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 has constant value ""Constant
지자체 시군구명 is highly overall correlated with 배출량(g) and 2 other fieldsHigh correlation
지자체코드 is highly overall correlated with 배출량(g) and 2 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 1 other fieldsHigh correlation
배출횟수 is highly overall correlated with 배출량(g) and 2 other fieldsHigh correlation
배출횟수비율(%) is highly overall correlated with 배출요일 and 1 other fieldsHigh correlation
배출량(g) has unique valuesUnique

Reproduction

Analysis started2023-12-10 10:44:45.123476
Analysis finished2023-12-10 10:44:52.770719
Duration7.65 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
2019
100 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2019 100
100.0%

Length

2023-12-10T19:44:52.888746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:44:53.050803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 100
100.0%

배출월
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
12
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row12
2nd row12
3rd row12
4th row12
5th row12

Common Values

ValueCountFrequency (%)
12 100
100.0%

Length

2023-12-10T19:44:53.217559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:44:53.366842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
12 100
100.0%

배출일
Real number (ℝ)

Distinct31
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.16
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:44:53.543319image/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 deviation9.214306
Coefficient of variation (CV)0.60780382
Kurtosis-1.2602055
Mean15.16
Median Absolute Deviation (MAD)8
Skewness0.10720476
Sum1516
Variance84.903434
MonotonicityNot monotonic
2023-12-10T19:44:53.895572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
2 4
 
4.0%
4 4
 
4.0%
5 4
 
4.0%
6 4
 
4.0%
7 4
 
4.0%
1 4
 
4.0%
3 4
 
4.0%
26 3
 
3.0%
23 3
 
3.0%
24 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 4
4.0%
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%
Mean3.82
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:44:54.094207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0169985
Coefficient of variation (CV)0.52801007
Kurtosis-1.2496962
Mean3.82
Median Absolute Deviation (MAD)2
Skewness0.13826275
Sum382
Variance4.0682828
MonotonicityNot monotonic
2023-12-10T19:44:54.285315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 16
16.0%
3 16
16.0%
1 16
16.0%
4 13
13.0%
5 13
13.0%
6 13
13.0%
7 13
13.0%
ValueCountFrequency (%)
1 16
16.0%
2 16
16.0%
3 16
16.0%
4 13
13.0%
5 13
13.0%
6 13
13.0%
7 13
13.0%
ValueCountFrequency (%)
7 13
13.0%
6 13
13.0%
5 13
13.0%
4 13
13.0%
3 16
16.0%
2 16
16.0%
1 16
16.0%

지자체코드
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
W01
31 
W02
31 
W03
31 
W04
7 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

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 7
 
7.0%

Length

2023-12-10T19:44:54.515958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:44:54.712219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
w01 31
31.0%
w02 31
31.0%
w03 31
31.0%
w04 7
 
7.0%

지자체 시도명
Categorical

CONSTANT 

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

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 100
100.0%

Length

2023-12-10T19:44:55.054440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:44:55.236465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 100
100.0%

지자체 시군구명
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
종로구
31 
중구
31 
용산구
31 
성동구
7 

Length

Max length3
Median length3
Mean length2.69
Min length2

Unique

Unique0 ?
Unique (%)0.0%

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 7
 
7.0%

Length

2023-12-10T19:44:55.409806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:44:55.586847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
종로구 31
31.0%
중구 31
31.0%
용산구 31
31.0%
성동구 7
 
7.0%

배출량(g)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4085209.5
Minimum115600
Maximum18281450
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:44:55.813948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum115600
5-th percentile169210
Q1226812.5
median3446275
Q35900375
95-th percentile13060945
Maximum18281450
Range18165850
Interquartile range (IQR)5673562.5

Descriptive statistics

Standard deviation3872610.1
Coefficient of variation (CV)0.94795875
Kurtosis2.5457586
Mean4085209.5
Median Absolute Deviation (MAD)2706400
Skewness1.4174387
Sum4.0852095 × 108
Variance1.4997109 × 1013
MonotonicityNot monotonic
2023-12-10T19:44:56.091037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3999500 1
 
1.0%
169850 1
 
1.0%
186800 1
 
1.0%
166550 1
 
1.0%
169350 1
 
1.0%
182850 1
 
1.0%
209350 1
 
1.0%
158950 1
 
1.0%
250400 1
 
1.0%
271350 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
115600 1
1.0%
155550 1
1.0%
158950 1
1.0%
162450 1
1.0%
166550 1
1.0%
169350 1
1.0%
169850 1
1.0%
170400 1
1.0%
173550 1
1.0%
175950 1
1.0%
ValueCountFrequency (%)
18281450 1
1.0%
17122750 1
1.0%
14050950 1
1.0%
13770800 1
1.0%
13571000 1
1.0%
13034100 1
1.0%
13017950 1
1.0%
8698300 1
1.0%
8527750 1
1.0%
8448500 1
1.0%

배출량비율(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct75
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2289
Minimum1.87
Maximum4.45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:44:56.389931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.87
5-th percentile2.6995
Q12.8975
median3.07
Q33.4825
95-th percentile4.1965
Maximum4.45
Range2.58
Interquartile range (IQR)0.585

Descriptive statistics

Standard deviation0.48336969
Coefficient of variation (CV)0.14970104
Kurtosis0.46862146
Mean3.2289
Median Absolute Deviation (MAD)0.26
Skewness0.69920572
Sum322.89
Variance0.23364625
MonotonicityNot monotonic
2023-12-10T19:44:57.032284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.63 3
 
3.0%
3.14 3
 
3.0%
2.91 3
 
3.0%
3.07 3
 
3.0%
3.04 3
 
3.0%
3.03 3
 
3.0%
3.84 2
 
2.0%
2.81 2
 
2.0%
4.45 2
 
2.0%
3.26 2
 
2.0%
Other values (65) 74
74.0%
ValueCountFrequency (%)
1.87 1
1.0%
2.52 1
1.0%
2.58 1
1.0%
2.63 1
1.0%
2.69 1
1.0%
2.7 1
1.0%
2.74 1
1.0%
2.75 2
2.0%
2.76 2
2.0%
2.77 1
1.0%
ValueCountFrequency (%)
4.45 2
2.0%
4.4 1
1.0%
4.36 1
1.0%
4.32 1
1.0%
4.19 1
1.0%
4.09 1
1.0%
4.08 1
1.0%
4.06 1
1.0%
3.95 1
1.0%
3.92 1
1.0%

배출횟수
Real number (ℝ)

HIGH CORRELATION 

Distinct93
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2821.91
Minimum111
Maximum11085
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:44:57.289570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum111
5-th percentile128
Q1165.75
median2278.5
Q34286
95-th percentile8823.2
Maximum11085
Range10974
Interquartile range (IQR)4120.25

Descriptive statistics

Standard deviation2594.3469
Coefficient of variation (CV)0.91935847
Kurtosis1.3697931
Mean2821.91
Median Absolute Deviation (MAD)2102
Skewness1.1492246
Sum282191
Variance6730635.7
MonotonicityNot monotonic
2023-12-10T19:44:57.574795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
144 3
 
3.0%
128 3
 
3.0%
154 2
 
2.0%
164 2
 
2.0%
4086 2
 
2.0%
2514 1
 
1.0%
169 1
 
1.0%
141 1
 
1.0%
126 1
 
1.0%
127 1
 
1.0%
Other values (83) 83
83.0%
ValueCountFrequency (%)
111 1
 
1.0%
126 1
 
1.0%
127 1
 
1.0%
128 3
3.0%
133 1
 
1.0%
134 1
 
1.0%
135 1
 
1.0%
138 1
 
1.0%
139 1
 
1.0%
140 1
 
1.0%
ValueCountFrequency (%)
11085 1
1.0%
10854 1
1.0%
9669 1
1.0%
9307 1
1.0%
9055 1
1.0%
8811 1
1.0%
8676 1
1.0%
5882 1
1.0%
5726 1
1.0%
5550 1
1.0%

배출횟수비율(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct62
Distinct (%)62.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.225
Minimum2.41
Maximum4.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:44:57.883468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.41
5-th percentile2.78
Q12.9875
median3.12
Q33.43
95-th percentile3.931
Maximum4.25
Range1.84
Interquartile range (IQR)0.4425

Descriptive statistics

Standard deviation0.35198571
Coefficient of variation (CV)0.10914286
Kurtosis0.41329846
Mean3.225
Median Absolute Deviation (MAD)0.21
Skewness0.80754701
Sum322.5
Variance0.12389394
MonotonicityNot monotonic
2023-12-10T19:44:58.202614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.89 5
 
5.0%
2.78 3
 
3.0%
3.34 3
 
3.0%
3.02 3
 
3.0%
3.62 3
 
3.0%
3.11 3
 
3.0%
2.93 3
 
3.0%
3.09 3
 
3.0%
3.12 3
 
3.0%
2.99 3
 
3.0%
Other values (52) 68
68.0%
ValueCountFrequency (%)
2.41 1
 
1.0%
2.73 1
 
1.0%
2.75 1
 
1.0%
2.78 3
3.0%
2.82 1
 
1.0%
2.87 2
 
2.0%
2.88 2
 
2.0%
2.89 5
5.0%
2.91 2
 
2.0%
2.92 1
 
1.0%
ValueCountFrequency (%)
4.25 1
1.0%
4.16 1
1.0%
4.08 1
1.0%
4.05 1
1.0%
3.95 1
1.0%
3.93 1
1.0%
3.91 1
1.0%
3.84 1
1.0%
3.7 1
1.0%
3.67 1
1.0%

Interactions

2023-12-10T19:44:51.142689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:45.606859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:46.603748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:47.969574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:49.225920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:50.167262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:51.317763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:45.741073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:46.771803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:48.136972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:49.364498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:50.362208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:51.504476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:45.883587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:47.273127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:48.317144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:49.521733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:50.518104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:51.715607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:46.106125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:47.442229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:48.506998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:49.708171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:50.683809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:51.893070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:46.274417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:47.637440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:48.720481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:49.856105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:50.835852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:52.059441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:46.414108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:47.801765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:48.983555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:49.991991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:50.982650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:44:58.420103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
배출일배출요일지자체코드지자체 시군구명배출량(g)배출량비율(%)배출횟수배출횟수비율(%)
배출일1.0000.6110.0000.0000.0000.4500.0000.497
배출요일0.6111.0000.0000.0000.3850.6490.3670.653
지자체코드0.0000.0001.0001.0000.9590.3351.0000.179
지자체 시군구명0.0000.0001.0001.0000.9590.3351.0000.179
배출량(g)0.0000.3850.9590.9591.0000.5970.9390.601
배출량비율(%)0.4500.6490.3350.3350.5971.0000.7400.837
배출횟수0.0000.3671.0001.0000.9390.7401.0000.603
배출횟수비율(%)0.4970.6530.1790.1790.6010.8370.6031.000
2023-12-10T19:44:58.844311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지자체 시군구명지자체코드
지자체 시군구명1.0001.000
지자체코드1.0001.000
2023-12-10T19:44:59.088640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
배출일배출요일배출량(g)배출량비율(%)배출횟수배출횟수비율(%)지자체코드지자체 시군구명
배출일1.0000.069-0.178-0.091-0.154-0.0130.0000.000
배출요일0.0691.000-0.172-0.617-0.196-0.7150.0000.000
배출량(g)-0.178-0.1721.0000.2980.9900.2310.9470.947
배출량비율(%)-0.091-0.6170.2981.0000.2660.8810.2120.212
배출횟수-0.154-0.1960.9900.2661.0000.2640.9740.974
배출횟수비율(%)-0.013-0.7150.2310.8810.2641.0000.1090.109
지자체코드0.0000.0000.9470.2120.9740.1091.0001.000
지자체 시군구명0.0000.0000.9470.2120.9740.1091.0001.000

Missing values

2023-12-10T19:44:52.307376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:44:52.664788image/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(%)
020191222W01서울특별시종로구39995003.8425143.65
120191233W01서울특별시종로구35653003.4223493.41
220191244W01서울특별시종로구32720503.1422613.28
320191255W01서울특별시종로구31103502.9920733.01
420191266W01서울특별시종로구28631002.7519822.88
520191277W01서울특별시종로구29366502.8219922.89
620191281W01서울특별시종로구40704003.9124953.62
720191292W01서울특별시종로구38374503.6924633.58
8201912103W01서울특별시종로구35007503.3622783.31
9201912114W01서울특별시종로구31170502.9921313.09
배출년도배출월배출일배출요일지자체코드지자체 시도명지자체 시군구명배출량(g)배출량비율(%)배출횟수배출횟수비율(%)
90201912302W03서울특별시용산구2156503.491583.43
91201912313W03서울특별시용산구2070003.351643.56
9220191211W04서울특별시성동구182814504.08110853.7
9320191222W04서울특별시성동구171227503.82108543.62
9420191233W04서울특별시성동구137708003.0793073.1
9520191244W04서울특별시성동구140509503.1496693.23
9620191255W04서울특별시성동구130179502.9190553.02
9720191266W04서울특별시성동구130341002.9188112.94
9820191277W04서울특별시성동구135710003.0386762.89
9920191211W01서울특별시종로구39965003.8422793.31