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 서울시 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/15066518/fileData.do

Alerts

지자체명 has constant value ""Constant
연도 is highly overall correlated with 생활폐기물 반입량(kg) and 1 other fieldsHigh correlation
생활폐기물 반입량(kg) is highly overall correlated with 연도 and 1 other fieldsHigh correlation
건설폐기물 반입량(kg) is highly overall correlated with 연도 and 1 other fieldsHigh 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 started2023-12-13 00:58:43.549359
Analysis finished2023-12-13 00:58:44.937221
Duration1.39 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
2023-12-13T09:58:44.981039image/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
2023-12-13T09:58:45.071077image/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
서울특별시
24 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울특별시 24
100.0%

Length

2023-12-13T09:58:45.375417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:58:45.444242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 24
100.0%

생활폐기물 반입량(kg)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.1453808 × 108
Minimum2.241709 × 108
Maximum1.8637659 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T09:58:45.509975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.241709 × 108
5-th percentile2.4749345 × 108
Q13.075634 × 108
median3.5411947 × 108
Q39.5754064 × 108
95-th percentile1.7743708 × 109
Maximum1.8637659 × 109
Range1.639595 × 109
Interquartile range (IQR)6.4997724 × 108

Descriptive statistics

Standard deviation5.8397108 × 108
Coefficient of variation (CV)0.81727076
Kurtosis-0.56360586
Mean7.1453808 × 108
Median Absolute Deviation (MAD)1.0193282 × 108
Skewness1.0582706
Sum1.7148914 × 1010
Variance3.4102223 × 1017
MonotonicityNot monotonic
2023-12-13T09:58:45.597418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1863765887 1
 
4.2%
224170895 1
 
4.2%
308011280 1
 
4.2%
328661560 1
 
4.2%
343836260 1
 
4.2%
346429120 1
 
4.2%
306219780 1
 
4.2%
274781630 1
 
4.2%
263663750 1
 
4.2%
245482070 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
224170895 1
4.2%
245482070 1
4.2%
258891244 1
4.2%
263663750 1
4.2%
274781630 1
4.2%
306219780 1
4.2%
308011280 1
4.2%
315667431 1
4.2%
328661560 1
4.2%
330858070 1
4.2%
ValueCountFrequency (%)
1863765887 1
4.2%
1789157849 1
4.2%
1690577794 1
4.2%
1684950240 1
4.2%
1541943387 1
4.2%
1346257217 1
4.2%
827968449 1
4.2%
825500431 1
4.2%
702722318 1
4.2%
550327640 1
4.2%

사업장폐기물 반입량(kg)
Real number (ℝ)

UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5099313 × 108
Minimum2.1817575 × 108
Maximum8.3468912 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T09:58:45.686052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.1817575 × 108
5-th percentile3.500537 × 108
Q14.862794 × 108
median5.7037399 × 108
Q36.3072283 × 108
95-th percentile7.183273 × 108
Maximum8.3468912 × 108
Range6.1651337 × 108
Interquartile range (IQR)1.4444343 × 108

Descriptive statistics

Standard deviation1.3845458 × 108
Coefficient of variation (CV)0.25128186
Kurtosis0.41683954
Mean5.5099313 × 108
Median Absolute Deviation (MAD)76428829
Skewness-0.39419846
Sum1.3223835 × 1010
Variance1.9169671 × 1016
MonotonicityNot monotonic
2023-12-13T09:58:45.773127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
834689123 1
 
4.2%
564165643 1
 
4.2%
218175750 1
 
4.2%
347951620 1
 
4.2%
512399980 1
 
4.2%
668020260 1
 
4.2%
719360920 1
 
4.2%
712470100 1
 
4.2%
684144960 1
 
4.2%
625070090 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
218175750 1
4.2%
347951620 1
4.2%
361965476 1
4.2%
371264849 1
4.2%
455213827 1
4.2%
465395203 1
4.2%
493240803 1
4.2%
494649510 1
4.2%
503808074 1
4.2%
512399980 1
4.2%
ValueCountFrequency (%)
834689123 1
4.2%
719360920 1
4.2%
712470100 1
4.2%
684144960 1
4.2%
668020260 1
4.2%
637718960 1
4.2%
628390791 1
4.2%
625070090 1
4.2%
613828053 1
4.2%
608131690 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%
Mean9.6104044 × 108
Minimum0
Maximum1.9947944 × 109
Zeros1
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T09:58:45.881614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.4792442 × 108
Q16.872057 × 108
median9.3079535 × 108
Q31.2551103 × 109
95-th percentile1.8196214 × 109
Maximum1.9947944 × 109
Range1.9947944 × 109
Interquartile range (IQR)5.6790464 × 108

Descriptive statistics

Standard deviation4.8565633 × 108
Coefficient of variation (CV)0.50534432
Kurtosis-0.054941524
Mean9.6104044 × 108
Median Absolute Deviation (MAD)3.0843296 × 108
Skewness0.31818484
Sum2.3064971 × 1010
Variance2.3586207 × 1017
MonotonicityNot monotonic
2023-12-13T09:58:45.974843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1082648830 1
 
4.2%
764287990 1
 
4.2%
0 1
 
4.2%
400829410 1
 
4.2%
338588240 1
 
4.2%
409717330 1
 
4.2%
561447770 1
 
4.2%
690748940 1
 
4.2%
676668030 1
 
4.2%
815835060 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
0 1
4.2%
338588240 1
4.2%
400829410 1
4.2%
409717330 1
4.2%
561447770 1
4.2%
676668030 1
4.2%
690718260 1
4.2%
690748940 1
4.2%
754207830 1
4.2%
764287990 1
4.2%
ValueCountFrequency (%)
1994794380 1
4.2%
1855677180 1
4.2%
1615305160 1
4.2%
1486638430 1
4.2%
1354291520 1
4.2%
1287495030 1
4.2%
1244315440 1
4.2%
1234141180 1
4.2%
1082648830 1
4.2%
973297290 1
4.2%

Interactions

2023-12-13T09:58:44.491354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:58:43.657553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:58:43.941275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:58:44.228182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:58:44.570055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:58:43.731529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:58:44.008837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:58:44.286913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:58:44.655940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:58:43.805304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:58:44.077837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:58:44.352088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:58:44.723871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:58:43.871354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:58:44.146386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:58:44.415823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T09:58:46.042529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도생활폐기물 반입량(kg)사업장폐기물 반입량(kg)건설폐기물 반입량(kg)
연도1.0000.6220.5160.791
생활폐기물 반입량(kg)0.6221.0000.3920.819
사업장폐기물 반입량(kg)0.5160.3921.0000.603
건설폐기물 반입량(kg)0.7910.8190.6031.000
2023-12-13T09:58:46.117056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도생활폐기물 반입량(kg)사업장폐기물 반입량(kg)건설폐기물 반입량(kg)
연도1.000-0.8190.185-0.943
생활폐기물 반입량(kg)-0.8191.000-0.3650.762
사업장폐기물 반입량(kg)0.185-0.3651.000-0.307
건설폐기물 반입량(kg)-0.9430.762-0.3071.000

Missing values

2023-12-13T09:58:44.814909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T09:58:44.903347image/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)
01999서울특별시18637658878346891231082648830
12000서울특별시17891578495484334011354291520
22001서울특별시16905777943619654761615305160
32002서울특별시16849502404946495101994794380
42003서울특별시15419433874932408031855677180
52004서울특별시13462572174653952031486638430
62005서울특별시8255004313712648491244315440
72006서울특별시8279684494552138271287495030
82007서울특별시7027223185038080741234141180
92008서울특별시550327640576582328973297290
연도지자체명생활폐기물 반입량(kg)사업장폐기물 반입량(kg)건설폐기물 반입량(kg)
142013서울특별시258891244637718960754207830
152014서울특별시315667431608131690690718260
162015서울특별시245482070625070090815835060
172016서울특별시263663750684144960676668030
182017서울특별시274781630712470100690748940
192018서울특별시306219780719360920561447770
202019서울특별시346429120668020260409717330
212020서울특별시343836260512399980338588240
222021서울특별시328661560347951620400829410
232022서울특별시3080112802181757500