Overview

Dataset statistics

Number of variables8
Number of observations119
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.0 KiB
Average record size in memory69.1 B

Variable types

Numeric4
Categorical2
Text2

Dataset

Description대구광역시 서구에서 처리하는 대형폐기물의 처리 품목의 목록입니다. 이 파일에는 행정동과 대형폐기물 품목의 종류, 수수료 금액의 정보를 포함하고 있습니다.
Author대구광역시 서구
URLhttps://www.data.go.kr/data/15084156/fileData.do

Alerts

번호 is highly overall correlated with 종류High correlation
건수 is highly overall correlated with 총금액 High correlation
총금액 is highly overall correlated with 건수 High correlation
종류 is highly overall correlated with 번호High correlation
번호 has unique valuesUnique

Reproduction

Analysis started2024-03-14 11:55:05.494616
Analysis finished2024-03-14 11:55:10.318378
Duration4.82 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct119
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60
Minimum1
Maximum119
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-14T20:55:10.523151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.9
Q130.5
median60
Q389.5
95-th percentile113.1
Maximum119
Range118
Interquartile range (IQR)59

Descriptive statistics

Standard deviation34.496377
Coefficient of variation (CV)0.57493961
Kurtosis-1.2
Mean60
Median Absolute Deviation (MAD)30
Skewness0
Sum7140
Variance1190
MonotonicityStrictly increasing
2024-03-14T20:55:10.884028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.8%
2 1
 
0.8%
89 1
 
0.8%
88 1
 
0.8%
87 1
 
0.8%
86 1
 
0.8%
85 1
 
0.8%
84 1
 
0.8%
83 1
 
0.8%
82 1
 
0.8%
Other values (109) 109
91.6%
ValueCountFrequency (%)
1 1
0.8%
2 1
0.8%
3 1
0.8%
4 1
0.8%
5 1
0.8%
6 1
0.8%
7 1
0.8%
8 1
0.8%
9 1
0.8%
10 1
0.8%
ValueCountFrequency (%)
119 1
0.8%
118 1
0.8%
117 1
0.8%
116 1
0.8%
115 1
0.8%
114 1
0.8%
113 1
0.8%
112 1
0.8%
111 1
0.8%
110 1
0.8%

동명
Categorical

Distinct8
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
평리동
45 
내당동
33 
중리동
17 
비산동
16 
이현동
 
4
Other values (3)
 
4

Length

Max length5
Median length3
Mean length3.0504202
Min length3

Unique

Unique2 ?
Unique (%)1.7%

Sample

1st row내당동
2nd row내당동
3rd row평리동
4th row비산동
5th row평리동

Common Values

ValueCountFrequency (%)
평리동 45
37.8%
내당동 33
27.7%
중리동 17
 
14.3%
비산동 16
 
13.4%
이현동 4
 
3.4%
원대동2가 2
 
1.7%
상리동 1
 
0.8%
원대동3가 1
 
0.8%

Length

2024-03-14T20:55:11.533637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T20:55:11.905593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
평리동 45
37.8%
내당동 33
27.7%
중리동 17
 
14.3%
비산동 16
 
13.4%
이현동 4
 
3.4%
원대동2가 2
 
1.7%
상리동 1
 
0.8%
원대동3가 1
 
0.8%

종류
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
기타
53 
가구류
38 
가전제품
28 

Length

Max length4
Median length3
Mean length2.789916
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
기타 53
44.5%
가구류 38
31.9%
가전제품 28
23.5%

Length

2024-03-14T20:55:12.356611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T20:55:12.701869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기타 53
44.5%
가구류 38
31.9%
가전제품 28
23.5%

품목
Text

Distinct75
Distinct (%)63.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-03-14T20:55:13.731534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length12
Mean length4.1848739
Min length2

Characters and Unicode

Total characters498
Distinct characters151
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)37.0%

Sample

1st row가스오븐렌지
2nd row가스전자렌지
3rd row가습기
4th row건조기(탈수기)
5th row공기청정기
ValueCountFrequency (%)
등 5
 
3.5%
식탁(탁자 4
 
2.8%
유아용 4
 
2.8%
침대 4
 
2.8%
서랍장 4
 
2.8%
쇼파 4
 
2.8%
장식장 3
 
2.1%
냉장고 3
 
2.1%
책장(책꽂이 3
 
2.1%
전기담요 3
 
2.1%
Other values (77) 106
74.1%
2024-03-14T20:55:14.967071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 28
 
5.6%
24
 
4.8%
기 23
 
4.6%
자 17
 
3.4%
( 16
 
3.2%
) 16
 
3.2%
, 13
 
2.6%
대 12
 
2.4%
전 11
 
2.2%
스 9
 
1.8%
Other values (141) 329
66.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 429
86.1%
Space Separator 24
 
4.8%
Open Punctuation 16
 
3.2%
Close Punctuation 16
 
3.2%
Other Punctuation 13
 
2.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 28
 
6.5%
기 23
 
5.4%
자 17
 
4.0%
대 12
 
2.8%
전 11
 
2.6%
스 9
 
2.1%
아 9
 
2.1%
이 9
 
2.1%
탁 9
 
2.1%
책 8
 
1.9%
Other values (137) 294
68.5%
Space Separator
ValueCountFrequency (%)
24
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%
Other Punctuation
ValueCountFrequency (%)
, 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 429
86.1%
Common 69
 
13.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 28
 
6.5%
기 23
 
5.4%
자 17
 
4.0%
대 12
 
2.8%
전 11
 
2.6%
스 9
 
2.1%
아 9
 
2.1%
이 9
 
2.1%
탁 9
 
2.1%
책 8
 
1.9%
Other values (137) 294
68.5%
Common
ValueCountFrequency (%)
24
34.8%
( 16
23.2%
) 16
23.2%
, 13
18.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 429
86.1%
ASCII 69
 
13.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 28
 
6.5%
기 23
 
5.4%
자 17
 
4.0%
대 12
 
2.8%
전 11
 
2.6%
스 9
 
2.1%
아 9
 
2.1%
이 9
 
2.1%
탁 9
 
2.1%
책 8
 
1.9%
Other values (137) 294
68.5%
ASCII
ValueCountFrequency (%)
24
34.8%
( 16
23.2%
) 16
23.2%
, 13
18.8%

규격
Text

Distinct86
Distinct (%)72.3%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-03-14T20:55:15.987630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length19
Mean length8.1680672
Min length2

Characters and Unicode

Total characters972
Distinct characters120
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique74 ?
Unique (%)62.2%

Sample

1st row높이 1m이상
2nd row모든 규격
3rd row모든 규격
4th row모든 규격
5th row높이 1m이상
ValueCountFrequency (%)
모든 24
 
9.4%
미만 22
 
8.6%
규격 21
 
8.2%
이상 19
 
7.5%
이하 5
 
2.0%
가로 5
 
2.0%
면 4
 
1.6%
당 4
 
1.6%
1.5m 4
 
1.6%
가장 4
 
1.6%
Other values (92) 143
56.1%
2024-03-14T20:55:17.255846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
136
 
14.0%
0 57
 
5.9%
m 50
 
5.1%
1 45
 
4.6%
이 40
 
4.1%
c 38
 
3.9%
5 26
 
2.7%
모 26
 
2.7%
든 25
 
2.6%
상 24
 
2.5%
Other values (110) 505
52.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 466
47.9%
Decimal Number 184
 
18.9%
Space Separator 136
 
14.0%
Lowercase Letter 98
 
10.1%
Other Punctuation 42
 
4.3%
Close Punctuation 13
 
1.3%
Open Punctuation 13
 
1.3%
Other Symbol 11
 
1.1%
Math Symbol 5
 
0.5%
Uppercase Letter 4
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 40
 
8.6%
모 26
 
5.6%
든 25
 
5.4%
상 24
 
5.2%
격 22
 
4.7%
만 22
 
4.7%
미 22
 
4.7%
규 22
 
4.7%
용 19
 
4.1%
인 17
 
3.6%
Other values (84) 227
48.7%
Decimal Number
ValueCountFrequency (%)
0 57
31.0%
1 45
24.5%
5 26
14.1%
2 15
 
8.2%
9 9
 
4.9%
3 9
 
4.9%
8 9
 
4.9%
6 6
 
3.3%
4 6
 
3.3%
7 2
 
1.1%
Lowercase Letter
ValueCountFrequency (%)
m 50
51.0%
c 38
38.8%
k 5
 
5.1%
g 5
 
5.1%
Other Punctuation
ValueCountFrequency (%)
. 19
45.2%
* 18
42.9%
, 5
 
11.9%
Math Symbol
ValueCountFrequency (%)
~ 3
60.0%
< 1
 
20.0%
> 1
 
20.0%
Other Symbol
ValueCountFrequency (%)
㎥ 7
63.6%
㎡ 4
36.4%
Space Separator
ValueCountFrequency (%)
136
100.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Uppercase Letter
ValueCountFrequency (%)
L 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 466
47.9%
Common 404
41.6%
Latin 102
 
10.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 40
 
8.6%
모 26
 
5.6%
든 25
 
5.4%
상 24
 
5.2%
격 22
 
4.7%
만 22
 
4.7%
미 22
 
4.7%
규 22
 
4.7%
용 19
 
4.1%
인 17
 
3.6%
Other values (84) 227
48.7%
Common
ValueCountFrequency (%)
136
33.7%
0 57
14.1%
1 45
 
11.1%
5 26
 
6.4%
. 19
 
4.7%
* 18
 
4.5%
2 15
 
3.7%
) 13
 
3.2%
( 13
 
3.2%
9 9
 
2.2%
Other values (11) 53
 
13.1%
Latin
ValueCountFrequency (%)
m 50
49.0%
c 38
37.3%
k 5
 
4.9%
g 5
 
4.9%
L 4
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 495
50.9%
Hangul 466
47.9%
CJK Compat 11
 
1.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
136
27.5%
0 57
11.5%
m 50
 
10.1%
1 45
 
9.1%
c 38
 
7.7%
5 26
 
5.3%
. 19
 
3.8%
* 18
 
3.6%
2 15
 
3.0%
) 13
 
2.6%
Other values (14) 78
15.8%
Hangul
ValueCountFrequency (%)
이 40
 
8.6%
모 26
 
5.6%
든 25
 
5.4%
상 24
 
5.2%
격 22
 
4.7%
만 22
 
4.7%
미 22
 
4.7%
규 22
 
4.7%
용 19
 
4.1%
인 17
 
3.6%
Other values (84) 227
48.7%
CJK Compat
ValueCountFrequency (%)
㎥ 7
63.6%
㎡ 4
36.4%

부과금액
Real number (ℝ)

Distinct13
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4218.4874
Minimum1000
Maximum20000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-14T20:55:17.457070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1000
Q12000
median3000
Q35000
95-th percentile10300
Maximum20000
Range19000
Interquartile range (IQR)3000

Descriptive statistics

Standard deviation3405.2792
Coefficient of variation (CV)0.80722754
Kurtosis5.0370617
Mean4218.4874
Median Absolute Deviation (MAD)1000
Skewness2.0439799
Sum502000
Variance11595927
MonotonicityNot monotonic
2024-03-14T20:55:17.649133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2000 34
28.6%
3000 26
21.8%
5000 16
13.4%
1000 13
 
10.9%
6000 9
 
7.6%
7000 7
 
5.9%
9000 3
 
2.5%
15000 3
 
2.5%
10000 3
 
2.5%
13000 2
 
1.7%
Other values (3) 3
 
2.5%
ValueCountFrequency (%)
1000 13
 
10.9%
2000 34
28.6%
3000 26
21.8%
4000 1
 
0.8%
5000 16
13.4%
6000 9
 
7.6%
7000 7
 
5.9%
8000 1
 
0.8%
9000 3
 
2.5%
10000 3
 
2.5%
ValueCountFrequency (%)
20000 1
 
0.8%
15000 3
 
2.5%
13000 2
 
1.7%
10000 3
 
2.5%
9000 3
 
2.5%
8000 1
 
0.8%
7000 7
5.9%
6000 9
7.6%
5000 16
13.4%
4000 1
 
0.8%

건수
Real number (ℝ)

HIGH CORRELATION 

Distinct46
Distinct (%)38.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.663866
Minimum1
Maximum181
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-14T20:55:18.013230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q324
95-th percentile78.4
Maximum181
Range180
Interquartile range (IQR)21

Descriptive statistics

Standard deviation30.857595
Coefficient of variation (CV)1.4933118
Kurtosis9.3072914
Mean20.663866
Median Absolute Deviation (MAD)5
Skewness2.8177299
Sum2459
Variance952.19114
MonotonicityNot monotonic
2024-03-14T20:55:18.445074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
3 14
 
11.8%
2 13
 
10.9%
4 12
 
10.1%
1 8
 
6.7%
5 7
 
5.9%
6 7
 
5.9%
13 4
 
3.4%
20 4
 
3.4%
8 3
 
2.5%
47 3
 
2.5%
Other values (36) 44
37.0%
ValueCountFrequency (%)
1 8
6.7%
2 13
10.9%
3 14
11.8%
4 12
10.1%
5 7
5.9%
6 7
5.9%
7 2
 
1.7%
8 3
 
2.5%
10 1
 
0.8%
12 2
 
1.7%
ValueCountFrequency (%)
181 1
0.8%
143 1
0.8%
133 1
0.8%
130 1
0.8%
86 1
0.8%
82 1
0.8%
78 1
0.8%
72 1
0.8%
71 1
0.8%
62 1
0.8%

총금액
Real number (ℝ)

HIGH CORRELATION 

Distinct66
Distinct (%)55.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80226.891
Minimum2000
Maximum780000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-14T20:55:18.865015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile3000
Q110000
median26000
Q393500
95-th percentile386400
Maximum780000
Range778000
Interquartile range (IQR)83500

Descriptive statistics

Standard deviation132770.83
Coefficient of variation (CV)1.6549417
Kurtosis10.616378
Mean80226.891
Median Absolute Deviation (MAD)18000
Skewness3.0290105
Sum9547000
Variance1.7628092 × 1010
MonotonicityNot monotonic
2024-03-14T20:55:19.326367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 6
 
5.0%
8000 5
 
4.2%
12000 5
 
4.2%
2000 5
 
4.2%
24000 5
 
4.2%
14000 4
 
3.4%
15000 4
 
3.4%
6000 4
 
3.4%
9000 4
 
3.4%
3000 3
 
2.5%
Other values (56) 74
62.2%
ValueCountFrequency (%)
2000 5
4.2%
3000 3
2.5%
4000 3
2.5%
5000 2
 
1.7%
6000 4
3.4%
8000 5
4.2%
9000 4
3.4%
10000 6
5.0%
12000 5
4.2%
13000 1
 
0.8%
ValueCountFrequency (%)
780000 1
0.8%
705000 1
0.8%
470000 1
0.8%
432000 1
0.8%
430000 1
0.8%
399000 1
0.8%
385000 1
0.8%
362000 1
0.8%
310000 1
0.8%
252000 1
0.8%

Interactions

2024-03-14T20:55:08.573322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:55:06.061139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:55:06.828499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:55:07.534164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:55:08.837746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:55:06.321470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:55:06.984629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:55:07.789501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:55:09.108503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:55:06.504724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:55:07.143548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:55:08.050046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:55:09.374617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:55:06.661487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:55:07.305311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:55:08.306797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T20:55:19.612225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호동명종류품목규격부과금액건수총금액
번호1.0000.0320.9350.9970.8860.1250.3630.341
동명0.0321.0000.2100.0000.9440.0000.0000.000
종류0.9350.2101.0001.0000.8810.3710.3040.528
품목0.9970.0001.0001.0000.0000.0000.0000.000
규격0.8860.9440.8810.0001.0000.8990.9370.994
부과금액0.1250.0000.3710.0000.8991.0000.0000.608
건수0.3630.0000.3040.0000.9370.0001.0000.712
총금액0.3410.0000.5280.0000.9940.6080.7121.000
2024-03-14T20:55:19.893396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
동명종류
동명1.0000.131
종류0.1311.000
2024-03-14T20:55:20.136485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호부과금액건수총금액동명종류
번호1.000-0.151-0.146-0.2220.0000.890
부과금액-0.1511.000-0.1410.3220.0000.247
건수-0.146-0.1411.0000.8570.0000.209
총금액-0.2220.3220.8571.0000.0000.392
동명0.0000.0000.0000.0001.0000.131
종류0.8900.2470.2090.3920.1311.000

Missing values

2024-03-14T20:55:09.746308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T20:55:10.160921image/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

번호동명종류품목규격부과금액건수총금액
01내당동가전제품가스오븐렌지높이 1m이상5000210000
12내당동가전제품가스전자렌지모든 규격20001326000
23평리동가전제품가습기모든 규격100044000
34비산동가전제품건조기(탈수기)모든 규격200048000
45평리동가전제품공기청정기높이 1m이상3000412000
56내당동가전제품기타소형가전모든 규격10004040000
67평리동가전제품난로전기난로200048000
78내당동가전제품냉장고300L 미만5000525000
89비산동가전제품냉장고300L 이상~500L 미만7000428000
910평리동가전제품냉장고500L 이상9000654000
번호동명종류품목규격부과금액건수총금액
109110평리동기타카시트모든 규격30002678000
110111내당동기타카펫3.3㎡ 당500015000
111112중리동기타파렛트모든규격300043129000
112113중리동기타피아노디지털5000315000
113114중리동기타피아노업라이트13000226000
114115평리동기타항아리소형 1개당 0.5㎥ 미만100022000
115116평리동기타항아리중형 1개당 0.5㎥~1㎥ 미만200036000
116117내당동기타화분소형(높이 0.5m 미만)100022000
117118중리동기타화분대형(높이 0.5m 이상)200012000
118119평리동기타화환모든 규격300026000