Overview

Dataset statistics

Number of variables3
Number of observations21
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory657.0 B
Average record size in memory31.3 B

Variable types

Text1
Categorical1
Numeric1

Dataset

Description전라남도 여수시 수도요금에 대한 요금정보 부과기준 데이터이며, 구간별, 업종별, 톤당 상수도요금을 제공하고 있습니다.
URLhttps://www.data.go.kr/data/15093343/fileData.do

Reproduction

Analysis started2023-12-12 02:28:00.770891
Analysis finished2023-12-12 02:28:01.116590
Duration0.35 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

사용량
Text

Distinct20
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-12T11:28:01.234755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.3809524
Min length4

Characters and Unicode

Total characters113
Distinct characters9
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)90.5%

Sample

1st row1-10
2nd row11-20
3rd row21-30
4th row31-40
5th row41-50
ValueCountFrequency (%)
51-100 2
 
9.5%
1-10 1
 
4.8%
1-30 1
 
4.8%
1-40000 1
 
4.8%
501이상 1
 
4.8%
301-500 1
 
4.8%
201-300 1
 
4.8%
1-200 1
 
4.8%
101이상 1
 
4.8%
31-50 1
 
4.8%
Other values (10) 10
47.6%
2023-12-12T11:28:01.569019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 34
30.1%
1 27
23.9%
- 16
14.2%
5 8
 
7.1%
3 8
 
7.1%
2 6
 
5.3%
이 5
 
4.4%
상 5
 
4.4%
4 4
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 87
77.0%
Dash Punctuation 16
 
14.2%
Other Letter 10
 
8.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 34
39.1%
1 27
31.0%
5 8
 
9.2%
3 8
 
9.2%
2 6
 
6.9%
4 4
 
4.6%
Other Letter
ValueCountFrequency (%)
이 5
50.0%
상 5
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 103
91.2%
Hangul 10
 
8.8%

Most frequent character per script

Common
ValueCountFrequency (%)
0 34
33.0%
1 27
26.2%
- 16
15.5%
5 8
 
7.8%
3 8
 
7.8%
2 6
 
5.8%
4 4
 
3.9%
Hangul
ValueCountFrequency (%)
이 5
50.0%
상 5
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 103
91.2%
Hangul 10
 
8.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 34
33.0%
1 27
26.2%
- 16
15.5%
5 8
 
7.8%
3 8
 
7.8%
2 6
 
5.8%
4 4
 
3.9%
Hangul
ValueCountFrequency (%)
이 5
50.0%
상 5
50.0%

업종별
Categorical

Distinct5
Distinct (%)23.8%
Missing0
Missing (%)0.0%
Memory size300.0 B
가정용
6 
업무용
5 
영업용
4 
대중탕용
4 
공업용
2 

Length

Max length4
Median length3
Mean length3.1904762
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
가정용 6
28.6%
업무용 5
23.8%
영업용 4
19.0%
대중탕용 4
19.0%
공업용 2
 
9.5%

Length

2023-12-12T11:28:01.724452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:28:01.841424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
가정용 6
28.6%
업무용 5
23.8%
영업용 4
19.0%
대중탕용 4
19.0%
공업용 2
 
9.5%

상수도(원_톤)
Real number (ℝ)

Distinct20
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1393.8095
Minimum550
Maximum2660
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T11:28:01.967641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum550
5-th percentile590
Q1950
median1400
Q31780
95-th percentile2450
Maximum2660
Range2110
Interquartile range (IQR)830

Descriptive statistics

Standard deviation612.85786
Coefficient of variation (CV)0.43969987
Kurtosis-0.54624057
Mean1393.8095
Median Absolute Deviation (MAD)420
Skewness0.53536178
Sum29270
Variance375594.76
MonotonicityNot monotonic
2023-12-12T11:28:02.093707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
770 2
 
9.5%
590 1
 
4.8%
1400 1
 
4.8%
710 1
 
4.8%
550 1
 
4.8%
1470 1
 
4.8%
1230 1
 
4.8%
1000 1
 
4.8%
2660 1
 
4.8%
2240 1
 
4.8%
Other values (10) 10
47.6%
ValueCountFrequency (%)
550 1
4.8%
590 1
4.8%
710 1
4.8%
770 2
9.5%
950 1
4.8%
1000 1
4.8%
1110 1
4.8%
1120 1
4.8%
1230 1
4.8%
1400 1
4.8%
ValueCountFrequency (%)
2660 1
4.8%
2450 1
4.8%
2240 1
4.8%
2120 1
4.8%
1820 1
4.8%
1780 1
4.8%
1660 1
4.8%
1470 1
4.8%
1450 1
4.8%
1420 1
4.8%

Interactions

2023-12-12T11:28:00.879482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:28:02.168950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사용량업종별상수도(원_톤)
사용량1.0000.9090.760
업종별0.9091.0000.000
상수도(원_톤)0.7600.0001.000
2023-12-12T11:28:02.258737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상수도(원_톤)업종별
상수도(원_톤)1.0000.051
업종별0.0511.000

Missing values

2023-12-12T11:28:00.995476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:28:01.082792image/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

사용량업종별상수도(원_톤)
01-10가정용590
111-20가정용770
221-30가정용950
331-40가정용1120
441-50가정용1420
551이상가정용1660
61-20업무용1110
721-50업무용1450
851-100업무용1780
9101-300업무용2120
사용량업종별상수도(원_톤)
111-30영업용1400
1231-50영업용1820
1351-100영업용2240
14101이상영업용2660
151-200대중탕용770
16201-300대중탕용1000
17301-500대중탕용1230
18501이상대중탕용1470
191-40000공업용550
2040001이상공업용710