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 10:43:36.903977
Analysis finished2023-12-10 10:43:43.732127
Duration6.83 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

2023-12-10T19:43:43.848201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:43:44.106737image/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
5
100 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 100
100.0%

Length

2023-12-10T19:43:44.278663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:43:44.430845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5 100
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
2023-12-10T19:43:44.609344image/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
2023-12-10T19:43:44.832225image/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%
Mean4.06
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:43:45.008244image/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 deviation2.0832182
Coefficient of variation (CV)0.51310793
Kurtosis-1.3348586
Mean4.06
Median Absolute Deviation (MAD)2
Skewness-0.060830899
Sum406
Variance4.339798
MonotonicityNot monotonic
2023-12-10T19:43:45.195760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
7 16
16.0%
1 16
16.0%
6 16
16.0%
2 13
13.0%
3 13
13.0%
4 13
13.0%
5 13
13.0%
ValueCountFrequency (%)
1 16
16.0%
2 13
13.0%
3 13
13.0%
4 13
13.0%
5 13
13.0%
6 16
16.0%
7 16
16.0%
ValueCountFrequency (%)
7 16
16.0%
6 16
16.0%
5 13
13.0%
4 13
13.0%
3 13
13.0%
2 13
13.0%
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

2023-12-10T19:43:45.423834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:43:45.611151image/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

2023-12-10T19:43:45.801391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:43:45.972529image/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

2023-12-10T19:43:46.146311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:43:46.355742image/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%
Mean4573653.5
Minimum475350
Maximum18579550
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:43:46.565107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum475350
5-th percentile515425
Q1664712.5
median3906300
Q36302875
95-th percentile14436515
Maximum18579550
Range18104200
Interquartile range (IQR)5638162.5

Descriptive statistics

Standard deviation4013402.3
Coefficient of variation (CV)0.87750467
Kurtosis2.3088752
Mean4573653.5
Median Absolute Deviation (MAD)2779900
Skewness1.4186168
Sum4.5736535 × 108
Variance1.6107398 × 1013
MonotonicityNot monotonic
2023-12-10T19:43:46.831028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3435400 1
 
1.0%
592500 1
 
1.0%
511150 1
 
1.0%
546950 1
 
1.0%
511000 1
 
1.0%
666750 1
 
1.0%
712150 1
 
1.0%
525400 1
 
1.0%
526000 1
 
1.0%
571100 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
475350 1
1.0%
481900 1
1.0%
508100 1
1.0%
511000 1
1.0%
511150 1
1.0%
515650 1
1.0%
522750 1
1.0%
523750 1
1.0%
525400 1
1.0%
526000 1
1.0%
ValueCountFrequency (%)
18579550 1
1.0%
16176049 1
1.0%
15939750 1
1.0%
15883700 1
1.0%
15117000 1
1.0%
14400700 1
1.0%
14309350 1
1.0%
9563200 1
1.0%
9322800 1
1.0%
9145450 1
1.0%

배출량비율(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct71
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.22
Minimum2.42
Maximum4.54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:43:47.093291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.42
5-th percentile2.6595
Q12.9375
median3.135
Q33.385
95-th percentile4.01
Maximum4.54
Range2.12
Interquartile range (IQR)0.4475

Descriptive statistics

Standard deviation0.41738242
Coefficient of variation (CV)0.12962187
Kurtosis0.98039156
Mean3.22
Median Absolute Deviation (MAD)0.22
Skewness1.0088445
Sum322
Variance0.17420808
MonotonicityNot monotonic
2023-12-10T19:43:47.310608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.12 4
 
4.0%
2.91 3
 
3.0%
2.86 3
 
3.0%
3.14 3
 
3.0%
3.2 3
 
3.0%
3.03 3
 
3.0%
3.18 2
 
2.0%
3.23 2
 
2.0%
2.89 2
 
2.0%
2.81 2
 
2.0%
Other values (61) 73
73.0%
ValueCountFrequency (%)
2.42 1
1.0%
2.48 1
1.0%
2.62 1
1.0%
2.63 1
1.0%
2.65 1
1.0%
2.66 1
1.0%
2.78 2
2.0%
2.8 1
1.0%
2.81 2
2.0%
2.84 1
1.0%
ValueCountFrequency (%)
4.54 1
1.0%
4.43 1
1.0%
4.34 1
1.0%
4.15 1
1.0%
4.01 2
2.0%
3.99 1
1.0%
3.95 1
1.0%
3.94 1
1.0%
3.92 1
1.0%
3.81 1
1.0%

배출횟수
Real number (ℝ)

HIGH CORRELATION 

Distinct94
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3377.25
Minimum363
Maximum12743
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:43:47.570135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum363
5-th percentile402
Q1497.5
median2739.5
Q34984.5
95-th percentile10704.85
Maximum12743
Range12380
Interquartile range (IQR)4487

Descriptive statistics

Standard deviation2920.6822
Coefficient of variation (CV)0.86481079
Kurtosis1.6566209
Mean3377.25
Median Absolute Deviation (MAD)2245.5
Skewness1.2650646
Sum337725
Variance8530384.8
MonotonicityNot monotonic
2023-12-10T19:43:47.823819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4901 2
 
2.0%
5123 2
 
2.0%
418 2
 
2.0%
2602 2
 
2.0%
402 2
 
2.0%
2766 2
 
2.0%
477 1
 
1.0%
373 1
 
1.0%
432 1
 
1.0%
412 1
 
1.0%
Other values (84) 84
84.0%
ValueCountFrequency (%)
363 1
1.0%
366 1
1.0%
373 1
1.0%
382 1
1.0%
402 2
2.0%
404 1
1.0%
405 1
1.0%
412 1
1.0%
418 2
2.0%
420 1
1.0%
ValueCountFrequency (%)
12743 1
1.0%
11653 1
1.0%
11606 1
1.0%
11334 1
1.0%
10930 1
1.0%
10693 1
1.0%
10142 1
1.0%
6764 1
1.0%
6715 1
1.0%
6600 1
1.0%

배출횟수비율(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct62
Distinct (%)62.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2232
Minimum2.5
Maximum4.19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:43:48.064670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile2.6585
Q13.0275
median3.175
Q33.3425
95-th percentile3.8025
Maximum4.19
Range1.69
Interquartile range (IQR)0.315

Descriptive statistics

Standard deviation0.34293705
Coefficient of variation (CV)0.10639645
Kurtosis0.58212147
Mean3.2232
Median Absolute Deviation (MAD)0.16
Skewness0.60269545
Sum322.32
Variance0.11760582
MonotonicityNot monotonic
2023-12-10T19:43:48.667684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.03 5
 
5.0%
3.09 4
 
4.0%
2.92 4
 
4.0%
3.27 4
 
4.0%
3.17 4
 
4.0%
3.16 3
 
3.0%
3.24 3
 
3.0%
3.8 3
 
3.0%
2.93 2
 
2.0%
3.31 2
 
2.0%
Other values (52) 66
66.0%
ValueCountFrequency (%)
2.5 1
 
1.0%
2.55 1
 
1.0%
2.56 1
 
1.0%
2.63 2
2.0%
2.66 1
 
1.0%
2.71 1
 
1.0%
2.77 1
 
1.0%
2.84 1
 
1.0%
2.86 1
 
1.0%
2.92 4
4.0%
ValueCountFrequency (%)
4.19 1
 
1.0%
4.15 1
 
1.0%
4.08 1
 
1.0%
3.99 1
 
1.0%
3.85 1
 
1.0%
3.8 3
3.0%
3.78 1
 
1.0%
3.76 1
 
1.0%
3.75 1
 
1.0%
3.73 2
2.0%

Interactions

2023-12-10T19:43:42.355643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:37.359887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:38.213292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:39.378078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:40.452228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:41.359629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:42.498333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:37.489269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:38.349486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:39.634895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:40.620532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:41.527651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:42.671746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:37.632824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:38.489531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:39.802915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:40.769072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:41.708373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:42.834093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:37.779975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:38.963632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:39.964423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:40.947628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:41.892303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:42.982684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:37.907169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:39.077872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:40.109778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:41.079368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:42.030814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:43.142872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:38.054423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:39.211101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:40.276675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:41.218745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:43:42.182676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:43:48.873315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
배출일배출요일지자체코드지자체 시군구명배출량(g)배출량비율(%)배출횟수배출횟수비율(%)
배출일1.0000.6110.0000.0000.0000.5240.0000.602
배출요일0.6111.0000.0000.0000.2980.6040.2300.580
지자체코드0.0000.0001.0001.0000.9760.3750.9850.136
지자체 시군구명0.0000.0001.0001.0000.9760.3750.9850.136
배출량(g)0.0000.2980.9760.9761.0000.6980.9840.630
배출량비율(%)0.5240.6040.3750.3750.6981.0000.7420.962
배출횟수0.0000.2300.9850.9850.9840.7421.0000.694
배출횟수비율(%)0.6020.5800.1360.1360.6300.9620.6941.000
2023-12-10T19:43:49.083747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지자체 시군구명지자체코드
지자체 시군구명1.0001.000
지자체코드1.0001.000
2023-12-10T19:43:49.240659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
배출일배출요일배출량(g)배출량비율(%)배출횟수배출횟수비율(%)지자체코드지자체 시군구명
배출일1.000-0.014-0.0730.265-0.0590.2920.0000.000
배출요일-0.0141.000-0.164-0.558-0.162-0.5460.0000.000
배출량(g)-0.073-0.1641.0000.2440.9930.2370.9490.949
배출량비율(%)0.265-0.5580.2441.0000.2240.9330.2230.223
배출횟수-0.059-0.1620.9930.2241.0000.2570.9640.964
배출횟수비율(%)0.292-0.5460.2370.9330.2571.0000.0730.073
지자체코드0.0000.0000.9490.2230.9640.0731.0001.000
지자체 시군구명0.0000.0000.9490.2230.9640.0731.0001.000

Missing values

2023-12-10T19:43:43.348325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:43:43.636898image/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(%)
02020527W01서울특별시종로구34354002.9124682.97
12020531W01서울특별시종로구41075503.4727553.31
22020542W01서울특별시종로구38966503.327143.26
32020553W01서울특별시종로구39159503.3128353.41
42020564W01서울특별시종로구36858503.1226573.19
52020575W01서울특별시종로구35098002.9725763.09
62020586W01서울특별시종로구32910502.7824312.92
72020597W01서울특별시종로구31126002.6321242.55
820205101W01서울특별시종로구46635003.9431303.76
920205112W01서울특별시종로구41720003.5328833.46
배출년도배출월배출일배출요일지자체코드지자체 시도명지자체 시군구명배출량(g)배출량비율(%)배출횟수배출횟수비율(%)
9020205307W03서울특별시용산구6803003.744903.56
9120205311W03서울특별시용산구7290004.015213.78
922020516W04서울특별시성동구151170003.02106933.02
932020527W04서울특별시성동구143093502.86101422.86
942020531W04서울특별시성동구185795503.71127433.6
952020542W04서울특별시성동구159397503.18113343.2
962020553W04서울특별시성동구161760493.23116063.28
972020564W04서울특별시성동구158837003.17116533.29
982020575W04서울특별시성동구144007002.87109303.09
992020516W01서울특별시종로구35903503.0425713.09