Overview

Dataset statistics

Number of variables20
Number of observations21
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.8 KiB
Average record size in memory184.1 B

Variable types

Numeric18
Categorical1
Text1

Dataset

Description에너지다소비사업자의 에너지사용량신고에 대한 데이터로 지역(지자체)별, 연도별, 부문별 다소비사업자의 연료사용량에 대한 데이터 개방
Author한국에너지공단
URLhttps://www.data.go.kr/data/15086729/fileData.do

Alerts

강원(toe) is highly overall correlated with 경기(toe) and 14 other fieldsHigh correlation
경기(toe) is highly overall correlated with 강원(toe) and 14 other fieldsHigh correlation
경남(toe) is highly overall correlated with 강원(toe) and 14 other fieldsHigh correlation
경북(toe) is highly overall correlated with 강원(toe) and 14 other fieldsHigh correlation
광주(toe) is highly overall correlated with 강원(toe) and 14 other fieldsHigh correlation
대구(toe) is highly overall correlated with 강원(toe) and 14 other fieldsHigh correlation
대전(toe) is highly overall correlated with 강원(toe) and 14 other fieldsHigh correlation
부산(toe) is highly overall correlated with 경기(toe) and 13 other fieldsHigh correlation
서울(toe) is highly overall correlated with 강원(toe) and 2 other fieldsHigh correlation
울산(toe) is highly overall correlated with 경기(toe) and 13 other fieldsHigh correlation
인천(toe) is highly overall correlated with 강원(toe) and 14 other fieldsHigh correlation
전남(toe) is highly overall correlated with 강원(toe) and 14 other fieldsHigh correlation
전북(toe) is highly overall correlated with 강원(toe) and 14 other fieldsHigh correlation
제주(toe) is highly overall correlated with 강원(toe) and 2 other fieldsHigh correlation
충남(toe) is highly overall correlated with 강원(toe) and 14 other fieldsHigh correlation
충북(toe) is highly overall correlated with 강원(toe) and 14 other fieldsHigh correlation
총합계(toe) is highly overall correlated with 강원(toe) and 14 other fieldsHigh correlation
부문 is highly overall correlated with 강원(toe) and 16 other fieldsHigh correlation
강원(toe) has unique valuesUnique
경기(toe) has unique valuesUnique
경남(toe) has unique valuesUnique
경북(toe) has unique valuesUnique
광주(toe) has unique valuesUnique
대구(toe) has unique valuesUnique
대전(toe) has unique valuesUnique
부산(toe) has unique valuesUnique
서울(toe) has unique valuesUnique
울산(toe) has unique valuesUnique
인천(toe) has unique valuesUnique
전남(toe) has unique valuesUnique
전북(toe) has unique valuesUnique
제주(toe) has unique valuesUnique
충남(toe) has unique valuesUnique
충북(toe) has unique valuesUnique
총합계(toe) has unique valuesUnique

Reproduction

Analysis started2024-04-20 19:32:26.932314
Analysis finished2024-04-20 19:33:46.353742
Duration1 minute and 19.42 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

Distinct7
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019
Minimum2016
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size317.0 B
2024-04-21T04:33:46.489006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2016
Q12017
median2019
Q32021
95-th percentile2022
Maximum2022
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0493902
Coefficient of variation (CV)0.0010150521
Kurtosis-1.2573099
Mean2019
Median Absolute Deviation (MAD)2
Skewness0
Sum42399
Variance4.2
MonotonicityIncreasing
2024-04-21T04:33:46.839938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2016 3
14.3%
2017 3
14.3%
2018 3
14.3%
2019 3
14.3%
2020 3
14.3%
2021 3
14.3%
2022 3
14.3%
ValueCountFrequency (%)
2016 3
14.3%
2017 3
14.3%
2018 3
14.3%
2019 3
14.3%
2020 3
14.3%
2021 3
14.3%
2022 3
14.3%
ValueCountFrequency (%)
2022 3
14.3%
2021 3
14.3%
2020 3
14.3%
2019 3
14.3%
2018 3
14.3%
2017 3
14.3%
2016 3
14.3%

부문
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size296.0 B
산업
건물
수송

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row산업
2nd row건물
3rd row수송
4th row산업
5th row건물

Common Values

ValueCountFrequency (%)
산업 7
33.3%
건물 7
33.3%
수송 7
33.3%

Length

2024-04-21T04:33:47.223850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T04:33:47.541574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
산업 7
33.3%
건물 7
33.3%
수송 7
33.3%

강원(toe)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1352738
Minimum26340.844
Maximum4577436.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size317.0 B
2024-04-21T04:33:47.865997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26340.844
5-th percentile32147.854
Q152810.067
median60634.584
Q33657098.7
95-th percentile4513676.6
Maximum4577436.7
Range4551095.9
Interquartile range (IQR)3604288.7

Descriptive statistics

Standard deviation1926122.1
Coefficient of variation (CV)1.4238693
Kurtosis-1.224381
Mean1352738
Median Absolute Deviation (MAD)12186.122
Skewness0.88468657
Sum28407498
Variance3.7099465 × 1012
MonotonicityNot monotonic
2024-04-21T04:33:48.243693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
2467781.737 1
 
4.8%
60634.584 1
 
4.8%
37663.2 1
 
4.8%
61608.359 1
 
4.8%
4513676.558 1
 
4.8%
26340.844 1
 
4.8%
52810.067 1
 
4.8%
3977894.147 1
 
4.8%
32147.854 1
 
4.8%
53083.689 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
26340.844 1
4.8%
32147.854 1
4.8%
37663.2 1
4.8%
48448.462 1
4.8%
52583.759 1
4.8%
52810.067 1
4.8%
53083.689 1
4.8%
57445.231 1
4.8%
59361.181 1
4.8%
59783.899 1
4.8%
ValueCountFrequency (%)
4577436.73 1
4.8%
4513676.558 1
4.8%
4357936.978 1
4.8%
4120736.877 1
4.8%
3977894.147 1
4.8%
3657098.724 1
4.8%
2467781.737 1
4.8%
66747.981 1
4.8%
66277.335 1
4.8%
61608.359 1
4.8%

경기(toe)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4502578.5
Minimum138922.32
Maximum14822916
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size317.0 B
2024-04-21T04:33:48.595247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum138922.32
5-th percentile148121.21
Q1159188.48
median557205.68
Q311451360
95-th percentile14182089
Maximum14822916
Range14683993
Interquartile range (IQR)11292171

Descriptive statistics

Standard deviation6109987.1
Coefficient of variation (CV)1.3569974
Kurtosis-1.3369253
Mean4502578.5
Median Absolute Deviation (MAD)402059.02
Skewness0.83698427
Sum94554148
Variance3.7331942 × 1013
MonotonicityNot monotonic
2024-04-21T04:33:48.988245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
9695982.976 1
 
4.8%
151836.261 1
 
4.8%
420971.164 1
 
4.8%
148121.211 1
 
4.8%
14822915.59 1
 
4.8%
446652.646 1
 
4.8%
138922.315 1
 
4.8%
14182088.92 1
 
4.8%
458377.637 1
 
4.8%
157851.218 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
138922.315 1
4.8%
148121.211 1
4.8%
151836.261 1
4.8%
155146.657 1
4.8%
157851.218 1
4.8%
159188.478 1
4.8%
180004.954 1
4.8%
420971.164 1
4.8%
446652.646 1
4.8%
458377.637 1
4.8%
ValueCountFrequency (%)
14822915.59 1
4.8%
14182088.92 1
4.8%
13523257.45 1
4.8%
13109898.82 1
4.8%
13019753.29 1
4.8%
11451359.69 1
4.8%
9695982.976 1
4.8%
597925.1 1
4.8%
594198.808 1
4.8%
582489.45 1
4.8%

경남(toe)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean509290.23
Minimum26882.905
Maximum3511650.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size317.0 B
2024-04-21T04:33:49.357553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26882.905
5-th percentile28325.714
Q135002.783
median94381.539
Q3696069.12
95-th percentile2623858.2
Maximum3511650.8
Range3484767.9
Interquartile range (IQR)661066.34

Descriptive statistics

Standard deviation910918.78
Coefficient of variation (CV)1.7886045
Kurtosis6.4825238
Mean509290.23
Median Absolute Deviation (MAD)60281.474
Skewness2.5819561
Sum10695095
Variance8.2977303 × 1011
MonotonicityNot monotonic
2024-04-21T04:33:49.749550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
792973.959 1
 
4.8%
28325.714 1
 
4.8%
75586.619 1
 
4.8%
33259.664 1
 
4.8%
3511650.779 1
 
4.8%
74135.964 1
 
4.8%
35002.783 1
 
4.8%
2623858.244 1
 
4.8%
81692.687 1
 
4.8%
34100.065 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
26882.905 1
4.8%
28325.714 1
4.8%
33259.664 1
4.8%
34100.065 1
4.8%
34198.297 1
4.8%
35002.783 1
4.8%
36415.286 1
4.8%
74135.964 1
4.8%
75586.619 1
4.8%
81692.687 1
4.8%
ValueCountFrequency (%)
3511650.779 1
4.8%
2623858.244 1
4.8%
821069.379 1
4.8%
792973.959 1
4.8%
711936.008 1
4.8%
696069.121 1
4.8%
683689.173 1
4.8%
102076.165 1
4.8%
99163.562 1
4.8%
98626.999 1
4.8%

경북(toe)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3247457
Minimum16728.539
Maximum10100949
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size317.0 B
2024-04-21T04:33:50.364540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16728.539
5-th percentile17904.434
Q124054.627
median81059.629
Q39654532.7
95-th percentile9901747.8
Maximum10100949
Range10084220
Interquartile range (IQR)9630478.1

Descriptive statistics

Standard deviation4646003.1
Coefficient of variation (CV)1.4306588
Kurtosis-1.5292236
Mean3247457
Median Absolute Deviation (MAD)60887.072
Skewness0.77845241
Sum68196597
Variance2.1585345 × 1013
MonotonicityNot monotonic
2024-04-21T04:33:50.755222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
9897609.388 1
 
4.8%
24054.627 1
 
4.8%
66501.155 1
 
4.8%
17904.434 1
 
4.8%
8321800.961 1
 
4.8%
70771.75 1
 
4.8%
18278.542 1
 
4.8%
9770158.375 1
 
4.8%
71246.72 1
 
4.8%
16728.539 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
16728.539 1
4.8%
17904.434 1
4.8%
18278.542 1
4.8%
20172.557 1
4.8%
24016.434 1
4.8%
24054.627 1
4.8%
24268.683 1
4.8%
66501.155 1
4.8%
70771.75 1
4.8%
71246.72 1
4.8%
ValueCountFrequency (%)
10100948.84 1
4.8%
9901747.823 1
4.8%
9897609.388 1
4.8%
9857552.286 1
4.8%
9770158.375 1
4.8%
9654532.724 1
4.8%
8321800.961 1
4.8%
88807.044 1
4.8%
87320.465 1
4.8%
81116.011 1
4.8%

광주(toe)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80649.113
Minimum12280.298
Maximum200269.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size317.0 B
2024-04-21T04:33:51.110607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12280.298
5-th percentile13558.131
Q115405.894
median30452.983
Q3189811.21
95-th percentile197748.5
Maximum200269.43
Range187989.13
Interquartile range (IQR)174405.32

Descriptive statistics

Standard deviation81924.796
Coefficient of variation (CV)1.0158177
Kurtosis-1.569871
Mean80649.113
Median Absolute Deviation (MAD)15547.745
Skewness0.71987472
Sum1693631.4
Variance6.7116723 × 109
MonotonicityNot monotonic
2024-04-21T04:33:51.492991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
189811.209 1
 
4.8%
15186.66 1
 
4.8%
24405.798 1
 
4.8%
14905.238 1
 
4.8%
197748.501 1
 
4.8%
28226.487 1
 
4.8%
13558.131 1
 
4.8%
200269.43 1
 
4.8%
25999.786 1
 
4.8%
12280.298 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
12280.298 1
4.8%
13558.131 1
4.8%
14905.238 1
4.8%
15090.645 1
4.8%
15186.66 1
4.8%
15405.894 1
4.8%
15798.151 1
4.8%
24405.798 1
4.8%
25999.786 1
4.8%
28226.487 1
4.8%
ValueCountFrequency (%)
200269.43 1
4.8%
197748.501 1
4.8%
194914.636 1
4.8%
191611.801 1
4.8%
191048.954 1
4.8%
189811.209 1
4.8%
184433.114 1
4.8%
46252.663 1
4.8%
44788.692 1
4.8%
41442.311 1
4.8%

대구(toe)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean319492
Minimum27168.572
Maximum928168.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size317.0 B
2024-04-21T04:33:51.858433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27168.572
5-th percentile27208.158
Q129964.191
median98240.862
Q3778471.54
95-th percentile862497.09
Maximum928168.66
Range901000.09
Interquartile range (IQR)748507.35

Descriptive statistics

Standard deviation379514.57
Coefficient of variation (CV)1.1878688
Kurtosis-1.5120972
Mean319492
Median Absolute Deviation (MAD)70411.649
Skewness0.7634846
Sum6709331.9
Variance1.4403131 × 1011
MonotonicityNot monotonic
2024-04-21T04:33:52.216032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
928168.659 1
 
4.8%
27168.572 1
 
4.8%
69123.35 1
 
4.8%
31238.017 1
 
4.8%
778471.543 1
 
4.8%
62630.917 1
 
4.8%
29964.191 1
 
4.8%
850018.003 1
 
4.8%
61774.091 1
 
4.8%
27558.974 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
27168.572 1
4.8%
27208.158 1
4.8%
27558.974 1
4.8%
27829.213 1
4.8%
29805.284 1
4.8%
29964.191 1
4.8%
31238.017 1
4.8%
61774.091 1
4.8%
62630.917 1
4.8%
69123.35 1
4.8%
ValueCountFrequency (%)
928168.659 1
4.8%
862497.091 1
4.8%
850018.003 1
4.8%
845770.84 1
4.8%
844429.489 1
4.8%
778471.543 1
4.8%
772296.452 1
4.8%
114596.516 1
4.8%
114264.369 1
4.8%
106277.309 1
4.8%

대전(toe)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean148105.02
Minimum70831.04
Maximum227112.31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size317.0 B
2024-04-21T04:33:52.542922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum70831.04
5-th percentile71415.066
Q176027.83
median146879.98
Q3217368.84
95-th percentile226192.77
Maximum227112.31
Range156281.27
Interquartile range (IQR)141341.01

Descriptive statistics

Standard deviation66029.832
Coefficient of variation (CV)0.44583114
Kurtosis-1.9130401
Mean148105.02
Median Absolute Deviation (MAD)70852.145
Skewness0.005366284
Sum3110205.5
Variance4.3599387 × 109
MonotonicityNot monotonic
2024-04-21T04:33:52.917796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
220440.233 1
 
4.8%
76027.83 1
 
4.8%
98037.402 1
 
4.8%
71415.066 1
 
4.8%
217368.839 1
 
4.8%
111434.327 1
 
4.8%
72147.623 1
 
4.8%
227112.314 1
 
4.8%
122723.58 1
 
4.8%
70831.04 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
70831.04 1
4.8%
71415.066 1
4.8%
72147.623 1
4.8%
72255.198 1
4.8%
75757.063 1
4.8%
76027.83 1
4.8%
77204.051 1
4.8%
98037.402 1
4.8%
111434.327 1
4.8%
122723.58 1
4.8%
ValueCountFrequency (%)
227112.314 1
4.8%
226192.766 1
4.8%
223802.841 1
4.8%
220440.233 1
4.8%
218588.063 1
4.8%
217368.839 1
4.8%
216196.791 1
4.8%
207846.381 1
4.8%
197561.309 1
4.8%
160382.804 1
4.8%

부산(toe)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean285391.79
Minimum58050.221
Maximum488910.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size317.0 B
2024-04-21T04:33:53.289113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum58050.221
5-th percentile62262.798
Q172134.205
median360270.07
Q3413930.1
95-th percentile449458.32
Maximum488910.94
Range430860.72
Interquartile range (IQR)341795.9

Descriptive statistics

Standard deviation164559.82
Coefficient of variation (CV)0.57661021
Kurtosis-1.5923464
Mean285391.79
Median Absolute Deviation (MAD)70717.485
Skewness-0.51830361
Sum5993227.7
Variance2.7079935 × 1010
MonotonicityNot monotonic
2024-04-21T04:33:53.672236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
440607.253 1
 
4.8%
78332.969 1
 
4.8%
308967.499 1
 
4.8%
62433.003 1
 
4.8%
408622.778 1
 
4.8%
310090.88 1
 
4.8%
66451.838 1
 
4.8%
404058.166 1
 
4.8%
305594.988 1
 
4.8%
58050.221 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
58050.221 1
4.8%
62262.798 1
4.8%
62433.003 1
4.8%
66451.838 1
4.8%
71285.874 1
4.8%
72134.205 1
4.8%
78332.969 1
4.8%
305594.988 1
4.8%
308967.499 1
4.8%
310090.88 1
4.8%
ValueCountFrequency (%)
488910.94 1
4.8%
449458.315 1
4.8%
440607.253 1
4.8%
430987.555 1
4.8%
424504.386 1
4.8%
413930.102 1
4.8%
412889.699 1
4.8%
408622.778 1
4.8%
404058.166 1
4.8%
363384.13 1
4.8%

서울(toe)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean526895.77
Minimum219833.72
Maximum1077618.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size317.0 B
2024-04-21T04:33:54.027988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum219833.72
5-th percentile235406.43
Q1266436.94
median341808.39
Q3931089.21
95-th percentile1075403.6
Maximum1077618.8
Range857785.12
Interquartile range (IQR)664652.26

Descriptive statistics

Standard deviation338778.19
Coefficient of variation (CV)0.64297004
Kurtosis-1.3827518
Mean526895.77
Median Absolute Deviation (MAD)99309.685
Skewness0.77083773
Sum11064811
Variance1.1477066 × 1011
MonotonicityNot monotonic
2024-04-21T04:33:54.398525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
238918.852 1
 
4.8%
366194.438 1
 
4.8%
946190.316 1
 
4.8%
332362.892 1
 
4.8%
307994.849 1
 
4.8%
931089.207 1
 
4.8%
312974.47 1
 
4.8%
266436.943 1
 
4.8%
851912.8 1
 
4.8%
307590.173 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
219833.719 1
4.8%
235406.434 1
4.8%
238918.852 1
4.8%
242498.702 1
4.8%
263553.496 1
4.8%
266436.943 1
4.8%
307590.173 1
4.8%
307994.849 1
4.8%
312974.47 1
4.8%
332362.892 1
4.8%
ValueCountFrequency (%)
1077618.843 1
4.8%
1075403.6 1
4.8%
1040911.964 1
4.8%
982610.442 1
4.8%
946190.316 1
4.8%
931089.207 1
4.8%
851912.8 1
4.8%
366845.911 1
4.8%
366194.438 1
4.8%
356654.639 1
4.8%
Distinct15
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Memory size296.0 B
2024-04-21T04:33:54.967283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length6.9047619
Min length4

Characters and Unicode

Total characters145
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)66.7%

Sample

1st row52861.618
2nd row1078.792
3rd row해당없음
4th row52064.005
5th row1019.925
ValueCountFrequency (%)
해당없음 7
33.3%
52861.618 1
 
4.8%
1078.792 1
 
4.8%
52064.005 1
 
4.8%
1019.925 1
 
4.8%
54423.472 1
 
4.8%
871.707 1
 
4.8%
64087.232 1
 
4.8%
791.885 1
 
4.8%
58580.371 1
 
4.8%
Other values (5) 5
23.8%
2024-04-21T04:33:55.938016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 17
11.7%
. 14
9.7%
7 14
9.7%
0 13
 
9.0%
2 11
 
7.6%
8 11
 
7.6%
5 9
 
6.2%
6 8
 
5.5%
7
 
4.8%
7
 
4.8%
Other values (5) 34
23.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 103
71.0%
Other Letter 28
 
19.3%
Other Punctuation 14
 
9.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 17
16.5%
7 14
13.6%
0 13
12.6%
2 11
10.7%
8 11
10.7%
5 9
8.7%
6 8
7.8%
9 7
6.8%
4 7
6.8%
3 6
 
5.8%
Other Letter
ValueCountFrequency (%)
7
25.0%
7
25.0%
7
25.0%
7
25.0%
Other Punctuation
ValueCountFrequency (%)
. 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 117
80.7%
Hangul 28
 
19.3%

Most frequent character per script

Common
ValueCountFrequency (%)
1 17
14.5%
. 14
12.0%
7 14
12.0%
0 13
11.1%
2 11
9.4%
8 11
9.4%
5 9
7.7%
6 8
6.8%
9 7
6.0%
4 7
6.0%
Hangul
ValueCountFrequency (%)
7
25.0%
7
25.0%
7
25.0%
7
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 117
80.7%
Hangul 28
 
19.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 17
14.5%
. 14
12.0%
7 14
12.0%
0 13
11.1%
2 11
9.4%
8 11
9.4%
5 9
7.7%
6 8
6.8%
9 7
6.0%
4 7
6.0%
Hangul
ValueCountFrequency (%)
7
25.0%
7
25.0%
7
25.0%
7
25.0%

울산(toe)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2964006.6
Minimum5948.584
Maximum9650944.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size317.0 B
2024-04-21T04:33:56.321353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5948.584
5-th percentile6598.161
Q17574.82
median42530.009
Q38182314.5
95-th percentile9407314.4
Maximum9650944.1
Range9644995.5
Interquartile range (IQR)8174739.7

Descriptive statistics

Standard deviation4274467.2
Coefficient of variation (CV)1.4421247
Kurtosis-1.5002996
Mean2964006.6
Median Absolute Deviation (MAD)35356.015
Skewness0.7864344
Sum62244138
Variance1.827107 × 1013
MonotonicityNot monotonic
2024-04-21T04:33:56.713918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
7804328.578 1
 
4.8%
5948.584 1
 
4.8%
44956.584 1
 
4.8%
6598.161 1
 
4.8%
8749397.923 1
 
4.8%
42530.009 1
 
4.8%
6771.307 1
 
4.8%
9124017.86 1
 
4.8%
43524.567 1
 
4.8%
7775.23 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
5948.584 1
4.8%
6598.161 1
4.8%
6771.307 1
4.8%
7173.994 1
4.8%
7263.066 1
4.8%
7574.82 1
4.8%
7775.23 1
4.8%
37634.24 1
4.8%
40750.296 1
4.8%
41208.8 1
4.8%
ValueCountFrequency (%)
9650944.094 1
4.8%
9407314.423 1
4.8%
9124017.86 1
4.8%
8978000.783 1
4.8%
8749397.923 1
4.8%
8182314.514 1
4.8%
7804328.578 1
4.8%
48109.926 1
4.8%
44956.584 1
4.8%
43524.567 1
4.8%

인천(toe)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1277441.2
Minimum24125.798
Maximum4069071.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size317.0 B
2024-04-21T04:33:57.079014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum24125.798
5-th percentile26406.01
Q135547.075
median111197.74
Q33480424
95-th percentile3982716.4
Maximum4069071.1
Range4044945.3
Interquartile range (IQR)3444877

Descriptive statistics

Standard deviation1758577.2
Coefficient of variation (CV)1.3766405
Kurtosis-1.4782629
Mean1277441.2
Median Absolute Deviation (MAD)82465.906
Skewness0.79139745
Sum26826264
Variance3.0925938 × 1012
MonotonicityNot monotonic
2024-04-21T04:33:57.484513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
3480424.027 1
 
4.8%
35547.075 1
 
4.8%
97336.263 1
 
4.8%
26406.01 1
 
4.8%
3500067.886 1
 
4.8%
95663.962 1
 
4.8%
26735.815 1
 
4.8%
3982716.366 1
 
4.8%
100148.499 1
 
4.8%
24125.798 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
24125.798 1
4.8%
26406.01 1
4.8%
26735.815 1
4.8%
28731.837 1
4.8%
30160.458 1
4.8%
35547.075 1
4.8%
35839.913 1
4.8%
95663.962 1
4.8%
97336.263 1
4.8%
100148.499 1
4.8%
ValueCountFrequency (%)
4069071.131 1
4.8%
3982716.366 1
4.8%
3911675.701 1
4.8%
3649468.801 1
4.8%
3500067.886 1
4.8%
3480424.027 1
4.8%
3259912.164 1
4.8%
123937.918 1
4.8%
122512.072 1
4.8%
114584.842 1
4.8%

전남(toe)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7243448.2
Minimum4599.402
Maximum22448718
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size317.0 B
2024-04-21T04:33:57.870795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4599.402
5-th percentile5621.037
Q17997.371
median76178.379
Q320833209
95-th percentile22303816
Maximum22448718
Range22444119
Interquartile range (IQR)20825211

Descriptive statistics

Standard deviation10443071
Coefficient of variation (CV)1.4417264
Kurtosis-1.5596082
Mean7243448.2
Median Absolute Deviation (MAD)68647.633
Skewness0.76844816
Sum1.5211241 × 108
Variance1.0905773 × 1014
MonotonicityNot monotonic
2024-04-21T04:33:58.242784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
21118198.07 1
 
4.8%
4599.402 1
 
4.8%
92055.997 1
 
4.8%
7631.968 1
 
4.8%
20564816.91 1
 
4.8%
76178.379 1
 
4.8%
7997.371 1
 
4.8%
22303816.08 1
 
4.8%
73973.588 1
 
4.8%
7530.746 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
4599.402 1
4.8%
5621.037 1
4.8%
7017.332 1
4.8%
7530.746 1
4.8%
7631.968 1
4.8%
7997.371 1
4.8%
8191.669 1
4.8%
73973.588 1
4.8%
75559.115 1
4.8%
75825.017 1
4.8%
ValueCountFrequency (%)
22448717.97 1
4.8%
22303816.08 1
4.8%
22298871.03 1
4.8%
21940163.75 1
4.8%
21118198.07 1
4.8%
20833208.83 1
4.8%
20564816.91 1
4.8%
92055.997 1
4.8%
81701.733 1
4.8%
80736.992 1
4.8%

전북(toe)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean778945.37
Minimum16347.706
Maximum2473157
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size317.0 B
2024-04-21T04:33:58.601586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16347.706
5-th percentile16924.961
Q119033.29
median50383.123
Q32122238.7
95-th percentile2386846
Maximum2473157
Range2456809.3
Interquartile range (IQR)2103205.4

Descriptive statistics

Standard deviation1074475.6
Coefficient of variation (CV)1.3793979
Kurtosis-1.515789
Mean778945.37
Median Absolute Deviation (MAD)33458.162
Skewness0.77966714
Sum16357853
Variance1.1544978 × 1012
MonotonicityNot monotonic
2024-04-21T04:33:58.994549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
2473157.05 1
 
4.8%
18566.765 1
 
4.8%
39044.604 1
 
4.8%
17712.696 1
 
4.8%
2070419.467 1
 
4.8%
38437.521 1
 
4.8%
16347.706 1
 
4.8%
2315257.027 1
 
4.8%
45876.697 1
 
4.8%
16924.961 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
16347.706 1
4.8%
16924.961 1
4.8%
17712.696 1
4.8%
18566.765 1
4.8%
18622.354 1
4.8%
19033.29 1
4.8%
19140.721 1
4.8%
38437.521 1
4.8%
39044.604 1
4.8%
45876.697 1
4.8%
ValueCountFrequency (%)
2473157.05 1
4.8%
2386845.996 1
4.8%
2315257.027 1
4.8%
2245513.409 1
4.8%
2189009.105 1
4.8%
2122238.701 1
4.8%
2070419.467 1
4.8%
103130.377 1
4.8%
99556.066 1
4.8%
52635.089 1
4.8%

제주(toe)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22400.172
Minimum673.058
Maximum128254.77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size317.0 B
2024-04-21T04:33:59.351617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum673.058
5-th percentile2056.443
Q14086.238
median10227.885
Q327804.375
95-th percentile93638.015
Maximum128254.77
Range127581.72
Interquartile range (IQR)23718.137

Descriptive statistics

Standard deviation31637.856
Coefficient of variation (CV)1.4123934
Kurtosis6.7680431
Mean22400.172
Median Absolute Deviation (MAD)6768.405
Skewness2.6012846
Sum470403.61
Variance1.0009539 × 109
MonotonicityNot monotonic
2024-04-21T04:33:59.741580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
673.058 1
 
4.8%
8086.413 1
 
4.8%
34064.628 1
 
4.8%
16039.45 1
 
4.8%
4086.238 1
 
4.8%
27804.375 1
 
4.8%
15621.277 1
 
4.8%
3885.333 1
 
4.8%
32550.794 1
 
4.8%
10265.849 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
673.058 1
4.8%
2056.443 1
4.8%
2095.657 1
4.8%
3459.48 1
4.8%
3885.333 1
4.8%
4086.238 1
4.8%
8086.413 1
4.8%
8385.292 1
4.8%
9777.783 1
4.8%
10133.319 1
4.8%
ValueCountFrequency (%)
128254.774 1
4.8%
93638.015 1
4.8%
34064.628 1
4.8%
32550.794 1
4.8%
28683.739 1
4.8%
27804.375 1
4.8%
20613.805 1
4.8%
16039.45 1
4.8%
15621.277 1
4.8%
10265.849 1
4.8%

충남(toe)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4908874
Minimum15767.604
Maximum15521820
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size317.0 B
2024-04-21T04:34:00.102508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15767.604
5-th percentile16312.391
Q122563.317
median40702.775
Q314225665
95-th percentile15207202
Maximum15521820
Range15506053
Interquartile range (IQR)14203102

Descriptive statistics

Standard deviation7076934.8
Coefficient of variation (CV)1.4416615
Kurtosis-1.556596
Mean4908874
Median Absolute Deviation (MAD)19067.411
Skewness0.76926995
Sum1.0308635 × 108
Variance5.0083006 × 1013
MonotonicityNot monotonic
2024-04-21T04:34:00.503093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
13997372.42 1
 
4.8%
24255.877 1
 
4.8%
33164.129 1
 
4.8%
21635.364 1
 
4.8%
14526442.8 1
 
4.8%
32883.253 1
 
4.8%
16312.391 1
 
4.8%
14225665.43 1
 
4.8%
34843.02 1
 
4.8%
15767.604 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
15767.604 1
4.8%
16312.391 1
4.8%
21119.365 1
4.8%
21635.364 1
4.8%
21650.373 1
4.8%
22563.317 1
4.8%
24255.877 1
4.8%
32883.253 1
4.8%
33164.129 1
4.8%
34843.02 1
4.8%
ValueCountFrequency (%)
15521820.28 1
4.8%
15207202.41 1
4.8%
14916527.56 1
4.8%
14526442.8 1
4.8%
14271710.22 1
4.8%
14225665.43 1
4.8%
13997372.42 1
4.8%
46236.54 1
4.8%
45367.567 1
4.8%
43111.635 1
4.8%

충북(toe)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean670931.34
Minimum17101.789
Maximum2019325.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size317.0 B
2024-04-21T04:34:00.892345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17101.789
5-th percentile17971.446
Q120370.548
median55213.423
Q31889540.9
95-th percentile2000642.5
Maximum2019325.2
Range2002223.4
Interquartile range (IQR)1869170.3

Descriptive statistics

Standard deviation919339.27
Coefficient of variation (CV)1.3702434
Kurtosis-1.5618912
Mean670931.34
Median Absolute Deviation (MAD)36749.883
Skewness0.76679439
Sum14089558
Variance8.4518469 × 1011
MonotonicityNot monotonic
2024-04-21T04:34:01.299977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1967547.752 1
 
4.8%
17971.446 1
 
4.8%
49035.715 1
 
4.8%
18463.54 1
 
4.8%
2000642.467 1
 
4.8%
49736.166 1
 
4.8%
18046.404 1
 
4.8%
2019325.237 1
 
4.8%
51175.157 1
 
4.8%
17101.789 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
17101.789 1
4.8%
17971.446 1
4.8%
18046.404 1
4.8%
18463.54 1
4.8%
19617.63 1
4.8%
20370.548 1
4.8%
20519.822 1
4.8%
49035.715 1
4.8%
49736.166 1
4.8%
51175.157 1
4.8%
ValueCountFrequency (%)
2019325.237 1
4.8%
2000642.467 1
4.8%
1967547.752 1
4.8%
1944217.109 1
4.8%
1915890.486 1
4.8%
1889540.878 1
4.8%
1833066.515 1
4.8%
69477.056 1
4.8%
57330.898 1
4.8%
55268.166 1
4.8%

총합계(toe)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28858365
Minimum838576.51
Maximum86534226
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size317.0 B
2024-04-21T04:34:01.687360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum838576.51
5-th percentile849212.4
Q1943816.01
median2943615.3
Q380161498
95-th percentile86306830
Maximum86534226
Range85695650
Interquartile range (IQR)79217682

Descriptive statistics

Standard deviation39207839
Coefficient of variation (CV)1.3586299
Kurtosis-1.5462074
Mean28858365
Median Absolute Deviation (MAD)2018540.1
Skewness0.77119134
Sum6.0602567 × 108
Variance1.5372547 × 1015
MonotonicityNot monotonic
2024-04-21T04:34:02.058175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
75766856.84 1
 
4.8%
943816.009 1
 
4.8%
2437104.423 1
 
4.8%
888965.155 1
 
4.8%
84563323.87 1
 
4.8%
2424606.687 1
 
4.8%
849212.398 1
 
4.8%
86534226.33 1
 
4.8%
2393562.465 1
 
4.8%
838576.507 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
838576.507 1
4.8%
849212.398 1
4.8%
888965.155 1
4.8%
925075.178 1
4.8%
937408.249 1
4.8%
943816.009 1
4.8%
968828.815 1
4.8%
2393562.465 1
4.8%
2424606.687 1
4.8%
2437104.423 1
4.8%
ValueCountFrequency (%)
86534226.33 1
4.8%
86306830.12 1
4.8%
85014452.09 1
4.8%
84563323.87 1
4.8%
81798752.09 1
4.8%
80161497.53 1
4.8%
75766856.84 1
4.8%
3223329.074 1
4.8%
3126530.284 1
4.8%
2979098.235 1
4.8%

Interactions

2024-04-21T04:33:40.842116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:27.971396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:32.523043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:35.853486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:38.587617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:43.003395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:47.448154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:52.212297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:56.566085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:01.142874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:05.724228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:10.086662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:14.362775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:18.692623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:23.017739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:26.763691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:31.349490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:35.995161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:41.088357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:28.122126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:32.765695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:36.009074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:38.953781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:43.264942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:47.707143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:52.455639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:56.823939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:01.386976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:05.975034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:10.347369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:14.522503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:18.934737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:23.265077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:27.132649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:31.610838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:36.254078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:41.318135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:28.345488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:32.991228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:36.149162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:39.096069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:43.499595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:47.951308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:52.684386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:57.065055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:01.619245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:06.202851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:10.585547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:14.666515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:19.162565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:23.496509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:27.364560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:31.861637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:36.500966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:41.565323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:28.604250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:33.241580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:36.310158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:39.256456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:43.755267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:48.214715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:52.937415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:57.330655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:01.869713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:06.453150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:10.848887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:14.833279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:19.412938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:23.751117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:27.620141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:32.131075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:36.765149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:41.815614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:28.862508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:33.489112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:36.471074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:39.451442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:44.012266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:48.477296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:53.187571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:57.592880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:02.122732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:06.703973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:11.051065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:14.996905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:19.661976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:24.001736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:27.874579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:32.401412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:37.031543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:42.050345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:29.105147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:33.726228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:36.619957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:39.702818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:44.250324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:48.724613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:53.427798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:57.845386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:02.359614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:06.938888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:11.286293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:15.148284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:19.897207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:24.242785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:28.119079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:32.658036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:37.286844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:42.301523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:29.567626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:33.972512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:36.779780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:39.962958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:44.506541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:48.982677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:53.676053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:58.105356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:02.610583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:07.186437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:11.544525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:15.630795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:20.144122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:24.493647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:28.375358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:32.925539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:37.551771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:42.526228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:29.799650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:34.199489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:36.920077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:40.204824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:44.737977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:49.220535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:53.904194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:58.347413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:02.836847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:07.415473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:11.776972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:15.871685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:20.373358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:24.729341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:28.616718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:33.171486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:37.797417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:42.775289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:30.054945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:34.450040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:37.078632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:40.465751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:44.993982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:49.481796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:54.152412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:58.604549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:03.088032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:07.662679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:12.028197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:16.140055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:20.620243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:24.980350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:28.879434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:33.433079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:38.061859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:43.001866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:30.288588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:34.584269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:37.217878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:40.707947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:45.227486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:49.720711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:54.382675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:58.847820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:03.521695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:07.892941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:12.262078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:16.382424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:20.850027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:25.219859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:29.114881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:33.676157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:38.521024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:43.235730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:30.527323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:34.714322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:37.358905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:40.951483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:45.464332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:49.966172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:54.613617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:59.091018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:03.753068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:08.120786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:12.494673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:16.628716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:21.079780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:25.443010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:29.356337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:33.918384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:38.768293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:43.478869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:30.769636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:34.853128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:37.506016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:41.203170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:45.704176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:50.214972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:54.853111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:59.343354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:03.993207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:08.357565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:12.733913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:16.878799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:21.317122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:25.684156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:29.600318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:34.168961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:39.020602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:43.740762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:31.033964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:35.006239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:37.670097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:41.468840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:45.967429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:50.696592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:55.109413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:59.614488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:04.252585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:08.613809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:12.992751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:17.147320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:21.571382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:25.892384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:29.864797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:34.438011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:39.292345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:43.979669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:31.271937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:35.138559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:37.811073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:41.711895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:46.201818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:50.936469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:55.342267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:59.857160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:04.483495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:08.848609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:13.226633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:17.394778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:21.800531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:26.029901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:30.100401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:34.685458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:39.540920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:44.225292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:31.514807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:35.274668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:37.955708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:41.958586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:46.441776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:51.185557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:55.578683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:00.109144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:04.719907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:09.085535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:13.465678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:17.645418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:22.035050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:26.167550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:30.344633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:34.940050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:39.794737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:44.465904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:31.762427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:35.414837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:38.108816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:42.212194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:46.689812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:51.437489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:55.821678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:00.360901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:04.961358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:09.326919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:13.710203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:17.906356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:22.276968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:26.312551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:30.586925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:35.195246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:40.049747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:44.725312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:32.023768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:35.568146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:38.273223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:42.478704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:46.948965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:51.704927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:56.078082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:00.629943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:05.221341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:09.587566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:13.966822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:18.175962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:22.531509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:26.471675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:30.850410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:35.467298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:40.324096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:44.982572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:32.288831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:35.725550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:38.444530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:42.754334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:47.215822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:51.973059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:32:56.337616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:00.898912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:05.487071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:09.852491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:14.227551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:18.448039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:22.789585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:26.632016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:31.112294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:35.745787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T04:33:40.592734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T04:34:02.337313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도부문강원(toe)경기(toe)경남(toe)경북(toe)광주(toe)대구(toe)대전(toe)부산(toe)서울(toe)세종(toe)울산(toe)인천(toe)전남(toe)전북(toe)제주(toe)충남(toe)충북(toe)총합계(toe)
연도1.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
부문0.0001.0000.6470.6470.6470.9260.9640.9261.0000.8310.8681.0000.9260.6231.0000.9260.6331.0001.0000.926
강원(toe)0.0000.6471.0000.9870.9590.7220.6420.7090.0000.6880.3331.0001.0000.8991.0000.7520.0001.0001.0001.000
경기(toe)0.0000.6470.9871.0000.9590.7220.6420.7090.0000.7470.3611.0001.0000.8441.0000.7550.0001.0001.0001.000
경남(toe)0.0000.6470.9590.9591.0001.0000.6420.8140.0000.0840.6191.0001.0000.7761.0000.8350.0001.0001.0000.781
경북(toe)0.0000.9260.7220.7221.0001.0000.9260.9790.6110.5270.6711.0000.9370.7001.0000.9590.0001.0001.0000.931
광주(toe)0.0000.9640.6420.6420.6420.9261.0000.9241.0000.6850.7120.0000.9240.6231.0000.9240.6691.0001.0000.926
대구(toe)0.0000.9260.7090.7090.8140.9790.9241.0000.5890.5610.6011.0000.9520.6441.0000.9870.0001.0001.0000.937
대전(toe)0.0001.0000.0000.0000.0000.6111.0000.5891.0000.8870.9210.0000.5890.0001.0000.5890.7681.0001.0000.611
부산(toe)0.0000.8310.6880.7470.0840.5270.6850.5610.8871.0000.9330.0000.5250.5630.6400.6220.7720.6500.6500.527
서울(toe)0.0000.8680.3330.3610.6190.6710.7120.6010.9210.9331.0000.0000.5590.3620.7230.6010.8170.7230.7230.549
세종(toe)0.0001.0001.0001.0001.0001.0000.0001.0000.0000.0000.0001.0001.0001.0001.0001.0000.0001.0001.0001.000
울산(toe)0.0000.9261.0001.0001.0000.9370.9240.9520.5890.5250.5591.0001.0000.7901.0000.9680.0001.0001.0000.979
인천(toe)0.0000.6230.8990.8440.7760.7000.6230.6440.0000.5630.3621.0000.7901.0001.0000.6980.0001.0001.0000.700
전남(toe)0.0001.0001.0001.0001.0001.0001.0001.0001.0000.6400.7231.0001.0001.0001.0001.0000.4180.9820.9821.000
전북(toe)0.0000.9260.7520.7550.8350.9590.9240.9870.5890.6220.6011.0000.9680.6981.0001.0000.0001.0001.0000.946
제주(toe)0.0000.6330.0000.0000.0000.0000.6690.0000.7680.7720.8170.0000.0000.0000.4180.0001.0000.4180.4180.418
충남(toe)0.0001.0001.0001.0001.0001.0001.0001.0001.0000.6500.7231.0001.0001.0000.9821.0000.4181.0000.9831.000
충북(toe)0.0001.0001.0001.0001.0001.0001.0001.0001.0000.6500.7231.0001.0001.0000.9821.0000.4180.9831.0001.000
총합계(toe)0.0000.9261.0001.0000.7810.9310.9260.9370.6110.5270.5491.0000.9790.7001.0000.9460.4181.0001.0001.000
2024-04-21T04:34:02.764602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도강원(toe)경기(toe)경남(toe)경북(toe)광주(toe)대구(toe)대전(toe)부산(toe)서울(toe)울산(toe)인천(toe)전남(toe)전북(toe)제주(toe)충남(toe)충북(toe)총합계(toe)부문
연도1.000-0.090-0.051-0.020-0.204-0.102-0.051-0.224-0.346-0.0590.067-0.1140.079-0.2600.177-0.145-0.039-0.1020.000
강원(toe)-0.0901.0000.5770.5270.5620.5680.5290.5490.475-0.7470.4770.5560.5120.539-0.8030.5840.5340.5900.577
경기(toe)-0.0510.5771.0000.9520.9160.9640.9030.9380.848-0.3710.9300.9450.8920.922-0.3560.9230.9430.9660.577
경남(toe)-0.0200.5270.9521.0000.8820.9530.9380.9320.844-0.3880.8860.9010.9100.918-0.3650.8870.9340.9230.577
경북(toe)-0.2040.5620.9160.8821.0000.9130.9180.9530.929-0.3820.8830.9650.8830.942-0.4220.9700.9290.9510.667
광주(toe)-0.1020.5680.9640.9530.9131.0000.8990.9660.879-0.3420.8750.9520.8730.943-0.4130.9230.9560.9660.772
대구(toe)-0.0510.5290.9030.9380.9180.8991.0000.9250.897-0.4310.8830.8940.9520.931-0.3360.8950.9160.9210.667
대전(toe)-0.2240.5490.9380.9320.9530.9660.9251.0000.931-0.3680.8840.9510.8880.970-0.4270.9430.9320.9690.850
부산(toe)-0.3460.4750.8480.8440.9290.8790.8970.9311.000-0.3130.8140.8820.8420.926-0.3320.9250.8340.8970.814
서울(toe)-0.059-0.747-0.371-0.388-0.382-0.342-0.431-0.368-0.3131.000-0.455-0.353-0.466-0.3990.812-0.371-0.349-0.3530.866
울산(toe)0.0670.4770.9300.8860.8830.8750.8830.8840.814-0.4551.0000.9080.9170.869-0.3290.8770.8690.9130.667
인천(toe)-0.1140.5560.9450.9010.9650.9520.8940.9510.882-0.3530.9081.0000.8680.922-0.3940.9490.9510.9740.624
전남(toe)0.0790.5120.8920.9100.8830.8730.9520.8880.842-0.4660.9170.8681.0000.895-0.3310.8740.8660.9100.973
전북(toe)-0.2600.5390.9220.9180.9420.9430.9310.9700.926-0.3990.8690.9220.8951.000-0.4340.9220.9380.9320.667
제주(toe)0.177-0.803-0.356-0.365-0.422-0.413-0.336-0.427-0.3320.812-0.329-0.394-0.331-0.4341.000-0.401-0.404-0.3700.566
충남(toe)-0.1450.5840.9230.8870.9700.9230.8950.9430.925-0.3710.8770.9490.8740.922-0.4011.0000.9130.9550.973
충북(toe)-0.0390.5340.9430.9340.9290.9560.9160.9320.834-0.3490.8690.9510.8660.938-0.4040.9131.0000.9350.973
총합계(toe)-0.1020.5900.9660.9230.9510.9660.9210.9690.897-0.3530.9130.9740.9100.932-0.3700.9550.9351.0000.667
부문0.0000.5770.5770.5770.6670.7720.6670.8500.8140.8660.6670.6240.9730.6670.5660.9730.9730.6671.000

Missing values

2024-04-21T04:33:45.355391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T04:33:46.067734image/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

연도부문강원(toe)경기(toe)경남(toe)경북(toe)광주(toe)대구(toe)대전(toe)부산(toe)서울(toe)세종(toe)울산(toe)인천(toe)전남(toe)전북(toe)제주(toe)충남(toe)충북(toe)총합계(toe)
02016산업2467781.7379695982.976792973.9599897609.388189811.209928168.659220440.233440607.253238918.85252861.6187804328.5783480424.02721118198.072473157.05673.05813997372.421967547.75275766856.84
12016건물60634.584151836.26128325.71424054.62715186.6627168.57276027.8378332.969366194.4381078.7925948.58435547.0754599.40218566.7658086.41324255.87717971.446943816.009
22016수송48448.462597925.1102076.16581059.62946252.663114264.369207846.381412889.6991040911.964해당없음48109.926123937.91875825.01799556.066128254.77440702.77555268.1663223329.074
32017산업3657098.72411451359.69821069.3799654532.724194914.636844429.489223802.841449458.315235406.43452064.0058182314.5143259912.16421940163.752386845.9962056.44314916527.561889540.87880161497.53
42017건물59361.181155146.65726882.90524016.43415090.64527208.15875757.06371285.874356654.6391019.9257173.99430160.4585621.03719140.7218385.29221650.37320519.822925075.178
52017수송59783.899594198.80898626.99981116.01144788.692114596.516197561.309424504.386982610.442해당없음40750.296111197.74381701.733103130.37793638.01543111.63555213.4233126530.284
62018산업4577436.7313109898.82711936.0089901747.823191048.954845770.84226192.766488910.94263553.49654423.4729407314.4233649468.80122298871.032245513.4092095.65715207202.411833066.51585014452.09
72018건물66747.981159188.47834198.29724268.68315798.15129805.28477204.05172134.205366845.911871.7077574.8235839.9137017.33218622.3549777.78322563.31720370.548968828.815
82018수송57445.231582489.4599163.56287320.46541442.311106277.309160382.804360270.071075403.6해당없음37634.24114584.84280736.99252635.08920613.80545367.56757330.8982979098.235
92019산업4357936.97813523257.45696069.12110100948.84184433.114862497.091218588.063430987.555219833.71964087.2329650944.0943911675.70122448717.972189009.10510133.31915521820.281915890.48686306830.12
연도부문강원(toe)경기(toe)경남(toe)경북(toe)광주(toe)대구(toe)대전(toe)부산(toe)서울(toe)세종(toe)울산(toe)인천(toe)전남(toe)전북(toe)제주(toe)충남(toe)충북(toe)총합계(toe)
112019수송52583.759557205.67594381.53988807.04430452.98398240.862146879.975363384.131077618.843해당없음41208.8122512.07275559.11550383.12328683.73946236.5469477.0562943615.255
122020산업4120736.87713019753.29683689.1739857552.286191611.801772296.452216196.791413930.102242498.70258580.3718978000.7834069071.13120833208.832122238.7013459.4814271710.221944217.10981798752.09
132020건물53083.689157851.21834100.06516728.53912280.29827558.97470831.0458050.221307590.1731010.3137775.2324125.7987530.74616924.96110265.84915767.60417101.789838576.507
142020수송32147.854458377.63781692.68771246.7225999.78661774.091122723.58305594.988851912.8해당없음43524.567100148.49973973.58845876.69732550.79434843.0251175.1572393562.465
152021산업3977894.14714182088.922623858.2449770158.375200269.43850018.003227112.314404058.166266436.94357648.4619124017.863982716.36622303816.082315257.0273885.33314225665.432019325.23786534226.33
162021건물52810.067138922.31535002.78318278.54213558.13129964.19172147.62366451.838312974.471270.1676771.30726735.8157997.37116347.70615621.27716312.39118046.404849212.398
172021수송26340.844446652.64674135.96470771.7528226.48762630.917111434.327310090.88931089.207해당없음42530.00995663.96276178.37938437.52127804.37532883.25349736.1662424606.687
182022산업4513676.55814822915.593511650.7798321800.961197748.501778471.543217368.839408622.778307994.84967199.7918749397.9233500067.88620564816.912070419.4674086.23814526442.82000642.46784563323.87
192022건물61608.359148121.21133259.66417904.43414905.23831238.01771415.06662433.003332362.8921230.0826598.16126406.017631.96817712.69616039.4521635.36418463.54888965.155
202022수송37663.2420971.16475586.61966501.15524405.79869123.3598037.402308967.499946190.316해당없음44956.58497336.26392055.99739044.60434064.62833164.12949035.7152437104.423