Overview

Dataset statistics

Number of variables19
Number of observations24
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.1 KiB
Average record size in memory175.5 B

Variable types

Text1
Numeric18

Dataset

Description지역별(광역시, 도) 공무원 퇴직자수 현황(2001~)에 대한 데이터입니다. 2001년부터 시작되며 연 단위로 구분됩니다.
URLhttps://www.data.go.kr/data/15054050/fileData.do

Alerts

is highly overall correlated with 서울 and 16 other fieldsHigh correlation
서울 is highly overall correlated with and 16 other fieldsHigh correlation
부산 is highly overall correlated with and 16 other fieldsHigh correlation
대구 is highly overall correlated with and 16 other fieldsHigh correlation
인천 is highly overall correlated with and 16 other fieldsHigh correlation
광주 is highly overall correlated with and 16 other fieldsHigh correlation
대전 is highly overall correlated with and 16 other fieldsHigh correlation
세종 is highly overall correlated with and 16 other fieldsHigh correlation
울산 is highly overall correlated with and 16 other fieldsHigh correlation
경기 is highly overall correlated with and 16 other fieldsHigh correlation
강원 is highly overall correlated with and 16 other fieldsHigh correlation
충북 is highly overall correlated with and 16 other fieldsHigh correlation
충남 is highly overall correlated with and 16 other fieldsHigh correlation
경북 is highly overall correlated with and 16 other fieldsHigh correlation
경남 is highly overall correlated with and 16 other fieldsHigh correlation
전북 is highly overall correlated with and 16 other fieldsHigh correlation
전남 is highly overall correlated with and 16 other fieldsHigh correlation
제주 is highly overall correlated with and 16 other fieldsHigh correlation
구분 has unique valuesUnique
has unique valuesUnique
서울 has unique valuesUnique
부산 has unique valuesUnique
대구 has unique valuesUnique
광주 has unique valuesUnique
대전 has unique valuesUnique
울산 has unique valuesUnique
경기 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
세종 has 11 (45.8%) zerosZeros

Reproduction

Analysis started2023-12-12 09:06:20.508577
Analysis finished2023-12-12 09:06:59.264266
Duration38.76 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size324.0 B
2023-12-12T18:06:59.410531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.75
Min length1

Characters and Unicode

Total characters90
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

Unique24 ?
Unique (%)100.0%

Sample

1st row2001
2nd row2002
3rd row2003
4th row2004
5th row2005
ValueCountFrequency (%)
2001 1
 
4.2%
2002 1
 
4.2%
1
 
4.2%
2022 1
 
4.2%
2021 1
 
4.2%
2020 1
 
4.2%
2019 1
 
4.2%
2018 1
 
4.2%
2017 1
 
4.2%
2016 1
 
4.2%
Other values (14) 14
58.3%
2023-12-12T18:06:59.771423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 33
36.7%
2 28
31.1%
1 13
 
14.4%
3 2
 
2.2%
4 2
 
2.2%
5 2
 
2.2%
6 2
 
2.2%
7 2
 
2.2%
8 2
 
2.2%
9 2
 
2.2%
Other values (2) 2
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 88
97.8%
Other Letter 2
 
2.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 33
37.5%
2 28
31.8%
1 13
 
14.8%
3 2
 
2.3%
4 2
 
2.3%
5 2
 
2.3%
6 2
 
2.3%
7 2
 
2.3%
8 2
 
2.3%
9 2
 
2.3%
Other Letter
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 88
97.8%
Hangul 2
 
2.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 33
37.5%
2 28
31.8%
1 13
 
14.8%
3 2
 
2.3%
4 2
 
2.3%
5 2
 
2.3%
6 2
 
2.3%
7 2
 
2.3%
8 2
 
2.3%
9 2
 
2.3%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 88
97.8%
Hangul 2
 
2.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 33
37.5%
2 28
31.8%
1 13
 
14.8%
3 2
 
2.3%
4 2
 
2.3%
5 2
 
2.3%
6 2
 
2.3%
7 2
 
2.3%
8 2
 
2.3%
9 2
 
2.3%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%


Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34251.75
Minimum19722
Maximum54993
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T18:06:59.916572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19722
5-th percentile23272.75
Q128869
median35016.5
Q338743.75
95-th percentile46922.55
Maximum54993
Range35271
Interquartile range (IQR)9874.75

Descriptive statistics

Standard deviation8463.915
Coefficient of variation (CV)0.24710898
Kurtosis0.12882763
Mean34251.75
Median Absolute Deviation (MAD)5415.5
Skewness0.48790235
Sum822042
Variance71637858
MonotonicityNot monotonic
2023-12-12T18:07:00.074188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
29509 1
 
4.2%
44010 1
 
4.2%
19722 1
 
4.2%
35271 1
 
4.2%
54993 1
 
4.2%
44676 1
 
4.2%
47319 1
 
4.2%
39781 1
 
4.2%
37710 1
 
4.2%
37059 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
19722 1
4.2%
23095 1
4.2%
24280 1
4.2%
24899 1
4.2%
26163 1
4.2%
27384 1
4.2%
29364 1
4.2%
29509 1
4.2%
30021 1
4.2%
30035 1
4.2%
ValueCountFrequency (%)
54993 1
4.2%
47319 1
4.2%
44676 1
4.2%
44010 1
4.2%
40340 1
4.2%
39781 1
4.2%
38398 1
4.2%
37710 1
4.2%
37059 1
4.2%
36934 1
4.2%

서울
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8870.875
Minimum5288
Maximum13784
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T18:07:00.516610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5288
5-th percentile6400.6
Q17648.5
median8764.5
Q39780
95-th percentile11658.15
Maximum13784
Range8496
Interquartile range (IQR)2131.5

Descriptive statistics

Standard deviation1938.9901
Coefficient of variation (CV)0.21857935
Kurtosis0.50886515
Mean8870.875
Median Absolute Deviation (MAD)1066
Skewness0.47972542
Sum212901
Variance3759682.7
MonotonicityNot monotonic
2023-12-12T18:07:00.648390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
7780 1
 
4.2%
9912 1
 
4.2%
5288 1
 
4.2%
8496 1
 
4.2%
13784 1
 
4.2%
10694 1
 
4.2%
11679 1
 
4.2%
9705 1
 
4.2%
9436 1
 
4.2%
9033 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
5288 1
4.2%
6364 1
4.2%
6608 1
4.2%
6822 1
4.2%
6848 1
4.2%
7254 1
4.2%
7780 1
4.2%
7820 1
4.2%
8172 1
4.2%
8320 1
4.2%
ValueCountFrequency (%)
13784 1
4.2%
11679 1
4.2%
11540 1
4.2%
10694 1
4.2%
10216 1
4.2%
9912 1
4.2%
9736 1
4.2%
9705 1
4.2%
9577 1
4.2%
9436 1
4.2%

부산
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2252.8333
Minimum1378
Maximum3456
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T18:07:00.808577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1378
5-th percentile1434
Q11796.5
median2089
Q32615.5
95-th percentile3388.65
Maximum3456
Range2078
Interquartile range (IQR)819

Descriptive statistics

Standard deviation622.61878
Coefficient of variation (CV)0.27637143
Kurtosis-0.75293842
Mean2252.8333
Median Absolute Deviation (MAD)487.5
Skewness0.44225805
Sum54068
Variance387654.14
MonotonicityNot monotonic
2023-12-12T18:07:00.981882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1829 1
 
4.2%
3456 1
 
4.2%
1378 1
 
4.2%
2063 1
 
4.2%
3441 1
 
4.2%
2861 1
 
4.2%
3092 1
 
4.2%
2592 1
 
4.2%
2561 1
 
4.2%
2478 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
1378 1
4.2%
1416 1
4.2%
1536 1
4.2%
1538 1
4.2%
1579 1
4.2%
1699 1
4.2%
1829 1
4.2%
1935 1
4.2%
1949 1
4.2%
1963 1
4.2%
ValueCountFrequency (%)
3456 1
4.2%
3441 1
4.2%
3092 1
4.2%
3041 1
4.2%
2861 1
4.2%
2686 1
4.2%
2592 1
4.2%
2561 1
4.2%
2513 1
4.2%
2478 1
4.2%

대구
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1524.375
Minimum759
Maximum2561
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T18:07:01.221849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum759
5-th percentile904.65
Q11130
median1536.5
Q31763.75
95-th percentile2142.3
Maximum2561
Range1802
Interquartile range (IQR)633.75

Descriptive statistics

Standard deviation452.62104
Coefficient of variation (CV)0.29692237
Kurtosis-0.3462026
Mean1524.375
Median Absolute Deviation (MAD)346.5
Skewness0.26899287
Sum36585
Variance204865.81
MonotonicityNot monotonic
2023-12-12T18:07:01.425066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1157 1
 
4.2%
2093 1
 
4.2%
759 1
 
4.2%
1802 1
 
4.2%
2561 1
 
4.2%
1964 1
 
4.2%
2151 1
 
4.2%
1751 1
 
4.2%
1599 1
 
4.2%
1697 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
759 1
4.2%
888 1
4.2%
999 1
4.2%
1033 1
4.2%
1036 1
4.2%
1049 1
4.2%
1157 1
4.2%
1223 1
4.2%
1397 1
4.2%
1419 1
4.2%
ValueCountFrequency (%)
2561 1
4.2%
2151 1
4.2%
2093 1
4.2%
2009 1
4.2%
1964 1
4.2%
1802 1
4.2%
1751 1
4.2%
1744 1
4.2%
1723 1
4.2%
1697 1
4.2%

인천
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1192.4583
Minimum735
Maximum2292
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T18:07:01.574211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum735
5-th percentile751.4
Q1886
median1098.5
Q31368.75
95-th percentile1969.7
Maximum2292
Range1557
Interquartile range (IQR)482.75

Descriptive statistics

Standard deviation421.7843
Coefficient of variation (CV)0.35370989
Kurtosis0.76144074
Mean1192.4583
Median Absolute Deviation (MAD)249
Skewness1.0967664
Sum28619
Variance177902
MonotonicityNot monotonic
2023-12-12T18:07:01.769904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
886 2
 
8.3%
900 1
 
4.2%
1404 1
 
4.2%
1406 1
 
4.2%
2292 1
 
4.2%
1982 1
 
4.2%
1900 1
 
4.2%
1609 1
 
4.2%
1338 1
 
4.2%
1203 1
 
4.2%
Other values (13) 13
54.2%
ValueCountFrequency (%)
735 1
4.2%
749 1
4.2%
765 1
4.2%
771 1
4.2%
790 1
4.2%
886 2
8.3%
887 1
4.2%
896 1
4.2%
900 1
4.2%
1052 1
4.2%
ValueCountFrequency (%)
2292 1
4.2%
1982 1
4.2%
1900 1
4.2%
1609 1
4.2%
1406 1
4.2%
1404 1
4.2%
1357 1
4.2%
1338 1
4.2%
1313 1
4.2%
1301 1
4.2%

광주
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1030.625
Minimum409
Maximum1449
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T18:07:01.963538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum409
5-th percentile762.5
Q1893.75
median1042
Q31176.25
95-th percentile1385.45
Maximum1449
Range1040
Interquartile range (IQR)282.5

Descriptive statistics

Standard deviation233.51079
Coefficient of variation (CV)0.22657202
Kurtosis0.9022108
Mean1030.625
Median Absolute Deviation (MAD)152.5
Skewness-0.53831548
Sum24735
Variance54527.288
MonotonicityNot monotonic
2023-12-12T18:07:02.129477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1023 1
 
4.2%
1400 1
 
4.2%
409 1
 
4.2%
1040 1
 
4.2%
1449 1
 
4.2%
1167 1
 
4.2%
1303 1
 
4.2%
1129 1
 
4.2%
1117 1
 
4.2%
1102 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
409 1
4.2%
761 1
4.2%
771 1
4.2%
775 1
4.2%
782 1
4.2%
878 1
4.2%
899 1
4.2%
923 1
4.2%
981 1
4.2%
1023 1
4.2%
ValueCountFrequency (%)
1449 1
4.2%
1400 1
4.2%
1303 1
4.2%
1263 1
4.2%
1211 1
4.2%
1204 1
4.2%
1167 1
4.2%
1129 1
4.2%
1117 1
4.2%
1102 1
4.2%

대전
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1505.5417
Minimum685
Maximum2719
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T18:07:02.296533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum685
5-th percentile904.8
Q11219.25
median1437
Q31796.5
95-th percentile2212.9
Maximum2719
Range2034
Interquartile range (IQR)577.25

Descriptive statistics

Standard deviation468.29636
Coefficient of variation (CV)0.31104842
Kurtosis0.56948259
Mean1505.5417
Median Absolute Deviation (MAD)334.5
Skewness0.6090212
Sum36133
Variance219301.48
MonotonicityNot monotonic
2023-12-12T18:07:02.466878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1359 1
 
4.2%
2031 1
 
4.2%
685 1
 
4.2%
1560 1
 
4.2%
2245 1
 
4.2%
1748 1
 
4.2%
1947 1
 
4.2%
1666 1
 
4.2%
1634 1
 
4.2%
1879 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
685 1
4.2%
885 1
4.2%
1017 1
4.2%
1023 1
4.2%
1026 1
4.2%
1190 1
4.2%
1229 1
4.2%
1246 1
4.2%
1291 1
4.2%
1312 1
4.2%
ValueCountFrequency (%)
2719 1
4.2%
2245 1
4.2%
2031 1
4.2%
1947 1
4.2%
1879 1
4.2%
1801 1
4.2%
1795 1
4.2%
1748 1
4.2%
1666 1
4.2%
1634 1
4.2%

세종
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)58.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean308.91667
Minimum0
Maximum1139
Zeros11
Zeros (%)45.8%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T18:07:02.640270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median84.5
Q3553
95-th percentile906.6
Maximum1139
Range1139
Interquartile range (IQR)553

Descriptive statistics

Standard deviation364.46147
Coefficient of variation (CV)1.1798052
Kurtosis-0.65725489
Mean308.91667
Median Absolute Deviation (MAD)84.5
Skewness0.7589256
Sum7414
Variance132832.17
MonotonicityNot monotonic
2023-12-12T18:07:02.793240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 11
45.8%
5 1
 
4.2%
164 1
 
4.2%
465 1
 
4.2%
526 1
 
4.2%
519 1
 
4.2%
571 1
 
4.2%
547 1
 
4.2%
617 1
 
4.2%
798 1
 
4.2%
Other values (4) 4
 
16.7%
ValueCountFrequency (%)
0 11
45.8%
5 1
 
4.2%
164 1
 
4.2%
331 1
 
4.2%
465 1
 
4.2%
519 1
 
4.2%
526 1
 
4.2%
547 1
 
4.2%
571 1
 
4.2%
617 1
 
4.2%
ValueCountFrequency (%)
1139 1
4.2%
924 1
4.2%
808 1
4.2%
798 1
4.2%
617 1
4.2%
571 1
4.2%
547 1
4.2%
526 1
4.2%
519 1
4.2%
465 1
4.2%

울산
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean420.125
Minimum236
Maximum754
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T18:07:02.960609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum236
5-th percentile249.9
Q1286.75
median386.5
Q3520.75
95-th percentile675.7
Maximum754
Range518
Interquartile range (IQR)234

Descriptive statistics

Standard deviation147.6389
Coefficient of variation (CV)0.3514166
Kurtosis-0.47066234
Mean420.125
Median Absolute Deviation (MAD)106
Skewness0.65362799
Sum10083
Variance21797.245
MonotonicityNot monotonic
2023-12-12T18:07:03.117198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
283 1
 
4.2%
585 1
 
4.2%
340 1
 
4.2%
414 1
 
4.2%
754 1
 
4.2%
623 1
 
4.2%
685 1
 
4.2%
554 1
 
4.2%
489 1
 
4.2%
457 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
236 1
4.2%
249 1
4.2%
255 1
4.2%
268 1
4.2%
278 1
4.2%
283 1
4.2%
288 1
4.2%
327 1
4.2%
333 1
4.2%
340 1
4.2%
ValueCountFrequency (%)
754 1
4.2%
685 1
4.2%
623 1
4.2%
585 1
4.2%
554 1
4.2%
529 1
4.2%
518 1
4.2%
489 1
4.2%
487 1
4.2%
457 1
4.2%

경기
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5048.0417
Minimum3424
Maximum8948
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T18:07:03.281572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3424
5-th percentile3633.75
Q14170.5
median4679.5
Q35576.5
95-th percentile7407.8
Maximum8948
Range5524
Interquartile range (IQR)1406

Descriptive statistics

Standard deviation1365.7655
Coefficient of variation (CV)0.27055354
Kurtosis1.6711158
Mean5048.0417
Median Absolute Deviation (MAD)705
Skewness1.3067261
Sum121153
Variance1865315.5
MonotonicityNot monotonic
2023-12-12T18:07:03.453158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
4255 1
 
4.2%
6246 1
 
4.2%
3627 1
 
4.2%
5321 1
 
4.2%
8948 1
 
4.2%
7299 1
 
4.2%
7427 1
 
4.2%
6023 1
 
4.2%
5671 1
 
4.2%
5156 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
3424 1
4.2%
3627 1
4.2%
3672 1
4.2%
3686 1
4.2%
3911 1
4.2%
4058 1
4.2%
4208 1
4.2%
4255 1
4.2%
4290 1
4.2%
4327 1
4.2%
ValueCountFrequency (%)
8948 1
4.2%
7427 1
4.2%
7299 1
4.2%
6246 1
4.2%
6023 1
4.2%
5671 1
4.2%
5545 1
4.2%
5321 1
4.2%
5227 1
4.2%
5156 1
4.2%

강원
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1567.4167
Minimum732
Maximum2429
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T18:07:03.620358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum732
5-th percentile1007.95
Q11203
median1582.5
Q31824.25
95-th percentile2228.2
Maximum2429
Range1697
Interquartile range (IQR)621.25

Descriptive statistics

Standard deviation437.18715
Coefficient of variation (CV)0.2789221
Kurtosis-0.72108541
Mean1567.4167
Median Absolute Deviation (MAD)343
Skewness0.15700792
Sum37618
Variance191132.6
MonotonicityNot monotonic
2023-12-12T18:07:03.795006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1203 2
 
8.3%
1615 1
 
4.2%
2074 1
 
4.2%
732 1
 
4.2%
1697 1
 
4.2%
2429 1
 
4.2%
2078 1
 
4.2%
2116 1
 
4.2%
1837 1
 
4.2%
1807 1
 
4.2%
Other values (13) 13
54.2%
ValueCountFrequency (%)
732 1
4.2%
991 1
4.2%
1104 1
4.2%
1164 1
4.2%
1166 1
4.2%
1203 2
8.3%
1276 1
4.2%
1288 1
4.2%
1295 1
4.2%
1426 1
4.2%
ValueCountFrequency (%)
2429 1
4.2%
2248 1
4.2%
2116 1
4.2%
2078 1
4.2%
2074 1
4.2%
1837 1
4.2%
1820 1
4.2%
1807 1
4.2%
1773 1
4.2%
1726 1
4.2%

충북
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1224.4167
Minimum601
Maximum1879
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T18:07:03.965205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum601
5-th percentile815.1
Q11016.25
median1190.5
Q31378.5
95-th percentile1744.8
Maximum1879
Range1278
Interquartile range (IQR)362.25

Descriptive statistics

Standard deviation311.13703
Coefficient of variation (CV)0.25411042
Kurtosis-0.13918662
Mean1224.4167
Median Absolute Deviation (MAD)184
Skewness0.1981869
Sum29386
Variance96806.254
MonotonicityNot monotonic
2023-12-12T18:07:04.114867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1008 1
 
4.2%
1879 1
 
4.2%
601 1
 
4.2%
1169 1
 
4.2%
1770 1
 
4.2%
1583 1
 
4.2%
1602 1
 
4.2%
1386 1
 
4.2%
1346 1
 
4.2%
1278 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
601 1
4.2%
813 1
4.2%
827 1
4.2%
919 1
4.2%
923 1
4.2%
1008 1
4.2%
1019 1
4.2%
1025 1
4.2%
1149 1
4.2%
1150 1
4.2%
ValueCountFrequency (%)
1879 1
4.2%
1770 1
4.2%
1602 1
4.2%
1583 1
4.2%
1532 1
4.2%
1386 1
4.2%
1376 1
4.2%
1358 1
4.2%
1346 1
4.2%
1292 1
4.2%

충남
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1382.625
Minimum771
Maximum2381
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T18:07:04.252180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum771
5-th percentile819.5
Q11150.75
median1283.5
Q31616.5
95-th percentile2029
Maximum2381
Range1610
Interquartile range (IQR)465.75

Descriptive statistics

Standard deviation413.80229
Coefficient of variation (CV)0.29928743
Kurtosis0.018816281
Mean1382.625
Median Absolute Deviation (MAD)265
Skewness0.58392522
Sum33183
Variance171232.33
MonotonicityNot monotonic
2023-12-12T18:07:04.414418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1201 1
 
4.2%
1867 1
 
4.2%
815 1
 
4.2%
1566 1
 
4.2%
2381 1
 
4.2%
2041 1
 
4.2%
1961 1
 
4.2%
1728 1
 
4.2%
1606 1
 
4.2%
1531 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
771 1
4.2%
815 1
4.2%
845 1
4.2%
869 1
4.2%
1040 1
4.2%
1144 1
4.2%
1153 1
4.2%
1185 1
4.2%
1194 1
4.2%
1201 1
4.2%
ValueCountFrequency (%)
2381 1
4.2%
2041 1
4.2%
1961 1
4.2%
1867 1
4.2%
1728 1
4.2%
1648 1
4.2%
1606 1
4.2%
1566 1
4.2%
1531 1
4.2%
1476 1
4.2%

경북
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1871.4167
Minimum896
Maximum2847
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T18:07:04.588331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum896
5-th percentile1086.05
Q11540.75
median1871.5
Q32288.25
95-th percentile2644.1
Maximum2847
Range1951
Interquartile range (IQR)747.5

Descriptive statistics

Standard deviation533.51084
Coefficient of variation (CV)0.28508394
Kurtosis-0.89881341
Mean1871.4167
Median Absolute Deviation (MAD)403.5
Skewness-0.10041848
Sum44914
Variance284633.82
MonotonicityNot monotonic
2023-12-12T18:07:04.739196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1576 1
 
4.2%
2418 1
 
4.2%
896 1
 
4.2%
1951 1
 
4.2%
2847 1
 
4.2%
2402 1
 
4.2%
2684 1
 
4.2%
2310 1
 
4.2%
2184 1
 
4.2%
2189 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
896 1
4.2%
1082 1
4.2%
1109 1
4.2%
1161 1
4.2%
1429 1
4.2%
1474 1
4.2%
1563 1
4.2%
1576 1
4.2%
1578 1
4.2%
1617 1
4.2%
ValueCountFrequency (%)
2847 1
4.2%
2684 1
4.2%
2418 1
4.2%
2402 1
4.2%
2324 1
4.2%
2310 1
4.2%
2281 1
4.2%
2246 1
4.2%
2189 1
4.2%
2184 1
4.2%

경남
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1919.25
Minimum1088
Maximum2955
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T18:07:04.914188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1088
5-th percentile1316.75
Q11603.5
median1845.5
Q32223.25
95-th percentile2611.55
Maximum2955
Range1867
Interquartile range (IQR)619.75

Descriptive statistics

Standard deviation469.47679
Coefficient of variation (CV)0.24461471
Kurtosis-0.45831737
Mean1919.25
Median Absolute Deviation (MAD)295
Skewness0.36796977
Sum46062
Variance220408.46
MonotonicityNot monotonic
2023-12-12T18:07:05.070823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1696 1
 
4.2%
2558 1
 
4.2%
1088 1
 
4.2%
1867 1
 
4.2%
2955 1
 
4.2%
2423 1
 
4.2%
2621 1
 
4.2%
2178 1
 
4.2%
2070 1
 
4.2%
2141 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
1088 1
4.2%
1307 1
4.2%
1372 1
4.2%
1401 1
4.2%
1563 1
4.2%
1593 1
4.2%
1607 1
4.2%
1616 1
4.2%
1646 1
4.2%
1696 1
4.2%
ValueCountFrequency (%)
2955 1
4.2%
2621 1
4.2%
2558 1
4.2%
2423 1
4.2%
2404 1
4.2%
2359 1
4.2%
2178 1
4.2%
2141 1
4.2%
2140 1
4.2%
2070 1
4.2%

전북
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1670.875
Minimum753
Maximum2443
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T18:07:05.241237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum753
5-th percentile1184.75
Q11410.5
median1663.5
Q31910.5
95-th percentile2312.95
Maximum2443
Range1690
Interquartile range (IQR)500

Descriptive statistics

Standard deviation394.95287
Coefficient of variation (CV)0.23637487
Kurtosis0.1583347
Mean1670.875
Median Absolute Deviation (MAD)252.5
Skewness-0.053457182
Sum40101
Variance155987.77
MonotonicityNot monotonic
2023-12-12T18:07:05.389806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1454 1
 
4.2%
2329 1
 
4.2%
753 1
 
4.2%
1690 1
 
4.2%
2443 1
 
4.2%
2035 1
 
4.2%
2222 1
 
4.2%
1905 1
 
4.2%
1678 1
 
4.2%
1880 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
753 1
4.2%
1178 1
4.2%
1223 1
4.2%
1262 1
4.2%
1362 1
4.2%
1376 1
4.2%
1422 1
4.2%
1454 1
4.2%
1514 1
4.2%
1600 1
4.2%
ValueCountFrequency (%)
2443 1
4.2%
2329 1
4.2%
2222 1
4.2%
2071 1
4.2%
2035 1
4.2%
1927 1
4.2%
1905 1
4.2%
1880 1
4.2%
1814 1
4.2%
1690 1
4.2%

전남
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1958.6667
Minimum874
Maximum2715
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T18:07:05.538369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum874
5-th percentile1460.95
Q11794
median1982.5
Q32149
95-th percentile2499.25
Maximum2715
Range1841
Interquartile range (IQR)355

Descriptive statistics

Standard deviation389.62442
Coefficient of variation (CV)0.19892329
Kurtosis1.5957853
Mean1958.6667
Median Absolute Deviation (MAD)171
Skewness-0.57493229
Sum47008
Variance151807.19
MonotonicityNot monotonic
2023-12-12T18:07:05.696827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1987 1
 
4.2%
2500 1
 
4.2%
874 1
 
4.2%
1841 1
 
4.2%
2715 1
 
4.2%
2155 1
 
4.2%
2388 1
 
4.2%
2133 1
 
4.2%
2044 1
 
4.2%
2037 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
874 1
4.2%
1453 1
4.2%
1506 1
4.2%
1613 1
4.2%
1676 1
4.2%
1737 1
4.2%
1813 1
4.2%
1835 1
4.2%
1841 1
4.2%
1857 1
4.2%
ValueCountFrequency (%)
2715 1
4.2%
2500 1
4.2%
2495 1
4.2%
2388 1
4.2%
2198 1
4.2%
2155 1
4.2%
2147 1
4.2%
2133 1
4.2%
2127 1
4.2%
2044 1
4.2%

제주
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean503.29167
Minimum260
Maximum840
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T18:07:05.854001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum260
5-th percentile325.95
Q1383
median461
Q3610
95-th percentile736.1
Maximum840
Range580
Interquartile range (IQR)227

Descriptive statistics

Standard deviation150.7836
Coefficient of variation (CV)0.29959487
Kurtosis-0.5698594
Mean503.29167
Median Absolute Deviation (MAD)104
Skewness0.51487929
Sum12079
Variance22735.694
MonotonicityNot monotonic
2023-12-12T18:07:06.005646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
386 1
 
4.2%
619 1
 
4.2%
260 1
 
4.2%
580 1
 
4.2%
840 1
 
4.2%
697 1
 
4.2%
743 1
 
4.2%
658 1
 
4.2%
583 1
 
4.2%
607 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
260 1
4.2%
321 1
4.2%
354 1
4.2%
362 1
4.2%
368 1
4.2%
374 1
4.2%
386 1
4.2%
393 1
4.2%
410 1
4.2%
438 1
4.2%
ValueCountFrequency (%)
840 1
4.2%
743 1
4.2%
697 1
4.2%
687 1
4.2%
658 1
4.2%
619 1
4.2%
607 1
4.2%
583 1
4.2%
580 1
4.2%
562 1
4.2%

Interactions

2023-12-12T18:06:56.967309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:21.213276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:23.182944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:24.871510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:27.030307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:29.141746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:30.828174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:32.728975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:34.812616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:36.918252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:39.135856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:40.755169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:42.976349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:44.951930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:47.423951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:49.678484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:52.045730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:54.646322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:57.102137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:21.346289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:23.296803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:24.995165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:27.150254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:29.275279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:30.906778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:32.824160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:34.935453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:37.044576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:39.257691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:40.865166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-12T18:06:42.106015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:44.322444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:46.696321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:48.954324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:51.277412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:53.885131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:56.191551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:58.254583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:22.646577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:24.282919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:26.447811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:28.542579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:30.410384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:32.204440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:34.204764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:36.293227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:38.234667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:40.300905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:42.236785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:44.414424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:46.829849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:49.081627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:51.443011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:53.994697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:56.331819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:58.353403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:22.765266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:24.377814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:26.560128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:28.664994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:30.490496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:32.307858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:34.316148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:36.432919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:38.343397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:40.384742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:42.360096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:44.512882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:46.950294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:49.179794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:51.550766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:54.117413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:56.442965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:58.482453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:22.881781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:24.503470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:26.669192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:28.785184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:30.581623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:32.416993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:34.454681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:36.551414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:38.451462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:40.480088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:42.506159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:44.614865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:47.072841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:49.289163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:51.683887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:54.269112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:56.566827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:58.614100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:22.981015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:24.646002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:26.775984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:28.911592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:30.669340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:32.523344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:34.578175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:36.670780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:38.583671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:40.567838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:42.634234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:44.710981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:47.214819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:49.437889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:51.801021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:54.405245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:56.699415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:58.729307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:23.096470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:24.752277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:26.914866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:29.035331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:30.756917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:32.638311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:34.699460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:36.779004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:39.050119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:40.660352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:42.778693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:44.828779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:47.316195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:49.578227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:51.919131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:54.531219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:06:56.844587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:07:06.123789image/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
1.0001.0000.8180.7060.9350.8770.7410.9490.6940.7310.9560.8880.8350.9100.9110.8960.7600.7380.807
서울1.0000.8181.0000.8160.8120.7720.7760.7420.7900.8780.7550.7570.8790.7330.6590.8030.8790.7720.924
부산1.0000.7060.8161.0000.7450.7700.8560.4870.7000.8740.7320.6080.7870.7820.6710.7410.8470.8460.681
대구1.0000.9350.8120.7451.0000.8200.7630.9660.7190.7020.8730.9200.6830.7790.9240.8650.7230.8170.831
인천1.0000.8770.7720.7700.8201.0000.4400.6240.8410.8560.8700.8500.6940.9150.7790.8850.7530.5250.865
광주1.0000.7410.7760.8560.7630.4401.0000.8560.6980.4820.5330.8040.7480.6940.4870.7840.8660.8620.788
대전1.0000.9490.7420.4870.9660.6240.8561.0000.5100.4890.8840.9450.7430.7470.8690.8350.7590.8030.797
세종1.0000.6940.7900.7000.7190.8410.6980.5101.0000.8450.8550.7440.3190.6850.2900.7560.6410.8240.775
울산1.0000.7310.8780.8740.7020.8560.4820.4890.8451.0000.7630.7700.6830.6660.6080.8460.7550.6890.887
경기1.0000.9560.7550.7320.8730.8700.5330.8840.8550.7631.0000.7030.6710.9580.0000.7110.8390.4990.928
강원1.0000.8880.7570.6080.9200.8500.8040.9450.7440.7700.7031.0000.6170.8640.4170.9090.6900.7580.812
충북1.0000.8350.8790.7870.6830.6940.7480.7430.3190.6830.6710.6171.0000.7610.6620.7340.9080.8030.870
충남1.0000.9100.7330.7820.7790.9150.6940.7470.6850.6660.9580.8640.7611.0000.7340.9160.6520.6290.839
경북1.0000.9110.6590.6710.9240.7790.4870.8690.2900.6080.0000.4170.6620.7341.0000.8770.4550.5660.629
경남1.0000.8960.8030.7410.8650.8850.7840.8350.7560.8460.7110.9090.7340.9160.8771.0000.7980.7820.854
전북1.0000.7600.8790.8470.7230.7530.8660.7590.6410.7550.8390.6900.9080.6520.4550.7981.0000.5840.831
전남1.0000.7380.7720.8460.8170.5250.8620.8030.8240.6890.4990.7580.8030.6290.5660.7820.5841.0000.661
제주1.0000.8070.9240.6810.8310.8650.7880.7970.7750.8870.9280.8120.8700.8390.6290.8540.8310.6611.000
2023-12-12T18:07:06.321719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
서울부산대구인천광주대전세종울산경기강원충북충남경북경남전북전남제주
1.0000.9370.9700.9560.8450.9460.8840.7470.8980.9350.9620.9460.9470.9570.9490.9600.9180.929
서울0.9371.0000.8990.8830.7180.8960.8130.6140.8010.8370.8680.9000.8560.8990.8750.9310.8620.824
부산0.9700.8991.0000.9510.7950.9420.9100.7100.8540.9020.9330.9560.9150.9660.9010.9170.9250.888
대구0.9560.8830.9511.0000.8000.9150.9030.7400.8270.8960.9240.9330.9030.9540.8960.9410.8830.878
인천0.8450.7180.7950.8001.0000.7450.6240.8720.9370.9320.8310.7250.9310.7640.8630.7940.6710.919
광주0.9460.8960.9420.9150.7451.0000.8920.6080.8230.8320.9220.9230.8700.9300.9270.9420.9560.831
대전0.8840.8130.9100.9030.6240.8921.0000.6030.6720.7670.8960.9080.7740.9430.7980.8460.9110.739
세종0.7470.6140.7100.7400.8720.6080.6031.0000.8170.8290.7440.6160.8330.6890.6850.6680.5700.833
울산0.8980.8010.8540.8270.9370.8230.6720.8171.0000.9150.8510.8030.9060.8100.9010.8640.7410.904
경기0.9350.8370.9020.8960.9320.8320.7670.8290.9151.0000.8960.8410.9700.8590.8910.8700.7630.944
강원0.9620.8680.9330.9240.8310.9220.8960.7440.8510.8961.0000.9180.9220.9350.9460.9460.9320.910
충북0.9460.9000.9560.9330.7250.9230.9080.6160.8030.8410.9181.0000.8520.9810.9090.9260.9240.820
충남0.9470.8560.9150.9030.9310.8700.7740.8330.9060.9700.9220.8521.0000.8770.9230.8970.8180.966
경북0.9570.8990.9660.9540.7640.9300.9430.6890.8100.8590.9350.9810.8771.0000.9060.9200.9350.840
경남0.9490.8750.9010.8960.8630.9270.7980.6850.9010.8910.9460.9090.9230.9061.0000.9630.8890.905
전북0.9600.9310.9170.9410.7940.9420.8460.6680.8640.8700.9460.9260.8970.9200.9631.0000.8970.884
전남0.9180.8620.9250.8830.6710.9560.9110.5700.7410.7630.9320.9240.8180.9350.8890.8971.0000.785
제주0.9290.8240.8880.8780.9190.8310.7390.8330.9040.9440.9100.8200.9660.8400.9050.8840.7851.000

Missing values

2023-12-12T18:06:58.900253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:06:59.166511image/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
020012950977801829115790010231359028342551615100812011576169614541987386
1200223095636414168887657618850268342411648137711082137212621506354
220032489968481579103679077510170278367211669198451161140113621676374
32004273847820153810337718991246024936861288101910401474160714221899393
420053476293792513172373510792719025540581550153211442324161616262147362
520063002183201949139788710441190032739111203115012141617182416001978410
620073090981721935144711039231291042244311295119811851690189516491835438
72008369341021623601610109412111515048748631726135813232111240420712127458
82009242806608153610497497711023023642089918278691109130712231453321
920103003584381963141988610251330033343271203102511941563156315141813439
구분서울부산대구인천광주대전세종울산경기강원충북충남경북경남전북전남제주
1420154034095773041200913131204179552651855452074137616482246235919272495687
1520163839897362686174413571263180151952952271773129214762281214018142198562
1620173705990332478169712031102187957145751561820127815312189214118802037607
1720183771094362561159913381117163454748956711807134616062184207016782044583
1820193978197052592175116091129166661755460231837138617282310217819052133658
19202047319116793092215119001303194779868574272116160219612684262122222388743
20202144676106942861196419821167174892462372992078158320412402242320352155697
212022549931378434412561229214492245113975489482429177023812847295524432715840
223527184962063180214061040156080841453211697116915661951186716901841580
23197225288137875988640968533134036277326018158961088753874260