Overview

Dataset statistics

Number of variables5
Number of observations24
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 KiB
Average record size in memory49.5 B

Variable types

Numeric4
Categorical1

Dataset

Description수도권매립지관리공사로 반입되는 경기도 연도별 폐기물 반입량입니다. 개방항목 : 연도, 지자체명, 생활폐기물 반입량(kg), 사업장폐기물(kg), 건설폐기물 반입량(kg)의 항목을 제공합니다. * 생활소각재 분류(2010년까지 : 생활폐기물, 2011년 이후 : 사업장 배출시설계)
Author수도권매립지관리공사
URLhttps://www.data.go.kr/data/15066515/fileData.do

Alerts

지자체명 has constant value ""Constant
연도 is highly overall correlated with 생활폐기물 반입량(kg) and 2 other fieldsHigh correlation
생활폐기물 반입량(kg) is highly overall correlated with 연도 and 1 other fieldsHigh correlation
사업장폐기물 반입량(kg) is highly overall correlated with 연도 and 1 other fieldsHigh correlation
건설폐기물 반입량(kg) is highly overall correlated with 연도High correlation
연도 has unique valuesUnique
생활폐기물 반입량(kg) has unique valuesUnique
사업장폐기물 반입량(kg) has unique valuesUnique
건설폐기물 반입량(kg) has unique valuesUnique
건설폐기물 반입량(kg) has 1 (4.2%) zerosZeros

Reproduction

Analysis started2023-12-12 05:48:25.832794
Analysis finished2023-12-12 05:48:27.642616
Duration1.81 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2010.5
Minimum1999
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T14:48:27.715252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1999
5-th percentile2000.15
Q12004.75
median2010.5
Q32016.25
95-th percentile2020.85
Maximum2022
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation7.0710678
Coefficient of variation (CV)0.0035170693
Kurtosis-1.2
Mean2010.5
Median Absolute Deviation (MAD)6
Skewness0
Sum48252
Variance50
MonotonicityStrictly increasing
2023-12-12T14:48:27.852916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1999 1
 
4.2%
2012 1
 
4.2%
2022 1
 
4.2%
2021 1
 
4.2%
2020 1
 
4.2%
2019 1
 
4.2%
2018 1
 
4.2%
2017 1
 
4.2%
2016 1
 
4.2%
2015 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
1999 1
4.2%
2000 1
4.2%
2001 1
4.2%
2002 1
4.2%
2003 1
4.2%
2004 1
4.2%
2005 1
4.2%
2006 1
4.2%
2007 1
4.2%
2008 1
4.2%
ValueCountFrequency (%)
2022 1
4.2%
2021 1
4.2%
2020 1
4.2%
2019 1
4.2%
2018 1
4.2%
2017 1
4.2%
2016 1
4.2%
2015 1
4.2%
2014 1
4.2%
2013 1
4.2%

지자체명
Categorical

CONSTANT 

Distinct1
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size324.0 B
경기도
24 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 24
100.0%

Length

2023-12-12T14:48:28.005239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:48:28.162570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 24
100.0%

생활폐기물 반입량(kg)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.334737 × 108
Minimum1.4047623 × 108
Maximum7.5411498 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T14:48:28.265293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.4047623 × 108
5-th percentile1.4375566 × 108
Q12.0134649 × 108
median2.971684 × 108
Q33.7649068 × 108
95-th percentile6.6573701 × 108
Maximum7.5411498 × 108
Range6.1363875 × 108
Interquartile range (IQR)1.751442 × 108

Descriptive statistics

Standard deviation1.6915573 × 108
Coefficient of variation (CV)0.50725358
Kurtosis0.56929702
Mean3.334737 × 108
Median Absolute Deviation (MAD)97609140
Skewness1.1122449
Sum8.0033687 × 109
Variance2.861366 × 1016
MonotonicityNot monotonic
2023-12-12T14:48:28.384155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
754114982 1
 
4.2%
140476230 1
 
4.2%
317860370 1
 
4.2%
324017450 1
 
4.2%
292191140 1
 
4.2%
313085670 1
 
4.2%
292877320 1
 
4.2%
206619770 1
 
4.2%
194952090 1
 
4.2%
161708420 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
140476230 1
4.2%
140587530 1
4.2%
161708420 1
4.2%
177282945 1
4.2%
194952090 1
4.2%
195984820 1
4.2%
203133710 1
4.2%
206619770 1
4.2%
252012590 1
4.2%
292191140 1
4.2%
ValueCountFrequency (%)
754114982 1
4.2%
680370457 1
4.2%
582814120 1
4.2%
534484680 1
4.2%
513936930 1
4.2%
444465050 1
4.2%
353832560 1
4.2%
332546689 1
4.2%
324017450 1
4.2%
317860370 1
4.2%

사업장폐기물 반입량(kg)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.69261 × 108
Minimum2.1037721 × 108
Maximum1.0336288 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T14:48:28.524994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.1037721 × 108
5-th percentile2.4979392 × 108
Q13.3263793 × 108
median6.0930456 × 108
Q37.3779304 × 108
95-th percentile8.976324 × 108
Maximum1.0336288 × 109
Range8.232516 × 108
Interquartile range (IQR)4.0515511 × 108

Descriptive statistics

Standard deviation2.385474 × 108
Coefficient of variation (CV)0.4190475
Kurtosis-1.1005146
Mean5.69261 × 108
Median Absolute Deviation (MAD)1.7853033 × 108
Skewness0.0043340553
Sum1.3662264 × 1010
Variance5.6904861 × 1016
MonotonicityNot monotonic
2023-12-12T14:48:28.653935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
339706768 1
 
4.2%
536270540 1
 
4.2%
543849270 1
 
4.2%
718384580 1
 
4.2%
715835490 1
 
4.2%
763763780 1
 
4.2%
907283090 1
 
4.2%
729136120 1
 
4.2%
673355560 1
 
4.2%
662000060 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
210377210 1
4.2%
248732133 1
4.2%
255810690 1
4.2%
272065600 1
4.2%
278801000 1
4.2%
311431400 1
4.2%
339706768 1
4.2%
368749330 1
4.2%
455858490 1
4.2%
536270540 1
4.2%
ValueCountFrequency (%)
1033628807 1
4.2%
907283090 1
4.2%
842945190 1
4.2%
806622110 1
4.2%
769047678 1
4.2%
763763780 1
4.2%
729136120 1
4.2%
718384580 1
4.2%
715835490 1
4.2%
673355560 1
4.2%

건설폐기물 반입량(kg)
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4543033 × 108
Minimum0
Maximum1.2013747 × 109
Zeros1
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T14:48:28.780405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.4727371 × 108
Q13.3652932 × 108
median4.6088178 × 108
Q37.841119 × 108
95-th percentile1.1312618 × 109
Maximum1.2013747 × 109
Range1.2013747 × 109
Interquartile range (IQR)4.4758258 × 108

Descriptive statistics

Standard deviation3.3249827 × 108
Coefficient of variation (CV)0.60960722
Kurtosis-0.69153006
Mean5.4543033 × 108
Median Absolute Deviation (MAD)2.6114918 × 108
Skewness0.45689009
Sum1.3090328 × 1010
Variance1.105551 × 1017
MonotonicityNot monotonic
2023-12-12T14:48:28.936050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
205875340 1
 
4.2%
479594280 1
 
4.2%
0 1
 
4.2%
193589870 1
 
4.2%
142685820 1
 
4.2%
173271750 1
 
4.2%
263064490 1
 
4.2%
390093990 1
 
4.2%
367609460 1
 
4.2%
440850890 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
0 1
4.2%
142685820 1
4.2%
173271750 1
4.2%
193589870 1
4.2%
205875340 1
4.2%
263064490 1
4.2%
361017590 1
4.2%
367609460 1
4.2%
390093990 1
4.2%
396767570 1
4.2%
ValueCountFrequency (%)
1201374740 1
4.2%
1158879240 1
4.2%
974762866 1
4.2%
972045070 1
4.2%
893144620 1
4.2%
855082920 1
4.2%
760454890 1
4.2%
742926420 1
4.2%
641721254 1
4.2%
549002033 1
4.2%

Interactions

2023-12-12T14:48:27.042636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:48:25.964776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:48:26.313058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:48:26.662131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:48:27.142978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:48:26.046354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:48:26.389033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:48:26.742429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:48:27.269561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:48:26.125275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:48:26.473106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:48:26.843836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:48:27.356158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:48:26.220965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:48:26.565032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:48:26.953442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:48:29.038710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도생활폐기물 반입량(kg)사업장폐기물 반입량(kg)건설폐기물 반입량(kg)
연도1.0000.8470.6720.859
생활폐기물 반입량(kg)0.8471.0000.5500.750
사업장폐기물 반입량(kg)0.6720.5501.0000.856
건설폐기물 반입량(kg)0.8590.7500.8561.000
2023-12-12T14:48:29.138407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도생활폐기물 반입량(kg)사업장폐기물 반입량(kg)건설폐기물 반입량(kg)
연도1.000-0.5850.657-0.737
생활폐기물 반입량(kg)-0.5851.000-0.5990.300
사업장폐기물 반입량(kg)0.657-0.5991.000-0.457
건설폐기물 반입량(kg)-0.7370.300-0.4571.000

Missing values

2023-12-12T14:48:27.478467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:48:27.597312image/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

연도지자체명생활폐기물 반입량(kg)사업장폐기물 반입량(kg)건설폐기물 반입량(kg)
01999경기도754114982339706768205875340
12000경기도680370457248732133484343530
22001경기도582814120210377210742926420
32002경기도5139369302720656001158879240
42003경기도5344846802558106901201374740
52004경기도444465050311431400972045070
62005경기도301459490278801000855082920
72006경기도332546689368749330893144620
82007경기도353832560455858490974762866
92008경기도292553690806622110760454890
연도지자체명생활폐기물 반입량(kg)사업장폐기물 반입량(kg)건설폐기물 반입량(kg)
142013경기도177282945581457700396767570
152014경기도140587530637151420361017590
162015경기도161708420662000060440850890
172016경기도194952090673355560367609460
182017경기도206619770729136120390093990
192018경기도292877320907283090263064490
202019경기도313085670763763780173271750
212020경기도292191140715835490142685820
222021경기도324017450718384580193589870
232022경기도3178603705438492700