Overview

Dataset statistics

Number of variables18
Number of observations408
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory64.3 KiB
Average record size in memory161.3 B

Variable types

DateTime1
Numeric17

Dataset

Description한국가스공사의 월간 시도별(강원,서울,경기,인천,경남,경북,광주,대구,대전,부산,세종,울산,전남,전북,제주,충남,충북) 도시가스 판매현황 데이터입니다. 단위는 천㎥ 입니다.
Author한국가스공사
URLhttps://www.data.go.kr/data/15040819/fileData.do

Alerts

강원 is highly overall correlated with 서울 and 15 other fieldsHigh correlation
서울 is highly overall correlated with 강원 and 13 other fieldsHigh correlation
경기 is highly overall correlated with 강원 and 15 other fieldsHigh correlation
인천 is highly overall correlated with 강원 and 14 other fieldsHigh correlation
경남 is highly overall correlated with 강원 and 15 other fieldsHigh correlation
경북 is highly overall correlated with 강원 and 15 other fieldsHigh correlation
광주 is highly overall correlated with 강원 and 15 other fieldsHigh correlation
대구 is highly overall correlated with 강원 and 14 other fieldsHigh correlation
대전 is highly overall correlated with 강원 and 15 other fieldsHigh correlation
부산 is highly overall correlated with 강원 and 15 other fieldsHigh correlation
세종 is highly overall correlated with 강원 and 12 other fieldsHigh correlation
울산 is highly overall correlated with 강원 and 15 other fieldsHigh correlation
전남 is highly overall correlated with 강원 and 15 other fieldsHigh correlation
전북 is highly overall correlated with 강원 and 15 other fieldsHigh correlation
제주 is highly overall correlated with 강원 and 14 other fieldsHigh correlation
충남 is highly overall correlated with 강원 and 15 other fieldsHigh correlation
충북 is highly overall correlated with 강원 and 15 other fieldsHigh correlation
연월 has unique valuesUnique
서울 has unique valuesUnique
인천 has unique valuesUnique
경남 has unique valuesUnique
광주 has unique valuesUnique
경기 has 48 (11.8%) zerosZeros
세종 has 288 (70.6%) zerosZeros
제주 has 192 (47.1%) zerosZeros
충남 has 58 (14.2%) zerosZeros
충북 has 7 (1.7%) zerosZeros

Reproduction

Analysis started2024-01-06 12:53:29.330159
Analysis finished2024-01-06 12:55:00.879428
Duration1 minute and 31.55 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연월
Date

UNIQUE 

Distinct408
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
Minimum1989-01-01 00:00:00
Maximum2022-12-01 00:00:00
2024-01-06T12:55:01.074149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:55:01.642854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

강원
Real number (ℝ)

HIGH CORRELATION 

Distinct405
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16556.363
Minimum105
Maximum74051
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-01-06T12:55:02.233646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum105
5-th percentile438.05
Q14306.75
median12585.5
Q324230
95-th percentile48962.75
Maximum74051
Range73946
Interquartile range (IQR)19923.25

Descriptive statistics

Standard deviation15578.443
Coefficient of variation (CV)0.94093389
Kurtosis1.3689581
Mean16556.363
Median Absolute Deviation (MAD)9135.5
Skewness1.3066687
Sum6754996
Variance2.4268788 × 108
MonotonicityNot monotonic
2024-01-06T12:55:02.819148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13001 2
 
0.5%
16280 2
 
0.5%
15614 2
 
0.5%
326 1
 
0.2%
12653 1
 
0.2%
34754 1
 
0.2%
42131 1
 
0.2%
44479 1
 
0.2%
35317 1
 
0.2%
21521 1
 
0.2%
Other values (395) 395
96.8%
ValueCountFrequency (%)
105 1
0.2%
108 1
0.2%
113 1
0.2%
134 1
0.2%
161 1
0.2%
163 1
0.2%
189 1
0.2%
191 1
0.2%
229 1
0.2%
240 1
0.2%
ValueCountFrequency (%)
74051 1
0.2%
72792 1
0.2%
69717 1
0.2%
66441 1
0.2%
64248 1
0.2%
64031 1
0.2%
61782 1
0.2%
61530 1
0.2%
60387 1
0.2%
59603 1
0.2%

서울
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct408
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean309465.69
Minimum15151
Maximum928709
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-01-06T12:55:03.263655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15151
5-th percentile33963.6
Q1150187.5
median207211
Q3466845.5
95-th percentile752034.1
Maximum928709
Range913558
Interquartile range (IQR)316658

Descriptive statistics

Standard deviation229159.12
Coefficient of variation (CV)0.74049928
Kurtosis-0.5581231
Mean309465.69
Median Absolute Deviation (MAD)123337.5
Skewness0.79326217
Sum1.26262 × 108
Variance5.2513904 × 1010
MonotonicityNot monotonic
2024-01-06T12:55:03.795784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37560 1
 
0.2%
198037 1
 
0.2%
416297 1
 
0.2%
598720 1
 
0.2%
787565 1
 
0.2%
802350 1
 
0.2%
639756 1
 
0.2%
347194 1
 
0.2%
227152 1
 
0.2%
157947 1
 
0.2%
Other values (398) 398
97.5%
ValueCountFrequency (%)
15151 1
0.2%
15521 1
0.2%
15790 1
0.2%
16609 1
0.2%
17090 1
0.2%
18668 1
0.2%
20214 1
0.2%
20668 1
0.2%
21701 1
0.2%
21820 1
0.2%
ValueCountFrequency (%)
928709 1
0.2%
905732 1
0.2%
859992 1
0.2%
855091 1
0.2%
822116 1
0.2%
820626 1
0.2%
810317 1
0.2%
806820 1
0.2%
802350 1
0.2%
797092 1
0.2%

경기
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct361
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean272097.74
Minimum0
Maximum868530
Zeros48
Zeros (%)11.8%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-01-06T12:55:04.288873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q194801
median234795
Q3407826.25
95-th percentile711083.4
Maximum868530
Range868530
Interquartile range (IQR)313025.25

Descriptive statistics

Standard deviation218890.85
Coefficient of variation (CV)0.80445671
Kurtosis-0.27277006
Mean272097.74
Median Absolute Deviation (MAD)153763
Skewness0.73552527
Sum1.1101588 × 108
Variance4.7913206 × 1010
MonotonicityNot monotonic
2024-01-06T12:55:04.702889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 48
 
11.8%
240402 1
 
0.2%
240791 1
 
0.2%
320129 1
 
0.2%
450269 1
 
0.2%
556162 1
 
0.2%
652787 1
 
0.2%
821469 1
 
0.2%
772003 1
 
0.2%
503008 1
 
0.2%
Other values (351) 351
86.0%
ValueCountFrequency (%)
0 48
11.8%
15180 1
 
0.2%
16023 1
 
0.2%
16119 1
 
0.2%
20179 1
 
0.2%
20378 1
 
0.2%
20933 1
 
0.2%
21693 1
 
0.2%
22850 1
 
0.2%
28254 1
 
0.2%
ValueCountFrequency (%)
868530 1
0.2%
865976 1
0.2%
859780 1
0.2%
821469 1
0.2%
816518 1
0.2%
800552 1
0.2%
800313 1
0.2%
791139 1
0.2%
776179 1
0.2%
772003 1
0.2%

인천
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct408
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92246.868
Minimum6191
Maximum241088
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-01-06T12:55:05.110591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6191
5-th percentile13981.4
Q148528.25
median78622.5
Q3129166.25
95-th percentile198339.95
Maximum241088
Range234897
Interquartile range (IQR)80638

Descriptive statistics

Standard deviation57365.815
Coefficient of variation (CV)0.62187276
Kurtosis-0.58513782
Mean92246.868
Median Absolute Deviation (MAD)40073.5
Skewness0.56823407
Sum37636722
Variance3.2908367 × 109
MonotonicityNot monotonic
2024-01-06T12:55:05.580690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10468 1
 
0.2%
83313 1
 
0.2%
138957 1
 
0.2%
173944 1
 
0.2%
198234 1
 
0.2%
214146 1
 
0.2%
171694 1
 
0.2%
109145 1
 
0.2%
90064 1
 
0.2%
72299 1
 
0.2%
Other values (398) 398
97.5%
ValueCountFrequency (%)
6191 1
0.2%
6484 1
0.2%
6802 1
0.2%
7091 1
0.2%
7317 1
0.2%
7736 1
0.2%
9211 1
0.2%
9608 1
0.2%
9790 1
0.2%
10279 1
0.2%
ValueCountFrequency (%)
241088 1
0.2%
233451 1
0.2%
230689 1
0.2%
224628 1
0.2%
224622 1
0.2%
224584 1
0.2%
218911 1
0.2%
217026 1
0.2%
214146 1
0.2%
213458 1
0.2%

경남
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct408
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52827.375
Minimum1564
Maximum193539
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-01-06T12:55:06.057277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1564
5-th percentile3345.5
Q115769.75
median48558
Q377334.5
95-th percentile131488.4
Maximum193539
Range191975
Interquartile range (IQR)61564.75

Descriptive statistics

Standard deviation41443.253
Coefficient of variation (CV)0.78450336
Kurtosis-0.0069100012
Mean52827.375
Median Absolute Deviation (MAD)30875.5
Skewness0.76596374
Sum21553569
Variance1.7175432 × 109
MonotonicityNot monotonic
2024-01-06T12:55:06.550283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2942 1
 
0.2%
59792 1
 
0.2%
93434 1
 
0.2%
116782 1
 
0.2%
126191 1
 
0.2%
120835 1
 
0.2%
95814 1
 
0.2%
71783 1
 
0.2%
57525 1
 
0.2%
49507 1
 
0.2%
Other values (398) 398
97.5%
ValueCountFrequency (%)
1564 1
0.2%
1669 1
0.2%
1722 1
0.2%
1727 1
0.2%
1740 1
0.2%
1951 1
0.2%
2282 1
0.2%
2548 1
0.2%
2625 1
0.2%
2636 1
0.2%
ValueCountFrequency (%)
193539 1
0.2%
182553 1
0.2%
179907 1
0.2%
171797 1
0.2%
159302 1
0.2%
157687 1
0.2%
151007 1
0.2%
149527 1
0.2%
147553 1
0.2%
146294 1
0.2%

경북
Real number (ℝ)

HIGH CORRELATION 

Distinct406
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68060.922
Minimum139
Maximum208314
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-01-06T12:55:07.069018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum139
5-th percentile1116.05
Q122651
median64965
Q3101260.75
95-th percentile164648.95
Maximum208314
Range208175
Interquartile range (IQR)78609.75

Descriptive statistics

Standard deviation51563.174
Coefficient of variation (CV)0.75760323
Kurtosis-0.67399357
Mean68060.922
Median Absolute Deviation (MAD)39075
Skewness0.44871226
Sum27768856
Variance2.6587609 × 109
MonotonicityNot monotonic
2024-01-06T12:55:07.490637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3764 2
 
0.5%
1017 2
 
0.5%
82062 1
 
0.2%
106270 1
 
0.2%
123783 1
 
0.2%
150698 1
 
0.2%
157314 1
 
0.2%
159092 1
 
0.2%
144802 1
 
0.2%
109101 1
 
0.2%
Other values (396) 396
97.1%
ValueCountFrequency (%)
139 1
0.2%
150 1
0.2%
239 1
0.2%
271 1
0.2%
359 1
0.2%
593 1
0.2%
603 1
0.2%
661 1
0.2%
740 1
0.2%
818 1
0.2%
ValueCountFrequency (%)
208314 1
0.2%
203081 1
0.2%
192264 1
0.2%
188792 1
0.2%
186183 1
0.2%
183273 1
0.2%
183207 1
0.2%
181227 1
0.2%
179607 1
0.2%
177749 1
0.2%

광주
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct408
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32372.301
Minimum815
Maximum102090
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-01-06T12:55:08.156883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum815
5-th percentile2115.3
Q112812.25
median27424
Q344950.5
95-th percentile83539
Maximum102090
Range101275
Interquartile range (IQR)32138.25

Descriptive statistics

Standard deviation24636.876
Coefficient of variation (CV)0.76104802
Kurtosis0.01477589
Mean32372.301
Median Absolute Deviation (MAD)16343
Skewness0.86129647
Sum13207899
Variance6.0697565 × 108
MonotonicityNot monotonic
2024-01-06T12:55:08.673373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2203 1
 
0.2%
27185 1
 
0.2%
44227 1
 
0.2%
68239 1
 
0.2%
80087 1
 
0.2%
85824 1
 
0.2%
78625 1
 
0.2%
44215 1
 
0.2%
36243 1
 
0.2%
26714 1
 
0.2%
Other values (398) 398
97.5%
ValueCountFrequency (%)
815 1
0.2%
821 1
0.2%
823 1
0.2%
847 1
0.2%
903 1
0.2%
1030 1
0.2%
1064 1
0.2%
1098 1
0.2%
1124 1
0.2%
1225 1
0.2%
ValueCountFrequency (%)
102090 1
0.2%
101969 1
0.2%
100875 1
0.2%
99162 1
0.2%
96670 1
0.2%
94589 1
0.2%
94441 1
0.2%
93959 1
0.2%
93418 1
0.2%
90364 1
0.2%

대구
Real number (ℝ)

HIGH CORRELATION 

Distinct407
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54394.703
Minimum1118
Maximum159602
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-01-06T12:55:09.124910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1118
5-th percentile2586.45
Q128083.75
median46064
Q381613.5
95-th percentile129302.8
Maximum159602
Range158484
Interquartile range (IQR)53529.75

Descriptive statistics

Standard deviation38761.57
Coefficient of variation (CV)0.71259823
Kurtosis-0.34698189
Mean54394.703
Median Absolute Deviation (MAD)26108.5
Skewness0.61280796
Sum22193039
Variance1.5024593 × 109
MonotonicityNot monotonic
2024-01-06T12:55:09.598445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37955 2
 
0.5%
1940 1
 
0.2%
50864 1
 
0.2%
98983 1
 
0.2%
122833 1
 
0.2%
135280 1
 
0.2%
130060 1
 
0.2%
91544 1
 
0.2%
64437 1
 
0.2%
49613 1
 
0.2%
Other values (397) 397
97.3%
ValueCountFrequency (%)
1118 1
0.2%
1120 1
0.2%
1161 1
0.2%
1228 1
0.2%
1270 1
0.2%
1416 1
0.2%
1470 1
0.2%
1570 1
0.2%
1630 1
0.2%
1756 1
0.2%
ValueCountFrequency (%)
159602 1
0.2%
156060 1
0.2%
154893 1
0.2%
154535 1
0.2%
149169 1
0.2%
148346 1
0.2%
144808 1
0.2%
141192 1
0.2%
141067 1
0.2%
139938 1
0.2%

대전
Real number (ℝ)

HIGH CORRELATION 

Distinct406
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37965.426
Minimum158
Maximum124430
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-01-06T12:55:10.142131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum158
5-th percentile971.5
Q115096.5
median30284
Q356615.75
95-th percentile102864.75
Maximum124430
Range124272
Interquartile range (IQR)41519.25

Descriptive statistics

Standard deviation30686.446
Coefficient of variation (CV)0.80827343
Kurtosis0.10260827
Mean37965.426
Median Absolute Deviation (MAD)18641
Skewness0.936564
Sum15489894
Variance9.4165794 × 108
MonotonicityNot monotonic
2024-01-06T12:55:10.750770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10624 2
 
0.5%
110897 2
 
0.5%
30867 1
 
0.2%
86729 1
 
0.2%
107664 1
 
0.2%
116389 1
 
0.2%
98455 1
 
0.2%
56485 1
 
0.2%
46031 1
 
0.2%
31020 1
 
0.2%
Other values (396) 396
97.1%
ValueCountFrequency (%)
158 1
0.2%
186 1
0.2%
188 1
0.2%
196 1
0.2%
206 1
0.2%
257 1
0.2%
268 1
0.2%
322 1
0.2%
338 1
0.2%
345 1
0.2%
ValueCountFrequency (%)
124430 1
0.2%
123727 1
0.2%
121801 1
0.2%
121150 1
0.2%
120524 1
0.2%
120022 1
0.2%
118824 1
0.2%
116389 1
0.2%
115709 1
0.2%
113070 1
0.2%

부산
Real number (ℝ)

HIGH CORRELATION 

Distinct407
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73059.814
Minimum2268
Maximum225359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-01-06T12:55:11.518231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2268
5-th percentile4243.7
Q128395.25
median68631
Q3103420.75
95-th percentile178011.2
Maximum225359
Range223091
Interquartile range (IQR)75025.5

Descriptive statistics

Standard deviation52566.896
Coefficient of variation (CV)0.71950493
Kurtosis-0.17334847
Mean73059.814
Median Absolute Deviation (MAD)37417.5
Skewness0.68057189
Sum29808404
Variance2.7632786 × 109
MonotonicityNot monotonic
2024-01-06T12:55:12.303208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2497 2
 
0.5%
3112 1
 
0.2%
70169 1
 
0.2%
83927 1
 
0.2%
103329 1
 
0.2%
144653 1
 
0.2%
173008 1
 
0.2%
178441 1
 
0.2%
164255 1
 
0.2%
99190 1
 
0.2%
Other values (397) 397
97.3%
ValueCountFrequency (%)
2268 1
0.2%
2301 1
0.2%
2497 2
0.5%
2582 1
0.2%
2619 1
0.2%
3013 1
0.2%
3052 1
0.2%
3069 1
0.2%
3112 1
0.2%
3184 1
0.2%
ValueCountFrequency (%)
225359 1
0.2%
216643 1
0.2%
215573 1
0.2%
210982 1
0.2%
204669 1
0.2%
200683 1
0.2%
200163 1
0.2%
199756 1
0.2%
192790 1
0.2%
191681 1
0.2%

세종
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct120
Distinct (%)29.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2204.3799
Minimum0
Maximum12855
Zeros288
Zeros (%)70.6%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-01-06T12:55:12.938876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34856
95-th percentile10210.45
Maximum12855
Range12855
Interquartile range (IQR)4856

Descriptive statistics

Standard deviation3662.8253
Coefficient of variation (CV)1.6616126
Kurtosis0.31718628
Mean2204.3799
Median Absolute Deviation (MAD)0
Skewness1.3310416
Sum899387
Variance13416289
MonotonicityNot monotonic
2024-01-06T12:55:14.334407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 288
70.6%
5138 2
 
0.5%
6886 1
 
0.2%
6059 1
 
0.2%
6909 1
 
0.2%
7749 1
 
0.2%
9913 1
 
0.2%
10263 1
 
0.2%
10640 1
 
0.2%
10629 1
 
0.2%
Other values (110) 110
 
27.0%
ValueCountFrequency (%)
0 288
70.6%
3962 1
 
0.2%
3983 1
 
0.2%
3997 1
 
0.2%
4071 1
 
0.2%
4107 1
 
0.2%
4217 1
 
0.2%
4291 1
 
0.2%
4317 1
 
0.2%
4376 1
 
0.2%
ValueCountFrequency (%)
12855 1
0.2%
12823 1
0.2%
12222 1
0.2%
12113 1
0.2%
12087 1
0.2%
11904 1
0.2%
11875 1
0.2%
11610 1
0.2%
11351 1
0.2%
11264 1
0.2%

울산
Real number (ℝ)

HIGH CORRELATION 

Distinct407
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100868.63
Minimum1222
Maximum352116
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-01-06T12:55:15.476911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1222
5-th percentile3039.85
Q121635
median83779
Q3164711.25
95-th percentile262807.1
Maximum352116
Range350894
Interquartile range (IQR)143076.25

Descriptive statistics

Standard deviation84651.975
Coefficient of variation (CV)0.83922991
Kurtosis-0.51928675
Mean100868.63
Median Absolute Deviation (MAD)66915.5
Skewness0.66000911
Sum41154403
Variance7.1659569 × 109
MonotonicityNot monotonic
2024-01-06T12:55:16.375030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7277 2
 
0.5%
2006 1
 
0.2%
214146 1
 
0.2%
190155 1
 
0.2%
196637 1
 
0.2%
246244 1
 
0.2%
267942 1
 
0.2%
270006 1
 
0.2%
265100 1
 
0.2%
219141 1
 
0.2%
Other values (397) 397
97.3%
ValueCountFrequency (%)
1222 1
0.2%
1228 1
0.2%
1338 1
0.2%
1402 1
0.2%
1445 1
0.2%
1541 1
0.2%
1626 1
0.2%
1797 1
0.2%
1863 1
0.2%
1872 1
0.2%
ValueCountFrequency (%)
352116 1
0.2%
351014 1
0.2%
328057 1
0.2%
307958 1
0.2%
305735 1
0.2%
299474 1
0.2%
298701 1
0.2%
292135 1
0.2%
289470 1
0.2%
289048 1
0.2%

전남
Real number (ℝ)

HIGH CORRELATION 

Distinct406
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29035.373
Minimum405
Maximum107310
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-01-06T12:55:16.844641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum405
5-th percentile1099.25
Q19007.25
median23745.5
Q343958.75
95-th percentile77347.35
Maximum107310
Range106905
Interquartile range (IQR)34951.5

Descriptive statistics

Standard deviation24678.204
Coefficient of variation (CV)0.84993586
Kurtosis0.12536322
Mean29035.373
Median Absolute Deviation (MAD)17298.5
Skewness0.9008802
Sum11846432
Variance6.0901377 × 108
MonotonicityNot monotonic
2024-01-06T12:55:17.365232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19787 2
 
0.5%
1339 2
 
0.5%
29894 1
 
0.2%
35251 1
 
0.2%
43905 1
 
0.2%
55178 1
 
0.2%
58251 1
 
0.2%
57098 1
 
0.2%
54181 1
 
0.2%
37562 1
 
0.2%
Other values (396) 396
97.1%
ValueCountFrequency (%)
405 1
0.2%
416 1
0.2%
434 1
0.2%
474 1
0.2%
486 1
0.2%
507 1
0.2%
533 1
0.2%
574 1
0.2%
579 1
0.2%
581 1
0.2%
ValueCountFrequency (%)
107310 1
0.2%
105857 1
0.2%
101995 1
0.2%
101903 1
0.2%
97863 1
0.2%
96787 1
0.2%
94564 1
0.2%
91532 1
0.2%
89544 1
0.2%
87950 1
0.2%

전북
Real number (ℝ)

HIGH CORRELATION 

Distinct407
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39508.314
Minimum328
Maximum129959
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-01-06T12:55:17.872787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum328
5-th percentile999.4
Q110049.5
median34756.5
Q358968.75
95-th percentile103363.45
Maximum129959
Range129631
Interquartile range (IQR)48919.25

Descriptive statistics

Standard deviation32685.924
Coefficient of variation (CV)0.82731763
Kurtosis-0.32610199
Mean39508.314
Median Absolute Deviation (MAD)24636
Skewness0.71949375
Sum16119392
Variance1.0683697 × 109
MonotonicityNot monotonic
2024-01-06T12:55:18.409844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
607 2
 
0.5%
586 1
 
0.2%
43339 1
 
0.2%
50390 1
 
0.2%
66345 1
 
0.2%
83988 1
 
0.2%
96421 1
 
0.2%
101691 1
 
0.2%
88899 1
 
0.2%
62670 1
 
0.2%
Other values (397) 397
97.3%
ValueCountFrequency (%)
328 1
0.2%
349 1
0.2%
411 1
0.2%
432 1
0.2%
461 1
0.2%
522 1
0.2%
523 1
0.2%
565 1
0.2%
586 1
0.2%
600 1
0.2%
ValueCountFrequency (%)
129959 1
0.2%
129623 1
0.2%
128621 1
0.2%
125735 1
0.2%
124247 1
0.2%
121355 1
0.2%
116089 1
0.2%
115432 1
0.2%
113880 1
0.2%
113530 1
0.2%

제주
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct205
Distinct (%)50.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean659.34804
Minimum0
Maximum5602
Zeros192
Zeros (%)47.1%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-01-06T12:55:19.054310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median83.5
Q31095.5
95-th percentile2727.85
Maximum5602
Range5602
Interquartile range (IQR)1095.5

Descriptive statistics

Standard deviation976.56295
Coefficient of variation (CV)1.4811039
Kurtosis4.7718448
Mean659.34804
Median Absolute Deviation (MAD)83.5
Skewness2.0209335
Sum269014
Variance953675.2
MonotonicityNot monotonic
2024-01-06T12:55:19.540555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 192
47.1%
1065 3
 
0.7%
81 2
 
0.5%
922 2
 
0.5%
2077 2
 
0.5%
1154 2
 
0.5%
1275 2
 
0.5%
718 2
 
0.5%
580 2
 
0.5%
426 2
 
0.5%
Other values (195) 197
48.3%
ValueCountFrequency (%)
0 192
47.1%
35 1
 
0.2%
36 1
 
0.2%
41 1
 
0.2%
43 1
 
0.2%
46 1
 
0.2%
47 1
 
0.2%
51 1
 
0.2%
54 1
 
0.2%
65 1
 
0.2%
ValueCountFrequency (%)
5602 1
0.2%
5353 1
0.2%
4874 1
0.2%
4519 1
0.2%
4497 1
0.2%
4220 1
0.2%
3604 1
0.2%
3586 1
0.2%
3448 1
0.2%
3379 1
0.2%

충남
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct351
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60929.985
Minimum0
Maximum220643
Zeros58
Zeros (%)14.2%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-01-06T12:55:20.139511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15344
median51263
Q398117
95-th percentile169505.75
Maximum220643
Range220643
Interquartile range (IQR)92773

Descriptive statistics

Standard deviation56905.423
Coefficient of variation (CV)0.93394776
Kurtosis-0.56740374
Mean60929.985
Median Absolute Deviation (MAD)46113
Skewness0.65963934
Sum24859434
Variance3.2382272 × 109
MonotonicityNot monotonic
2024-01-06T12:55:20.683390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 58
 
14.2%
12 1
 
0.2%
114582 1
 
0.2%
96042 1
 
0.2%
99485 1
 
0.2%
106313 1
 
0.2%
109835 1
 
0.2%
128468 1
 
0.2%
146806 1
 
0.2%
157717 1
 
0.2%
Other values (341) 341
83.6%
ValueCountFrequency (%)
0 58
14.2%
12 1
 
0.2%
43 1
 
0.2%
49 1
 
0.2%
78 1
 
0.2%
98 1
 
0.2%
142 1
 
0.2%
166 1
 
0.2%
256 1
 
0.2%
270 1
 
0.2%
ValueCountFrequency (%)
220643 1
0.2%
217447 1
0.2%
198939 1
0.2%
198190 1
0.2%
197928 1
0.2%
196450 1
0.2%
192888 1
0.2%
192508 1
0.2%
192276 1
0.2%
187985 1
0.2%

충북
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct402
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34134.169
Minimum0
Maximum134218
Zeros7
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-01-06T12:55:21.346693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile414.9
Q18124.75
median26919.5
Q351427.75
95-th percentile94576.15
Maximum134218
Range134218
Interquartile range (IQR)43303

Descriptive statistics

Standard deviation30732.265
Coefficient of variation (CV)0.90033727
Kurtosis0.15503859
Mean34134.169
Median Absolute Deviation (MAD)21587
Skewness0.9134113
Sum13926741
Variance9.444721 × 108
MonotonicityNot monotonic
2024-01-06T12:55:22.085484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7
 
1.7%
35691 1
 
0.2%
40623 1
 
0.2%
44377 1
 
0.2%
53799 1
 
0.2%
71216 1
 
0.2%
83915 1
 
0.2%
87616 1
 
0.2%
81516 1
 
0.2%
53543 1
 
0.2%
Other values (392) 392
96.1%
ValueCountFrequency (%)
0 7
1.7%
9 1
 
0.2%
21 1
 
0.2%
71 1
 
0.2%
125 1
 
0.2%
145 1
 
0.2%
153 1
 
0.2%
174 1
 
0.2%
191 1
 
0.2%
237 1
 
0.2%
ValueCountFrequency (%)
134218 1
0.2%
133416 1
0.2%
128159 1
0.2%
119471 1
0.2%
117834 1
0.2%
117146 1
0.2%
114597 1
0.2%
113620 1
0.2%
109718 1
0.2%
106175 1
0.2%

Interactions

2024-01-06T12:54:53.287342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:53:33.441716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:53:38.016389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:53:42.806762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:53:47.812350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:53:52.310527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:53:57.257264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:02.204378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:07.356542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:12.041893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:18.261168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:23.050385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:27.946720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:32.839579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:37.580681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:43.288561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:47.707073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:53.670360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:53:33.709402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:53:38.275338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:53:43.153680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:53:48.085899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:53:52.628427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:53:57.502113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:02.486678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:07.642603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:12.312576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:18.545321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:23.335919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:28.223448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:33.100201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:37.976075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:43.557254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:48.101429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:53.938495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:53:33.974806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:53:38.509750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:53:43.398746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:53:48.322523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:53:52.881109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:53:57.820894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:02.716235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:07.883431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:12.557003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:19.011562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:23.616095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:28.530650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:33.340846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:38.274029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:43.797132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:48.447611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:54.220594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:53:34.202112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:53:38.804631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:53:43.624181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:53:48.556399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:53:53.133618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-01-06T12:53:37.506531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:53:42.307758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:53:47.339231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:53:51.800983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:53:56.451731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:01.690637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:06.530042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:11.517406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:17.707958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:22.583418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:27.310011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:32.281868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:36.865970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:42.748274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:47.193551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:52.641759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:58.955637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:53:37.749002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:53:42.543672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:53:47.576237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:53:52.043722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:53:56.855292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:01.949422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:07.047083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:11.770728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:17.972800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:22.801399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:27.668675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:32.577327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:37.105286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:42.998082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:47.452512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:54:52.967117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-06T12:55:22.547974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강원서울경기인천경남경북광주대구대전부산세종울산전남전북제주충남충북
강원1.0000.7700.9110.8620.9680.9390.9200.9040.8960.9260.7490.8270.9240.9510.9230.9230.954
서울0.7701.0000.8650.9300.7600.8140.8640.8780.8760.8480.4580.6960.7070.8020.5560.7430.727
경기0.9110.8651.0000.9410.8990.9350.9810.9260.9770.9760.6310.8370.8710.9320.7970.8850.896
인천0.8620.9300.9411.0000.8560.8940.9290.9220.9350.9200.5310.7970.8000.8780.6840.8350.832
경남0.9680.7600.8990.8561.0000.9500.9020.9070.8820.9240.7170.8870.9430.9500.9070.9290.940
경북0.9390.8140.9350.8940.9501.0000.9310.9070.9160.9660.7450.9040.9350.9710.8760.9500.960
광주0.9200.8640.9810.9290.9020.9311.0000.9220.9630.9700.6570.8290.8840.9310.8110.8970.911
대구0.9040.8780.9260.9220.9070.9070.9221.0000.8980.9250.6040.8230.8510.8950.7570.8490.860
대전0.8960.8760.9770.9350.8820.9160.9630.8981.0000.9610.6540.8400.8450.9090.8000.8840.885
부산0.9260.8480.9760.9200.9240.9660.9700.9250.9611.0000.6700.8600.9000.9420.8210.9140.927
세종0.7490.4580.6310.5310.7170.7450.6570.6040.6540.6701.0000.5930.7790.7270.8000.7330.806
울산0.8270.6960.8370.7970.8870.9040.8290.8230.8400.8600.5931.0000.8980.8820.7620.9020.864
전남0.9240.7070.8710.8000.9430.9350.8840.8510.8450.9000.7790.8981.0000.9360.9530.9310.966
전북0.9510.8020.9320.8780.9500.9710.9310.8950.9090.9420.7270.8820.9361.0000.8920.9480.962
제주0.9230.5560.7970.6840.9070.8760.8110.7570.8000.8210.8000.7620.9530.8921.0000.8960.947
충남0.9230.7430.8850.8350.9290.9500.8970.8490.8840.9140.7330.9020.9310.9480.8961.0000.948
충북0.9540.7270.8960.8320.9400.9600.9110.8600.8850.9270.8060.8640.9660.9620.9470.9481.000
2024-01-06T12:55:23.074857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강원서울경기인천경남경북광주대구대전부산세종울산전남전북제주충남충북
강원1.0000.8260.9740.9380.9810.9680.9790.9310.9590.9780.6600.9140.9580.9830.8450.9430.974
서울0.8261.0000.8740.9340.7620.7270.8790.8860.9120.8410.2600.6530.6680.7580.4490.6690.712
경기0.9740.8741.0000.9690.9550.9470.9890.9640.9810.9880.5530.8880.9100.9520.7590.9150.934
인천0.9380.9340.9691.0000.9080.8830.9720.9690.9750.9530.4420.8270.8360.8990.6550.8410.867
경남0.9810.7620.9550.9081.0000.9880.9610.9250.9240.9720.6720.9460.9720.9870.8810.9750.979
경북0.9680.7270.9470.8830.9881.0000.9450.9030.9160.9670.6790.9670.9760.9890.8900.9880.986
광주0.9790.8790.9890.9720.9610.9451.0000.9600.9780.9870.5590.8910.9110.9570.7720.9150.935
대구0.9310.8860.9640.9690.9250.9030.9601.0000.9480.9540.4800.8490.8530.9010.6770.8650.877
대전0.9590.9120.9810.9750.9240.9160.9780.9481.0000.9720.5040.8630.8740.9320.7090.8790.908
부산0.9780.8410.9880.9530.9720.9670.9870.9540.9721.0000.5800.9160.9300.9710.7980.9390.953
세종0.6600.2600.5530.4420.6720.6790.5590.4800.5040.5801.0000.6590.7600.6890.8170.7050.732
울산0.9140.6530.8880.8270.9460.9670.8910.8490.8630.9160.6591.0000.9570.9580.8780.9730.958
전남0.9580.6680.9100.8360.9720.9760.9110.8530.8740.9300.7600.9571.0000.9790.9230.9760.991
전북0.9830.7580.9520.8990.9870.9890.9570.9010.9320.9710.6890.9580.9791.0000.8960.9810.992
제주0.8450.4490.7590.6550.8810.8900.7720.6770.7090.7980.8170.8780.9230.8961.0000.9160.914
충남0.9430.6690.9150.8410.9750.9880.9150.8650.8790.9390.7050.9730.9760.9810.9161.0000.981
충북0.9740.7120.9340.8670.9790.9860.9350.8770.9080.9530.7320.9580.9910.9920.9140.9811.000

Missing values

2024-01-06T12:54:59.743684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-06T12:55:00.610751image/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

연월강원서울경기인천경남경북광주대구대전부산세종울산전남전북제주충남충북
01989-0132637560010468294215022031940322311202006764586000
11989-022723356609211294013920341756345305201900722864000
21989-032533022409608270227119401896268371402135734980000
31989-041631866807736195123912251416257249701402533600000
41989-05134157900709115643599031270196226801445474461000
51989-06113151510619117225938211161188230101338434411000
61989-07108166090648417276618471120158249701228416349000
71989-08105170900680216696038151118186261901626405328009
81989-091891552107317174074082312282062582015414864320021
91989-10321206680102792282905130618413383013018635816070071
연월강원서울경기인천경남경북광주대구대전부산세종울산전남전북제주충남충북
3982022-035587850858060168616779515930216532270266134151787981588069963220928879501019743379155922106175
3992022-0440254324808430826135139120351131911465821025034908711182179682014298359976890224412001279743
4002022-052872420527831942210317193334106832373486305536001886607458158299730886657117139421466093
4012022-06239851669892594938393378912951993081649993302407605162901595376817061874155110442659516
4022022-0721910165743278576811267307091136290654549930261695546252171226736835926517389852557782
4032022-0821332152928257556748446382686249277243871528525665906732122696542935431317809095155662
4042022-0921213153182250248793316279780465272703903028461691599710116200496675236415637884055276
4052022-102842619189834655690949729781016053853843270391928425211131132535518856585119219926266501
4062022-11385093332324646731173649463612684252472606306018610781485191392276686183703255812714384864
4072022-126644166464380055218152314238518618310087591225115709199756122222049661019031299595602192888128159