Overview

Dataset statistics

Number of variables18
Number of observations41
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.6 KiB
Average record size in memory165.2 B

Variable types

Numeric18

Dataset

Description공무원 지역별(서울 부산 등 광역시 도별) 가입자 추이에 대한 데이터입니다. 1982년부터 시작되며 연 단위로 구분됩니다.
URLhttps://www.data.go.kr/data/15054016/fileData.do

Alerts

구분 is highly overall correlated with 서울 and 12 other fieldsHigh correlation
서울 is highly overall correlated with 구분 and 9 other fieldsHigh correlation
부산 is highly overall correlated with 구분 and 12 other fieldsHigh correlation
대구 is highly overall correlated with 구분 and 13 other fieldsHigh correlation
인천 is highly overall correlated with 구분 and 13 other fieldsHigh correlation
광주 is highly overall correlated with 구분 and 12 other fieldsHigh correlation
대전 is highly overall correlated with 구분 and 11 other fieldsHigh correlation
세종 is highly overall correlated with 구분 and 12 other fieldsHigh correlation
울산 is highly overall correlated with 구분 and 12 other fieldsHigh correlation
경기 is highly overall correlated with 구분 and 12 other fieldsHigh correlation
강원 is highly overall correlated with 구분 and 12 other fieldsHigh correlation
충북 is highly overall correlated with 구분 and 12 other fieldsHigh correlation
경북 is highly overall correlated with 구분 and 13 other fieldsHigh correlation
경남 is highly overall correlated with 대구 and 5 other fieldsHigh correlation
전북 is highly overall correlated with 전남High correlation
전남 is highly overall correlated with 전북High correlation
제주 is highly overall correlated with 구분 and 13 other fieldsHigh correlation
구분 has unique valuesUnique
서울 has unique valuesUnique
부산 has unique valuesUnique
대구 has unique valuesUnique
인천 has unique valuesUnique
경기 has unique valuesUnique
강원 has unique valuesUnique
충북 has unique valuesUnique
충남 has unique valuesUnique
경북 has unique valuesUnique
경남 has unique valuesUnique
전북 has unique valuesUnique
전남 has unique valuesUnique
제주 has unique valuesUnique
광주 has 4 (9.8%) zerosZeros
대전 has 7 (17.1%) zerosZeros
세종 has 30 (73.2%) zerosZeros
울산 has 15 (36.6%) zerosZeros

Reproduction

Analysis started2023-12-12 13:26:13.039944
Analysis finished2023-12-12 13:26:45.834633
Duration32.79 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2002
Minimum1982
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T22:26:45.908546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1982
5-th percentile1984
Q11992
median2002
Q32012
95-th percentile2020
Maximum2022
Range40
Interquartile range (IQR)20

Descriptive statistics

Standard deviation11.979149
Coefficient of variation (CV)0.0059835907
Kurtosis-1.2
Mean2002
Median Absolute Deviation (MAD)10
Skewness0
Sum82082
Variance143.5
MonotonicityStrictly increasing
2023-12-12T22:26:46.093428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
1982 1
 
2.4%
2013 1
 
2.4%
2005 1
 
2.4%
2006 1
 
2.4%
2007 1
 
2.4%
2008 1
 
2.4%
2009 1
 
2.4%
2010 1
 
2.4%
2011 1
 
2.4%
2012 1
 
2.4%
Other values (31) 31
75.6%
ValueCountFrequency (%)
1982 1
2.4%
1983 1
2.4%
1984 1
2.4%
1985 1
2.4%
1986 1
2.4%
1987 1
2.4%
1988 1
2.4%
1989 1
2.4%
1990 1
2.4%
1991 1
2.4%
ValueCountFrequency (%)
2022 1
2.4%
2021 1
2.4%
2020 1
2.4%
2019 1
2.4%
2018 1
2.4%
2017 1
2.4%
2016 1
2.4%
2015 1
2.4%
2014 1
2.4%
2013 1
2.4%

서울
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean213127.07
Minimum164485
Maximum241420
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T22:26:46.241502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum164485
5-th percentile174575
Q1209788
median217450
Q3223070
95-th percentile230396
Maximum241420
Range76935
Interquartile range (IQR)13282

Descriptive statistics

Standard deviation17098.446
Coefficient of variation (CV)0.080226532
Kurtosis1.4590395
Mean213127.07
Median Absolute Deviation (MAD)7428
Skewness-1.272864
Sum8738210
Variance2.9235685 × 108
MonotonicityNot monotonic
2023-12-12T22:26:46.384998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
164485 1
 
2.4%
218164 1
 
2.4%
214138 1
 
2.4%
216855 1
 
2.4%
218526 1
 
2.4%
219090 1
 
2.4%
224292 1
 
2.4%
220472 1
 
2.4%
219507 1
 
2.4%
219079 1
 
2.4%
Other values (31) 31
75.6%
ValueCountFrequency (%)
164485 1
2.4%
174243 1
2.4%
174575 1
2.4%
181631 1
2.4%
181766 1
2.4%
189997 1
2.4%
199768 1
2.4%
206952 1
2.4%
208820 1
2.4%
209251 1
2.4%
ValueCountFrequency (%)
241420 1
2.4%
239095 1
2.4%
230396 1
2.4%
229543 1
2.4%
228131 1
2.4%
226436 1
2.4%
226407 1
2.4%
225604 1
2.4%
225529 1
2.4%
224292 1
2.4%

부산
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65551.78
Minimum49635
Maximum78354
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T22:26:46.521762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum49635
5-th percentile53232
Q163279
median66054
Q369681
95-th percentile75619
Maximum78354
Range28719
Interquartile range (IQR)6402

Descriptive statistics

Standard deviation6799.1974
Coefficient of variation (CV)0.10372254
Kurtosis0.10770699
Mean65551.78
Median Absolute Deviation (MAD)3580
Skewness-0.49060472
Sum2687623
Variance46229085
MonotonicityNot monotonic
2023-12-12T22:26:46.640698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
58213 1
 
2.4%
70708 1
 
2.4%
66210 1
 
2.4%
67014 1
 
2.4%
66754 1
 
2.4%
66054 1
 
2.4%
66717 1
 
2.4%
69221 1
 
2.4%
69634 1
 
2.4%
69681 1
 
2.4%
Other values (31) 31
75.6%
ValueCountFrequency (%)
49635 1
2.4%
50993 1
2.4%
53232 1
2.4%
53478 1
2.4%
55621 1
2.4%
57571 1
2.4%
58213 1
2.4%
58605 1
2.4%
61530 1
2.4%
62987 1
2.4%
ValueCountFrequency (%)
78354 1
2.4%
77164 1
2.4%
75619 1
2.4%
74759 1
2.4%
73705 1
2.4%
71838 1
2.4%
71611 1
2.4%
71301 1
2.4%
71172 1
2.4%
70708 1
2.4%

대구
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46591.22
Minimum30237
Maximum64205
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T22:26:46.824691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30237
5-th percentile33168
Q137535
median46904
Q354193
95-th percentile62681
Maximum64205
Range33968
Interquartile range (IQR)16658

Descriptive statistics

Standard deviation10352.087
Coefficient of variation (CV)0.22218965
Kurtosis-1.3374761
Mean46591.22
Median Absolute Deviation (MAD)7717
Skewness0.18915526
Sum1910240
Variance1.071657 × 108
MonotonicityNot monotonic
2023-12-12T22:26:46.996131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
30237 1
 
2.4%
52400 1
 
2.4%
51429 1
 
2.4%
52752 1
 
2.4%
53101 1
 
2.4%
53297 1
 
2.4%
54193 1
 
2.4%
54470 1
 
2.4%
50906 1
 
2.4%
51258 1
 
2.4%
Other values (31) 31
75.6%
ValueCountFrequency (%)
30237 1
2.4%
32945 1
2.4%
33168 1
2.4%
33781 1
2.4%
33941 1
2.4%
34615 1
2.4%
35152 1
2.4%
35619 1
2.4%
36469 1
2.4%
37161 1
2.4%
ValueCountFrequency (%)
64205 1
2.4%
63959 1
2.4%
62681 1
2.4%
62324 1
2.4%
61117 1
2.4%
59653 1
2.4%
58667 1
2.4%
58663 1
2.4%
58062 1
2.4%
54470 1
2.4%

인천
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39986.341
Minimum12721
Maximum64406
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T22:26:47.129441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12721
5-th percentile18915
Q128194
median41680
Q351142
95-th percentile60458
Maximum64406
Range51685
Interquartile range (IQR)22948

Descriptive statistics

Standard deviation14299.764
Coefficient of variation (CV)0.35761621
Kurtosis-1.0536813
Mean39986.341
Median Absolute Deviation (MAD)9683
Skewness-0.26642895
Sum1639440
Variance2.0448325 × 108
MonotonicityNot monotonic
2023-12-12T22:26:47.258605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
12721 1
 
2.4%
51142 1
 
2.4%
46278 1
 
2.4%
48547 1
 
2.4%
49875 1
 
2.4%
50984 1
 
2.4%
51922 1
 
2.4%
51261 1
 
2.4%
51109 1
 
2.4%
51363 1
 
2.4%
Other values (31) 31
75.6%
ValueCountFrequency (%)
12721 1
2.4%
16509 1
2.4%
18915 1
2.4%
19160 1
2.4%
19451 1
2.4%
20403 1
2.4%
20701 1
2.4%
21918 1
2.4%
23446 1
2.4%
25995 1
2.4%
ValueCountFrequency (%)
64406 1
2.4%
63343 1
2.4%
60458 1
2.4%
59065 1
2.4%
54289 1
2.4%
51922 1
2.4%
51848 1
2.4%
51363 1
2.4%
51333 1
2.4%
51261 1
2.4%

광주
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)92.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27300.415
Minimum0
Maximum41608
Zeros4
Zeros (%)9.8%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T22:26:47.383808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q125169
median27137
Q333610
95-th percentile40196
Maximum41608
Range41608
Interquartile range (IQR)8441

Descriptive statistics

Standard deviation10685.697
Coefficient of variation (CV)0.39141151
Kurtosis2.1550068
Mean27300.415
Median Absolute Deviation (MAD)4283
Skewness-1.4085067
Sum1119317
Variance1.1418411 × 108
MonotonicityNot monotonic
2023-12-12T22:26:47.507007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0 4
 
9.8%
37167 1
 
2.4%
30309 1
 
2.4%
30496 1
 
2.4%
31420 1
 
2.4%
32668 1
 
2.4%
33202 1
 
2.4%
33610 1
 
2.4%
34501 1
 
2.4%
37513 1
 
2.4%
Other values (28) 28
68.3%
ValueCountFrequency (%)
0 4
9.8%
22389 1
 
2.4%
22435 1
 
2.4%
22538 1
 
2.4%
23250 1
 
2.4%
23673 1
 
2.4%
24850 1
 
2.4%
25169 1
 
2.4%
25242 1
 
2.4%
25594 1
 
2.4%
ValueCountFrequency (%)
41608 1
2.4%
41086 1
2.4%
40196 1
2.4%
39475 1
2.4%
38959 1
2.4%
37846 1
2.4%
37570 1
2.4%
37513 1
2.4%
37167 1
2.4%
34501 1
2.4%

대전
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)82.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33751.707
Minimum0
Maximum53064
Zeros7
Zeros (%)17.1%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T22:26:47.650713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q129596
median40345
Q345090
95-th percentile52561
Maximum53064
Range53064
Interquartile range (IQR)15494

Descriptive statistics

Standard deviation17499.256
Coefficient of variation (CV)0.51847025
Kurtosis-0.071852771
Mean33751.707
Median Absolute Deviation (MAD)10066
Skewness-1.0056202
Sum1383820
Variance3.0622397 × 108
MonotonicityNot monotonic
2023-12-12T22:26:47.775080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 7
 
17.1%
30279 2
 
4.9%
51805 1
 
2.4%
45090 1
 
2.4%
45435 1
 
2.4%
42468 1
 
2.4%
42943 1
 
2.4%
44706 1
 
2.4%
49080 1
 
2.4%
52248 1
 
2.4%
Other values (24) 24
58.5%
ValueCountFrequency (%)
0 7
17.1%
23723 1
 
2.4%
29225 1
 
2.4%
29279 1
 
2.4%
29596 1
 
2.4%
29666 1
 
2.4%
30279 2
 
4.9%
30853 1
 
2.4%
32279 1
 
2.4%
32337 1
 
2.4%
ValueCountFrequency (%)
53064 1
2.4%
52931 1
2.4%
52561 1
2.4%
52389 1
2.4%
52297 1
2.4%
52270 1
2.4%
52248 1
2.4%
51805 1
2.4%
49080 1
2.4%
45435 1
2.4%

세종
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)29.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5825.439
Minimum0
Maximum32533
Zeros30
Zeros (%)73.2%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T22:26:47.903387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q38112
95-th percentile29661
Maximum32533
Range32533
Interquartile range (IQR)8112

Descriptive statistics

Standard deviation10633.649
Coefficient of variation (CV)1.8253816
Kurtosis0.89031378
Mean5825.439
Median Absolute Deviation (MAD)0
Skewness1.5528883
Sum238843
Variance1.130745 × 108
MonotonicityIncreasing
2023-12-12T22:26:48.290353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 30
73.2%
8112 1
 
2.4%
8504 1
 
2.4%
16705 1
 
2.4%
17955 1
 
2.4%
20753 1
 
2.4%
21337 1
 
2.4%
23490 1
 
2.4%
27522 1
 
2.4%
29661 1
 
2.4%
Other values (2) 2
 
4.9%
ValueCountFrequency (%)
0 30
73.2%
8112 1
 
2.4%
8504 1
 
2.4%
16705 1
 
2.4%
17955 1
 
2.4%
20753 1
 
2.4%
21337 1
 
2.4%
23490 1
 
2.4%
27522 1
 
2.4%
29661 1
 
2.4%
ValueCountFrequency (%)
32533 1
2.4%
32271 1
2.4%
29661 1
2.4%
27522 1
2.4%
23490 1
2.4%
21337 1
2.4%
20753 1
2.4%
17955 1
2.4%
16705 1
2.4%
8504 1
2.4%

울산
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)65.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12160.488
Minimum0
Maximum24000
Zeros15
Zeros (%)36.6%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T22:26:48.420244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median16418
Q320011
95-th percentile22583
Maximum24000
Range24000
Interquartile range (IQR)20011

Descriptive statistics

Standard deviation9596.8727
Coefficient of variation (CV)0.78918485
Kurtosis-1.7182214
Mean12160.488
Median Absolute Deviation (MAD)4310
Skewness-0.42402243
Sum498580
Variance92099967
MonotonicityNot monotonic
2023-12-12T22:26:48.532285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 15
36.6%
14257 1
 
2.4%
24000 1
 
2.4%
23433 1
 
2.4%
22583 1
 
2.4%
22028 1
 
2.4%
21563 1
 
2.4%
20728 1
 
2.4%
20572 1
 
2.4%
20276 1
 
2.4%
Other values (17) 17
41.5%
ValueCountFrequency (%)
0 15
36.6%
14257 1
 
2.4%
14267 1
 
2.4%
14442 1
 
2.4%
15479 1
 
2.4%
15913 1
 
2.4%
16418 1
 
2.4%
17209 1
 
2.4%
17901 1
 
2.4%
18561 1
 
2.4%
ValueCountFrequency (%)
24000 1
2.4%
23433 1
2.4%
22583 1
2.4%
22028 1
2.4%
21563 1
2.4%
20728 1
2.4%
20572 1
2.4%
20299 1
2.4%
20276 1
2.4%
20231 1
2.4%

경기
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean145237.9
Minimum48937
Maximum242893
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T22:26:48.669526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum48937
5-th percentile52440
Q1107235
median142430
Q3194161
95-th percentile229299
Maximum242893
Range193956
Interquartile range (IQR)86926

Descriptive statistics

Standard deviation56919.207
Coefficient of variation (CV)0.39190325
Kurtosis-1.0517244
Mean145237.9
Median Absolute Deviation (MAD)48675
Skewness-0.1257644
Sum5954754
Variance3.2397961 × 109
MonotonicityNot monotonic
2023-12-12T22:26:48.802349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
52440 1
 
2.4%
195983 1
 
2.4%
161200 1
 
2.4%
167398 1
 
2.4%
173199 1
 
2.4%
179951 1
 
2.4%
184389 1
 
2.4%
190591 1
 
2.4%
194161 1
 
2.4%
191105 1
 
2.4%
Other values (31) 31
75.6%
ValueCountFrequency (%)
48937 1
2.4%
51242 1
2.4%
52440 1
2.4%
54434 1
2.4%
64350 1
2.4%
68013 1
2.4%
69551 1
2.4%
85147 1
2.4%
91538 1
2.4%
99859 1
2.4%
ValueCountFrequency (%)
242893 1
2.4%
237594 1
2.4%
229299 1
2.4%
223505 1
2.4%
217553 1
2.4%
205988 1
2.4%
201810 1
2.4%
197883 1
2.4%
196320 1
2.4%
195983 1
2.4%

강원
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47108.146
Minimum36511
Maximum55353
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T22:26:48.929444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36511
5-th percentile38315
Q145144
median47955
Q349395
95-th percentile52755
Maximum55353
Range18842
Interquartile range (IQR)4251

Descriptive statistics

Standard deviation4222.9196
Coefficient of variation (CV)0.089643086
Kurtosis0.43606802
Mean47108.146
Median Absolute Deviation (MAD)2271
Skewness-0.66362302
Sum1931434
Variance17833050
MonotonicityNot monotonic
2023-12-12T22:26:49.083067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
36511 1
 
2.4%
48838 1
 
2.4%
46627 1
 
2.4%
47932 1
 
2.4%
47973 1
 
2.4%
47748 1
 
2.4%
47955 1
 
2.4%
48316 1
 
2.4%
48523 1
 
2.4%
48650 1
 
2.4%
Other values (31) 31
75.6%
ValueCountFrequency (%)
36511 1
2.4%
38305 1
2.4%
38315 1
2.4%
39902 1
2.4%
41357 1
2.4%
41705 1
2.4%
42288 1
2.4%
43902 1
2.4%
44533 1
2.4%
44907 1
2.4%
ValueCountFrequency (%)
55353 1
2.4%
54630 1
2.4%
52755 1
2.4%
51858 1
2.4%
50978 1
2.4%
50830 1
2.4%
50435 1
2.4%
50263 1
2.4%
50221 1
2.4%
50112 1
2.4%

충북
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36522.244
Minimum27045
Maximum45771
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T22:26:49.205813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27045
5-th percentile27655
Q134101
median37356
Q338707
95-th percentile43929
Maximum45771
Range18726
Interquartile range (IQR)4606

Descriptive statistics

Standard deviation4629.0565
Coefficient of variation (CV)0.12674622
Kurtosis0.13646644
Mean36522.244
Median Absolute Deviation (MAD)2147
Skewness-0.40091323
Sum1497412
Variance21428164
MonotonicityNot monotonic
2023-12-12T22:26:49.331628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
33321 1
 
2.4%
38669 1
 
2.4%
36179 1
 
2.4%
36738 1
 
2.4%
37098 1
 
2.4%
37356 1
 
2.4%
36821 1
 
2.4%
37744 1
 
2.4%
38121 1
 
2.4%
38487 1
 
2.4%
Other values (31) 31
75.6%
ValueCountFrequency (%)
27045 1
2.4%
27199 1
2.4%
27655 1
2.4%
28390 1
2.4%
28527 1
2.4%
28957 1
2.4%
33321 1
2.4%
33390 1
2.4%
33448 1
2.4%
33798 1
2.4%
ValueCountFrequency (%)
45771 1
2.4%
45530 1
2.4%
43929 1
2.4%
42582 1
2.4%
41438 1
2.4%
40050 1
2.4%
39754 1
2.4%
39503 1
2.4%
39138 1
2.4%
38923 1
2.4%

충남
Real number (ℝ)

UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47125
Minimum34708
Maximum59091
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T22:26:49.470613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34708
5-th percentile36972
Q142297
median47394
Q351068
95-th percentile57195
Maximum59091
Range24383
Interquartile range (IQR)8771

Descriptive statistics

Standard deviation6579.974
Coefficient of variation (CV)0.1396281
Kurtosis-0.84654599
Mean47125
Median Absolute Deviation (MAD)4843
Skewness0.0098089199
Sum1932125
Variance43296058
MonotonicityNot monotonic
2023-12-12T22:26:49.611223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
50822 1
 
2.4%
45832 1
 
2.4%
39427 1
 
2.4%
42297 1
 
2.4%
42551 1
 
2.4%
43023 1
 
2.4%
42780 1
 
2.4%
43651 1
 
2.4%
47778 1
 
2.4%
45900 1
 
2.4%
Other values (31) 31
75.6%
ValueCountFrequency (%)
34708 1
2.4%
35219 1
2.4%
36972 1
2.4%
37969 1
2.4%
38429 1
2.4%
39427 1
2.4%
39673 1
2.4%
41293 1
2.4%
42024 1
2.4%
42277 1
2.4%
ValueCountFrequency (%)
59091 1
2.4%
57443 1
2.4%
57195 1
2.4%
56695 1
2.4%
56512 1
2.4%
56316 1
2.4%
55387 1
2.4%
54851 1
2.4%
53263 1
2.4%
51796 1
2.4%

경북
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51974.976
Minimum41709
Maximum65819
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T22:26:49.723391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum41709
5-th percentile42422
Q146477
median52266
Q355341
95-th percentile62702
Maximum65819
Range24110
Interquartile range (IQR)8864

Descriptive statistics

Standard deviation6148.055
Coefficient of variation (CV)0.11828875
Kurtosis-0.35252902
Mean51974.976
Median Absolute Deviation (MAD)4423
Skewness0.23881722
Sum2130974
Variance37798580
MonotonicityNot monotonic
2023-12-12T22:26:49.838435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
44853 1
 
2.4%
56689 1
 
2.4%
52266 1
 
2.4%
53149 1
 
2.4%
53547 1
 
2.4%
53143 1
 
2.4%
53767 1
 
2.4%
53195 1
 
2.4%
57139 1
 
2.4%
57154 1
 
2.4%
Other values (31) 31
75.6%
ValueCountFrequency (%)
41709 1
2.4%
42286 1
2.4%
42422 1
2.4%
43040 1
2.4%
43494 1
2.4%
44062 1
2.4%
44853 1
2.4%
45397 1
2.4%
46313 1
2.4%
46444 1
2.4%
ValueCountFrequency (%)
65819 1
2.4%
64549 1
2.4%
62702 1
2.4%
61235 1
2.4%
59587 1
2.4%
58265 1
2.4%
57980 1
2.4%
57154 1
2.4%
57139 1
2.4%
56689 1
2.4%

경남
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64582.61
Minimum52014
Maximum76081
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T22:26:49.958734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum52014
5-th percentile53965
Q160124
median65178
Q368161
95-th percentile73760
Maximum76081
Range24067
Interquartile range (IQR)8037

Descriptive statistics

Standard deviation6216.9868
Coefficient of variation (CV)0.0962641
Kurtosis-0.78109343
Mean64582.61
Median Absolute Deviation (MAD)4440
Skewness-0.068941787
Sum2647887
Variance38650925
MonotonicityNot monotonic
2023-12-12T22:26:50.090232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
71623 1
 
2.4%
67517 1
 
2.4%
65178 1
 
2.4%
66736 1
 
2.4%
67492 1
 
2.4%
67659 1
 
2.4%
68429 1
 
2.4%
67128 1
 
2.4%
67209 1
 
2.4%
68161 1
 
2.4%
Other values (31) 31
75.6%
ValueCountFrequency (%)
52014 1
2.4%
53857 1
2.4%
53965 1
2.4%
55712 1
2.4%
57139 1
2.4%
57399 1
2.4%
58086 1
2.4%
58499 1
2.4%
59256 1
2.4%
59644 1
2.4%
ValueCountFrequency (%)
76081 1
2.4%
74890 1
2.4%
73760 1
2.4%
73246 1
2.4%
72980 1
2.4%
72818 1
2.4%
71845 1
2.4%
71623 1
2.4%
69882 1
2.4%
68429 1
2.4%

전북
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54699.098
Minimum46633
Maximum70713
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T22:26:50.217468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum46633
5-th percentile46967
Q148600
median51161
Q361647
95-th percentile68823
Maximum70713
Range24080
Interquartile range (IQR)13047

Descriptive statistics

Standard deviation7797.8193
Coefficient of variation (CV)0.14255846
Kurtosis-0.87025734
Mean54699.098
Median Absolute Deviation (MAD)3498
Skewness0.79599719
Sum2242663
Variance60805985
MonotonicityNot monotonic
2023-12-12T22:26:50.364150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
52013 1
 
2.4%
48600 1
 
2.4%
47947 1
 
2.4%
48564 1
 
2.4%
48682 1
 
2.4%
48420 1
 
2.4%
48943 1
 
2.4%
48725 1
 
2.4%
48961 1
 
2.4%
49074 1
 
2.4%
Other values (31) 31
75.6%
ValueCountFrequency (%)
46633 1
2.4%
46819 1
2.4%
46967 1
2.4%
47307 1
2.4%
47455 1
2.4%
47663 1
2.4%
47947 1
2.4%
48047 1
2.4%
48420 1
2.4%
48564 1
2.4%
ValueCountFrequency (%)
70713 1
2.4%
69748 1
2.4%
68823 1
2.4%
67362 1
2.4%
67286 1
2.4%
66187 1
2.4%
66107 1
2.4%
64754 1
2.4%
62949 1
2.4%
62849 1
2.4%

전남
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59292.22
Minimum50343
Maximum78143
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T22:26:50.530255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50343
5-th percentile52042
Q153524
median54899
Q362128
95-th percentile77345
Maximum78143
Range27800
Interquartile range (IQR)8604

Descriptive statistics

Standard deviation8148.9504
Coefficient of variation (CV)0.1374371
Kurtosis0.53726433
Mean59292.22
Median Absolute Deviation (MAD)2460
Skewness1.2921812
Sum2430981
Variance66405393
MonotonicityNot monotonic
2023-12-12T22:26:50.890248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
50343 1
 
2.4%
53348 1
 
2.4%
54529 1
 
2.4%
54667 1
 
2.4%
54706 1
 
2.4%
54575 1
 
2.4%
55025 1
 
2.4%
53432 1
 
2.4%
52884 1
 
2.4%
52989 1
 
2.4%
Other values (31) 31
75.6%
ValueCountFrequency (%)
50343 1
2.4%
50932 1
2.4%
52042 1
2.4%
52439 1
2.4%
52884 1
2.4%
52924 1
2.4%
52978 1
2.4%
52989 1
2.4%
53348 1
2.4%
53432 1
2.4%
ValueCountFrequency (%)
78143 1
2.4%
77499 1
2.4%
77345 1
2.4%
77021 1
2.4%
75869 1
2.4%
70989 1
2.4%
67288 1
2.4%
65448 1
2.4%
64529 1
2.4%
62878 1
2.4%

제주
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14848.341
Minimum9650
Maximum20273
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T22:26:51.038348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9650
5-th percentile10190
Q113674
median14788
Q316512
95-th percentile19898
Maximum20273
Range10623
Interquartile range (IQR)2838

Descriptive statistics

Standard deviation2811.9941
Coefficient of variation (CV)0.18938102
Kurtosis-0.4459855
Mean14848.341
Median Absolute Deviation (MAD)1633
Skewness-0.039436422
Sum608782
Variance7907310.8
MonotonicityNot monotonic
2023-12-12T22:26:51.217368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
9972 1
 
2.4%
16710 1
 
2.4%
15353 1
 
2.4%
15764 1
 
2.4%
15913 1
 
2.4%
16208 1
 
2.4%
16214 1
 
2.4%
16283 1
 
2.4%
16404 1
 
2.4%
16675 1
 
2.4%
Other values (31) 31
75.6%
ValueCountFrequency (%)
9650 1
2.4%
9972 1
2.4%
10190 1
2.4%
10292 1
2.4%
10618 1
2.4%
10972 1
2.4%
11294 1
2.4%
11915 1
2.4%
12429 1
2.4%
13155 1
2.4%
ValueCountFrequency (%)
20273 1
2.4%
19926 1
2.4%
19898 1
2.4%
19329 1
2.4%
18748 1
2.4%
17721 1
2.4%
17268 1
2.4%
16955 1
2.4%
16710 1
2.4%
16675 1
2.4%

Interactions

2023-12-12T22:26:43.906919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:13.553622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:15.187994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:17.060309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:19.124172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:21.013897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:22.745061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:24.686363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:26.479989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:28.380978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:29.993802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:32.164570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:33.799569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:35.437258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:37.390683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:38.922232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:40.447611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:41.934624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:44.023919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:13.649814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:15.292121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:17.177660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:19.236399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:21.143746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:22.836909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:24.812983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:26.603426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:28.494640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:30.110949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:32.261641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:33.887979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:35.569179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:37.477016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:39.028997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:40.548895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:42.017487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:44.132550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:13.742459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:15.425199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:17.282367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:19.357369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:21.249803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:22.939361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:24.925969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:26.714192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:28.593994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:30.226117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:32.362188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:33.973741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:35.671475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:37.581470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:39.132865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:40.665207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:42.097254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:44.208974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:13.828608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:15.526931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:17.363117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:19.452798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:21.336780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:23.021193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:25.014863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:26.799256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:28.690226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:30.328658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:32.453336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:34.058195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:35.757288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:37.650959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:39.212368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:40.745188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:42.184191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:44.296916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:13.919412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:15.641670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:17.449990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:19.547338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:21.421789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:23.132024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:25.138734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:26.900521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:28.791268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:30.427795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:32.542710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:34.143066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:35.880293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:37.749621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:39.297306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:40.837325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:42.267225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:44.385662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:13.996074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:15.729505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:17.534701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:19.668139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:21.502163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:23.208604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:25.243607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:26.998970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:28.872063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:30.516399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:32.619616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:34.219663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:35.977829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:37.835481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:39.400860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:40.904153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:42.641413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:44.459745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:14.069836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:15.814960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:17.986845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:19.789822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:21.574300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:23.303269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:25.340409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:27.109369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:28.945044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:30.615295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:32.700776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:34.290654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:36.079009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:37.902122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:39.480331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:40.971172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:42.714864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:44.567127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:14.151717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:15.926396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:18.090775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:19.887787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:21.657064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:23.393482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:25.415535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:27.227223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:29.032327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:30.728687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:32.790574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:34.362059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:36.171238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:37.971835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:39.561370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:41.038945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:42.805015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:44.660881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:14.237295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:16.035116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:18.182200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:20.013150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:21.769318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:23.488384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:25.515007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:27.347605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:29.114388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:31.173903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:32.897047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:34.444068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:36.251851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:38.051432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:39.658870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:41.122436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:42.926981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:44.763027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:14.317672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:16.123571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:18.264367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:20.112284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:21.880946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:23.579183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:25.611498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:27.460826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:29.190102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:31.285188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:32.994961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:34.532248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:36.634425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:38.133965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:39.742204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:41.213293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:43.027984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:44.872743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:14.415048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:16.222365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:18.368813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:20.212300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:21.991073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:23.672372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:25.722284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:27.591210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:29.296240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:31.405128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:33.103814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:34.628833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:36.720054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:38.225710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:39.824908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:41.312151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:43.130144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:44.965731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:14.528814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:16.334962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:18.472912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:20.337170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:22.097363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:23.769837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:25.827738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:27.703488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:29.380780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:31.515412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:33.189482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:34.742758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:36.802507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:38.323242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:39.895662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:41.397515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:43.244428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:45.044574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:14.635463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:16.448267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:18.585585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:20.440852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:22.192933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:23.858028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:25.911261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:27.831822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:29.465913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:31.621978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:33.275591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:34.849062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:36.920043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:38.442819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:39.979285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:41.487510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:43.343320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:45.116062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:14.727511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:16.557652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:18.668398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:20.525312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:22.275696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:23.944769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:26.001619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:27.916870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:29.551087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:31.711127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:33.379077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:34.950630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:36.996030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:38.541135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:40.054681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:41.566542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:43.448765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:45.182653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:14.821603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:16.648583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:18.745100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:20.613495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:22.367320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:24.021865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:26.081842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:28.002676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:29.647515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:31.793787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:33.476877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:35.090434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:37.073046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:38.605154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:40.123132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:41.638257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:43.527730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:45.271084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:14.905770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:16.730651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:18.842419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:20.703053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:22.469170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:24.107724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:26.167978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:28.106930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:29.722695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:31.876177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:33.555514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:35.185074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:37.160629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:38.672448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:40.201651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:41.713775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:43.611056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:45.361041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:14.998883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:16.842467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:18.931524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:20.792838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:22.561090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:24.206507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:26.269007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:28.191470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:29.798185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:31.976976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:33.636815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:35.273277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:37.233418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:38.745927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:40.273574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:41.788195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:43.701262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:45.433576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:15.092095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:16.957909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:19.015532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:20.912077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:22.648331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:24.314513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:26.359997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:28.283759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:29.896003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:32.067534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:33.719532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:35.354661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:37.315247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:38.850633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:40.351060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:41.859189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:26:43.787627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:26:51.356621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분서울부산대구인천광주대전세종울산경기강원충북충남경북경남전북전남제주
구분1.0000.7620.9250.9470.9540.9400.8690.7360.9120.9690.8630.8300.9160.9260.8330.9040.6510.958
서울0.7621.0000.8840.6530.7610.7320.6880.0000.5540.6720.9090.9170.7050.6820.8560.6340.7020.778
부산0.9250.8841.0000.8900.9180.7990.8380.8090.7080.8870.9170.8570.8010.9050.8920.8140.5220.931
대구0.9470.6530.8901.0000.9370.8540.8850.6860.9050.9260.7820.6470.8500.8770.7400.5930.6080.917
인천0.9540.7610.9180.9371.0000.7700.8470.7320.8760.9450.9010.8080.9320.9210.6980.8490.6170.928
광주0.9400.7320.7990.8540.7701.0000.8050.6830.8660.8770.7700.7540.8030.7550.8020.6920.4760.878
대전0.8690.6880.8380.8850.8470.8051.0000.6620.7420.8660.7080.6520.7220.8270.6570.7230.7120.860
세종0.7360.0000.8090.6860.7320.6830.6621.0000.5920.7100.5330.8500.5860.8840.7590.4120.0000.859
울산0.9120.5540.7080.9050.8760.8660.7420.5921.0000.9420.7470.7940.7290.7760.6640.6250.6220.791
경기0.9690.6720.8870.9260.9450.8770.8660.7100.9421.0000.8610.8470.9270.9340.8750.8440.7900.947
강원0.8630.9090.9170.7820.9010.7700.7080.5330.7470.8611.0000.8270.9250.9150.9430.8740.6280.856
충북0.8300.9170.8570.6470.8080.7540.6520.8500.7940.8470.8271.0000.7020.8910.8480.7860.5570.834
충남0.9160.7050.8010.8500.9320.8030.7220.5860.7290.9270.9250.7021.0000.9180.8170.8830.7550.810
경북0.9260.6820.9050.8770.9210.7550.8270.8840.7760.9340.9150.8910.9181.0000.9340.9230.8220.953
경남0.8330.8560.8920.7400.6980.8020.6570.7590.6640.8750.9430.8480.8170.9341.0000.9000.6060.935
전북0.9040.6340.8140.5930.8490.6920.7230.4120.6250.8440.8740.7860.8830.9230.9001.0000.8700.841
전남0.6510.7020.5220.6080.6170.4760.7120.0000.6220.7900.6280.5570.7550.8220.6060.8701.0000.631
제주0.9580.7780.9310.9170.9280.8780.8600.8590.7910.9470.8560.8340.8100.9530.9350.8410.6311.000
2023-12-12T22:26:51.542851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분서울부산대구인천광주대전세종울산경기강원충북충남경북경남전북전남제주
구분1.0000.5420.9120.9600.9820.9440.9790.7800.9740.9980.6380.743-0.1450.9670.459-0.237-0.2460.973
서울0.5421.0000.7230.5650.5830.6820.4960.3510.4100.5540.9080.8440.1400.5790.2630.4690.4750.647
부산0.9120.7231.0000.9000.8980.9630.8730.7800.8470.9250.8460.9150.0510.9330.4890.041-0.0770.970
대구0.9600.5650.9001.0000.9600.9500.9520.7410.9340.9620.6630.752-0.0910.9490.552-0.286-0.2500.952
인천0.9820.5830.8980.9601.0000.9300.9680.7290.9630.9800.6540.737-0.1440.9630.530-0.251-0.2340.966
광주0.9440.6820.9630.9500.9301.0000.9160.7800.8920.9500.7890.8660.0170.9480.498-0.061-0.1260.974
대전0.9790.4960.8730.9520.9680.9161.0000.7650.9600.9740.5980.695-0.2080.9450.472-0.272-0.2720.943
세종0.7800.3510.7800.7410.7290.7800.7651.0000.7990.7770.5740.6870.2620.7660.545-0.037-0.2100.779
울산0.9740.4100.8470.9340.9630.8920.9600.7991.0000.9710.5190.640-0.0960.9350.545-0.356-0.3930.931
경기0.9980.5540.9250.9620.9800.9500.9740.7770.9711.0000.6560.760-0.1320.9750.465-0.225-0.2420.980
강원0.6380.9080.8460.6630.6540.7890.5980.5740.5190.6561.0000.9640.1890.7030.3250.4360.3830.758
충북0.7430.8440.9150.7520.7370.8660.6950.6870.6400.7600.9641.0000.1660.7880.3840.3320.2210.842
충남-0.1450.1400.051-0.091-0.1440.017-0.2080.262-0.096-0.1320.1890.1661.000-0.1190.3350.3600.122-0.034
경북0.9670.5790.9330.9490.9630.9480.9450.7660.9350.9750.7030.788-0.1191.0000.505-0.211-0.2140.967
경남0.4590.2630.4890.5520.5300.4980.4720.5450.5450.4650.3250.3840.3350.5051.000-0.318-0.4450.501
전북-0.2370.4690.041-0.286-0.251-0.061-0.272-0.037-0.356-0.2250.4360.3320.360-0.211-0.3181.0000.806-0.113
전남-0.2460.475-0.077-0.250-0.234-0.126-0.272-0.210-0.393-0.2420.3830.2210.122-0.214-0.4450.8061.000-0.164
제주0.9730.6470.9700.9520.9660.9740.9430.7790.9310.9800.7580.842-0.0340.9670.501-0.113-0.1641.000

Missing values

2023-12-12T22:26:45.555157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:26:45.762790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

구분서울부산대구인천광주대전세종울산경기강원충북충남경북경남전북전남제주
01982164485582133023712721000052440365113332150822448537162352013503439972
11983174243496353561919160000048937383152704554851417097281846819509329650
219841745755099336469207010000512423830527199563164228672980480475297810190
319851817665323233941194510000544343990227655565124242273760490435454110292
4198618163153478331681650922538000643504135728527574434349452014541755732710618
5198718999755621337811891522435000680134170528390571954304053857546445912310972
6198819976857571351522040323673000695514228828957590914406255712574736212811294
71989206952586053294521918223892372300851474568433390384294539757399616476452911915
81990210022615303461523446232503027900915384683934461396734644458499629496728812429
91991217450642063716125995251693227900998594787035926412934841860124647547098913155
구분서울부산대구인천광주대전세종울산경기강원충북충남경북경남전북전남제주
31201321816470708524005114234501447068504202991959834883838669458325668967517486005334816710
322014215402711725806250569371674908016705200111963204755438095451765472364894476635204216512
332015214256713015866351021375135180517955202761978834830138627457075534165803491925243916955
342016213532716115866751333375705224820753205722018104892839503466605798066965496485292417268
352017215234718385965351848378465238921337207282059884924440050473945826567591498085352417721
362018220977737056111754289389595229723490215632175535083041438500915958769882511615489918748
372019225529747596232459065394755256127522220282235055185842582517966123571845527875685119329
382020229543756196268160458401965306429661225832292995275543929532636270273246541035829419926
392021239095771646395963343410865227032271234332375945463045530553876454974890556756064719898
402022241420783546420564406416085293132533240002428935535345771566956581976081565356211720273