Overview

Dataset statistics

Number of variables8
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory771.5 KiB
Average record size in memory79.0 B

Variable types

Categorical2
Numeric6

Dataset

Description음식물쓰레기 배출 현황
Author한국환경공단
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=8FUJGXN2C3TZ1Y5TVT4N28532739&infSeq=1

Alerts

배출량(g) is highly overall correlated with 배출횟수 and 1 other fieldsHigh correlation
배출량비율(%) is highly overall correlated with 배출횟수비율(%)High correlation
배출횟수 is highly overall correlated with 배출량(g) and 1 other fieldsHigh correlation
배출횟수비율(%) is highly overall correlated with 배출량비율(%)High correlation
시군구명 is highly overall correlated with 배출량(g) and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-12-10 21:37:30.467080
Analysis finished2023-12-10 21:37:35.771706
Duration5.3 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

배출년도
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2019
4014 
2020
3052 
2018
2934 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2018
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2019 4014
40.1%
2020 3052
30.5%
2018 2934
29.3%

Length

2023-12-11T06:37:35.843085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:37:36.012311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 4014
40.1%
2020 3052
30.5%
2018 2934
29.3%

배출월
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.7618
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T06:37:36.116788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.1276891
Coefficient of variation (CV)0.46255274
Kurtosis-0.94149637
Mean6.7618
Median Absolute Deviation (MAD)2
Skewness-0.12624387
Sum67618
Variance9.782439
MonotonicityNot monotonic
2023-12-11T06:37:36.249586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
8 1038
10.4%
10 1024
10.2%
5 997
10.0%
6 991
9.9%
9 989
9.9%
7 989
9.9%
4 986
9.9%
1 701
7.0%
11 672
6.7%
3 666
6.7%
Other values (2) 947
9.5%
ValueCountFrequency (%)
1 701
7.0%
2 308
 
3.1%
3 666
6.7%
4 986
9.9%
5 997
10.0%
6 991
9.9%
7 989
9.9%
8 1038
10.4%
9 989
9.9%
10 1024
10.2%
ValueCountFrequency (%)
12 639
6.4%
11 672
6.7%
10 1024
10.2%
9 989
9.9%
8 1038
10.4%
7 989
9.9%
6 991
9.9%
5 997
10.0%
4 986
9.9%
3 666
6.7%

배출일
Real number (ℝ)

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.7131
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T06:37:36.401164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7989392
Coefficient of variation (CV)0.55997475
Kurtosis-1.1893245
Mean15.7131
Median Absolute Deviation (MAD)8
Skewness0.024790403
Sum157131
Variance77.421331
MonotonicityNot monotonic
2023-12-11T06:37:36.565105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
11 350
 
3.5%
4 346
 
3.5%
12 345
 
3.5%
18 341
 
3.4%
19 340
 
3.4%
14 339
 
3.4%
5 339
 
3.4%
7 338
 
3.4%
10 335
 
3.4%
27 335
 
3.4%
Other values (21) 6592
65.9%
ValueCountFrequency (%)
1 315
3.1%
2 311
3.1%
3 328
3.3%
4 346
3.5%
5 339
3.4%
6 332
3.3%
7 338
3.4%
8 332
3.3%
9 308
3.1%
10 335
3.4%
ValueCountFrequency (%)
31 207
2.1%
30 325
3.2%
29 300
3.0%
28 311
3.1%
27 335
3.4%
26 302
3.0%
25 326
3.3%
24 325
3.2%
23 326
3.3%
22 320
3.2%

시군구명
Categorical

HIGH CORRELATION 

Distinct28
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
양주시
 
406
수원시권선구
 
396
고양시
 
393
수원시팔달구
 
390
수원시영통구
 
389
Other values (23)
8026 

Length

Max length6
Median length3
Mean length3.8057
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row고양시
2nd row파주시
3rd row수원시권선구
4th row포천시
5th row수원시영통구

Common Values

ValueCountFrequency (%)
양주시 406
 
4.1%
수원시권선구 396
 
4.0%
고양시 393
 
3.9%
수원시팔달구 390
 
3.9%
수원시영통구 389
 
3.9%
이천시 389
 
3.9%
성남시중원구 389
 
3.9%
동두천시 388
 
3.9%
평택시 387
 
3.9%
성남시분당구 383
 
3.8%
Other values (18) 6090
60.9%

Length

2023-12-11T06:37:36.710172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
양주시 406
 
4.1%
수원시권선구 396
 
4.0%
고양시 393
 
3.9%
수원시팔달구 390
 
3.9%
수원시영통구 389
 
3.9%
이천시 389
 
3.9%
성남시중원구 389
 
3.9%
동두천시 388
 
3.9%
평택시 387
 
3.9%
성남시분당구 383
 
3.8%
Other values (18) 6090
60.9%

배출량(g)
Real number (ℝ)

HIGH CORRELATION 

Distinct9849
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20659902
Minimum50
Maximum1.6930315 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T06:37:36.845023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile312715
Q15186775
median11723050
Q325054111
95-th percentile89347443
Maximum1.6930315 × 108
Range1.693031 × 108
Interquartile range (IQR)19867336

Descriptive statistics

Standard deviation26611045
Coefficient of variation (CV)1.2880529
Kurtosis6.4475582
Mean20659902
Median Absolute Deviation (MAD)9154650
Skewness2.4681596
Sum2.0659902 × 1011
Variance7.0814774 × 1014
MonotonicityNot monotonic
2023-12-11T06:37:37.016655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
464100 4
 
< 0.1%
123650 3
 
< 0.1%
424950 3
 
< 0.1%
447650 3
 
< 0.1%
372800 3
 
< 0.1%
405300 3
 
< 0.1%
484250 3
 
< 0.1%
455950 3
 
< 0.1%
6968600 3
 
< 0.1%
5605350 2
 
< 0.1%
Other values (9839) 9970
99.7%
ValueCountFrequency (%)
50 1
< 0.1%
200 1
< 0.1%
14950 1
< 0.1%
18150 1
< 0.1%
19650 1
< 0.1%
38450 1
< 0.1%
45700 1
< 0.1%
47350 1
< 0.1%
56050 1
< 0.1%
60450 1
< 0.1%
ValueCountFrequency (%)
169303149 1
< 0.1%
167002450 1
< 0.1%
166952750 1
< 0.1%
166824898 1
< 0.1%
163024850 1
< 0.1%
161344750 1
< 0.1%
160920750 1
< 0.1%
160699300 1
< 0.1%
159563350 1
< 0.1%
159546699 1
< 0.1%

배출량비율(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct512
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.283342
Minimum0
Maximum29.21
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T06:37:37.193006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.5
Q12.93
median3.18
Q33.52
95-th percentile4.37
Maximum29.21
Range29.21
Interquartile range (IQR)0.59

Descriptive statistics

Standard deviation0.80126214
Coefficient of variation (CV)0.24403859
Kurtosis224.5356
Mean3.283342
Median Absolute Deviation (MAD)0.285
Skewness9.0566511
Sum32833.42
Variance0.64202101
MonotonicityNot monotonic
2023-12-11T06:37:37.662596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.0 125
 
1.2%
2.98 125
 
1.2%
2.96 113
 
1.1%
3.09 113
 
1.1%
3.01 111
 
1.1%
3.14 109
 
1.1%
2.99 108
 
1.1%
3.02 107
 
1.1%
3.06 104
 
1.0%
3.27 104
 
1.0%
Other values (502) 8881
88.8%
ValueCountFrequency (%)
0.0 1
< 0.1%
0.01 1
< 0.1%
0.06 1
< 0.1%
0.12 2
< 0.1%
0.14 1
< 0.1%
0.27 1
< 0.1%
0.28 1
< 0.1%
0.29 1
< 0.1%
0.34 1
< 0.1%
0.35 2
< 0.1%
ValueCountFrequency (%)
29.21 1
< 0.1%
25.43 1
< 0.1%
19.52 1
< 0.1%
18.97 1
< 0.1%
17.85 1
< 0.1%
16.79 1
< 0.1%
12.67 1
< 0.1%
12.55 1
< 0.1%
10.94 1
< 0.1%
10.72 1
< 0.1%

배출횟수
Real number (ℝ)

HIGH CORRELATION 

Distinct7257
Distinct (%)72.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11209.575
Minimum1
Maximum98530
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T06:37:37.851478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile158.95
Q12642.25
median5742.5
Q313484
95-th percentile47922.05
Maximum98530
Range98529
Interquartile range (IQR)10841.75

Descriptive statistics

Standard deviation15266.995
Coefficient of variation (CV)1.3619601
Kurtosis7.0509184
Mean11209.575
Median Absolute Deviation (MAD)4792
Skewness2.5676874
Sum1.1209575 × 108
Variance2.3308113 × 108
MonotonicityNot monotonic
2023-12-11T06:37:38.028818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56 12
 
0.1%
265 12
 
0.1%
59 11
 
0.1%
261 11
 
0.1%
365 11
 
0.1%
258 11
 
0.1%
281 11
 
0.1%
53 10
 
0.1%
82 10
 
0.1%
61 10
 
0.1%
Other values (7247) 9891
98.9%
ValueCountFrequency (%)
1 2
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
17 1
< 0.1%
21 1
< 0.1%
24 2
< 0.1%
31 2
< 0.1%
32 2
< 0.1%
35 1
< 0.1%
36 1
< 0.1%
ValueCountFrequency (%)
98530 1
< 0.1%
94471 1
< 0.1%
94029 1
< 0.1%
93841 1
< 0.1%
93605 1
< 0.1%
93094 1
< 0.1%
92823 1
< 0.1%
92702 1
< 0.1%
91627 1
< 0.1%
90986 1
< 0.1%

배출횟수비율(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct346
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.289214
Minimum0.05
Maximum31.39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T06:37:38.200596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.05
5-th percentile2.73
Q13.0475
median3.225
Q33.46
95-th percentile4.02
Maximum31.39
Range31.34
Interquartile range (IQR)0.4125

Descriptive statistics

Standard deviation0.64065537
Coefficient of variation (CV)0.19477461
Kurtosis587.10281
Mean3.289214
Median Absolute Deviation (MAD)0.205
Skewness17.424078
Sum32892.14
Variance0.41043931
MonotonicityNot monotonic
2023-12-11T06:37:38.364731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.17 156
 
1.6%
3.18 156
 
1.6%
3.15 155
 
1.6%
3.16 149
 
1.5%
3.1 147
 
1.5%
3.19 145
 
1.5%
3.11 145
 
1.5%
3.08 144
 
1.4%
3.23 142
 
1.4%
3.13 142
 
1.4%
Other values (336) 8519
85.2%
ValueCountFrequency (%)
0.05 2
< 0.1%
0.44 1
< 0.1%
0.71 1
< 0.1%
0.76 1
< 0.1%
0.83 2
< 0.1%
1.0 1
< 0.1%
1.48 1
< 0.1%
1.5 1
< 0.1%
1.52 1
< 0.1%
1.53 1
< 0.1%
ValueCountFrequency (%)
31.39 1
< 0.1%
24.09 1
< 0.1%
17.59 1
< 0.1%
17.1 1
< 0.1%
16.93 1
< 0.1%
16.26 1
< 0.1%
14.92 1
< 0.1%
13.76 1
< 0.1%
11.28 1
< 0.1%
10.13 1
< 0.1%

Interactions

2023-12-11T06:37:34.756244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:37:31.472949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:37:32.072343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:37:32.773883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:37:33.490331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:37:34.139558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:37:34.887373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:37:31.566186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:37:32.215290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:37:32.894026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:37:33.608177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:37:34.238530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:37:35.005643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:37:31.649261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:37:32.321076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:37:33.009489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:37:33.725809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:37:34.331241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:37:35.131501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:37:31.754759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:37:32.476400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:37:33.150703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:37:33.827050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:37:34.445728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:37:35.259073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:37:31.851844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:37:32.573869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:37:33.273670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:37:33.928297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:37:34.559333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:37:35.378031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:37:31.949215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:37:32.663698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:37:33.386732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:37:34.028530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:37:34.648925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:37:38.466994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
배출년도배출월배출일시군구명배출량(g)배출량비율(%)배출횟수배출횟수비율(%)
배출년도1.0000.4140.0000.2740.0950.0840.1120.055
배출월0.4141.0000.0000.0000.1060.0760.1680.100
배출일0.0000.0001.0000.0000.0000.0490.0000.059
시군구명0.2740.0000.0001.0000.8750.2820.8830.159
배출량(g)0.0950.1060.0000.8751.0000.1370.9570.118
배출량비율(%)0.0840.0760.0490.2820.1371.0000.0800.949
배출횟수0.1120.1680.0000.8830.9570.0801.0000.045
배출횟수비율(%)0.0550.1000.0590.1590.1180.9490.0451.000
2023-12-11T06:37:38.625173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
배출년도시군구명
배출년도1.0000.144
시군구명0.1441.000
2023-12-11T06:37:38.717325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
배출월배출일배출량(g)배출량비율(%)배출횟수배출횟수비율(%)배출년도시군구명
배출월1.0000.0050.031-0.0110.051-0.0170.2740.000
배출일0.0051.000-0.0040.014-0.0050.0350.0000.000
배출량(g)0.031-0.0041.0000.1480.9500.0980.0560.555
배출량비율(%)-0.0110.0140.1481.0000.0850.8130.0360.109
배출횟수0.051-0.0050.9500.0851.0000.0780.0660.569
배출횟수비율(%)-0.0170.0350.0980.8130.0781.0000.0340.062
배출년도0.2740.0000.0560.0360.0660.0341.0000.144
시군구명0.0000.0000.5550.1090.5690.0620.1441.000

Missing values

2023-12-11T06:37:35.508059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:37:35.707767image/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

배출년도배출월배출일시군구명배출량(g)배출량비율(%)배출횟수배출횟수비율(%)
68562020122고양시6351004.264464.1
205782018829파주시229025502.62153852.79
108202019828수원시권선구367409002.89258023.16
166632019124포천시59390003.031053.09
145432019412수원시영통구365415002.85229043.03
11449201985의정부시84893003.7950133.55
240642018417화성시711162603.11363613.18
752020191229여주시52369003.8721993.71
753720191228부천시164723003.1889223.15
60462020322화성시1278473744.51627584.34
배출년도배출월배출일시군구명배출량(g)배출량비율(%)배출횟수배출횟수비율(%)
16364201924파주시413151505.54187665.06
798620191211부천시156742003.0288163.11
24820201022동두천시63224002.8840562.98
1839120181119구리시492851505.7377043.73
150322019325수원시권선구369112003.43206353.42
1945120181011양주시149111502.9893303.09
10526201998양주시283841504.7165284.35
158602019222군포시48079502.9827343.1
223532018621군포시59252003.0440493.26
1842420181118성남시분당구5964003.943203.36