13:00.798363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
2 2
 
6.2%
0 2
 
6.2%
124 1
 
3.1%
20 1
 
3.1%
8 1
 
3.1%
68 1
 
3.1%
688 1
 
3.1%
3 1
 
3.1%
25 1
 
3.1%
13 1
 
3.1%
Other values (20) 20
62.5%
ValueCountFrequency (%)
0 2
6.2%
1 1
3.1%
2 2
6.2%
3 1
3.1%
4 1
3.1%
5 1
3.1%
7 1
3.1%
8 1
3.1%
9 1
3.1%
13 1
3.1%
ValueCountFrequency (%)
1134 1
3.1%
1002 1
3.1%
945 1
3.1%
688 1
3.1%
316 1
3.1%
296 1
3.1%
287 1
3.1%
223 1
3.1%
217 1
3.1%
128 1
3.1%

실기응시
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)84.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean167.5
Minimum0
Maximum978
Zeros2
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T12:13:00.970708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.55
Q16.5
median21
Q3197.5
95-th percentile926.4
Maximum978
Range978
Interquartile range (IQR)191

Descriptive statistics

Standard deviation289.84768
Coefficient of variation (CV)1.7304339
Kurtosis3.2855466
Mean167.5
Median Absolute Deviation (MAD)19.5
Skewness2.0923056
Sum5360
Variance84011.677
MonotonicityNot monotonic
2023-12-12T12:13:01.164201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
123 2
 
6.2%
7 2
 
6.2%
2 2
 
6.2%
0 2
 
6.2%
21 2
 
6.2%
4 1
 
3.1%
68 1
 
3.1%
679 1
 
3.1%
3 1
 
3.1%
24 1
 
3.1%
Other values (17) 17
53.1%
ValueCountFrequency (%)
0 2
6.2%
1 1
3.1%
2 2
6.2%
3 1
3.1%
4 1
3.1%
5 1
3.1%
7 2
6.2%
9 1
3.1%
13 1
3.1%
14 1
3.1%
ValueCountFrequency (%)
978 1
3.1%
955 1
3.1%
903 1
3.1%
679 1
3.1%
292 1
3.1%
283 1
3.1%
241 1
3.1%
214 1
3.1%
192 1
3.1%
123 2
6.2%

실기합격
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean119.21875
Minimum0
Maximum894
Zeros3
Zeros (%)9.4%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T12:13:01.347251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.75
median14
Q398.75
95-th percentile688.2
Maximum894
Range894
Interquartile range (IQR)95

Descriptive statistics

Standard deviation228.13787
Coefficient of variation (CV)1.9136073
Kurtosis5.2471321
Mean119.21875
Median Absolute Deviation (MAD)13.5
Skewness2.4496389
Sum3815
Variance52046.886
MonotonicityNot monotonic
2023-12-12T12:13:01.500134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 3
 
9.4%
3 3
 
9.4%
5 2
 
6.2%
12 2
 
6.2%
97 1
 
3.1%
87 1
 
3.1%
4 1
 
3.1%
65 1
 
3.1%
654 1
 
3.1%
17 1
 
3.1%
Other values (16) 16
50.0%
ValueCountFrequency (%)
0 3
9.4%
1 1
 
3.1%
2 1
 
3.1%
3 3
9.4%
4 1
 
3.1%
5 2
6.2%
7 1
 
3.1%
8 1
 
3.1%
12 2
6.2%
13 1
 
3.1%
ValueCountFrequency (%)
894 1
3.1%
730 1
3.1%
654 1
3.1%
416 1
3.1%
213 1
3.1%
188 1
3.1%
117 1
3.1%
104 1
3.1%
97 1
3.1%
87 1
3.1%

실기합격율(퍼센트)
Real number (ℝ)

ZEROS 

Distinct26
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.274375
Minimum0
Maximum100
Zeros3
Zeros (%)9.4%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T12:13:01.659421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q144.8725
median72.19
Q389.52
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)44.6475

Descriptive statistics

Standard deviation31.00595
Coefficient of variation (CV)0.48239987
Kurtosis-0.34949537
Mean64.274375
Median Absolute Deviation (MAD)21.02
Skewness-0.81344548
Sum2056.78
Variance961.36896
MonotonicityNot monotonic
2023-12-12T12:13:01.842774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
100.0 3
 
9.4%
0.0 3
 
9.4%
71.43 2
 
6.2%
57.14 2
 
6.2%
78.86 1
 
3.1%
28.63 1
 
3.1%
95.59 1
 
3.1%
96.32 1
 
3.1%
70.83 1
 
3.1%
38.46 1
 
3.1%
Other values (16) 16
50.0%
ValueCountFrequency (%)
0.0 3
9.4%
18.02 1
 
3.1%
27.78 1
 
3.1%
28.63 1
 
3.1%
38.46 1
 
3.1%
43.56 1
 
3.1%
45.31 1
 
3.1%
50.0 1
 
3.1%
57.14 2
6.2%
60.0 1
 
3.1%
ValueCountFrequency (%)
100.0 3
9.4%
96.32 1
 
3.1%
95.59 1
 
3.1%
95.12 1
 
3.1%
92.04 1
 
3.1%
91.41 1
 
3.1%
88.89 1
 
3.1%
87.85 1
 
3.1%
85.71 1
 
3.1%
80.84 1
 
3.1%

Interactions

2023-12-12T12:12:56.820647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:50.855154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:51.819324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:52.693379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:53.650601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:54.490427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:55.234144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:55.992061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:57.245683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:50.978520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:51.922954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:52.824432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:53.757646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:54.590782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:55.360682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:56.103046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:57.333196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:51.095419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:52.025687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:52.934342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:53.892652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:54.680237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:55.442046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:56.193297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:57.424002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:51.218909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:52.153466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:53.060256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:54.009954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:54.775480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:55.537986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:56.298108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:57.504238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:51.336057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:52.270970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:53.181203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:54.118547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:54.859822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:55.626917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:56.402250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:57.593364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:51.454633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:52.384261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:53.309942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:54.209629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:54.945681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:55.721316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:56.497977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:57.676664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:51.571352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:52.490291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:53.417758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:54.317900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:55.033411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:55.821671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:56.596987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:57.752562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:51.680230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:52.581103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:53.532864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:54.403319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:55.112411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:55.904854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:56.696751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T12:13:01.982373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
종목성별필기접수필기응시필기합격필기합격률(퍼센트)실기접수실기응시실기합격실기합격율(퍼센트)
종목1.0000.0000.2720.1790.2720.5440.0000.0000.3300.895
성별0.0001.0000.1730.3050.0000.1090.5530.2590.0000.000
필기접수0.2720.1731.0000.9990.9910.0000.9670.7480.8820.359
필기응시0.1790.3050.9991.0000.9860.0000.9600.7220.8550.371
필기합격0.2720.0000.9910.9861.0000.0000.9610.7280.8510.560
필기합격률(퍼센트)0.5440.1090.0000.0000.0001.0000.0000.0000.0000.553
실기접수0.0000.5530.9670.9600.9610.0001.0000.9300.9020.000
실기응시0.0000.2590.7480.7220.7280.0000.9301.0000.9100.000
실기합격0.3300.0000.8820.8550.8510.0000.9020.9101.0000.000
실기합격율(퍼센트)0.8950.0000.3590.3710.5600.5530.0000.0000.0001.000
2023-12-12T12:13:02.188069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
필기접수필기응시필기합격필기합격률(퍼센트)실기접수실기응시실기합격실기합격율(퍼센트)성별
필기접수1.0000.9970.9700.2320.9370.9360.8690.0210.091
필기응시0.9971.0000.9730.2450.9420.9410.8730.0220.192
필기합격0.9700.9731.0000.3960.9620.9570.9030.0390.000
필기합격률(퍼센트)0.2320.2450.3961.0000.4020.3990.4360.2470.030
실기접수0.9370.9420.9620.4021.0000.9990.9660.1600.367
실기응시0.9360.9410.9570.3990.9991.0000.9720.1780.294
실기합격0.8690.8730.9030.4360.9660.9721.0000.3420.000
실기합격율(퍼센트)0.0210.0220.0390.2470.1600.1780.3421.0000.000
성별0.0910.1920.0000.0300.3670.2940.0000.0001.000

Missing values

2023-12-12T12:12:57.873973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:12:58.027828image/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

종목성별필기접수필기응시필기합격필기합격률(퍼센트)실기접수실기응시실기합격실기합격율(퍼센트)
0전파전자통신기사전체2081759654.861241239778.86
1전파전자통신기사여성17171482.3514141285.71
2전파전자통신산업기사전체4331825.81777100.0
3전파전자통신산업기사여성97228.57222100.0
4전파전자통신기능사전체52946524452.47100297889491.41
5전파전자통신기능사여성71633758.7311311310492.04
6무선설비기사전체2350189896750.9594590373080.84
7무선설비기사여성50742223054.521721418887.85
8무선설비산업기사전체103088528632.3229629221372.95
9무선설비산업기사여성68582543.128282071.43
종목성별필기접수필기응시필기합격필기합격률(퍼센트)실기접수실기응시실기합격실기합격율(퍼센트)
22방송통신기능사전체107228.571100.0
23방송통신기능사여성3200.00000.0
24통신설비기능장전체80570026738.143162835118.02
25통신설비기능장여성48391333.331313538.46
26통신선로산업기사전체1551293124.0325241770.83
27통신선로산업기사여성1616425.0333100.0
28통신선로기능사전체85168823434.0168867965496.32
29통신선로기능사여성51431432.5668686595.59
30통신기기기능사전체27221045.4587457.14
31통신기기기능사여성1100.00000.0