Overview

Dataset statistics

Number of variables13
Number of observations10000
Missing cells26216
Missing cells (%)20.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory114.0 B

Variable types

Text3
Categorical7
DateTime2
Boolean1

Dataset

Description부산시설공단_영락공원묘지사용현황_20201118
Author부산시설공단
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15067559

Alerts

개장여부 has constant value ""Constant
순번 is highly overall correlated with 구분 and 1 other fieldsHigh correlation
구분 is highly overall correlated with 순번 and 1 other fieldsHigh correlation
매장종류 is highly overall correlated with 묘지수납구분High correlation
감면구분 is highly overall correlated with 묘지수납구분High correlation
묘지수납구분 is highly overall correlated with 순번 and 3 other fieldsHigh correlation
순번 is highly imbalanced (79.6%)Imbalance
시도구분 is highly imbalanced (74.6%)Imbalance
매장종류 is highly imbalanced (80.4%)Imbalance
감면구분 is highly imbalanced (86.9%)Imbalance
묘지수납구분 is highly imbalanced (79.0%)Imbalance
사용료 is highly imbalanced (98.3%)Imbalance
만료일자 has 9355 (93.5%) missing valuesMissing
개장여부 has 8397 (84.0%) missing valuesMissing
개장일자 has 8397 (84.0%) missing valuesMissing

Reproduction

Analysis started2023-12-10 16:43:36.836922
Analysis finished2023-12-10 16:43:38.261754
Duration1.42 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

묘지정보
Text

Distinct9801
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T01:43:38.513593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters150000
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9603 ?
Unique (%)96.0%

Sample

1st row01묘원 09블럭 0063호
2nd row06묘원 33블럭 0072호
3rd row14묘원 61블럭 0332호
4th row13묘원 60블럭 0494호
5th row11묘원 56블럭 0024호
ValueCountFrequency (%)
01묘원 1364
 
4.5%
11묘원 987
 
3.3%
14묘원 940
 
3.1%
08묘원 923
 
3.1%
07묘원 875
 
2.9%
02묘원 740
 
2.5%
03묘원 665
 
2.2%
09묘원 615
 
2.1%
13묘원 591
 
2.0%
10묘원 561
 
1.9%
Other values (927) 21739
72.5%
2023-12-11T01:43:38.963843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 23465
15.6%
20000
13.3%
1 12151
8.1%
묘 10000
 
6.7%
원 10000
 
6.7%
블 10000
 
6.7%
럭 10000
 
6.7%
호 10000
 
6.7%
3 7656
 
5.1%
2 7528
 
5.0%
Other values (6) 29200
19.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 80000
53.3%
Other Letter 50000
33.3%
Space Separator 20000
 
13.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23465
29.3%
1 12151
15.2%
3 7656
 
9.6%
2 7528
 
9.4%
4 7146
 
8.9%
5 6262
 
7.8%
6 4822
 
6.0%
8 3866
 
4.8%
7 3786
 
4.7%
9 3318
 
4.1%
Other Letter
ValueCountFrequency (%)
묘 10000
20.0%
원 10000
20.0%
블 10000
20.0%
럭 10000
20.0%
호 10000
20.0%
Space Separator
ValueCountFrequency (%)
20000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 100000
66.7%
Hangul 50000
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23465
23.5%
20000
20.0%
1 12151
12.2%
3 7656
 
7.7%
2 7528
 
7.5%
4 7146
 
7.1%
5 6262
 
6.3%
6 4822
 
4.8%
8 3866
 
3.9%
7 3786
 
3.8%
Hangul
ValueCountFrequency (%)
묘 10000
20.0%
원 10000
20.0%
블 10000
20.0%
럭 10000
20.0%
호 10000
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 100000
66.7%
Hangul 50000
33.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23465
23.5%
20000
20.0%
1 12151
12.2%
3 7656
 
7.7%
2 7528
 
7.5%
4 7146
 
7.1%
5 6262
 
6.3%
6 4822
 
4.8%
8 3866
 
3.9%
7 3786
 
3.8%
Hangul
ValueCountFrequency (%)
묘 10000
20.0%
원 10000
20.0%
블 10000
20.0%
럭 10000
20.0%
호 10000
20.0%

순번
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9436 
2
 
549
3
 
15

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 9436
94.4%
2 549
 
5.5%
3 15
 
0.1%

Length

2023-12-11T01:43:39.103596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:43:39.192300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9436
94.4%
2 549
 
5.5%
3 15
 
0.1%

사망일자
Text

Distinct5423
Distinct (%)54.6%
Missing67
Missing (%)0.7%
Memory size156.2 KiB
2023-12-11T01:43:39.455659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters99330
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3050 ?
Unique (%)30.7%

Sample

1st row1970-09-14
2nd row1974-04-18
3rd row1981-06-10
4th row1980-06-23
5th row1979-01-08
ValueCountFrequency (%)
1901-01-01 49
 
0.5%
1927-08-27 12
 
0.1%
1976-01-25 9
 
0.1%
1978-12-21 8
 
0.1%
1981-07-01 8
 
0.1%
1980-01-24 8
 
0.1%
1979-10-13 8
 
0.1%
1978-12-28 8
 
0.1%
1979-05-12 7
 
0.1%
1981-09-15 7
 
0.1%
Other values (5413) 9809
98.8%
2023-12-11T01:43:39.885679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 19866
20.0%
1 19713
19.8%
0 14678
14.8%
9 12438
12.5%
7 8773
8.8%
2 7216
 
7.3%
8 4585
 
4.6%
6 3245
 
3.3%
3 3175
 
3.2%
5 2899
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 79464
80.0%
Dash Punctuation 19866
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 19713
24.8%
0 14678
18.5%
9 12438
15.7%
7 8773
11.0%
2 7216
 
9.1%
8 4585
 
5.8%
6 3245
 
4.1%
3 3175
 
4.0%
5 2899
 
3.6%
4 2742
 
3.5%
Dash Punctuation
ValueCountFrequency (%)
- 19866
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 99330
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 19866
20.0%
1 19713
19.8%
0 14678
14.8%
9 12438
12.5%
7 8773
8.8%
2 7216
 
7.3%
8 4585
 
4.6%
6 3245
 
3.3%
3 3175
 
3.2%
5 2899
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 99330
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 19866
20.0%
1 19713
19.8%
0 14678
14.8%
9 12438
12.5%
7 8773
8.8%
2 7216
 
7.3%
8 4585
 
4.6%
6 3245
 
3.3%
3 3175
 
3.2%
5 2899
 
2.9%

매장일자
Date

Distinct5134
Distinct (%)51.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum1957-06-17 00:00:00
Maximum2020-08-14 00:00:00
2023-12-11T01:43:40.061611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:43:40.231458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

만료일자
Text

MISSING 

Distinct614
Distinct (%)95.2%
Missing9355
Missing (%)93.5%
Memory size156.2 KiB
2023-12-11T01:43:40.610649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters6450
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique583 ?
Unique (%)90.4%

Sample

1st row2049-06-18
2nd row2033-06-11
3rd row2046-06-14
4th row2050-05-26
5th row2047-02-08
ValueCountFrequency (%)
2023-12-09 2
 
0.3%
2032-05-13 2
 
0.3%
2039-01-23 2
 
0.3%
2040-12-22 2
 
0.3%
2037-03-25 2
 
0.3%
2042-03-07 2
 
0.3%
2045-02-10 2
 
0.3%
2036-09-17 2
 
0.3%
2034-09-29 2
 
0.3%
2038-09-08 2
 
0.3%
Other values (604) 625
96.9%
2023-12-11T01:43:41.092408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1486
23.0%
- 1290
20.0%
2 1122
17.4%
1 633
9.8%
4 518
 
8.0%
3 506
 
7.8%
5 202
 
3.1%
6 184
 
2.9%
7 177
 
2.7%
9 168
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5160
80.0%
Dash Punctuation 1290
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1486
28.8%
2 1122
21.7%
1 633
12.3%
4 518
 
10.0%
3 506
 
9.8%
5 202
 
3.9%
6 184
 
3.6%
7 177
 
3.4%
9 168
 
3.3%
8 164
 
3.2%
Dash Punctuation
ValueCountFrequency (%)
- 1290
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6450
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1486
23.0%
- 1290
20.0%
2 1122
17.4%
1 633
9.8%
4 518
 
8.0%
3 506
 
7.8%
5 202
 
3.1%
6 184
 
2.9%
7 177
 
2.7%
9 168
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6450
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1486
23.0%
- 1290
20.0%
2 1122
17.4%
1 633
9.8%
4 518
 
8.0%
3 506
 
7.8%
5 202
 
3.1%
6 184
 
2.9%
7 177
 
2.7%
9 168
 
2.6%

구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
신규
7860 
개장
1603 
재사용
 
537

Length

Max length3
Median length2
Mean length2.0537
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
신규 7860
78.6%
개장 1603
 
16.0%
재사용 537
 
5.4%

Length

2023-12-11T01:43:41.237841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:43:41.327528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신규 7860
78.6%
개장 1603
 
16.0%
재사용 537
 
5.4%

개장여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing8397
Missing (%)84.0%
Memory size97.7 KiB
True
1603 
(Missing)
8397 
ValueCountFrequency (%)
True 1603
 
16.0%
(Missing) 8397
84.0%
2023-12-11T01:43:41.408863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

개장일자
Date

MISSING 

Distinct1270
Distinct (%)79.2%
Missing8397
Missing (%)84.0%
Memory size156.2 KiB
Minimum1969-05-01 00:00:00
Maximum2020-10-22 00:00:00
2023-12-11T01:43:41.551689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:43:41.710487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

시도구분
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
자시
9368 
인접
 
356
타시
 
276

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
자시 9368
93.7%
인접 356
 
3.6%
타시 276
 
2.8%

Length

2023-12-11T01:43:41.837229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:43:41.918450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자시 9368
93.7%
인접 356
 
3.6%
타시 276
 
2.8%

매장종류
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
시체
9330 
개장유골
 
407
부부합장
 
139
화장유골
 
99
유골합장
 
25

Length

Max length4
Median length2
Mean length2.134
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
시체 9330
93.3%
개장유골 407
 
4.1%
부부합장 139
 
1.4%
화장유골 99
 
1.0%
유골합장 25
 
0.2%

Length

2023-12-11T01:43:42.018584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:43:42.118469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
시체 9330
93.3%
개장유골 407
 
4.1%
부부합장 139
 
1.4%
화장유골 99
 
1.0%
유골합장 25
 
0.2%

감면구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
일반
9456 
기타면제
 
400
사전예매
 
98
생활수급
 
29
지역주민
 
14
Other values (2)
 
3

Length

Max length4
Median length2
Mean length2.1088
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
일반 9456
94.6%
기타면제 400
 
4.0%
사전예매 98
 
1.0%
생활수급 29
 
0.3%
지역주민 14
 
0.1%
참전유공 2
 
< 0.1%
국가유공 1
 
< 0.1%

Length

2023-12-11T01:43:42.215846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:43:42.312056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반 9456
94.6%
기타면제 400
 
4.0%
사전예매 98
 
1.0%
생활수급 29
 
0.3%
지역주민 14
 
0.1%
참전유공 2
 
< 0.1%
국가유공 1
 
< 0.1%

묘지수납구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
영구(신규불가)
9390 
최초(15년)
 
608
<NA>
 
2

Length

Max length8
Median length8
Mean length7.9384
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row영구(신규불가)
2nd row영구(신규불가)
3rd row영구(신규불가)
4th row영구(신규불가)
5th row영구(신규불가)

Common Values

ValueCountFrequency (%)
영구(신규불가) 9390
93.9%
최초(15년) 608
 
6.1%
<NA> 2
 
< 0.1%

Length

2023-12-11T01:43:42.455868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:43:42.592388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영구(신규불가 9390
93.9%
최초(15년 608
 
6.1%
na 2
 
< 0.1%

사용료
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
300000
9962 
600000
 
31
0
 
4
<NA>
 
2
40000
 
1

Length

Max length6
Median length6
Mean length5.9975
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row300000
2nd row300000
3rd row300000
4th row300000
5th row300000

Common Values

ValueCountFrequency (%)
300000 9962
99.6%
600000 31
 
0.3%
0 4
 
< 0.1%
<NA> 2
 
< 0.1%
40000 1
 
< 0.1%

Length

2023-12-11T01:43:42.916064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:43:43.042871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
300000 9962
99.6%
600000 31
 
0.3%
0 4
 
< 0.1%
na 2
 
< 0.1%
40000 1
 
< 0.1%

Correlations

2023-12-11T01:43:43.120267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번구분시도구분매장종류감면구분묘지수납구분사용료
순번1.0000.9350.0000.4580.4210.5380.101
구분0.9351.0000.0320.4450.4250.5260.109
시도구분0.0000.0321.0000.0610.0980.0240.138
매장종류0.4580.4450.0611.0000.2590.4480.118
감면구분0.4210.4250.0980.2591.0000.5410.272
묘지수납구분0.5380.5260.0240.4480.5411.0000.238
사용료0.1010.1090.1380.1180.2720.2381.000
2023-12-11T01:43:43.244254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도구분사용료구분매장종류순번묘지수납구분감면구분
시도구분1.0000.1300.0100.0460.0000.0390.066
사용료0.1301.0000.1030.0970.0950.1580.190
구분0.0100.1031.0000.3760.6890.7910.316
매장종류0.0460.0970.3761.0000.3890.5440.169
순번0.0000.0950.6890.3891.0000.8050.313
묘지수납구분0.0390.1580.7910.5440.8051.0000.581
감면구분0.0660.1900.3160.1690.3130.5811.000
2023-12-11T01:43:43.384054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번구분시도구분매장종류감면구분묘지수납구분사용료
순번1.0000.6890.0000.3890.3130.8050.095
구분0.6891.0000.0100.3760.3160.7910.103
시도구분0.0000.0101.0000.0460.0660.0390.130
매장종류0.3890.3760.0461.0000.1690.5440.097
감면구분0.3130.3160.0660.1691.0000.5810.190
묘지수납구분0.8050.7910.0390.5440.5811.0000.158
사용료0.0950.1030.1300.0970.1900.1581.000

Missing values

2023-12-11T01:43:37.805187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:43:37.972648image/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.
2023-12-11T01:43:38.167400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

묘지정보순번사망일자매장일자만료일자구분개장여부개장일자시도구분매장종류감면구분묘지수납구분사용료
298701묘원 09블럭 0063호11970-09-141970-09-16<NA>신규<NA><NA>자시시체일반영구(신규불가)300000
1218606묘원 33블럭 0072호11974-04-181974-04-20<NA>신규<NA><NA>자시시체일반영구(신규불가)300000
2693214묘원 61블럭 0332호11981-06-101981-06-12<NA>신규<NA><NA>자시시체일반영구(신규불가)300000
2638413묘원 60블럭 0494호11980-06-231980-06-25<NA>신규<NA><NA>자시시체일반영구(신규불가)300000
2351411묘원 56블럭 0024호11979-01-081979-01-10<NA>신규<NA><NA>자시시체일반영구(신규불가)300000
1111205묘원 31블럭 0291호11973-10-261973-10-28<NA>신규<NA><NA>자시시체일반영구(신규불가)300000
146801묘원 04블럭 0370호11995-10-171995-10-19<NA>신규<NA><NA>자시시체일반영구(신규불가)300000
2220811묘원 54블럭 0249호11978-10-311978-11-04<NA>신규<NA><NA>자시시체일반영구(신규불가)300000
1737908묘원 44블럭 0270호11976-10-041976-10-06<NA>개장Y2001-12-20자시시체일반영구(신규불가)300000
1020805묘원 29블럭 0399호11974-12-061974-12-08<NA>개장Y2012-05-11자시시체일반영구(신규불가)300000
묘지정보순번사망일자매장일자만료일자구분개장여부개장일자시도구분매장종류감면구분묘지수납구분사용료
1511407묘원 39블럭 0464호11975-03-251975-03-27<NA>개장Y2013-04-21자시시체일반영구(신규불가)300000
2667714묘원 61블럭 0078호11981-05-081981-05-10<NA>신규<NA><NA>자시시체일반영구(신규불가)300000
641203묘원 17블럭 0227호12016-01-162016-01-18<NA>신규<NA><NA>자시시체사전예매영구(신규불가)300000
2057710묘원 51블럭 0410호11978-05-041978-05-06<NA>신규<NA><NA>자시시체일반영구(신규불가)300000
26101묘원 01블럭 0268호11974-02-101974-02-11<NA>개장Y2019-01-03자시시체일반영구(신규불가)300000
1733108묘원 44블럭 0211호11976-08-251976-08-27<NA>신규<NA><NA>인접시체일반영구(신규불가)300000
564702묘원 16블럭 0131호11971-04-111971-04-13<NA>신규<NA><NA>인접시체일반영구(신규불가)300000
1326407묘원 36블럭 0274호11974-12-061974-12-08<NA>신규<NA><NA>자시시체일반영구(신규불가)300000
1184506묘원 32블럭 0464호11974-04-131974-04-15<NA>신규<NA><NA>자시시체일반영구(신규불가)300000
2284911묘원 55블럭 0241호11979-04-171979-04-19<NA>신규<NA><NA>자시시체일반영구(신규불가)300000