Overview

Dataset statistics

Number of variables18
Number of observations23
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.8 KiB
Average record size in memory167.7 B

Variable types

Numeric18

Dataset

Description동력수상레저기구 중 추진기관의 최대 출력이 5마력 이상의 기구 운전에 필요한 조종면허증 취득 현황으로 제1급, 제2급, 요트 조종면허 취득 현황입니다. - 연도별, 지역별로 구분하여 작성됬습니다.
URLhttps://www.data.go.kr/data/15053878/fileData.do

Alerts

연도 is highly overall correlated with 합계 and 16 other fieldsHigh correlation
합계 is highly overall correlated with 연도 and 16 other fieldsHigh correlation
서울 is highly overall correlated with 연도 and 16 other fieldsHigh correlation
인천 is highly overall correlated with 연도 and 16 other fieldsHigh correlation
부산 is highly overall correlated with 연도 and 16 other fieldsHigh correlation
대구 is highly overall correlated with 연도 and 16 other fieldsHigh correlation
광주 is highly overall correlated with 연도 and 16 other fieldsHigh correlation
대전 is highly overall correlated with 연도 and 16 other fieldsHigh correlation
울산 is highly overall correlated with 연도 and 16 other fieldsHigh correlation
강원 is highly overall correlated with 연도 and 16 other fieldsHigh correlation
경기 is highly overall correlated with 연도 and 16 other fieldsHigh correlation
경남 is highly overall correlated with 연도 and 16 other fieldsHigh correlation
경북 is highly overall correlated with 연도 and 16 other fieldsHigh correlation
전남 is highly overall correlated with 연도 and 16 other fieldsHigh correlation
전북 is highly overall correlated with 연도 and 16 other fieldsHigh correlation
충남 is highly overall correlated with 연도 and 16 other fieldsHigh correlation
충북 is highly overall correlated with 연도 and 16 other fieldsHigh correlation
제주 is highly overall correlated with 연도 and 16 other fieldsHigh correlation
연도 has unique valuesUnique
합계 has unique valuesUnique
서울 has unique valuesUnique
인천 has unique valuesUnique
부산 has unique valuesUnique
대구 has unique valuesUnique
광주 has unique valuesUnique
대전 has unique valuesUnique
경기 has unique valuesUnique
경남 has unique valuesUnique
전남 has unique valuesUnique
전북 has unique valuesUnique
충남 has unique valuesUnique
제주 has unique valuesUnique

Reproduction

Analysis started2023-12-12 21:42:06.486251
Analysis finished2023-12-12 21:42:36.703982
Duration30.22 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2011
Minimum2000
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-13T06:42:36.797272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2001.1
Q12005.5
median2011
Q32016.5
95-th percentile2020.9
Maximum2022
Range22
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.78233
Coefficient of variation (CV)0.0033726156
Kurtosis-1.2
Mean2011
Median Absolute Deviation (MAD)6
Skewness0
Sum46253
Variance46
MonotonicityStrictly increasing
2023-12-13T06:42:36.945256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2000 1
 
4.3%
2001 1
 
4.3%
2022 1
 
4.3%
2021 1
 
4.3%
2020 1
 
4.3%
2019 1
 
4.3%
2018 1
 
4.3%
2017 1
 
4.3%
2016 1
 
4.3%
2015 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
2000 1
4.3%
2001 1
4.3%
2002 1
4.3%
2003 1
4.3%
2004 1
4.3%
2005 1
4.3%
2006 1
4.3%
2007 1
4.3%
2008 1
4.3%
2009 1
4.3%
ValueCountFrequency (%)
2022 1
4.3%
2021 1
4.3%
2020 1
4.3%
2019 1
4.3%
2018 1
4.3%
2017 1
4.3%
2016 1
4.3%
2015 1
4.3%
2014 1
4.3%
2013 1
4.3%

합계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13344.652
Minimum6556
Maximum21596
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-13T06:42:37.061839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6556
5-th percentile6804.9
Q19261
median13413
Q317166.5
95-th percentile21305.8
Maximum21596
Range15040
Interquartile range (IQR)7905.5

Descriptive statistics

Standard deviation5063.5733
Coefficient of variation (CV)0.37944588
Kurtosis-1.1538526
Mean13344.652
Median Absolute Deviation (MAD)4191
Skewness0.32732468
Sum306927
Variance25639774
MonotonicityNot monotonic
2023-12-13T06:42:37.190797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
6966 1
 
4.3%
9222 1
 
4.3%
19420 1
 
4.3%
21313 1
 
4.3%
20406 1
 
4.3%
17822 1
 
4.3%
21241 1
 
4.3%
21596 1
 
4.3%
16511 1
 
4.3%
15059 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
6556 1
4.3%
6787 1
4.3%
6966 1
4.3%
6985 1
4.3%
9205 1
4.3%
9222 1
4.3%
9300 1
4.3%
9413 1
4.3%
10529 1
4.3%
11500 1
4.3%
ValueCountFrequency (%)
21596 1
4.3%
21313 1
4.3%
21241 1
4.3%
20406 1
4.3%
19420 1
4.3%
17822 1
4.3%
16511 1
4.3%
15059 1
4.3%
14233 1
4.3%
13973 1
4.3%

서울
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1379.913
Minimum898
Maximum2274
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-13T06:42:37.331446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum898
5-th percentile912.6
Q11029.5
median1318
Q31625.5
95-th percentile2119.4
Maximum2274
Range1376
Interquartile range (IQR)596

Descriptive statistics

Standard deviation412.28542
Coefficient of variation (CV)0.29877637
Kurtosis-0.39018253
Mean1379.913
Median Absolute Deviation (MAD)318
Skewness0.76337475
Sum31738
Variance169979.26
MonotonicityNot monotonic
2023-12-13T06:42:37.459641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
972 1
 
4.3%
1637 1
 
4.3%
2130 1
 
4.3%
2274 1
 
4.3%
2024 1
 
4.3%
1614 1
 
4.3%
1775 1
 
4.3%
1913 1
 
4.3%
1390 1
 
4.3%
1345 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
898 1
4.3%
912 1
4.3%
918 1
4.3%
972 1
4.3%
978 1
4.3%
1000 1
4.3%
1059 1
4.3%
1070 1
4.3%
1217 1
4.3%
1238 1
4.3%
ValueCountFrequency (%)
2274 1
4.3%
2130 1
4.3%
2024 1
4.3%
1913 1
4.3%
1775 1
4.3%
1637 1
4.3%
1614 1
4.3%
1404 1
4.3%
1390 1
4.3%
1387 1
4.3%

인천
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean720.82609
Minimum389
Maximum1237
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-13T06:42:37.611918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum389
5-th percentile393
Q1522.5
median667
Q3829.5
95-th percentile1193.3
Maximum1237
Range848
Interquartile range (IQR)307

Descriptive statistics

Standard deviation268.06693
Coefficient of variation (CV)0.37188849
Kurtosis-0.52606123
Mean720.82609
Median Absolute Deviation (MAD)150
Skewness0.76370777
Sum16579
Variance71859.877
MonotonicityNot monotonic
2023-12-13T06:42:37.765018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
390 1
 
4.3%
740 1
 
4.3%
919 1
 
4.3%
1195 1
 
4.3%
1150 1
 
4.3%
1013 1
 
4.3%
1237 1
 
4.3%
1178 1
 
4.3%
728 1
 
4.3%
713 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
389 1
4.3%
390 1
4.3%
420 1
4.3%
433 1
4.3%
497 1
4.3%
517 1
4.3%
528 1
4.3%
587 1
4.3%
612 1
4.3%
619 1
4.3%
ValueCountFrequency (%)
1237 1
4.3%
1195 1
4.3%
1178 1
4.3%
1150 1
4.3%
1013 1
4.3%
919 1
4.3%
740 1
4.3%
728 1
4.3%
717 1
4.3%
713 1
4.3%

부산
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1260.1739
Minimum612
Maximum2057
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-13T06:42:37.882771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum612
5-th percentile653.6
Q1991
median1226
Q31600
95-th percentile1990.5
Maximum2057
Range1445
Interquartile range (IQR)609

Descriptive statistics

Standard deviation444.46982
Coefficient of variation (CV)0.35270514
Kurtosis-0.9970196
Mean1260.1739
Median Absolute Deviation (MAD)328
Skewness0.28503776
Sum28984
Variance197553.42
MonotonicityNot monotonic
2023-12-13T06:42:38.015441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
726 1
 
4.3%
1012 1
 
4.3%
1821 1
 
4.3%
2000 1
 
4.3%
1905 1
 
4.3%
1646 1
 
4.3%
2057 1
 
4.3%
1675 1
 
4.3%
1554 1
 
4.3%
1391 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
612 1
4.3%
653 1
4.3%
659 1
4.3%
726 1
4.3%
867 1
4.3%
978 1
4.3%
1004 1
4.3%
1012 1
4.3%
1021 1
4.3%
1030 1
4.3%
ValueCountFrequency (%)
2057 1
4.3%
2000 1
4.3%
1905 1
4.3%
1821 1
4.3%
1675 1
4.3%
1646 1
4.3%
1554 1
4.3%
1462 1
4.3%
1391 1
4.3%
1357 1
4.3%

대구
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean399.04348
Minimum180
Maximum645
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-13T06:42:38.173302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum180
5-th percentile199.1
Q1276
median392
Q3559.5
95-th percentile610.7
Maximum645
Range465
Interquartile range (IQR)283.5

Descriptive statistics

Standard deviation153.75999
Coefficient of variation (CV)0.38532139
Kurtosis-1.4045898
Mean399.04348
Median Absolute Deviation (MAD)150
Skewness0.18084924
Sum9178
Variance23642.134
MonotonicityNot monotonic
2023-12-13T06:42:38.318311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
200 1
 
4.3%
199 1
 
4.3%
480 1
 
4.3%
612 1
 
4.3%
597 1
 
4.3%
577 1
 
4.3%
580 1
 
4.3%
645 1
 
4.3%
599 1
 
4.3%
542 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
180 1
4.3%
199 1
4.3%
200 1
4.3%
220 1
4.3%
240 1
4.3%
270 1
4.3%
282 1
4.3%
294 1
4.3%
309 1
4.3%
341 1
4.3%
ValueCountFrequency (%)
645 1
4.3%
612 1
4.3%
599 1
4.3%
597 1
4.3%
580 1
4.3%
577 1
4.3%
542 1
4.3%
480 1
4.3%
438 1
4.3%
430 1
4.3%

광주
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean298.86957
Minimum81
Maximum591
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-13T06:42:38.462602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum81
5-th percentile99.9
Q1144
median243
Q3444
95-th percentile575.8
Maximum591
Range510
Interquartile range (IQR)300

Descriptive statistics

Standard deviation172.66871
Coefficient of variation (CV)0.57773934
Kurtosis-1.2177192
Mean298.86957
Median Absolute Deviation (MAD)116
Skewness0.50414086
Sum6874
Variance29814.482
MonotonicityNot monotonic
2023-12-13T06:42:38.924594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
127 1
 
4.3%
126 1
 
4.3%
484 1
 
4.3%
576 1
 
4.3%
537 1
 
4.3%
504 1
 
4.3%
574 1
 
4.3%
591 1
 
4.3%
404 1
 
4.3%
385 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
81 1
4.3%
97 1
4.3%
126 1
4.3%
127 1
4.3%
132 1
4.3%
143 1
4.3%
145 1
4.3%
180 1
4.3%
182 1
4.3%
212 1
4.3%
ValueCountFrequency (%)
591 1
4.3%
576 1
4.3%
574 1
4.3%
537 1
4.3%
504 1
4.3%
484 1
4.3%
404 1
4.3%
385 1
4.3%
327 1
4.3%
302 1
4.3%

대전
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean209.86957
Minimum86
Maximum351
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-13T06:42:39.076648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum86
5-th percentile104.5
Q1136.5
median201
Q3299.5
95-th percentile344.1
Maximum351
Range265
Interquartile range (IQR)163

Descriptive statistics

Standard deviation87.713016
Coefficient of variation (CV)0.41794062
Kurtosis-1.3120584
Mean209.86957
Median Absolute Deviation (MAD)80
Skewness0.32492898
Sum4827
Variance7693.5731
MonotonicityNot monotonic
2023-12-13T06:42:39.218928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
158 1
 
4.3%
166 1
 
4.3%
325 1
 
4.3%
351 1
 
4.3%
316 1
 
4.3%
300 1
 
4.3%
336 1
 
4.3%
299 1
 
4.3%
215 1
 
4.3%
345 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
86 1
4.3%
104 1
4.3%
109 1
4.3%
110 1
4.3%
121 1
4.3%
131 1
4.3%
142 1
4.3%
150 1
4.3%
158 1
4.3%
166 1
4.3%
ValueCountFrequency (%)
351 1
4.3%
345 1
4.3%
336 1
4.3%
325 1
4.3%
316 1
4.3%
300 1
4.3%
299 1
4.3%
232 1
4.3%
224 1
4.3%
215 1
4.3%

울산
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean437.08696
Minimum120
Maximum689
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-13T06:42:39.345876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum120
5-th percentile192
Q1367
median458
Q3548
95-th percentile612.3
Maximum689
Range569
Interquartile range (IQR)181

Descriptive statistics

Standard deviation145.72481
Coefficient of variation (CV)0.33340004
Kurtosis-0.15445039
Mean437.08696
Median Absolute Deviation (MAD)92
Skewness-0.54674606
Sum10053
Variance21235.719
MonotonicityNot monotonic
2023-12-13T06:42:39.472796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
546 2
 
8.7%
120 1
 
4.3%
357 1
 
4.3%
423 1
 
4.3%
550 1
 
4.3%
579 1
 
4.3%
469 1
 
4.3%
588 1
 
4.3%
689 1
 
4.3%
566 1
 
4.3%
Other values (12) 12
52.2%
ValueCountFrequency (%)
120 1
4.3%
191 1
4.3%
201 1
4.3%
249 1
4.3%
349 1
4.3%
357 1
4.3%
377 1
4.3%
388 1
4.3%
415 1
4.3%
423 1
4.3%
ValueCountFrequency (%)
689 1
4.3%
615 1
4.3%
588 1
4.3%
579 1
4.3%
566 1
4.3%
550 1
4.3%
546 2
8.7%
469 1
4.3%
463 1
4.3%
462 1
4.3%

강원
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean588.95652
Minimum314
Maximum994
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-13T06:42:39.620564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum314
5-th percentile349.3
Q1490.5
median579
Q3646
95-th percentile921.6
Maximum994
Range680
Interquartile range (IQR)155.5

Descriptive statistics

Standard deviation167.37315
Coefficient of variation (CV)0.28418592
Kurtosis0.76357542
Mean588.95652
Median Absolute Deviation (MAD)87
Skewness0.78278061
Sum13546
Variance28013.771
MonotonicityNot monotonic
2023-12-13T06:42:39.783227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
493 2
 
8.7%
451 1
 
4.3%
603 1
 
4.3%
783 1
 
4.3%
598 1
 
4.3%
666 1
 
4.3%
752 1
 
4.3%
937 1
 
4.3%
994 1
 
4.3%
626 1
 
4.3%
Other values (12) 12
52.2%
ValueCountFrequency (%)
314 1
4.3%
340 1
4.3%
433 1
4.3%
437 1
4.3%
451 1
4.3%
488 1
4.3%
493 2
8.7%
508 1
4.3%
558 1
4.3%
569 1
4.3%
ValueCountFrequency (%)
994 1
4.3%
937 1
4.3%
783 1
4.3%
752 1
4.3%
724 1
4.3%
666 1
4.3%
626 1
4.3%
609 1
4.3%
603 1
4.3%
598 1
4.3%

경기
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2123.1739
Minimum987
Maximum4035
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-13T06:42:39.937821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum987
5-th percentile1114.2
Q11274.5
median1858
Q32651.5
95-th percentile3934.6
Maximum4035
Range3048
Interquartile range (IQR)1377

Descriptive statistics

Standard deviation999.25029
Coefficient of variation (CV)0.47063987
Kurtosis-0.69785032
Mean2123.1739
Median Absolute Deviation (MAD)596
Skewness0.82977724
Sum48833
Variance998501.15
MonotonicityNot monotonic
2023-12-13T06:42:40.082568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1152 1
 
4.3%
987 1
 
4.3%
4035 1
 
4.3%
3969 1
 
4.3%
3592 1
 
4.3%
2904 1
 
4.3%
3625 1
 
4.3%
3586 1
 
4.3%
2399 1
 
4.3%
2176 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
987 1
4.3%
1110 1
4.3%
1152 1
4.3%
1215 1
4.3%
1248 1
4.3%
1262 1
4.3%
1287 1
4.3%
1411 1
4.3%
1498 1
4.3%
1718 1
4.3%
ValueCountFrequency (%)
4035 1
4.3%
3969 1
4.3%
3625 1
4.3%
3592 1
4.3%
3586 1
4.3%
2904 1
4.3%
2399 1
4.3%
2216 1
4.3%
2176 1
4.3%
1938 1
4.3%

경남
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1669.5652
Minimum643
Maximum3250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-13T06:42:40.212026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum643
5-th percentile661.7
Q11064.5
median1661
Q32225.5
95-th percentile2974.2
Maximum3250
Range2607
Interquartile range (IQR)1161

Descriptive statistics

Standard deviation778.47858
Coefficient of variation (CV)0.46627623
Kurtosis-0.83604641
Mean1669.5652
Median Absolute Deviation (MAD)596
Skewness0.34579086
Sum38400
Variance606028.89
MonotonicityNot monotonic
2023-12-13T06:42:40.361130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
760 1
 
4.3%
769 1
 
4.3%
2212 1
 
4.3%
2589 1
 
4.3%
2531 1
 
4.3%
2239 1
 
4.3%
3017 1
 
4.3%
3250 1
 
4.3%
2344 1
 
4.3%
2141 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
643 1
4.3%
658 1
4.3%
695 1
4.3%
760 1
4.3%
769 1
4.3%
1064 1
4.3%
1065 1
4.3%
1078 1
4.3%
1162 1
4.3%
1498 1
4.3%
ValueCountFrequency (%)
3250 1
4.3%
3017 1
4.3%
2589 1
4.3%
2531 1
4.3%
2344 1
4.3%
2239 1
4.3%
2212 1
4.3%
2141 1
4.3%
1939 1
4.3%
1815 1
4.3%

경북
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean916.13043
Minimum453
Maximum1344
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-13T06:42:40.501903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum453
5-th percentile548.5
Q1729.5
median931
Q31120.5
95-th percentile1284.5
Maximum1344
Range891
Interquartile range (IQR)391

Descriptive statistics

Standard deviation259.48398
Coefficient of variation (CV)0.28323912
Kurtosis-1.0534074
Mean916.13043
Median Absolute Deviation (MAD)197
Skewness-0.15196332
Sum21071
Variance67331.937
MonotonicityNot monotonic
2023-12-13T06:42:40.618613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1188 2
 
8.7%
562 1
 
4.3%
1013 1
 
4.3%
1056 1
 
4.3%
1344 1
 
4.3%
1121 1
 
4.3%
1226 1
 
4.3%
1291 1
 
4.3%
1120 1
 
4.3%
990 1
 
4.3%
Other values (12) 12
52.2%
ValueCountFrequency (%)
453 1
4.3%
547 1
4.3%
562 1
4.3%
572 1
4.3%
621 1
4.3%
725 1
4.3%
734 1
4.3%
771 1
4.3%
860 1
4.3%
861 1
4.3%
ValueCountFrequency (%)
1344 1
4.3%
1291 1
4.3%
1226 1
4.3%
1188 2
8.7%
1121 1
4.3%
1120 1
4.3%
1056 1
4.3%
1035 1
4.3%
1013 1
4.3%
990 1
4.3%

전남
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1410.6957
Minimum244
Maximum2595
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-13T06:42:40.723706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum244
5-th percentile314.3
Q1775.5
median1379
Q32129
95-th percentile2342.3
Maximum2595
Range2351
Interquartile range (IQR)1353.5

Descriptive statistics

Standard deviation775.38509
Coefficient of variation (CV)0.54964732
Kurtosis-1.4290057
Mean1410.6957
Median Absolute Deviation (MAD)660
Skewness-0.14487327
Sum32446
Variance601222.04
MonotonicityNot monotonic
2023-12-13T06:42:40.878062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
244 1
 
4.3%
376 1
 
4.3%
2035 1
 
4.3%
2219 1
 
4.3%
2246 1
 
4.3%
2039 1
 
4.3%
2318 1
 
4.3%
2345 1
 
4.3%
1926 1
 
4.3%
1326 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
244 1
4.3%
308 1
4.3%
371 1
4.3%
376 1
4.3%
391 1
4.3%
754 1
4.3%
797 1
4.3%
976 1
4.3%
989 1
4.3%
1326 1
4.3%
ValueCountFrequency (%)
2595 1
4.3%
2345 1
4.3%
2318 1
4.3%
2246 1
4.3%
2225 1
4.3%
2219 1
4.3%
2039 1
4.3%
2035 1
4.3%
1926 1
4.3%
1732 1
4.3%

전북
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean390.69565
Minimum137
Maximum684
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-13T06:42:41.026460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum137
5-th percentile141.7
Q1291.5
median380
Q3507
95-th percentile667.3
Maximum684
Range547
Interquartile range (IQR)215.5

Descriptive statistics

Standard deviation168.37281
Coefficient of variation (CV)0.43095645
Kurtosis-0.90234215
Mean390.69565
Median Absolute Deviation (MAD)117
Skewness0.13380576
Sum8986
Variance28349.403
MonotonicityNot monotonic
2023-12-13T06:42:41.159712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
157 1
 
4.3%
212 1
 
4.3%
460 1
 
4.3%
616 1
 
4.3%
613 1
 
4.3%
497 1
 
4.3%
684 1
 
4.3%
673 1
 
4.3%
537 1
 
4.3%
517 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
137 1
4.3%
140 1
4.3%
157 1
4.3%
171 1
4.3%
212 1
4.3%
290 1
4.3%
293 1
4.3%
308 1
4.3%
319 1
4.3%
323 1
4.3%
ValueCountFrequency (%)
684 1
4.3%
673 1
4.3%
616 1
4.3%
613 1
4.3%
537 1
4.3%
517 1
4.3%
497 1
4.3%
460 1
4.3%
459 1
4.3%
423 1
4.3%

충남
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean791.52174
Minimum292
Maximum1262
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-13T06:42:41.316339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum292
5-th percentile325.1
Q1613
median702
Q31035.5
95-th percentile1226.4
Maximum1262
Range970
Interquartile range (IQR)422.5

Descriptive statistics

Standard deviation293.35038
Coefficient of variation (CV)0.37061569
Kurtosis-1.0413194
Mean791.52174
Median Absolute Deviation (MAD)216
Skewness0.0036508617
Sum18205
Variance86054.443
MonotonicityNot monotonic
2023-12-13T06:42:41.435558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
636 1
 
4.3%
625 1
 
4.3%
1055 1
 
4.3%
1229 1
 
4.3%
1203 1
 
4.3%
1016 1
 
4.3%
1103 1
 
4.3%
1262 1
 
4.3%
1111 1
 
4.3%
1009 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
292 1
4.3%
319 1
4.3%
380 1
4.3%
500 1
4.3%
535 1
4.3%
601 1
4.3%
625 1
4.3%
636 1
4.3%
681 1
4.3%
685 1
4.3%
ValueCountFrequency (%)
1262 1
4.3%
1229 1
4.3%
1203 1
4.3%
1111 1
4.3%
1103 1
4.3%
1055 1
4.3%
1016 1
4.3%
1009 1
4.3%
918 1
4.3%
873 1
4.3%

충북
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean278.47826
Minimum102
Maximum526
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-13T06:42:41.560863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum102
5-th percentile124.3
Q1145
median274
Q3408.5
95-th percentile492.8
Maximum526
Range424
Interquartile range (IQR)263.5

Descriptive statistics

Standard deviation139.62315
Coefficient of variation (CV)0.501379
Kurtosis-1.3883341
Mean278.47826
Median Absolute Deviation (MAD)131
Skewness0.33224143
Sum6405
Variance19494.625
MonotonicityNot monotonic
2023-12-13T06:42:41.681260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
127 2
 
8.7%
228 1
 
4.3%
274 1
 
4.3%
496 1
 
4.3%
464 1
 
4.3%
526 1
 
4.3%
415 1
 
4.3%
419 1
 
4.3%
449 1
 
4.3%
388 1
 
4.3%
Other values (12) 12
52.2%
ValueCountFrequency (%)
102 1
4.3%
124 1
4.3%
127 2
8.7%
137 1
4.3%
143 1
4.3%
147 1
4.3%
151 1
4.3%
176 1
4.3%
219 1
4.3%
228 1
4.3%
ValueCountFrequency (%)
526 1
4.3%
496 1
4.3%
464 1
4.3%
449 1
4.3%
419 1
4.3%
415 1
4.3%
402 1
4.3%
388 1
4.3%
316 1
4.3%
297 1
4.3%

제주
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean469.65217
Minimum83
Maximum765
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-13T06:42:41.792647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum83
5-th percentile236.2
Q1355
median442
Q3631
95-th percentile753.7
Maximum765
Range682
Interquartile range (IQR)276

Descriptive statistics

Standard deviation190.14578
Coefficient of variation (CV)0.40486512
Kurtosis-0.68678025
Mean469.65217
Median Absolute Deviation (MAD)114
Skewness0.11942258
Sum10802
Variance36155.419
MonotonicityNot monotonic
2023-12-13T06:42:41.903010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
83 1
 
4.3%
466 1
 
4.3%
706 1
 
4.3%
727 1
 
4.3%
733 1
 
4.3%
716 1
 
4.3%
765 1
 
4.3%
756 1
 
4.3%
556 1
 
4.3%
490 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
83 1
4.3%
234 1
4.3%
256 1
4.3%
267 1
4.3%
316 1
4.3%
348 1
4.3%
362 1
4.3%
388 1
4.3%
393 1
4.3%
406 1
4.3%
ValueCountFrequency (%)
765 1
4.3%
756 1
4.3%
733 1
4.3%
727 1
4.3%
716 1
4.3%
706 1
4.3%
556 1
4.3%
530 1
4.3%
490 1
4.3%
466 1
4.3%

Interactions

2023-12-13T06:42:34.610674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:07.002156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:08.898138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:10.499459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:11.850715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:13.271905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:15.146004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:16.706685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:18.212705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:19.802482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:21.912241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:23.434429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:25.070606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:26.950739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:28.588240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:30.014657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:31.381015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:33.039665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:34.706747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:07.074853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:08.986106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:10.567903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:11.913721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:13.351519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:15.227167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:16.782102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:18.300653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:19.909196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:22.005222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:23.526438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:25.151215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:27.038066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:28.664096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:30.085695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:31.450865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:33.109497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:34.838804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:07.195558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:09.124990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:10.646688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:11.993206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:13.456983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:15.319563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:16.883653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:18.438839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:20.021655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:22.092381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:23.635852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:25.238287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:27.134703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:28.748422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:30.182802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:31.533381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:33.219665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:34.937753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:07.305645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:09.245432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:10.725838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:12.078947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:13.541125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-13T06:42:17.855276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:19.442963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:21.564575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:23.124477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:24.704038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:26.609214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:28.254742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:29.731984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:31.085398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:32.708507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:34.226663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:36.026963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:08.661826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:10.274808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:11.642218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:13.025823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:14.901462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:16.474060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:17.938668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:19.546148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:21.660728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:23.201254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:24.797344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:26.690808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:28.335500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:29.800405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:31.153636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:32.785275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:34.308259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:36.120709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:08.741554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:10.346052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:11.712900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:13.088764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:14.987184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:16.546133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:18.023106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:19.632844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:21.750745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:23.276693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:24.892996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:26.772787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:28.408737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:29.868244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:31.229351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:32.872685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:34.398790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:36.203476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:08.811623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:10.419137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:11.782518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:13.172642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:15.066471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:16.623972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:18.109976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:19.710485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:21.830701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:23.348505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:24.978237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:26.853340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:28.492539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:29.942065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:31.306981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:32.961518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:42:34.495210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:42:42.058256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도합계서울인천부산대구광주대전울산강원경기경남경북전남전북충남충북제주
연도1.0000.7960.6850.1300.7890.8460.8520.5710.5490.6150.8060.8410.8280.8450.8410.6310.7510.774
합계0.7961.0000.5180.7810.8270.8770.9620.6370.0000.3570.8960.7850.8940.7430.9090.7610.7370.617
서울0.6850.5181.0000.8430.8630.0000.5360.5240.7660.6420.8020.6760.5130.0000.4490.7170.8810.000
인천0.1300.7810.8431.0000.7940.4350.7460.7320.5670.6960.8700.5300.5230.5470.7020.0000.6430.386
부산0.7890.8270.8630.7941.0000.5330.7860.7460.5940.4550.8040.6000.5070.7180.7600.3490.9170.677
대구0.8460.8770.0000.4350.5331.0000.9350.5410.5200.0000.6890.7350.7910.6950.6600.5570.6220.537
광주0.8520.9620.5360.7460.7860.9351.0000.5910.0000.4900.8200.9080.8650.7800.8710.6560.8240.632
대전0.5710.6370.5240.7320.7460.5410.5911.0000.5720.0000.7610.3620.7230.5930.7070.8250.5460.637
울산0.5490.0000.7660.5670.5940.5200.0000.5721.0000.6980.0970.6510.5300.0000.5850.4690.2030.619
강원0.6150.3570.6420.6960.4550.0000.4900.0000.6981.0000.5890.7650.2800.3500.6630.4400.0000.000
경기0.8060.8960.8020.8700.8040.6890.8200.7610.0970.5891.0000.8620.9180.6480.7530.5470.5890.548
경남0.8410.7850.6760.5300.6000.7350.9080.3620.6510.7650.8621.0000.9050.8510.8250.7480.5500.412
경북0.8280.8940.5130.5230.5070.7910.8650.7230.5300.2800.9180.9051.0000.7430.8310.8630.6890.507
전남0.8450.7430.0000.5470.7180.6950.7800.5930.0000.3500.6480.8510.7431.0000.6750.7870.4870.778
전북0.8410.9090.4490.7020.7600.6600.8710.7070.5850.6630.7530.8250.8310.6751.0000.7340.6780.503
충남0.6310.7610.7170.0000.3490.5570.6560.8250.4690.4400.5470.7480.8630.7870.7341.0000.9220.758
충북0.7510.7370.8810.6430.9170.6220.8240.5460.2030.0000.5890.5500.6890.4870.6780.9221.0000.744
제주0.7740.6170.0000.3860.6770.5370.6320.6370.6190.0000.5480.4120.5070.7780.5030.7580.7441.000
2023-12-13T06:42:42.247905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도합계서울인천부산대구광주대전울산강원경기경남경북전남전북충남충북제주
연도1.0000.9390.7860.7980.9220.9080.9540.8660.5710.6940.9750.9290.9300.8310.8880.8640.8940.856
합계0.9391.0000.8430.8880.9660.9340.9770.9130.7100.7990.9510.9750.9680.8820.9360.9230.8870.884
서울0.7860.8431.0000.8640.8400.7410.7770.8480.6730.6520.8050.7920.7680.7120.7170.7900.8390.858
인천0.7980.8880.8641.0000.8820.7760.8340.8580.8500.7120.7830.8650.8340.7770.8020.8210.8250.887
부산0.9220.9660.8400.8821.0000.8790.9550.9210.6900.8240.9120.9510.9350.8370.9430.9000.8830.898
대구0.9080.9340.7410.7760.8791.0000.9280.8310.7010.7590.9160.9270.9560.7680.9250.9370.7990.787
광주0.9540.9770.7770.8340.9550.9281.0000.8820.6380.7580.9430.9700.9700.8750.9450.9120.8970.888
대전0.8660.9130.8480.8580.9210.8310.8821.0000.6860.6770.8640.8830.8720.7320.8440.8600.9170.839
울산0.5710.7100.6730.8500.6900.7010.6380.6861.0000.6750.5420.7150.7070.5260.7130.7160.5760.722
강원0.6940.7990.6520.7120.8240.7590.7580.6770.6751.0000.7100.7660.7790.5920.8510.8050.5900.686
경기0.9750.9510.8050.7830.9120.9160.9430.8640.5420.7101.0000.9310.9290.8520.8800.8820.8810.808
경남0.9290.9750.7920.8650.9510.9270.9700.8830.7150.7660.9311.0000.9580.8600.9270.9040.8900.877
경북0.9300.9680.7680.8340.9350.9560.9700.8720.7070.7790.9290.9581.0000.8380.9580.9250.8390.849
전남0.8310.8820.7120.7770.8370.7680.8750.7320.5260.5920.8520.8600.8381.0000.7690.7400.7650.759
전북0.8880.9360.7170.8020.9430.9250.9450.8440.7130.8510.8800.9270.9580.7691.0000.9310.7980.836
충남0.8640.9230.7900.8210.9000.9370.9120.8600.7160.8050.8820.9040.9250.7400.9311.0000.8510.776
충북0.8940.8870.8390.8250.8830.7990.8970.9170.5760.5900.8810.8900.8390.7650.7980.8511.0000.868
제주0.8560.8840.8580.8870.8980.7870.8880.8390.7220.6860.8080.8770.8490.7590.8360.7760.8681.000

Missing values

2023-12-13T06:42:36.350405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:42:36.587157image/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

연도합계서울인천부산대구광주대전울산강원경기경남경북전남전북충남충북제주
020006966972390726200127158120451115276056224415763622883
12001922216377401012199126166615569987769572376212625151466
2200269851070433612270811102494371262643547371140380124256
320036556105938965322097861913401215695453308137319127267
4200467879125176591801321042013141110658621391171292137388
520059413918497103724018212134960912481078725976378535127393
62006105291000587102140918014246372414111064931989399873102234
72007930089861297828214313146249312871162771797293500143348
82008920597842086729414510937749314981065734754323685147316
920091205512177171030392212197546508171816888601379308702219362
연도합계서울인천부산대구광주대전울산강원경기경남경북전남전북충남충북제주
13201313973140461914624302832324586031938193910351732423695274446
1420141342212656521357438327224452558191416619901361459918316530
152015150591345713139154238534556659121762141112013265171009402490
162016165111390728155459940421554662623992344118819265371111388556
1720172159619131178167564559129968999435863250129123456731262449756
1820182124117751237205758057433658893736253017122623186841103419765
1920191782216141013164657750430046975229042239112120394971016415716
2020202040620241150190559753731657966635922531118822466131203526733
2120212131322741195200061257635155059839692589134422196161229464727
222022194202130919182148048432542378340352212105620354601055496706