Overview

Dataset statistics

Number of variables4
Number of observations283
Missing cells49
Missing cells (%)4.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.2 KiB
Average record size in memory33.5 B

Variable types

Text2
Numeric1
Categorical1

Dataset

Description서울특별시 광진구 대형폐기물 수집 운반 수수료와 품목, 규격, 부과 금액에 관한 상세 설명 파일입니다. 이외의 품목들 같은 경우에 해당 데이터를 보고 비슷한 항목으로 선택 요청 드리면 됩니다.
Author서울특별시 광진구
URLhttps://www.data.go.kr/data/15036341/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
규격 has 49 (17.3%) missing valuesMissing
부과금액 has 36 (12.7%) zerosZeros

Reproduction

Analysis started2024-03-14 15:26:29.988898
Analysis finished2024-03-14 15:26:31.074017
Duration1.09 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

품목
Text

Distinct150
Distinct (%)53.0%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2024-03-15T00:26:32.315931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length14
Mean length4.1872792
Min length2

Characters and Unicode

Total characters1185
Distinct characters233
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique69 ?
Unique (%)24.4%

Sample

1st rowDVD, 비디오 (VTR)
2nd rowTV
3rd rowTV
4th rowTV
5th rowTV 받침
ValueCountFrequency (%)
침대 8
 
2.3%
유리별도 7
 
2.0%
장판 5
 
1.5%
쇼파 5
 
1.5%
안락의자 5
 
1.5%
에어컨 5
 
1.5%
식탁 5
 
1.5%
의자별도 5
 
1.5%
탁자 5
 
1.5%
냉장고 4
 
1.2%
Other values (164) 288
84.2%
2024-03-15T00:26:34.186392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
기 72
 
6.1%
62
 
5.2%
장 43
 
3.6%
( 41
 
3.5%
) 41
 
3.5%
자 35
 
3.0%
스 24
 
2.0%
대 23
 
1.9%
리 17
 
1.4%
오 17
 
1.4%
Other values (223) 810
68.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1024
86.4%
Space Separator 62
 
5.2%
Open Punctuation 41
 
3.5%
Close Punctuation 41
 
3.5%
Uppercase Letter 14
 
1.2%
Other Punctuation 3
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 72
 
7.0%
장 43
 
4.2%
자 35
 
3.4%
스 24
 
2.3%
대 23
 
2.2%
리 17
 
1.7%
오 17
 
1.7%
판 17
 
1.7%
의 15
 
1.5%
이 15
 
1.5%
Other values (215) 746
72.9%
Uppercase Letter
ValueCountFrequency (%)
V 6
42.9%
T 5
35.7%
D 2
 
14.3%
R 1
 
7.1%
Space Separator
ValueCountFrequency (%)
62
100.0%
Open Punctuation
ValueCountFrequency (%)
( 41
100.0%
Close Punctuation
ValueCountFrequency (%)
) 41
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1024
86.4%
Common 147
 
12.4%
Latin 14
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 72
 
7.0%
장 43
 
4.2%
자 35
 
3.4%
스 24
 
2.3%
대 23
 
2.2%
리 17
 
1.7%
오 17
 
1.7%
판 17
 
1.7%
의 15
 
1.5%
이 15
 
1.5%
Other values (215) 746
72.9%
Common
ValueCountFrequency (%)
62
42.2%
( 41
27.9%
) 41
27.9%
, 3
 
2.0%
Latin
ValueCountFrequency (%)
V 6
42.9%
T 5
35.7%
D 2
 
14.3%
R 1
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1024
86.4%
ASCII 161
 
13.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
기 72
 
7.0%
장 43
 
4.2%
자 35
 
3.4%
스 24
 
2.3%
대 23
 
2.2%
리 17
 
1.7%
오 17
 
1.7%
판 17
 
1.7%
의 15
 
1.5%
이 15
 
1.5%
Other values (215) 746
72.9%
ASCII
ValueCountFrequency (%)
62
38.5%
( 41
25.5%
) 41
25.5%
V 6
 
3.7%
T 5
 
3.1%
, 3
 
1.9%
D 2
 
1.2%
R 1
 
0.6%

규격
Text

MISSING 

Distinct180
Distinct (%)76.9%
Missing49
Missing (%)17.3%
Memory size2.3 KiB
2024-03-15T00:26:35.494766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length14
Mean length7.1111111
Min length2

Characters and Unicode

Total characters1664
Distinct characters171
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique152 ?
Unique (%)65.0%

Sample

1st row42인치 이상
2nd row25인치 이상
3rd row25인치 미만
4th row골프채 가방
5th row길이 50cm 이상 (캐리어 등)
ValueCountFrequency (%)
이상 70
 
13.6%
미만 48
 
9.3%
1m 35
 
6.8%
높이 20
 
3.9%
50cm 16
 
3.1%
가장 15
 
2.9%
긴면 15
 
2.9%
길이 13
 
2.5%
지름 10
 
1.9%
2인용 9
 
1.7%
Other values (140) 264
51.3%
2024-03-15T00:26:37.260137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
281
 
16.9%
이 115
 
6.9%
m 112
 
6.7%
1 79
 
4.7%
0 77
 
4.6%
상 73
 
4.4%
용 55
 
3.3%
미 49
 
2.9%
만 49
 
2.9%
c 47
 
2.8%
Other values (161) 727
43.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 833
50.1%
Decimal Number 293
 
17.6%
Space Separator 281
 
16.9%
Lowercase Letter 181
 
10.9%
Other Punctuation 24
 
1.4%
Uppercase Letter 16
 
1.0%
Other Number 14
 
0.8%
Close Punctuation 11
 
0.7%
Open Punctuation 11
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 115
 
13.8%
상 73
 
8.8%
용 55
 
6.6%
미 49
 
5.9%
만 49
 
5.9%
인 37
 
4.4%
가 28
 
3.4%
높 20
 
2.4%
장 19
 
2.3%
당 16
 
1.9%
Other values (136) 372
44.7%
Decimal Number
ValueCountFrequency (%)
1 79
27.0%
0 77
26.3%
2 33
11.3%
5 28
 
9.6%
6 23
 
7.8%
3 22
 
7.5%
4 17
 
5.8%
9 7
 
2.4%
8 7
 
2.4%
Lowercase Letter
ValueCountFrequency (%)
m 112
61.9%
c 47
26.0%
k 9
 
5.0%
g 7
 
3.9%
l 2
 
1.1%
a 2
 
1.1%
h 2
 
1.1%
Other Punctuation
ValueCountFrequency (%)
, 14
58.3%
. 8
33.3%
/ 2
 
8.3%
Uppercase Letter
ValueCountFrequency (%)
L 10
62.5%
X 6
37.5%
Space Separator
ValueCountFrequency (%)
281
100.0%
Other Number
ValueCountFrequency (%)
² 14
100.0%
Close Punctuation
ValueCountFrequency (%)
) 11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 833
50.1%
Common 634
38.1%
Latin 197
 
11.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 115
 
13.8%
상 73
 
8.8%
용 55
 
6.6%
미 49
 
5.9%
만 49
 
5.9%
인 37
 
4.4%
가 28
 
3.4%
높 20
 
2.4%
장 19
 
2.3%
당 16
 
1.9%
Other values (136) 372
44.7%
Common
ValueCountFrequency (%)
281
44.3%
1 79
 
12.5%
0 77
 
12.1%
2 33
 
5.2%
5 28
 
4.4%
6 23
 
3.6%
3 22
 
3.5%
4 17
 
2.7%
, 14
 
2.2%
² 14
 
2.2%
Other values (6) 46
 
7.3%
Latin
ValueCountFrequency (%)
m 112
56.9%
c 47
23.9%
L 10
 
5.1%
k 9
 
4.6%
g 7
 
3.6%
X 6
 
3.0%
l 2
 
1.0%
a 2
 
1.0%
h 2
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 833
50.1%
ASCII 817
49.1%
None 14
 
0.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
281
34.4%
m 112
 
13.7%
1 79
 
9.7%
0 77
 
9.4%
c 47
 
5.8%
2 33
 
4.0%
5 28
 
3.4%
6 23
 
2.8%
3 22
 
2.7%
4 17
 
2.1%
Other values (14) 98
 
12.0%
Hangul
ValueCountFrequency (%)
이 115
 
13.8%
상 73
 
8.8%
용 55
 
6.6%
미 49
 
5.9%
만 49
 
5.9%
인 37
 
4.4%
가 28
 
3.4%
높 20
 
2.4%
장 19
 
2.3%
당 16
 
1.9%
Other values (136) 372
44.7%
None
ValueCountFrequency (%)
² 14
100.0%

부과금액
Real number (ℝ)

ZEROS 

Distinct16
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3692.5795
Minimum0
Maximum30000
Zeros36
Zeros (%)12.7%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-03-15T00:26:37.470350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11000
median3000
Q35000
95-th percentile10000
Maximum30000
Range30000
Interquartile range (IQR)4000

Descriptive statistics

Standard deviation3837.9194
Coefficient of variation (CV)1.03936
Kurtosis10.083022
Mean3692.5795
Median Absolute Deviation (MAD)2000
Skewness2.5465307
Sum1045000
Variance14729626
MonotonicityNot monotonic
2024-03-15T00:26:37.662609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
3000 51
18.0%
2000 49
17.3%
1000 41
14.5%
0 36
12.7%
4000 28
9.9%
5000 26
9.2%
7000 14
 
4.9%
10000 13
 
4.6%
15000 7
 
2.5%
6000 5
 
1.8%
Other values (6) 13
 
4.6%
ValueCountFrequency (%)
0 36
12.7%
500 2
 
0.7%
1000 41
14.5%
2000 49
17.3%
3000 51
18.0%
4000 28
9.9%
5000 26
9.2%
6000 5
 
1.8%
7000 14
 
4.9%
8000 5
 
1.8%
ValueCountFrequency (%)
30000 1
 
0.4%
20000 2
 
0.7%
15000 7
 
2.5%
13000 1
 
0.4%
12000 2
 
0.7%
10000 13
4.6%
8000 5
 
1.8%
7000 14
4.9%
6000 5
 
1.8%
5000 26
9.2%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2024-02-13
283 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-02-13
2nd row2024-02-13
3rd row2024-02-13
4th row2024-02-13
5th row2024-02-13

Common Values

ValueCountFrequency (%)
2024-02-13 283
100.0%

Length

2024-03-15T00:26:38.057864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T00:26:38.565911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024-02-13 283
100.0%

Interactions

2024-03-15T00:26:30.343139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Missing values

2024-03-15T00:26:30.679841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T00:26:30.957493image/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

품목규격부과금액데이터기준일자
0DVD, 비디오 (VTR)<NA>02024-02-13
1TV42인치 이상70002024-02-13
2TV25인치 이상50002024-02-13
3TV25인치 미만30002024-02-13
4TV 받침<NA>30002024-02-13
5가방류골프채 가방30002024-02-13
6가방류길이 50cm 이상 (캐리어 등)20002024-02-13
7가방류길이 50cm 미만10002024-02-13
8가스레인지<NA>02024-02-13
9가스오븐레인지높이 1m 이상50002024-02-13
품목규격부과금액데이터기준일자
273환풍기업소용 (대형)30002024-02-13
274휠체어<NA>30002024-02-13
275휴대폰충전기<NA>02024-02-13
276라지에타<NA>40002024-02-13
277태양광 폐패널20kg당60002024-02-13
278분말소화기3.3kg 초과30002024-02-13
279분말소화기3.3kg 이하20002024-02-13
280파렛트가로 60cm X 세로 60cm 이상40002024-02-13
281파렛트가로 60cm X 세로 60cm 미만30002024-02-13
282폐플라스틱5kg당10002024-02-13