Overview

Dataset statistics

Number of variables13
Number of observations27
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 KiB
Average record size in memory120.9 B

Variable types

Text1
Numeric12

Dataset

Description여수시 관내 차상위계층 현황(한부모가족, 차상위장애인, 차상위자활, 차상위본인부담경감, 차상위계층확인) 자료제공을 위한 자료입니다
URLhttps://www.data.go.kr/data/15055373/fileData.do

Alerts

합계(가구) is highly overall correlated with 합계(인원) and 10 other fieldsHigh correlation
합계(인원) is highly overall correlated with 합계(가구) and 10 other fieldsHigh correlation
한부모(가구) is highly overall correlated with 합계(가구) and 9 other fieldsHigh correlation
한부모(인원) is highly overall correlated with 합계(가구) and 9 other fieldsHigh correlation
차상위장애인(가구) is highly overall correlated with 합계(가구) and 10 other fieldsHigh correlation
차상위장애인(인원) is highly overall correlated with 합계(가구) and 10 other fieldsHigh correlation
차상위자활(가구) is highly overall correlated with 합계(가구) and 8 other fieldsHigh correlation
차상위자활(인원) is highly overall correlated with 합계(가구) and 8 other fieldsHigh correlation
차상위본인부담경감(가구) is highly overall correlated with 합계(가구) and 10 other fieldsHigh correlation
차상위본인부담경감(인원) is highly overall correlated with 합계(가구) and 10 other fieldsHigh correlation
차상위계층확인(가구) is highly overall correlated with 합계(가구) and 6 other fieldsHigh correlation
차상위계층확인(인원) is highly overall correlated with 합계(가구) and 8 other fieldsHigh correlation
읍면동 has unique valuesUnique
합계(인원) has unique valuesUnique
차상위자활(가구) has 5 (18.5%) zerosZeros
차상위자활(인원) has 5 (18.5%) zerosZeros

Reproduction

Analysis started2023-12-12 02:28:15.932890
Analysis finished2023-12-12 02:28:32.663197
Duration16.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

읍면동
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-12T11:28:32.837998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters108
Distinct characters46
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st row 돌산
2nd row 소라
3rd row 율촌
4th row 화양
5th row 남면
ValueCountFrequency (%)
돌산 1
 
3.7%
국동 1
 
3.7%
삼일 1
 
3.7%
주삼 1
 
3.7%
여천 1
 
3.7%
시전 1
 
3.7%
쌍봉 1
 
3.7%
만덕 1
 
3.7%
둔덕 1
 
3.7%
미평 1
 
3.7%
Other values (17) 17
63.0%
2023-12-12T11:28:33.235806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
54
50.0%
3
 
2.8%
2
 
1.9%
2
 
1.9%
2
 
1.9%
2
 
1.9%
2
 
1.9%
2
 
1.9%
2
 
1.9%
1
 
0.9%
Other values (36) 36
33.3%

Most occurring categories

ValueCountFrequency (%)
Space Separator 54
50.0%
Other Letter 54
50.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3
 
5.6%
2
 
3.7%
2
 
3.7%
2
 
3.7%
2
 
3.7%
2
 
3.7%
2
 
3.7%
2
 
3.7%
1
 
1.9%
1
 
1.9%
Other values (35) 35
64.8%
Space Separator
ValueCountFrequency (%)
54
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 54
50.0%
Hangul 54
50.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3
 
5.6%
2
 
3.7%
2
 
3.7%
2
 
3.7%
2
 
3.7%
2
 
3.7%
2
 
3.7%
2
 
3.7%
1
 
1.9%
1
 
1.9%
Other values (35) 35
64.8%
Common
ValueCountFrequency (%)
54
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 54
50.0%
Hangul 54
50.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
54
100.0%
Hangul
ValueCountFrequency (%)
3
 
5.6%
2
 
3.7%
2
 
3.7%
2
 
3.7%
2
 
3.7%
2
 
3.7%
2
 
3.7%
2
 
3.7%
1
 
1.9%
1
 
1.9%
Other values (35) 35
64.8%

합계(가구)
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean162.07407
Minimum25
Maximum378
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T11:28:33.388703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile35.1
Q199
median129
Q3241.5
95-th percentile355.6
Maximum378
Range353
Interquartile range (IQR)142.5

Descriptive statistics

Standard deviation100.42331
Coefficient of variation (CV)0.61961364
Kurtosis-0.36305825
Mean162.07407
Median Absolute Deviation (MAD)42
Skewness0.78593307
Sum4376
Variance10084.84
MonotonicityNot monotonic
2023-12-12T11:28:33.519471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
110 3
 
11.1%
141 2
 
7.4%
242 1
 
3.7%
60 1
 
3.7%
25 1
 
3.7%
27 1
 
3.7%
378 1
 
3.7%
284 1
 
3.7%
240 1
 
3.7%
111 1
 
3.7%
Other values (14) 14
51.9%
ValueCountFrequency (%)
25 1
 
3.7%
27 1
 
3.7%
54 1
 
3.7%
60 1
 
3.7%
87 1
 
3.7%
91 1
 
3.7%
93 1
 
3.7%
105 1
 
3.7%
110 3
11.1%
111 1
 
3.7%
ValueCountFrequency (%)
378 1
3.7%
361 1
3.7%
343 1
3.7%
284 1
3.7%
254 1
3.7%
248 1
3.7%
242 1
3.7%
241 1
3.7%
240 1
3.7%
142 1
3.7%

합계(인원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean245.48148
Minimum31
Maximum612
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T11:28:33.669458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile48.2
Q1133.5
median184
Q3375
95-th percentile572.7
Maximum612
Range581
Interquartile range (IQR)241.5

Descriptive statistics

Standard deviation167.33853
Coefficient of variation (CV)0.68167474
Kurtosis-0.2758395
Mean245.48148
Median Absolute Deviation (MAD)82
Skewness0.86824163
Sum6628
Variance28002.182
MonotonicityNot monotonic
2023-12-12T11:28:33.812931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
332 1
 
3.7%
585 1
 
3.7%
31 1
 
3.7%
38 1
 
3.7%
206 1
 
3.7%
612 1
 
3.7%
465 1
 
3.7%
395 1
 
3.7%
225 1
 
3.7%
202 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
31 1
3.7%
38 1
3.7%
72 1
3.7%
81 1
3.7%
102 1
3.7%
126 1
3.7%
130 1
3.7%
137 1
3.7%
142 1
3.7%
149 1
3.7%
ValueCountFrequency (%)
612 1
3.7%
585 1
3.7%
544 1
3.7%
465 1
3.7%
401 1
3.7%
395 1
3.7%
388 1
3.7%
362 1
3.7%
332 1
3.7%
225 1
3.7%

한부모(가구)
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.740741
Minimum1
Maximum125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T11:28:33.952586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.3
Q111
median28
Q366
95-th percentile101.3
Maximum125
Range124
Interquartile range (IQR)55

Descriptive statistics

Standard deviation35.523116
Coefficient of variation (CV)0.91694467
Kurtosis-0.11321669
Mean38.740741
Median Absolute Deviation (MAD)23
Skewness0.92938479
Sum1046
Variance1261.8917
MonotonicityNot monotonic
2023-12-12T11:28:34.086419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
19 3
 
11.1%
3 2
 
7.4%
36 1
 
3.7%
15 1
 
3.7%
41 1
 
3.7%
125 1
 
3.7%
93 1
 
3.7%
71 1
 
3.7%
43 1
 
3.7%
44 1
 
3.7%
Other values (14) 14
51.9%
ValueCountFrequency (%)
1 1
 
3.7%
2 1
 
3.7%
3 2
7.4%
4 1
 
3.7%
5 1
 
3.7%
10 1
 
3.7%
12 1
 
3.7%
15 1
 
3.7%
19 3
11.1%
22 1
 
3.7%
ValueCountFrequency (%)
125 1
3.7%
104 1
3.7%
95 1
3.7%
93 1
3.7%
71 1
3.7%
70 1
3.7%
69 1
3.7%
63 1
3.7%
44 1
3.7%
43 1
3.7%

한부모(인원)
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.222222
Minimum2
Maximum303
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T11:28:34.262993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6
Q130.5
median59
Q3164
95-th percentile265.1
Maximum303
Range301
Interquartile range (IQR)133.5

Descriptive statistics

Standard deviation90.534028
Coefficient of variation (CV)0.93120715
Kurtosis-0.32294505
Mean97.222222
Median Absolute Deviation (MAD)52
Skewness0.89892249
Sum2625
Variance8196.4103
MonotonicityNot monotonic
2023-12-12T11:28:34.424215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
6 2
 
7.4%
95 1
 
3.7%
182 1
 
3.7%
7 1
 
3.7%
115 1
 
3.7%
303 1
 
3.7%
233 1
 
3.7%
183 1
 
3.7%
98 1
 
3.7%
112 1
 
3.7%
Other values (16) 16
59.3%
ValueCountFrequency (%)
2 1
3.7%
6 2
7.4%
7 1
3.7%
9 1
3.7%
12 1
3.7%
29 1
3.7%
32 1
3.7%
34 1
3.7%
43 1
3.7%
44 1
3.7%
ValueCountFrequency (%)
303 1
3.7%
272 1
3.7%
249 1
3.7%
233 1
3.7%
183 1
3.7%
182 1
3.7%
176 1
3.7%
152 1
3.7%
115 1
3.7%
112 1
3.7%

차상위장애인(가구)
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)74.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.777778
Minimum8
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T11:28:34.561589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile11.1
Q125.5
median42
Q362.5
95-th percentile95
Maximum97
Range89
Interquartile range (IQR)37

Descriptive statistics

Standard deviation26.611642
Coefficient of variation (CV)0.58132228
Kurtosis-0.48137684
Mean45.777778
Median Absolute Deviation (MAD)18
Skewness0.63298692
Sum1236
Variance708.17949
MonotonicityNot monotonic
2023-12-12T11:28:34.690684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
50 3
 
11.1%
42 3
 
11.1%
95 2
 
7.4%
17 2
 
7.4%
34 2
 
7.4%
60 1
 
3.7%
9 1
 
3.7%
8 1
 
3.7%
97 1
 
3.7%
66 1
 
3.7%
Other values (10) 10
37.0%
ValueCountFrequency (%)
8 1
3.7%
9 1
3.7%
16 1
3.7%
17 2
7.4%
24 1
3.7%
25 1
3.7%
26 1
3.7%
34 2
7.4%
35 1
3.7%
36 1
3.7%
ValueCountFrequency (%)
97 1
 
3.7%
95 2
7.4%
90 1
 
3.7%
73 1
 
3.7%
66 1
 
3.7%
65 1
 
3.7%
60 1
 
3.7%
50 3
11.1%
42 3
11.1%
38 1
 
3.7%

차상위장애인(인원)
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)81.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.777778
Minimum9
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T11:28:34.825866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile11.4
Q127.5
median43
Q364
95-th percentile98.4
Maximum100
Range91
Interquartile range (IQR)36.5

Descriptive statistics

Standard deviation27.47493
Coefficient of variation (CV)0.57505668
Kurtosis-0.51893577
Mean47.777778
Median Absolute Deviation (MAD)18
Skewness0.59066336
Sum1290
Variance754.87179
MonotonicityNot monotonic
2023-12-12T11:28:34.942511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
17 3
 
11.1%
25 2
 
7.4%
9 2
 
7.4%
37 2
 
7.4%
55 1
 
3.7%
100 1
 
3.7%
70 1
 
3.7%
66 1
 
3.7%
44 1
 
3.7%
62 1
 
3.7%
Other values (12) 12
44.4%
ValueCountFrequency (%)
9 2
7.4%
17 3
11.1%
25 2
7.4%
30 1
 
3.7%
34 1
 
3.7%
36 1
 
3.7%
37 2
7.4%
41 1
 
3.7%
43 1
 
3.7%
44 1
 
3.7%
ValueCountFrequency (%)
100 1
3.7%
99 1
3.7%
97 1
3.7%
94 1
3.7%
75 1
3.7%
70 1
3.7%
66 1
3.7%
62 1
3.7%
55 1
3.7%
53 1
3.7%

차상위자활(가구)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)51.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1111111
Minimum0
Maximum18
Zeros5
Zeros (%)18.5%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T11:28:35.055680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q37.5
95-th percentile13.7
Maximum18
Range18
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation4.82249
Coefficient of variation (CV)0.94353066
Kurtosis0.61157801
Mean5.1111111
Median Absolute Deviation (MAD)3
Skewness1.0931142
Sum138
Variance23.25641
MonotonicityNot monotonic
2023-12-12T11:28:35.178129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 5
18.5%
4 4
14.8%
2 4
14.8%
3 2
 
7.4%
5 2
 
7.4%
11 2
 
7.4%
14 1
 
3.7%
1 1
 
3.7%
8 1
 
3.7%
6 1
 
3.7%
Other values (4) 4
14.8%
ValueCountFrequency (%)
0 5
18.5%
1 1
 
3.7%
2 4
14.8%
3 2
 
7.4%
4 4
14.8%
5 2
 
7.4%
6 1
 
3.7%
7 1
 
3.7%
8 1
 
3.7%
9 1
 
3.7%
ValueCountFrequency (%)
18 1
 
3.7%
14 1
 
3.7%
13 1
 
3.7%
11 2
7.4%
9 1
 
3.7%
8 1
 
3.7%
7 1
 
3.7%
6 1
 
3.7%
5 2
7.4%
4 4
14.8%

차상위자활(인원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)55.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2222222
Minimum0
Maximum18
Zeros5
Zeros (%)18.5%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T11:28:35.326174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q37.5
95-th percentile14.4
Maximum18
Range18
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation4.9250797
Coefficient of variation (CV)0.94310037
Kurtosis0.5239268
Mean5.2222222
Median Absolute Deviation (MAD)3
Skewness1.0950124
Sum141
Variance24.25641
MonotonicityNot monotonic
2023-12-12T11:28:35.454304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 5
18.5%
4 4
14.8%
2 3
11.1%
3 3
11.1%
5 2
 
7.4%
15 1
 
3.7%
1 1
 
3.7%
8 1
 
3.7%
6 1
 
3.7%
18 1
 
3.7%
Other values (5) 5
18.5%
ValueCountFrequency (%)
0 5
18.5%
1 1
 
3.7%
2 3
11.1%
3 3
11.1%
4 4
14.8%
5 2
 
7.4%
6 1
 
3.7%
7 1
 
3.7%
8 1
 
3.7%
9 1
 
3.7%
ValueCountFrequency (%)
18 1
3.7%
15 1
3.7%
13 1
3.7%
12 1
3.7%
11 1
3.7%
9 1
3.7%
8 1
3.7%
7 1
3.7%
6 1
3.7%
5 2
7.4%

차상위본인부담경감(가구)
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.851852
Minimum3
Maximum118
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T11:28:35.582381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile11
Q127.5
median39
Q369.5
95-th percentile111
Maximum118
Range115
Interquartile range (IQR)42

Descriptive statistics

Standard deviation31.244327
Coefficient of variation (CV)0.63957303
Kurtosis-0.084800953
Mean48.851852
Median Absolute Deviation (MAD)18
Skewness0.75419309
Sum1319
Variance976.20798
MonotonicityNot monotonic
2023-12-12T11:28:35.698238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
11 2
 
7.4%
32 2
 
7.4%
39 2
 
7.4%
71 1
 
3.7%
3 1
 
3.7%
23 1
 
3.7%
117 1
 
3.7%
77 1
 
3.7%
68 1
 
3.7%
28 1
 
3.7%
Other values (14) 14
51.9%
ValueCountFrequency (%)
3 1
3.7%
11 2
7.4%
15 1
3.7%
21 1
3.7%
23 1
3.7%
27 1
3.7%
28 1
3.7%
32 2
7.4%
35 1
3.7%
36 1
3.7%
ValueCountFrequency (%)
118 1
3.7%
117 1
3.7%
97 1
3.7%
86 1
3.7%
77 1
3.7%
75 1
3.7%
71 1
3.7%
68 1
3.7%
65 1
3.7%
54 1
3.7%

차상위본인부담경감(인원)
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.333333
Minimum4
Maximum156
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T11:28:35.844880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile11.9
Q137.5
median48
Q390.5
95-th percentile143.1
Maximum156
Range152
Interquartile range (IQR)53

Descriptive statistics

Standard deviation40.371353
Coefficient of variation (CV)0.64766877
Kurtosis0.1130407
Mean62.333333
Median Absolute Deviation (MAD)25
Skewness0.81617256
Sum1683
Variance1629.8462
MonotonicityNot monotonic
2023-12-12T11:28:35.976400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
96 2
 
7.4%
42 2
 
7.4%
11 1
 
3.7%
4 1
 
3.7%
14 1
 
3.7%
38 1
 
3.7%
156 1
 
3.7%
90 1
 
3.7%
57 1
 
3.7%
45 1
 
3.7%
Other values (15) 15
55.6%
ValueCountFrequency (%)
4 1
3.7%
11 1
3.7%
14 1
3.7%
18 1
3.7%
30 1
3.7%
31 1
3.7%
37 1
3.7%
38 1
3.7%
41 1
3.7%
42 2
7.4%
ValueCountFrequency (%)
156 1
3.7%
150 1
3.7%
127 1
3.7%
102 1
3.7%
96 2
7.4%
91 1
3.7%
90 1
3.7%
84 1
3.7%
73 1
3.7%
58 1
3.7%

차상위계층확인(가구)
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)77.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.592593
Minimum5
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T11:28:36.125443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile7.9
Q115
median22
Q329
95-th percentile47.4
Maximum54
Range49
Interquartile range (IQR)14

Descriptive statistics

Standard deviation11.975164
Coefficient of variation (CV)0.50758153
Kurtosis0.85135267
Mean23.592593
Median Absolute Deviation (MAD)7
Skewness0.87368033
Sum637
Variance143.40456
MonotonicityNot monotonic
2023-12-12T11:28:36.267479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
29 3
 
11.1%
15 2
 
7.4%
20 2
 
7.4%
22 2
 
7.4%
12 2
 
7.4%
36 1
 
3.7%
26 1
 
3.7%
10 1
 
3.7%
5 1
 
3.7%
27 1
 
3.7%
Other values (11) 11
40.7%
ValueCountFrequency (%)
5 1
3.7%
7 1
3.7%
10 1
3.7%
12 2
7.4%
14 1
3.7%
15 2
7.4%
17 1
3.7%
18 1
3.7%
19 1
3.7%
20 2
7.4%
ValueCountFrequency (%)
54 1
 
3.7%
51 1
 
3.7%
39 1
 
3.7%
36 1
 
3.7%
31 1
 
3.7%
30 1
 
3.7%
29 3
11.1%
28 1
 
3.7%
27 1
 
3.7%
26 1
 
3.7%

차상위계층확인(인원)
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)81.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.925926
Minimum9
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T11:28:36.375964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile9.6
Q120.5
median32
Q343.5
95-th percentile61.2
Maximum72
Range63
Interquartile range (IQR)23

Descriptive statistics

Standard deviation16.596862
Coefficient of variation (CV)0.50406669
Kurtosis-0.23795093
Mean32.925926
Median Absolute Deviation (MAD)13
Skewness0.55491374
Sum889
Variance275.45584
MonotonicityNot monotonic
2023-12-12T11:28:36.500438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
33 2
 
7.4%
24 2
 
7.4%
18 2
 
7.4%
25 2
 
7.4%
9 2
 
7.4%
52 1
 
3.7%
11 1
 
3.7%
34 1
 
3.7%
42 1
 
3.7%
57 1
 
3.7%
Other values (12) 12
44.4%
ValueCountFrequency (%)
9 2
7.4%
11 1
3.7%
16 1
3.7%
17 1
3.7%
18 2
7.4%
23 1
3.7%
24 2
7.4%
25 2
7.4%
31 1
3.7%
32 1
3.7%
ValueCountFrequency (%)
72 1
3.7%
63 1
3.7%
57 1
3.7%
52 1
3.7%
49 1
3.7%
47 1
3.7%
45 1
3.7%
42 1
3.7%
41 1
3.7%
39 1
3.7%

Interactions

2023-12-12T11:28:30.613959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:16.292548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:17.527149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:18.711291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:20.190764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:21.417927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:22.640002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:23.842343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:24.966175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:26.444671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:27.826839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:29.284492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:30.732222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:16.377349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:17.661510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:18.811685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:20.283424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:21.519373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:22.750005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:23.952369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:25.050689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:26.549752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:27.966212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:29.396164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:30.834602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:16.459179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:17.746647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:19.200665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:20.375369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:21.610711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:22.846918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:24.051687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:25.150221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:26.656687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:28.080728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:29.505950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:30.949868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:16.551283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:17.867654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:19.294564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:20.486524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:21.692724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:22.945678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:24.156393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:25.245313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:26.749456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:28.198152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:29.638026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:31.065080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:16.648868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:17.967533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:19.388979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:20.572030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:21.796923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:23.039638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:24.263276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:25.623717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:26.860928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:28.315934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:29.748004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:31.168319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:16.727641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:18.060729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:19.504172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:20.657405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:21.889066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:23.122438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:24.343224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:25.717803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:26.963417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:28.443660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:29.834142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:31.266006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:16.836642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:18.145357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:19.588664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:20.734773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:21.979728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:23.207696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:24.413613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:25.822316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:27.066195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:28.560412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:29.926390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:31.395672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:16.930417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:18.249547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:19.702251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:20.842048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:22.079742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:23.304276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:24.498852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:25.947716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:27.221416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:28.701294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:30.021209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:31.531932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:17.071056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:18.337145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:19.790408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:20.967842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:22.221976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:23.416178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:24.594459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:26.059742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:27.340377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:28.816289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:30.147533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:31.620679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:17.194679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:18.435876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:19.886887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:21.084313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:22.327920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:23.527768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:24.697776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:26.173671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:27.467994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:28.942695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:30.264152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:31.720424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:17.321787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:18.545926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:19.980523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:21.223532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:22.443151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:23.651476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:24.793311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:26.269919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:27.591202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:29.068106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:30.399842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:31.823501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:17.418638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:18.623322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:20.073260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:21.312294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:22.532233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:23.747507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:24.877600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:26.350260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:27.700784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:29.179577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:30.490391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:28:36.602671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면동합계(가구)합계(인원)한부모(가구)한부모(인원)차상위장애인(가구)차상위장애인(인원)차상위자활(가구)차상위자활(인원)차상위본인부담경감(가구)차상위본인부담경감(인원)차상위계층확인(가구)차상위계층확인(인원)
읍면동1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
합계(가구)1.0001.0000.9240.6630.7930.7300.7530.6920.7290.8600.8970.5700.773
합계(인원)1.0000.9241.0000.9400.8660.8110.7900.9120.8380.8210.9090.4270.728
한부모(가구)1.0000.6630.9401.0000.9500.7540.7140.9200.8880.8080.8860.3310.528
한부모(인원)1.0000.7930.8660.9501.0000.7560.7220.8640.9580.9170.7630.0000.683
차상위장애인(가구)1.0000.7300.8110.7540.7561.0000.9910.4540.5640.7370.7710.0000.545
차상위장애인(인원)1.0000.7530.7900.7140.7220.9911.0000.0000.0000.6280.8070.0000.640
차상위자활(가구)1.0000.6920.9120.9200.8640.4540.0001.0001.0000.7120.8570.0000.314
차상위자활(인원)1.0000.7290.8380.8880.9580.5640.0001.0001.0000.9110.7980.0000.654
차상위본인부담경감(가구)1.0000.8600.8210.8080.9170.7370.6280.7120.9111.0000.9510.4710.895
차상위본인부담경감(인원)1.0000.8970.9090.8860.7630.7710.8070.8570.7980.9511.0000.3830.731
차상위계층확인(가구)1.0000.5700.4270.3310.0000.0000.0000.0000.0000.4710.3831.0000.792
차상위계층확인(인원)1.0000.7730.7280.5280.6830.5450.6400.3140.6540.8950.7310.7921.000
2023-12-12T11:28:36.768974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
합계(가구)합계(인원)한부모(가구)한부모(인원)차상위장애인(가구)차상위장애인(인원)차상위자활(가구)차상위자활(인원)차상위본인부담경감(가구)차상위본인부담경감(인원)차상위계층확인(가구)차상위계층확인(인원)
합계(가구)1.0000.9710.8870.8800.8640.8780.8580.8470.9480.9560.6830.735
합계(인원)0.9711.0000.9510.9490.8000.8150.8590.8480.8950.9300.6440.713
한부모(가구)0.8870.9511.0000.9970.6490.6720.8920.8880.7940.8350.4790.559
한부모(인원)0.8800.9490.9971.0000.6440.6670.8800.8760.7890.8280.4910.569
차상위장애인(가구)0.8640.8000.6490.6441.0000.9980.6120.5970.8650.8410.7040.737
차상위장애인(인원)0.8780.8150.6720.6670.9981.0000.6360.6230.8770.8550.7040.737
차상위자활(가구)0.8580.8590.8920.8800.6120.6361.0000.9980.7770.7940.3840.451
차상위자활(인원)0.8470.8480.8880.8760.5970.6230.9981.0000.7740.7870.3680.435
차상위본인부담경감(가구)0.9480.8950.7940.7890.8650.8770.7770.7741.0000.9820.6610.700
차상위본인부담경감(인원)0.9560.9300.8350.8280.8410.8550.7940.7870.9821.0000.6440.683
차상위계층확인(가구)0.6830.6440.4790.4910.7040.7040.3840.3680.6610.6441.0000.979
차상위계층확인(인원)0.7350.7130.5590.5690.7370.7370.4510.4350.7000.6830.9791.000

Missing values

2023-12-12T11:28:32.004150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:28:32.554630image/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

읍면동합계(가구)합계(인원)한부모(가구)한부모(인원)차상위장애인(가구)차상위장애인(인원)차상위자활(가구)차상위자활(인원)차상위본인부담경감(가구)차상위본인부담경감(인원)차상위계층확인(가구)차상위계층확인(인원)
0돌산242332369595974471843652
1소라36158510427295991415971275172
2율촌91126102926301135421924
3화양12916151250532242473047
4남면1051374938410032423145
5화정871021250520021301518
6삼산54722617170015182031
7동문118167285935378827312032
8한려110149224834364432371824
9중앙110142123234343339482225
읍면동합계(가구)합계(인원)한부모(가구)한부모(인원)차상위장애인(가구)차상위장애인(인원)차상위자활(가구)차상위자활(인원)차상위본인부담경감(가구)차상위본인부담경감(인원)차상위계층확인(가구)차상위계층확인(인원)
17문수34354495249909418181181502233
18미평2483626315260621313861022633
19둔덕111202441121617111228451216
20만덕141225439842443339571423
21쌍봉2403957118365667768902949
22시전2844659323366709977963957
23여천3786121253039710011111171562842
24주삼1102064111517172223382734
25삼일2738368900111459
26묘도2531379900341011