Overview

Dataset statistics

Number of variables3
Number of observations24
Missing cells18
Missing cells (%)25.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory732.0 B
Average record size in memory30.5 B

Variable types

Text2
Numeric1

Dataset

Description영월군 상하수도요금 현황
Author강원도 영월군
URLhttps://www.data.go.kr/data/15010828/fileData.do

Alerts

업종별 has 18 (75.0%) missing valuesMissing

Reproduction

Analysis started2023-12-12 22:30:45.984686
Analysis finished2023-12-12 22:30:46.247337
Duration0.26 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종별
Text

MISSING 

Distinct6
Distinct (%)100.0%
Missing18
Missing (%)75.0%
Memory size324.0 B
2023-12-13T07:30:46.335833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.3333333
Min length3

Characters and Unicode

Total characters20
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)100.0%

Sample

1st row가정용
2nd row업무용
3rd row영업용
4th row욕탕1종
5th row욕탕2종
ValueCountFrequency (%)
가정용 1
16.7%
업무용 1
16.7%
영업용 1
16.7%
욕탕1종 1
16.7%
욕탕2종 1
16.7%
산업용 1
16.7%
2023-12-13T07:30:46.606384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
용 4
20.0%
업 3
15.0%
욕 2
10.0%
탕 2
10.0%
종 2
10.0%
가 1
 
5.0%
정 1
 
5.0%
무 1
 
5.0%
영 1
 
5.0%
1 1
 
5.0%
Other values (2) 2
10.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 18
90.0%
Decimal Number 2
 
10.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
용 4
22.2%
업 3
16.7%
욕 2
11.1%
탕 2
11.1%
종 2
11.1%
가 1
 
5.6%
정 1
 
5.6%
무 1
 
5.6%
영 1
 
5.6%
산 1
 
5.6%
Decimal Number
ValueCountFrequency (%)
1 1
50.0%
2 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 18
90.0%
Common 2
 
10.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
용 4
22.2%
업 3
16.7%
욕 2
11.1%
탕 2
11.1%
종 2
11.1%
가 1
 
5.6%
정 1
 
5.6%
무 1
 
5.6%
영 1
 
5.6%
산 1
 
5.6%
Common
ValueCountFrequency (%)
1 1
50.0%
2 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 18
90.0%
ASCII 2
 
10.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
용 4
22.2%
업 3
16.7%
욕 2
11.1%
탕 2
11.1%
종 2
11.1%
가 1
 
5.6%
정 1
 
5.6%
무 1
 
5.6%
영 1
 
5.6%
산 1
 
5.6%
ASCII
ValueCountFrequency (%)
1 1
50.0%
2 1
50.0%

용량별 
Text

Distinct22
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Memory size324.0 B
2023-12-13T07:30:46.744139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length7.9166667
Min length3

Characters and Unicode

Total characters190
Distinct characters12
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)83.3%

Sample

1st row1 ~ 10톤
2nd row11 ~ 20톤
3rd row21 ~ 30톤
4th row31 ~ 40톤
5th row41 ~ 50톤
ValueCountFrequency (%)
17
26.6%
이상 5
 
7.8%
1 4
 
6.2%
50톤 3
 
4.7%
100톤 2
 
3.1%
21 2
 
3.1%
30톤 2
 
3.1%
31 2
 
3.1%
500톤 2
 
3.1%
51 2
 
3.1%
Other values (20) 23
35.9%
2023-12-13T07:30:47.021682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
40
21.1%
0 38
20.0%
1 32
16.8%
톤 24
12.6%
~ 18
9.5%
5 10
 
5.3%
3 8
 
4.2%
2 7
 
3.7%
이 5
 
2.6%
상 5
 
2.6%
Other values (2) 3
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 97
51.1%
Space Separator 40
21.1%
Other Letter 35
 
18.4%
Math Symbol 18
 
9.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 38
39.2%
1 32
33.0%
5 10
 
10.3%
3 8
 
8.2%
2 7
 
7.2%
4 2
 
2.1%
Other Letter
ValueCountFrequency (%)
톤 24
68.6%
이 5
 
14.3%
상 5
 
14.3%
당 1
 
2.9%
Space Separator
ValueCountFrequency (%)
40
100.0%
Math Symbol
ValueCountFrequency (%)
~ 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 155
81.6%
Hangul 35
 
18.4%

Most frequent character per script

Common
ValueCountFrequency (%)
40
25.8%
0 38
24.5%
1 32
20.6%
~ 18
11.6%
5 10
 
6.5%
3 8
 
5.2%
2 7
 
4.5%
4 2
 
1.3%
Hangul
ValueCountFrequency (%)
톤 24
68.6%
이 5
 
14.3%
상 5
 
14.3%
당 1
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 155
81.6%
Hangul 35
 
18.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
40
25.8%
0 38
24.5%
1 32
20.6%
~ 18
11.6%
5 10
 
6.5%
3 8
 
5.2%
2 7
 
4.5%
4 2
 
1.3%
Hangul
ValueCountFrequency (%)
톤 24
68.6%
이 5
 
14.3%
상 5
 
14.3%
당 1
 
2.9%

톤당단가(원) 
Real number (ℝ)

Distinct22
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1403.75
Minimum630
Maximum3150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T07:30:47.140889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum630
5-th percentile723
Q1932.5
median1240
Q31632.5
95-th percentile2580
Maximum3150
Range2520
Interquartile range (IQR)700

Descriptive statistics

Standard deviation663.93958
Coefficient of variation (CV)0.47297566
Kurtosis0.8985343
Mean1403.75
Median Absolute Deviation (MAD)360
Skewness1.2025399
Sum33690
Variance440815.76
MonotonicityNot monotonic
2023-12-13T07:30:47.492339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
940 2
 
8.3%
2580 2
 
8.3%
720 1
 
4.2%
1860 1
 
4.2%
800 1
 
4.2%
3150 1
 
4.2%
2260 1
 
4.2%
1640 1
 
4.2%
1260 1
 
4.2%
1630 1
 
4.2%
Other values (12) 12
50.0%
ValueCountFrequency (%)
630 1
4.2%
720 1
4.2%
740 1
4.2%
800 1
4.2%
830 1
4.2%
910 1
4.2%
940 2
8.3%
1030 1
4.2%
1080 1
4.2%
1180 1
4.2%
ValueCountFrequency (%)
3150 1
4.2%
2580 2
8.3%
2260 1
4.2%
1860 1
4.2%
1640 1
4.2%
1630 1
4.2%
1560 1
4.2%
1510 1
4.2%
1340 1
4.2%
1300 1
4.2%

Interactions

2023-12-13T07:30:46.075717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:30:47.571424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종별용량별톤당단가(원)
업종별1.0001.0001.000
용량별1.0001.0000.898
톤당단가(원)1.0000.8981.000

Missing values

2023-12-13T07:30:46.162014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:30:46.222395image/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

업종별용량별톤당단가(원)
0가정용1 ~ 10톤720
1<NA>11 ~ 20톤830
2<NA>21 ~ 30톤940
3<NA>31 ~ 40톤1080
4<NA>41 ~ 50톤1300
5<NA>51톤 이상1510
6업무용1 ~ 10톤630
7<NA>21 ~ 50톤740
8<NA>51 ~ 100톤940
9<NA>101 ~ 300톤1220
업종별용량별톤당단가(원)
14<NA>101톤 이상2580
15욕탕1종1~200톤910
16<NA>201 ~ 300톤1180
17<NA>301 ~ 500톤1630
18<NA>501톤 이상2580
19욕탕2종1 ~ 200톤1260
20<NA>201 ~ 500톤1640
21<NA>501 ~ 1000톤2260
22<NA>1001톤 이상3150
23산업용톤 당800