Overview

Dataset statistics

Number of variables3
Number of observations84
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory26.6 B

Variable types

Text2
Numeric1

Dataset

Description경상남도 창녕군 대형생활폐기물 배출 시 처리비용에 대한 데이터를 포함하고 있습니다.(대형폐기물 품목, 세부규격, 규격별 처리비용)
URLhttps://www.data.go.kr/data/15014580/fileData.do

Reproduction

Analysis started2023-12-12 09:43:24.197912
Analysis finished2023-12-12 09:43:24.645688
Duration0.45 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

품명
Text

Distinct48
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Memory size804.0 B
2023-12-12T18:43:24.804061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length14
Mean length5.0952381
Min length2

Characters and Unicode

Total characters428
Distinct characters130
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

Unique26 ?
Unique (%)31.0%

Sample

1st row냉장고
2nd row냉장고
3rd row냉장고
4th row냉장고
5th row텔레비전
ValueCountFrequency (%)
냉장고 4
 
4.7%
오디오 4
 
4.7%
침대(일반용 4
 
4.7%
냉,난방기 3
 
3.5%
캐비닛 3
 
3.5%
책상 3
 
3.5%
침대(어린이용 3
 
3.5%
응접셋트 3
 
3.5%
장식장,찬장 3
 
3.5%
식탁 3
 
3.5%
Other values (39) 52
61.2%
2023-12-12T18:43:25.220571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
기 31
 
7.2%
, 30
 
7.0%
장 21
 
4.9%
오 14
 
3.3%
용 11
 
2.6%
대 10
 
2.3%
전 10
 
2.3%
냉 9
 
2.1%
비 8
 
1.9%
) 8
 
1.9%
Other values (120) 276
64.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 375
87.6%
Other Punctuation 30
 
7.0%
Close Punctuation 8
 
1.9%
Open Punctuation 8
 
1.9%
Uppercase Letter 6
 
1.4%
Space Separator 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 31
 
8.3%
장 21
 
5.6%
오 14
 
3.7%
용 11
 
2.9%
대 10
 
2.7%
전 10
 
2.7%
냉 9
 
2.4%
비 8
 
2.1%
자 7
 
1.9%
식 7
 
1.9%
Other values (111) 247
65.9%
Uppercase Letter
ValueCountFrequency (%)
R 2
33.3%
F 1
16.7%
P 1
16.7%
T 1
16.7%
V 1
16.7%
Other Punctuation
ValueCountFrequency (%)
, 30
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 375
87.6%
Common 47
 
11.0%
Latin 6
 
1.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 31
 
8.3%
장 21
 
5.6%
오 14
 
3.7%
용 11
 
2.9%
대 10
 
2.7%
전 10
 
2.7%
냉 9
 
2.4%
비 8
 
2.1%
자 7
 
1.9%
식 7
 
1.9%
Other values (111) 247
65.9%
Latin
ValueCountFrequency (%)
R 2
33.3%
F 1
16.7%
P 1
16.7%
T 1
16.7%
V 1
16.7%
Common
ValueCountFrequency (%)
, 30
63.8%
) 8
 
17.0%
( 8
 
17.0%
1
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 375
87.6%
ASCII 53
 
12.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
기 31
 
8.3%
장 21
 
5.6%
오 14
 
3.7%
용 11
 
2.9%
대 10
 
2.7%
전 10
 
2.7%
냉 9
 
2.4%
비 8
 
2.1%
자 7
 
1.9%
식 7
 
1.9%
Other values (111) 247
65.9%
ASCII
ValueCountFrequency (%)
, 30
56.6%
) 8
 
15.1%
( 8
 
15.1%
R 2
 
3.8%
F 1
 
1.9%
P 1
 
1.9%
1
 
1.9%
T 1
 
1.9%
V 1
 
1.9%

규격
Text

Distinct68
Distinct (%)81.0%
Missing0
Missing (%)0.0%
Memory size804.0 B
2023-12-12T18:43:25.543345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length5.0833333
Min length2

Characters and Unicode

Total characters427
Distinct characters99
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique65 ?
Unique (%)77.4%

Sample

1st row500ℓ이상
2nd row300ℓ이상
3rd row300ℓ미만
4th row100ℓ미만
5th row10인치당
ValueCountFrequency (%)
모든규격 15
 
17.2%
높이 2
 
2.3%
1인용 2
 
2.3%
폭1.5m이상 2
 
2.3%
pp포대당 1
 
1.1%
1층조합 1
 
1.1%
3인용 1
 
1.1%
테이블 1
 
1.1%
편수(소형 1
 
1.1%
2인용 1
 
1.1%
Other values (60) 60
69.0%
2023-12-12T18:43:25.963933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 25
 
5.9%
이 20
 
4.7%
0 19
 
4.4%
m 18
 
4.2%
모 16
 
3.7%
형 15
 
3.5%
규 15
 
3.5%
격 15
 
3.5%
당 15
 
3.5%
든 15
 
3.5%
Other values (89) 254
59.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 285
66.7%
Decimal Number 70
 
16.4%
Lowercase Letter 30
 
7.0%
Close Punctuation 11
 
2.6%
Open Punctuation 11
 
2.6%
Other Punctuation 9
 
2.1%
Uppercase Letter 5
 
1.2%
Space Separator 3
 
0.7%
Other Symbol 2
 
0.5%
Math Symbol 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 20
 
7.0%
모 16
 
5.6%
형 15
 
5.3%
규 15
 
5.3%
격 15
 
5.3%
당 15
 
5.3%
든 15
 
5.3%
인 12
 
4.2%
상 11
 
3.9%
용 11
 
3.9%
Other values (67) 140
49.1%
Decimal Number
ValueCountFrequency (%)
1 25
35.7%
0 19
27.1%
2 8
 
11.4%
3 7
 
10.0%
5 5
 
7.1%
4 3
 
4.3%
9 2
 
2.9%
8 1
 
1.4%
Lowercase Letter
ValueCountFrequency (%)
m 18
60.0%
ℓ 6
 
20.0%
c 4
 
13.3%
g 2
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
K 2
40.0%
P 2
40.0%
X 1
20.0%
Other Punctuation
ValueCountFrequency (%)
. 7
77.8%
, 2
 
22.2%
Close Punctuation
ValueCountFrequency (%)
) 11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%
Other Symbol
ValueCountFrequency (%)
㎡ 2
100.0%
Math Symbol
ValueCountFrequency (%)
× 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 285
66.7%
Common 113
 
26.5%
Latin 29
 
6.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 20
 
7.0%
모 16
 
5.6%
형 15
 
5.3%
규 15
 
5.3%
격 15
 
5.3%
당 15
 
5.3%
든 15
 
5.3%
인 12
 
4.2%
상 11
 
3.9%
용 11
 
3.9%
Other values (67) 140
49.1%
Common
ValueCountFrequency (%)
1 25
22.1%
0 19
16.8%
) 11
9.7%
( 11
9.7%
2 8
 
7.1%
3 7
 
6.2%
. 7
 
6.2%
ℓ 6
 
5.3%
5 5
 
4.4%
4 3
 
2.7%
Other values (6) 11
9.7%
Latin
ValueCountFrequency (%)
m 18
62.1%
c 4
 
13.8%
K 2
 
6.9%
g 2
 
6.9%
P 2
 
6.9%
X 1
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 285
66.7%
ASCII 133
31.1%
Letterlike Symbols 6
 
1.4%
CJK Compat 2
 
0.5%
None 1
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 25
18.8%
0 19
14.3%
m 18
13.5%
) 11
8.3%
( 11
8.3%
2 8
 
6.0%
3 7
 
5.3%
. 7
 
5.3%
5 5
 
3.8%
c 4
 
3.0%
Other values (9) 18
13.5%
Hangul
ValueCountFrequency (%)
이 20
 
7.0%
모 16
 
5.6%
형 15
 
5.3%
규 15
 
5.3%
격 15
 
5.3%
당 15
 
5.3%
든 15
 
5.3%
인 12
 
4.2%
상 11
 
3.9%
용 11
 
3.9%
Other values (67) 140
49.1%
Letterlike Symbols
ValueCountFrequency (%)
ℓ 6
100.0%
CJK Compat
ValueCountFrequency (%)
㎡ 2
100.0%
None
ValueCountFrequency (%)
× 1
100.0%

처리비
Real number (ℝ)

Distinct11
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3904.7619
Minimum1000
Maximum15000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2023-12-12T18:43:26.092624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1000
Q12000
median3000
Q34250
95-th percentile10000
Maximum15000
Range14000
Interquartile range (IQR)2250

Descriptive statistics

Standard deviation2960.0515
Coefficient of variation (CV)0.75806196
Kurtosis3.9407236
Mean3904.7619
Median Absolute Deviation (MAD)1000
Skewness1.9367273
Sum328000
Variance8761904.8
MonotonicityNot monotonic
2023-12-12T18:43:26.235157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2000 25
29.8%
3000 19
22.6%
4000 11
13.1%
1000 8
 
9.5%
5000 6
 
7.1%
8000 5
 
6.0%
6000 3
 
3.6%
10000 3
 
3.6%
15000 2
 
2.4%
12000 1
 
1.2%
ValueCountFrequency (%)
1000 8
 
9.5%
2000 25
29.8%
3000 19
22.6%
4000 11
13.1%
5000 6
 
7.1%
6000 3
 
3.6%
8000 5
 
6.0%
9000 1
 
1.2%
10000 3
 
3.6%
12000 1
 
1.2%
ValueCountFrequency (%)
15000 2
 
2.4%
12000 1
 
1.2%
10000 3
 
3.6%
9000 1
 
1.2%
8000 5
 
6.0%
6000 3
 
3.6%
5000 6
 
7.1%
4000 11
13.1%
3000 19
22.6%
2000 25
29.8%

Interactions

2023-12-12T18:43:24.391826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:43:26.332354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
품명규격처리비
품명1.0000.0000.000
규격0.0001.0000.958
처리비0.0000.9581.000

Missing values

2023-12-12T18:43:24.527568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:43:24.616447image/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

품명규격처리비
0냉장고500ℓ이상8000
1냉장고300ℓ이상6000
2냉장고300ℓ미만4000
3냉장고100ℓ미만2000
4텔레비전10인치당2000
5텔레비전20인치이상3000
6텔레비전(20인치미만)장식 대2000
7세탁기모든규격4000
8냉,난방기세로형(폭50cm당)5000
9냉,난방기벽면부착형3000
품명규격처리비
74쌀통모든규격2000
75장독20ℓ당1000
76세면기모든규격2000
77수족관1m당3000
78문짝0.9m×1.8m당3000
79옷장폭30cm당2000
80거울,유리㎡당1000
81기타잡품목PP포대당2000
82폐소화기소형(3.3Kg이하)2000
83폐소화기중,대형(3.4Kg이상)3000