Overview

Dataset statistics

Number of variables19
Number of observations26
Missing cells16
Missing cells (%)3.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.5 KiB
Average record size in memory176.1 B

Variable types

Numeric19

Dataset

Description국내 석유제품의 지역별 소비량(서울,경기,강원,충북,충남,전북,전남,경북,경남,부산,제주,대구,인천,광주,대전,울산,세종) 단위 : 물량(천Bbl)
URLhttps://www.data.go.kr/data/15054602/fileData.do

Alerts

is highly overall correlated with 서울 and 13 other fieldsHigh correlation
서울 is highly overall correlated with and 12 other fieldsHigh correlation
경기 is highly overall correlated with 인천 and 1 other fieldsHigh correlation
강원 is highly overall correlated with and 11 other fieldsHigh correlation
충북 is highly overall correlated with 강원High correlation
충남 is highly overall correlated with and 13 other fieldsHigh correlation
전북 is highly overall correlated with and 11 other fieldsHigh correlation
전남 is highly overall correlated with and 14 other fieldsHigh correlation
경북 is highly overall correlated with and 12 other fieldsHigh correlation
경남 is highly overall correlated with and 8 other fieldsHigh correlation
부산 is highly overall correlated with and 13 other fieldsHigh correlation
제주 is highly overall correlated with 강원 and 1 other fieldsHigh correlation
대구 is highly overall correlated with and 12 other fieldsHigh correlation
인천 is highly overall correlated with and 9 other fieldsHigh correlation
광주 is highly overall correlated with 세종High correlation
대전 is highly overall correlated with and 11 other fieldsHigh correlation
울산 is highly overall correlated with and 12 other fieldsHigh correlation
세종 is highly overall correlated with and 9 other fieldsHigh correlation
합계 is highly overall correlated with and 11 other fieldsHigh correlation
울산 has 1 (3.8%) missing valuesMissing
세종 has 15 (57.7%) missing valuesMissing
has unique valuesUnique
서울 has unique valuesUnique
경기 has unique valuesUnique
강원 has unique valuesUnique
충북 has unique valuesUnique
충남 has unique valuesUnique
전북 has unique valuesUnique
전남 has unique valuesUnique
경북 has unique valuesUnique
경남 has unique valuesUnique
부산 has unique valuesUnique
제주 has unique valuesUnique
대구 has unique valuesUnique
인천 has unique valuesUnique
광주 has unique valuesUnique
대전 has unique valuesUnique
합계 has unique valuesUnique

Reproduction

Analysis started2023-12-12 23:13:32.285785
Analysis finished2023-12-12 23:14:05.121538
Duration32.84 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables


Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2009.5
Minimum1997
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T08:14:05.177476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1997
5-th percentile1998.25
Q12003.25
median2009.5
Q32015.75
95-th percentile2020.75
Maximum2022
Range25
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation7.6485293
Coefficient of variation (CV)0.0038061853
Kurtosis-1.2
Mean2009.5
Median Absolute Deviation (MAD)6.5
Skewness0
Sum52247
Variance58.5
MonotonicityStrictly increasing
2023-12-13T08:14:05.297924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1997 1
 
3.8%
2011 1
 
3.8%
2022 1
 
3.8%
2021 1
 
3.8%
2020 1
 
3.8%
2019 1
 
3.8%
2018 1
 
3.8%
2017 1
 
3.8%
2016 1
 
3.8%
2015 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
1997 1
3.8%
1998 1
3.8%
1999 1
3.8%
2000 1
3.8%
2001 1
3.8%
2002 1
3.8%
2003 1
3.8%
2004 1
3.8%
2005 1
3.8%
2006 1
3.8%
ValueCountFrequency (%)
2022 1
3.8%
2021 1
3.8%
2020 1
3.8%
2019 1
3.8%
2018 1
3.8%
2017 1
3.8%
2016 1
3.8%
2015 1
3.8%
2014 1
3.8%
2013 1
3.8%

서울
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51983.885
Minimum32270
Maximum98871
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T08:14:05.636311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32270
5-th percentile35245.75
Q145252.25
median48735.5
Q352494
95-th percentile82465.25
Maximum98871
Range66601
Interquartile range (IQR)7241.75

Descriptive statistics

Standard deviation14919.855
Coefficient of variation (CV)0.28700923
Kurtosis3.4778703
Mean51983.885
Median Absolute Deviation (MAD)3738
Skewness1.7624462
Sum1351581
Variance2.2260207 × 108
MonotonicityNot monotonic
2023-12-13T08:14:05.738221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
98871 1
 
3.8%
46218 1
 
3.8%
32270 1
 
3.8%
35214 1
 
3.8%
35341 1
 
3.8%
41388 1
 
3.8%
42468 1
 
3.8%
46817 1
 
3.8%
49228 1
 
3.8%
49392 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
32270 1
3.8%
35214 1
3.8%
35341 1
3.8%
41388 1
3.8%
42468 1
3.8%
44548 1
3.8%
45060 1
3.8%
45829 1
3.8%
46218 1
3.8%
46817 1
3.8%
ValueCountFrequency (%)
98871 1
3.8%
84342 1
3.8%
76835 1
3.8%
66664 1
3.8%
58400 1
3.8%
54824 1
3.8%
52536 1
3.8%
52368 1
3.8%
51383 1
3.8%
49392 1
3.8%

경기
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87814.615
Minimum68154
Maximum97240
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T08:14:05.837928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum68154
5-th percentile78491.5
Q185939.75
median89249
Q391221
95-th percentile95583.5
Maximum97240
Range29086
Interquartile range (IQR)5281.25

Descriptive statistics

Standard deviation6168.7519
Coefficient of variation (CV)0.07024744
Kurtosis3.0133059
Mean87814.615
Median Absolute Deviation (MAD)2491
Skewness-1.4008543
Sum2283180
Variance38053501
MonotonicityNot monotonic
2023-12-13T08:14:05.928784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
94265 1
 
3.8%
87702 1
 
3.8%
91305 1
 
3.8%
89815 1
 
3.8%
87607 1
 
3.8%
90608 1
 
3.8%
92343 1
 
3.8%
91734 1
 
3.8%
97240 1
 
3.8%
88683 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
68154 1
3.8%
78458 1
3.8%
78592 1
3.8%
81408 1
3.8%
82711 1
3.8%
84596 1
3.8%
85669 1
3.8%
86752 1
3.8%
86962 1
3.8%
87607 1
3.8%
ValueCountFrequency (%)
97240 1
3.8%
96023 1
3.8%
94265 1
3.8%
92343 1
3.8%
92220 1
3.8%
91734 1
3.8%
91305 1
3.8%
90969 1
3.8%
90765 1
3.8%
90608 1
3.8%

강원
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16895.5
Minimum13722
Maximum22799
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T08:14:06.026761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13722
5-th percentile13977.5
Q114835.5
median16089.5
Q318203.25
95-th percentile21391.5
Maximum22799
Range9077
Interquartile range (IQR)3367.75

Descriptive statistics

Standard deviation2619.0786
Coefficient of variation (CV)0.15501634
Kurtosis-0.29142654
Mean16895.5
Median Absolute Deviation (MAD)1439
Skewness0.91910563
Sum439283
Variance6859572.5
MonotonicityNot monotonic
2023-12-13T08:14:06.119514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
22799 1
 
3.8%
13845 1
 
3.8%
16391 1
 
3.8%
16279 1
 
3.8%
15781 1
 
3.8%
16553 1
 
3.8%
15747 1
 
3.8%
15650 1
 
3.8%
15955 1
 
3.8%
14821 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
13722 1
3.8%
13845 1
3.8%
14375 1
3.8%
14432 1
3.8%
14575 1
3.8%
14726 1
3.8%
14821 1
3.8%
14879 1
3.8%
15650 1
3.8%
15747 1
3.8%
ValueCountFrequency (%)
22799 1
3.8%
21454 1
3.8%
21204 1
3.8%
21197 1
3.8%
20206 1
3.8%
19603 1
3.8%
18394 1
3.8%
17631 1
3.8%
17054 1
3.8%
16553 1
3.8%

충북
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16917.538
Minimum14429
Maximum19496
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T08:14:06.211703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14429
5-th percentile14673.75
Q116270.5
median17169.5
Q317594.25
95-th percentile18123.25
Maximum19496
Range5067
Interquartile range (IQR)1323.75

Descriptive statistics

Standard deviation1132.7312
Coefficient of variation (CV)0.066956028
Kurtosis0.81780772
Mean16917.538
Median Absolute Deviation (MAD)497
Skewness-0.4711001
Sum439856
Variance1283079.9
MonotonicityNot monotonic
2023-12-13T08:14:06.319694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
19496 1
 
3.8%
16231 1
 
3.8%
17610 1
 
3.8%
17625 1
 
3.8%
17243 1
 
3.8%
17962 1
 
3.8%
17461 1
 
3.8%
17189 1
 
3.8%
16991 1
 
3.8%
15279 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
14429 1
3.8%
14472 1
3.8%
15279 1
3.8%
15497 1
3.8%
16127 1
3.8%
16142 1
3.8%
16231 1
3.8%
16389 1
3.8%
16660 1
3.8%
16819 1
3.8%
ValueCountFrequency (%)
19496 1
3.8%
18177 1
3.8%
17962 1
3.8%
17896 1
3.8%
17654 1
3.8%
17625 1
3.8%
17610 1
3.8%
17547 1
3.8%
17461 1
3.8%
17407 1
3.8%

충남
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean132518
Minimum56417
Maximum216944
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T08:14:06.418469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum56417
5-th percentile77572
Q193510
median129637.5
Q3176034.75
95-th percentile196297.25
Maximum216944
Range160527
Interquartile range (IQR)82524.75

Descriptive statistics

Standard deviation45225.886
Coefficient of variation (CV)0.34128108
Kurtosis-1.10514
Mean132518
Median Absolute Deviation (MAD)36680.5
Skewness0.28963658
Sum3445468
Variance2.0453807 × 109
MonotonicityNot monotonic
2023-12-13T08:14:06.520000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
56417 1
 
3.8%
129217 1
 
3.8%
216944 1
 
3.8%
193343 1
 
3.8%
197282 1
 
3.8%
192256 1
 
3.8%
190542 1
 
3.8%
182663 1
 
3.8%
185541 1
 
3.8%
156150 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
56417 1
3.8%
75713 1
3.8%
83149 1
3.8%
87754 1
3.8%
88877 1
3.8%
92660 1
3.8%
93254 1
3.8%
94278 1
3.8%
95426 1
3.8%
100741 1
3.8%
ValueCountFrequency (%)
216944 1
3.8%
197282 1
3.8%
193343 1
3.8%
192256 1
3.8%
190542 1
3.8%
185541 1
3.8%
182663 1
3.8%
156150 1
3.8%
152750 1
3.8%
141519 1
3.8%

전북
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19653.654
Minimum16297
Maximum25042
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T08:14:06.621020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16297
5-th percentile16491.5
Q117518.25
median18971
Q321579.75
95-th percentile23699.25
Maximum25042
Range8745
Interquartile range (IQR)4061.5

Descriptive statistics

Standard deviation2547.9522
Coefficient of variation (CV)0.12964267
Kurtosis-0.84616303
Mean19653.654
Median Absolute Deviation (MAD)1919
Skewness0.55141892
Sum510995
Variance6492060.4
MonotonicityNot monotonic
2023-12-13T08:14:06.725335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
25042 1
 
3.8%
17374 1
 
3.8%
17014 1
 
3.8%
17090 1
 
3.8%
17424 1
 
3.8%
18131 1
 
3.8%
18821 1
 
3.8%
19782 1
 
3.8%
19912 1
 
3.8%
19121 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
16297 1
3.8%
16351 1
3.8%
16913 1
3.8%
17014 1
3.8%
17090 1
3.8%
17374 1
3.8%
17424 1
3.8%
17801 1
3.8%
18131 1
3.8%
18144 1
3.8%
ValueCountFrequency (%)
25042 1
3.8%
23745 1
3.8%
23562 1
3.8%
23165 1
3.8%
22560 1
3.8%
21954 1
3.8%
21617 1
3.8%
21468 1
3.8%
21147 1
3.8%
19912 1
3.8%

전남
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean160119.08
Minimum121763
Maximum220527
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T08:14:06.833183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum121763
5-th percentile125948.75
Q1139243.5
median163446
Q3175003
95-th percentile201195.25
Maximum220527
Range98764
Interquartile range (IQR)35759.5

Descriptive statistics

Standard deviation25747.331
Coefficient of variation (CV)0.16080115
Kurtosis-0.34068367
Mean160119.08
Median Absolute Deviation (MAD)19737
Skewness0.36820229
Sum4163096
Variance6.6292506 × 108
MonotonicityNot monotonic
2023-12-13T08:14:06.950835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
127607 1
 
3.8%
170223 1
 
3.8%
205344 1
 
3.8%
220527 1
 
3.8%
158549 1
 
3.8%
183288 1
 
3.8%
175616 1
 
3.8%
188749 1
 
3.8%
170690 1
 
3.8%
181091 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
121763 1
3.8%
125396 1
3.8%
127607 1
3.8%
128152 1
3.8%
129791 1
3.8%
136912 1
3.8%
138545 1
3.8%
141339 1
3.8%
142117 1
3.8%
143814 1
3.8%
ValueCountFrequency (%)
220527 1
3.8%
205344 1
3.8%
188749 1
3.8%
183288 1
3.8%
181091 1
3.8%
179978 1
3.8%
175616 1
3.8%
173164 1
3.8%
172963 1
3.8%
171337 1
3.8%

경북
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29682.538
Minimum24879
Maximum36842
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T08:14:07.067194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum24879
5-th percentile25805
Q127596.25
median28856
Q330955.5
95-th percentile34764
Maximum36842
Range11963
Interquartile range (IQR)3359.25

Descriptive statistics

Standard deviation3116.5456
Coefficient of variation (CV)0.10499593
Kurtosis-0.30944938
Mean29682.538
Median Absolute Deviation (MAD)1873
Skewness0.67242471
Sum771746
Variance9712856.4
MonotonicityNot monotonic
2023-12-13T08:14:07.180002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
36842 1
 
3.8%
28040 1
 
3.8%
27234 1
 
3.8%
28180 1
 
3.8%
27202 1
 
3.8%
28030 1
 
3.8%
27473 1
 
3.8%
28080 1
 
3.8%
27966 1
 
3.8%
26144 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
24879 1
3.8%
25692 1
3.8%
26144 1
3.8%
26404 1
3.8%
27202 1
3.8%
27234 1
3.8%
27473 1
3.8%
27966 1
3.8%
28030 1
3.8%
28040 1
3.8%
ValueCountFrequency (%)
36842 1
3.8%
34796 1
3.8%
34668 1
3.8%
33753 1
3.8%
33575 1
3.8%
32791 1
3.8%
31005 1
3.8%
30807 1
3.8%
30798 1
3.8%
30660 1
3.8%

경남
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36370.692
Minimum28197
Maximum174623
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T08:14:07.288923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28197
5-th percentile29184.75
Q130002.25
median30578
Q331769.5
95-th percentile35160.5
Maximum174623
Range146426
Interquartile range (IQR)1767.25

Descriptive statistics

Standard deviation28237.403
Coefficient of variation (CV)0.77637794
Kurtosis25.83713
Mean36370.692
Median Absolute Deviation (MAD)773.5
Skewness5.0761899
Sum945638
Variance7.9735093 × 108
MonotonicityNot monotonic
2023-12-13T08:14:07.410637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
174623 1
 
3.8%
31048 1
 
3.8%
30012 1
 
3.8%
29999 1
 
3.8%
29740 1
 
3.8%
30539 1
 
3.8%
30333 1
 
3.8%
32387 1
 
3.8%
30617 1
 
3.8%
29148 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
28197 1
3.8%
29148 1
3.8%
29295 1
3.8%
29323 1
3.8%
29740 1
3.8%
29932 1
3.8%
29999 1
3.8%
30012 1
3.8%
30211 1
3.8%
30333 1
3.8%
ValueCountFrequency (%)
174623 1
3.8%
36085 1
3.8%
32387 1
3.8%
32144 1
3.8%
32113 1
3.8%
32005 1
3.8%
31832 1
3.8%
31582 1
3.8%
31287 1
3.8%
31251 1
3.8%

부산
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27212.769
Minimum18326
Maximum43280
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T08:14:07.530143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18326
5-th percentile18960.25
Q121576.25
median25579
Q332267.25
95-th percentile37681
Maximum43280
Range24954
Interquartile range (IQR)10691

Descriptive statistics

Standard deviation6930.797
Coefficient of variation (CV)0.25468915
Kurtosis-0.62196702
Mean27212.769
Median Absolute Deviation (MAD)4977
Skewness0.56913591
Sum707532
Variance48035947
MonotonicityNot monotonic
2023-12-13T08:14:07.646019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
43280 1
 
3.8%
23965 1
 
3.8%
19150 1
 
3.8%
18326 1
 
3.8%
19390 1
 
3.8%
22190 1
 
3.8%
21799 1
 
3.8%
21424 1
 
3.8%
22543 1
 
3.8%
20244 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
18326 1
3.8%
18897 1
3.8%
19150 1
3.8%
19390 1
3.8%
20244 1
3.8%
21424 1
3.8%
21502 1
3.8%
21799 1
3.8%
22190 1
3.8%
22543 1
3.8%
ValueCountFrequency (%)
43280 1
3.8%
37925 1
3.8%
36949 1
3.8%
35758 1
3.8%
34196 1
3.8%
34064 1
3.8%
32632 1
3.8%
31173 1
3.8%
30198 1
3.8%
29708 1
3.8%

제주
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8367.6923
Minimum5511
Maximum10245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T08:14:07.741243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5511
5-th percentile7028.25
Q17832
median8333
Q39120.5
95-th percentile9971.5
Maximum10245
Range4734
Interquartile range (IQR)1288.5

Descriptive statistics

Standard deviation1052.771
Coefficient of variation (CV)0.12581378
Kurtosis0.95710612
Mean8367.6923
Median Absolute Deviation (MAD)511.5
Skewness-0.34469504
Sum217560
Variance1108326.8
MonotonicityNot monotonic
2023-12-13T08:14:07.841300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
7305 1
 
3.8%
9402 1
 
3.8%
8035 1
 
3.8%
7812 1
 
3.8%
7339 1
 
3.8%
9714 1
 
3.8%
9805 1
 
3.8%
9305 1
 
3.8%
8287 1
 
3.8%
8414 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
5511 1
3.8%
6936 1
3.8%
7305 1
3.8%
7339 1
3.8%
7689 1
3.8%
7812 1
3.8%
7831 1
3.8%
7835 1
3.8%
7958 1
3.8%
8035 1
3.8%
ValueCountFrequency (%)
10245 1
3.8%
10027 1
3.8%
9805 1
3.8%
9714 1
3.8%
9549 1
3.8%
9402 1
3.8%
9305 1
3.8%
8567 1
3.8%
8526 1
3.8%
8414 1
3.8%

대구
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15505.385
Minimum11910
Maximum21919
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T08:14:07.963425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11910
5-th percentile12570.5
Q113382.25
median14219.5
Q317473
95-th percentile21164
Maximum21919
Range10009
Interquartile range (IQR)4090.75

Descriptive statistics

Standard deviation3104.2545
Coefficient of variation (CV)0.20020493
Kurtosis-0.58174593
Mean15505.385
Median Absolute Deviation (MAD)1453
Skewness0.91698931
Sum403140
Variance9636396.1
MonotonicityNot monotonic
2023-12-13T08:14:08.083237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
21919 1
 
3.8%
13681 1
 
3.8%
14308 1
 
3.8%
12567 1
 
3.8%
11910 1
 
3.8%
13709 1
 
3.8%
13397 1
 
3.8%
13382 1
 
3.8%
13383 1
 
3.8%
12933 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
11910 1
3.8%
12567 1
3.8%
12581 1
3.8%
12721 1
3.8%
12766 1
3.8%
12933 1
3.8%
13382 1
3.8%
13383 1
3.8%
13397 1
3.8%
13681 1
3.8%
ValueCountFrequency (%)
21919 1
3.8%
21275 1
3.8%
20831 1
3.8%
20031 1
3.8%
19920 1
3.8%
18939 1
3.8%
17612 1
3.8%
17056 1
3.8%
15672 1
3.8%
15256 1
3.8%

인천
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51184.692
Minimum27985
Maximum73035
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T08:14:08.201704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27985
5-th percentile30418.5
Q146426.5
median50314.5
Q355023.5
95-th percentile71036
Maximum73035
Range45050
Interquartile range (IQR)8597

Descriptive statistics

Standard deviation11349.894
Coefficient of variation (CV)0.22174392
Kurtosis0.36550365
Mean51184.692
Median Absolute Deviation (MAD)4019.5
Skewness0.058213832
Sum1330802
Variance1.288201 × 108
MonotonicityNot monotonic
2023-12-13T08:14:08.326569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
46377 1
 
3.8%
47778 1
 
3.8%
53642 1
 
3.8%
53579 1
 
3.8%
57340 1
 
3.8%
71050 1
 
3.8%
73035 1
 
3.8%
70994 1
 
3.8%
65829 1
 
3.8%
63430 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
27985 1
3.8%
29433 1
3.8%
33375 1
3.8%
44556 1
3.8%
45473 1
3.8%
46213 1
3.8%
46377 1
3.8%
46575 1
3.8%
47027 1
3.8%
47778 1
3.8%
ValueCountFrequency (%)
73035 1
3.8%
71050 1
3.8%
70994 1
3.8%
65829 1
3.8%
63430 1
3.8%
57340 1
3.8%
55484 1
3.8%
53642 1
3.8%
53579 1
3.8%
52379 1
3.8%

광주
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7964.0769
Minimum6412
Maximum9016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T08:14:08.434572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6412
5-th percentile7082
Q17504.75
median8089
Q38430.75
95-th percentile8792
Maximum9016
Range2604
Interquartile range (IQR)926

Descriptive statistics

Standard deviation630.42944
Coefficient of variation (CV)0.079159134
Kurtosis-0.10921644
Mean7964.0769
Median Absolute Deviation (MAD)427.5
Skewness-0.4358689
Sum207066
Variance397441.27
MonotonicityNot monotonic
2023-12-13T08:14:08.544519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
8774 1
 
3.8%
7654 1
 
3.8%
7497 1
 
3.8%
7471 1
 
3.8%
7528 1
 
3.8%
8177 1
 
3.8%
8105 1
 
3.8%
8257 1
 
3.8%
8421 1
 
3.8%
8434 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
6412 1
3.8%
7060 1
3.8%
7148 1
3.8%
7239 1
3.8%
7471 1
3.8%
7490 1
3.8%
7497 1
3.8%
7528 1
3.8%
7654 1
3.8%
7696 1
3.8%
ValueCountFrequency (%)
9016 1
3.8%
8798 1
3.8%
8774 1
3.8%
8762 1
3.8%
8509 1
3.8%
8438 1
3.8%
8434 1
3.8%
8421 1
3.8%
8335 1
3.8%
8257 1
3.8%

대전
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8981.1154
Minimum7075
Maximum12389
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T08:14:08.651901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7075
5-th percentile7307
Q18103.25
median8720
Q39879.5
95-th percentile10820.75
Maximum12389
Range5314
Interquartile range (IQR)1776.25

Descriptive statistics

Standard deviation1283.8294
Coefficient of variation (CV)0.14294766
Kurtosis0.44748819
Mean8981.1154
Median Absolute Deviation (MAD)700.5
Skewness0.75715629
Sum233509
Variance1648218
MonotonicityNot monotonic
2023-12-13T08:14:08.781806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
10283 1
 
3.8%
7891 1
 
3.8%
7075 1
 
3.8%
7268 1
 
3.8%
7450 1
 
3.8%
8079 1
 
3.8%
8372 1
 
3.8%
8523 1
 
3.8%
9068 1
 
3.8%
9073 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
7075 1
3.8%
7268 1
3.8%
7424 1
3.8%
7450 1
3.8%
7891 1
3.8%
7960 1
3.8%
8079 1
3.8%
8176 1
3.8%
8372 1
3.8%
8523 1
3.8%
ValueCountFrequency (%)
12389 1
3.8%
10902 1
3.8%
10577 1
3.8%
10520 1
3.8%
10330 1
3.8%
10283 1
3.8%
10086 1
3.8%
9260 1
3.8%
9211 1
3.8%
9073 1
3.8%

울산
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct25
Distinct (%)100.0%
Missing1
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean150611.12
Minimum105782
Maximum188181
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T08:14:08.914768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum105782
5-th percentile119881.8
Q1128932
median149037
Q3178353
95-th percentile183314.8
Maximum188181
Range82399
Interquartile range (IQR)49421

Descriptive statistics

Standard deviation24262.604
Coefficient of variation (CV)0.16109438
Kurtosis-1.2291464
Mean150611.12
Median Absolute Deviation (MAD)21078
Skewness0.070730178
Sum3765278
Variance5.8867397 × 108
MonotonicityNot monotonic
2023-12-13T08:14:09.048083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
105782 1
 
3.8%
119034 1
 
3.8%
181542 1
 
3.8%
181233 1
 
3.8%
178353 1
 
3.8%
178830 1
 
3.8%
188181 1
 
3.8%
183758 1
 
3.8%
181162 1
 
3.8%
152579 1
 
3.8%
Other values (15) 15
57.7%
ValueCountFrequency (%)
105782 1
3.8%
119034 1
3.8%
123273 1
3.8%
125628 1
3.8%
127131 1
3.8%
127959 1
3.8%
128932 1
3.8%
132703 1
3.8%
135377 1
3.8%
138525 1
3.8%
ValueCountFrequency (%)
188181 1
3.8%
183758 1
3.8%
181542 1
3.8%
181233 1
3.8%
181162 1
3.8%
178830 1
3.8%
178353 1
3.8%
166354 1
3.8%
164529 1
3.8%
156900 1
3.8%

세종
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)100.0%
Missing15
Missing (%)57.7%
Infinite0
Infinite (%)0.0%
Mean1209.1209
Minimum0.12
Maximum1903
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T08:14:09.174325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.12
5-th percentile0.165
Q11173
median1365
Q31571.5
95-th percentile1873
Maximum1903
Range1902.88
Interquartile range (IQR)398.5

Descriptive statistics

Standard deviation648.54043
Coefficient of variation (CV)0.53637352
Kurtosis0.73441196
Mean1209.1209
Median Absolute Deviation (MAD)323
Skewness-1.2578267
Sum13300.33
Variance420604.7
MonotonicityNot monotonic
2023-12-13T08:14:09.325497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0.12 1
 
3.8%
0.21 1
 
3.8%
1042.0 1
 
3.8%
1311.0 1
 
3.8%
1365.0 1
 
3.8%
1389.0 1
 
3.8%
1304.0 1
 
3.8%
1443.0 1
 
3.8%
1700.0 1
 
3.8%
1843.0 1
 
3.8%
(Missing) 15
57.7%
ValueCountFrequency (%)
0.12 1
3.8%
0.21 1
3.8%
1042.0 1
3.8%
1304.0 1
3.8%
1311.0 1
3.8%
1365.0 1
3.8%
1389.0 1
3.8%
1443.0 1
3.8%
1700.0 1
3.8%
1843.0 1
3.8%
ValueCountFrequency (%)
1903.0 1
3.8%
1843.0 1
3.8%
1700.0 1
3.8%
1443.0 1
3.8%
1389.0 1
3.8%
1365.0 1
3.8%
1311.0 1
3.8%
1304.0 1
3.8%
1042.0 1
3.8%
0.21 1
3.8%

합계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean816501.13
Minimum670278
Maximum947276
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T08:14:09.481937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum670278
5-th percentile725381.75
Q1761526.05
median794613
Q3871946
95-th percentile939605
Maximum947276
Range276998
Interquartile range (IQR)110419.95

Descriptive statistics

Standard deviation78716.789
Coefficient of variation (CV)0.096407447
Kurtosis-0.83387041
Mean816501.13
Median Absolute Deviation (MAD)38127.5
Skewness0.41679704
Sum21229029
Variance6.1963329 × 109
MonotonicityNot monotonic
2023-12-13T08:14:09.629493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
793900.0 1
 
3.8%
801644.0 1
 
3.8%
947276.0 1
 
3.8%
938171.0 1
 
3.8%
877179.0 1
 
3.8%
931946.0 1
 
3.8%
934802.0 1
 
3.8%
940083.0 1
 
3.8%
924198.0 1
 
3.8%
856247.0 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
670278.0 1
3.8%
719657.0 1
3.8%
742556.0 1
3.8%
743668.12 1
3.8%
752330.0 1
3.8%
760641.0 1
3.8%
761079.0 1
3.8%
762867.21 1
3.8%
762942.0 1
3.8%
765520.0 1
3.8%
ValueCountFrequency (%)
947276.0 1
3.8%
940083.0 1
3.8%
938171.0 1
3.8%
934802.0 1
3.8%
931946.0 1
3.8%
924198.0 1
3.8%
877179.0 1
3.8%
856247.0 1
3.8%
827679.0 1
3.8%
825203.0 1
3.8%

Interactions

2023-12-13T08:14:03.184562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:32.766730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:34.165059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:35.850587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:37.744919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:39.288897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:40.765736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:42.797235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:44.432601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:46.236729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:48.030210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:50.285766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:51.927902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:53.469246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:55.120947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:56.482176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:58.360029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:59.626451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:14:01.152366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:14:03.276217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:32.829428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:34.236416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:35.921139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:37.816932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:39.358262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-13T08:13:54.326356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:55.854286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:57.704466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:59.110432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:14:00.614483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:14:02.270833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:14:04.184110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:33.688196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:35.249528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:37.157633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:38.675703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:40.167395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:41.810181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:43.865046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:45.550925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:47.374535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:49.584558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:51.309216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:52.934171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:54.391529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:55.912308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:57.823334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:59.170060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:14:00.671921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:14:02.381690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:14:04.256756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:33.757400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:35.338573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:37.236438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:38.755118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:40.258917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:41.898140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:43.944171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:45.648487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:47.471318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:49.671863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:51.383918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:53.016861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:54.693999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:55.974401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:57.935667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:59.230690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:14:00.730037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:14:02.507487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:14:04.324948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:33.817724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:35.411056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:37.315236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:38.863210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:40.333086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:42.286557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:44.019074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:45.762466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:47.549483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:49.763125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:51.454671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:53.078864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:54.759056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:56.036674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:58.014272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:59.295073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:14:00.799143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:14:02.614236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:14:04.395094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:33.885477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:35.508976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:37.424365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:38.943194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:40.413767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:42.372575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:44.090689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:45.873023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:47.636749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:49.888271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:51.544876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:53.156288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:54.825745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:56.107760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:58.089303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:59.356523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:14:00.863640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:14:02.740417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:14:04.470690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:33.958367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:35.611933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:37.502499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:39.036406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:40.496323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:42.486862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:44.164652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:45.965478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:47.733343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:49.994478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:51.618378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:53.259633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:54.907526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:56.207019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:58.155315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:59.419009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:14:00.927409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:14:02.866802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:14:04.546211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:34.018821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:35.693379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:37.575632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:39.136174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:40.597051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:42.590693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:44.243491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:46.054493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:47.812253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:50.087825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:51.706208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:53.327346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:54.970443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:56.295841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:58.221450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:59.477990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:14:00.986771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:14:02.956785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:14:04.627772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:34.100086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:35.767755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:37.665294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:39.218529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:40.693836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:42.690423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:44.356233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:46.156449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:47.925507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:50.186749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:51.824751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:53.400426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:55.051603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:56.393419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:58.297833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:13:59.563245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:14:01.070949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:14:03.063437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:14:09.748786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
서울경기강원충북충남전북전남경북경남부산제주대구인천광주대전울산세종합계
1.0000.8400.3080.6680.7280.7430.7030.7420.862NaN0.8240.2430.7320.7000.8040.7280.7510.8390.819
서울0.8401.0000.2230.8060.6980.5530.7400.5830.6841.0000.7560.5120.8480.6970.5250.8200.7030.7970.690
경기0.3080.2231.0000.0000.6860.3930.6680.0000.5590.0000.0000.4690.0000.6030.2380.5250.5810.5360.380
강원0.6680.8060.0001.0000.8180.3230.7800.3310.9421.0000.7930.0000.8170.0000.0000.5820.8990.3280.473
충북0.7280.6980.6860.8181.0000.5230.6080.0000.7831.0000.8170.0000.3960.3500.6480.0000.3530.4120.793
충남0.7430.5530.3930.3230.5231.0000.2460.8700.602NaN0.6590.0000.3960.6180.0000.3130.8810.5850.766
전북0.7030.7400.6680.7800.6080.2461.0000.7890.7831.0000.8330.4430.7820.6770.6570.5090.7510.6300.662
전남0.7420.5830.0000.3310.0000.8700.7891.0000.3860.0000.7100.5550.7620.0000.3330.0390.0000.7400.475
경북0.8620.6840.5590.9420.7830.6020.7830.3861.0001.0000.8600.0000.8620.0000.3020.7230.7860.4530.621
경남NaN1.0000.0001.0001.000NaN1.0000.0001.0001.0001.0000.4820.6740.0000.1780.000NaNNaN0.000
부산0.8240.7560.0000.7930.8170.6590.8330.7100.8601.0001.0000.4750.8040.6720.5930.3930.7330.5750.653
제주0.2430.5120.4690.0000.0000.0000.4430.5550.0000.4820.4751.0000.0000.0000.0000.0000.4610.9010.127
대구0.7320.8480.0000.8170.3960.3960.7820.7620.8620.6740.8040.0001.0000.0000.5300.5030.6130.8030.594
인천0.7000.6970.6030.0000.3500.6180.6770.0000.0000.0000.6720.0000.0001.0000.6330.8380.7170.7430.813
광주0.8040.5250.2380.0000.6480.0000.6570.3330.3020.1780.5930.0000.5300.6331.0000.0000.0000.5990.222
대전0.7280.8200.5250.5820.0000.3130.5090.0390.7230.0000.3930.0000.5030.8380.0001.0000.6860.0000.501
울산0.7510.7030.5810.8990.3530.8810.7510.0000.786NaN0.7330.4610.6130.7170.0000.6861.0000.8650.863
세종0.8390.7970.5360.3280.4120.5850.6300.7400.453NaN0.5750.9010.8030.7430.5990.0000.8651.0000.942
합계0.8190.6900.3800.4730.7930.7660.6620.4750.6210.0000.6530.1270.5940.8130.2220.5010.8630.9421.000
2023-12-13T08:14:09.964666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
서울경기강원충북충남전북전남경북경남부산제주대구인천광주대전울산세종합계
1.000-0.8910.270-0.584-0.0540.994-0.7160.930-0.824-0.594-0.9460.385-0.8620.861-0.056-0.7420.8950.9450.918
서울-0.8911.000-0.2660.577-0.000-0.8880.762-0.8390.6830.4720.820-0.4370.741-0.7180.2360.802-0.705-0.900-0.776
경기0.270-0.2661.0000.0940.4770.2790.2050.2720.0440.149-0.1060.255-0.0010.5510.1020.0110.5240.2820.483
강원-0.5840.5770.0941.0000.608-0.5740.787-0.6400.7970.5330.688-0.5260.794-0.4490.0520.589-0.396-0.036-0.472
충북-0.054-0.0000.4770.6081.000-0.0420.478-0.1070.4820.3620.288-0.0680.4000.0280.1560.2860.1280.1360.095
충남0.994-0.8880.279-0.574-0.0421.000-0.7070.912-0.825-0.582-0.9350.387-0.8540.863-0.051-0.7250.8710.9270.912
전북-0.7160.7620.2050.7870.478-0.7071.000-0.7250.8490.5960.809-0.3450.817-0.4400.2380.899-0.490-0.591-0.580
전남0.930-0.8390.272-0.640-0.1070.912-0.7251.000-0.796-0.556-0.9100.538-0.8190.8090.042-0.7050.8310.7360.897
경북-0.8240.6830.0440.7970.482-0.8250.849-0.7961.0000.7120.897-0.4100.909-0.698-0.0370.755-0.710-0.209-0.729
경남-0.5940.4720.1490.5330.362-0.5820.596-0.5560.7121.0000.664-0.1430.696-0.473-0.0800.426-0.523-0.245-0.422
부산-0.9460.820-0.1060.6880.288-0.9350.809-0.9100.8970.6641.000-0.3180.923-0.8000.0380.780-0.815-0.609-0.847
제주0.385-0.4370.255-0.526-0.0680.387-0.3450.538-0.410-0.143-0.3181.000-0.3670.4390.380-0.2120.325-0.4360.430
대구-0.8620.741-0.0010.7940.400-0.8540.817-0.8190.9090.6960.923-0.3671.000-0.7150.0170.752-0.746-0.409-0.758
인천0.861-0.7180.551-0.4490.0280.863-0.4400.809-0.698-0.473-0.8000.439-0.7151.0000.144-0.4970.8680.2000.883
광주-0.0560.2360.1020.0520.156-0.0510.2380.042-0.037-0.0800.0380.3800.0170.1441.0000.3850.165-0.8640.103
대전-0.7420.8020.0110.5890.286-0.7250.899-0.7050.7550.4260.780-0.2120.752-0.4970.3851.000-0.591-0.891-0.662
울산0.895-0.7050.524-0.3960.1280.871-0.4900.831-0.710-0.523-0.8150.325-0.7460.8680.165-0.5911.0000.5820.935
세종0.945-0.9000.282-0.0360.1360.927-0.5910.736-0.209-0.245-0.609-0.436-0.4090.200-0.864-0.8910.5821.0000.800
합계0.918-0.7760.483-0.4720.0950.912-0.5800.897-0.729-0.422-0.8470.430-0.7580.8830.103-0.6620.9350.8001.000

Missing values

2023-12-13T08:14:04.738105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:14:04.965196image/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.
2023-12-13T08:14:05.074808image/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

서울경기강원충북충남전북전남경북경남부산제주대구인천광주대전울산세종합계
01997988719426522799194965641725042127607368421746234328073052191946377877410283<NA><NA>793900.0
119988434268154176311447275713185771217632943136085326325511176122798564128176105782<NA>670278.0
2199976835785922145416142831492195412539634796321133575869362083129433714810086119034<NA>719657.0
3200066664827112120417654877542374512979133753312873792576892127533375820912389127131<NA>742556.0
42001584008566921197181778887723562128152346683183236949837920031454738509105201232730.12743668.12
52002548249011620206175479325423165136912335753200534196856719920462138762109021327030.21762867.21
6200352536903291960317896942782256013854532791315823406484001893947027843810577135377<NA>762942.0
7200452368867521839416819926602161714133931005299323117379581705646575833510330140017<NA>752330.0
820054775596023170541712895426214681438143080730489296798048156725237978039009138525<NA>761079.0
9200649364907651578617407100741211471421173066030934301987835152564990670609260147084<NA>765520.0
서울경기강원충북충남전북전남경북경남부산제주대구인천광주대전울산세종합계
162013469518140814375154971415191629717997825692292952150210245125814857687987960164529<NA>825203.0
172014482437845813722144291527501691317296324879281971889782521272155484901685911569001042.0821457.0
182015493928868314821152791561501912118109126144291482024484141293363430843490731525791311.0856247.0
192016492289724015955169911855411991217069027966306172254382871338365829842190681811621365.0924198.0
202017468179173415650171891826631978218874928080323872142493051338270994825785231837581389.0940083.0
212018424689234315747174611905421882117561627473303332179998051339773035810583721881811304.0934802.0
222019413889060816553179621922561813118328828030305392219097141370971050817780791788301443.0931946.0
232020353418760715781172431972821742415854927202297401939073391191057340752874501783531700.0877179.0
242021352148981516279176251933431709022052728180299991832678121256753579747172681812331843.0938171.0
252022322709130516391176102169441701420534427234300121915080351430853642749770751815421903.0947276.0