Overview

Dataset statistics

Number of variables18
Number of observations34
Missing cells48
Missing cells (%)7.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.5 KiB
Average record size in memory165.9 B

Variable types

Numeric18

Dataset

Description연간 지역별 판매되는 도시가스의 통계자료 데이터로 강원, 서울, 경기, 인천, 대전, 세종, 충남, 충북, 대구, 경남, 경북, 울산, 부산, 광주, 전남, 전북, 제주의 속성정보를 포함하고 있습니다.단위는 ㎥ 입니다.
Author한국가스공사
URLhttps://www.data.go.kr/data/15040818/fileData.do

Alerts

연도 is highly overall correlated with 강원 and 16 other fieldsHigh correlation
강원 is highly overall correlated with 연도 and 16 other fieldsHigh correlation
서울 is highly overall correlated with 연도 and 13 other fieldsHigh correlation
경기 is highly overall correlated with 연도 and 15 other fieldsHigh correlation
인천 is highly overall correlated with 연도 and 14 other fieldsHigh correlation
대전 is highly overall correlated with 연도 and 15 other fieldsHigh correlation
세종 is highly overall correlated with 연도 and 11 other fieldsHigh correlation
충남 is highly overall correlated with 연도 and 14 other fieldsHigh correlation
충북 is highly overall correlated with 연도 and 16 other fieldsHigh correlation
대구 is highly overall correlated with 연도 and 16 other fieldsHigh correlation
경남 is highly overall correlated with 연도 and 16 other fieldsHigh correlation
경북 is highly overall correlated with 연도 and 16 other fieldsHigh correlation
울산 is highly overall correlated with 연도 and 15 other fieldsHigh correlation
부산 is highly overall correlated with 연도 and 16 other fieldsHigh correlation
광주 is highly overall correlated with 연도 and 16 other fieldsHigh correlation
전남 is highly overall correlated with 연도 and 16 other fieldsHigh correlation
전북 is highly overall correlated with 연도 and 16 other fieldsHigh correlation
제주 is highly overall correlated with 연도 and 15 other fieldsHigh correlation
경기 has 4 (11.8%) missing valuesMissing
세종 has 24 (70.6%) missing valuesMissing
충남 has 4 (11.8%) missing valuesMissing
제주 has 16 (47.1%) missing valuesMissing
연도 has unique valuesUnique
강원 has unique valuesUnique
서울 has unique valuesUnique
인천 has unique valuesUnique
대전 has unique valuesUnique
충북 has unique valuesUnique
대구 has unique valuesUnique
경남 has unique valuesUnique
경북 has unique valuesUnique
울산 has unique valuesUnique
부산 has unique valuesUnique
광주 has unique valuesUnique
전남 has unique valuesUnique
전북 has unique valuesUnique

Reproduction

Analysis started2024-01-06 12:37:43.937024
Analysis finished2024-01-06 12:39:39.580095
Duration1 minute and 55.64 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2005.5
Minimum1989
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-01-06T12:39:39.777037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1989
5-th percentile1990.65
Q11997.25
median2005.5
Q32013.75
95-th percentile2020.35
Maximum2022
Range33
Interquartile range (IQR)16.5

Descriptive statistics

Standard deviation9.9582462
Coefficient of variation (CV)0.004965468
Kurtosis-1.2
Mean2005.5
Median Absolute Deviation (MAD)8.5
Skewness0
Sum68187
Variance99.166667
MonotonicityStrictly increasing
2024-01-06T12:39:40.184707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
1989 1
 
2.9%
2015 1
 
2.9%
2009 1
 
2.9%
2010 1
 
2.9%
2011 1
 
2.9%
2012 1
 
2.9%
2013 1
 
2.9%
2014 1
 
2.9%
2016 1
 
2.9%
2007 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
1989 1
2.9%
1990 1
2.9%
1991 1
2.9%
1992 1
2.9%
1993 1
2.9%
1994 1
2.9%
1995 1
2.9%
1996 1
2.9%
1997 1
2.9%
1998 1
2.9%
ValueCountFrequency (%)
2022 1
2.9%
2021 1
2.9%
2020 1
2.9%
2019 1
2.9%
2018 1
2.9%
2017 1
2.9%
2016 1
2.9%
2015 1
2.9%
2014 1
2.9%
2013 1
2.9%

강원
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean198676.41
Minimum3094
Maximum490442
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-01-06T12:39:40.618144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3094
5-th percentile10302.85
Q173089
median185525.5
Q3302625.75
95-th percentile431746.35
Maximum490442
Range487348
Interquartile range (IQR)229536.75

Descriptive statistics

Standard deviation145453.19
Coefficient of variation (CV)0.73211101
Kurtosis-1.0269149
Mean198676.41
Median Absolute Deviation (MAD)115779.5
Skewness0.36926337
Sum6754998
Variance2.115663 × 1010
MonotonicityNot monotonic
2024-01-06T12:39:41.263105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
3094 1
 
2.9%
303995 1
 
2.9%
234503 1
 
2.9%
265539 1
 
2.9%
282691 1
 
2.9%
298518 1
 
2.9%
307873 1
 
2.9%
296437 1
 
2.9%
329207 1
 
2.9%
201820 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
3094 1
2.9%
6397 1
2.9%
12406 1
2.9%
18897 1
2.9%
28601 1
2.9%
37948 1
2.9%
53370 1
2.9%
66705 1
2.9%
72436 1
2.9%
75048 1
2.9%
ValueCountFrequency (%)
490442 1
2.9%
459931 1
2.9%
416570 1
2.9%
413340 1
2.9%
407645 1
2.9%
363885 1
2.9%
329207 1
2.9%
307873 1
2.9%
303995 1
2.9%
298518 1
2.9%

서울
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3712948.8
Minimum319989
Maximum5133109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-01-06T12:39:41.702152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum319989
5-th percentile659275.95
Q13165549.5
median4259137
Q34703922
95-th percentile4941501.5
Maximum5133109
Range4813120
Interquartile range (IQR)1538372.5

Descriptive statistics

Standard deviation1427004.2
Coefficient of variation (CV)0.38433176
Kurtosis0.45712672
Mean3712948.8
Median Absolute Deviation (MAD)557530.5
Skewness-1.3118179
Sum1.2624026 × 108
Variance2.0363409 × 1012
MonotonicityNot monotonic
2024-01-06T12:39:42.169637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
319989.0 1
 
2.9%
4166211.0 1
 
2.9%
4820090.0 1
 
2.9%
5133109.0 1
 
2.9%
4927023.0 1
 
2.9%
4931781.0 1
 
2.9%
4731122.0 1
 
2.9%
4261204.0 1
 
2.9%
4250538.0 1
 
2.9%
4847689.0 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
319989.0 1
2.9%
501129.0 1
2.9%
744432.0 1
2.9%
1073312.0 1
2.9%
1421167.0 1
2.9%
1894184.0 1
2.9%
2452297.0 1
2.9%
2977508.0 1
2.9%
3138085.0 1
2.9%
3247943.0 1
2.9%
ValueCountFrequency (%)
5133109.0 1
2.9%
4959554.0 1
2.9%
4931781.0 1
2.9%
4927023.0 1
2.9%
4853863.0 1
2.9%
4847689.0 1
2.9%
4820090.0 1
2.9%
4813245.0 1
2.9%
4731122.0 1
2.9%
4622322.0 1
2.9%

경기
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct30
Distinct (%)100.0%
Missing4
Missing (%)11.8%
Infinite0
Infinite (%)0.0%
Mean3699854.3
Minimum386308
Maximum5631491
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-01-06T12:39:42.711859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum386308
5-th percentile702998.65
Q12650084.5
median4136028
Q35153239
95-th percentile5505900.8
Maximum5631491
Range5245183
Interquartile range (IQR)2503154.5

Descriptive statistics

Standard deviation1654659.4
Coefficient of variation (CV)0.44722284
Kurtosis-0.79234903
Mean3699854.3
Median Absolute Deviation (MAD)1090714.5
Skewness-0.72268609
Sum1.1099563 × 108
Variance2.7378976 × 1012
MonotonicityNot monotonic
2024-01-06T12:39:43.196755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
4291039.0 1
 
2.9%
5631491.0 1
 
2.9%
5498483.0 1
 
2.9%
5236434.0 1
 
2.9%
5279044.857 1
 
2.9%
5511970.0 1
 
2.9%
5260344.0 1
 
2.9%
4969753.0 1
 
2.9%
4763556.0 1
 
2.9%
4797282.0 1
 
2.9%
Other values (20) 20
58.8%
(Missing) 4
 
11.8%
ValueCountFrequency (%)
386308.0 1
2.9%
571618.0 1
2.9%
863575.0 1
2.9%
1174932.0 1
2.9%
1429039.0 1
2.9%
1580507.0 1
2.9%
2078424.0 1
2.9%
2600747.0 1
2.9%
2798097.0 1
2.9%
3116858.0 1
2.9%
ValueCountFrequency (%)
5631491.0 1
2.9%
5511970.0 1
2.9%
5498483.0 1
2.9%
5279044.857 1
2.9%
5260344.0 1
2.9%
5236434.0 1
2.9%
5217051.0 1
2.9%
5214401.0 1
2.9%
4969753.0 1
2.9%
4879639.0 1
2.9%

인천
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1106962.3
Minimum113349
Maximum1574147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-01-06T12:39:43.721227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum113349
5-th percentile275402.95
Q1694451.5
median1387572.5
Q31478078.2
95-th percentile1569850.9
Maximum1574147
Range1460798
Interquartile range (IQR)783626.75

Descriptive statistics

Standard deviation480216.87
Coefficient of variation (CV)0.43381502
Kurtosis-0.90479087
Mean1106962.3
Median Absolute Deviation (MAD)167754
Skewness-0.815466
Sum37636719
Variance2.3060825 × 1011
MonotonicityNot monotonic
2024-01-06T12:39:44.533205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
113349.0 1
 
2.9%
1393452.0 1
 
2.9%
1421974.0 1
 
2.9%
1527655.0 1
 
2.9%
1529080.0 1
 
2.9%
1569847.0 1
 
2.9%
1569858.0 1
 
2.9%
1479928.0 1
 
2.9%
1448692.0 1
 
2.9%
1441279.0 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
113349.0 1
2.9%
205370.0 1
2.9%
313113.0 1
2.9%
319128.0 1
2.9%
394821.0 1
2.9%
452930.0 1
2.9%
522337.0 1
2.9%
624902.0 1
2.9%
693540.0 1
2.9%
697186.0 1
2.9%
ValueCountFrequency (%)
1574147.0 1
2.9%
1569858.0 1
2.9%
1569847.0 1
2.9%
1543709.0 1
2.9%
1529080.0 1
2.9%
1527655.0 1
2.9%
1504512.709 1
2.9%
1485274.0 1
2.9%
1479928.0 1
2.9%
1472529.0 1
2.9%

대전
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean455585.15
Minimum3844
Maximum782858
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-01-06T12:39:44.990625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3844
5-th percentile20991
Q1244389.25
median538485
Q3680064.5
95-th percentile747711.5
Maximum782858
Range779014
Interquartile range (IQR)435675.25

Descriptive statistics

Standard deviation251465.11
Coefficient of variation (CV)0.55196073
Kurtosis-1.1324199
Mean455585.15
Median Absolute Deviation (MAD)179139.5
Skewness-0.4955773
Sum15489895
Variance6.3234701 × 1010
MonotonicityNot monotonic
2024-01-06T12:39:45.663570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
3844 1
 
2.9%
616966 1
 
2.9%
588324 1
 
2.9%
642407 1
 
2.9%
694119 1
 
2.9%
782858 1
 
2.9%
750539 1
 
2.9%
650047 1
 
2.9%
654218 1
 
2.9%
547170 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
3844 1
2.9%
11592 1
2.9%
26052 1
2.9%
49930 1
2.9%
93889 1
2.9%
139305 1
2.9%
190635 1
2.9%
229125 1
2.9%
243117 1
2.9%
248206 1
2.9%
ValueCountFrequency (%)
782858 1
2.9%
750539 1
2.9%
746189 1
2.9%
734652 1
2.9%
718098 1
2.9%
717151 1
2.9%
695599 1
2.9%
694119 1
2.9%
688680 1
2.9%
654218 1
2.9%

세종
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)100.0%
Missing24
Missing (%)70.6%
Infinite0
Infinite (%)0.0%
Mean89938.7
Minimum69119
Maximum109413
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-01-06T12:39:46.048218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum69119
5-th percentile71713.7
Q178222.25
median87177.5
Q3104105.25
95-th percentile107115.3
Maximum109413
Range40294
Interquartile range (IQR)25883

Descriptive statistics

Standard deviation14489.257
Coefficient of variation (CV)0.16110147
Kurtosis-1.6823604
Mean89938.7
Median Absolute Deviation (MAD)14357
Skewness0.027675887
Sum899387
Variance2.0993856 × 108
MonotonicityNot monotonic
2024-01-06T12:39:46.453350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
74885 1
 
2.9%
69119 1
 
2.9%
76727 1
 
2.9%
82708 1
 
2.9%
84999 1
 
2.9%
89356 1
 
2.9%
104307 1
 
2.9%
104274 1
 
2.9%
103599 1
 
2.9%
109413 1
 
2.9%
(Missing) 24
70.6%
ValueCountFrequency (%)
69119 1
2.9%
74885 1
2.9%
76727 1
2.9%
82708 1
2.9%
84999 1
2.9%
89356 1
2.9%
103599 1
2.9%
104274 1
2.9%
104307 1
2.9%
109413 1
2.9%
ValueCountFrequency (%)
109413 1
2.9%
104307 1
2.9%
104274 1
2.9%
103599 1
2.9%
89356 1
2.9%
84999 1
2.9%
82708 1
2.9%
76727 1
2.9%
74885 1
2.9%
69119 1
2.9%

충남
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct30
Distinct (%)100.0%
Missing4
Missing (%)11.8%
Infinite0
Infinite (%)0.0%
Mean828647.77
Minimum305
Maximum1660010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-01-06T12:39:46.979337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum305
5-th percentile9995.85
Q1228521.75
median888511
Q31425906.2
95-th percentile1584307.9
Maximum1660010
Range1659705
Interquartile range (IQR)1197384.5

Descriptive statistics

Standard deviation604063.83
Coefficient of variation (CV)0.72897539
Kurtosis-1.6552735
Mean828647.77
Median Absolute Deviation (MAD)597629.5
Skewness-0.088366255
Sum24859433
Variance3.6489311 × 1011
MonotonicityNot monotonic
2024-01-06T12:39:47.456123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
933738 1
 
2.9%
1571906 1
 
2.9%
1594455 1
 
2.9%
1478566 1
 
2.9%
1550913 1
 
2.9%
1504009 1
 
2.9%
1379695 1
 
2.9%
1302614 1
 
2.9%
1260344 1
 
2.9%
1504915 1
 
2.9%
Other values (20) 20
58.8%
(Missing) 4
 
11.8%
ValueCountFrequency (%)
305 1
2.9%
3762 1
2.9%
17615 1
2.9%
34880 1
2.9%
85368 1
2.9%
106758 1
2.9%
126453 1
2.9%
210260 1
2.9%
283307 1
2.9%
347226 1
2.9%
ValueCountFrequency (%)
1660010 1
2.9%
1594455 1
2.9%
1571906 1
2.9%
1550913 1
2.9%
1504915 1
2.9%
1504009 1
2.9%
1478566 1
2.9%
1441310 1
2.9%
1379695 1
2.9%
1302614 1
2.9%

충북
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean409610.24
Minimum700
Maximum1011823
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-01-06T12:39:48.032875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum700
5-th percentile8927.7
Q1119443.25
median316542.5
Q3714800
95-th percentile923355.55
Maximum1011823
Range1011123
Interquartile range (IQR)595356.75

Descriptive statistics

Standard deviation329262.92
Coefficient of variation (CV)0.80384445
Kurtosis-1.3414233
Mean409610.24
Median Absolute Deviation (MAD)281162.5
Skewness0.35054951
Sum13926748
Variance1.0841407 × 1011
MonotonicityNot monotonic
2024-01-06T12:39:48.592119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
700 1
 
2.9%
702872 1
 
2.9%
462297 1
 
2.9%
563490 1
 
2.9%
651311 1
 
2.9%
721053 1
 
2.9%
718776 1
 
2.9%
697543 1
 
2.9%
742339 1
 
2.9%
380580 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
700 1
2.9%
5302 1
2.9%
10880 1
2.9%
18367 1
2.9%
30373 1
2.9%
40387 1
2.9%
66302 1
2.9%
99191 1
2.9%
118028 1
2.9%
123689 1
2.9%
ValueCountFrequency (%)
1011823 1
2.9%
964028 1
2.9%
901455 1
2.9%
882465 1
2.9%
852493 1
2.9%
784914 1
2.9%
742339 1
2.9%
721053 1
2.9%
718776 1
2.9%
702872 1
2.9%

대구
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean652736.53
Minimum20855
Maximum1004383
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-01-06T12:39:49.188571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20855
5-th percentile43923.7
Q1372840
median778614.5
Q3948594.5
95-th percentile983148.65
Maximum1004383
Range983528
Interquartile range (IQR)575754.5

Descriptive statistics

Standard deviation346692.95
Coefficient of variation (CV)0.53113766
Kurtosis-0.9229092
Mean652736.53
Median Absolute Deviation (MAD)182943
Skewness-0.81069662
Sum22193042
Variance1.20196 × 1011
MonotonicityNot monotonic
2024-01-06T12:39:49.735046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
20855 1
 
2.9%
928277 1
 
2.9%
806218 1
 
2.9%
902882 1
 
2.9%
954672 1
 
2.9%
966132 1
 
2.9%
970475 1
 
2.9%
933462 1
 
2.9%
935036 1
 
2.9%
777571 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
20855 1
2.9%
34890 1
2.9%
48788 1
2.9%
66596 1
2.9%
95587 1
2.9%
126843 1
2.9%
172955 1
2.9%
241125 1
2.9%
343753 1
2.9%
460101 1
2.9%
ValueCountFrequency (%)
1004383 1
2.9%
1004013 1
2.9%
971914 1
2.9%
970951 1
2.9%
970475 1
2.9%
966132 1
2.9%
956983 1
2.9%
954672 1
2.9%
953114 1
2.9%
935036 1
2.9%

경남
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean633928.53
Minimum29263
Maximum1324051
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-01-06T12:39:50.375280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum29263
5-th percentile53848.7
Q1197054.25
median672840
Q31032626.5
95-th percentile1230103.5
Maximum1324051
Range1294788
Interquartile range (IQR)835572.25

Descriptive statistics

Standard deviation429335.23
Coefficient of variation (CV)0.67726125
Kurtosis-1.4956314
Mean633928.53
Median Absolute Deviation (MAD)393852
Skewness-0.0071179551
Sum21553570
Variance1.8432874 × 1011
MonotonicityNot monotonic
2024-01-06T12:39:50.981131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
29263 1
 
2.9%
1027795 1
 
2.9%
800429 1
 
2.9%
907217 1
 
2.9%
954781 1
 
2.9%
1005173 1
 
2.9%
1051350 1
 
2.9%
1034237 1
 
2.9%
1063225 1
 
2.9%
721413 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
29263 1
2.9%
45647 1
2.9%
58265 1
2.9%
74797 1
2.9%
90025 1
2.9%
116257 1
2.9%
146106 1
2.9%
173502 1
2.9%
196582 1
2.9%
198471 1
2.9%
ValueCountFrequency (%)
1324051 1
2.9%
1316482 1
2.9%
1183592 1
2.9%
1160107 1
2.9%
1122209 1
2.9%
1070159 1
2.9%
1063225 1
2.9%
1051350 1
2.9%
1034237 1
2.9%
1027795 1
2.9%

경북
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean816731.06
Minimum6437
Maximum1577407
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-01-06T12:39:51.938598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6437
5-th percentile20426.85
Q1311737.5
median836561.5
Q31374529.5
95-th percentile1519623.7
Maximum1577407
Range1570970
Interquartile range (IQR)1062792

Descriptive statistics

Standard deviation562168.18
Coefficient of variation (CV)0.68831493
Kurtosis-1.5314075
Mean816731.06
Median Absolute Deviation (MAD)540171.5
Skewness-0.11716915
Sum27768856
Variance3.1603307 × 1011
MonotonicityNot monotonic
2024-01-06T12:39:52.478881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
6437 1
 
2.9%
1319382 1
 
2.9%
1044778 1
 
2.9%
1231860 1
 
2.9%
1335269 1
 
2.9%
1451325 1
 
2.9%
1439696 1
 
2.9%
1383377 1
 
2.9%
1347987 1
 
2.9%
848062 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
6437 1
2.9%
13245 1
2.9%
24294 1
2.9%
38357 1
2.9%
55802 1
2.9%
71927 1
2.9%
120996 1
2.9%
210320 1
2.9%
303034 1
2.9%
337848 1
2.9%
ValueCountFrequency (%)
1577407 1
2.9%
1556051 1
2.9%
1500009 1
2.9%
1495929 1
2.9%
1479257 1
2.9%
1451325 1
2.9%
1439696 1
2.9%
1417020 1
2.9%
1383377 1
2.9%
1347987 1
2.9%

울산
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1210423.6
Minimum21628
Maximum3101053
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-01-06T12:39:53.016874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21628
5-th percentile54766.2
Q1282308
median985734.5
Q31997480.2
95-th percentile2848809.4
Maximum3101053
Range3079425
Interquartile range (IQR)1715172.2

Descriptive statistics

Standard deviation964897.69
Coefficient of variation (CV)0.797157
Kurtosis-1.1079607
Mean1210423.6
Median Absolute Deviation (MAD)814850
Skewness0.39767262
Sum41154404
Variance9.3102755 × 1011
MonotonicityNot monotonic
2024-01-06T12:39:53.574853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
21628 1
 
2.9%
1789084 1
 
2.9%
1360863 1
 
2.9%
1830750 1
 
2.9%
2382495 1
 
2.9%
2938566 1
 
2.9%
3101053 1
 
2.9%
2607319 1
 
2.9%
1708269 1
 
2.9%
1062211 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
21628 1
2.9%
34845 1
2.9%
65493 1
2.9%
87413 1
2.9%
97092 1
2.9%
121640 1
2.9%
159384 1
2.9%
213993 1
2.9%
268970 1
2.9%
322322 1
2.9%
ValueCountFrequency (%)
3101053 1
2.9%
2938566 1
2.9%
2800479 1
2.9%
2607319 1
2.9%
2382495 1
2.9%
2345387 1
2.9%
2134812 1
2.9%
2112563 1
2.9%
2053057 1
2.9%
1830750 1
2.9%

부산
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean876717.82
Minimum36623
Maximum1461840
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-01-06T12:39:54.216491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36623
5-th percentile62802.4
Q1428251
median1035203.5
Q31316795.2
95-th percentile1432461
Maximum1461840
Range1425217
Interquartile range (IQR)888544.25

Descriptive statistics

Standard deviation502245.56
Coefficient of variation (CV)0.57287025
Kurtosis-1.3390579
Mean876717.82
Median Absolute Deviation (MAD)370551
Skewness-0.45000554
Sum29808406
Variance2.522506 × 1011
MonotonicityNot monotonic
2024-01-06T12:39:55.197507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
36623 1
 
2.9%
1296982 1
 
2.9%
1160866 1
 
2.9%
1275714 1
 
2.9%
1297956 1
 
2.9%
1359726 1
 
2.9%
1318036 1
 
2.9%
1313073 1
 
2.9%
1361004 1
 
2.9%
1072919 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
36623 1
2.9%
51404 1
2.9%
68940 1
2.9%
102003 1
2.9%
153877 1
2.9%
196830 1
2.9%
253637 1
2.9%
325281 1
2.9%
418137 1
2.9%
458593 1
2.9%
ValueCountFrequency (%)
1461840 1
2.9%
1439559 1
2.9%
1428639 1
2.9%
1427371 1
2.9%
1421999 1
2.9%
1408886 1
2.9%
1361004 1
2.9%
1359726 1
2.9%
1318036 1
2.9%
1313073 1
2.9%

광주
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean388467.59
Minimum18064
Maximum653410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-01-06T12:39:55.964359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18064
5-th percentile36358.05
Q1196389.5
median456783
Q3587822
95-th percentile636058.4
Maximum653410
Range635346
Interquartile range (IQR)391432.5

Descriptive statistics

Standard deviation218594.04
Coefficient of variation (CV)0.56270857
Kurtosis-1.3926855
Mean388467.59
Median Absolute Deviation (MAD)169573
Skewness-0.38894124
Sum13207898
Variance4.7783354 × 1010
MonotonicityNot monotonic
2024-01-06T12:39:56.497622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
18064 1
 
2.9%
566883 1
 
2.9%
512363 1
 
2.9%
565168 1
 
2.9%
578463 1
 
2.9%
599276 1
 
2.9%
590363 1
 
2.9%
580199 1
 
2.9%
595195 1
 
2.9%
480851 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
18064 1
2.9%
27286 1
2.9%
41243 1
2.9%
63723 1
2.9%
85305 1
2.9%
102529 1
2.9%
130967 1
2.9%
172429 1
2.9%
196348 1
2.9%
196514 1
2.9%
ValueCountFrequency (%)
653410 1
2.9%
639844 1
2.9%
634020 1
2.9%
627613 1
2.9%
625099 1
2.9%
624409 1
2.9%
599276 1
2.9%
595195 1
2.9%
590363 1
2.9%
580199 1
2.9%

전남
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean348424.5
Minimum7458
Maximum916293
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-01-06T12:39:57.283542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7458
5-th percentile19018.05
Q195354.25
median271315.5
Q3567310.5
95-th percentile842130.25
Maximum916293
Range908835
Interquartile range (IQR)471956.25

Descriptive statistics

Standard deviation276199.1
Coefficient of variation (CV)0.79270862
Kurtosis-0.81507233
Mean348424.5
Median Absolute Deviation (MAD)205009.5
Skewness0.55669758
Sum11846433
Variance7.6285945 × 1010
MonotonicityNot monotonic
2024-01-06T12:39:58.126043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
7458 1
 
2.9%
547347 1
 
2.9%
374877 1
 
2.9%
457891 1
 
2.9%
465549 1
 
2.9%
518472 1
 
2.9%
594959 1
 
2.9%
573965 1
 
2.9%
588393 1
 
2.9%
309961 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
7458 1
2.9%
11727 1
2.9%
22944 1
2.9%
31651 1
2.9%
46688 1
2.9%
57721 1
2.9%
74891 1
2.9%
91364 1
2.9%
93368 1
2.9%
101313 1
2.9%
ValueCountFrequency (%)
916293 1
2.9%
913744 1
2.9%
803569 1
2.9%
742686 1
2.9%
727612 1
2.9%
627055 1
2.9%
594959 1
2.9%
588393 1
2.9%
573965 1
2.9%
547347 1
2.9%

전북
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean474099.74
Minimum8176
Maximum1003743
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-01-06T12:39:58.838800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8176
5-th percentile20709.7
Q1166936.75
median434529
Q3827869.75
95-th percentile931900.15
Maximum1003743
Range995567
Interquartile range (IQR)660933

Descriptive statistics

Standard deviation341822.71
Coefficient of variation (CV)0.72099325
Kurtosis-1.6074872
Mean474099.74
Median Absolute Deviation (MAD)351271.5
Skewness0.075762622
Sum16119391
Variance1.1684277 × 1011
MonotonicityNot monotonic
2024-01-06T12:39:59.416267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
8176 1
 
2.9%
820207 1
 
2.9%
585837 1
 
2.9%
700849 1
 
2.9%
768433 1
 
2.9%
812973 1
 
2.9%
834110 1
 
2.9%
830627 1
 
2.9%
830424 1
 
2.9%
540486 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
8176 1
2.9%
14510 1
2.9%
24048 1
2.9%
35541 1
2.9%
53022 1
2.9%
65890 1
2.9%
102419 1
2.9%
142757 1
2.9%
166360 1
2.9%
168667 1
2.9%
ValueCountFrequency (%)
1003743 1
2.9%
947207 1
2.9%
923658 1
2.9%
908431 1
2.9%
883121 1
2.9%
861812 1
2.9%
834110 1
2.9%
830627 1
2.9%
830424 1
2.9%
820207 1
2.9%

제주
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)100.0%
Missing16
Missing (%)47.1%
Infinite0
Infinite (%)0.0%
Mean14945.222
Minimum843
Maximum34276
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-01-06T12:39:59.916858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum843
5-th percentile2169
Q17907.5
median13126.5
Q322211.75
95-th percentile30465.45
Maximum34276
Range33433
Interquartile range (IQR)14304.25

Descriptive statistics

Standard deviation9710.8128
Coefficient of variation (CV)0.64976035
Kurtosis-0.7386024
Mean14945.222
Median Absolute Deviation (MAD)8313.5
Skewness0.3710241
Sum269014
Variance94299885
MonotonicityStrictly increasing
2024-01-06T12:40:00.403704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
15960 1
 
2.9%
34276 1
 
2.9%
29793 1
 
2.9%
25200 1
 
2.9%
23847 1
 
2.9%
22577 1
 
2.9%
21116 1
 
2.9%
19218 1
 
2.9%
843 1
 
2.9%
2403 1
 
2.9%
Other values (8) 8
23.5%
(Missing) 16
47.1%
ValueCountFrequency (%)
843 1
2.9%
2403 1
2.9%
4262 1
2.9%
4489 1
2.9%
7642 1
2.9%
8704 1
2.9%
10753 1
2.9%
11678 1
2.9%
12089 1
2.9%
14164 1
2.9%
ValueCountFrequency (%)
34276 1
2.9%
29793 1
2.9%
25200 1
2.9%
23847 1
2.9%
22577 1
2.9%
21116 1
2.9%
19218 1
2.9%
15960 1
2.9%
14164 1
2.9%
12089 1
2.9%

Interactions

2024-01-06T12:39:31.008649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:37:45.953604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:37:51.817315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:37:56.410127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:01.293491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:06.787005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:13.958587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:20.870766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:27.155640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:33.483856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:39.429678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:43.565388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:51.075491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:59.520401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:39:05.891541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:39:10.241341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:39:16.430758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:39:25.179490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:39:31.543656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:37:46.208442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:37:52.059385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:37:56.668166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:01.703409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:07.081100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:14.417604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:21.180673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-01-06T12:38:37.751562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:42.024592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:47.247596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:56.870065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:39:03.753880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:39:08.827646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:39:14.601017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:39:22.121745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:39:28.817425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:39:36.936232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:37:49.972258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:37:55.359504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:37:59.905127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:05.540152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:10.779207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:18.973308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:25.785209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:31.269654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:38.109256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:42.319936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:47.666260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:57.225952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:39:04.099554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:39:09.088381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:39:14.967011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:39:22.590656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:39:29.185466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:39:37.244365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:37:50.342325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:37:55.616780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:00.201224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:05.775845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:11.054678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:19.254463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:26.035187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:31.642888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:38.446684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:42.645925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:48.633821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:57.665660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:39:04.502035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:39:09.343321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:39:15.295820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:39:23.041431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:39:29.481534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:39:37.482924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:37:50.628026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:37:55.866375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:00.451752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:05.999497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:12.060142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:19.631752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:26.303586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:32.145497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:38.700230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:42.899556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:49.548959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:58.088509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:39:04.914584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:39:09.597334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:39:15.576825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:39:23.728627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:39:29.760883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:39:37.744296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:37:51.075941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:37:56.016705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:00.731103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:06.221845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:12.712050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:20.105300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:26.616271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:32.723390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:38.941458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:43.132978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:50.228417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:58.512968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:39:05.272873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:39:09.779980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:39:15.913634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:39:24.400214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:39:30.090003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:39:37.991537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:37:51.441305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:37:56.161439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:00.940775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:06.508001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:13.480887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:20.456457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:26.896822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:33.065067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:39.189353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:43.371763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:50.567914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:38:59.166417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:39:05.638404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:39:10.001728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:39:16.179662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:39:24.769274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:39:30.485654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-06T12:40:00.854128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도강원서울경기인천대전세종충남충북대구경남경북울산부산광주전남전북제주
연도1.0000.9430.8910.8400.8260.9161.0000.8710.9250.9180.9550.9380.8410.9720.9810.8940.9590.972
강원0.9431.0000.4970.9420.7900.9410.5890.8510.9710.7850.9870.9450.9220.9470.9280.9560.9780.875
서울0.8910.4971.0000.7980.8960.8800.3850.0000.0000.9430.4360.5820.0000.9050.9060.0000.3330.931
경기0.8400.9420.7981.0000.9140.9200.0000.8990.8770.9400.9750.9800.8940.9730.9450.7870.9510.894
인천0.8260.7900.8960.9141.0000.9470.0000.8060.4000.9320.9200.8900.7420.9650.9320.2570.8110.896
대전0.9160.9410.8800.9200.9471.0000.4430.9260.9120.9410.9520.9570.8780.9660.9540.8100.9030.586
세종1.0000.5890.3850.0000.0000.4431.0000.0000.589NaN0.5870.6160.0001.0000.0000.6820.5610.424
충남0.8710.8510.0000.8990.8060.9260.0001.0000.7940.8590.8850.7640.8170.9550.9620.6810.9120.937
충북0.9250.9710.0000.8770.4000.9120.5890.7941.0000.8030.9800.9470.9590.9140.9240.9820.9710.576
대구0.9180.7850.9430.9400.9320.941NaN0.8590.8031.0000.9280.9490.6450.9800.9590.7380.9100.000
경남0.9550.9870.4360.9750.9200.9520.5870.8850.9800.9281.0000.9710.8880.9860.9600.9410.9830.954
경북0.9380.9450.5820.9800.8900.9570.6160.7640.9470.9490.9711.0000.8660.9540.9630.8250.9730.000
울산0.8410.9220.0000.8940.7420.8780.0000.8170.9590.6450.8880.8661.0000.8720.9030.9450.9000.000
부산0.9720.9470.9050.9730.9650.9661.0000.9550.9140.9800.9860.9540.8721.0000.9920.7780.9540.787
광주0.9810.9280.9060.9450.9320.9540.0000.9620.9240.9590.9600.9630.9030.9921.0000.7550.9660.753
전남0.8940.9560.0000.7870.2570.8100.6820.6810.9820.7380.9410.8250.9450.7780.7551.0000.9200.706
전북0.9590.9780.3330.9510.8110.9030.5610.9120.9710.9100.9830.9730.9000.9540.9660.9201.0000.712
제주0.9720.8750.9310.8940.8960.5860.4240.9370.5760.0000.9540.0000.0000.7870.7530.7060.7121.000
2024-01-06T12:40:01.499434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도강원서울경기인천대전세종충남충북대구경남경북울산부산광주전남전북제주
연도1.0000.9980.5980.9850.9000.9560.9390.9660.9970.9730.9980.9880.9280.9900.9930.9960.9961.000
강원0.9981.0000.6090.9910.9090.9650.9390.9710.9990.9790.9980.9910.9320.9910.9950.9970.9960.990
서울0.5980.6091.0000.4870.7950.699-0.0420.4750.6170.6720.6090.6460.7270.6430.6350.6100.618-0.746
경기0.9850.9910.4871.0000.9150.9680.7940.9740.9920.9880.9900.9930.9320.9960.9960.9890.9930.932
인천0.9000.9090.7950.9151.0000.9580.0910.9110.9120.9540.9100.9330.9750.9360.9260.9120.9220.337
대전0.9560.9650.6990.9680.9581.0000.3820.9650.9690.9830.9630.9810.9780.9740.9750.9650.9660.705
세종0.9390.939-0.0420.7940.0910.3821.0000.2120.9390.5880.8790.733-0.2610.7700.8550.8180.8060.939
충남0.9660.9710.4750.9740.9110.9650.2121.0000.9690.9720.9730.9820.9540.9640.9710.9760.9810.841
충북0.9970.9990.6170.9920.9120.9690.9390.9691.0000.9800.9970.9930.9350.9930.9970.9950.9940.981
대구0.9730.9790.6720.9880.9540.9830.5880.9720.9801.0000.9780.9900.9700.9870.9870.9800.9830.822
경남0.9980.9980.6090.9900.9100.9630.8790.9730.9970.9781.0000.9910.9340.9920.9960.9990.9980.990
경북0.9880.9910.6460.9930.9330.9810.7330.9820.9930.9900.9911.0000.9580.9910.9960.9910.9930.917
울산0.9280.9320.7270.9320.9750.978-0.2610.9540.9350.9700.9340.9581.0000.9480.9450.9380.9450.511
부산0.9900.9910.6430.9960.9360.9740.7700.9640.9930.9870.9920.9910.9481.0000.9950.9890.9930.930
광주0.9930.9950.6350.9960.9260.9750.8550.9710.9970.9870.9960.9960.9450.9951.0000.9940.9950.955
전남0.9960.9970.6100.9890.9120.9650.8180.9760.9950.9800.9990.9910.9380.9890.9941.0000.9980.979
전북0.9960.9960.6180.9930.9220.9660.8060.9810.9940.9830.9980.9930.9450.9930.9950.9981.0000.973
제주1.0000.990-0.7460.9320.3370.7050.9390.8410.9810.8220.9900.9170.5110.9300.9550.9790.9731.000

Missing values

2024-01-06T12:39:38.384736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-06T12:39:39.081815image/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.
2024-01-06T12:39:39.427231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

연도강원서울경기인천대전세종충남충북대구경남경북울산부산광주전남전북제주
019893094319989.0<NA>113349.03844<NA><NA>7002085529263643721628366231806474588176<NA>
119906397501129.0<NA>205370.011592<NA><NA>53023489045647132453484551404272861172714510<NA>
2199112406744432.0<NA>319128.026052<NA><NA>108804878858265242946549368940412432294424048<NA>
31992188971073312.0<NA>452930.049930<NA><NA>1836766596747973835787413102003637233165135541<NA>
41993286011421167.0386308.0313113.093889<NA>3053037395587900255580297092153877853054668853022<NA>
51994379481894184.0571618.0394821.0139305<NA>376240387126843116257719271216401968301025295772165890<NA>
61995533702452297.0863575.0522337.0190635<NA>176156630217295514610612099615938425363713096774891102419<NA>
71996667052977508.01174932.0624902.0229125<NA>348809919124112517350221032021399332528117242993368142757<NA>
81997724363247943.01429039.0693540.0248206<NA>85368118028343753198471303034268970418137196514101313168667<NA>
91998750483138085.01580507.0697186.0243117<NA>10675812368946010119658233784832232245859319634891364166360<NA>
연도강원서울경기인천대전세종충남충북대구경남경북울산부산광주전남전북제주
2420133078734731122.05217051.01569858.0750539748851660010718776970475105135014396963101053131803659036359495983411012089
2520142964374261204.04797282.01479928.0650047691191504915697543933462103423713833772607319131307358019957396583062714164
2620153039954166211.04763556.01393452.0616966767271260344702872928277102779513193821789084129698256688354734782020715960
2720163292074250538.04969753.01448692.0654218827081302614742339935036106322513479871708269136100459519558839383042419218
2820173638854432120.05260344.01485274.0688680849991379695784914956983107015914170202112563142737162440962705586181221116
2920184076454622322.05511970.01574147.07461898935615040098524931004383116010715000092800479146184063402074268692365822577
3020194133404295798.855279044.8571504512.7097346521043071550913882465971914112220914959292345387142863962509972761290843123847
3120204165704180225.05236434.01393145.07180981042741478566901455953114118359214792571774920140888662761380356988312125200
3220214599314201860.05498483.01472529.069559910359915944559640281004013131648215774072134812142199963984491629394720729793
3320224904424318148.05631491.01543709.0717151109413157190610118239709511324051155605120530571439559653410913744100374334276