Overview

Dataset statistics

Number of variables9
Number of observations60
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.8 KiB
Average record size in memory82.2 B

Variable types

Text1
Numeric8

Dataset

Description태양광 발전설비 사용전검사 실시 건수를 한국전기안전공사의 사업소(60개소)별 및 연도별(2015년 ~ 2022년)로 제공하는 데이터입니다.
URLhttps://www.data.go.kr/data/15002343/fileData.do

Alerts

2015 is highly overall correlated with 2016 and 6 other fieldsHigh correlation
2016 is highly overall correlated with 2015 and 6 other fieldsHigh correlation
2017 is highly overall correlated with 2015 and 6 other fieldsHigh correlation
2018 is highly overall correlated with 2015 and 6 other fieldsHigh correlation
2019 is highly overall correlated with 2015 and 6 other fieldsHigh correlation
2020 is highly overall correlated with 2015 and 6 other fieldsHigh correlation
2021 is highly overall correlated with 2015 and 6 other fieldsHigh correlation
2022 is highly overall correlated with 2015 and 6 other fieldsHigh correlation
구분 has unique valuesUnique

Reproduction

Analysis started2023-12-12 07:39:17.467723
Analysis finished2023-12-12 07:39:24.222696
Duration6.75 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct60
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size612.0 B
2023-12-12T16:39:24.368744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length6
Mean length6.0166667
Min length4

Characters and Unicode

Total characters361
Distinct characters56
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

Unique60 ?
Unique (%)100.0%

Sample

1st row강원남부지사
2nd row강원동부지사
3rd row강원북부지사
4th row강원지역본부
5th row경기북동부지사
ValueCountFrequency (%)
강원남부지사 1
 
1.7%
강원동부지사 1
 
1.7%
인천지역본부 1
 
1.7%
서울북부지사 1
 
1.7%
서울서부지사 1
 
1.7%
서울지역본부 1
 
1.7%
안산시흥지사 1
 
1.7%
여수지사 1
 
1.7%
영동옥천지사 1
 
1.7%
용인지사 1
 
1.7%
Other values (50) 50
83.3%
2023-12-12T16:39:24.678563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 60
16.6%
사 47
13.0%
부 44
 
12.2%
남 18
 
5.0%
서 15
 
4.2%
경 15
 
4.2%
북 14
 
3.9%
역 13
 
3.6%
본 13
 
3.6%
산 10
 
2.8%
Other values (46) 112
31.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 361
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 60
16.6%
사 47
13.0%
부 44
 
12.2%
남 18
 
5.0%
서 15
 
4.2%
경 15
 
4.2%
북 14
 
3.9%
역 13
 
3.6%
본 13
 
3.6%
산 10
 
2.8%
Other values (46) 112
31.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 361
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 60
16.6%
사 47
13.0%
부 44
 
12.2%
남 18
 
5.0%
서 15
 
4.2%
경 15
 
4.2%
북 14
 
3.9%
역 13
 
3.6%
본 13
 
3.6%
산 10
 
2.8%
Other values (46) 112
31.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 361
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 60
16.6%
사 47
13.0%
부 44
 
12.2%
남 18
 
5.0%
서 15
 
4.2%
경 15
 
4.2%
북 14
 
3.9%
역 13
 
3.6%
본 13
 
3.6%
산 10
 
2.8%
Other values (46) 112
31.0%

2015
Real number (ℝ)

HIGH CORRELATION 

Distinct55
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174.96667
Minimum26
Maximum849
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-12T16:39:24.807616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile35.8
Q168
median107.5
Q3168.75
95-th percentile548.1
Maximum849
Range823
Interquartile range (IQR)100.75

Descriptive statistics

Standard deviation175.44355
Coefficient of variation (CV)1.0027256
Kurtosis3.7127354
Mean174.96667
Median Absolute Deviation (MAD)50
Skewness1.9964531
Sum10498
Variance30780.44
MonotonicityNot monotonic
2023-12-12T16:39:24.930753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68 2
 
3.3%
125 2
 
3.3%
99 2
 
3.3%
76 2
 
3.3%
66 2
 
3.3%
32 1
 
1.7%
168 1
 
1.7%
71 1
 
1.7%
38 1
 
1.7%
105 1
 
1.7%
Other values (45) 45
75.0%
ValueCountFrequency (%)
26 1
1.7%
27 1
1.7%
32 1
1.7%
36 1
1.7%
38 1
1.7%
47 1
1.7%
50 1
1.7%
53 1
1.7%
54 1
1.7%
56 1
1.7%
ValueCountFrequency (%)
849 1
1.7%
652 1
1.7%
569 1
1.7%
547 1
1.7%
519 1
1.7%
504 1
1.7%
496 1
1.7%
383 1
1.7%
377 1
1.7%
303 1
1.7%

2016
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)88.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean141.95
Minimum27
Maximum457
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-12T16:39:25.049113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile35.8
Q161.75
median107.5
Q3187.75
95-th percentile366.7
Maximum457
Range430
Interquartile range (IQR)126

Descriptive statistics

Standard deviation103.74944
Coefficient of variation (CV)0.73088722
Kurtosis1.2440889
Mean141.95
Median Absolute Deviation (MAD)52.5
Skewness1.328937
Sum8517
Variance10763.947
MonotonicityNot monotonic
2023-12-12T16:39:25.160786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105 2
 
3.3%
27 2
 
3.3%
119 2
 
3.3%
175 2
 
3.3%
146 2
 
3.3%
53 2
 
3.3%
57 2
 
3.3%
104 1
 
1.7%
54 1
 
1.7%
120 1
 
1.7%
Other values (43) 43
71.7%
ValueCountFrequency (%)
27 2
3.3%
32 1
1.7%
36 1
1.7%
43 1
1.7%
47 1
1.7%
51 1
1.7%
53 2
3.3%
54 1
1.7%
55 1
1.7%
57 2
3.3%
ValueCountFrequency (%)
457 1
1.7%
422 1
1.7%
380 1
1.7%
366 1
1.7%
342 1
1.7%
338 1
1.7%
273 1
1.7%
267 1
1.7%
242 1
1.7%
228 1
1.7%

2017
Real number (ℝ)

HIGH CORRELATION 

Distinct56
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean189.11667
Minimum43
Maximum644
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-12T16:39:25.297046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum43
5-th percentile59.9
Q1104
median158.5
Q3249.5
95-th percentile357
Maximum644
Range601
Interquartile range (IQR)145.5

Descriptive statistics

Standard deviation118.91032
Coefficient of variation (CV)0.628767
Kurtosis3.4543888
Mean189.11667
Median Absolute Deviation (MAD)67
Skewness1.5982095
Sum11347
Variance14139.664
MonotonicityNot monotonic
2023-12-12T16:39:25.427709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
247 3
 
5.0%
104 2
 
3.3%
105 2
 
3.3%
100 1
 
1.7%
181 1
 
1.7%
122 1
 
1.7%
268 1
 
1.7%
259 1
 
1.7%
142 1
 
1.7%
54 1
 
1.7%
Other values (46) 46
76.7%
ValueCountFrequency (%)
43 1
1.7%
54 1
1.7%
58 1
1.7%
60 1
1.7%
61 1
1.7%
71 1
1.7%
75 1
1.7%
78 1
1.7%
84 1
1.7%
86 1
1.7%
ValueCountFrequency (%)
644 1
1.7%
543 1
1.7%
471 1
1.7%
351 1
1.7%
345 1
1.7%
330 1
1.7%
309 1
1.7%
304 1
1.7%
303 1
1.7%
292 1
1.7%

2018
Real number (ℝ)

HIGH CORRELATION 

Distinct57
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean279.96667
Minimum65
Maximum1249
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-12T16:39:25.558953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum65
5-th percentile73.95
Q1133
median213.5
Q3334.75
95-th percentile620.4
Maximum1249
Range1184
Interquartile range (IQR)201.75

Descriptive statistics

Standard deviation223.82507
Coefficient of variation (CV)0.79947041
Kurtosis5.8068284
Mean279.96667
Median Absolute Deviation (MAD)85.5
Skewness2.1496766
Sum16798
Variance50097.66
MonotonicityNot monotonic
2023-12-12T16:39:25.688922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
133 2
 
3.3%
128 2
 
3.3%
329 2
 
3.3%
74 1
 
1.7%
393 1
 
1.7%
96 1
 
1.7%
225 1
 
1.7%
68 1
 
1.7%
163 1
 
1.7%
73 1
 
1.7%
Other values (47) 47
78.3%
ValueCountFrequency (%)
65 1
1.7%
68 1
1.7%
73 1
1.7%
74 1
1.7%
75 1
1.7%
96 1
1.7%
100 1
1.7%
106 1
1.7%
107 1
1.7%
113 1
1.7%
ValueCountFrequency (%)
1249 1
1.7%
883 1
1.7%
875 1
1.7%
607 1
1.7%
574 1
1.7%
550 1
1.7%
528 1
1.7%
527 1
1.7%
526 1
1.7%
479 1
1.7%

2019
Real number (ℝ)

HIGH CORRELATION 

Distinct59
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean493.48333
Minimum62
Maximum2535
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-12T16:39:25.843286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum62
5-th percentile110.9
Q1200.75
median393
Q3688.5
95-th percentile1144.4
Maximum2535
Range2473
Interquartile range (IQR)487.75

Descriptive statistics

Standard deviation410.32284
Coefficient of variation (CV)0.83148266
Kurtosis9.2886197
Mean493.48333
Median Absolute Deviation (MAD)209.5
Skewness2.3960637
Sum29609
Variance168364.83
MonotonicityNot monotonic
2023-12-12T16:39:25.991895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
363 2
 
3.3%
209 1
 
1.7%
196 1
 
1.7%
121 1
 
1.7%
147 1
 
1.7%
109 1
 
1.7%
416 1
 
1.7%
118 1
 
1.7%
173 1
 
1.7%
385 1
 
1.7%
Other values (49) 49
81.7%
ValueCountFrequency (%)
62 1
1.7%
106 1
1.7%
109 1
1.7%
111 1
1.7%
113 1
1.7%
118 1
1.7%
121 1
1.7%
132 1
1.7%
147 1
1.7%
163 1
1.7%
ValueCountFrequency (%)
2535 1
1.7%
1302 1
1.7%
1228 1
1.7%
1140 1
1.7%
1035 1
1.7%
967 1
1.7%
915 1
1.7%
902 1
1.7%
815 1
1.7%
813 1
1.7%

2020
Real number (ℝ)

HIGH CORRELATION 

Distinct58
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean588.05
Minimum68
Maximum3103
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-12T16:39:26.141027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum68
5-th percentile105.75
Q1194
median385.5
Q3850.5
95-th percentile1592.25
Maximum3103
Range3035
Interquartile range (IQR)656.5

Descriptive statistics

Standard deviation554.67885
Coefficient of variation (CV)0.94325117
Kurtosis6.1787787
Mean588.05
Median Absolute Deviation (MAD)230
Skewness2.0890843
Sum35283
Variance307668.62
MonotonicityNot monotonic
2023-12-12T16:39:26.685729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
479 2
 
3.3%
631 2
 
3.3%
218 1
 
1.7%
1231 1
 
1.7%
231 1
 
1.7%
123 1
 
1.7%
180 1
 
1.7%
226 1
 
1.7%
599 1
 
1.7%
101 1
 
1.7%
Other values (48) 48
80.0%
ValueCountFrequency (%)
68 1
1.7%
72 1
1.7%
101 1
1.7%
106 1
1.7%
109 1
1.7%
123 1
1.7%
142 1
1.7%
154 1
1.7%
156 1
1.7%
160 1
1.7%
ValueCountFrequency (%)
3103 1
1.7%
1797 1
1.7%
1730 1
1.7%
1585 1
1.7%
1502 1
1.7%
1254 1
1.7%
1231 1
1.7%
1169 1
1.7%
1125 1
1.7%
1124 1
1.7%

2021
Real number (ℝ)

HIGH CORRELATION 

Distinct59
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean531.7
Minimum75
Maximum2474
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-12T16:39:26.874484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum75
5-th percentile134.85
Q1198.75
median382.5
Q3675.75
95-th percentile1385.4
Maximum2474
Range2399
Interquartile range (IQR)477

Descriptive statistics

Standard deviation473.14839
Coefficient of variation (CV)0.88987849
Kurtosis5.6559617
Mean531.7
Median Absolute Deviation (MAD)194.5
Skewness2.1862772
Sum31902
Variance223869.4
MonotonicityNot monotonic
2023-12-12T16:39:27.021101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
521 2
 
3.3%
135 1
 
1.7%
105 1
 
1.7%
195 1
 
1.7%
151 1
 
1.7%
220 1
 
1.7%
185 1
 
1.7%
369 1
 
1.7%
191 1
 
1.7%
230 1
 
1.7%
Other values (49) 49
81.7%
ValueCountFrequency (%)
75 1
1.7%
105 1
1.7%
132 1
1.7%
135 1
1.7%
151 1
1.7%
153 1
1.7%
157 1
1.7%
169 1
1.7%
176 1
1.7%
184 1
1.7%
ValueCountFrequency (%)
2474 1
1.7%
2100 1
1.7%
1697 1
1.7%
1369 1
1.7%
1217 1
1.7%
1213 1
1.7%
1033 1
1.7%
876 1
1.7%
856 1
1.7%
830 1
1.7%

2022
Real number (ℝ)

HIGH CORRELATION 

Distinct59
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean461.68333
Minimum88
Maximum1904
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-12T16:39:27.151173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum88
5-th percentile106.95
Q1190.5
median337
Q3615
95-th percentile1155.75
Maximum1904
Range1816
Interquartile range (IQR)424.5

Descriptive statistics

Standard deviation392.55143
Coefficient of variation (CV)0.85026122
Kurtosis4.7554827
Mean461.68333
Median Absolute Deviation (MAD)186.5
Skewness2.0374384
Sum27701
Variance154096.63
MonotonicityNot monotonic
2023-12-12T16:39:27.331275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
256 2
 
3.3%
128 1
 
1.7%
633 1
 
1.7%
167 1
 
1.7%
139 1
 
1.7%
221 1
 
1.7%
88 1
 
1.7%
378 1
 
1.7%
107 1
 
1.7%
155 1
 
1.7%
Other values (49) 49
81.7%
ValueCountFrequency (%)
88 1
1.7%
89 1
1.7%
106 1
1.7%
107 1
1.7%
121 1
1.7%
128 1
1.7%
129 1
1.7%
133 1
1.7%
139 1
1.7%
150 1
1.7%
ValueCountFrequency (%)
1904 1
1.7%
1882 1
1.7%
1436 1
1.7%
1141 1
1.7%
1058 1
1.7%
1014 1
1.7%
959 1
1.7%
749 1
1.7%
696 1
1.7%
669 1
1.7%

Interactions

2023-12-12T16:39:23.413221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:17.799450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:18.526547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:19.222395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:20.119834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:20.908011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:22.008419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:22.730658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:23.512095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:17.881742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:18.608869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:19.327223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:20.220934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:21.312809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:22.084874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:22.809370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:23.586082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:17.985223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:18.682609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:19.416238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:20.319952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:21.410510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:22.167712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:22.899009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:23.669110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:18.094132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:18.759034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:19.616043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:20.415962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:21.521572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:22.254317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:22.985020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:23.745474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:18.183942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:18.849976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:19.703619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:20.508138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:21.622778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:22.340367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:23.068183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:23.817617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:18.260828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:18.939216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:19.789378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:20.619606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:21.702760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:22.430861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:23.171085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:23.897196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:18.346254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:19.022388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:19.894312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:20.715332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:21.812019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:22.543447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:23.258745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:23.969651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:18.420345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:19.096851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:20.001957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:20.802947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:21.909528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:22.632811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:39:23.333330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T16:39:27.466102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분20152016201720182019202020212022
구분1.0001.0001.0001.0001.0001.0001.0001.0001.000
20151.0001.0000.9190.9580.8340.8480.8440.9600.815
20161.0000.9191.0000.9130.7580.7140.7340.8970.683
20171.0000.9580.9131.0000.7990.7760.7840.9340.774
20181.0000.8340.7580.7991.0000.9200.9570.8550.859
20191.0000.8480.7140.7760.9201.0000.9500.8410.787
20201.0000.8440.7340.7840.9570.9501.0000.9000.865
20211.0000.9600.8970.9340.8550.8410.9001.0000.933
20221.0000.8150.6830.7740.8590.7870.8650.9331.000
2023-12-12T16:39:27.610711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
20152016201720182019202020212022
20151.0000.7670.7910.7190.7050.7040.6650.690
20160.7671.0000.8420.6840.5570.5520.5170.524
20170.7910.8421.0000.7410.6000.5700.5660.543
20180.7190.6840.7411.0000.8700.8470.8190.859
20190.7050.5570.6000.8701.0000.9560.9250.922
20200.7040.5520.5700.8470.9561.0000.9450.911
20210.6650.5170.5660.8190.9250.9451.0000.923
20220.6900.5240.5430.8590.9220.9110.9231.000

Missing values

2023-12-12T16:39:24.069410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:39:24.183011image/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

구분20152016201720182019202020212022
0강원남부지사3210510074209218184128
1강원동부지사7367137187363353384297
2강원북부지사63105154295363407405256
3강원지역본부125222249550813857738575
4경기북동부지사6047109229292195234245
5경기북부지역본부9988174234453527592465
6경기서부지사26276065627275106
7경기중부지사156183150106163154194228
8경기지역본부161136251329459479464523
9경남남부지사7551105100186142210133
구분20152016201720182019202020212022
50제주지역본부162226309352497631381191
51제천단양지사6861133139324308245246
52천안아산지사94119247249474625588460
53충남남부지사569267255526130215851033749
54충남서부지사235175200198708740642554
55충남중부지사171146215329915855708666
56충북지역본부3832283516071228116912131058
57충주음성지사124119104239794631565406
58파주고양지사8871105177269272270375
59평택안성지사120102172235410447449346