Overview

Dataset statistics

Number of variables4
Number of observations636
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory20.6 KiB
Average record size in memory33.2 B

Variable types

Numeric1
Categorical2
Text1

Dataset

Description양양군 관내 상수도 원격검침시스템을 설치한 수용가에 대하여 현재 시점으로 지역, 주소, 검침방식 현황 전체 자료를 제공합니다.
Author강원특별자치도 양양군
URLhttps://www.data.go.kr/data/15103370/fileData.do

Alerts

검침방식 has constant value ""Constant
연번 is highly overall correlated with 지역High correlation
지역 is highly overall correlated with 연번High correlation
연번 has unique valuesUnique

Reproduction

Analysis started2024-03-14 13:22:21.163475
Analysis finished2024-03-14 13:22:22.167229
Duration1 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct636
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean318.5
Minimum1
Maximum636
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2024-03-14T22:22:22.384069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile32.75
Q1159.75
median318.5
Q3477.25
95-th percentile604.25
Maximum636
Range635
Interquartile range (IQR)317.5

Descriptive statistics

Standard deviation183.74167
Coefficient of variation (CV)0.57689691
Kurtosis-1.2
Mean318.5
Median Absolute Deviation (MAD)159
Skewness0
Sum202566
Variance33761
MonotonicityStrictly increasing
2024-03-14T22:22:22.772345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.2%
429 1
 
0.2%
422 1
 
0.2%
423 1
 
0.2%
424 1
 
0.2%
425 1
 
0.2%
426 1
 
0.2%
427 1
 
0.2%
428 1
 
0.2%
430 1
 
0.2%
Other values (626) 626
98.4%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
636 1
0.2%
635 1
0.2%
634 1
0.2%
633 1
0.2%
632 1
0.2%
631 1
0.2%
630 1
0.2%
629 1
0.2%
628 1
0.2%
627 1
0.2%

지역
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
양양읍
147 
현북면
130 
서면
107 
강현면
103 
손양면
98 

Length

Max length3
Median length3
Mean length2.831761
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row양양읍
2nd row서면
3rd row양양읍
4th row양양읍
5th row양양읍

Common Values

ValueCountFrequency (%)
양양읍 147
23.1%
현북면 130
20.4%
서면 107
16.8%
강현면 103
16.2%
손양면 98
15.4%
현남면 51
 
8.0%

Length

2024-03-14T22:22:23.013132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T22:22:23.501719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
양양읍 147
23.1%
현북면 130
20.4%
서면 107
16.8%
강현면 103
16.2%
손양면 98
15.4%
현남면 51
 
8.0%

수용가 주소
Text

Distinct617
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
2024-03-14T22:22:24.468419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length20.5
Mean length9.5
Min length4

Characters and Unicode

Total characters6042
Distinct characters224
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique602 ?
Unique (%)94.7%

Sample

1st row월리 318-3
2nd row장승1길 128
3rd row남문8길 6 (서원독서실)
4th row양양로 83-1 (황실유럽자수)
5th row양양로 101
ValueCountFrequency (%)
어성전길 44
 
3.4%
쟁기동길 37
 
2.9%
용호길 37
 
2.9%
양양로 32
 
2.5%
수리1길 28
 
2.2%
굴개길 20
 
1.5%
화채봉길 20
 
1.5%
쟁기동길58번길 20
 
1.5%
밀양고개길 19
 
1.5%
장승1길 17
 
1.3%
Other values (644) 1020
78.8%
2024-03-14T22:22:25.853561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
662
 
11.0%
1 620
 
10.3%
길 493
 
8.2%
- 388
 
6.4%
2 365
 
6.0%
8 192
 
3.2%
5 185
 
3.1%
4 179
 
3.0%
3 167
 
2.8%
6 162
 
2.7%
Other values (214) 2629
43.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2568
42.5%
Decimal Number 2285
37.8%
Space Separator 662
 
11.0%
Dash Punctuation 388
 
6.4%
Open Punctuation 68
 
1.1%
Close Punctuation 67
 
1.1%
Uppercase Letter 3
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
길 493
 
19.2%
로 141
 
5.5%
양 102
 
4.0%
동 101
 
3.9%
리 82
 
3.2%
기 59
 
2.3%
쟁 57
 
2.2%
호 52
 
2.0%
수 52
 
2.0%
전 50
 
1.9%
Other values (194) 1379
53.7%
Decimal Number
ValueCountFrequency (%)
1 620
27.1%
2 365
16.0%
8 192
 
8.4%
5 185
 
8.1%
4 179
 
7.8%
3 167
 
7.3%
6 162
 
7.1%
9 144
 
6.3%
0 137
 
6.0%
7 134
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
C 1
33.3%
B 1
33.3%
H 1
33.3%
Open Punctuation
ValueCountFrequency (%)
( 66
97.1%
（ 2
 
2.9%
Close Punctuation
ValueCountFrequency (%)
) 65
97.0%
） 2
 
3.0%
Space Separator
ValueCountFrequency (%)
662
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 388
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3471
57.4%
Hangul 2568
42.5%
Latin 3
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
길 493
 
19.2%
로 141
 
5.5%
양 102
 
4.0%
동 101
 
3.9%
리 82
 
3.2%
기 59
 
2.3%
쟁 57
 
2.2%
호 52
 
2.0%
수 52
 
2.0%
전 50
 
1.9%
Other values (194) 1379
53.7%
Common
ValueCountFrequency (%)
662
19.1%
1 620
17.9%
- 388
11.2%
2 365
10.5%
8 192
 
5.5%
5 185
 
5.3%
4 179
 
5.2%
3 167
 
4.8%
6 162
 
4.7%
9 144
 
4.1%
Other values (7) 407
11.7%
Latin
ValueCountFrequency (%)
C 1
33.3%
B 1
33.3%
H 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3470
57.4%
Hangul 2568
42.5%
None 4
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
662
19.1%
1 620
17.9%
- 388
11.2%
2 365
10.5%
8 192
 
5.5%
5 185
 
5.3%
4 179
 
5.2%
3 167
 
4.8%
6 162
 
4.7%
9 144
 
4.1%
Other values (8) 406
11.7%
Hangul
ValueCountFrequency (%)
길 493
 
19.2%
로 141
 
5.5%
양 102
 
4.0%
동 101
 
3.9%
리 82
 
3.2%
기 59
 
2.3%
쟁 57
 
2.2%
호 52
 
2.0%
수 52
 
2.0%
전 50
 
1.9%
Other values (194) 1379
53.7%
None
ValueCountFrequency (%)
） 2
50.0%
（ 2
50.0%

검침방식
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
무선검침
636 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
무선검침 636
100.0%

Length

2024-03-14T22:22:26.258038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T22:22:26.561902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
무선검침 636
100.0%

Interactions

2024-03-14T22:22:21.425335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T22:22:26.682597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번지역
연번1.0000.911
지역0.9111.000
2024-03-14T22:22:26.819354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번지역
연번1.0000.778
지역0.7781.000

Missing values

2024-03-14T22:22:21.771044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T22:22:22.055150image/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

연번지역수용가 주소검침방식
01양양읍월리 318-3무선검침
12서면장승1길 128무선검침
23양양읍남문8길 6 (서원독서실)무선검침
34양양읍양양로 83-1 (황실유럽자수)무선검침
45양양읍양양로 101무선검침
56양양읍서문1길 10-6무선검침
67양양읍서문1길 10-6무선검침
78양양읍서문1길 10-6무선검침
89양양읍서문2길 2-2무선검침
910양양읍양양로 37 (목화장)무선검침
연번지역수용가 주소검침방식
626627강현면화채봉길 215-6무선검침
627628강현면화채봉길 215-4무선검침
628629강현면화채봉길 215-8무선검침
629630강현면화채봉길 215-15무선검침
630631강현면강현면 화채봉길 215-9무선검침
631632강현면화채봉길 211무선검침
632633강현면화채봉길 215-41무선검침
633634강현면화재봉길 197무선검침
634635강현면화채봉길 190무선검침
635636강현면화채봉길 206무선검침