Overview

Dataset statistics

Number of variables11
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.5 KiB
Average record size in memory97.3 B

Variable types

Categorical5
Numeric6

Alerts

배출년도 has constant value ""Constant
배출월 has constant value ""Constant
지자체 시도명 is highly overall correlated with 배출량비율(%) and 3 other fieldsHigh correlation
지자체 시군구명 is highly overall correlated with 배출량(g) and 5 other fieldsHigh correlation
지자체코드 is highly overall correlated with 배출량(g) and 5 other fieldsHigh correlation
배출요일 is highly overall correlated with 배출량비율(%) and 1 other fieldsHigh correlation
배출량(g) is highly overall correlated with 배출횟수 and 2 other fieldsHigh correlation
배출량비율(%) is highly overall correlated with 배출요일 and 4 other fieldsHigh correlation
배출횟수 is highly overall correlated with 배출량(g) and 2 other fieldsHigh correlation
배출횟수비율(%) is highly overall correlated with 배출요일 and 4 other fieldsHigh correlation
지자체 시도명 is highly imbalanced (91.9%)Imbalance
배출량(g) has unique valuesUnique

Reproduction

Analysis started2023-12-10 10:44:04.145490
Analysis finished2023-12-10 10:44:10.723463
Duration6.58 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

배출년도
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2020
100 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2020 100
100.0%

Length

2023-12-10T19:44:10.797221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:44:10.900702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 100
100.0%

배출월
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
3
100 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 100
100.0%

Length

2023-12-10T19:44:11.059836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:44:11.257500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 100
100.0%

배출일
Real number (ℝ)

Distinct31
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.25
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:44:11.404184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median15
Q323
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.1776793
Coefficient of variation (CV)0.60181504
Kurtosis-1.2415912
Mean15.25
Median Absolute Deviation (MAD)8
Skewness0.085725267
Sum1525
Variance84.229798
MonotonicityNot monotonic
2023-12-10T19:44:11.612997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 4
 
4.0%
3 4
 
4.0%
4 4
 
4.0%
5 4
 
4.0%
6 4
 
4.0%
2 4
 
4.0%
16 4
 
4.0%
25 3
 
3.0%
22 3
 
3.0%
23 3
 
3.0%
Other values (21) 63
63.0%
ValueCountFrequency (%)
1 4
4.0%
2 4
4.0%
3 4
4.0%
4 4
4.0%
5 4
4.0%
6 4
4.0%
7 3
3.0%
8 3
3.0%
9 3
3.0%
10 3
3.0%
ValueCountFrequency (%)
31 3
3.0%
30 3
3.0%
29 3
3.0%
28 3
3.0%
27 3
3.0%
26 3
3.0%
25 3
3.0%
24 3
3.0%
23 3
3.0%
22 3
3.0%

배출요일
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.77
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:44:11.791036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35.25
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3.25

Descriptive statistics

Standard deviation1.9992675
Coefficient of variation (CV)0.53030969
Kurtosis-1.2311029
Mean3.77
Median Absolute Deviation (MAD)2
Skewness0.16929617
Sum377
Variance3.9970707
MonotonicityNot monotonic
2023-12-10T19:44:11.965795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 17
17.0%
1 16
16.0%
3 16
16.0%
4 13
13.0%
5 13
13.0%
6 13
13.0%
7 12
12.0%
ValueCountFrequency (%)
1 16
16.0%
2 17
17.0%
3 16
16.0%
4 13
13.0%
5 13
13.0%
6 13
13.0%
7 12
12.0%
ValueCountFrequency (%)
7 12
12.0%
6 13
13.0%
5 13
13.0%
4 13
13.0%
3 16
16.0%
2 17
17.0%
1 16
16.0%

지자체코드
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
W01
31 
W02
31 
W03
31 
W04
6 
W3L
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st rowW01
2nd rowW01
3rd rowW01
4th rowW01
5th rowW01

Common Values

ValueCountFrequency (%)
W01 31
31.0%
W02 31
31.0%
W03 31
31.0%
W04 6
 
6.0%
W3L 1
 
1.0%

Length

2023-12-10T19:44:12.165347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:44:12.324584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
w01 31
31.0%
w02 31
31.0%
w03 31
31.0%
w04 6
 
6.0%
w3l 1
 
1.0%

지자체 시도명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
서울특별시
99 
강원도
 
1

Length

Max length5
Median length5
Mean length4.98
Min length3

Unique

Unique1 ?
Unique (%)1.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울특별시 99
99.0%
강원도 1
 
1.0%

Length

2023-12-10T19:44:12.549344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:44:12.750070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 99
99.0%
강원도 1
 
1.0%

지자체 시군구명
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
종로구
31 
중구
31 
용산구
31 
성동구
6 
영월군
 
1

Length

Max length3
Median length3
Mean length2.69
Min length2

Unique

Unique1 ?
Unique (%)1.0%

Sample

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

Common Values

ValueCountFrequency (%)
종로구 31
31.0%
중구 31
31.0%
용산구 31
31.0%
성동구 6
 
6.0%
영월군 1
 
1.0%

Length

2023-12-10T19:44:12.930980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:44:13.100456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
종로구 31
31.0%
중구 31
31.0%
용산구 31
31.0%
성동구 6
 
6.0%
영월군 1
 
1.0%

배출량(g)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4172388
Minimum1850
Maximum20522500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:44:13.320904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1850
5-th percentile482110
Q1567287.5
median3490300
Q35910275
95-th percentile13591495
Maximum20522500
Range20520650
Interquartile range (IQR)5342987.5

Descriptive statistics

Standard deviation3799780.3
Coefficient of variation (CV)0.91069678
Kurtosis4.1221765
Mean4172388
Median Absolute Deviation (MAD)2862475
Skewness1.701199
Sum4.172388 × 108
Variance1.443833 × 1013
MonotonicityNot monotonic
2023-12-10T19:44:13.568074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4040050 1
 
1.0%
589000 1
 
1.0%
478500 1
 
1.0%
552600 1
 
1.0%
488150 1
 
1.0%
567650 1
 
1.0%
501800 1
 
1.0%
598950 1
 
1.0%
547100 1
 
1.0%
520900 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1850 1
1.0%
438300 1
1.0%
455000 1
1.0%
476250 1
1.0%
478500 1
1.0%
482300 1
1.0%
485350 1
1.0%
488150 1
1.0%
501800 1
1.0%
507350 1
1.0%
ValueCountFrequency (%)
20522500 1
1.0%
16339500 1
1.0%
14634400 1
1.0%
14489400 1
1.0%
13616100 1
1.0%
13590200 1
1.0%
8592150 1
1.0%
8565700 1
1.0%
8480000 1
1.0%
8463000 1
1.0%

배출량비율(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct65
Distinct (%)65.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.202
Minimum0.49
Maximum4.35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:44:13.797903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.49
5-th percentile2.737
Q12.98
median3.12
Q33.375
95-th percentile3.9005
Maximum4.35
Range3.86
Interquartile range (IQR)0.395

Descriptive statistics

Standard deviation0.46930854
Coefficient of variation (CV)0.14656731
Kurtosis11.005094
Mean3.202
Median Absolute Deviation (MAD)0.205
Skewness-1.2651521
Sum320.2
Variance0.22025051
MonotonicityNot monotonic
2023-12-10T19:44:14.065947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.3 4
 
4.0%
3.33 3
 
3.0%
3.06 3
 
3.0%
3.37 3
 
3.0%
3.1 3
 
3.0%
3.29 3
 
3.0%
2.88 3
 
3.0%
3.07 3
 
3.0%
3.0 3
 
3.0%
2.98 3
 
3.0%
Other values (55) 69
69.0%
ValueCountFrequency (%)
0.49 1
1.0%
2.6 1
1.0%
2.62 1
1.0%
2.64 1
1.0%
2.68 1
1.0%
2.74 2
2.0%
2.78 1
1.0%
2.8 1
1.0%
2.82 1
1.0%
2.84 1
1.0%
ValueCountFrequency (%)
4.35 2
2.0%
4.34 1
1.0%
4.29 2
2.0%
3.88 1
1.0%
3.83 1
1.0%
3.74 1
1.0%
3.71 1
1.0%
3.68 2
2.0%
3.67 2
2.0%
3.66 1
1.0%

배출횟수
Real number (ℝ)

HIGH CORRELATION 

Distinct93
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3027.19
Minimum3
Maximum13079
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:44:14.265613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile390.95
Q1442
median2459
Q34608.75
95-th percentile9676.05
Maximum13079
Range13076
Interquartile range (IQR)4166.75

Descriptive statistics

Standard deviation2662.5999
Coefficient of variation (CV)0.87956153
Kurtosis2.497101
Mean3027.19
Median Absolute Deviation (MAD)2042.5
Skewness1.3979456
Sum302719
Variance7089438
MonotonicityNot monotonic
2023-12-10T19:44:14.529215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
413 2
 
2.0%
396 2
 
2.0%
390 2
 
2.0%
445 2
 
2.0%
402 2
 
2.0%
400 2
 
2.0%
2717 2
 
2.0%
496 1
 
1.0%
399 1
 
1.0%
405 1
 
1.0%
Other values (83) 83
83.0%
ValueCountFrequency (%)
3 1
1.0%
370 1
1.0%
373 1
1.0%
390 2
2.0%
391 1
1.0%
396 2
2.0%
397 1
1.0%
399 1
1.0%
400 2
2.0%
402 2
2.0%
ValueCountFrequency (%)
13079 1
1.0%
11183 1
1.0%
10382 1
1.0%
10133 1
1.0%
9734 1
1.0%
9673 1
1.0%
6175 1
1.0%
6119 1
1.0%
6101 1
1.0%
5988 1
1.0%

배출횟수비율(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct61
Distinct (%)61.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2071
Minimum1.2
Maximum4.14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:44:14.836798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.2
5-th percentile2.8595
Q13.0475
median3.155
Q33.38
95-th percentile3.846
Maximum4.14
Range2.94
Interquartile range (IQR)0.3325

Descriptive statistics

Standard deviation0.35908778
Coefficient of variation (CV)0.1119665
Kurtosis9.7050533
Mean3.2071
Median Absolute Deviation (MAD)0.135
Skewness-1.108035
Sum320.71
Variance0.12894403
MonotonicityNot monotonic
2023-12-10T19:44:15.453656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.1 5
 
5.0%
3.19 4
 
4.0%
3.5 3
 
3.0%
3.15 3
 
3.0%
3.13 3
 
3.0%
3.56 3
 
3.0%
3.02 3
 
3.0%
3.2 3
 
3.0%
3.22 3
 
3.0%
3.16 3
 
3.0%
Other values (51) 67
67.0%
ValueCountFrequency (%)
1.2 1
1.0%
2.56 1
1.0%
2.61 1
1.0%
2.76 1
1.0%
2.85 1
1.0%
2.86 2
2.0%
2.89 1
1.0%
2.9 1
1.0%
2.91 1
1.0%
2.93 1
1.0%
ValueCountFrequency (%)
4.14 1
1.0%
4.1 1
1.0%
4.09 1
1.0%
4.01 1
1.0%
3.96 1
1.0%
3.84 1
1.0%
3.7 1
1.0%
3.6 1
1.0%
3.59 1
1.0%
3.58 1
1.0%

Interactions

2023-12-10T19:44:09.609649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:04.773982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:05.714587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:06.619705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:07.846889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:08.746500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:09.742377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:04.952216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:05.867967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:06.759506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:08.005159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:08.893763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:09.885363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:05.121468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:06.007499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:06.905673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:08.155076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:09.045196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:10.019693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:05.267780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:06.139561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:07.378054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:08.313354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:09.193732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:10.152554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:05.418787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:06.314119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:07.551991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:08.473452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:09.338517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:10.273818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:05.567766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:06.457558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:07.701124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:08.618522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:09.475665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:44:15.635336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
배출일배출요일지자체코드지자체 시도명지자체 시군구명배출량(g)배출량비율(%)배출횟수배출횟수비율(%)
배출일1.0000.6090.0000.0410.0000.0000.3700.0000.445
배출요일0.6091.0000.0000.0000.0000.3320.5950.3330.739
지자체코드0.0000.0001.0001.0001.0001.0000.6750.9090.669
지자체 시도명0.0410.0001.0001.0001.000NaN1.0000.0001.000
지자체 시군구명0.0000.0001.0001.0001.0001.0000.6750.9090.669
배출량(g)0.0000.3321.000NaN1.0001.0000.7330.9900.686
배출량비율(%)0.3700.5950.6751.0000.6750.7331.0000.7610.912
배출횟수0.0000.3330.9090.0000.9090.9900.7611.0000.698
배출횟수비율(%)0.4450.7390.6691.0000.6690.6860.9120.6981.000
2023-12-10T19:44:15.862665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지자체 시도명지자체 시군구명지자체코드
지자체 시도명1.0000.9850.985
지자체 시군구명0.9851.0001.000
지자체코드0.9851.0001.000
2023-12-10T19:44:16.037746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
배출일배출요일배출량(g)배출량비율(%)배출횟수배출횟수비율(%)지자체코드지자체 시도명지자체 시군구명
배출일1.0000.081-0.160-0.079-0.1110.0730.0000.0000.000
배출요일0.0811.000-0.158-0.526-0.170-0.5740.0000.0000.000
배출량(g)-0.160-0.1581.0000.2200.9890.1910.8230.0000.823
배출량비율(%)-0.079-0.5260.2201.0000.1910.9030.5330.9790.533
배출횟수-0.111-0.1700.9890.1911.0000.2210.8320.0000.832
배출횟수비율(%)0.073-0.5740.1910.9030.2211.0000.5070.9740.507
지자체코드0.0000.0000.8230.5330.8320.5071.0000.9851.000
지자체 시도명0.0000.0000.0000.9790.0000.9740.9851.0000.985
지자체 시군구명0.0000.0000.8230.5330.8320.5071.0000.9851.000

Missing values

2023-12-10T19:44:10.448707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:44:10.656760image/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

배출년도배출월배출일배출요일지자체코드지자체 시도명지자체 시군구명배출량(g)배출량비율(%)배출횟수배출횟수비율(%)
02020311W01서울특별시종로구40400503.6726683.5
12020322W01서울특별시종로구39940503.6327143.56
22020333W01서울특별시종로구36115003.2824373.19
32020344W01서울특별시종로구30101502.7421752.85
42020355W01서울특별시종로구32826002.9823633.1
52020366W01서울특별시종로구32965003.022682.97
62020377W01서울특별시종로구34733003.1623593.09
72020381W01서울특별시종로구40427003.6827413.59
82020392W01서울특별시종로구42109503.8326853.52
920203103W01서울특별시종로구32744002.9822732.98
배출년도배출월배출일배출요일지자체코드지자체 시도명지자체 시군구명배출량(g)배출량비율(%)배출횟수배출횟수비율(%)
9020203291W03서울특별시용산구6106003.684633.58
9120203302W03서울특별시용산구4853502.924023.11
9220203313W03서울특별시용산구5384503.244173.23
932020311W04서울특별시성동구205225004.35130793.96
942020322W04서울특별시성동구163395003.46111833.39
952020333W04서울특별시성동구144894003.07101333.07
962020344W04서울특별시성동구136161002.8896732.93
972020355W04서울특별시성동구146344003.1103823.15
982020366W04서울특별시성동구135902002.8897342.95
9920203162W3L강원도영월군18500.4931.2