Overview

Dataset statistics

Number of variables19
Number of observations43
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.3 KiB
Average record size in memory173.1 B

Variable types

Text1
Numeric18

Dataset

Description지역별 (서울, 부산, 대구, 인천, 대전, 전주, 제주 등)공무원연금 가입자 추이로 1982년 부터 연 단위로 구분됩니다.
URLhttps://www.data.go.kr/data/15053034/fileData.do

Alerts

is highly overall correlated with 서울 and 13 other fieldsHigh correlation
서울 is highly overall correlated with and 12 other fieldsHigh correlation
부산 is highly overall correlated with and 13 other fieldsHigh correlation
대구 is highly overall correlated with and 13 other fieldsHigh correlation
인천 is highly overall correlated with and 13 other fieldsHigh correlation
광주 is highly overall correlated with and 13 other fieldsHigh correlation
대전 is highly overall correlated with and 12 other fieldsHigh correlation
세종 is highly overall correlated with and 10 other fieldsHigh correlation
울산 is highly overall correlated with and 12 other fieldsHigh correlation
경기 is highly overall correlated with and 12 other fieldsHigh correlation
강원 is highly overall correlated with and 12 other fieldsHigh correlation
충북 is highly overall correlated with and 12 other fieldsHigh correlation
경북 is highly overall correlated with and 13 other fieldsHigh correlation
경남 is highly overall correlated with and 7 other fieldsHigh correlation
전북 is highly overall correlated with 서울 and 2 other fieldsHigh correlation
전남 is highly overall correlated with 서울 and 1 other fieldsHigh correlation
제주 is highly overall correlated with and 13 other fieldsHigh correlation
구분 has unique valuesUnique
has unique valuesUnique
서울 has unique valuesUnique
부산 has unique valuesUnique
대구 has unique valuesUnique
인천 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
제주 has unique valuesUnique
광주 has 4 (9.3%) zerosZeros
대전 has 7 (16.3%) zerosZeros
세종 has 30 (69.8%) zerosZeros
울산 has 15 (34.9%) zerosZeros

Reproduction

Analysis started2023-12-12 16:31:54.424038
Analysis finished2023-12-12 16:32:30.316260
Duration35.89 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size476.0 B
2023-12-13T01:32:30.459666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.0465116
Min length4

Characters and Unicode

Total characters174
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)100.0%

Sample

1st row1982
2nd row1983
3rd row1984
4th row1985
5th row1986
ValueCountFrequency (%)
1982 1
 
2.3%
2004 1
 
2.3%
2006 1
 
2.3%
2007 1
 
2.3%
2008 1
 
2.3%
2009 1
 
2.3%
2010 1
 
2.3%
2011 1
 
2.3%
2012 1
 
2.3%
2013 1
 
2.3%
Other values (33) 33
76.7%
2023-12-13T01:32:30.797880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 39
22.4%
2 37
21.3%
1 32
18.4%
9 32
18.4%
8 12
 
6.9%
3 4
 
2.3%
4 4
 
2.3%
5 4
 
2.3%
6 4
 
2.3%
7 4
 
2.3%
Other values (2) 2
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 172
98.9%
Other Letter 2
 
1.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 39
22.7%
2 37
21.5%
1 32
18.6%
9 32
18.6%
8 12
 
7.0%
3 4
 
2.3%
4 4
 
2.3%
5 4
 
2.3%
6 4
 
2.3%
7 4
 
2.3%
Other Letter
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 172
98.9%
Hangul 2
 
1.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 39
22.7%
2 37
21.5%
1 32
18.6%
9 32
18.6%
8 12
 
7.0%
3 4
 
2.3%
4 4
 
2.3%
5 4
 
2.3%
6 4
 
2.3%
7 4
 
2.3%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 172
98.9%
Hangul 2
 
1.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 39
22.7%
2 37
21.5%
1 32
18.6%
9 32
18.6%
8 12
 
7.0%
3 4
 
2.3%
4 4
 
2.3%
5 4
 
2.3%
6 4
 
2.3%
7 4
 
2.3%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%


Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean950559.98
Minimum623723
Maximum1280994
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-13T01:32:30.934135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum623723
5-th percentile667771.9
Q1863955
median957882
Q31061215
95-th percentile1218694.9
Maximum1280994
Range657271
Interquartile range (IQR)197260

Descriptive statistics

Standard deviation170029.42
Coefficient of variation (CV)0.1788729
Kurtosis-0.51234863
Mean950559.98
Median Absolute Deviation (MAD)106590
Skewness-0.21464587
Sum40874079
Variance2.8910004 × 1010
MonotonicityNot monotonic
2023-12-13T01:32:31.064062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
667554 1
 
2.3%
669733 1
 
2.3%
1009145 1
 
2.3%
1021771 1
 
2.3%
1030256 1
 
2.3%
1047897 1
 
2.3%
1052407 1
 
2.3%
1057958 1
 
2.3%
1064472 1
 
2.3%
1072610 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
623723 1
2.3%
657271 1
2.3%
667554 1
2.3%
669733 1
2.3%
682281 1
2.3%
696951 1
2.3%
716629 1
2.3%
737688 1
2.3%
767123 1
2.3%
810069 1
2.3%
ValueCountFrequency (%)
1280994 1
2.3%
1261421 1
2.3%
1221322 1
2.3%
1195051 1
2.3%
1160586 1
2.3%
1120458 1
2.3%
1107972 1
2.3%
1093038 1
2.3%
1081147 1
2.3%
1072610 1
2.3%

서울
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean208828.6
Minimum114949
Maximum241420
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-13T01:32:31.194128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum114949
5-th percentile165460.8
Q1209035.5
median216855
Q3222023.5
95-th percentile230310.7
Maximum241420
Range126471
Interquartile range (IQR)12988

Descriptive statistics

Standard deviation25842.001
Coefficient of variation (CV)0.12374742
Kurtosis5.0666792
Mean208828.6
Median Absolute Deviation (MAD)7437
Skewness-2.1328929
Sum8979630
Variance6.6780903 × 108
MonotonicityNot monotonic
2023-12-13T01:32:31.339974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
164485 1
 
2.3%
174243 1
 
2.3%
216855 1
 
2.3%
218526 1
 
2.3%
219090 1
 
2.3%
224292 1
 
2.3%
220472 1
 
2.3%
219507 1
 
2.3%
219079 1
 
2.3%
218164 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
114949 1
2.3%
126471 1
2.3%
164485 1
2.3%
174243 1
2.3%
174575 1
2.3%
181631 1
2.3%
181766 1
2.3%
189997 1
2.3%
199768 1
2.3%
206952 1
2.3%
ValueCountFrequency (%)
241420 1
2.3%
239095 1
2.3%
230396 1
2.3%
229543 1
2.3%
228131 1
2.3%
226436 1
2.3%
226407 1
2.3%
225604 1
2.3%
225529 1
2.3%
224292 1
2.3%

부산
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64325.047
Minimum38358
Maximum78354
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-13T01:32:31.499827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum38358
5-th percentile49770.8
Q162258.5
median65797
Q369657.5
95-th percentile75533
Maximum78354
Range39996
Interquartile range (IQR)7399

Descriptive statistics

Standard deviation8697.3632
Coefficient of variation (CV)0.13520959
Kurtosis1.7618482
Mean64325.047
Median Absolute Deviation (MAD)3884
Skewness-1.1726705
Sum2765977
Variance75644127
MonotonicityNot monotonic
2023-12-13T01:32:31.656976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
58213 1
 
2.3%
49635 1
 
2.3%
67014 1
 
2.3%
66754 1
 
2.3%
66054 1
 
2.3%
66717 1
 
2.3%
69221 1
 
2.3%
69634 1
 
2.3%
69681 1
 
2.3%
70708 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
38358 1
2.3%
39996 1
2.3%
49635 1
2.3%
50993 1
2.3%
53232 1
2.3%
53478 1
2.3%
55621 1
2.3%
57571 1
2.3%
58213 1
2.3%
58605 1
2.3%
ValueCountFrequency (%)
78354 1
2.3%
77164 1
2.3%
75619 1
2.3%
74759 1
2.3%
73705 1
2.3%
71838 1
2.3%
71611 1
2.3%
71301 1
2.3%
71172 1
2.3%
70708 1
2.3%

대구
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45917.326
Minimum28798
Maximum64205
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-13T01:32:31.779529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28798
5-th percentile32967.3
Q136815
median45982
Q353745
95-th percentile62645.3
Maximum64205
Range35407
Interquartile range (IQR)16930

Descriptive statistics

Standard deviation10588.387
Coefficient of variation (CV)0.23059677
Kurtosis-1.2971583
Mean45917.326
Median Absolute Deviation (MAD)8488
Skewness0.22055218
Sum1974445
Variance1.1211394 × 108
MonotonicityNot monotonic
2023-12-13T01:32:31.894861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
30237 1
 
2.3%
35619 1
 
2.3%
52752 1
 
2.3%
53101 1
 
2.3%
53297 1
 
2.3%
54193 1
 
2.3%
54470 1
 
2.3%
50906 1
 
2.3%
51258 1
 
2.3%
52400 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
28798 1
2.3%
30237 1
2.3%
32945 1
2.3%
33168 1
2.3%
33781 1
2.3%
33941 1
2.3%
34615 1
2.3%
35152 1
2.3%
35407 1
2.3%
35619 1
2.3%
ValueCountFrequency (%)
64205 1
2.3%
63959 1
2.3%
62681 1
2.3%
62324 1
2.3%
61117 1
2.3%
59653 1
2.3%
58667 1
2.3%
58663 1
2.3%
58062 1
2.3%
54470 1
2.3%

인천
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39624.326
Minimum12721
Maximum64406
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-13T01:32:32.007828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12721
5-th percentile18939.5
Q128708.5
median40427
Q351125.5
95-th percentile60318.7
Maximum64406
Range51685
Interquartile range (IQR)22417

Descriptive statistics

Standard deviation14055.918
Coefficient of variation (CV)0.35472953
Kurtosis-1.0340792
Mean39624.326
Median Absolute Deviation (MAD)10834
Skewness-0.194606
Sum1703846
Variance1.9756884 × 108
MonotonicityNot monotonic
2023-12-13T01:32:32.123094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
12721 1
 
2.3%
19160 1
 
2.3%
48547 1
 
2.3%
49875 1
 
2.3%
50984 1
 
2.3%
51922 1
 
2.3%
51261 1
 
2.3%
51109 1
 
2.3%
51363 1
 
2.3%
51142 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
12721 1
2.3%
16509 1
2.3%
18915 1
2.3%
19160 1
2.3%
19451 1
2.3%
20403 1
2.3%
20701 1
2.3%
21918 1
2.3%
23446 1
2.3%
25995 1
2.3%
ValueCountFrequency (%)
64406 1
2.3%
63343 1
2.3%
60458 1
2.3%
59065 1
2.3%
54289 1
2.3%
51922 1
2.3%
51848 1
2.3%
51363 1
2.3%
51333 1
2.3%
51261 1
2.3%

광주
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26998.256
Minimum0
Maximum41608
Zeros4
Zeros (%)9.3%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-13T01:32:32.233183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q124261.5
median27105
Q333406
95-th percentile40123.9
Maximum41608
Range41608
Interquartile range (IQR)9144.5

Descriptive statistics

Standard deviation10523.681
Coefficient of variation (CV)0.38979113
Kurtosis2.0320383
Mean26998.256
Median Absolute Deviation (MAD)4567
Skewness-1.3238805
Sum1160925
Variance1.1074785 × 108
MonotonicityNot monotonic
2023-12-13T01:32:32.338309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0 4
 
9.3%
29927 1
 
2.3%
30496 1
 
2.3%
31420 1
 
2.3%
32668 1
 
2.3%
33202 1
 
2.3%
33610 1
 
2.3%
34501 1
 
2.3%
37167 1
 
2.3%
37513 1
 
2.3%
Other values (30) 30
69.8%
ValueCountFrequency (%)
0 4
9.3%
19469 1
 
2.3%
22139 1
 
2.3%
22389 1
 
2.3%
22435 1
 
2.3%
22538 1
 
2.3%
23250 1
 
2.3%
23673 1
 
2.3%
24850 1
 
2.3%
25169 1
 
2.3%
ValueCountFrequency (%)
41608 1
2.3%
41086 1
2.3%
40196 1
2.3%
39475 1
2.3%
38959 1
2.3%
37846 1
2.3%
37570 1
2.3%
37513 1
2.3%
37167 1
2.3%
34501 1
2.3%

대전
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct36
Distinct (%)83.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33412.814
Minimum0
Maximum53064
Zeros7
Zeros (%)16.3%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-13T01:32:32.444725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q129252
median40318
Q344898
95-th percentile52543.8
Maximum53064
Range53064
Interquartile range (IQR)15646

Descriptive statistics

Standard deviation17151.305
Coefficient of variation (CV)0.51331518
Kurtosis-0.065240304
Mean33412.814
Median Absolute Deviation (MAD)10652
Skewness-0.95696678
Sum1436751
Variance2.9416725 × 108
MonotonicityNot monotonic
2023-12-13T01:32:32.557689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 7
 
16.3%
30279 2
 
4.7%
52389 1
 
2.3%
45435 1
 
2.3%
42468 1
 
2.3%
42943 1
 
2.3%
44706 1
 
2.3%
49080 1
 
2.3%
51805 1
 
2.3%
52248 1
 
2.3%
Other values (26) 26
60.5%
ValueCountFrequency (%)
0 7
16.3%
23723 1
 
2.3%
24912 1
 
2.3%
28019 1
 
2.3%
29225 1
 
2.3%
29279 1
 
2.3%
29596 1
 
2.3%
29666 1
 
2.3%
30279 2
 
4.7%
30853 1
 
2.3%
ValueCountFrequency (%)
53064 1
2.3%
52931 1
2.3%
52561 1
2.3%
52389 1
2.3%
52297 1
2.3%
52270 1
2.3%
52248 1
2.3%
51805 1
2.3%
49080 1
2.3%
45435 1
2.3%

세종
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)32.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6311.0698
Minimum0
Maximum32533
Zeros30
Zeros (%)69.8%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-13T01:32:32.659004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q311448
95-th percentile29447.1
Maximum32533
Range32533
Interquartile range (IQR)11448

Descriptive statistics

Standard deviation10621.068
Coefficient of variation (CV)1.6829268
Kurtosis0.46363042
Mean6311.0698
Median Absolute Deviation (MAD)0
Skewness1.3887406
Sum271376
Variance1.1280709 × 108
MonotonicityNot monotonic
2023-12-13T01:32:32.802981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 30
69.8%
8112 1
 
2.3%
8504 1
 
2.3%
16705 1
 
2.3%
17955 1
 
2.3%
20753 1
 
2.3%
21337 1
 
2.3%
23490 1
 
2.3%
27522 1
 
2.3%
29661 1
 
2.3%
Other values (4) 4
 
9.3%
ValueCountFrequency (%)
0 30
69.8%
8112 1
 
2.3%
8504 1
 
2.3%
14392 1
 
2.3%
16705 1
 
2.3%
17955 1
 
2.3%
18141 1
 
2.3%
20753 1
 
2.3%
21337 1
 
2.3%
23490 1
 
2.3%
ValueCountFrequency (%)
32533 1
2.3%
32271 1
2.3%
29661 1
2.3%
27522 1
2.3%
23490 1
2.3%
21337 1
2.3%
20753 1
2.3%
18141 1
2.3%
17955 1
2.3%
16705 1
2.3%

울산
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)67.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12153.023
Minimum0
Maximum24000
Zeros15
Zeros (%)34.9%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-13T01:32:32.939187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median15913
Q319981.5
95-th percentile22527.5
Maximum24000
Range24000
Interquartile range (IQR)19981.5

Descriptive statistics

Standard deviation9368.5832
Coefficient of variation (CV)0.77088499
Kurtosis-1.6450654
Mean12153.023
Median Absolute Deviation (MAD)4815
Skewness-0.4305714
Sum522580
Variance87770351
MonotonicityNot monotonic
2023-12-13T01:32:33.066242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 15
34.9%
14257 1
 
2.3%
13074 1
 
2.3%
10926 1
 
2.3%
24000 1
 
2.3%
23433 1
 
2.3%
22583 1
 
2.3%
22028 1
 
2.3%
21563 1
 
2.3%
20728 1
 
2.3%
Other values (19) 19
44.2%
ValueCountFrequency (%)
0 15
34.9%
10926 1
 
2.3%
13074 1
 
2.3%
14257 1
 
2.3%
14267 1
 
2.3%
14442 1
 
2.3%
15479 1
 
2.3%
15913 1
 
2.3%
16418 1
 
2.3%
17209 1
 
2.3%
ValueCountFrequency (%)
24000 1
2.3%
23433 1
2.3%
22583 1
2.3%
22028 1
2.3%
21563 1
2.3%
20728 1
2.3%
20572 1
2.3%
20299 1
2.3%
20276 1
2.3%
20231 1
2.3%

경기
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean144131.33
Minimum48937
Maximum242893
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-13T01:32:33.198216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum48937
5-th percentile52639.4
Q1107970.5
median134777
Q3192633
95-th percentile228719.6
Maximum242893
Range193956
Interquartile range (IQR)84662.5

Descriptive statistics

Standard deviation55847.559
Coefficient of variation (CV)0.3874769
Kurtosis-0.99918597
Mean144131.33
Median Absolute Deviation (MAD)49612
Skewness-0.071334247
Sum6197647
Variance3.1189498 × 109
MonotonicityNot monotonic
2023-12-13T01:32:33.337939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
52440 1
 
2.3%
48937 1
 
2.3%
167398 1
 
2.3%
173199 1
 
2.3%
179951 1
 
2.3%
184389 1
 
2.3%
190591 1
 
2.3%
194161 1
 
2.3%
191105 1
 
2.3%
195983 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
48937 1
2.3%
51242 1
2.3%
52440 1
2.3%
54434 1
2.3%
64350 1
2.3%
68013 1
2.3%
69551 1
2.3%
85147 1
2.3%
91538 1
2.3%
99859 1
2.3%
ValueCountFrequency (%)
242893 1
2.3%
237594 1
2.3%
229299 1
2.3%
223505 1
2.3%
217553 1
2.3%
205988 1
2.3%
201810 1
2.3%
197883 1
2.3%
196320 1
2.3%
195983 1
2.3%

강원
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46204.349
Minimum22929
Maximum55353
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-13T01:32:33.477927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22929
5-th percentile36690.4
Q144720
median47932
Q349319.5
95-th percentile52665.3
Maximum55353
Range32424
Interquartile range (IQR)4599.5

Descriptive statistics

Standard deviation5933.0646
Coefficient of variation (CV)0.12840922
Kurtosis4.8958464
Mean46204.349
Median Absolute Deviation (MAD)2307
Skewness-1.8129685
Sum1986787
Variance35201255
MonotonicityNot monotonic
2023-12-13T01:32:33.948352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
36511 1
 
2.3%
38315 1
 
2.3%
47932 1
 
2.3%
47973 1
 
2.3%
47748 1
 
2.3%
47955 1
 
2.3%
48316 1
 
2.3%
48523 1
 
2.3%
48650 1
 
2.3%
48838 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
22929 1
2.3%
32424 1
2.3%
36511 1
2.3%
38305 1
2.3%
38315 1
2.3%
39902 1
2.3%
41357 1
2.3%
41705 1
2.3%
42288 1
2.3%
43902 1
2.3%
ValueCountFrequency (%)
55353 1
2.3%
54630 1
2.3%
52755 1
2.3%
51858 1
2.3%
50978 1
2.3%
50830 1
2.3%
50435 1
2.3%
50263 1
2.3%
50221 1
2.3%
50112 1
2.3%

충북
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35887.977
Minimum22075
Maximum45771
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-13T01:32:34.074706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22075
5-th percentile27060.4
Q133623
median37098
Q338703
95-th percentile43794.3
Maximum45771
Range23696
Interquartile range (IQR)5080

Descriptive statistics

Standard deviation5374.2403
Coefficient of variation (CV)0.14975044
Kurtosis0.39813907
Mean35887.977
Median Absolute Deviation (MAD)2623
Skewness-0.69488603
Sum1543183
Variance28882459
MonotonicityNot monotonic
2023-12-13T01:32:34.195139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
33321 1
 
2.3%
27045 1
 
2.3%
36738 1
 
2.3%
37098 1
 
2.3%
37356 1
 
2.3%
36821 1
 
2.3%
37744 1
 
2.3%
38121 1
 
2.3%
38487 1
 
2.3%
38669 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
22075 1
2.3%
23696 1
2.3%
27045 1
2.3%
27199 1
2.3%
27655 1
2.3%
28390 1
2.3%
28527 1
2.3%
28957 1
2.3%
33321 1
2.3%
33390 1
2.3%
ValueCountFrequency (%)
45771 1
2.3%
45530 1
2.3%
43929 1
2.3%
42582 1
2.3%
41438 1
2.3%
40050 1
2.3%
39754 1
2.3%
39503 1
2.3%
39138 1
2.3%
38923 1
2.3%

충남
Real number (ℝ)

UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46251.628
Minimum26842
Maximum59091
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-13T01:32:34.322277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26842
5-th percentile34759.1
Q142150.5
median46660
Q351007
95-th percentile57145
Maximum59091
Range32249
Interquartile range (IQR)8856.5

Descriptive statistics

Standard deviation7573.0803
Coefficient of variation (CV)0.16373651
Kurtosis-0.078967561
Mean46251.628
Median Absolute Deviation (MAD)4408
Skewness-0.40537586
Sum1988820
Variance57351545
MonotonicityNot monotonic
2023-12-13T01:32:34.456869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
50822 1
 
2.3%
54851 1
 
2.3%
42297 1
 
2.3%
42551 1
 
2.3%
43023 1
 
2.3%
42780 1
 
2.3%
43651 1
 
2.3%
47778 1
 
2.3%
45900 1
 
2.3%
45832 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
26842 1
2.3%
29853 1
2.3%
34708 1
2.3%
35219 1
2.3%
36972 1
2.3%
37969 1
2.3%
38429 1
2.3%
39427 1
2.3%
39673 1
2.3%
41293 1
2.3%
ValueCountFrequency (%)
59091 1
2.3%
57443 1
2.3%
57195 1
2.3%
56695 1
2.3%
56512 1
2.3%
56316 1
2.3%
55387 1
2.3%
54851 1
2.3%
53263 1
2.3%
51796 1
2.3%

경북
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51088.209
Minimum29802
Maximum65819
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-13T01:32:34.600836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum29802
5-th percentile41766.7
Q146378.5
median52131
Q355032
95-th percentile62555.3
Maximum65819
Range36017
Interquartile range (IQR)8653.5

Descriptive statistics

Standard deviation7277.5385
Coefficient of variation (CV)0.14245045
Kurtosis0.82240022
Mean51088.209
Median Absolute Deviation (MAD)5008
Skewness-0.44739231
Sum2196793
Variance52962566
MonotonicityNot monotonic
2023-12-13T01:32:34.787740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
44853 1
 
2.3%
41709 1
 
2.3%
53149 1
 
2.3%
53547 1
 
2.3%
53143 1
 
2.3%
53767 1
 
2.3%
53195 1
 
2.3%
57139 1
 
2.3%
57154 1
 
2.3%
56689 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
29802 1
2.3%
36017 1
2.3%
41709 1
2.3%
42286 1
2.3%
42422 1
2.3%
43040 1
2.3%
43494 1
2.3%
44062 1
2.3%
44853 1
2.3%
45397 1
2.3%
ValueCountFrequency (%)
65819 1
2.3%
64549 1
2.3%
62702 1
2.3%
61235 1
2.3%
59587 1
2.3%
58265 1
2.3%
57980 1
2.3%
57154 1
2.3%
57139 1
2.3%
56689 1
2.3%

경남
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63348.093
Minimum37560
Maximum76081
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-13T01:32:34.984072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37560
5-th percentile52198.3
Q159450
median64894
Q367910
95-th percentile73708.6
Maximum76081
Range38521
Interquartile range (IQR)8460

Descriptive statistics

Standard deviation8295.0539
Coefficient of variation (CV)0.13094402
Kurtosis2.3410443
Mean63348.093
Median Absolute Deviation (MAD)4770
Skewness-1.1935051
Sum2723968
Variance68807919
MonotonicityNot monotonic
2023-12-13T01:32:35.144714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
71623 1
 
2.3%
72818 1
 
2.3%
66736 1
 
2.3%
67492 1
 
2.3%
67659 1
 
2.3%
68429 1
 
2.3%
67128 1
 
2.3%
67209 1
 
2.3%
68161 1
 
2.3%
67517 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
37560 1
2.3%
38521 1
2.3%
52014 1
2.3%
53857 1
2.3%
53965 1
2.3%
55712 1
2.3%
57139 1
2.3%
57399 1
2.3%
58086 1
2.3%
58499 1
2.3%
ValueCountFrequency (%)
76081 1
2.3%
74890 1
2.3%
73760 1
2.3%
73246 1
2.3%
72980 1
2.3%
72818 1
2.3%
71845 1
2.3%
71623 1
2.3%
69882 1
2.3%
68429 1
2.3%

전북
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53469.721
Minimum26110
Maximum70713
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-13T01:32:35.294704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26110
5-th percentile46651.6
Q148492
median49808
Q360410.5
95-th percentile68676.9
Maximum70713
Range44603
Interquartile range (IQR)11918.5

Descriptive statistics

Standard deviation9479.0734
Coefficient of variation (CV)0.17727928
Kurtosis0.98040015
Mean53469.721
Median Absolute Deviation (MAD)2989
Skewness-0.27298727
Sum2299198
Variance89852832
MonotonicityNot monotonic
2023-12-13T01:32:35.418002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
52013 1
 
2.3%
46819 1
 
2.3%
48564 1
 
2.3%
48682 1
 
2.3%
48420 1
 
2.3%
48943 1
 
2.3%
48725 1
 
2.3%
48961 1
 
2.3%
49074 1
 
2.3%
48600 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
26110 1
2.3%
30425 1
2.3%
46633 1
2.3%
46819 1
2.3%
46967 1
2.3%
47307 1
2.3%
47455 1
2.3%
47663 1
2.3%
47947 1
2.3%
48047 1
2.3%
ValueCountFrequency (%)
70713 1
2.3%
69748 1
2.3%
68823 1
2.3%
67362 1
2.3%
67286 1
2.3%
66187 1
2.3%
66107 1
2.3%
64754 1
2.3%
62949 1
2.3%
62849 1
2.3%

전남
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57979.023
Minimum26862
Maximum78143
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-13T01:32:35.531237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26862
5-th percentile50401.9
Q153390
median54817
Q362122.5
95-th percentile77312.6
Maximum78143
Range51281
Interquartile range (IQR)8732.5

Descriptive statistics

Standard deviation10013.764
Coefficient of variation (CV)0.17271357
Kurtosis2.0858002
Mean57979.023
Median Absolute Deviation (MAD)2775
Skewness-0.11691268
Sum2493098
Variance1.0027548 × 108
MonotonicityNot monotonic
2023-12-13T01:32:35.686437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
50343 1
 
2.3%
50932 1
 
2.3%
54667 1
 
2.3%
54706 1
 
2.3%
54575 1
 
2.3%
55025 1
 
2.3%
53432 1
 
2.3%
52884 1
 
2.3%
52989 1
 
2.3%
53348 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
26862 1
2.3%
35255 1
2.3%
50343 1
2.3%
50932 1
2.3%
52042 1
2.3%
52439 1
2.3%
52884 1
2.3%
52924 1
2.3%
52978 1
2.3%
52989 1
2.3%
ValueCountFrequency (%)
78143 1
2.3%
77499 1
2.3%
77345 1
2.3%
77021 1
2.3%
75869 1
2.3%
70989 1
2.3%
67288 1
2.3%
65448 1
2.3%
64529 1
2.3%
62878 1
2.3%

제주
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14629.186
Minimum9008
Maximum20273
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-13T01:32:35.826565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9008
5-th percentile9993.8
Q112792
median14521
Q316458
95-th percentile19841.1
Maximum20273
Range11265
Interquartile range (IQR)3666

Descriptive statistics

Standard deviation2932.4827
Coefficient of variation (CV)0.20045426
Kurtosis-0.55246448
Mean14629.186
Median Absolute Deviation (MAD)1991
Skewness-0.040274804
Sum629055
Variance8599454.9
MonotonicityNot monotonic
2023-12-13T01:32:35.955282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
9972 1
 
2.3%
9650 1
 
2.3%
15764 1
 
2.3%
15913 1
 
2.3%
16208 1
 
2.3%
16214 1
 
2.3%
16283 1
 
2.3%
16404 1
 
2.3%
16675 1
 
2.3%
16710 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
9008 1
2.3%
9650 1
2.3%
9972 1
2.3%
10190 1
2.3%
10292 1
2.3%
10618 1
2.3%
10972 1
2.3%
11265 1
2.3%
11294 1
2.3%
11915 1
2.3%
ValueCountFrequency (%)
20273 1
2.3%
19926 1
2.3%
19898 1
2.3%
19329 1
2.3%
18748 1
2.3%
17721 1
2.3%
17268 1
2.3%
16955 1
2.3%
16710 1
2.3%
16675 1
2.3%

Interactions

2023-12-13T01:32:28.050401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:31:54.947681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:31:56.984334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:31:58.957943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:00.908005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:03.099992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:04.645364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:06.087106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:07.601050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:09.424768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:11.159555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:13.095868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:16.282860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:18.106341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:20.511299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:22.844816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:24.798486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:26.374180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:28.144148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:31:55.061414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:31:57.087085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:31:59.057002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-13T01:32:00.234644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:02.485391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:04.162128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:05.578884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:07.097218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:08.941156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:10.497281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:12.457633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:15.610169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:17.512832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:19.557972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:21.935990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:24.132553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:25.863380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:27.478304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:29.471717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:31:56.491790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:31:58.434471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:00.357061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:02.577399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:04.252616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:05.663026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:07.191719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:09.018515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:10.597664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:12.573963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:15.731589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:17.630508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:19.717356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:22.014734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:24.243943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:25.955200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:27.571203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:29.564144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:31:56.590935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:31:58.538961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:00.491645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:02.695714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:04.343484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:05.755216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:07.293311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:09.105387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:10.734370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:12.682640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:15.865339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:17.746891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:19.874294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:22.097561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:24.385175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:26.031466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:27.678932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:29.661557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:31:56.707754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:31:58.650012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:00.611962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:02.815394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:04.435741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:05.853999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:07.382891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:09.191677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:10.856305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:12.785671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:15.983726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:17.854986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:20.044884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:22.194151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:24.485787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:26.139682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:27.790870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:29.783727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:31:56.791508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:31:58.749879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:00.708889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:02.910376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:04.507215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:05.938809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:07.451525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:09.261807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:10.970752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:12.897511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:16.074305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:17.937641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:20.182376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:22.278870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:24.583711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:26.233042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:27.876113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:29.861730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:31:56.888319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:31:58.852472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:00.797990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:03.012037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:04.578390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:06.014927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:07.529722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:09.348619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:11.066746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:12.996134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:16.185534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:18.013199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:20.344262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:22.738609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:24.697546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:26.303374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:32:27.963852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:32:36.062565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분서울부산대구인천광주대전세종울산경기강원충북충남경북경남전북전남제주
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
1.0001.0000.8400.8380.8920.9680.8720.8330.8260.7650.9640.8940.8670.7330.8760.8190.8130.6520.994
서울1.0000.8401.0000.8610.7460.7250.6530.5840.4640.7260.7120.9010.9330.7790.9300.8340.7950.6890.794
부산1.0000.8380.8611.0000.7390.7470.8650.7920.6910.8290.8400.8460.8290.9080.8580.7340.7570.7580.877
대구1.0000.8920.7460.7391.0000.8300.8500.8600.6600.7860.8720.7660.7350.6800.7870.7500.6460.5530.909
인천1.0000.9680.7250.7470.8301.0000.7120.8460.7150.8610.9490.8600.8710.8010.8950.8550.8390.6500.948
광주1.0000.8720.6530.8650.8500.7121.0000.9380.7960.8710.8340.7490.7190.6690.7740.8110.6030.4750.888
대전1.0000.8330.5840.7920.8600.8460.9381.0000.6080.8890.8520.8830.7370.7160.7960.7340.8930.8390.824
세종1.0000.8260.4640.6910.6600.7150.7960.6081.0000.6860.6750.8150.7880.6030.8300.8500.6240.5980.830
울산1.0000.7650.7260.8290.7860.8610.8710.8890.6861.0000.9020.9330.8120.8160.8300.7140.9190.8560.803
경기1.0000.9640.7120.8400.8720.9490.8340.8520.6750.9021.0000.7920.7880.7770.8220.7310.8100.7070.970
강원1.0000.8940.9010.8460.7660.8600.7490.8830.8150.9330.7921.0000.9030.8710.8790.7270.9180.8980.854
충북1.0000.8670.9330.8290.7350.8710.7190.7370.7880.8120.7880.9031.0000.8340.9820.8970.7670.6970.823
충남1.0000.7330.7790.9080.6800.8010.6690.7160.6030.8160.7770.8710.8341.0000.8310.7230.7640.6980.715
경북1.0000.8760.9300.8580.7870.8950.7740.7960.8300.8300.8220.8790.9820.8311.0000.9080.8370.7940.845
경남1.0000.8190.8340.7340.7500.8550.8110.7340.8500.7140.7310.7270.8970.7230.9081.0000.7720.6510.758
전북1.0000.8130.7950.7570.6460.8390.6030.8930.6240.9190.8100.9180.7670.7640.8370.7721.0000.9390.742
전남1.0000.6520.6890.7580.5530.6500.4750.8390.5980.8560.7070.8980.6970.6980.7940.6510.9391.0000.697
제주1.0000.9940.7940.8770.9090.9480.8880.8240.8300.8030.9700.8540.8230.7150.8450.7580.7420.6971.000
2023-12-13T01:32:36.250797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
서울부산대구인천광주대전세종울산경기강원충북충남경북경남전북전남제주
1.0000.7130.9800.9520.9310.9810.9280.5980.8790.9300.8090.8780.1230.9660.5710.0580.0100.994
서울0.7131.0000.7600.6160.5910.7210.5510.2150.4130.5430.9200.8650.2540.6350.3610.5400.5450.691
부산0.9800.7601.0000.9070.8780.9630.8780.5980.8150.8860.8670.9260.1770.9420.5560.1690.0670.970
대구0.9520.6160.9071.0000.9430.9480.9500.5920.9080.9310.7000.7790.0250.9490.596-0.142-0.1080.954
인천0.9310.5910.8780.9431.0000.9160.9640.6400.9620.9800.6500.730-0.0940.9360.526-0.183-0.1650.950
광주0.9810.7210.9630.9480.9161.0000.9220.6270.8660.9260.8140.8790.1060.9510.5200.0660.0160.975
대전0.9280.5510.8780.9500.9640.9221.0000.6420.9450.9640.6380.724-0.1270.9410.493-0.151-0.1450.944
세종0.5980.2150.5980.5920.6400.6270.6421.0000.7170.6980.4160.5170.1390.5860.388-0.128-0.2810.620
울산0.8790.4130.8150.9080.9620.8660.9450.7171.0000.9680.5070.620-0.0740.8990.525-0.303-0.3310.903
경기0.9300.5430.8860.9310.9800.9260.9640.6980.9681.0000.6340.730-0.1020.9360.463-0.188-0.1960.946
강원0.8090.9200.8670.7000.6500.8140.6380.4160.5070.6341.0000.9690.2970.7430.4150.5110.4650.787
충북0.8780.8650.9260.7790.7300.8790.7240.5170.6200.7300.9691.0000.2770.8160.4660.4210.3240.860
충남0.1230.2540.1770.025-0.0940.106-0.1270.139-0.074-0.1020.2970.2771.0000.0300.4230.4450.2390.071
경북0.9660.6350.9420.9490.9360.9510.9410.5860.8990.9360.7430.8160.0301.0000.571-0.049-0.0520.968
경남0.5710.3610.5560.5960.5260.5200.4930.3880.5250.4630.4150.4660.4230.5711.000-0.142-0.2530.544
전북0.0580.5400.169-0.142-0.1830.066-0.151-0.128-0.303-0.1880.5110.4210.445-0.049-0.1421.0000.8320.020
전남0.0100.5450.067-0.108-0.1650.016-0.145-0.281-0.331-0.1960.4650.3240.239-0.052-0.2530.8321.000-0.020
제주0.9940.6910.9700.9540.9500.9750.9440.6200.9030.9460.7870.8600.0710.9680.5440.020-0.0201.000

Missing values

2023-12-13T01:32:30.000206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:32:30.237026image/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

구분서울부산대구인천광주대전세종울산경기강원충북충남경북경남전북전남제주
01982667554164485582133023712721000052440365113332150822448537162352013503439972
11983669733174243496353561919160000048937383152704554851417097281846819509329650
219846822811745755099336469207010000512423830527199563164228672980480475297810190
319856969511817665323233941194510000544343990227655565124242273760490435454110292
4198671662918163153478331681650922538000643504135728527574434349452014541755732710618
5198773768818999755621337811891522435000680134170528390571954304053857546445912310972
6198876712319976857571351522040323673000695514228828957590914406255712574736212811294
71989810069206952586053294521918223892372300851474568433390384294539757399616476452911915
81990843262210022615303461523446232503027900915384683934461396734644458499629496728812429
91991884648217450642063716125995251693227900998594787035926412934841860124647547098913155
구분서울부산대구인천광주대전세종울산경기강원충북충남경북경남전북전남제주
3320151093038214256713015866351021375135180517955202761978834830138627457075534165803491925243916955
3420161107972213532716115866751333375705224820753205722018104892839503466605798066965496485292417268
3520171120458215234718385965351848378465238921337207282059884924440050473945826567591498085352417721
3620181160586220977737056111754289389595229723490215632175535083041438500915958769882511615489918748
3720191195051225529747596232459065394755256127522220282235055185842582517966123571845527875685119329
3820201221322229543756196268160458401965306429661225832292995275543929532636270273246541035829419926
3920211261421239095771646395963343410865227032271234332375945463045530553876454974890556756064719898
4020221280994241420783546420564406416085293132533240002428935535345771566956581976081565356211720273
412022남657271126471399963540730971221392801918141109261087063242423696298533601737560304253525511265
422022여62372311494938358287983343519469249121439213074134187229292207526842298023852126110268629008