Overview

Dataset statistics

Number of variables13
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 KiB
Average record size in memory119.4 B

Variable types

Categorical3
Numeric10

Dataset

Description연도/지역별 무선통신사 국가자격검정 취득교육 수료자 현황 통계 입니다. (2015~2019년) 컬럼 : 교육년도, 종목, 구분, 서울, 부산, 전남, 충청, 경북, 전북, 강원, 제주, 경인, 합계
URLhttps://www.data.go.kr/data/15042405/fileData.do

Alerts

서울 is highly overall correlated with 부산 and 4 other fieldsHigh correlation
부산 is highly overall correlated with 서울 and 8 other fieldsHigh correlation
전남 is highly overall correlated with 부산 and 6 other fieldsHigh correlation
충청 is highly overall correlated with 서울 and 4 other fieldsHigh correlation
경북 is highly overall correlated with 서울 and 8 other fieldsHigh correlation
전북 is highly overall correlated with 부산 and 6 other fieldsHigh correlation
강원 is highly overall correlated with 부산 and 5 other fieldsHigh correlation
제주 is highly overall correlated with 부산 and 5 other fieldsHigh correlation
경인 is highly overall correlated with 부산 and 5 other fieldsHigh correlation
합계 is highly overall correlated with 서울 and 8 other fieldsHigh correlation
종목 is highly overall correlated with 합계High correlation
구분 is highly overall correlated with 서울 and 3 other fieldsHigh correlation
서울 has unique valuesUnique
전남 has 9 (30.0%) zerosZeros
경북 has 1 (3.3%) zerosZeros
전북 has 17 (56.7%) zerosZeros
강원 has 10 (33.3%) zerosZeros
제주 has 18 (60.0%) zerosZeros
경인 has 20 (66.7%) zerosZeros

Reproduction

Analysis started2023-12-12 01:28:31.906258
Analysis finished2023-12-12 01:28:43.689660
Duration11.78 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

교육년도
Categorical

Distinct5
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2015
6 
2016
6 
2017
6 
2018
6 
2019
6 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015
2nd row2015
3rd row2015
4th row2015
5th row2015

Common Values

ValueCountFrequency (%)
2015 6
20.0%
2016 6
20.0%
2017 6
20.0%
2018 6
20.0%
2019 6
20.0%

Length

2023-12-12T10:28:43.767204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:28:43.892210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2015 6
20.0%
2016 6
20.0%
2017 6
20.0%
2018 6
20.0%
2019 6
20.0%

종목
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
육상무선통신사
10 
제한무선통신사
10 
항공무선통신사
10 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row육상무선통신사
2nd row육상무선통신사
3rd row제한무선통신사
4th row제한무선통신사
5th row항공무선통신사

Common Values

ValueCountFrequency (%)
육상무선통신사 10
33.3%
제한무선통신사 10
33.3%
항공무선통신사 10
33.3%

Length

2023-12-12T10:28:44.029649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:28:44.143342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
육상무선통신사 10
33.3%
제한무선통신사 10
33.3%
항공무선통신사 10
33.3%

구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
전체
15 
여성
15 

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 15
50.0%
여성 15
50.0%

Length

2023-12-12T10:28:44.269522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:28:44.391012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전체 15
50.0%
여성 15
50.0%

서울
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean521.63333
Minimum14
Maximum1777
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T10:28:44.518636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile15.9
Q173.75
median151.5
Q31090.75
95-th percentile1749.5
Maximum1777
Range1763
Interquartile range (IQR)1017

Descriptive statistics

Standard deviation623.77027
Coefficient of variation (CV)1.1958022
Kurtosis-0.6836549
Mean521.63333
Median Absolute Deviation (MAD)125.5
Skewness0.9716239
Sum15649
Variance389089.34
MonotonicityNot monotonic
2023-12-12T10:28:44.671989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
279 1
 
3.3%
1135 1
 
3.3%
151 1
 
3.3%
1772 1
 
3.3%
102 1
 
3.3%
1722 1
 
3.3%
14 1
 
3.3%
152 1
 
3.3%
138 1
 
3.3%
1777 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
14 1
3.3%
15 1
3.3%
17 1
3.3%
24 1
3.3%
30 1
3.3%
40 1
3.3%
54 1
3.3%
73 1
3.3%
76 1
3.3%
78 1
3.3%
ValueCountFrequency (%)
1777 1
3.3%
1772 1
3.3%
1722 1
3.3%
1422 1
3.3%
1328 1
3.3%
1263 1
3.3%
1153 1
3.3%
1135 1
3.3%
958 1
3.3%
938 1
3.3%

부산
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean251
Minimum1
Maximum1582
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T10:28:44.833993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q15
median37
Q381
95-th percentile1450.7
Maximum1582
Range1581
Interquartile range (IQR)76

Descriptive statistics

Standard deviation496.21714
Coefficient of variation (CV)1.9769607
Kurtosis2.555543
Mean251
Median Absolute Deviation (MAD)32.5
Skewness2.0317754
Sum7530
Variance246231.45
MonotonicityNot monotonic
2023-12-12T10:28:44.997203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
5 3
 
10.0%
3 3
 
10.0%
37 2
 
6.7%
4 2
 
6.7%
22 1
 
3.3%
16 1
 
3.3%
179 1
 
3.3%
82 1
 
3.3%
1249 1
 
3.3%
32 1
 
3.3%
Other values (14) 14
46.7%
ValueCountFrequency (%)
1 1
 
3.3%
3 3
10.0%
4 2
6.7%
5 3
10.0%
16 1
 
3.3%
22 1
 
3.3%
28 1
 
3.3%
30 1
 
3.3%
32 1
 
3.3%
37 2
6.7%
ValueCountFrequency (%)
1582 1
3.3%
1484 1
3.3%
1410 1
3.3%
1249 1
3.3%
835 1
3.3%
179 1
3.3%
112 1
3.3%
82 1
3.3%
78 1
3.3%
69 1
3.3%

전남
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean216.8
Minimum0
Maximum1418
Zeros9
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T10:28:45.177506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median29.5
Q368.75
95-th percentile1179.3
Maximum1418
Range1418
Interquartile range (IQR)68.75

Descriptive statistics

Standard deviation436.76265
Coefficient of variation (CV)2.0145879
Kurtosis2.1835794
Mean216.8
Median Absolute Deviation (MAD)29.5
Skewness1.9570809
Sum6504
Variance190761.61
MonotonicityNot monotonic
2023-12-12T10:28:45.322410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 9
30.0%
4 2
 
6.7%
1 2
 
6.7%
64 2
 
6.7%
92 1
 
3.3%
60 1
 
3.3%
1109 1
 
3.3%
8 1
 
3.3%
68 1
 
3.3%
1209 1
 
3.3%
Other values (9) 9
30.0%
ValueCountFrequency (%)
0 9
30.0%
1 2
 
6.7%
4 2
 
6.7%
8 1
 
3.3%
16 1
 
3.3%
43 1
 
3.3%
49 1
 
3.3%
58 1
 
3.3%
60 1
 
3.3%
64 2
 
6.7%
ValueCountFrequency (%)
1418 1
3.3%
1209 1
3.3%
1143 1
3.3%
1109 1
3.3%
943 1
3.3%
92 1
3.3%
81 1
3.3%
69 1
3.3%
68 1
3.3%
64 2
6.7%

충청
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean144.86667
Minimum3
Maximum517
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T10:28:45.472040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q118.25
median42
Q3224.25
95-th percentile502.1
Maximum517
Range514
Interquartile range (IQR)206

Descriptive statistics

Standard deviation177.91353
Coefficient of variation (CV)1.2281191
Kurtosis-0.045281446
Mean144.86667
Median Absolute Deviation (MAD)37
Skewness1.1877685
Sum4346
Variance31653.223
MonotonicityNot monotonic
2023-12-12T10:28:45.636648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
42 2
 
6.7%
5 2
 
6.7%
80 1
 
3.3%
16 1
 
3.3%
28 1
 
3.3%
272 1
 
3.3%
47 1
 
3.3%
466 1
 
3.3%
30 1
 
3.3%
213 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
3 1
3.3%
5 2
6.7%
6 1
3.3%
8 1
3.3%
12 1
3.3%
16 1
3.3%
18 1
3.3%
19 1
3.3%
22 1
3.3%
23 1
3.3%
ValueCountFrequency (%)
517 1
3.3%
512 1
3.3%
490 1
3.3%
466 1
3.3%
464 1
3.3%
302 1
3.3%
272 1
3.3%
228 1
3.3%
213 1
3.3%
173 1
3.3%

경북
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105.13333
Minimum0
Maximum524
Zeros1
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T10:28:45.791913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q116
median37
Q3126.75
95-th percentile450.1
Maximum524
Range524
Interquartile range (IQR)110.75

Descriptive statistics

Standard deviation148.59381
Coefficient of variation (CV)1.4133843
Kurtosis2.5149744
Mean105.13333
Median Absolute Deviation (MAD)31.5
Skewness1.878007
Sum3154
Variance22080.12
MonotonicityNot monotonic
2023-12-12T10:28:45.954567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
16 2
 
6.7%
22 2
 
6.7%
2 2
 
6.7%
108 1
 
3.3%
0 1
 
3.3%
32 1
 
3.3%
487 1
 
3.3%
34 1
 
3.3%
135 1
 
3.3%
55 1
 
3.3%
Other values (17) 17
56.7%
ValueCountFrequency (%)
0 1
3.3%
2 2
6.7%
5 1
3.3%
6 1
3.3%
13 1
3.3%
14 1
3.3%
16 2
6.7%
19 1
3.3%
22 2
6.7%
29 1
3.3%
ValueCountFrequency (%)
524 1
3.3%
487 1
3.3%
405 1
3.3%
382 1
3.3%
215 1
3.3%
155 1
3.3%
135 1
3.3%
133 1
3.3%
108 1
3.3%
101 1
3.3%

전북
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)43.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.2
Minimum0
Maximum524
Zeros17
Zeros (%)56.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T10:28:46.153582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q333.5
95-th percentile455.8
Maximum524
Range524
Interquartile range (IQR)33.5

Descriptive statistics

Standard deviation154.02136
Coefficient of variation (CV)2.1041169
Kurtosis3.3759657
Mean73.2
Median Absolute Deviation (MAD)0
Skewness2.1676869
Sum2196
Variance23722.579
MonotonicityNot monotonic
2023-12-12T10:28:46.274776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 17
56.7%
17 2
 
6.7%
213 1
 
3.3%
524 1
 
3.3%
56 1
 
3.3%
44 1
 
3.3%
1 1
 
3.3%
436 1
 
3.3%
35 1
 
3.3%
328 1
 
3.3%
Other values (3) 3
 
10.0%
ValueCountFrequency (%)
0 17
56.7%
1 1
 
3.3%
17 2
 
6.7%
24 1
 
3.3%
29 1
 
3.3%
35 1
 
3.3%
44 1
 
3.3%
56 1
 
3.3%
213 1
 
3.3%
328 1
 
3.3%
ValueCountFrequency (%)
524 1
3.3%
472 1
3.3%
436 1
3.3%
328 1
3.3%
213 1
3.3%
56 1
3.3%
44 1
3.3%
35 1
3.3%
29 1
3.3%
24 1
3.3%

강원
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean151.43333
Minimum0
Maximum936
Zeros10
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T10:28:46.398899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median28.5
Q389.75
95-th percentile803.4
Maximum936
Range936
Interquartile range (IQR)89.75

Descriptive statistics

Standard deviation281.52965
Coefficient of variation (CV)1.8590996
Kurtosis2.6072855
Mean151.43333
Median Absolute Deviation (MAD)28.5
Skewness2.0145259
Sum4543
Variance79258.944
MonotonicityNot monotonic
2023-12-12T10:28:46.541874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 10
33.3%
14 2
 
6.7%
99 1
 
3.3%
73 1
 
3.3%
92 1
 
3.3%
861 1
 
3.3%
16 1
 
3.3%
68 1
 
3.3%
79 1
 
3.3%
733 1
 
3.3%
Other values (10) 10
33.3%
ValueCountFrequency (%)
0 10
33.3%
11 1
 
3.3%
14 2
 
6.7%
16 1
 
3.3%
19 1
 
3.3%
38 1
 
3.3%
42 1
 
3.3%
47 1
 
3.3%
68 1
 
3.3%
73 1
 
3.3%
ValueCountFrequency (%)
936 1
3.3%
861 1
3.3%
733 1
3.3%
731 1
3.3%
490 1
3.3%
99 1
3.3%
97 1
3.3%
92 1
3.3%
83 1
3.3%
79 1
3.3%

제주
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)43.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.066667
Minimum0
Maximum227
Zeros18
Zeros (%)60.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T10:28:46.670961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q39.75
95-th percentile192.3
Maximum227
Range227
Interquartile range (IQR)9.75

Descriptive statistics

Standard deviation68.890184
Coefficient of variation (CV)2.0833725
Kurtosis2.5947889
Mean33.066667
Median Absolute Deviation (MAD)0
Skewness2.0017943
Sum992
Variance4745.8575
MonotonicityNot monotonic
2023-12-12T10:28:46.796736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 18
60.0%
49 1
 
3.3%
1 1
 
3.3%
136 1
 
3.3%
5 1
 
3.3%
156 1
 
3.3%
6 1
 
3.3%
167 1
 
3.3%
9 1
 
3.3%
213 1
 
3.3%
Other values (3) 3
 
10.0%
ValueCountFrequency (%)
0 18
60.0%
1 1
 
3.3%
5 1
 
3.3%
6 1
 
3.3%
9 1
 
3.3%
10 1
 
3.3%
13 1
 
3.3%
49 1
 
3.3%
136 1
 
3.3%
156 1
 
3.3%
ValueCountFrequency (%)
227 1
3.3%
213 1
3.3%
167 1
3.3%
156 1
3.3%
136 1
3.3%
49 1
3.3%
13 1
3.3%
10 1
3.3%
9 1
3.3%
6 1
3.3%

경인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.03333
Minimum0
Maximum857
Zeros20
Zeros (%)66.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T10:28:46.939111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q333
95-th percentile721.75
Maximum857
Range857
Interquartile range (IQR)33

Descriptive statistics

Standard deviation272.28895
Coefficient of variation (CV)2.1604519
Kurtosis2.0121836
Mean126.03333
Median Absolute Deviation (MAD)0
Skewness1.9338453
Sum3781
Variance74141.275
MonotonicityNot monotonic
2023-12-12T10:28:47.095023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 20
66.7%
33 2
 
6.7%
635 1
 
3.3%
26 1
 
3.3%
664 1
 
3.3%
48 1
 
3.3%
719 1
 
3.3%
724 1
 
3.3%
857 1
 
3.3%
42 1
 
3.3%
ValueCountFrequency (%)
0 20
66.7%
26 1
 
3.3%
33 2
 
6.7%
42 1
 
3.3%
48 1
 
3.3%
635 1
 
3.3%
664 1
 
3.3%
719 1
 
3.3%
724 1
 
3.3%
857 1
 
3.3%
ValueCountFrequency (%)
857 1
 
3.3%
724 1
 
3.3%
719 1
 
3.3%
664 1
 
3.3%
635 1
 
3.3%
48 1
 
3.3%
42 1
 
3.3%
33 2
 
6.7%
26 1
 
3.3%
0 20
66.7%

합계
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1623.1667
Minimum59
Maximum7450
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T10:28:47.262017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum59
5-th percentile61.35
Q1119.75
median536.5
Q31409
95-th percentile7189.2
Maximum7450
Range7391
Interquartile range (IQR)1289.25

Descriptive statistics

Standard deviation2445.3033
Coefficient of variation (CV)1.5065017
Kurtosis1.574448
Mean1623.1667
Median Absolute Deviation (MAD)465.5
Skewness1.7523704
Sum48695
Variance5979508.4
MonotonicityNot monotonic
2023-12-12T10:28:47.457989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
71 2
 
6.7%
670 1
 
3.3%
92 1
 
3.3%
195 1
 
3.3%
2245 1
 
3.3%
491 1
 
3.3%
7450 1
 
3.3%
105 1
 
3.3%
673 1
 
3.3%
164 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
59 1
3.3%
60 1
3.3%
63 1
3.3%
64 1
3.3%
71 2
6.7%
92 1
3.3%
105 1
3.3%
164 1
3.3%
195 1
3.3%
300 1
3.3%
ValueCountFrequency (%)
7450 1
3.3%
7299 1
3.3%
7055 1
3.3%
7040 1
3.3%
5110 1
3.3%
2245 1
3.3%
2166 1
3.3%
1450 1
3.3%
1286 1
3.3%
1204 1
3.3%

Interactions

2023-12-12T10:28:42.348921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:32.476564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:33.514310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:34.600001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:36.145170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:37.255385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:38.289566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:39.297178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:40.260049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:41.221668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:42.449819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:32.600111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:33.627110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:35.082882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:36.278122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:37.379763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:38.383842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:39.406603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:40.377929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:41.340445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:42.549779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:32.751394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:33.752337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:35.229866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:36.408434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:37.492085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:38.487298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:39.542875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:40.481742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:41.451149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:42.651354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:32.846723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:33.858041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:35.355987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:36.527435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:37.605612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:38.604239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:39.643315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:40.564979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:41.555065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:42.741059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:32.940253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:33.959441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:35.453470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:36.651679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:37.714899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:38.719783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:39.748242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:40.666225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:41.642811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:42.848112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:33.038870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:34.051327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:35.548174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:36.769891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:37.820123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:38.801128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:39.853337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:40.752837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:41.721801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:42.962082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:33.150217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:34.139445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:35.656266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:36.879446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:37.914592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:38.896605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:39.939728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:40.831857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:41.795036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:43.068706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:33.233508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:34.246617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:35.772107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:36.968395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:38.002477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:38.989352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:40.015084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:40.918405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:42.113504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:43.147720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:33.320693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:34.369152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:35.882940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:37.062289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:38.099014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:39.102782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:40.091883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:41.006429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:42.201231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:43.224708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:33.415728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:34.473739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:35.995375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:37.154169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:38.188729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:39.193100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:40.165561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:41.103496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:28:42.268417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T10:28:47.615475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
교육년도종목구분서울부산전남충청경북전북강원제주경인합계
교육년도1.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
종목0.0001.0000.0000.7910.5560.3440.5090.7890.6170.4520.5560.3440.580
구분0.0000.0001.0000.9920.4390.2270.9640.9360.3590.3640.4390.4920.537
서울0.0000.7910.9921.0000.9010.7130.8330.9190.7840.7390.8330.7520.879
부산0.0000.5560.4390.9011.0000.9210.6390.9370.9650.8970.9751.0000.835
전남0.0000.3440.2270.7130.9211.0000.5510.8370.9210.9700.9210.8980.938
충청0.0000.5090.9640.8330.6390.5511.0000.7840.6870.7710.7640.8620.860
경북0.0000.7890.9360.9190.9370.8370.7841.0000.9370.7970.9740.8450.796
전북0.0000.6170.3590.7840.9650.9210.6870.9371.0000.8970.9750.9390.759
강원0.0000.4520.3640.7390.8970.9700.7710.7970.8971.0000.8940.8600.939
제주0.0000.5560.4390.8330.9750.9210.7640.9740.9750.8941.0000.9390.759
경인0.0000.3440.4920.7521.0000.8980.8620.8450.9390.8600.9391.0000.679
합계0.0000.5800.5370.8790.8350.9380.8600.7960.7590.9390.7590.6791.000
2023-12-12T10:28:47.787132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
교육년도구분종목
교육년도1.0000.0000.000
구분0.0001.0000.000
종목0.0000.0001.000
2023-12-12T10:28:47.926878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
서울부산전남충청경북전북강원제주경인합계교육년도종목구분
서울1.0000.7210.3190.8980.6900.3530.2930.3340.3450.9480.0000.4390.853
부산0.7211.0000.7440.7480.7230.7110.6490.6560.7560.8260.0000.2470.283
전남0.3190.7441.0000.4980.7280.8010.8430.6950.7560.5430.0000.2580.254
충청0.8980.7480.4981.0000.7730.4960.4650.4670.4940.9570.0000.3270.736
경북0.6900.7230.7280.7731.0000.5220.7270.5070.4850.8260.0000.4370.715
전북0.3530.7110.8010.4960.5221.0000.8060.8740.8900.5090.0000.2890.227
강원0.2930.6490.8430.4650.7270.8061.0000.7750.7140.4770.0000.3630.415
제주0.3340.6560.6950.4670.5070.8740.7751.0000.9050.4700.0000.2470.283
경인0.3450.7560.7560.4940.4850.8900.7140.9051.0000.5030.0000.3220.316
합계0.9480.8260.5430.9570.8260.5090.4770.4700.5031.0000.0000.5050.612
교육년도0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.000
종목0.4390.2470.2580.3270.4370.2890.3630.2470.3220.5050.0001.0000.000
구분0.8530.2830.2540.7360.7150.2270.4150.2830.3160.6120.0000.0001.000

Missing values

2023-12-12T10:28:43.365737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T10:28:43.592513image/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

교육년도종목구분서울부산전남충청경북전북강원제주경인합계
02015육상무선통신사전체2792216801081799490670
12015육상무선통신사여성24305160141063
22015제한무선통신사전체11538359434902152134901366355110
32015제한무선통신사여성76394936141738526300
42015항공무선통신사전체93850811288900001286
52015항공무선통신사여성545156000071
62016육상무선통신사전체25328434213308300582
72016육상무선통신사여성15416190140059
82016제한무선통신사전체1328141011435123825249361566647055
92016제한무선통신사여성86646418295642648413
교육년도종목구분서울부산전남충청경북전북강원제주경인합계
202018제한무선통신사전체1263158212094645243287332137247040
212018제한무선통신사여성887864422229791333448
222018항공무선통신사전체17773203025500002166
232018항공무선통신사여성138501920000164
242019육상무선통신사전체152376821313506800673
252019육상무선통신사여성1438303401600105
262019제한무선통신사전체1722124911094664874728612278577450
272019제한무선통신사여성1028260473224921042491
282019항공무선통신사전체177217902722200002245
292019항공무선통신사여성1511602800000195