Overview

Dataset statistics

Number of variables18
Number of observations21
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory168.1 B

Variable types

Numeric18

Dataset

Description공무원연금 지역별(서울 부산 대구 인천 등 광역시와 도) 신규가입자수에 대한 데이터입니다. 2001년부터 시작되며 연 단위로 구분됩니다.
Author공무원연금공단
URLhttps://www.data.go.kr/data/15052944/fileData.do

Alerts

서울 is highly overall correlated with 연도 and 15 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
인천 is highly overall correlated with 서울 and 14 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
세종 is highly overall correlated with 연도 and 14 other fieldsHigh correlation
경기 is highly overall correlated with 서울 and 15 other fieldsHigh correlation
강원 is highly overall correlated with 연도 and 16 other fieldsHigh correlation
충북 is highly overall correlated with 연도 and 16 other fieldsHigh correlation
충남 is highly overall correlated with 연도 and 16 other fieldsHigh correlation
경북 is highly overall correlated with 연도 and 16 other fieldsHigh correlation
전북 is highly overall correlated with 연도 and 16 other fieldsHigh correlation
전남 is highly overall correlated with 연도 and 16 other fieldsHigh correlation
연도 is highly overall correlated with 서울 and 13 other fieldsHigh correlation
울산 is highly overall correlated with 부산 and 13 other fieldsHigh correlation
경남 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
서울 has unique valuesUnique
부산 has unique valuesUnique
대구 has unique valuesUnique
인천 has unique valuesUnique
광주 has unique valuesUnique
대전 has unique valuesUnique
경기 has unique valuesUnique
강원 has unique valuesUnique
충북 has unique valuesUnique
충남 has unique valuesUnique
경북 has unique valuesUnique
경남 has unique valuesUnique
전북 has unique valuesUnique
전남 has unique valuesUnique
세종 has 11 (52.4%) zerosZeros

Reproduction

Analysis started2023-06-12 11:23:41.123428
Analysis finished2023-06-12 11:25:19.041596
Duration1 minute and 37.92 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

연도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2011
Minimum2001
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size317.0 B
2023-06-12T20:25:19.223596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2001
5-th percentile2002
Q12006
median2011
Q32016
95-th percentile2020
Maximum2021
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.2048368
Coefficient of variation (CV)0.0030854484
Kurtosis-1.2
Mean2011
Median Absolute Deviation (MAD)5
Skewness0
Sum42231
Variance38.5
MonotonicityStrictly increasing
2023-06-12T20:25:19.643873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
2001 1
 
4.8%
2012 1
 
4.8%
2021 1
 
4.8%
2003 1
 
4.8%
2004 1
 
4.8%
2005 1
 
4.8%
2006 1
 
4.8%
2007 1
 
4.8%
2008 1
 
4.8%
2009 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
2001 1
4.8%
2002 1
4.8%
2003 1
4.8%
2004 1
4.8%
2005 1
4.8%
2006 1
4.8%
2007 1
4.8%
2008 1
4.8%
2009 1
4.8%
2010 1
4.8%
ValueCountFrequency (%)
2021 1
4.8%
2020 1
4.8%
2019 1
4.8%
2018 1
4.8%
2017 1
4.8%
2016 1
4.8%
2015 1
4.8%
2014 1
4.8%
2013 1
4.8%
2012 1
4.8%

서울
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9797.0476
Minimum4410
Maximum17998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size317.0 B
2023-06-12T20:25:19.980191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4410
5-th percentile5138
Q17249
median9362
Q311122
95-th percentile16702
Maximum17998
Range13588
Interquartile range (IQR)3873

Descriptive statistics

Standard deviation3684.8858
Coefficient of variation (CV)0.37612207
Kurtosis0.13551878
Mean9797.0476
Median Absolute Deviation (MAD)2011
Skewness0.85466965
Sum205738
Variance13578383
MonotonicityNot monotonic
2023-06-12T20:25:20.373866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
6477 1
 
4.8%
9249 1
 
4.8%
17998 1
 
4.8%
7351 1
 
4.8%
9362 1
 
4.8%
5138 1
 
4.8%
7249 1
 
4.8%
8937 1
 
4.8%
11248 1
 
4.8%
9656 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
4410 1
4.8%
5138 1
4.8%
6477 1
4.8%
6548 1
4.8%
7145 1
4.8%
7249 1
4.8%
7351 1
4.8%
7512 1
4.8%
8937 1
4.8%
9249 1
4.8%
ValueCountFrequency (%)
17998 1
4.8%
16702 1
4.8%
14985 1
4.8%
14972 1
4.8%
11248 1
4.8%
11122 1
4.8%
10591 1
4.8%
9671 1
4.8%
9656 1
4.8%
9415 1
4.8%

부산
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2407.1429
Minimum1383
Maximum4239
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size317.0 B
2023-06-12T20:25:20.690801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1383
5-th percentile1516
Q11643
median2071
Q33007
95-th percentile4015
Maximum4239
Range2856
Interquartile range (IQR)1364

Descriptive statistics

Standard deviation920.10419
Coefficient of variation (CV)0.38223913
Kurtosis-0.81777205
Mean2407.1429
Median Absolute Deviation (MAD)543
Skewness0.74270588
Sum50550
Variance846591.73
MonotonicityNot monotonic
2023-06-12T20:25:20.978840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1528 1
 
4.8%
1714 1
 
4.8%
4015 1
 
4.8%
2071 1
 
4.8%
3007 1
 
4.8%
1383 1
 
4.8%
1643 1
 
4.8%
1576 1
 
4.8%
1617 1
 
4.8%
1728 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
1383 1
4.8%
1516 1
4.8%
1528 1
4.8%
1576 1
4.8%
1617 1
4.8%
1643 1
4.8%
1714 1
4.8%
1728 1
4.8%
1783 1
4.8%
1889 1
4.8%
ValueCountFrequency (%)
4239 1
4.8%
4015 1
4.8%
3733 1
4.8%
3711 1
4.8%
3072 1
4.8%
3007 1
4.8%
2887 1
4.8%
2812 1
4.8%
2527 1
4.8%
2099 1
4.8%

대구
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1835.7619
Minimum966
Maximum2951
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size317.0 B
2023-06-12T20:25:21.237543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum966
5-th percentile1082
Q11412
median1807
Q32179
95-th percentile2950
Maximum2951
Range1985
Interquartile range (IQR)767

Descriptive statistics

Standard deviation597.42857
Coefficient of variation (CV)0.32543903
Kurtosis-0.58174005
Mean1835.7619
Median Absolute Deviation (MAD)395
Skewness0.54892413
Sum38551
Variance356920.89
MonotonicityNot monotonic
2023-06-12T20:25:21.603051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1209 1
 
4.8%
1456 1
 
4.8%
2950 1
 
4.8%
1839 1
 
4.8%
2123 1
 
4.8%
1276 1
 
4.8%
1807 1
 
4.8%
1449 1
 
4.8%
1599 1
 
4.8%
1443 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
966 1
4.8%
1082 1
4.8%
1209 1
4.8%
1276 1
4.8%
1321 1
4.8%
1412 1
4.8%
1443 1
4.8%
1449 1
4.8%
1456 1
4.8%
1599 1
4.8%
ValueCountFrequency (%)
2951 1
4.8%
2950 1
4.8%
2842 1
4.8%
2250 1
4.8%
2247 1
4.8%
2179 1
4.8%
2124 1
4.8%
2123 1
4.8%
2026 1
4.8%
1839 1
4.8%

인천
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2232.0952
Minimum1183
Maximum3750
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size317.0 B
2023-06-12T20:25:21.931387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1183
5-th percentile1469
Q11789
median1924
Q32491
95-th percentile3694
Maximum3750
Range2567
Interquartile range (IQR)702

Descriptive statistics

Standard deviation742.08449
Coefficient of variation (CV)0.33246094
Kurtosis0.061474641
Mean2232.0952
Median Absolute Deviation (MAD)319
Skewness1.0005607
Sum46874
Variance550689.39
MonotonicityNot monotonic
2023-06-12T20:25:22.234415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1635 1
 
4.8%
1605 1
 
4.8%
3583 1
 
4.8%
2491 1
 
4.8%
2430 1
 
4.8%
1643 1
 
4.8%
2044 1
 
4.8%
2629 1
 
4.8%
2268 1
 
4.8%
1911 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
1183 1
4.8%
1469 1
4.8%
1605 1
4.8%
1635 1
4.8%
1643 1
4.8%
1789 1
4.8%
1841 1
4.8%
1872 1
4.8%
1911 1
4.8%
1914 1
4.8%
ValueCountFrequency (%)
3750 1
4.8%
3694 1
4.8%
3583 1
4.8%
3156 1
4.8%
2629 1
4.8%
2491 1
4.8%
2430 1
4.8%
2268 1
4.8%
2044 1
4.8%
2043 1
4.8%

광주
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1309.2381
Minimum591
Maximum2038
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size317.0 B
2023-06-12T20:25:22.484977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum591
5-th percentile795
Q11089
median1243
Q31578
95-th percentile1969
Maximum2038
Range1447
Interquartile range (IQR)489

Descriptive statistics

Standard deviation402.09836
Coefficient of variation (CV)0.30712394
Kurtosis-0.67423908
Mean1309.2381
Median Absolute Deviation (MAD)270
Skewness0.36184753
Sum27494
Variance161683.09
MonotonicityNot monotonic
2023-06-12T20:25:22.734687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
795 1
 
4.8%
1157 1
 
4.8%
1969 1
 
4.8%
1446 1
 
4.8%
1788 1
 
4.8%
960 1
 
4.8%
1339 1
 
4.8%
1095 1
 
4.8%
1094 1
 
4.8%
1254 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
591 1
4.8%
795 1
4.8%
904 1
4.8%
960 1
4.8%
973 1
4.8%
1089 1
4.8%
1094 1
4.8%
1095 1
4.8%
1157 1
4.8%
1166 1
4.8%
ValueCountFrequency (%)
2038 1
4.8%
1969 1
4.8%
1886 1
4.8%
1801 1
4.8%
1788 1
4.8%
1578 1
4.8%
1446 1
4.8%
1339 1
4.8%
1328 1
4.8%
1254 1
4.8%

대전
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1657.4286
Minimum730
Maximum2981
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size317.0 B
2023-06-12T20:25:23.027031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum730
5-th percentile1000
Q11205
median1409
Q32018
95-th percentile2871
Maximum2981
Range2251
Interquartile range (IQR)813

Descriptive statistics

Standard deviation616.03422
Coefficient of variation (CV)0.3716807
Kurtosis-0.12319082
Mean1657.4286
Median Absolute Deviation (MAD)323
Skewness0.78043142
Sum34806
Variance379498.16
MonotonicityNot monotonic
2023-06-12T20:25:23.321119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1307 1
 
4.8%
1192 1
 
4.8%
2525 1
 
4.8%
1372 1
 
4.8%
1732 1
 
4.8%
1000 1
 
4.8%
1640 1
 
4.8%
1409 1
 
4.8%
1228 1
 
4.8%
1183 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
730 1
4.8%
1000 1
4.8%
1096 1
4.8%
1183 1
4.8%
1192 1
4.8%
1205 1
4.8%
1228 1
4.8%
1247 1
4.8%
1307 1
4.8%
1372 1
4.8%
ValueCountFrequency (%)
2981 1
4.8%
2871 1
4.8%
2525 1
4.8%
2233 1
4.8%
2070 1
4.8%
2018 1
4.8%
1896 1
4.8%
1871 1
4.8%
1732 1
4.8%
1640 1
4.8%

세종
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)52.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean524.85714
Minimum0
Maximum1751
Zeros11
Zeros (%)52.4%
Negative0
Negative (%)0.0%
Memory size317.0 B
2023-06-12T20:25:23.543529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31172
95-th percentile1644
Maximum1751
Range1751
Interquartile range (IQR)1172

Descriptive statistics

Standard deviation679.81654
Coefficient of variation (CV)1.2952411
Kurtosis-1.2438533
Mean524.85714
Median Absolute Deviation (MAD)0
Skewness0.76123097
Sum11022
Variance462150.53
MonotonicityNot monotonic
2023-06-12T20:25:23.777120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 11
52.4%
118 1
 
4.8%
268 1
 
4.8%
790 1
 
4.8%
944 1
 
4.8%
1172 1
 
4.8%
1343 1
 
4.8%
1644 1
 
4.8%
1751 1
 
4.8%
1546 1
 
4.8%
ValueCountFrequency (%)
0 11
52.4%
118 1
 
4.8%
268 1
 
4.8%
790 1
 
4.8%
944 1
 
4.8%
1172 1
 
4.8%
1343 1
 
4.8%
1446 1
 
4.8%
1546 1
 
4.8%
1644 1
 
4.8%
ValueCountFrequency (%)
1751 1
4.8%
1644 1
4.8%
1546 1
4.8%
1446 1
4.8%
1343 1
4.8%
1172 1
4.8%
944 1
4.8%
790 1
4.8%
268 1
4.8%
118 1
4.8%

울산
Real number (ℝ)

Distinct20
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean787.66667
Minimum402
Maximum1334
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size317.0 B
2023-06-12T20:25:24.050306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum402
5-th percentile426
Q1580
median694
Q3969
95-th percentile1301
Maximum1334
Range932
Interquartile range (IQR)389

Descriptive statistics

Standard deviation275.14293
Coefficient of variation (CV)0.34931392
Kurtosis-0.49768408
Mean787.66667
Median Absolute Deviation (MAD)120
Skewness0.71805503
Sum16541
Variance75703.633
MonotonicityNot monotonic
2023-06-12T20:25:24.799803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
713 2
 
9.5%
692 1
 
4.8%
1301 1
 
4.8%
1166 1
 
4.8%
969 1
 
4.8%
658 1
 
4.8%
580 1
 
4.8%
402 1
 
4.8%
694 1
 
4.8%
426 1
 
4.8%
Other values (10) 10
47.6%
ValueCountFrequency (%)
402 1
4.8%
426 1
4.8%
524 1
4.8%
574 1
4.8%
578 1
4.8%
580 1
4.8%
658 1
4.8%
670 1
4.8%
677 1
4.8%
692 1
4.8%
ValueCountFrequency (%)
1334 1
4.8%
1301 1
4.8%
1172 1
4.8%
1166 1
4.8%
999 1
4.8%
969 1
4.8%
955 1
4.8%
744 1
4.8%
713 2
9.5%
694 1
4.8%

경기
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9586.381
Minimum5912
Maximum15393
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size317.0 B
2023-06-12T20:25:25.065503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5912
5-th percentile6451
Q17536
median9035
Q310217
95-th percentile15032
Maximum15393
Range9481
Interquartile range (IQR)2681

Descriptive statistics

Standard deviation2773.9402
Coefficient of variation (CV)0.28936261
Kurtosis-0.035174804
Mean9586.381
Median Absolute Deviation (MAD)1499
Skewness0.93254713
Sum201314
Variance7694744
MonotonicityNot monotonic
2023-06-12T20:25:25.363159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
6625 1
 
4.8%
7536 1
 
4.8%
15032 1
 
4.8%
10217 1
 
4.8%
11462 1
 
4.8%
6451 1
 
4.8%
8113 1
 
4.8%
9064 1
 
4.8%
9504 1
 
4.8%
8092 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
5912 1
4.8%
6451 1
4.8%
6625 1
4.8%
7382 1
4.8%
7515 1
4.8%
7536 1
4.8%
8092 1
4.8%
8113 1
4.8%
8397 1
4.8%
8705 1
4.8%
ValueCountFrequency (%)
15393 1
4.8%
15032 1
4.8%
14099 1
4.8%
13215 1
4.8%
11462 1
4.8%
10217 1
4.8%
10210 1
4.8%
9504 1
4.8%
9355 1
4.8%
9064 1
4.8%

강원
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2205.5238
Minimum1312
Maximum3920
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size317.0 B
2023-06-12T20:25:25.650574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1312
5-th percentile1473
Q11758
median1880
Q32462
95-th percentile3547
Maximum3920
Range2608
Interquartile range (IQR)704

Descriptive statistics

Standard deviation713.62964
Coefficient of variation (CV)0.3235647
Kurtosis0.31233313
Mean2205.5238
Median Absolute Deviation (MAD)336
Skewness1.0600855
Sum46316
Variance509267.26
MonotonicityNot monotonic
2023-06-12T20:25:25.957978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1758 1
 
4.8%
1663 1
 
4.8%
3920 1
 
4.8%
1891 1
 
4.8%
2183 1
 
4.8%
1880 1
 
4.8%
1867 1
 
4.8%
1804 1
 
4.8%
1769 1
 
4.8%
1473 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
1312 1
4.8%
1473 1
4.8%
1544 1
4.8%
1663 1
4.8%
1681 1
4.8%
1758 1
4.8%
1769 1
4.8%
1797 1
4.8%
1804 1
4.8%
1867 1
4.8%
ValueCountFrequency (%)
3920 1
4.8%
3547 1
4.8%
3096 1
4.8%
3074 1
4.8%
2822 1
4.8%
2462 1
4.8%
2445 1
4.8%
2328 1
4.8%
2183 1
4.8%
1891 1
4.8%

충북
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1848.5238
Minimum910
Maximum3040
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size317.0 B
2023-06-12T20:25:26.284826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum910
5-th percentile1104
Q11435
median1683
Q32165
95-th percentile2859
Maximum3040
Range2130
Interquartile range (IQR)730

Descriptive statistics

Standard deviation577.54278
Coefficient of variation (CV)0.31243459
Kurtosis-0.3458313
Mean1848.5238
Median Absolute Deviation (MAD)284
Skewness0.62596696
Sum38819
Variance333555.66
MonotonicityNot monotonic
2023-06-12T20:25:26.657055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
910 1
 
4.8%
1757 1
 
4.8%
3040 1
 
4.8%
1683 1
 
4.8%
2165 1
 
4.8%
1353 1
 
4.8%
1482 1
 
4.8%
1628 1
 
4.8%
1623 1
 
4.8%
1435 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
910 1
4.8%
1104 1
4.8%
1353 1
4.8%
1402 1
4.8%
1431 1
4.8%
1435 1
4.8%
1482 1
4.8%
1542 1
4.8%
1623 1
4.8%
1628 1
4.8%
ValueCountFrequency (%)
3040 1
4.8%
2859 1
4.8%
2688 1
4.8%
2604 1
4.8%
2302 1
4.8%
2165 1
4.8%
2055 1
4.8%
1967 1
4.8%
1789 1
4.8%
1757 1
4.8%

충남
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2256.4286
Minimum877
Maximum3970
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size317.0 B
2023-06-12T20:25:27.107569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum877
5-th percentile1309
Q11805
median1988
Q32556
95-th percentile3738
Maximum3970
Range3093
Interquartile range (IQR)751

Descriptive statistics

Standard deviation814.61393
Coefficient of variation (CV)0.36101915
Kurtosis0.038838206
Mean2256.4286
Median Absolute Deviation (MAD)342
Skewness0.76903765
Sum47385
Variance663595.86
MonotonicityNot monotonic
2023-06-12T20:25:27.396195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
877 1
 
4.8%
1964 1
 
4.8%
3970 1
 
4.8%
1646 1
 
4.8%
2554 1
 
4.8%
2001 1
 
4.8%
1805 1
 
4.8%
1988 1
 
4.8%
1859 1
 
4.8%
1906 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
877 1
4.8%
1309 1
4.8%
1489 1
4.8%
1646 1
4.8%
1770 1
4.8%
1805 1
4.8%
1859 1
4.8%
1906 1
4.8%
1920 1
4.8%
1964 1
4.8%
ValueCountFrequency (%)
3970 1
4.8%
3738 1
4.8%
3527 1
4.8%
3442 1
4.8%
2585 1
4.8%
2556 1
4.8%
2554 1
4.8%
2325 1
4.8%
2154 1
4.8%
2001 1
4.8%

경북
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2577.2857
Minimum983
Maximum4391
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size317.0 B
2023-06-12T20:25:27.760731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum983
5-th percentile1485
Q11939
median2457
Q32963
95-th percentile4276
Maximum4391
Range3408
Interquartile range (IQR)1024

Descriptive statistics

Standard deviation935.79448
Coefficient of variation (CV)0.36309303
Kurtosis-0.30264821
Mean2577.2857
Median Absolute Deviation (MAD)518
Skewness0.62516039
Sum54123
Variance875711.31
MonotonicityNot monotonic
2023-06-12T20:25:28.084116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
983 1
 
4.8%
2042 1
 
4.8%
4391 1
 
4.8%
1887 1
 
4.8%
2963 1
 
4.8%
1871 1
 
4.8%
2457 1
 
4.8%
2490 1
 
4.8%
1905 1
 
4.8%
2049 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
983 1
4.8%
1485 1
4.8%
1871 1
4.8%
1887 1
4.8%
1905 1
4.8%
1939 1
4.8%
1946 1
4.8%
2021 1
4.8%
2042 1
4.8%
2049 1
4.8%
ValueCountFrequency (%)
4391 1
4.8%
4276 1
4.8%
4173 1
4.8%
3674 1
4.8%
3178 1
4.8%
2963 1
4.8%
2955 1
4.8%
2846 1
4.8%
2592 1
4.8%
2490 1
4.8%

경남
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2859.2857
Minimum1574
Maximum4340
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size317.0 B
2023-06-12T20:25:28.396508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1574
5-th percentile1685
Q12273
median2580
Q33098
95-th percentile4289
Maximum4340
Range2766
Interquartile range (IQR)825

Descriptive statistics

Standard deviation872.01784
Coefficient of variation (CV)0.30497751
Kurtosis-0.72587496
Mean2859.2857
Median Absolute Deviation (MAD)372
Skewness0.59472338
Sum60045
Variance760415.11
MonotonicityNot monotonic
2023-06-12T20:25:28.712134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1574 1
 
4.8%
2580 1
 
4.8%
4273 1
 
4.8%
2322 1
 
4.8%
4340 1
 
4.8%
2273 1
 
4.8%
3055 1
 
4.8%
2576 1
 
4.8%
2405 1
 
4.8%
2231 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
1574 1
4.8%
1685 1
4.8%
1894 1
4.8%
2231 1
4.8%
2262 1
4.8%
2273 1
4.8%
2322 1
4.8%
2405 1
4.8%
2497 1
4.8%
2576 1
4.8%
ValueCountFrequency (%)
4340 1
4.8%
4289 1
4.8%
4273 1
4.8%
4225 1
4.8%
3928 1
4.8%
3098 1
4.8%
3055 1
4.8%
2952 1
4.8%
2866 1
4.8%
2720 1
4.8%

전북
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2009.7619
Minimum1097
Maximum3551
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size317.0 B
2023-06-12T20:25:29.033818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1097
5-th percentile1363
Q11448
median1769
Q32149
95-th percentile3501
Maximum3551
Range2454
Interquartile range (IQR)701

Descriptive statistics

Standard deviation725.88497
Coefficient of variation (CV)0.36117958
Kurtosis0.34505124
Mean2009.7619
Median Absolute Deviation (MAD)342
Skewness1.1809937
Sum42205
Variance526908.99
MonotonicityNot monotonic
2023-06-12T20:25:29.384105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1363 1
 
4.8%
1769 1
 
4.8%
3551 1
 
4.8%
1625 1
 
4.8%
1871 1
 
4.8%
1427 1
 
4.8%
1697 1
 
4.8%
1741 1
 
4.8%
1763 1
 
4.8%
1786 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
1097 1
4.8%
1363 1
4.8%
1396 1
4.8%
1399 1
4.8%
1427 1
4.8%
1448 1
4.8%
1625 1
4.8%
1697 1
4.8%
1741 1
4.8%
1763 1
4.8%
ValueCountFrequency (%)
3551 1
4.8%
3501 1
4.8%
3301 1
4.8%
3024 1
4.8%
2239 1
4.8%
2149 1
4.8%
2091 1
4.8%
1967 1
4.8%
1871 1
4.8%
1786 1
4.8%

전남
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2545.2381
Minimum1522
Maximum4211
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size317.0 B
2023-06-12T20:25:29.684689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1522
5-th percentile1625
Q11935
median2183
Q32947
95-th percentile4160
Maximum4211
Range2689
Interquartile range (IQR)1012

Descriptive statistics

Standard deviation866.99163
Coefficient of variation (CV)0.34063282
Kurtosis-0.58198986
Mean2545.2381
Median Absolute Deviation (MAD)558
Skewness0.80875306
Sum53450
Variance751674.49
MonotonicityNot monotonic
2023-06-12T20:25:29.987046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
2183 1
 
4.8%
1935 1
 
4.8%
4211 1
 
4.8%
1809 1
 
4.8%
2833 1
 
4.8%
1652 1
 
4.8%
2009 1
 
4.8%
2233 1
 
4.8%
2153 1
 
4.8%
2018 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
1522 1
4.8%
1625 1
4.8%
1652 1
4.8%
1744 1
4.8%
1809 1
4.8%
1935 1
4.8%
2009 1
4.8%
2018 1
4.8%
2031 1
4.8%
2153 1
4.8%
ValueCountFrequency (%)
4211 1
4.8%
4160 1
4.8%
3963 1
4.8%
3763 1
4.8%
3150 1
4.8%
2947 1
4.8%
2833 1
4.8%
2760 1
4.8%
2749 1
4.8%
2233 1
4.8%

제주
Real number (ℝ)

Distinct20
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean803.09524
Minimum402
Maximum1622
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size317.0 B
2023-06-12T20:25:30.285007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum402
5-th percentile473
Q1606
median662
Q3965
95-th percentile1287
Maximum1622
Range1220
Interquartile range (IQR)359

Descriptive statistics

Standard deviation318.05706
Coefficient of variation (CV)0.39603903
Kurtosis0.64609728
Mean803.09524
Median Absolute Deviation (MAD)180
Skewness1.076183
Sum16865
Variance101160.29
MonotonicityNot monotonic
2023-06-12T20:25:30.643814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
630 2
 
9.5%
481 1
 
4.8%
1092 1
 
4.8%
845 1
 
4.8%
706 1
 
4.8%
655 1
 
4.8%
662 1
 
4.8%
402 1
 
4.8%
585 1
 
4.8%
519 1
 
4.8%
Other values (10) 10
47.6%
ValueCountFrequency (%)
402 1
4.8%
473 1
4.8%
481 1
4.8%
519 1
4.8%
585 1
4.8%
606 1
4.8%
630 2
9.5%
633 1
4.8%
655 1
4.8%
662 1
4.8%
ValueCountFrequency (%)
1622 1
4.8%
1287 1
4.8%
1246 1
4.8%
1161 1
4.8%
1092 1
4.8%
965 1
4.8%
845 1
4.8%
842 1
4.8%
823 1
4.8%
706 1
4.8%

Interactions

2023-06-12T20:25:11.922607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:23:42.796696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:23:48.236454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:23:53.091631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:23:59.190047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:06.102401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:12.500931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:17.458750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:21.907857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:27.418016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:31.755272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:36.179676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:40.653663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:45.660295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:50.951476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:56.378572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:25:01.894091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:25:06.480795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:25:12.315715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:23:43.040400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:23:48.460410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:23:53.430577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:23:59.565977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:06.442479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:12.832021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:17.675526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:22.145255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:27.651448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:31.972358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:36.469623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:40.931617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:45.963076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:51.245426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:56.689698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:25:02.148342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:25:06.743263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:25:12.599576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:23:43.746890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:23:48.694280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:23:53.744956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:23:59.956555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:06.781214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:13.152446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:17.881153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:22.357608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:27.857634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:32.162983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:36.831001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:41.166148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:46.285213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:51.969476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:56.976070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:25:02.402259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:25:07.010204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:25:12.875882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:23:44.056732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-06-12T20:23:58.037130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:04.666081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:10.792796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:16.549601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:21.027116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:25.929618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:30.955698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:35.205353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:39.708325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:44.754682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:49.805912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:55.268685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:25:00.901439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:25:05.379217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:25:10.921575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:25:16.358594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:23:47.549095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:23:52.342577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:23:58.308022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:05.035759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:11.571187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:16.781068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:21.222396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:26.224002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:31.124146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:35.498180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:39.918517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:45.020347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:50.176083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:55.550886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:25:01.142450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:25:05.636548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:25:11.177740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:25:16.765792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:23:47.777272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:23:52.571965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:23:58.612072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:05.383439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:11.871301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:16.980066image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:21.439098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:26.659749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:31.336000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:35.736595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:40.132420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:45.210029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:50.438074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:55.839180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:25:01.403430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:25:05.970939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:25:11.441758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:25:17.060394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:23:47.986682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:23:52.836480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:23:58.902129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:05.747978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:12.185920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:17.236710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:21.652687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:26.902364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:31.543674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:35.929687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:40.385069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:45.401725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:50.688002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:24:56.097665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:25:01.627549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:25:06.238339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:25:11.677022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-12T20:25:30.948361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
연도서울부산대구인천광주대전세종울산경기강원충북충남경북경남전북전남제주
연도1.0000.8050.7330.7140.5110.6160.7630.8840.3580.6010.7520.8040.8100.8180.5530.8070.7820.717
서울0.8051.0000.8430.8800.8460.8250.8770.8260.6290.8830.8730.9130.9180.9040.7950.9660.9280.760
부산0.7330.8431.0000.9230.7960.8390.9120.8850.7940.9150.9400.9130.9070.8990.8780.8930.9330.893
대구0.7140.8800.9231.0000.8270.8900.9590.8610.7610.9060.9400.9000.9050.9010.8780.9030.9200.855
인천0.5110.8460.7960.8271.0000.8380.8110.6800.8080.9540.8120.8150.8420.8100.8140.8670.8280.740
광주0.6160.8250.8390.8900.8381.0000.8580.6860.7930.8940.8190.9060.8890.8600.8560.8610.8490.822
대전0.7630.8770.9120.9590.8110.8581.0000.9040.7080.8650.9260.8760.8890.9010.8280.9150.9430.889
세종0.8840.8260.8850.8610.6800.6860.9041.0000.5720.7600.8880.8320.8450.8600.6990.8790.9020.842
울산0.3580.6290.7940.7610.8080.7930.7080.5721.0000.8630.8010.7610.7100.6770.7410.7110.7070.778
경기0.6010.8830.9150.9060.9540.8940.8650.7600.8631.0000.8860.9030.8960.8680.8930.8990.8870.833
강원0.7520.8730.9400.9400.8120.8190.9260.8880.8010.8861.0000.9010.9200.8960.8260.9230.9440.885
충북0.8040.9130.9130.9000.8150.9060.8760.8320.7610.9030.9011.0000.9670.9640.8750.9430.9250.872
충남0.8100.9180.9070.9050.8420.8890.8890.8450.7100.8960.9200.9671.0000.9690.8820.9560.9300.864
경북0.8180.9040.8990.9010.8100.8600.9010.8600.6770.8680.8960.9640.9691.0000.8950.9480.9320.831
경남0.5530.7950.8780.8780.8140.8560.8280.6990.7410.8930.8260.8750.8820.8951.0000.8380.8430.785
전북0.8070.9660.8930.9030.8670.8610.9150.8790.7110.8990.9230.9430.9560.9480.8381.0000.9630.831
전남0.7820.9280.9330.9200.8280.8490.9430.9020.7070.8870.9440.9250.9300.9320.8430.9631.0000.861
제주0.7170.7600.8930.8550.7400.8220.8890.8420.7780.8330.8850.8720.8640.8310.7850.8310.8611.000
2023-06-12T20:25:31.885872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
연도서울부산대구인천광주대전세종울산경기강원충북충남경북경남전북전남제주
연도1.0000.7870.6700.7190.3690.5740.6960.9130.2640.4840.6420.7680.7840.8080.5310.8040.7010.685
서울0.7871.0000.7320.8770.6840.7220.7840.7430.4680.7910.7250.8710.8050.8420.7030.9430.8480.746
부산0.6700.7321.0000.8510.6320.7640.8040.7840.6980.8140.7950.8390.7600.8010.7780.8010.7620.739
대구0.7190.8770.8511.0000.7600.8480.9290.7980.6850.8660.9100.9320.8560.8830.8620.9380.8650.873
인천0.3690.6840.6320.7601.0000.8010.7450.4730.7550.9130.7130.7360.6440.6700.7100.6820.7210.657
광주0.5740.7220.7640.8480.8011.0000.7920.6280.6990.8120.7520.8520.7580.7920.7340.8140.7510.741
대전0.6960.7840.8040.9290.7450.7921.0000.8050.7220.8000.9120.8740.8060.8700.7870.8520.9270.831
세종0.9130.7430.7840.7980.4730.6280.8051.0000.4110.6000.7570.8090.8190.7940.6280.8070.7840.769
울산0.2640.4680.6980.6850.7550.6990.7220.4111.0000.7890.7970.6540.5490.5480.6800.5550.6100.681
경기0.4840.7910.8140.8660.9130.8120.8000.6000.7891.0000.7900.8660.7100.7380.8100.7900.7740.761
강원0.6420.7250.7950.9100.7130.7520.9120.7570.7970.7901.0000.8440.8570.7920.7990.8320.8350.910
충북0.7680.8710.8390.9320.7360.8520.8740.8090.6540.8660.8441.0000.8940.9130.8450.9340.8690.878
충남0.7840.8050.7600.8560.6440.7580.8060.8190.5490.7100.8570.8941.0000.9030.8000.9170.8610.898
경북0.8080.8420.8010.8830.6700.7920.8700.7940.5480.7380.7920.9130.9031.0000.8560.9260.8700.798
경남0.5310.7030.7780.8620.7100.7340.7870.6280.6800.8100.7990.8450.8000.8561.0000.8160.7350.837
전북0.8040.9430.8010.9380.6820.8140.8520.8070.5550.7900.8320.9340.9170.9260.8161.0000.8860.846
전남0.7010.8480.7620.8650.7210.7510.9270.7840.6100.7740.8350.8690.8610.8700.7350.8861.0000.796
제주0.6850.7460.7390.8730.6570.7410.8310.7690.6810.7610.9100.8780.8980.7980.8370.8460.7961.000
2023-06-12T20:25:32.327394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
연도서울부산대구인천광주대전세종울산경기강원충북충남경북경남전북전남제주
연도1.0000.6290.5050.5050.2290.4100.4950.8040.1670.3240.4100.6100.6100.6670.3900.6380.5330.473
서울0.6291.0000.5710.7240.5050.5710.6380.6040.3290.6190.5710.7330.6570.7330.5520.8380.6950.558
부산0.5050.5711.0000.6950.4570.6190.6480.6370.5010.6670.6380.6480.5710.5900.6000.6380.5710.539
대구0.5050.7240.6951.0000.5710.7140.8000.6490.5200.7240.7900.7620.6480.7240.6760.7900.6860.692
인천0.2290.5050.4570.5711.0000.6480.5430.3600.5580.7520.5140.5620.4860.5050.5330.4950.5430.482
광주0.4100.5710.6190.7140.6481.0000.6290.5040.4820.6670.5810.6860.5710.6480.5620.6570.5710.558
대전0.4950.6380.6480.8000.5430.6291.0000.6600.5490.6000.8000.6950.6380.6950.5900.6860.7900.673
세종0.8040.6040.6370.6490.3600.5040.6601.0000.2940.4600.5820.6490.6710.6490.5380.6370.6260.606
울산0.1670.3290.5010.5200.5580.4820.5490.2941.0000.6160.6350.5010.4250.3870.4820.3960.4630.555
경기0.3240.6190.6670.7240.7520.6670.6000.4600.6161.0000.5900.6950.5430.6190.5900.6100.6000.587
강원0.4100.5710.6380.7900.5140.5810.8000.5820.6350.5901.0000.6670.7050.6100.6190.6760.6860.749
충북0.6100.7330.6480.7620.5620.6860.6950.6490.5010.6950.6671.0000.7710.7900.6670.8000.7520.721
충남0.6100.6570.5710.6480.4860.5710.6380.6710.4250.5430.7050.7711.0000.7710.6290.8000.7520.768
경북0.6670.7330.5900.7240.5050.6480.6950.6490.3870.6190.6100.7900.7711.0000.6670.8000.7330.597
경남0.3900.5520.6000.6760.5330.5620.5900.5380.4820.5900.6190.6670.6290.6671.0000.6570.5520.644
전북0.6380.8380.6380.7900.4950.6570.6860.6370.3960.6100.6760.8000.8000.8000.6571.0000.7810.683
전남0.5330.6950.5710.6860.5430.5710.7900.6260.4630.6000.6860.7520.7520.7330.5520.7811.0000.635
제주0.4730.5580.5390.6920.4820.5580.6730.6060.5550.5870.7490.7210.7680.5970.6440.6830.6351.000
2023-06-12T20:25:32.733523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
연도서울부산대구인천광주대전세종울산경기강원충북충남경북경남전북전남제주
연도1.0000.0000.7980.3880.5540.5740.7980.8220.4990.4510.1100.6420.7950.8330.5950.7040.7760.584
서울0.0001.0000.6330.8440.4860.1040.7300.0000.6390.5650.3870.0000.8450.5500.8120.7950.7960.789
부산0.7980.6331.0000.6860.6510.6690.6080.6720.6670.7780.8190.7440.8710.8220.4990.8840.6900.721
대구0.3880.8440.6861.0000.6870.5110.5920.4040.6110.6220.6690.5460.7080.7900.6520.9170.5310.511
인천0.5540.4860.6510.6871.0000.5970.7760.4260.7700.8200.2860.5330.5970.0000.8260.8860.5170.597
광주0.5740.1040.6690.5110.5971.0000.3630.6610.8280.7920.7440.7220.4730.7310.5890.5890.9200.528
대전0.7980.7300.6080.5920.7760.3631.0000.7820.6730.7140.7250.8430.6860.6450.8910.8280.8010.778
세종0.8220.0000.6720.4040.4260.6610.7821.0000.1860.1340.7900.8560.6080.8720.8760.7280.9320.780
울산0.4990.6390.6670.6110.7700.8280.6730.1861.0000.8680.5180.5640.6560.6790.7710.5060.0000.451
경기0.4510.5650.7780.6220.8200.7920.7140.1340.8681.0000.8110.7950.3010.0000.5070.8040.4340.810
강원0.1100.3870.8190.6690.2860.7440.7250.7900.5180.8111.0000.8980.6580.7580.2970.8770.8620.963
충북0.6420.0000.7440.5460.5330.7220.8430.8560.5640.7950.8981.0000.6920.8240.5320.8450.9680.929
충남0.7950.8450.8710.7080.5970.4730.6860.6080.6560.3010.6580.6921.0000.8370.6750.9150.7280.864
경북0.8330.5500.8220.7900.0000.7310.6450.8720.6790.0000.7580.8240.8371.0000.6370.9150.8360.623
경남0.5950.8120.4990.6520.8260.5890.8910.8760.7710.5070.2970.5320.6750.6371.0000.7250.6130.414
전북0.7040.7950.8840.9170.8860.5890.8280.7280.5060.8040.8770.8450.9150.9150.7251.0000.8570.870
전남0.7760.7960.6900.5310.5170.9200.8010.9320.0000.4340.8620.9680.7280.8360.6130.8571.0000.847
제주0.5840.7890.7210.5110.5970.5280.7780.7800.4510.8100.9630.9290.8640.6230.4140.8700.8471.000
2023-06-12T20:25:33.162764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
연도서울부산대구인천광주대전세종울산경기강원충북충남경북경남전북전남제주
연도1.0000.7870.6700.7190.3690.5740.6960.9130.2640.4840.6420.7680.7840.8080.5310.8040.7010.685
서울0.7871.0000.7320.8770.6840.7220.7840.7430.4680.7910.7250.8710.8050.8420.7030.9430.8480.746
부산0.6700.7321.0000.8510.6320.7640.8040.7840.6980.8140.7950.8390.7600.8010.7780.8010.7620.739
대구0.7190.8770.8511.0000.7600.8480.9290.7980.6850.8660.9100.9320.8560.8830.8620.9380.8650.873
인천0.3690.6840.6320.7601.0000.8010.7450.4730.7550.9130.7130.7360.6440.6700.7100.6820.7210.657
광주0.5740.7220.7640.8480.8011.0000.7920.6280.6990.8120.7520.8520.7580.7920.7340.8140.7510.741
대전0.6960.7840.8040.9290.7450.7921.0000.8050.7220.8000.9120.8740.8060.8700.7870.8520.9270.831
세종0.9130.7430.7840.7980.4730.6280.8051.0000.4110.6000.7570.8090.8190.7940.6280.8070.7840.769
울산0.2640.4680.6980.6850.7550.6990.7220.4111.0000.7890.7970.6540.5490.5480.6800.5550.6100.681
경기0.4840.7910.8140.8660.9130.8120.8000.6000.7891.0000.7900.8660.7100.7380.8100.7900.7740.761
강원0.6420.7250.7950.9100.7130.7520.9120.7570.7970.7901.0000.8440.8570.7920.7990.8320.8350.910
충북0.7680.8710.8390.9320.7360.8520.8740.8090.6540.8660.8441.0000.8940.9130.8450.9340.8690.878
충남0.7840.8050.7600.8560.6440.7580.8060.8190.5490.7100.8570.8941.0000.9030.8000.9170.8610.898
경북0.8080.8420.8010.8830.6700.7920.8700.7940.5480.7380.7920.9130.9031.0000.8560.9260.8700.798
경남0.5310.7030.7780.8620.7100.7340.7870.6280.6800.8100.7990.8450.8000.8561.0000.8160.7350.837
전북0.8040.9430.8010.9380.6820.8140.8520.8070.5550.7900.8320.9340.9170.9260.8161.0000.8860.846
전남0.7010.8480.7620.8650.7210.7510.9270.7840.6100.7740.8350.8690.8610.8700.7350.8861.0000.796
제주0.6850.7460.7390.8730.6570.7410.8310.7690.6810.7610.9100.8780.8980.7980.8370.8460.7961.000

Missing values

2023-06-12T20:25:17.948421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-12T20:25:18.715433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

연도서울부산대구인천광주대전세종울산경기강원충북충남경북경남전북전남제주
0200164771528120916357951307067766251758910877983157413632183473
120024410209910821841591730069283971544110413091485249710971522481
2200373512071183924911446137201166102171891168316461887232216251809630
320049362300721232430178817320969114622183216525542963434018712833845
4200551381383127616439601000065864511880135320011871227314271652706
52006724916431807204413391640071381131867148218052457305516972009655
62007893715761449262910951409071390641804162819882490257617412233630
720081124816171599226810941228058095041769162318591905240517632153662
82009965617281443191112541183040280921473143519062049223117862018402
9201075121889141214699731205069473821797143117701946226214481744585
연도서울부산대구인천광주대전세종울산경기강원충북충남경북경남전북전남제주
11201292491714145616051157119211857475361663175719642042258017691935633
12201371451783132118721166124726852475151681154219202021168513992031606
1320149671288720262043157818967909991021024622302255628462866214929471246
142015105913072224719241328207094474490352822196725852955295222393150965
1520161112228122179191410892018117267093552445205523253178309820912760842
162017941525272124178912431871134357887052328178921542592272019672749823
1720181497242392951369420382981164413341539335472604352736744225302437631622
182019167023733284237501886287117519551409930962688373842764289350141601161
1920201498537112250315618012233154611721321530742859344241733928330139631287
2020211799840152950358319692525144613011503239203040397043914273355142111092