Overview

Dataset statistics

Number of variables6
Number of observations138
Missing cells142
Missing cells (%)17.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.9 KiB
Average record size in memory51.0 B

Variable types

Categorical1
Text2
Numeric1
DateTime1
Unsupported1

Dataset

Description제주특별자치도 서귀포시 관내의 대형폐기물 수수료 정보에 대한 데이터로 구분, 폐기물명, 규격에 대한 정보를 제공합니다.
Author제주특별자치도 서귀포시
URLhttps://www.data.go.kr/data/15056474/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
Unnamed: 5 has 138 (100.0%) missing valuesMissing
Unnamed: 5 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-12 07:02:43.389269
Analysis finished2023-12-12 07:02:44.213776
Duration0.82 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

폐기물 구분
Categorical

Distinct5
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
기타
38 
전자제품
37 
가구류
34 
생활용품
28 
<NA>
 
1

Length

Max length4
Median length3
Mean length3.2028986
Min length2

Unique

Unique1 ?
Unique (%)0.7%

Sample

1st row생활용품
2nd row생활용품
3rd row기타
4th row기타
5th row기타

Common Values

ValueCountFrequency (%)
기타 38
27.5%
전자제품 37
26.8%
가구류 34
24.6%
생활용품 28
20.3%
<NA> 1
 
0.7%

Length

2023-12-12T16:02:44.309502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:02:44.474552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기타 38
27.5%
전자제품 37
26.8%
가구류 34
24.6%
생활용품 28
20.3%
na 1
 
0.7%

폐기물명
Text

Distinct81
Distinct (%)59.1%
Missing1
Missing (%)0.7%
Memory size1.2 KiB
2023-12-12T16:02:44.663027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length20
Mean length6.4014599
Min length2

Characters and Unicode

Total characters877
Distinct characters181
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

Unique35 ?
Unique (%)25.5%

Sample

1st row싱크대(상부장)
2nd row싱크대(상부장 )
3rd row캐비닛
4th row천막,블라인드
5th row책상유리
ValueCountFrequency (%)
에어컨 5
 
3.2%
냉장고 4
 
2.5%
가전제품 3
 
1.9%
수족관 3
 
1.9%
싱크대(상부장별도 3
 
1.9%
서랍장,화장대,문갑,장식장,신발장,진열장 3
 
1.9%
및 3
 
1.9%
사무용품 3
 
1.9%
그밖의 3
 
1.9%
의자 3
 
1.9%
Other values (80) 124
79.0%
2023-12-12T16:02:45.088034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 56
 
6.4%
장 40
 
4.6%
기 36
 
4.1%
( 25
 
2.9%
) 25
 
2.9%
이 23
 
2.6%
20
 
2.3%
스 17
 
1.9%
대 16
 
1.8%
자 15
 
1.7%
Other values (171) 604
68.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 745
84.9%
Other Punctuation 56
 
6.4%
Open Punctuation 25
 
2.9%
Close Punctuation 25
 
2.9%
Space Separator 20
 
2.3%
Uppercase Letter 6
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 40
 
5.4%
기 36
 
4.8%
이 23
 
3.1%
스 17
 
2.3%
대 16
 
2.1%
자 15
 
2.0%
상 14
 
1.9%
트 13
 
1.7%
식 12
 
1.6%
매 11
 
1.5%
Other values (163) 548
73.6%
Uppercase Letter
ValueCountFrequency (%)
V 2
33.3%
D 2
33.3%
T 1
16.7%
R 1
16.7%
Other Punctuation
ValueCountFrequency (%)
, 56
100.0%
Open Punctuation
ValueCountFrequency (%)
( 25
100.0%
Close Punctuation
ValueCountFrequency (%)
) 25
100.0%
Space Separator
ValueCountFrequency (%)
20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 745
84.9%
Common 126
 
14.4%
Latin 6
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 40
 
5.4%
기 36
 
4.8%
이 23
 
3.1%
스 17
 
2.3%
대 16
 
2.1%
자 15
 
2.0%
상 14
 
1.9%
트 13
 
1.7%
식 12
 
1.6%
매 11
 
1.5%
Other values (163) 548
73.6%
Common
ValueCountFrequency (%)
, 56
44.4%
( 25
19.8%
) 25
19.8%
20
 
15.9%
Latin
ValueCountFrequency (%)
V 2
33.3%
D 2
33.3%
T 1
16.7%
R 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 745
84.9%
ASCII 132
 
15.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 56
42.4%
( 25
18.9%
) 25
18.9%
20
 
15.2%
V 2
 
1.5%
D 2
 
1.5%
T 1
 
0.8%
R 1
 
0.8%
Hangul
ValueCountFrequency (%)
장 40
 
5.4%
기 36
 
4.8%
이 23
 
3.1%
스 17
 
2.3%
대 16
 
2.1%
자 15
 
2.0%
상 14
 
1.9%
트 13
 
1.7%
식 12
 
1.6%
매 11
 
1.5%
Other values (163) 548
73.6%

폐기물 규격
Text

Distinct74
Distinct (%)54.0%
Missing1
Missing (%)0.7%
Memory size1.2 KiB
2023-12-12T16:02:45.366277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length17
Mean length7.9051095
Min length2

Characters and Unicode

Total characters1083
Distinct characters136
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique57 ?
Unique (%)41.6%

Sample

1st row가로1m이상, 1짝당
2nd row가로1m미만, 1짝당
3rd row모든규격
4th row묶음단위(길이2m×지름30cm)
5th row모든규격
ValueCountFrequency (%)
모든규격 25
 
10.6%
이상 21
 
8.9%
x 20
 
8.5%
미만 14
 
5.9%
1.2m 12
 
5.1%
1m 9
 
3.8%
소형(가정용 8
 
3.4%
0.6m 8
 
3.4%
대형(업소형 7
 
3.0%
1인용 6
 
2.5%
Other values (68) 106
44.9%
2023-12-12T16:02:45.861066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
99
 
9.1%
m 63
 
5.8%
1 46
 
4.2%
형 46
 
4.2%
용 42
 
3.9%
이 40
 
3.7%
) 40
 
3.7%
( 40
 
3.7%
0 37
 
3.4%
. 34
 
3.1%
Other values (126) 596
55.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 584
53.9%
Decimal Number 159
 
14.7%
Space Separator 99
 
9.1%
Lowercase Letter 94
 
8.7%
Other Punctuation 44
 
4.1%
Close Punctuation 40
 
3.7%
Open Punctuation 40
 
3.7%
Uppercase Letter 12
 
1.1%
Math Symbol 7
 
0.6%
Other Symbol 4
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
형 46
 
7.9%
용 42
 
7.2%
이 40
 
6.8%
든 25
 
4.3%
격 25
 
4.3%
규 25
 
4.3%
모 25
 
4.3%
소 24
 
4.1%
상 23
 
3.9%
인 23
 
3.9%
Other values (98) 286
49.0%
Decimal Number
ValueCountFrequency (%)
1 46
28.9%
0 37
23.3%
2 30
18.9%
6 17
 
10.7%
3 8
 
5.0%
9 7
 
4.4%
8 5
 
3.1%
5 4
 
2.5%
4 3
 
1.9%
7 2
 
1.3%
Uppercase Letter
ValueCountFrequency (%)
L 5
41.7%
P 3
25.0%
F 1
 
8.3%
R 1
 
8.3%
C 1
 
8.3%
E 1
 
8.3%
Lowercase Letter
ValueCountFrequency (%)
m 63
67.0%
x 23
 
24.5%
c 8
 
8.5%
Other Punctuation
ValueCountFrequency (%)
. 34
77.3%
, 10
 
22.7%
Math Symbol
ValueCountFrequency (%)
× 4
57.1%
+ 3
42.9%
Other Symbol
ValueCountFrequency (%)
㎡ 3
75.0%
㎥ 1
 
25.0%
Space Separator
ValueCountFrequency (%)
99
100.0%
Close Punctuation
ValueCountFrequency (%)
) 40
100.0%
Open Punctuation
ValueCountFrequency (%)
( 40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 584
53.9%
Common 393
36.3%
Latin 106
 
9.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
형 46
 
7.9%
용 42
 
7.2%
이 40
 
6.8%
든 25
 
4.3%
격 25
 
4.3%
규 25
 
4.3%
모 25
 
4.3%
소 24
 
4.1%
상 23
 
3.9%
인 23
 
3.9%
Other values (98) 286
49.0%
Common
ValueCountFrequency (%)
99
25.2%
1 46
11.7%
) 40
10.2%
( 40
10.2%
0 37
 
9.4%
. 34
 
8.7%
2 30
 
7.6%
6 17
 
4.3%
, 10
 
2.5%
3 8
 
2.0%
Other values (9) 32
 
8.1%
Latin
ValueCountFrequency (%)
m 63
59.4%
x 23
 
21.7%
c 8
 
7.5%
L 5
 
4.7%
P 3
 
2.8%
F 1
 
0.9%
R 1
 
0.9%
C 1
 
0.9%
E 1
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 584
53.9%
ASCII 491
45.3%
None 4
 
0.4%
CJK Compat 4
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
99
20.2%
m 63
12.8%
1 46
9.4%
) 40
8.1%
( 40
8.1%
0 37
 
7.5%
. 34
 
6.9%
2 30
 
6.1%
x 23
 
4.7%
6 17
 
3.5%
Other values (15) 62
12.6%
Hangul
ValueCountFrequency (%)
형 46
 
7.9%
용 42
 
7.2%
이 40
 
6.8%
든 25
 
4.3%
격 25
 
4.3%
규 25
 
4.3%
모 25
 
4.3%
소 24
 
4.1%
상 23
 
3.9%
인 23
 
3.9%
Other values (98) 286
49.0%
None
ValueCountFrequency (%)
× 4
100.0%
CJK Compat
ValueCountFrequency (%)
㎡ 3
75.0%
㎥ 1
 
25.0%

수수료(원)
Real number (ℝ)

Distinct12
Distinct (%)8.8%
Missing1
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean5974.4526
Minimum1500
Maximum30000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T16:02:46.358725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1500
5-th percentile3000
Q13000
median4500
Q37500
95-th percentile15000
Maximum30000
Range28500
Interquartile range (IQR)4500

Descriptive statistics

Standard deviation4370.4056
Coefficient of variation (CV)0.73151567
Kurtosis8.2495677
Mean5974.4526
Median Absolute Deviation (MAD)1500
Skewness2.4608458
Sum818500
Variance19100445
MonotonicityNot monotonic
2023-12-12T16:02:46.494088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3000 44
31.9%
4500 30
21.7%
7500 19
13.8%
6000 14
 
10.1%
12000 12
 
8.7%
15000 5
 
3.6%
1500 4
 
2.9%
4000 3
 
2.2%
2000 2
 
1.4%
22500 2
 
1.4%
Other values (2) 2
 
1.4%
ValueCountFrequency (%)
1500 4
 
2.9%
2000 2
 
1.4%
3000 44
31.9%
4000 3
 
2.2%
4500 30
21.7%
6000 14
 
10.1%
7500 19
13.8%
9000 1
 
0.7%
12000 12
 
8.7%
15000 5
 
3.6%
ValueCountFrequency (%)
30000 1
 
0.7%
22500 2
 
1.4%
15000 5
 
3.6%
12000 12
 
8.7%
9000 1
 
0.7%
7500 19
13.8%
6000 14
 
10.1%
4500 30
21.7%
4000 3
 
2.2%
3000 44
31.9%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing1
Missing (%)0.7%
Memory size1.2 KiB
Minimum2022-08-31 00:00:00
Maximum2022-08-31 00:00:00
2023-12-12T16:02:46.634674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:02:46.754734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Unnamed: 5
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing138
Missing (%)100.0%
Memory size1.3 KiB

Interactions

2023-12-12T16:02:43.716984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T16:02:46.863563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
폐기물 구분폐기물명폐기물 규격수수료(원)
폐기물 구분1.0001.0000.8270.072
폐기물명1.0001.0000.0000.000
폐기물 규격0.8270.0001.0000.964
수수료(원)0.0720.0000.9641.000
2023-12-12T16:02:46.985110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수수료(원)폐기물 구분
수수료(원)1.0000.000
폐기물 구분0.0001.000

Missing values

2023-12-12T16:02:43.866772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:02:44.004982image/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.
2023-12-12T16:02:44.136003image/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

폐기물 구분폐기물명폐기물 규격수수료(원)데이터기준일자Unnamed: 5
0생활용품싱크대(상부장)가로1m이상, 1짝당60002022-08-31<NA>
1생활용품싱크대(상부장 )가로1m미만, 1짝당30002022-08-31<NA>
2기타캐비닛모든규격60002022-08-31<NA>
3기타천막,블라인드묶음단위(길이2m×지름30cm)40002022-08-31<NA>
4기타책상유리모든규격30002022-08-31<NA>
5가구류좌식의자모든규격15002022-08-31<NA>
6기타장판,놀이방매트묶음단위(길이2mx지름30cm)45002022-08-31<NA>
7기타변기통모든규격45002022-08-31<NA>
8가구류간이침대(접이식침대)2인용120002022-08-31<NA>
9가구류간이침대(접이식침대)1인용75002022-08-31<NA>
폐기물 구분폐기물명폐기물 규격수수료(원)데이터기준일자Unnamed: 5
128가구류밥상1.2m x 0.9m 미만20002022-08-31<NA>
129가구류밥상1.2m x 0.9m 이상30002022-08-31<NA>
130기타물탱크모든규격60002022-08-31<NA>
131기타문짝1m x 1m 기준30002022-08-31<NA>
132기타문짝0.9m x 1.8m 기준60002022-08-31<NA>
133가구류매트리스1인용75002022-08-31<NA>
134가구류매트리스2인용120002022-08-31<NA>
135가구류거울1.2m × 0.6m 미만30002022-08-31<NA>
136가구류거울1.25m × 0.6m 이상45002022-08-31<NA>
137<NA><NA><NA><NA><NA><NA>