Overview

Dataset statistics

Number of variables20
Number of observations520
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory90.0 KiB
Average record size in memory177.3 B

Variable types

Categorical2
Text1
Numeric17

Dataset

Description주요관광지 방문자의 비율정보로 강릉, 경주, 부산, 여수, 전주 5개 지자체 관광지 방문자의 연령 및 거주지역 비율정보를 제공합니다.
Author한국철도공사
URLhttps://www.data.go.kr/data/15106038/fileData.do

Alerts

서울 is highly overall correlated with 경기High correlation
대구 is highly overall correlated with 울산 and 2 other fieldsHigh correlation
인천 is highly overall correlated with 경기 and 1 other fieldsHigh correlation
광주 is highly overall correlated with 전북 and 1 other fieldsHigh correlation
울산 is highly overall correlated with 대구 and 2 other fieldsHigh correlation
경기 is highly overall correlated with 서울 and 1 other fieldsHigh correlation
충북 is highly overall correlated with 인천High correlation
충남 is highly overall correlated with 전북High correlation
전북 is highly overall correlated with 광주 and 1 other fieldsHigh correlation
전남 is highly overall correlated with 광주High correlation
경북 is highly overall correlated with 대구 and 2 other fieldsHigh correlation
경남 is highly overall correlated with 대구 and 3 other fieldsHigh correlation
방문지역 is highly overall correlated with 경남High correlation
서울 has 19 (3.7%) zerosZeros
부산 has 195 (37.5%) zerosZeros
대구 has 60 (11.5%) zerosZeros
인천 has 58 (11.2%) zerosZeros
광주 has 56 (10.8%) zerosZeros
대전 has 66 (12.7%) zerosZeros
울산 has 87 (16.7%) zerosZeros
세종 has 143 (27.5%) zerosZeros
경기 has 14 (2.7%) zerosZeros
강원 has 83 (16.0%) zerosZeros
충북 has 77 (14.8%) zerosZeros
충남 has 63 (12.1%) zerosZeros
전북 has 68 (13.1%) zerosZeros
전남 has 42 (8.1%) zerosZeros
경북 has 62 (11.9%) zerosZeros
경남 has 36 (6.9%) zerosZeros
제주 has 149 (28.7%) zerosZeros

Reproduction

Analysis started2023-12-12 00:24:59.731728
Analysis finished2023-12-12 00:25:29.404479
Duration29.67 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

방문지역
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
부산
160 
여수
125 
경주
80 
전주
80 
강릉
75 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강릉
2nd row강릉
3rd row강릉
4th row강릉
5th row강릉

Common Values

ValueCountFrequency (%)
부산 160
30.8%
여수 125
24.0%
경주 80
15.4%
전주 80
15.4%
강릉 75
14.4%

Length

2023-12-12T09:25:29.469892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:25:29.603728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산 160
30.8%
여수 125
24.0%
경주 80
15.4%
전주 80
15.4%
강릉 75
14.4%

연령대
Categorical

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
20대
104 
30대
104 
40대
104 
50대
104 
60대 이상
104 

Length

Max length6
Median length3
Mean length3.6
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20대
2nd row20대
3rd row20대
4th row20대
5th row20대

Common Values

ValueCountFrequency (%)
20대 104
20.0%
30대 104
20.0%
40대 104
20.0%
50대 104
20.0%
60대 이상 104
20.0%

Length

2023-12-12T09:25:29.758727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:25:29.912354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20대 104
16.7%
30대 104
16.7%
40대 104
16.7%
50대 104
16.7%
60대 104
16.7%
이상 104
16.7%
Distinct100
Distinct (%)19.2%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
2023-12-12T09:25:30.178515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length4.7980769
Min length2

Characters and Unicode

Total characters2495
Distinct characters179
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row합계(중복제외)
2nd row강릉교동
3rd row강릉역앞
4th row강문해변
5th row경포호
ValueCountFrequency (%)
합계(중복제외 25
 
4.8%
거문도 5
 
1.0%
여서동사거리 5
 
1.0%
손죽도 5
 
1.0%
소호항 5
 
1.0%
백야도 5
 
1.0%
돌산우두리 5
 
1.0%
돌산도 5
 
1.0%
대여자도 5
 
1.0%
대두라도 5
 
1.0%
Other values (90) 450
86.5%
2023-12-12T09:25:30.568896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
95
 
3.8%
60
 
2.4%
55
 
2.2%
50
 
2.0%
50
 
2.0%
45
 
1.8%
45
 
1.8%
40
 
1.6%
40
 
1.6%
40
 
1.6%
Other values (169) 1975
79.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2395
96.0%
Other Punctuation 50
 
2.0%
Open Punctuation 25
 
1.0%
Close Punctuation 25
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
95
 
4.0%
60
 
2.5%
55
 
2.3%
50
 
2.1%
50
 
2.1%
45
 
1.9%
45
 
1.9%
40
 
1.7%
40
 
1.7%
40
 
1.7%
Other values (165) 1875
78.3%
Other Punctuation
ValueCountFrequency (%)
, 25
50.0%
. 25
50.0%
Open Punctuation
ValueCountFrequency (%)
( 25
100.0%
Close Punctuation
ValueCountFrequency (%)
) 25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2395
96.0%
Common 100
 
4.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
95
 
4.0%
60
 
2.5%
55
 
2.3%
50
 
2.1%
50
 
2.1%
45
 
1.9%
45
 
1.9%
40
 
1.7%
40
 
1.7%
40
 
1.7%
Other values (165) 1875
78.3%
Common
ValueCountFrequency (%)
, 25
25.0%
. 25
25.0%
( 25
25.0%
) 25
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2395
96.0%
ASCII 100
 
4.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
95
 
4.0%
60
 
2.5%
55
 
2.3%
50
 
2.1%
50
 
2.1%
45
 
1.9%
45
 
1.9%
40
 
1.7%
40
 
1.7%
40
 
1.7%
Other values (165) 1875
78.3%
ASCII
ValueCountFrequency (%)
, 25
25.0%
. 25
25.0%
( 25
25.0%
) 25
25.0%

서울
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct469
Distinct (%)90.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.07251
Minimum0
Maximum46.153846
Zeros19
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-12-12T09:25:30.704052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.0662571
Q113.023349
median17.259975
Q323.580968
95-th percentile32.286376
Maximum46.153846
Range46.153846
Interquartile range (IQR)10.557618

Descriptive statistics

Standard deviation8.4415575
Coefficient of variation (CV)0.4670938
Kurtosis0.31346328
Mean18.07251
Median Absolute Deviation (MAD)5.0676979
Skewness0.23877514
Sum9397.7053
Variance71.259892
MonotonicityNot monotonic
2023-12-12T09:25:30.844896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 19
 
3.7%
12.5 6
 
1.2%
25.0 5
 
1.0%
20.0 4
 
0.8%
14.285714285714285 4
 
0.8%
12.0 3
 
0.6%
33.33333333333333 3
 
0.6%
18.181818181818183 3
 
0.6%
28.57142857142857 3
 
0.6%
9.090909090909092 2
 
0.4%
Other values (459) 468
90.0%
ValueCountFrequency (%)
0.0 19
3.7%
1.3333333333333335 2
 
0.4%
2.380952380952381 1
 
0.2%
2.7877055039313796 1
 
0.2%
3.5502958579881656 1
 
0.2%
3.7947621592731156 1
 
0.2%
4.037005887300253 1
 
0.2%
4.067796610169491 1
 
0.2%
4.158790170132325 1
 
0.2%
4.181034482758621 1
 
0.2%
ValueCountFrequency (%)
46.15384615384615 1
0.2%
45.3125 1
0.2%
42.857142857142854 2
0.4%
41.0958904109589 1
0.2%
40.458015267175576 1
0.2%
40.25974025974026 1
0.2%
39.130434782608695 1
0.2%
37.96296296296296 1
0.2%
36.91246226821906 1
0.2%
36.73005374121538 1
0.2%

부산
Real number (ℝ)

ZEROS 

Distinct303
Distinct (%)58.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.11248
Minimum0
Maximum50
Zeros195
Zeros (%)37.5%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-12-12T09:25:30.993938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.9554313
Q35.2576982
95-th percentile14.423878
Maximum50
Range50
Interquartile range (IQR)5.2576982

Descriptive statistics

Standard deviation6.3547907
Coefficient of variation (CV)1.5452454
Kurtosis18.027499
Mean4.11248
Median Absolute Deviation (MAD)1.9554313
Skewness3.4734171
Sum2138.4896
Variance40.383365
MonotonicityNot monotonic
2023-12-12T09:25:31.132564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 195
37.5%
11.11111111111111 4
 
0.8%
50.0 3
 
0.6%
13.333333333333334 3
 
0.6%
10.0 3
 
0.6%
25.0 2
 
0.4%
4.918032786885246 2
 
0.4%
6.666666666666667 2
 
0.4%
4.0 2
 
0.4%
2.898550724637681 2
 
0.4%
Other values (293) 302
58.1%
ValueCountFrequency (%)
0.0 195
37.5%
0.6216006216006216 1
 
0.2%
0.656624179219776 1
 
0.2%
0.8048289738430584 1
 
0.2%
0.8130081300813009 1
 
0.2%
0.8620689655172413 1
 
0.2%
0.9036144578313252 1
 
0.2%
0.9116409537166901 1
 
0.2%
0.9259259259259258 1
 
0.2%
0.9345794392523363 2
 
0.4%
ValueCountFrequency (%)
50.0 3
0.6%
38.46153846153847 1
 
0.2%
33.33333333333333 2
0.4%
26.490066225165563 1
 
0.2%
25.0 2
0.4%
23.28767123287671 1
 
0.2%
20.0 1
 
0.2%
18.571428571428573 1
 
0.2%
18.181818181818183 1
 
0.2%
16.86375321336761 1
 
0.2%

대구
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct439
Distinct (%)84.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0371287
Minimum0
Maximum50
Zeros60
Zeros (%)11.5%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-12-12T09:25:31.302963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.6836902
median3.6341078
Q37.3645948
95-th percentile13.434271
Maximum50
Range50
Interquartile range (IQR)5.6809046

Descriptive statistics

Standard deviation5.1609495
Coefficient of variation (CV)1.0245816
Kurtosis21.788569
Mean5.0371287
Median Absolute Deviation (MAD)2.3439109
Skewness3.2353325
Sum2619.3069
Variance26.6354
MonotonicityNot monotonic
2023-12-12T09:25:31.457044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 60
 
11.5%
6.666666666666667 3
 
0.6%
3.125 3
 
0.6%
2.5 3
 
0.6%
8.695652173913043 3
 
0.6%
4.761904761904762 2
 
0.4%
1.9607843137254901 2
 
0.4%
5.0 2
 
0.4%
5.88235294117647 2
 
0.4%
8.024691358024691 2
 
0.4%
Other values (429) 438
84.2%
ValueCountFrequency (%)
0.0 60
11.5%
0.2958579881656805 1
 
0.2%
0.5 1
 
0.2%
0.6097560975609756 1
 
0.2%
0.646551724137931 1
 
0.2%
0.6756756756756757 1
 
0.2%
0.697350069735007 1
 
0.2%
0.7299270072992701 1
 
0.2%
0.7462686567164178 1
 
0.2%
0.8111239860950173 1
 
0.2%
ValueCountFrequency (%)
50.0 2
0.4%
28.57142857142857 1
0.2%
20.0 1
0.2%
19.612068965517242 1
0.2%
18.51851851851852 1
0.2%
18.461538461538463 1
0.2%
17.99307958477509 1
0.2%
17.218543046357617 1
0.2%
16.70235546038544 1
0.2%
16.680227827502033 1
0.2%

인천
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct429
Distinct (%)82.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3973678
Minimum0
Maximum50
Zeros58
Zeros (%)11.2%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-12-12T09:25:31.606214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.0593377
median3.1915302
Q34.3214324
95-th percentile6.6680614
Maximum50
Range50
Interquartile range (IQR)2.2620947

Descriptive statistics

Standard deviation3.1940756
Coefficient of variation (CV)0.94016185
Kurtosis101.24118
Mean3.3973678
Median Absolute Deviation (MAD)1.1310953
Skewness7.7627187
Sum1766.6313
Variance10.202119
MonotonicityNot monotonic
2023-12-12T09:25:31.803997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 58
 
11.2%
6.25 4
 
0.8%
4.3478260869565215 4
 
0.8%
6.666666666666667 3
 
0.6%
2.5974025974025974 3
 
0.6%
2.5 3
 
0.6%
3.8461538461538463 3
 
0.6%
2.0408163265306123 3
 
0.6%
4.10958904109589 2
 
0.4%
3.75 2
 
0.4%
Other values (419) 435
83.7%
ValueCountFrequency (%)
0.0 58
11.2%
0.29069767441860467 1
 
0.2%
0.411522633744856 1
 
0.2%
0.5 1
 
0.2%
0.5970149253731344 1
 
0.2%
0.6851448146994706 1
 
0.2%
0.7117437722419928 1
 
0.2%
0.7142857142857143 1
 
0.2%
0.7462686567164178 1
 
0.2%
0.7569386038687973 1
 
0.2%
ValueCountFrequency (%)
50.0 1
0.2%
33.33333333333333 1
0.2%
14.754098360655737 1
0.2%
11.76470588235294 1
0.2%
10.365853658536585 1
0.2%
10.0 1
0.2%
9.210526315789473 1
0.2%
9.16030534351145 1
0.2%
9.090909090909092 1
0.2%
8.695652173913043 2
0.4%

광주
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct436
Distinct (%)83.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0981541
Minimum0
Maximum50
Zeros56
Zeros (%)10.8%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-12-12T09:25:31.981096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.82730828
median1.8143283
Q35.6711972
95-th percentile14.285714
Maximum50
Range50
Interquartile range (IQR)4.843889

Descriptive statistics

Standard deviation5.851201
Coefficient of variation (CV)1.427765
Kurtosis16.960858
Mean4.0981541
Median Absolute Deviation (MAD)1.2458279
Skewness3.3862409
Sum2131.0401
Variance34.236554
MonotonicityNot monotonic
2023-12-12T09:25:32.137203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 56
 
10.8%
3.125 4
 
0.8%
0.7633587786259541 4
 
0.8%
2.0408163265306123 3
 
0.6%
11.11111111111111 3
 
0.6%
14.285714285714285 3
 
0.6%
8.333333333333332 2
 
0.4%
0.975609756097561 2
 
0.4%
1.1111111111111112 2
 
0.4%
6.666666666666667 2
 
0.4%
Other values (426) 439
84.4%
ValueCountFrequency (%)
0.0 56
10.8%
0.09342883836810963 1
 
0.2%
0.17094017094017094 1
 
0.2%
0.1724137931034483 1
 
0.2%
0.21321961620469082 1
 
0.2%
0.21413276231263384 1
 
0.2%
0.21551724137931033 1
 
0.2%
0.21691973969631237 1
 
0.2%
0.23696682464454977 1
 
0.2%
0.25017869907076484 1
 
0.2%
ValueCountFrequency (%)
50.0 1
0.2%
41.66666666666667 2
0.4%
38.46153846153847 1
0.2%
33.33333333333333 1
0.2%
30.0 1
0.2%
25.0 1
0.2%
22.22222222222222 1
0.2%
20.0 1
0.2%
19.672131147540984 1
0.2%
19.27710843373494 1
0.2%

대전
Real number (ℝ)

ZEROS 

Distinct429
Distinct (%)82.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4529706
Minimum0
Maximum25
Zeros66
Zeros (%)12.7%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-12-12T09:25:32.283413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.2791428
median2.0183883
Q33.2556073
95-th percentile5.5634921
Maximum25
Range25
Interquartile range (IQR)1.9764645

Descriptive statistics

Standard deviation2.385481
Coefficient of variation (CV)0.97248655
Kurtosis33.130244
Mean2.4529706
Median Absolute Deviation (MAD)0.90785908
Skewness4.4448914
Sum1275.5447
Variance5.6905194
MonotonicityNot monotonic
2023-12-12T09:25:32.410912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 66
 
12.7%
2.7777777777777777 4
 
0.8%
4.3478260869565215 4
 
0.8%
5.88235294117647 3
 
0.6%
1.6666666666666667 3
 
0.6%
3.3333333333333335 3
 
0.6%
1.0869565217391304 2
 
0.4%
1.8072289156626504 2
 
0.4%
1.2195121951219512 2
 
0.4%
2.7137042062415198 2
 
0.4%
Other values (419) 429
82.5%
ValueCountFrequency (%)
0.0 66
12.7%
0.48602673147023084 1
 
0.2%
0.5046257359125316 1
 
0.2%
0.5198487712665406 1
 
0.2%
0.5344735435595939 1
 
0.2%
0.5597014925373134 1
 
0.2%
0.5865102639296188 1
 
0.2%
0.591715976331361 1
 
0.2%
0.6396588486140725 1
 
0.2%
0.7142857142857143 1
 
0.2%
ValueCountFrequency (%)
25.0 1
0.2%
23.076923076923077 1
0.2%
20.0 1
0.2%
16.666666666666664 1
0.2%
11.059907834101383 1
0.2%
10.0 1
0.2%
9.523809523809524 1
0.2%
9.45945945945946 1
0.2%
9.090909090909092 1
0.2%
8.080808080808081 1
0.2%

울산
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct418
Distinct (%)80.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4493352
Minimum0
Maximum58.536585
Zeros87
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-12-12T09:25:32.535316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.67786547
median1.6734587
Q36.4070901
95-th percentile13.245922
Maximum58.536585
Range58.536585
Interquartile range (IQR)5.7292247

Descriptive statistics

Standard deviation6.4322034
Coefficient of variation (CV)1.4456549
Kurtosis21.933789
Mean4.4493352
Median Absolute Deviation (MAD)1.6734587
Skewness3.8631522
Sum2313.6543
Variance41.37324
MonotonicityNot monotonic
2023-12-12T09:25:32.686452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 87
 
16.7%
5.88235294117647 3
 
0.6%
5.555555555555555 3
 
0.6%
1.5552099533437014 2
 
0.4%
2.1739130434782608 2
 
0.4%
1.4492753623188406 2
 
0.4%
2.5 2
 
0.4%
1.5267175572519083 2
 
0.4%
1.639344262295082 2
 
0.4%
6.172839506172839 2
 
0.4%
Other values (408) 413
79.4%
ValueCountFrequency (%)
0.0 87
16.7%
0.2638522427440633 1
 
0.2%
0.2805049088359046 1
 
0.2%
0.29411764705882354 1
 
0.2%
0.2958579881656805 1
 
0.2%
0.30181086519114686 1
 
0.2%
0.3089996137504828 1
 
0.2%
0.3259452411994785 1
 
0.2%
0.33783783783783783 1
 
0.2%
0.3658536585365854 1
 
0.2%
ValueCountFrequency (%)
58.536585365853654 1
0.2%
47.01492537313433 1
0.2%
45.85783003741315 1
0.2%
43.28358208955223 1
0.2%
42.95841209829867 1
0.2%
33.33333333333333 1
0.2%
32.0 1
0.2%
29.545454545454547 1
0.2%
26.76056338028169 1
0.2%
25.961538461538463 1
0.2%

세종
Real number (ℝ)

ZEROS 

Distinct368
Distinct (%)70.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.62826818
Minimum0
Maximum14.285714
Zeros143
Zeros (%)27.5%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-12-12T09:25:32.827420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.46550395
Q30.82950783
95-th percentile1.78679
Maximum14.285714
Range14.285714
Interquartile range (IQR)0.82950783

Descriptive statistics

Standard deviation1.0104338
Coefficient of variation (CV)1.6082842
Kurtosis82.695741
Mean0.62826818
Median Absolute Deviation (MAD)0.39409683
Skewness7.4296001
Sum326.69946
Variance1.0209765
MonotonicityNot monotonic
2023-12-12T09:25:33.256856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 143
 
27.5%
0.9803921568627451 2
 
0.4%
0.5102040816326531 2
 
0.4%
1.4285714285714286 2
 
0.4%
0.7974481658692184 2
 
0.4%
0.4434589800443459 2
 
0.4%
3.278688524590164 2
 
0.4%
0.5291005291005291 2
 
0.4%
0.6024096385542169 2
 
0.4%
0.6666666666666667 2
 
0.4%
Other values (358) 359
69.0%
ValueCountFrequency (%)
0.0 143
27.5%
0.05279831045406547 1
 
0.2%
0.07017543859649122 1
 
0.2%
0.08695652173913043 1
 
0.2%
0.0945179584120983 1
 
0.2%
0.10141987829614604 1
 
0.2%
0.10384215991692627 1
 
0.2%
0.13054830287206268 1
 
0.2%
0.13774104683195593 1
 
0.2%
0.1394700139470014 1
 
0.2%
ValueCountFrequency (%)
14.285714285714285 1
0.2%
10.0 1
0.2%
7.834101382488479 1
0.2%
3.896103896103896 1
0.2%
3.614457831325301 1
0.2%
3.361344537815126 1
0.2%
3.3333333333333335 1
0.2%
3.278688524590164 2
0.4%
3.2520325203252036 1
0.2%
3.125 1
0.2%

경기
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct464
Distinct (%)89.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.498106
Minimum0
Maximum50
Zeros14
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-12-12T09:25:33.400689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.8794974
Q113.142317
median17.70414
Q322.979617
95-th percentile35.52818
Maximum50
Range50
Interquartile range (IQR)9.8373004

Descriptive statistics

Standard deviation8.869882
Coefficient of variation (CV)0.47950217
Kurtosis0.60910109
Mean18.498106
Median Absolute Deviation (MAD)5.0782165
Skewness0.57475848
Sum9619.0152
Variance78.674807
MonotonicityNot monotonic
2023-12-12T09:25:33.568142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 14
 
2.7%
20.0 7
 
1.3%
16.666666666666664 5
 
1.0%
14.285714285714285 5
 
1.0%
25.0 4
 
0.8%
5.88235294117647 3
 
0.6%
17.391304347826086 3
 
0.6%
19.047619047619047 3
 
0.6%
50.0 3
 
0.6%
13.333333333333334 3
 
0.6%
Other values (454) 470
90.4%
ValueCountFrequency (%)
0.0 14
2.7%
1.4925373134328357 1
 
0.2%
2.666666666666667 1
 
0.2%
3.125 1
 
0.2%
3.278688524590164 1
 
0.2%
3.768296480847088 1
 
0.2%
3.937007874015748 1
 
0.2%
4.580152671755725 1
 
0.2%
4.957264957264957 1
 
0.2%
5.47945205479452 1
 
0.2%
ValueCountFrequency (%)
50.0 3
0.6%
43.02819621475473 1
 
0.2%
42.27642276422765 1
 
0.2%
41.91304347826087 1
 
0.2%
40.3420523138833 1
 
0.2%
39.32741709481551 1
 
0.2%
39.02439024390244 1
 
0.2%
38.93805309734513 1
 
0.2%
38.77233877233877 1
 
0.2%
38.2648401826484 1
 
0.2%

강원
Real number (ℝ)

ZEROS 

Distinct416
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1623252
Minimum0
Maximum50
Zeros83
Zeros (%)16.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-12-12T09:25:33.721808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.79053535
median1.2345679
Q31.9770241
95-th percentile17.2227
Maximum50
Range50
Interquartile range (IQR)1.1864887

Descriptive statistics

Standard deviation5.8354375
Coefficient of variation (CV)1.8452996
Kurtosis15.82527
Mean3.1623252
Median Absolute Deviation (MAD)0.53267408
Skewness3.5824431
Sum1644.4091
Variance34.052331
MonotonicityNot monotonic
2023-12-12T09:25:33.853328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 83
 
16.0%
1.639344262295082 3
 
0.6%
2.1739130434782608 3
 
0.6%
1.5267175572519083 2
 
0.4%
7.4074074074074066 2
 
0.4%
1.2048192771084338 2
 
0.4%
0.4878048780487805 2
 
0.4%
3.9215686274509802 2
 
0.4%
1.0416666666666665 2
 
0.4%
1.2345679012345678 2
 
0.4%
Other values (406) 417
80.2%
ValueCountFrequency (%)
0.0 83
16.0%
0.24183796856106407 1
 
0.2%
0.2789400278940028 1
 
0.2%
0.2966381015161503 1
 
0.2%
0.32858707557502737 1
 
0.2%
0.33707865168539325 1
 
0.2%
0.40650406504065045 1
 
0.2%
0.4434589800443459 1
 
0.2%
0.45662100456621 1
 
0.2%
0.4878048780487805 2
 
0.4%
ValueCountFrequency (%)
50.0 1
0.2%
38.57343969967151 1
0.2%
34.05172413793103 1
0.2%
30.35190615835777 1
0.2%
30.081300813008134 1
0.2%
28.599033816425123 1
0.2%
25.956284153005466 1
0.2%
24.46043165467626 1
0.2%
24.358974358974358 1
0.2%
23.438836612489307 1
0.2%

충북
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct409
Distinct (%)78.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.798843
Minimum0
Maximum12.5
Zeros77
Zeros (%)14.8%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-12-12T09:25:34.007811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.98707303
median1.6507584
Q32.4281042
95-th percentile4.2059772
Maximum12.5
Range12.5
Interquartile range (IQR)1.4410312

Descriptive statistics

Standard deviation1.4179847
Coefficient of variation (CV)0.78827596
Kurtosis8.4369653
Mean1.798843
Median Absolute Deviation (MAD)0.72750857
Skewness1.8802266
Sum935.39835
Variance2.0106806
MonotonicityNot monotonic
2023-12-12T09:25:34.153923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 77
 
14.8%
1.639344262295082 4
 
0.8%
5.555555555555555 3
 
0.6%
2.898550724637681 3
 
0.6%
6.666666666666667 3
 
0.6%
3.225806451612903 3
 
0.6%
2.564102564102564 2
 
0.4%
1.2195121951219512 2
 
0.4%
0.7142857142857143 2
 
0.4%
0.7462686567164178 2
 
0.4%
Other values (399) 419
80.6%
ValueCountFrequency (%)
0.0 77
14.8%
0.234192037470726 1
 
0.2%
0.2617801047120419 1
 
0.2%
0.27932960893854747 1
 
0.2%
0.37735849056603776 1
 
0.2%
0.3913894324853229 1
 
0.2%
0.4434589800443459 1
 
0.2%
0.45558086560364464 1
 
0.2%
0.4671441918405481 1
 
0.2%
0.4912280701754386 1
 
0.2%
ValueCountFrequency (%)
12.5 1
 
0.2%
9.523809523809524 1
 
0.2%
7.112068965517242 1
 
0.2%
6.666666666666667 3
0.6%
6.438631790744467 1
 
0.2%
6.323337679269883 1
 
0.2%
6.25 1
 
0.2%
5.913043478260869 1
 
0.2%
5.555555555555555 3
0.6%
5.487804878048781 1
 
0.2%

충남
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct428
Distinct (%)82.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3920777
Minimum0
Maximum25
Zeros63
Zeros (%)12.1%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-12-12T09:25:34.288423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.4330544
median2.1021884
Q33.0603937
95-th percentile4.559024
Maximum25
Range25
Interquartile range (IQR)1.6273393

Descriptive statistics

Standard deviation2.2048369
Coefficient of variation (CV)0.92172463
Kurtosis35.562006
Mean2.3920777
Median Absolute Deviation (MAD)0.79687293
Skewness4.6451144
Sum1243.8804
Variance4.8613059
MonotonicityNot monotonic
2023-12-12T09:25:34.412696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 63
 
12.1%
3.3333333333333335 5
 
1.0%
4.3478260869565215 4
 
0.8%
1.639344262295082 3
 
0.6%
4.166666666666666 3
 
0.6%
4.0 3
 
0.6%
3.4482758620689653 2
 
0.4%
0.8130081300813009 2
 
0.4%
1.3333333333333335 2
 
0.4%
1.7726798748696557 2
 
0.4%
Other values (418) 431
82.9%
ValueCountFrequency (%)
0.0 63
12.1%
0.29069767441860467 1
 
0.2%
0.5249343832020997 1
 
0.2%
0.5555555555555556 1
 
0.2%
0.591715976331361 1
 
0.2%
0.5970149253731344 1
 
0.2%
0.6571741511500547 1
 
0.2%
0.6616257088846881 1
 
0.2%
0.7246376811594203 1
 
0.2%
0.7249466950959489 1
 
0.2%
ValueCountFrequency (%)
25.0 1
0.2%
20.0 1
0.2%
15.789473684210526 2
0.4%
14.285714285714285 1
0.2%
12.903225806451612 1
0.2%
9.67741935483871 1
0.2%
9.523809523809524 1
0.2%
9.090909090909092 1
0.2%
7.8431372549019605 1
0.2%
7.8125 1
0.2%

전북
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct436
Distinct (%)83.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6857703
Minimum0
Maximum63.571429
Zeros68
Zeros (%)13.1%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-12-12T09:25:34.565645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.81375117
median1.4349916
Q35.9170227
95-th percentile36.732906
Maximum63.571429
Range63.571429
Interquartile range (IQR)5.1032715

Descriptive statistics

Standard deviation12.130781
Coefficient of variation (CV)1.8144179
Kurtosis5.8224239
Mean6.6857703
Median Absolute Deviation (MAD)1.139092
Skewness2.5302715
Sum3476.6005
Variance147.15585
MonotonicityNot monotonic
2023-12-12T09:25:34.728606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 68
 
13.1%
6.25 3
 
0.6%
0.8849557522123894 2
 
0.4%
3.278688524590164 2
 
0.4%
8.695652173913043 2
 
0.4%
2.73972602739726 2
 
0.4%
1.5873015873015872 2
 
0.4%
8.0 2
 
0.4%
6.666666666666667 2
 
0.4%
42.857142857142854 2
 
0.4%
Other values (426) 433
83.3%
ValueCountFrequency (%)
0.0 68
13.1%
0.1779359430604982 1
 
0.2%
0.19569471624266144 1
 
0.2%
0.24390243902439024 1
 
0.2%
0.24914356898162568 1
 
0.2%
0.27932960893854747 1
 
0.2%
0.28591851322373124 1
 
0.2%
0.3498542274052478 1
 
0.2%
0.411522633744856 1
 
0.2%
0.4291845493562232 1
 
0.2%
ValueCountFrequency (%)
63.57142857142857 1
0.2%
61.969111969111964 1
0.2%
58.048780487804876 1
0.2%
55.172413793103445 1
0.2%
54.437869822485204 1
0.2%
54.41354292623942 1
0.2%
52.0 1
0.2%
51.4018691588785 1
0.2%
50.0 1
0.2%
49.72375690607735 1
0.2%

전남
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct451
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3273012
Minimum0
Maximum80
Zeros42
Zeros (%)8.1%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-12-12T09:25:34.889614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.1425996
median2.349006
Q36.7465623
95-th percentile27.627666
Maximum80
Range80
Interquartile range (IQR)5.6039627

Descriptive statistics

Standard deviation9.7081039
Coefficient of variation (CV)1.5343199
Kurtosis10.604509
Mean6.3273012
Median Absolute Deviation (MAD)1.5240373
Skewness2.8192357
Sum3290.1966
Variance94.247282
MonotonicityNot monotonic
2023-12-12T09:25:35.045931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 42
 
8.1%
25.0 5
 
1.0%
33.33333333333333 4
 
0.8%
2.1739130434782608 3
 
0.6%
20.0 3
 
0.6%
0.8130081300813009 2
 
0.4%
1.864406779661017 2
 
0.4%
6.666666666666667 2
 
0.4%
3.125 2
 
0.4%
5.555555555555555 2
 
0.4%
Other values (441) 453
87.1%
ValueCountFrequency (%)
0.0 42
8.1%
0.2145922746781116 1
 
0.2%
0.3089996137504828 1
 
0.2%
0.3658536585365854 1
 
0.2%
0.3885003885003885 1
 
0.2%
0.3911342894393742 1
 
0.2%
0.4048582995951417 1
 
0.2%
0.40683482506102525 1
 
0.2%
0.4098360655737705 1
 
0.2%
0.4363001745200698 1
 
0.2%
ValueCountFrequency (%)
80.0 1
0.2%
57.14285714285714 1
0.2%
55.55555555555556 1
0.2%
50.0 1
0.2%
41.66666666666667 1
0.2%
40.0 2
0.4%
38.46153846153847 1
0.2%
37.5 1
0.2%
34.78260869565217 1
0.2%
34.01450230718523 1
0.2%

경북
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct432
Distinct (%)83.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.458249
Minimum0
Maximum64.247898
Zeros62
Zeros (%)11.9%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-12-12T09:25:35.181836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.642453
median3.125
Q36.3126231
95-th percentile18.069743
Maximum64.247898
Range64.247898
Interquartile range (IQR)4.6701701

Descriptive statistics

Standard deviation7.8527173
Coefficient of variation (CV)1.438688
Kurtosis21.326751
Mean5.458249
Median Absolute Deviation (MAD)2.0606884
Skewness4.112263
Sum2838.2895
Variance61.66517
MonotonicityNot monotonic
2023-12-12T09:25:35.319434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 62
 
11.9%
1.639344262295082 3
 
0.6%
14.285714285714285 3
 
0.6%
4.3478260869565215 3
 
0.6%
3.4482758620689653 3
 
0.6%
5.555555555555555 3
 
0.6%
4.878048780487805 3
 
0.6%
2.0 3
 
0.6%
3.125 3
 
0.6%
7.142857142857142 3
 
0.6%
Other values (422) 431
82.9%
ValueCountFrequency (%)
0.0 62
11.9%
0.2958579881656805 1
 
0.2%
0.4878048780487805 1
 
0.2%
0.546448087431694 1
 
0.2%
0.5494505494505495 1
 
0.2%
0.6756756756756757 1
 
0.2%
0.7255139056831923 1
 
0.2%
0.7444168734491315 1
 
0.2%
0.7537688442211055 1
 
0.2%
0.7633587786259541 1
 
0.2%
ValueCountFrequency (%)
64.24789785113671 1
0.2%
58.8277340957827 1
0.2%
55.72519083969466 1
0.2%
53.96551724137931 1
0.2%
50.0 1
0.2%
47.57281553398058 1
0.2%
44.54810495626822 1
0.2%
43.48341232227489 1
0.2%
39.37007874015748 1
0.2%
32.30769230769231 1
0.2%

경남
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct453
Distinct (%)87.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.824504
Minimum0
Maximum67.142857
Zeros36
Zeros (%)6.9%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-12-12T09:25:35.455221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.5748382
median6.4761343
Q319.61763
95-th percentile45.681828
Maximum67.142857
Range67.142857
Interquartile range (IQR)17.042792

Descriptive statistics

Standard deviation14.744233
Coefficient of variation (CV)1.1496922
Kurtosis1.5552065
Mean12.824504
Median Absolute Deviation (MAD)4.7896443
Skewness1.513698
Sum6668.7422
Variance217.3924
MonotonicityNot monotonic
2023-12-12T09:25:35.592719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 36
 
6.9%
25.0 4
 
0.8%
14.285714285714285 3
 
0.6%
6.666666666666667 3
 
0.6%
4.0 3
 
0.6%
3.225806451612903 3
 
0.6%
7.6923076923076925 3
 
0.6%
50.0 3
 
0.6%
9.375 2
 
0.4%
6.25 2
 
0.4%
Other values (443) 458
88.1%
ValueCountFrequency (%)
0.0 36
6.9%
0.5578800557880056 1
 
0.2%
0.6493506493506493 2
 
0.4%
0.7633587786259541 1
 
0.2%
0.853658536585366 1
 
0.2%
0.8620689655172413 1
 
0.2%
0.8875739644970414 1
 
0.2%
0.9345794392523363 2
 
0.4%
0.975609756097561 1
 
0.2%
0.9900990099009901 1
 
0.2%
ValueCountFrequency (%)
67.14285714285714 1
0.2%
66.81270536692223 1
0.2%
66.77966101694915 1
0.2%
64.04494382022472 1
0.2%
62.56410256410256 1
0.2%
61.64383561643836 1
0.2%
58.2089552238806 1
0.2%
55.793991416309005 1
0.2%
54.06976744186046 1
0.2%
53.40909090909091 1
0.2%

제주
Real number (ℝ)

ZEROS 

Distinct360
Distinct (%)69.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.60460839
Minimum0
Maximum50
Zeros149
Zeros (%)28.7%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-12-12T09:25:35.753891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.34745434
Q30.7279867
95-th percentile1.5625
Maximum50
Range50
Interquartile range (IQR)0.7279867

Descriptive statistics

Standard deviation2.2746883
Coefficient of variation (CV)3.7622506
Kurtosis430.67353
Mean0.60460839
Median Absolute Deviation (MAD)0.34745434
Skewness19.909729
Sum314.39636
Variance5.1742068
MonotonicityNot monotonic
2023-12-12T09:25:35.928917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 149
28.7%
0.2857142857142857 3
 
0.6%
1.5625 3
 
0.6%
1.25 2
 
0.4%
0.7352941176470588 2
 
0.4%
1.36986301369863 2
 
0.4%
0.8130081300813009 2
 
0.4%
0.5221932114882507 2
 
0.4%
0.6451612903225806 2
 
0.4%
0.425531914893617 2
 
0.4%
Other values (350) 351
67.5%
ValueCountFrequency (%)
0.0 149
28.7%
0.043122035360069 1
 
0.2%
0.0546448087431694 1
 
0.2%
0.06200909466721786 1
 
0.2%
0.06455777921239508 1
 
0.2%
0.06591957811470006 1
 
0.2%
0.0716075904045829 1
 
0.2%
0.076103500761035 1
 
0.2%
0.08136696501220504 1
 
0.2%
0.08403361344537816 1
 
0.2%
ValueCountFrequency (%)
50.0 1
0.2%
7.6923076923076925 1
0.2%
5.555555555555555 1
0.2%
4.651162790697675 1
0.2%
3.896103896103896 1
0.2%
3.278688524590164 1
0.2%
2.8446389496717726 1
0.2%
2.6178010471204187 1
0.2%
2.1739130434782608 1
0.2%
2.158273381294964 1
0.2%

Interactions

2023-12-12T09:25:27.099341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:01.028410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:02.738734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:04.494412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:05.806848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:07.153194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:08.654221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:10.407035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:11.823227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:13.541647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:15.212954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:17.201490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:18.723647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:20.057747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:21.802261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:23.596977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:25.316388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:27.180546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:01.105196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:02.845147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:04.569439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:05.874557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:07.221267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:08.731172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:10.477504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:11.899046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:13.661989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:15.580559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:17.287513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:18.794797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:20.144094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:21.894699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:23.689585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:25.444120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:27.285548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:01.207465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:02.941994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:04.652673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:05.952296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:07.292182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:08.814025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:10.547130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:11.972990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:13.760350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:15.687224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:17.365464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:18.882841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:20.232363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:21.986606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:23.776726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:25.548770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:27.367819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:01.303158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:03.034823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:04.738760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:06.028861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:07.371780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:08.892011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:10.628375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:12.060701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:13.861675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:15.772743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:17.464091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:18.963024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:20.312240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:22.084567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:23.868442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:25.664200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:27.470074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:01.393864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:03.138820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:04.817317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:06.104486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:07.457084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:08.974739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:10.710907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:12.163123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:13.955529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:15.861373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:17.563044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:19.037302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:20.381138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:22.182317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:23.973227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:25.762010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:27.554389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:01.476948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:03.231442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:04.897460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:06.191563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:07.541208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:09.327240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:10.789331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:12.244644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:14.056235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:15.969836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:17.642625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:19.108794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:20.456813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:22.286113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:24.067890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:25.876909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:27.906309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:01.560652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:03.303189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:04.970104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:06.283039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:07.631836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:09.415145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:10.864469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:12.334863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:14.156444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:16.058366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:17.726580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:19.179905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:20.535537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:22.378776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:24.153544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:25.997053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:28.006419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:01.658811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:03.613670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:05.044731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:06.364161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:07.719851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:09.536898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:10.939126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:12.457054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:14.250206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:16.170747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:17.817541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:19.257763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:20.628640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:22.475942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:24.253007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:26.117449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:28.113152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:01.761652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:03.708265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:05.128048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:06.441958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:07.813077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:09.667449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:11.014809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:12.556305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:14.351777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:16.283470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:17.907787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:19.331785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:20.712038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:22.597375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:24.369614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:26.264511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:28.196362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:01.896712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:03.791748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:05.196537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:06.513533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:07.940496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:09.763468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:11.085342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:12.645277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:14.443143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:16.388832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:17.990478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:19.414808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:20.803989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:22.712998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:24.467360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:26.377064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:28.275996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:01.988285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:03.872732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:05.271514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:06.584058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:08.044680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:09.834442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:11.158417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:12.752710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:14.553245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:16.487658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:18.073607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:19.487283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:20.903180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:22.829951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:24.569684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:26.482079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:28.362045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:02.099116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:03.958412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:05.353153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:06.662855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:08.129477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:09.922543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:11.264635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:12.860192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:14.655827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:16.583504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:18.151601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:19.582381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:20.983152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:22.928991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:24.682140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:26.574194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:28.447949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:02.207870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:04.038975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:05.424785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:06.741863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:08.220607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:09.999697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:11.381918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:12.975453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:14.740281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:16.704545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:18.228042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:19.668919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:21.059114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:23.020476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:24.767434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:26.661880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:28.551532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:02.319128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:04.124902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:05.497121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:06.831775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:08.299576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:10.079446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:11.470972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:13.111361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:14.834586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:16.816306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:18.345170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:19.751138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:21.147570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:23.152507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:24.860703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:26.746017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:28.638710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:02.448329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:04.207482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:05.576975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:06.913741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:08.392894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:10.171132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:11.569401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:13.237033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:14.929271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:16.921830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:18.449585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:19.829095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:21.458549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:23.264119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:24.995708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:26.839026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:28.739411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:02.548043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:04.295064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:05.648766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:06.996274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:08.477048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:10.257646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:11.649547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:13.330078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:15.015642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:17.017630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:18.529986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:19.903670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:21.536047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:23.377230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:25.092255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:26.924450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:28.872653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:02.655561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:04.420782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:05.730305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:07.078099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:08.562489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:10.336035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:11.732488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:13.443208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:15.132924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:17.117861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:18.612753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:19.986967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:21.639401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:23.486557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:25.225002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:27.016685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:25:36.081378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
방문지역연령대관광지서울부산대구인천광주대전울산세종경기강원충북충남전북전남경북경남제주
방문지역1.0000.0000.9990.6620.6310.6600.6460.7720.3710.5370.1460.7620.6400.4450.4030.8270.6240.6940.8520.000
연령대0.0001.0000.0000.2430.0000.0000.0000.1830.0630.0000.1140.2370.0000.1160.1070.2530.0000.1270.0000.000
관광지0.9990.0001.0000.8760.7950.7950.5910.7130.6980.8760.4690.8670.7880.7610.5840.7240.8090.8180.9250.609
서울0.6620.2430.8761.0000.2860.3100.5440.4010.1880.3620.1010.7910.4980.4520.2120.1950.2780.4960.5450.395
부산0.6310.0000.7950.2861.0000.6060.4390.4300.0000.3430.0000.2950.0000.0000.4000.0300.6150.3470.3990.682
대구0.6600.0000.7950.3100.6061.0000.1190.2120.0000.4400.0000.3970.0120.1260.1520.1670.2820.6420.5260.000
인천0.6460.0000.5910.5440.4390.1191.0000.0000.3670.0770.0000.6110.4010.3690.0000.0000.0000.3210.2350.000
광주0.7720.1830.7130.4010.4300.2120.0001.0000.7120.0000.5370.4070.0000.0000.2630.5050.7050.0000.1520.000
대전0.3710.0630.6980.1880.0000.0000.3670.7121.0000.0990.5180.2980.0000.1980.7530.3680.2720.4050.0000.000
울산0.5370.0000.8760.3620.3430.4400.0770.0000.0991.0000.0000.3390.0000.1130.1620.0000.0000.6030.4180.000
세종0.1460.1140.4690.1010.0000.0000.0000.5370.5180.0001.0000.0000.0000.0000.3670.2320.1270.0000.0400.000
경기0.7620.2370.8670.7910.2950.3970.6110.4070.2980.3390.0001.0000.6030.5060.3260.2460.3810.4340.5900.227
강원0.6400.0000.7880.4980.0000.0120.4010.0000.0000.0000.0000.6031.0000.4100.0000.0000.0000.0000.1910.000
충북0.4450.1160.7610.4520.0000.1260.3690.0000.1980.1130.0000.5060.4101.0000.4980.1910.0000.0000.2950.238
충남0.4030.1070.5840.2120.4000.1520.0000.2630.7530.1620.3670.3260.0000.4981.0000.4220.3030.2180.2050.000
전북0.8270.2530.7240.1950.0300.1670.0000.5050.3680.0000.2320.2460.0000.1910.4221.0000.1860.0000.1940.000
전남0.6240.0000.8090.2780.6150.2820.0000.7050.2720.0000.1270.3810.0000.0000.3030.1861.0000.0000.0000.000
경북0.6940.1270.8180.4960.3470.6420.3210.0000.4050.6030.0000.4340.0000.0000.2180.0000.0001.0000.4170.114
경남0.8520.0000.9250.5450.3990.5260.2350.1520.0000.4180.0400.5900.1910.2950.2050.1940.0000.4171.0000.000
제주0.0000.0000.6090.3950.6820.0000.0000.0000.0000.0000.0000.2270.0000.2380.0000.0000.0000.1140.0001.000
2023-12-12T09:25:36.236507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
방문지역연령대
방문지역1.0000.000
연령대0.0001.000
2023-12-12T09:25:36.356193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
서울부산대구인천광주대전울산세종경기강원충북충남전북전남경북경남제주방문지역연령대
서울1.000-0.086-0.1710.483-0.0750.178-0.2570.1070.6490.3360.4250.257-0.027-0.273-0.175-0.283-0.0010.3330.103
부산-0.0861.000-0.0310.0460.2350.138-0.1550.0190.0290.0120.1780.1570.1090.094-0.072-0.394-0.3290.4280.000
대구-0.171-0.0311.000-0.137-0.2080.0010.7030.074-0.245-0.056-0.128-0.150-0.206-0.2760.7340.5580.2270.4970.000
인천0.4830.046-0.1371.0000.0300.131-0.1410.2360.5800.4090.5270.3850.146-0.167-0.148-0.2470.0660.2880.000
광주-0.0750.235-0.2080.0301.0000.404-0.2570.329-0.006-0.2190.1200.3640.5900.654-0.395-0.127-0.0100.4240.070
대전0.1780.1380.0010.1310.4041.000-0.1160.3520.162-0.0290.2410.4800.4500.261-0.179-0.123-0.0020.2420.059
울산-0.257-0.1550.703-0.141-0.257-0.1161.0000.101-0.3680.016-0.184-0.214-0.262-0.2330.7490.6430.3210.3450.000
세종0.1070.0190.0740.2360.3290.3520.1011.0000.1830.0330.1870.3940.3150.073-0.0350.0530.2630.0990.077
경기0.6490.029-0.2450.580-0.0060.162-0.3680.1831.0000.3700.4760.2980.001-0.183-0.300-0.402-0.0520.4200.104
강원0.3360.012-0.0560.409-0.219-0.0290.0160.0330.3701.0000.3950.043-0.127-0.3540.042-0.0930.1320.4390.000
충북0.4250.178-0.1280.5270.1200.241-0.1840.1870.4760.3951.0000.3760.188-0.072-0.114-0.2800.0180.2910.060
충남0.2570.157-0.1500.3850.3640.480-0.2140.3940.2980.0430.3761.0000.5160.172-0.267-0.2900.0040.2630.066
전북-0.0270.109-0.2060.1460.5900.450-0.2620.3150.001-0.1270.1880.5161.0000.450-0.357-0.2020.0350.4840.107
전남-0.2730.094-0.276-0.1670.6540.261-0.2330.073-0.183-0.354-0.0720.1720.4501.000-0.3900.0030.0400.4220.000
경북-0.175-0.0720.734-0.148-0.395-0.1790.749-0.035-0.3000.042-0.114-0.267-0.357-0.3901.0000.5430.2130.3570.053
경남-0.283-0.3940.558-0.247-0.127-0.1230.6430.053-0.402-0.093-0.280-0.290-0.2020.0030.5431.0000.4640.5160.000
제주-0.001-0.3290.2270.066-0.010-0.0020.3210.263-0.0520.1320.0180.0040.0350.0400.2130.4641.0000.0000.000
방문지역0.3330.4280.4970.2880.4240.2420.3450.0990.4200.4390.2910.2630.4840.4220.3570.5160.0001.0000.000
연령대0.1030.0000.0000.0000.0700.0590.0000.0770.1040.0000.0600.0660.1070.0000.0530.0000.0000.0001.000

Missing values

2023-12-12T09:25:29.014781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:25:29.308322image/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

방문지역연령대관광지서울부산대구인천광주대전울산세종경기강원충북충남전북전남경북경남제주
0강릉20대합계(중복제외)30.0186941.5889771.7638555.8131821.0402221.9387320.6361940.30452933.84188610.4926734.20312.4814571.1397210.8683592.0201411.6070670.241211
1강릉20대강릉교동25.1303251.6178321.7077124.7995690.8448682.0133020.5213010.26963929.12097821.1037214.7276651.7796150.8987960.8268922.6604351.5459280.431422
2강릉20대강릉역앞29.5739351.7185821.9692096.9459361.324742.5062660.6444680.25062733.9778028.9867534.2606522.1840321.0741140.6444682.3988541.4679560.071608
3강릉20대강문해변36.7300541.1781731.8189335.7668460.8474582.1496490.5167420.26870634.931795.3741223.3691612.4390241.0334850.5787521.5502271.384870.062009
4강릉20대경포호30.0797391.2969551.3930256.1677390.8742431.8637720.6724950.33624737.7077536.2830245.3415312.8148721.1624560.7493521.7484871.3353830.172927
5강릉20대도깨비촬영지28.0701751.7543861.9736849.2105260.6578951.7543860.6578950.031.79824610.9649124.6052633.9473680.4385961.3157891.0964911.5350880.219298
6강릉20대안목해변33.9124011.1827761.3491045.6551471.1273331.7556830.5544260.24025135.8159318.0946223.6776941.8480871.1088520.6653111.4415081.3860650.184809
7강릉20대역전상권29.627662.2340431.7553196.1170211.063831.4893620.6914890.26595731.80851114.2021282.5531911.9148941.436170.7446812.3404261.3297870.425532
8강릉20대영진항24.836353.3500194.1201394.8902581.7712752.1178281.0781670.46207229.9961498.3172893.3500193.3115132.0408162.3488642.7339244.8132460.462072
9강릉20대오죽헌14.870690.8620690.6465524.9568970.02.1551720.4310340.028.66379334.0517247.1120691.2931030.6465520.6465522.5862070.8620690.215517
방문지역연령대관광지서울부산대구인천광주대전울산세종경기강원충북충남전북전남경북경남제주
510전주60대 이상막걸리골목13.3136092.958580.2958582.3668642.0710061.7751480.2958580.29585811.834321.1834322.0710061.77514854.437874.1420120.2958580.8875740.0
511전주60대 이상세병호10.3448281.1494252.2988510.05.7471266.8965520.00.06.8965521.1494250.03.44827655.1724145.7471260.01.1494250.0
512전주60대 이상아중호수19.0476190.04.7619050.00.00.00.00.019.0476190.00.00.042.8571439.523810.04.7619050.0
513전주60대 이상완산공원9.652510.9652511.5444023.0888030.9652511.1583010.38610.57915110.424711.5444020.5791512.12355261.9691121.5444021.5444021.7374520.19305
514전주60대 이상웨딩거리16.01.00.50.52.03.00.01.018.01.00.51.547.02.02.04.00.0
515전주60대 이상자연생태관23.4234235.4054052.7027031.8018023.6036044.5045050.00.90090118.9189190.00.9009013.60360424.3243242.7027032.7027033.6036040.900901
516전주60대 이상전북도청13.0939232.4033151.2983432.1546963.204422.0165751.1325970.41436511.5469611.215471.1602212.65193449.7237573.9502761.4640882.2375690.331492
517전주60대 이상전주동물원10.924371.6806720.8403363.3613452.5210085.8823532.5210083.3613455.8823530.02.5210082.52100849.5798326.7226890.01.6806720.0
518전주60대 이상전주터미널14.3117042.7895692.0921772.1831412.152821.8799270.5761070.15160714.1297761.1825351.0309282.30442749.4238931.9708910.8489992.577320.394178
519전주60대 이상한옥마을19.9630314.4362293.1423294.0665432.8650653.0961180.9242140.46210720.8872461.4325321.4787433.55822625.0462112.3567472.541593.6044360.138632