Overview

Dataset statistics

Number of variables14
Number of observations26
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.3 KiB
Average record size in memory130.1 B

Variable types

Categorical1
Numeric13

Dataset

Description부산광역시영도구_생활쓰레기배출량현황_20221231
Author부산광역시 영도구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15053276

Alerts

1월 is highly overall correlated with 2월 and 11 other fieldsHigh correlation
2월 is highly overall correlated with 1월 and 11 other fieldsHigh correlation
3월 is highly overall correlated with 1월 and 11 other fieldsHigh correlation
4월 is highly overall correlated with 1월 and 11 other fieldsHigh correlation
5월 is highly overall correlated with 1월 and 11 other fieldsHigh correlation
6월 is highly overall correlated with 1월 and 11 other fieldsHigh correlation
7월 is highly overall correlated with 1월 and 11 other fieldsHigh correlation
8월 is highly overall correlated with 1월 and 11 other fieldsHigh correlation
9월 is highly overall correlated with 1월 and 11 other fieldsHigh correlation
10월 is highly overall correlated with 1월 and 11 other fieldsHigh correlation
11월 is highly overall correlated with 1월 and 11 other fieldsHigh correlation
12월 is highly overall correlated with 1월 and 11 other fieldsHigh correlation
구분 is highly overall correlated with 1월 and 11 other fieldsHigh correlation
1월 has unique valuesUnique
2월 has unique valuesUnique
3월 has unique valuesUnique
4월 has unique valuesUnique
5월 has unique valuesUnique
6월 has unique valuesUnique
7월 has unique valuesUnique
8월 has unique valuesUnique
9월 has unique valuesUnique
10월 has unique valuesUnique
11월 has unique valuesUnique
12월 has unique valuesUnique

Reproduction

Analysis started2023-12-10 16:20:04.884424
Analysis finished2023-12-10 16:20:23.725108
Duration18.84 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Memory size340.0 B
음식물쓰레기
재활용품
일반쓰레기

Length

Max length6
Median length5
Mean length5
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
음식물쓰레기 9
34.6%
재활용품 9
34.6%
일반쓰레기 8
30.8%

Length

2023-12-11T01:20:23.803143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:20:23.910414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
음식물쓰레기 9
34.6%
재활용품 9
34.6%
일반쓰레기 8
30.8%

년도
Real number (ℝ)

Distinct9
Distinct (%)34.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.1538
Minimum2014
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T01:20:23.995592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2014
5-th percentile2014.25
Q12016
median2018
Q32020
95-th percentile2022
Maximum2022
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.5564398
Coefficient of variation (CV)0.001266722
Kurtosis-1.1911518
Mean2018.1538
Median Absolute Deviation (MAD)2
Skewness-0.029834923
Sum52472
Variance6.5353846
MonotonicityNot monotonic
2023-12-11T01:20:24.102767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2015 3
11.5%
2016 3
11.5%
2017 3
11.5%
2018 3
11.5%
2019 3
11.5%
2020 3
11.5%
2021 3
11.5%
2022 3
11.5%
2014 2
7.7%
ValueCountFrequency (%)
2014 2
7.7%
2015 3
11.5%
2016 3
11.5%
2017 3
11.5%
2018 3
11.5%
2019 3
11.5%
2020 3
11.5%
2021 3
11.5%
2022 3
11.5%
ValueCountFrequency (%)
2022 3
11.5%
2021 3
11.5%
2020 3
11.5%
2019 3
11.5%
2018 3
11.5%
2017 3
11.5%
2016 3
11.5%
2015 3
11.5%
2014 2
7.7%

1월
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean620.98962
Minimum229.6
Maximum922.37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T01:20:24.218518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum229.6
5-th percentile365.125
Q1433.925
median702.85
Q3730.55
95-th percentile892.965
Maximum922.37
Range692.77
Interquartile range (IQR)296.625

Descriptive statistics

Standard deviation187.42555
Coefficient of variation (CV)0.30181753
Kurtosis-0.8606443
Mean620.98962
Median Absolute Deviation (MAD)85.19
Skewness-0.38837404
Sum16145.73
Variance35128.337
MonotonicityNot monotonic
2023-12-11T01:20:24.316654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
731.7 1
 
3.8%
734.9 1
 
3.8%
444.2 1
 
3.8%
430.5 1
 
3.8%
393.1 1
 
3.8%
466.2 1
 
3.8%
423.8 1
 
3.8%
403.2 1
 
3.8%
229.6 1
 
3.8%
367.3 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
229.6 1
3.8%
364.4 1
3.8%
367.3 1
3.8%
393.1 1
3.8%
403.2 1
3.8%
423.8 1
3.8%
430.5 1
3.8%
444.2 1
3.8%
466.2 1
3.8%
594.82 1
3.8%
ValueCountFrequency (%)
922.37 1
3.8%
896.8 1
3.8%
881.46 1
3.8%
765.2 1
3.8%
752.1 1
3.8%
734.9 1
3.8%
731.7 1
3.8%
727.1 1
3.8%
724.6 1
3.8%
713.8 1
3.8%

2월
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean599.10346
Minimum271.7
Maximum860.72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T01:20:24.418245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum271.7
5-th percentile354.875
Q1418.5
median651.2
Q3730.525
95-th percentile833.735
Maximum860.72
Range589.02
Interquartile range (IQR)312.025

Descriptive statistics

Standard deviation174.20749
Coefficient of variation (CV)0.29078031
Kurtosis-1.2274823
Mean599.10346
Median Absolute Deviation (MAD)95.85
Skewness-0.37335597
Sum15576.69
Variance30348.249
MonotonicityNot monotonic
2023-12-11T01:20:24.520572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
730.6 1
 
3.8%
607.7 1
 
3.8%
394.2 1
 
3.8%
384.6 1
 
3.8%
459.8 1
 
3.8%
417.7 1
 
3.8%
388.9 1
 
3.8%
420.9 1
 
3.8%
271.7 1
 
3.8%
353.0 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
271.7 1
3.8%
353.0 1
3.8%
360.5 1
3.8%
384.6 1
3.8%
388.9 1
3.8%
394.2 1
3.8%
417.7 1
3.8%
420.9 1
3.8%
459.8 1
3.8%
594.19 1
3.8%
ValueCountFrequency (%)
860.72 1
3.8%
841.08 1
3.8%
811.7 1
3.8%
751.5 1
3.8%
742.6 1
3.8%
739.8 1
3.8%
730.6 1
3.8%
730.3 1
3.8%
712.6 1
3.8%
707.7 1
3.8%

3월
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean637.24231
Minimum262.9
Maximum1059.85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T01:20:24.626591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum262.9
5-th percentile354.95
Q1471.3
median669.2
Q3766.25
95-th percentile908.7875
Maximum1059.85
Range796.95
Interquartile range (IQR)294.95

Descriptive statistics

Standard deviation192.73909
Coefficient of variation (CV)0.30245808
Kurtosis-0.32946161
Mean637.24231
Median Absolute Deviation (MAD)132.65
Skewness-0.037647574
Sum16568.3
Variance37148.356
MonotonicityNot monotonic
2023-12-11T01:20:24.748471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
786.2 1
 
3.8%
645.5 1
 
3.8%
470.9 1
 
3.8%
520.9 1
 
3.8%
420.4 1
 
3.8%
400.9 1
 
3.8%
465.2 1
 
3.8%
375.2 1
 
3.8%
262.9 1
 
3.8%
472.5 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
262.9 1
3.8%
348.2 1
3.8%
375.2 1
3.8%
400.9 1
3.8%
420.4 1
3.8%
465.2 1
3.8%
470.9 1
3.8%
472.5 1
3.8%
520.9 1
3.8%
645.5 1
3.8%
ValueCountFrequency (%)
1059.85 1
3.8%
937.35 1
3.8%
823.1 1
3.8%
822.5 1
3.8%
786.2 1
3.8%
778.4 1
3.8%
768.7 1
3.8%
758.9 1
3.8%
749.27 1
3.8%
713.5 1
3.8%

4월
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean605.83115
Minimum255.7
Maximum962.03
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T01:20:24.880121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum255.7
5-th percentile353.45
Q1417.925
median634.24
Q3748.3
95-th percentile906.86
Maximum962.03
Range706.33
Interquartile range (IQR)330.375

Descriptive statistics

Standard deviation187.84707
Coefficient of variation (CV)0.31006505
Kurtosis-0.7699173
Mean605.83115
Median Absolute Deviation (MAD)145.26
Skewness-0.018644682
Sum15751.61
Variance35286.521
MonotonicityNot monotonic
2023-12-11T01:20:24.991345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
765.4 1
 
3.8%
618.7 1
 
3.8%
400.8 1
 
3.8%
407.1 1
 
3.8%
462.4 1
 
3.8%
421.3 1
 
3.8%
416.8 1
 
3.8%
344.8 1
 
3.8%
255.7 1
 
3.8%
388.7 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
255.7 1
3.8%
344.8 1
3.8%
379.4 1
3.8%
388.7 1
3.8%
400.8 1
3.8%
407.1 1
3.8%
416.8 1
3.8%
421.3 1
3.8%
462.4 1
3.8%
603.42 1
3.8%
ValueCountFrequency (%)
962.03 1
3.8%
937.88 1
3.8%
813.8 1
3.8%
779.9 1
3.8%
779.1 1
3.8%
777.1 1
3.8%
765.4 1
3.8%
697.0 1
3.8%
678.0 1
3.8%
669.9 1
3.8%

5월
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean656.37115
Minimum324.2
Maximum1075.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T01:20:25.098853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum324.2
5-th percentile372.325
Q1446.05
median696.55
Q3809.5
95-th percentile953.5275
Maximum1075.2
Range751
Interquartile range (IQR)363.45

Descriptive statistics

Standard deviation206.83859
Coefficient of variation (CV)0.31512444
Kurtosis-0.92748781
Mean656.37115
Median Absolute Deviation (MAD)158.95
Skewness0.0064849735
Sum17065.65
Variance42782.204
MonotonicityNot monotonic
2023-12-11T01:20:25.207318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
809.2 1
 
3.8%
660.9 1
 
3.8%
442.7 1
 
3.8%
469.6 1
 
3.8%
389.2 1
 
3.8%
429.5 1
 
3.8%
421.3 1
 
3.8%
456.1 1
 
3.8%
324.2 1
 
3.8%
366.7 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
324.2 1
3.8%
366.7 1
3.8%
389.2 1
3.8%
396.8 1
3.8%
421.3 1
3.8%
429.5 1
3.8%
442.7 1
3.8%
456.1 1
3.8%
469.6 1
3.8%
644.97 1
3.8%
ValueCountFrequency (%)
1075.2 1
3.8%
982.27 1
3.8%
867.3 1
3.8%
860.0 1
3.8%
851.0 1
3.8%
830.4 1
3.8%
809.6 1
3.8%
809.2 1
3.8%
763.2 1
3.8%
724.2 1
3.8%

6월
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean666.48115
Minimum346.1
Maximum1052.34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T01:20:25.315250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum346.1
5-th percentile360.225
Q1456.325
median718.4
Q3775.05
95-th percentile954.58
Maximum1052.34
Range706.24
Interquartile range (IQR)318.725

Descriptive statistics

Standard deviation202.4422
Coefficient of variation (CV)0.30374782
Kurtosis-0.93479596
Mean666.48115
Median Absolute Deviation (MAD)139.25
Skewness-0.04884284
Sum17328.51
Variance40982.845
MonotonicityNot monotonic
2023-12-11T01:20:25.424262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
827.9 1
 
3.8%
724.7 1
 
3.8%
451.7 1
 
3.8%
549.4 1
 
3.8%
453.8 1
 
3.8%
416.2 1
 
3.8%
463.9 1
 
3.8%
423.9 1
 
3.8%
346.6 1
 
3.8%
401.1 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
346.1 1
3.8%
346.6 1
3.8%
401.1 1
3.8%
416.2 1
3.8%
423.9 1
3.8%
451.7 1
3.8%
453.8 1
3.8%
463.9 1
3.8%
549.4 1
3.8%
653.84 1
3.8%
ValueCountFrequency (%)
1052.34 1
3.8%
955.94 1
3.8%
950.5 1
3.8%
926.7 1
3.8%
827.9 1
3.8%
817.3 1
3.8%
779.8 1
3.8%
760.8 1
3.8%
752.2 1
3.8%
751.8 1
3.8%

7월
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean700.21615
Minimum343.8
Maximum1060.71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T01:20:25.756128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum343.8
5-th percentile361.775
Q1466.925
median760.35
Q3851.425
95-th percentile994.4
Maximum1060.71
Range716.91
Interquartile range (IQR)384.5

Descriptive statistics

Standard deviation225.62206
Coefficient of variation (CV)0.32221773
Kurtosis-1.2425142
Mean700.21615
Median Absolute Deviation (MAD)180.85
Skewness-0.26970603
Sum18205.62
Variance50905.313
MonotonicityNot monotonic
2023-12-11T01:20:25.866698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
872.0 1
 
3.8%
746.2 1
 
3.8%
465.8 1
 
3.8%
544.6 1
 
3.8%
470.3 1
 
3.8%
360.6 1
 
3.8%
441.1 1
 
3.8%
414.4 1
 
3.8%
343.8 1
 
3.8%
405.0 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
343.8 1
3.8%
360.6 1
3.8%
365.3 1
3.8%
405.0 1
3.8%
414.4 1
3.8%
441.1 1
3.8%
465.8 1
3.8%
470.3 1
3.8%
544.6 1
3.8%
694.04 1
3.8%
ValueCountFrequency (%)
1060.71 1
3.8%
995.2 1
3.8%
992.0 1
3.8%
986.9 1
3.8%
906.3 1
3.8%
872.0 1
3.8%
856.7 1
3.8%
835.6 1
3.8%
810.6 1
3.8%
806.8 1
3.8%

8월
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean715.05692
Minimum338.2
Maximum1791.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T01:20:25.971721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum338.2
5-th percentile378.725
Q1460.425
median734.04
Q3798.5
95-th percentile1027.11
Maximum1791.8
Range1453.6
Interquartile range (IQR)338.075

Descriptive statistics

Standard deviation295.84056
Coefficient of variation (CV)0.41373009
Kurtosis6.137614
Mean715.05692
Median Absolute Deviation (MAD)144.65
Skewness1.8378912
Sum18591.48
Variance87521.639
MonotonicityNot monotonic
2023-12-11T01:20:26.079366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
800.7 1
 
3.8%
711.8 1
 
3.8%
436.5 1
 
3.8%
505.2 1
 
3.8%
445.5 1
 
3.8%
539.7 1
 
3.8%
440.6 1
 
3.8%
438.5 1
 
3.8%
390.8 1
 
3.8%
374.7 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
338.2 1
3.8%
374.7 1
3.8%
390.8 1
3.8%
436.5 1
3.8%
438.5 1
3.8%
440.6 1
3.8%
445.5 1
3.8%
505.2 1
3.8%
539.7 1
3.8%
705.06 1
3.8%
ValueCountFrequency (%)
1791.8 1
3.8%
1030.45 1
3.8%
1017.09 1
3.8%
940.6 1
3.8%
829.0 1
3.8%
817.1 1
3.8%
800.7 1
3.8%
791.9 1
3.8%
771.0 1
3.8%
766.5 1
3.8%

9월
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean689.20308
Minimum349.4
Maximum1180.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T01:20:26.190641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum349.4
5-th percentile369.275
Q1471.625
median682.03
Q3808.85
95-th percentile1146.4025
Maximum1180.7
Range831.3
Interquartile range (IQR)337.225

Descriptive statistics

Standard deviation239.80015
Coefficient of variation (CV)0.34793831
Kurtosis-0.41057023
Mean689.20308
Median Absolute Deviation (MAD)163.98
Skewness0.43405549
Sum17919.28
Variance57504.114
MonotonicityNot monotonic
2023-12-11T01:20:26.311323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
820.5 1
 
3.8%
647.9 1
 
3.8%
434.5 1
 
3.8%
537.4 1
 
3.8%
498.7 1
 
3.8%
462.6 1
 
3.8%
365.5 1
 
3.8%
403.0 1
 
3.8%
349.4 1
 
3.8%
432.6 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
349.4 1
3.8%
365.5 1
3.8%
380.6 1
3.8%
403.0 1
3.8%
432.6 1
3.8%
434.5 1
3.8%
462.6 1
3.8%
498.7 1
3.8%
537.4 1
3.8%
647.9 1
3.8%
ValueCountFrequency (%)
1180.7 1
3.8%
1174.61 1
3.8%
1061.78 1
3.8%
962.0 1
3.8%
886.8 1
3.8%
820.5 1
3.8%
811.3 1
3.8%
801.5 1
3.8%
769.4 1
3.8%
764.2 1
3.8%

10월
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean661.14154
Minimum331.3
Maximum1004.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T01:20:26.434964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum331.3
5-th percentile366
Q1462.475
median671.55
Q3824.875
95-th percentile908.1225
Maximum1004.96
Range673.66
Interquartile range (IQR)362.4

Descriptive statistics

Standard deviation195.25214
Coefficient of variation (CV)0.29532578
Kurtosis-1.1647631
Mean661.14154
Median Absolute Deviation (MAD)184.15
Skewness-0.1700864
Sum17189.68
Variance38123.398
MonotonicityNot monotonic
2023-12-11T01:20:26.585819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
783.4 1
 
3.8%
650.3 1
 
3.8%
395.4 1
 
3.8%
477.4 1
 
3.8%
457.5 1
 
3.8%
440.2 1
 
3.8%
565.7 1
 
3.8%
454.6 1
 
3.8%
356.2 1
 
3.8%
412.6 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
331.3 1
3.8%
356.2 1
3.8%
395.4 1
3.8%
412.6 1
3.8%
440.2 1
3.8%
454.6 1
3.8%
457.5 1
3.8%
477.4 1
3.8%
565.7 1
3.8%
610.68 1
3.8%
ValueCountFrequency (%)
1004.96 1
3.8%
916.23 1
3.8%
883.8 1
3.8%
875.9 1
3.8%
865.7 1
3.8%
845.7 1
3.8%
838.7 1
3.8%
783.4 1
3.8%
782.4 1
3.8%
758.8 1
3.8%

11월
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean626.60231
Minimum276
Maximum979.04
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T01:20:26.766428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum276
5-th percentile316.825
Q1401.9
median693.175
Q3764.85
95-th percentile906.725
Maximum979.04
Range703.04
Interquartile range (IQR)362.95

Descriptive statistics

Standard deviation206.8304
Coefficient of variation (CV)0.33008241
Kurtosis-1.1440851
Mean626.60231
Median Absolute Deviation (MAD)124.125
Skewness-0.30189855
Sum16291.66
Variance42778.813
MonotonicityNot monotonic
2023-12-11T01:20:26.922102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
772.2 1
 
3.8%
670.2 1
 
3.8%
371.5 1
 
3.8%
474.8 1
 
3.8%
391.9 1
 
3.8%
348.4 1
 
3.8%
431.9 1
 
3.8%
365.3 1
 
3.8%
369.1 1
 
3.8%
276.0 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
276.0 1
3.8%
306.3 1
3.8%
348.4 1
3.8%
365.3 1
3.8%
369.1 1
3.8%
371.5 1
3.8%
391.9 1
3.8%
431.9 1
3.8%
474.8 1
3.8%
664.07 1
3.8%
ValueCountFrequency (%)
979.04 1
3.8%
911.7 1
3.8%
891.8 1
3.8%
831.6 1
3.8%
803.0 1
3.8%
773.3 1
3.8%
772.2 1
3.8%
742.8 1
3.8%
727.7 1
3.8%
713.3 1
3.8%

12월
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean644.275
Minimum234.6
Maximum971.81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T01:20:27.090052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum234.6
5-th percentile319.85
Q1408.025
median745.95
Q3782.925
95-th percentile915.055
Maximum971.81
Range737.21
Interquartile range (IQR)374.9

Descriptive statistics

Standard deviation219.26289
Coefficient of variation (CV)0.340325
Kurtosis-1.2488347
Mean644.275
Median Absolute Deviation (MAD)93.55
Skewness-0.48118762
Sum16751.15
Variance48076.214
MonotonicityNot monotonic
2023-12-11T01:20:27.241107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
789.8 1
 
3.8%
717.4 1
 
3.8%
307.1 1
 
3.8%
420.1 1
 
3.8%
420.3 1
 
3.8%
358.1 1
 
3.8%
373.1 1
 
3.8%
404.0 1
 
3.8%
373.3 1
 
3.8%
234.6 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
234.6 1
3.8%
307.1 1
3.8%
358.1 1
3.8%
373.1 1
3.8%
373.3 1
3.8%
393.9 1
3.8%
404.0 1
3.8%
420.1 1
3.8%
420.3 1
3.8%
671.03 1
3.8%
ValueCountFrequency (%)
971.81 1
3.8%
921.2 1
3.8%
896.62 1
3.8%
848.1 1
3.8%
830.9 1
3.8%
809.2 1
3.8%
789.8 1
3.8%
762.3 1
3.8%
760.1 1
3.8%
758.7 1
3.8%

Interactions

2023-12-11T01:20:22.328029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:05.250923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:06.729648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:08.088302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:09.876785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:11.240981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:12.471724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:13.908920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:15.635323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:16.959867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:18.311792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:19.601632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:21.128768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:22.437034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:05.339214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:06.831655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:08.198279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:09.986836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:11.360879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:12.576739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:14.017951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:15.735251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:17.075644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:18.432941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:19.709890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:21.238908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:22.547598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:05.425170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:06.916550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:08.309356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:10.081407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:11.465240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:12.713561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:14.126340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:15.830304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:17.183404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:18.543914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:19.797496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:21.348506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:22.642719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:05.530896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:07.023049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:08.400721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:10.179050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:11.565236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:12.838495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:14.226491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:15.916829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:17.287472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:18.618045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:19.893773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:21.432909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:22.736234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:05.625636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:07.135274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:08.523793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:10.288697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:11.669856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:12.942235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:14.313090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:16.021666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:17.384483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:18.704347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:19.995686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:21.539977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:22.817329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:05.720322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:07.226213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:08.646919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:10.388646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:11.753019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:13.057519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:14.418307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:16.144979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:17.483203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:18.794236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:20.082815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:21.621664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:22.901110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:05.837277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:07.335760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:08.773671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:10.490082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:11.839965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:13.164471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:14.514418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:16.266984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:17.571833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:18.888719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:20.175599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:21.712125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:22.977123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:05.938242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:07.447196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:08.897070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:10.592999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:11.927086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:13.260662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:14.612802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:16.364622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:17.675888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:18.981083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:20.276365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:21.790677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:23.056571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:06.057657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:07.544255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:09.007637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:10.724931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:12.010184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:13.352336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:14.733714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:16.486342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:17.774517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:19.080183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:20.381091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:21.874859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:23.139623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:06.187746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:07.665755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:09.123741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:10.829198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:12.096692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:13.469113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:14.857271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:16.589560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:17.865938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:19.198888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:20.476772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:21.971074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:23.216331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:06.295051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:07.746752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:09.215762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:10.919590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:12.176547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:13.592238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:14.948451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:16.671476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:17.949841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:19.281187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:20.554664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:22.056493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:23.298649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:06.438428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:07.849524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:09.664046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:11.020699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:12.283956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:13.700108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:15.401469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:16.755523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:18.056495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:19.387534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:20.643387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:22.139166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:23.385910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:06.612212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:07.954584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:09.767150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:11.132030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:12.376227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:13.815883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:15.521797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:16.847323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:18.171314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:19.491917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:20.724245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:20:22.239120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:20:27.359578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분년도1월2월3월4월5월6월7월8월9월10월11월12월
구분1.0000.0000.7910.8790.9971.0001.0000.9270.9970.9890.9420.9390.9300.795
년도0.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
1월0.7910.0001.0000.7380.5180.9220.6850.7380.7710.7270.7240.3500.8280.505
2월0.8790.0000.7381.0000.6730.6180.9100.8450.7120.7610.7040.7200.9310.806
3월0.9970.0000.5180.6731.0000.7430.8460.8590.8850.8080.8140.9040.8210.716
4월1.0000.0000.9220.6180.7431.0000.8290.6830.8280.7530.6800.6370.8430.582
5월1.0000.0000.6850.9100.8460.8291.0000.8870.6470.7640.7910.7590.9140.792
6월0.9270.0000.7380.8450.8590.6830.8871.0000.8520.8260.9100.8810.7880.829
7월0.9970.0000.7710.7120.8850.8280.6470.8521.0000.9800.8630.7240.8570.726
8월0.9890.0000.7270.7610.8080.7530.7640.8260.9801.0000.9170.7560.8750.654
9월0.9420.0000.7240.7040.8140.6800.7910.9100.8630.9171.0000.9160.8220.612
10월0.9390.0000.3500.7200.9040.6370.7590.8810.7240.7560.9161.0000.8590.731
11월0.9300.0000.8280.9310.8210.8430.9140.7880.8570.8750.8220.8591.0000.749
12월0.7950.0000.5050.8060.7160.5820.7920.8290.7260.6540.6120.7310.7491.000
2023-12-11T01:20:27.527081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도1월2월3월4월5월6월7월8월9월10월11월12월구분
년도1.0000.2000.0760.1780.1300.1500.0780.1460.1000.1120.0910.134-0.0140.000
1월0.2001.0000.8660.8880.9050.9040.8990.8970.8670.8760.9080.8890.8370.638
2월0.0760.8661.0000.8890.9500.9430.9430.9450.9730.9560.9380.9410.9450.721
3월0.1780.8880.8891.0000.9450.9240.9300.9410.9080.9230.9340.9340.8780.770
4월0.1300.9050.9500.9451.0000.9300.9460.9690.9580.9580.9560.9390.9280.885
5월0.1500.9040.9430.9240.9301.0000.9200.9540.9420.9390.9590.9340.8910.885
6월0.0780.8990.9430.9300.9460.9201.0000.9460.9410.9370.9510.9640.9290.820
7월0.1460.8970.9450.9410.9690.9540.9461.0000.9320.9480.9620.9540.9320.798
8월0.1000.8670.9730.9080.9580.9420.9410.9321.0000.9620.9430.9410.9330.812
9월0.1120.8760.9560.9230.9580.9390.9370.9480.9621.0000.9320.9250.9200.774
10월0.0910.9080.9380.9340.9560.9590.9510.9620.9430.9321.0000.9680.9290.770
11월0.1340.8890.9410.9340.9390.9340.9640.9540.9410.9250.9681.0000.9530.825
12월-0.0140.8370.9450.8780.9280.8910.9290.9320.9330.9200.9290.9531.0000.634
구분0.0000.6380.7210.7700.8850.8850.8200.7980.8120.7740.7700.8250.6341.000

Missing values

2023-12-11T01:20:23.507043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:20:23.669420image/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

구분년도1월2월3월4월5월6월7월8월9월10월11월12월
0일반쓰레기2015731.7730.6786.2765.4809.2827.9872.0800.7820.5783.4772.2789.8
1일반쓰레기2016713.8811.7778.4779.9830.4950.5986.91791.8962.0875.9891.8921.2
2일반쓰레기2017710.9712.6758.9697.0851.0779.8835.6771.0801.5845.7727.7750.4
3일반쓰레기2018765.2730.3822.5777.1809.6817.3856.7791.9769.4838.7773.3762.3
4일반쓰레기2019752.1739.8768.7779.1860.0743.5906.3829.0886.8883.8803.0830.9
5일반쓰레기2020896.8751.5823.1813.8867.3926.7992.0940.61180.7865.7831.6809.2
6일반쓰레기2021881.46860.721059.85962.031075.21052.341060.711017.091061.781004.96979.04971.81
7일반쓰레기2022922.37841.08937.35937.88982.27955.94995.21030.451174.61916.23911.7896.62
8음식물쓰레기2014724.6707.7669.8678.0709.4752.2801.1766.5811.3746.9693.1758.7
9음식물쓰레기2015712.2697.4713.5669.9691.3760.8785.4765.6751.9758.8742.8848.1
구분년도1월2월3월4월5월6월7월8월9월10월11월12월
16음식물쓰레기2022672.78594.19749.27621.58667.61653.84694.04736.88659.13610.68664.07671.03
17재활용품2014364.4360.5348.2379.4396.8346.1365.3338.2380.6331.3306.3393.9
18재활용품2015367.3353.0472.5388.7366.7401.1405.0374.7432.6412.6276.0234.6
19재활용품2016229.6271.7262.9255.7324.2346.6343.8390.8349.4356.2369.1373.3
20재활용품2017403.2420.9375.2344.8456.1423.9414.4438.5403.0454.6365.3404.0
21재활용품2018423.8388.9465.2416.8421.3463.9441.1440.6365.5565.7431.9373.1
22재활용품2019466.2417.7400.9421.3429.5416.2360.6539.7462.6440.2348.4358.1
23재활용품2020393.1459.8420.4462.4389.2453.8470.3445.5498.7457.5391.9420.3
24재활용품2021430.5384.6520.9407.1469.6549.4544.6505.2537.4477.4474.8420.1
25재활용품2022444.2394.2470.9400.8442.7451.7465.8436.5434.5395.4371.5307.1