Overview

Dataset statistics

Number of variables4
Number of observations131
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.5 KiB
Average record size in memory35.0 B

Variable types

Numeric2
Text2

Dataset

Description대전광역시 서구 대형 폐기물 처리 수수료 정보(대형 폐기물 품명, 대형 폐기물 규격, 대형 폐기물 수수료)를 제공합니다
Author대전광역시 서구
URLhttps://www.data.go.kr/data/15089837/fileData.do

Alerts

순번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 20:00:14.486838
Analysis finished2023-12-12 20:00:15.426964
Duration0.94 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct131
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66
Minimum1
Maximum131
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-13T05:00:15.496412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7.5
Q133.5
median66
Q398.5
95-th percentile124.5
Maximum131
Range130
Interquartile range (IQR)65

Descriptive statistics

Standard deviation37.960506
Coefficient of variation (CV)0.57515918
Kurtosis-1.2
Mean66
Median Absolute Deviation (MAD)33
Skewness0
Sum8646
Variance1441
MonotonicityStrictly increasing
2023-12-13T05:00:15.624066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.8%
84 1
 
0.8%
98 1
 
0.8%
97 1
 
0.8%
96 1
 
0.8%
95 1
 
0.8%
94 1
 
0.8%
93 1
 
0.8%
92 1
 
0.8%
91 1
 
0.8%
Other values (121) 121
92.4%
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 (%)
131 1
0.8%
130 1
0.8%
129 1
0.8%
128 1
0.8%
127 1
0.8%
126 1
0.8%
125 1
0.8%
124 1
0.8%
123 1
0.8%
122 1
0.8%

품명
Text

Distinct67
Distinct (%)51.1%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-13T05:00:15.829094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length3.2748092
Min length1

Characters and Unicode

Total characters429
Distinct characters123
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)23.7%

Sample

1st row냉장고
2nd row냉장고
3rd row냉장고
4th row냉장고
5th row냉장고
ValueCountFrequency (%)
냉장고 7
 
5.3%
침대 6
 
4.6%
tv 6
 
4.6%
에어컨(온풍기 5
 
3.8%
소파 5
 
3.8%
광고판 4
 
3.1%
장농 4
 
3.1%
컴퓨터 4
 
3.1%
피아노 3
 
2.3%
책상 3
 
2.3%
Other values (57) 84
64.1%
2023-12-13T05:00:16.169191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 27
 
6.3%
기 25
 
5.8%
대 16
 
3.7%
( 15
 
3.5%
) 15
 
3.5%
자 11
 
2.6%
고 11
 
2.6%
오 11
 
2.6%
풍 7
 
1.6%
냉 7
 
1.6%
Other values (113) 284
66.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 383
89.3%
Open Punctuation 15
 
3.5%
Close Punctuation 15
 
3.5%
Uppercase Letter 14
 
3.3%
Decimal Number 2
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 27
 
7.0%
기 25
 
6.5%
대 16
 
4.2%
자 11
 
2.9%
고 11
 
2.9%
오 11
 
2.9%
풍 7
 
1.8%
냉 7
 
1.8%
침 7
 
1.8%
소 6
 
1.6%
Other values (108) 255
66.6%
Uppercase Letter
ValueCountFrequency (%)
V 7
50.0%
T 7
50.0%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%
Decimal Number
ValueCountFrequency (%)
1 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 383
89.3%
Common 32
 
7.5%
Latin 14
 
3.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 27
 
7.0%
기 25
 
6.5%
대 16
 
4.2%
자 11
 
2.9%
고 11
 
2.9%
오 11
 
2.9%
풍 7
 
1.8%
냉 7
 
1.8%
침 7
 
1.8%
소 6
 
1.6%
Other values (108) 255
66.6%
Common
ValueCountFrequency (%)
( 15
46.9%
) 15
46.9%
1 2
 
6.2%
Latin
ValueCountFrequency (%)
V 7
50.0%
T 7
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 383
89.3%
ASCII 46
 
10.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 27
 
7.0%
기 25
 
6.5%
대 16
 
4.2%
자 11
 
2.9%
고 11
 
2.9%
오 11
 
2.9%
풍 7
 
1.8%
냉 7
 
1.8%
침 7
 
1.8%
소 6
 
1.6%
Other values (108) 255
66.6%
ASCII
ValueCountFrequency (%)
( 15
32.6%
) 15
32.6%
V 7
15.2%
T 7
15.2%
1 2
 
4.3%

규격
Text

Distinct88
Distinct (%)67.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-13T05:00:16.415786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length5.5038168
Min length2

Characters and Unicode

Total characters721
Distinct characters108
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique81 ?
Unique (%)61.8%

Sample

1st row800리터이상
2nd row800리터미만
3rd row600리터미만
4th row400리터미만
5th row300리터미만
ValueCountFrequency (%)
모든규격 29
 
22.0%
높이1m미만 6
 
4.5%
높이1m이상 6
 
4.5%
5.5제곱미터이상 3
 
2.3%
편수 2
 
1.5%
양수 2
 
1.5%
5.5제곱미터미만 2
 
1.5%
전신(f.r.p 1
 
0.8%
어프라이드 1
 
0.8%
그랜드 1
 
0.8%
Other values (79) 79
59.8%
2023-12-13T05:00:16.764509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 61
 
8.5%
1 41
 
5.7%
미 39
 
5.4%
상 31
 
4.3%
모 30
 
4.2%
규 29
 
4.0%
격 29
 
4.0%
든 29
 
4.0%
m 28
 
3.9%
만 27
 
3.7%
Other values (98) 377
52.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 522
72.4%
Decimal Number 125
 
17.3%
Lowercase Letter 42
 
5.8%
Other Punctuation 12
 
1.7%
Open Punctuation 7
 
1.0%
Close Punctuation 6
 
0.8%
Uppercase Letter 6
 
0.8%
Space Separator 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 61
 
11.7%
미 39
 
7.5%
상 31
 
5.9%
모 30
 
5.7%
규 29
 
5.6%
격 29
 
5.6%
든 29
 
5.6%
만 27
 
5.2%
터 22
 
4.2%
인 17
 
3.3%
Other values (76) 208
39.8%
Decimal Number
ValueCountFrequency (%)
1 41
32.8%
5 27
21.6%
0 26
20.8%
2 10
 
8.0%
4 7
 
5.6%
9 4
 
3.2%
3 4
 
3.2%
6 4
 
3.2%
8 2
 
1.6%
Lowercase Letter
ValueCountFrequency (%)
m 28
66.7%
c 8
 
19.0%
x 2
 
4.8%
k 2
 
4.8%
g 2
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
P 2
33.3%
R 2
33.3%
F 2
33.3%
Other Punctuation
ValueCountFrequency (%)
. 11
91.7%
/ 1
 
8.3%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 522
72.4%
Common 151
 
20.9%
Latin 48
 
6.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 61
 
11.7%
미 39
 
7.5%
상 31
 
5.9%
모 30
 
5.7%
규 29
 
5.6%
격 29
 
5.6%
든 29
 
5.6%
만 27
 
5.2%
터 22
 
4.2%
인 17
 
3.3%
Other values (76) 208
39.8%
Common
ValueCountFrequency (%)
1 41
27.2%
5 27
17.9%
0 26
17.2%
. 11
 
7.3%
2 10
 
6.6%
4 7
 
4.6%
( 7
 
4.6%
) 6
 
4.0%
9 4
 
2.6%
3 4
 
2.6%
Other values (4) 8
 
5.3%
Latin
ValueCountFrequency (%)
m 28
58.3%
c 8
 
16.7%
P 2
 
4.2%
R 2
 
4.2%
F 2
 
4.2%
x 2
 
4.2%
k 2
 
4.2%
g 2
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 522
72.4%
ASCII 199
 
27.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 61
 
11.7%
미 39
 
7.5%
상 31
 
5.9%
모 30
 
5.7%
규 29
 
5.6%
격 29
 
5.6%
든 29
 
5.6%
만 27
 
5.2%
터 22
 
4.2%
인 17
 
3.3%
Other values (76) 208
39.8%
ASCII
ValueCountFrequency (%)
1 41
20.6%
m 28
14.1%
5 27
13.6%
0 26
13.1%
. 11
 
5.5%
2 10
 
5.0%
c 8
 
4.0%
4 7
 
3.5%
( 7
 
3.5%
) 6
 
3.0%
Other values (12) 28
14.1%

수수료
Real number (ℝ)

Distinct13
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5118.3206
Minimum500
Maximum20000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-13T05:00:16.886331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile2000
Q13000
median4000
Q36000
95-th percentile11000
Maximum20000
Range19500
Interquartile range (IQR)3000

Descriptive statistics

Standard deviation3457.8962
Coefficient of variation (CV)0.67559196
Kurtosis2.7839778
Mean5118.3206
Median Absolute Deviation (MAD)1000
Skewness1.5645155
Sum670500
Variance11957046
MonotonicityNot monotonic
2023-12-13T05:00:16.983556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
3000 30
22.9%
2000 20
15.3%
4000 19
14.5%
5000 18
13.7%
10000 13
9.9%
8000 10
 
7.6%
6000 7
 
5.3%
15000 4
 
3.1%
1000 4
 
3.1%
12000 2
 
1.5%
Other values (3) 4
 
3.1%
ValueCountFrequency (%)
500 1
 
0.8%
1000 4
 
3.1%
2000 20
15.3%
3000 30
22.9%
4000 19
14.5%
5000 18
13.7%
6000 7
 
5.3%
7000 2
 
1.5%
8000 10
 
7.6%
10000 13
9.9%
ValueCountFrequency (%)
20000 1
 
0.8%
15000 4
 
3.1%
12000 2
 
1.5%
10000 13
9.9%
8000 10
 
7.6%
7000 2
 
1.5%
6000 7
 
5.3%
5000 18
13.7%
4000 19
14.5%
3000 30
22.9%

Interactions

2023-12-13T05:00:14.850588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:00:14.694214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:00:15.211394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:00:14.767499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:00:17.058388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번품명규격수수료
순번1.0000.9970.6630.174
품명0.9971.0000.0000.000
규격0.6630.0001.0000.971
수수료0.1740.0000.9711.000
2023-12-13T05:00:17.141035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번수수료
순번1.000-0.167
수수료-0.1671.000

Missing values

2023-12-13T05:00:15.318501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:00:15.394972image/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

순번품명규격수수료
01냉장고800리터이상15000
12냉장고800리터미만12000
23냉장고600리터미만10000
34냉장고400리터미만8000
45냉장고300리터미만6000
56냉장고200리터미만4000
67냉장고1000리터 이상20000
78TV55인치이상15000
89TV55인치미만10000
910TV45인치이하8000
순번품명규격수수료
121122목재길이1m미만500
122123오락기높이1m이상10000
123124오락기높이1m미만5000
124125광고판5제곱미터이상10000
125126광고판3제곱미터이상7000
126127광고판1제곱미터이상5000
127128광고판1제곱미터이하3000
128129물탱크1톤당(용랑기준)10000
129130이불류솜이불1채5000
130131이불류홑이불1채3000