Overview

Dataset statistics

Number of variables14
Number of observations29
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.7 KiB
Average record size in memory129.4 B

Variable types

Categorical1
Numeric13

Dataset

Description5년간 영도구의 월별 생활쓰레기 배출량에 대한 데이터로 일반쓰레기, 음식물쓰레기, 재활용품 배출량에 대한 항목을 제공합니다.
Author부산광역시 영도구
URLhttps://www.data.go.kr/data/15053276/fileData.do

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 started2024-03-14 20:40:44.701967
Analysis finished2024-03-14 20:41:27.255721
Duration42.55 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Memory size360.0 B
음식물쓰레기
10 
재활용품
10 
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 10
34.5%
재활용품 10
34.5%
일반쓰레기 9
31.0%

Length

2024-03-15T05:41:27.489112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T05:41:27.910957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
음식물쓰레기 10
34.5%
재활용품 10
34.5%
일반쓰레기 9
31.0%

년도
Real number (ℝ)

Distinct10
Distinct (%)34.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.6552
Minimum2014
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size389.0 B
2024-03-15T05:41:28.224450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2014
5-th percentile2014.4
Q12016
median2019
Q32021
95-th percentile2023
Maximum2023
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8444917
Coefficient of variation (CV)0.0014091023
Kurtosis-1.1923214
Mean2018.6552
Median Absolute Deviation (MAD)2
Skewness-0.024079223
Sum58541
Variance8.091133
MonotonicityNot monotonic
2024-03-15T05:41:28.675798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2015 3
10.3%
2016 3
10.3%
2017 3
10.3%
2018 3
10.3%
2019 3
10.3%
2020 3
10.3%
2021 3
10.3%
2022 3
10.3%
2023 3
10.3%
2014 2
6.9%
ValueCountFrequency (%)
2014 2
6.9%
2015 3
10.3%
2016 3
10.3%
2017 3
10.3%
2018 3
10.3%
2019 3
10.3%
2020 3
10.3%
2021 3
10.3%
2022 3
10.3%
2023 3
10.3%
ValueCountFrequency (%)
2023 3
10.3%
2022 3
10.3%
2021 3
10.3%
2020 3
10.3%
2019 3
10.3%
2018 3
10.3%
2017 3
10.3%
2016 3
10.3%
2015 3
10.3%
2014 2
6.9%

1월
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean618.91517
Minimum229.6
Maximum922.37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size389.0 B
2024-03-15T05:41:28.974569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum229.6
5-th percentile320.12
Q1430.5
median694.8
Q3731.7
95-th percentile890.664
Maximum922.37
Range692.77
Interquartile range (IQR)301.2

Descriptive statistics

Standard deviation192.52303
Coefficient of variation (CV)0.31106529
Kurtosis-0.9318795
Mean618.91517
Median Absolute Deviation (MAD)99.98
Skewness-0.41917612
Sum17948.54
Variance37065.117
MonotonicityNot monotonic
2024-03-15T05:41:29.337123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
731.7 1
 
3.4%
713.8 1
 
3.4%
290.6 1
 
3.4%
444.2 1
 
3.4%
430.5 1
 
3.4%
393.1 1
 
3.4%
466.2 1
 
3.4%
423.8 1
 
3.4%
403.2 1
 
3.4%
229.6 1
 
3.4%
Other values (19) 19
65.5%
ValueCountFrequency (%)
229.6 1
3.4%
290.6 1
3.4%
364.4 1
3.4%
367.3 1
3.4%
393.1 1
3.4%
403.2 1
3.4%
423.8 1
3.4%
430.5 1
3.4%
444.2 1
3.4%
466.2 1
3.4%
ValueCountFrequency (%)
922.37 1
3.4%
896.8 1
3.4%
881.46 1
3.4%
839.98 1
3.4%
765.2 1
3.4%
752.1 1
3.4%
734.9 1
3.4%
731.7 1
3.4%
727.1 1
3.4%
724.6 1
3.4%

2월
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean591.82724
Minimum264.3
Maximum860.72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size389.0 B
2024-03-15T05:41:29.713807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum264.3
5-th percentile304.22
Q1417.7
median632.5
Q3730.6
95-th percentile829.328
Maximum860.72
Range596.42
Interquartile range (IQR)312.9

Descriptive statistics

Standard deviation178.77377
Coefficient of variation (CV)0.30207087
Kurtosis-1.1655892
Mean591.82724
Median Absolute Deviation (MAD)119
Skewness-0.40099195
Sum17162.99
Variance31960.061
MonotonicityNot monotonic
2024-03-15T05:41:30.088029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
730.6 1
 
3.4%
811.7 1
 
3.4%
264.3 1
 
3.4%
394.2 1
 
3.4%
384.6 1
 
3.4%
459.8 1
 
3.4%
417.7 1
 
3.4%
388.9 1
 
3.4%
420.9 1
 
3.4%
271.7 1
 
3.4%
Other values (19) 19
65.5%
ValueCountFrequency (%)
264.3 1
3.4%
271.7 1
3.4%
353.0 1
3.4%
360.5 1
3.4%
384.6 1
3.4%
388.9 1
3.4%
394.2 1
3.4%
417.7 1
3.4%
420.9 1
3.4%
459.8 1
3.4%
ValueCountFrequency (%)
860.72 1
3.4%
841.08 1
3.4%
811.7 1
3.4%
755.75 1
3.4%
751.5 1
3.4%
742.6 1
3.4%
739.8 1
3.4%
730.6 1
3.4%
730.3 1
3.4%
712.6 1
3.4%

3월
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean631.20345
Minimum262.9
Maximum1059.85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size389.0 B
2024-03-15T05:41:30.462791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum262.9
5-th percentile309.86
Q1470.9
median668.6
Q3768.7
95-th percentile898.678
Maximum1059.85
Range796.95
Interquartile range (IQR)297.8

Descriptive statistics

Standard deviation197.68828
Coefficient of variation (CV)0.31319266
Kurtosis-0.46197147
Mean631.20345
Median Absolute Deviation (MAD)147.7
Skewness-0.10900359
Sum18304.9
Variance39080.658
MonotonicityNot monotonic
2024-03-15T05:41:30.859326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
786.2 1
 
3.4%
778.4 1
 
3.4%
284.3 1
 
3.4%
470.9 1
 
3.4%
520.9 1
 
3.4%
420.4 1
 
3.4%
400.9 1
 
3.4%
465.2 1
 
3.4%
375.2 1
 
3.4%
262.9 1
 
3.4%
Other values (19) 19
65.5%
ValueCountFrequency (%)
262.9 1
3.4%
284.3 1
3.4%
348.2 1
3.4%
375.2 1
3.4%
400.9 1
3.4%
420.4 1
3.4%
465.2 1
3.4%
470.9 1
3.4%
472.5 1
3.4%
520.9 1
3.4%
ValueCountFrequency (%)
1059.85 1
3.4%
937.35 1
3.4%
840.67 1
3.4%
823.1 1
3.4%
822.5 1
3.4%
786.2 1
3.4%
778.4 1
3.4%
768.7 1
3.4%
758.9 1
3.4%
749.27 1
3.4%

4월
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean596.28172
Minimum255.7
Maximum962.03
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size389.0 B
2024-03-15T05:41:31.224919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum255.7
5-th percentile293.32
Q1416.8
median621.58
Q3748.9
95-th percentile888.248
Maximum962.03
Range706.33
Interquartile range (IQR)332.1

Descriptive statistics

Standard deviation191.389
Coefficient of variation (CV)0.32097077
Kurtosis-0.77172437
Mean596.28172
Median Absolute Deviation (MAD)157.52
Skewness-0.069099183
Sum17292.17
Variance36629.751
MonotonicityNot monotonic
2024-03-15T05:41:31.620988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
765.4 1
 
3.4%
779.9 1
 
3.4%
259.0 1
 
3.4%
400.8 1
 
3.4%
407.1 1
 
3.4%
462.4 1
 
3.4%
421.3 1
 
3.4%
416.8 1
 
3.4%
344.8 1
 
3.4%
255.7 1
 
3.4%
Other values (19) 19
65.5%
ValueCountFrequency (%)
255.7 1
3.4%
259.0 1
3.4%
344.8 1
3.4%
379.4 1
3.4%
388.7 1
3.4%
400.8 1
3.4%
407.1 1
3.4%
416.8 1
3.4%
421.3 1
3.4%
462.4 1
3.4%
ValueCountFrequency (%)
962.03 1
3.4%
937.88 1
3.4%
813.8 1
3.4%
779.9 1
3.4%
779.1 1
3.4%
777.1 1
3.4%
765.4 1
3.4%
748.9 1
3.4%
697.0 1
3.4%
678.0 1
3.4%

5월
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean651.78621
Minimum305.9
Maximum1075.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size389.0 B
2024-03-15T05:41:32.075116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum305.9
5-th percentile341.2
Q1442.7
median691.3
Q3809.6
95-th percentile937.722
Maximum1075.2
Range769.3
Interquartile range (IQR)366.9

Descriptive statistics

Standard deviation210.25554
Coefficient of variation (CV)0.3225836
Kurtosis-0.95826576
Mean651.78621
Median Absolute Deviation (MAD)168.7
Skewness-0.061171373
Sum18901.8
Variance44207.393
MonotonicityNot monotonic
2024-03-15T05:41:32.486819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
809.2 1
 
3.4%
830.4 1
 
3.4%
305.9 1
 
3.4%
442.7 1
 
3.4%
469.6 1
 
3.4%
389.2 1
 
3.4%
429.5 1
 
3.4%
421.3 1
 
3.4%
456.1 1
 
3.4%
324.2 1
 
3.4%
Other values (19) 19
65.5%
ValueCountFrequency (%)
305.9 1
3.4%
324.2 1
3.4%
366.7 1
3.4%
389.2 1
3.4%
396.8 1
3.4%
421.3 1
3.4%
429.5 1
3.4%
442.7 1
3.4%
456.1 1
3.4%
469.6 1
3.4%
ValueCountFrequency (%)
1075.2 1
3.4%
982.27 1
3.4%
870.9 1
3.4%
867.3 1
3.4%
860.0 1
3.4%
851.0 1
3.4%
830.4 1
3.4%
809.6 1
3.4%
809.2 1
3.4%
763.2 1
3.4%

6월
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean659.0969
Minimum285.6
Maximum1052.34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size389.0 B
2024-03-15T05:41:33.101328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum285.6
5-th percentile346.3
Q1453.8
median715.9
Q3779.8
95-th percentile953.764
Maximum1052.34
Range766.74
Interquartile range (IQR)326

Descriptive statistics

Standard deviation207.26436
Coefficient of variation (CV)0.31446721
Kurtosis-0.89942794
Mean659.0969
Median Absolute Deviation (MAD)136.12
Skewness-0.13600228
Sum19113.81
Variance42958.515
MonotonicityNot monotonic
2024-03-15T05:41:33.501408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
827.9 1
 
3.4%
950.5 1
 
3.4%
285.6 1
 
3.4%
451.7 1
 
3.4%
549.4 1
 
3.4%
453.8 1
 
3.4%
416.2 1
 
3.4%
463.9 1
 
3.4%
423.9 1
 
3.4%
346.6 1
 
3.4%
Other values (19) 19
65.5%
ValueCountFrequency (%)
285.6 1
3.4%
346.1 1
3.4%
346.6 1
3.4%
401.1 1
3.4%
416.2 1
3.4%
423.9 1
3.4%
451.7 1
3.4%
453.8 1
3.4%
463.9 1
3.4%
549.4 1
3.4%
ValueCountFrequency (%)
1052.34 1
3.4%
955.94 1
3.4%
950.5 1
3.4%
926.7 1
3.4%
852.02 1
3.4%
827.9 1
3.4%
817.3 1
3.4%
779.8 1
3.4%
760.8 1
3.4%
752.2 1
3.4%

7월
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean692.49931
Minimum286.4
Maximum1060.71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size389.0 B
2024-03-15T05:41:33.775772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum286.4
5-th percentile350.52
Q1465.8
median756.2
Q3856.7
95-th percentile993.92
Maximum1060.71
Range774.31
Interquartile range (IQR)390.9

Descriptive statistics

Standard deviation230.6515
Coefficient of variation (CV)0.33307109
Kurtosis-1.1969751
Mean692.49931
Median Absolute Deviation (MAD)160.27
Skewness-0.30559354
Sum20082.48
Variance53200.116
MonotonicityNot monotonic
2024-03-15T05:41:34.096391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
872.0 1
 
3.4%
986.9 1
 
3.4%
286.4 1
 
3.4%
465.8 1
 
3.4%
544.6 1
 
3.4%
470.3 1
 
3.4%
360.6 1
 
3.4%
441.1 1
 
3.4%
414.4 1
 
3.4%
343.8 1
 
3.4%
Other values (19) 19
65.5%
ValueCountFrequency (%)
286.4 1
3.4%
343.8 1
3.4%
360.6 1
3.4%
365.3 1
3.4%
405.0 1
3.4%
414.4 1
3.4%
441.1 1
3.4%
465.8 1
3.4%
470.3 1
3.4%
544.6 1
3.4%
ValueCountFrequency (%)
1060.71 1
3.4%
995.2 1
3.4%
992.0 1
3.4%
986.9 1
3.4%
916.47 1
3.4%
906.3 1
3.4%
872.0 1
3.4%
856.7 1
3.4%
835.6 1
3.4%
810.6 1
3.4%

8월
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean703.65517
Minimum338.2
Maximum1791.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size389.0 B
2024-03-15T05:41:34.385323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum338.2
5-th percentile369
Q1445.5
median731.2
Q3800.7
95-th percentile1025.106
Maximum1791.8
Range1453.6
Interquartile range (IQR)355.2

Descriptive statistics

Standard deviation288.29453
Coefficient of variation (CV)0.40970996
Kurtosis6.2662101
Mean703.65517
Median Absolute Deviation (MAD)107.36
Skewness1.8179668
Sum20406
Variance83113.737
MonotonicityNot monotonic
2024-03-15T05:41:34.609581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
800.7 1
 
3.4%
1791.8 1
 
3.4%
365.2 1
 
3.4%
436.5 1
 
3.4%
505.2 1
 
3.4%
445.5 1
 
3.4%
539.7 1
 
3.4%
440.6 1
 
3.4%
438.5 1
 
3.4%
390.8 1
 
3.4%
Other values (19) 19
65.5%
ValueCountFrequency (%)
338.2 1
3.4%
365.2 1
3.4%
374.7 1
3.4%
390.8 1
3.4%
436.5 1
3.4%
438.5 1
3.4%
440.6 1
3.4%
445.5 1
3.4%
505.2 1
3.4%
539.7 1
3.4%
ValueCountFrequency (%)
1791.8 1
3.4%
1030.45 1
3.4%
1017.09 1
3.4%
940.6 1
3.4%
829.0 1
3.4%
825.48 1
3.4%
817.1 1
3.4%
800.7 1
3.4%
791.9 1
3.4%
771.0 1
3.4%

9월
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean675.69517
Minimum282.2
Maximum1180.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size389.0 B
2024-03-15T05:41:34.845435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum282.2
5-th percentile355.84
Q1462.6
median667.9
Q3811.3
95-th percentile1129.478
Maximum1180.7
Range898.5
Interquartile range (IQR)348.7

Descriptive statistics

Standard deviation240.87969
Coefficient of variation (CV)0.35649165
Kurtosis-0.35884886
Mean675.69517
Median Absolute Deviation (MAD)152.6
Skewness0.37661577
Sum19595.16
Variance58023.024
MonotonicityNot monotonic
2024-03-15T05:41:35.159756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
820.5 1
 
3.4%
962.0 1
 
3.4%
282.2 1
 
3.4%
434.5 1
 
3.4%
537.4 1
 
3.4%
498.7 1
 
3.4%
462.6 1
 
3.4%
365.5 1
 
3.4%
403.0 1
 
3.4%
349.4 1
 
3.4%
Other values (19) 19
65.5%
ValueCountFrequency (%)
282.2 1
3.4%
349.4 1
3.4%
365.5 1
3.4%
380.6 1
3.4%
403.0 1
3.4%
432.6 1
3.4%
434.5 1
3.4%
462.6 1
3.4%
498.7 1
3.4%
537.4 1
3.4%
ValueCountFrequency (%)
1180.7 1
3.4%
1174.61 1
3.4%
1061.78 1
3.4%
962.0 1
3.4%
886.8 1
3.4%
820.5 1
3.4%
812.09 1
3.4%
811.3 1
3.4%
801.5 1
3.4%
769.4 1
3.4%

10월
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean657.83414
Minimum331.3
Maximum1004.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size389.0 B
2024-03-15T05:41:35.439743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum331.3
5-th percentile360.04
Q1457.5
median664.8
Q3838.7
95-th percentile903.258
Maximum1004.96
Range673.66
Interquartile range (IQR)381.2

Descriptive statistics

Standard deviation197.12344
Coefficient of variation (CV)0.29965523
Kurtosis-1.2066626
Mean657.83414
Median Absolute Deviation (MAD)187.4
Skewness-0.16918459
Sum19077.19
Variance38857.649
MonotonicityNot monotonic
2024-03-15T05:41:35.726357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
783.4 1
 
3.4%
875.9 1
 
3.4%
365.8 1
 
3.4%
395.4 1
 
3.4%
477.4 1
 
3.4%
457.5 1
 
3.4%
440.2 1
 
3.4%
565.7 1
 
3.4%
454.6 1
 
3.4%
356.2 1
 
3.4%
Other values (19) 19
65.5%
ValueCountFrequency (%)
331.3 1
3.4%
356.2 1
3.4%
365.8 1
3.4%
395.4 1
3.4%
412.6 1
3.4%
440.2 1
3.4%
454.6 1
3.4%
457.5 1
3.4%
477.4 1
3.4%
565.7 1
3.4%
ValueCountFrequency (%)
1004.96 1
3.4%
916.23 1
3.4%
883.8 1
3.4%
879.46 1
3.4%
875.9 1
3.4%
865.7 1
3.4%
845.7 1
3.4%
838.7 1
3.4%
783.4 1
3.4%
782.4 1
3.4%

11월
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean619.19
Minimum276
Maximum979.04
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size389.0 B
2024-03-15T05:41:36.010501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum276
5-th percentile299.04
Q1391.9
median693.1
Q3769.55
95-th percentile903.74
Maximum979.04
Range703.04
Interquartile range (IQR)377.65

Descriptive statistics

Standard deviation206.97245
Coefficient of variation (CV)0.33426324
Kurtosis-1.1499661
Mean619.19
Median Absolute Deviation (MAD)109.9
Skewness-0.29758639
Sum17956.51
Variance42837.597
MonotonicityNot monotonic
2024-03-15T05:41:36.230291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
772.2 1
 
3.4%
891.8 1
 
3.4%
294.2 1
 
3.4%
371.5 1
 
3.4%
474.8 1
 
3.4%
391.9 1
 
3.4%
348.4 1
 
3.4%
431.9 1
 
3.4%
365.3 1
 
3.4%
369.1 1
 
3.4%
Other values (19) 19
65.5%
ValueCountFrequency (%)
276.0 1
3.4%
294.2 1
3.4%
306.3 1
3.4%
348.4 1
3.4%
365.3 1
3.4%
369.1 1
3.4%
371.5 1
3.4%
391.9 1
3.4%
431.9 1
3.4%
474.8 1
3.4%
ValueCountFrequency (%)
979.04 1
3.4%
911.7 1
3.4%
891.8 1
3.4%
831.6 1
3.4%
803.0 1
3.4%
773.3 1
3.4%
772.2 1
3.4%
769.55 1
3.4%
742.8 1
3.4%
727.7 1
3.4%

12월
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean633.90655
Minimum233.8
Maximum971.81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size389.0 B
2024-03-15T05:41:36.571954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum233.8
5-th percentile263.6
Q1404
median743.2
Q3770.36
95-th percentile911.368
Maximum971.81
Range738.01
Interquartile range (IQR)366.36

Descriptive statistics

Standard deviation222.28042
Coefficient of variation (CV)0.35065172
Kurtosis-1.1840105
Mean633.90655
Median Absolute Deviation (MAD)104.9
Skewness-0.49300883
Sum18383.29
Variance49408.585
MonotonicityNot monotonic
2024-03-15T05:41:36.810127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
789.8 1
 
3.4%
921.2 1
 
3.4%
233.8 1
 
3.4%
307.1 1
 
3.4%
420.1 1
 
3.4%
420.3 1
 
3.4%
358.1 1
 
3.4%
373.1 1
 
3.4%
404.0 1
 
3.4%
373.3 1
 
3.4%
Other values (19) 19
65.5%
ValueCountFrequency (%)
233.8 1
3.4%
234.6 1
3.4%
307.1 1
3.4%
358.1 1
3.4%
373.1 1
3.4%
373.3 1
3.4%
393.9 1
3.4%
404.0 1
3.4%
420.1 1
3.4%
420.3 1
3.4%
ValueCountFrequency (%)
971.81 1
3.4%
921.2 1
3.4%
896.62 1
3.4%
848.1 1
3.4%
830.9 1
3.4%
809.2 1
3.4%
789.8 1
3.4%
770.36 1
3.4%
762.3 1
3.4%
760.1 1
3.4%

Interactions

2024-03-15T05:41:23.417054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:45.614098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:48.988891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:51.761701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:54.640920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:57.712378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:00.371780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:03.493231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:08.230116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:11.731146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:14.521554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:17.968238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:20.658716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:23.571348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:45.843974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:49.237668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:51.996273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:54.847106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:57.998440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:00.621304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:03.635416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:08.474421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:11.872596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:14.767004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:18.240947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:20.820281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:23.742435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:46.095120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:49.493534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:52.248053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:55.054314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:58.258670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:00.884570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:03.879436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:08.733319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:12.031400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:15.025336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:18.503885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:21.081946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:23.889585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:46.333454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:49.737758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:52.486843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:55.199321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:58.444248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:01.137563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:04.301826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:08.976393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:12.246691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:15.253067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:18.782048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:21.448677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:24.051457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:46.581634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:50.009108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:52.740704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:55.360504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:58.595961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:01.401827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:04.695343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:09.243942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:12.481259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:15.509234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:19.049129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:21.617034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:24.242309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:46.824459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:50.164737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:52.983797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:55.604608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:58.744481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:01.658526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:05.163013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:09.492718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:12.681616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:15.732348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:19.290802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:21.768081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:24.521610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:47.078055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:50.331121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:53.224414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:55.884684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:58.905899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:01.967592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:05.632875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:09.952096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:12.891468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:15.974888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:19.459270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:21.935827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:24.780210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:47.406770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:50.488037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:53.379038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:56.145654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:59.090589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:02.231159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:06.057760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:10.221480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:13.051396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:16.257231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:19.631051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:22.176985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:25.040818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:47.767314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:50.649257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:53.618104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:56.406075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:59.244655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:02.489252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:06.395428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:10.526966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:13.275740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:16.529103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:19.846101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:22.334872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:25.300992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:48.007417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:50.808723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:53.770700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:56.618008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:59.480602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:02.757363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:06.758944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:10.784185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:13.474965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:16.790663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:20.004348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:22.586464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:25.545237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:48.231784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:50.976309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:53.900958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:56.796399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:59.712801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:02.916567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:07.065166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:11.019747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:13.674668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:17.021305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:20.150071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:22.743965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:25.812451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:48.384745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:51.240983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:54.144284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:57.067886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:59.903681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:03.146251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:07.513309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:11.282218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:13.977566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:17.314202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:20.324099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:22.910725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:26.073567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:48.636335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:51.500609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:54.367387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:57.534202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:00.112284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:03.325794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:07.837857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:11.565135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:14.235617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:17.682046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:20.485278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:41:23.179135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T05:41:37.014931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분년도1월2월3월4월5월6월7월8월9월10월11월12월
구분1.0000.0000.9540.9820.9461.0001.0000.8871.0000.9820.8870.9460.9980.812
년도0.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
1월0.9540.0001.0000.9000.6940.9450.6240.7300.7650.7570.5920.3400.8740.635
2월0.9820.0000.9001.0000.6710.8650.9100.7350.9390.7570.7520.7040.9130.749
3월0.9460.0000.6940.6711.0000.8020.9800.8130.9040.7460.7500.9600.8370.755
4월1.0000.0000.9450.8650.8021.0000.8220.7450.7510.8220.8470.5810.9520.786
5월1.0000.0000.6240.9100.9800.8221.0000.8170.9600.8220.7960.8620.9090.690
6월0.8870.0000.7300.7350.8130.7450.8171.0000.8380.8080.9200.9400.7460.645
7월1.0000.0000.7650.9390.9040.7510.9600.8381.0000.8640.7640.8430.8930.701
8월0.9820.0000.7570.7570.7460.8220.8220.8080.8641.0000.9400.7200.9400.714
9월0.8870.0000.5920.7520.7500.8470.7960.9200.7640.9401.0000.8400.8390.838
10월0.9460.0000.3400.7040.9600.5810.8620.9400.8430.7200.8401.0000.7910.687
11월0.9980.0000.8740.9130.8370.9520.9090.7460.8930.9400.8390.7911.0000.690
12월0.8120.0000.6350.7490.7550.7860.6900.6450.7010.7140.8380.6870.6901.000
2024-03-15T05:41:37.297681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도1월2월3월4월5월6월7월8월9월10월11월12월구분
년도1.0000.1210.0130.1070.0330.0800.0100.0590.0330.0170.0540.037-0.0860.000
1월0.1211.0000.8970.9170.9240.9290.9210.9210.8960.9010.9280.9060.8670.654
2월0.0130.8971.0000.9170.9570.9550.9580.9600.9770.9630.9500.9500.9520.736
3월0.1070.9170.9171.0000.9530.9430.9460.9540.9260.9360.9450.9390.8990.800
4월0.0330.9240.9570.9531.0000.9400.9550.9740.9650.9670.9580.9520.9410.877
5월0.0800.9290.9550.9430.9401.0000.9380.9630.9510.9460.9650.9400.9070.877
6월0.0100.9210.9580.9460.9550.9381.0000.9610.9540.9510.9570.9670.9420.710
7월0.0590.9210.9600.9540.9740.9630.9611.0000.9490.9610.9670.9620.9450.877
8월0.0330.8960.9770.9260.9650.9510.9540.9491.0000.9700.9520.9540.9440.788
9월0.0170.9010.9630.9360.9670.9460.9510.9610.9701.0000.9420.9420.9380.710
10월0.0540.9280.9500.9450.9580.9650.9570.9670.9520.9421.0000.9650.9340.800
11월0.0370.9060.9500.9390.9520.9400.9670.9620.9540.9420.9651.0000.9620.824
12월-0.0860.8670.9520.8990.9410.9070.9420.9450.9440.9380.9340.9621.0000.668
구분0.0000.6540.7360.8000.8770.8770.7100.8770.7880.7100.8000.8240.6681.000

Missing values

2024-03-15T05:41:26.468018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T05:41:27.038367image/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
8일반쓰레기2023839.98755.75840.67748.9870.9852.02916.47825.48812.09879.46769.55770.36
9음식물쓰레기2014724.6707.7669.8678.0709.4752.2801.1766.5811.3746.9693.1758.7
구분년도1월2월3월4월5월6월7월8월9월10월11월12월
19재활용품2014364.4360.5348.2379.4396.8346.1365.3338.2380.6331.3306.3393.9
20재활용품2015367.3353.0472.5388.7366.7401.1405.0374.7432.6412.6276.0234.6
21재활용품2016229.6271.7262.9255.7324.2346.6343.8390.8349.4356.2369.1373.3
22재활용품2017403.2420.9375.2344.8456.1423.9414.4438.5403.0454.6365.3404.0
23재활용품2018423.8388.9465.2416.8421.3463.9441.1440.6365.5565.7431.9373.1
24재활용품2019466.2417.7400.9421.3429.5416.2360.6539.7462.6440.2348.4358.1
25재활용품2020393.1459.8420.4462.4389.2453.8470.3445.5498.7457.5391.9420.3
26재활용품2021430.5384.6520.9407.1469.6549.4544.6505.2537.4477.4474.8420.1
27재활용품2022444.2394.2470.9400.8442.7451.7465.8436.5434.5395.4371.5307.1
28재활용품2023290.6264.3284.3259.0305.9285.6286.4365.2282.2365.8294.2233.8