Overview

Dataset statistics

Number of variables5
Number of observations123
Missing cells1
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.2 KiB
Average record size in memory43.1 B

Variable types

Numeric2
Categorical1
Text1
DateTime1

Dataset

Description전라남도 영암군 스마트 워터미터기 설치현황에 대한 자료이며, 설치장소, 설치 수량, 데이터기준일자를 표시한 데이터 자료 입니다.
Author공공데이터포털
URLhttps://www.data.go.kr/data/15113898/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
번호 is highly overall correlated with 읍면동High correlation
읍면동 is highly overall correlated with 번호High correlation
번호 has unique valuesUnique
설치수량 has 8 (6.5%) zerosZeros

Reproduction

Analysis started2024-04-17 11:39:40.150147
Analysis finished2024-04-17 11:39:40.717389
Duration0.57 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct123
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62
Minimum1
Maximum123
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-04-17T20:39:40.783695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7.1
Q131.5
median62
Q392.5
95-th percentile116.9
Maximum123
Range122
Interquartile range (IQR)61

Descriptive statistics

Standard deviation35.651087
Coefficient of variation (CV)0.57501753
Kurtosis-1.2
Mean62
Median Absolute Deviation (MAD)31
Skewness0
Sum7626
Variance1271
MonotonicityStrictly increasing
2024-04-17T20:39:40.906660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.8%
79 1
 
0.8%
92 1
 
0.8%
91 1
 
0.8%
90 1
 
0.8%
89 1
 
0.8%
88 1
 
0.8%
87 1
 
0.8%
86 1
 
0.8%
85 1
 
0.8%
Other values (113) 113
91.9%
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 (%)
123 1
0.8%
122 1
0.8%
121 1
0.8%
120 1
0.8%
119 1
0.8%
118 1
0.8%
117 1
0.8%
116 1
0.8%
115 1
0.8%
114 1
0.8%

읍면동
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
영암읍
16 
시종면
14 
신북면
13 
삼호읍
12 
금정면
11 
Other values (6)
57 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
영암읍 16
13.0%
시종면 14
11.4%
신북면 13
10.6%
삼호읍 12
9.8%
금정면 11
8.9%
군서면 11
8.9%
서호면 10
8.1%
학산면 10
8.1%
덕진면 9
7.3%
미암면 9
7.3%

Length

2024-04-17T20:39:41.022732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
영암읍 16
13.0%
시종면 14
11.4%
신북면 13
10.6%
삼호읍 12
9.8%
금정면 11
8.9%
군서면 11
8.9%
서호면 10
8.1%
학산면 10
8.1%
덕진면 9
7.3%
미암면 9
7.3%


Text

Distinct114
Distinct (%)92.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-04-17T20:39:41.278525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0162602
Min length2

Characters and Unicode

Total characters371
Distinct characters105
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

Unique106 ?
Unique (%)86.2%

Sample

1st row회문리
2nd row교동리
3rd row서남리
4th row동무리
5th row역리
ValueCountFrequency (%)
용산리 3
 
2.4%
용흥리 2
 
1.6%
금강리 2
 
1.6%
난전리 2
 
1.6%
나불리 2
 
1.6%
청용리 2
 
1.6%
월평리 2
 
1.6%
동호리 2
 
1.6%
성재리 1
 
0.8%
해창리 1
 
0.8%
Other values (104) 104
84.6%
2024-04-17T20:39:41.675096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
123
33.2%
13
 
3.5%
10
 
2.7%
10
 
2.7%
9
 
2.4%
8
 
2.2%
8
 
2.2%
6
 
1.6%
6
 
1.6%
5
 
1.3%
Other values (95) 173
46.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 370
99.7%
Space Separator 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
123
33.2%
13
 
3.5%
10
 
2.7%
10
 
2.7%
9
 
2.4%
8
 
2.2%
8
 
2.2%
6
 
1.6%
6
 
1.6%
5
 
1.4%
Other values (94) 172
46.5%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 370
99.7%
Common 1
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
123
33.2%
13
 
3.5%
10
 
2.7%
10
 
2.7%
9
 
2.4%
8
 
2.2%
8
 
2.2%
6
 
1.6%
6
 
1.6%
5
 
1.4%
Other values (94) 172
46.5%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 370
99.7%
ASCII 1
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
123
33.2%
13
 
3.5%
10
 
2.7%
10
 
2.7%
9
 
2.4%
8
 
2.2%
8
 
2.2%
6
 
1.6%
6
 
1.6%
5
 
1.4%
Other values (94) 172
46.5%
ASCII
ValueCountFrequency (%)
1
100.0%

설치수량
Real number (ℝ)

ZEROS 

Distinct50
Distinct (%)41.0%
Missing1
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean39.622951
Minimum0
Maximum759
Zeros8
Zeros (%)6.5%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-04-17T20:39:41.796993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median5
Q328.5
95-th percentile169.9
Maximum759
Range759
Interquartile range (IQR)25.5

Descriptive statistics

Standard deviation92.773369
Coefficient of variation (CV)2.3414048
Kurtosis32.617195
Mean39.622951
Median Absolute Deviation (MAD)4
Skewness5.0233595
Sum4834
Variance8606.898
MonotonicityNot monotonic
2024-04-17T20:39:41.909366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 15
 
12.2%
4 11
 
8.9%
1 11
 
8.9%
2 10
 
8.1%
0 8
 
6.5%
5 7
 
5.7%
6 6
 
4.9%
9 5
 
4.1%
10 3
 
2.4%
12 2
 
1.6%
Other values (40) 44
35.8%
ValueCountFrequency (%)
0 8
6.5%
1 11
8.9%
2 10
8.1%
3 15
12.2%
4 11
8.9%
5 7
5.7%
6 6
 
4.9%
7 2
 
1.6%
8 1
 
0.8%
9 5
 
4.1%
ValueCountFrequency (%)
759 1
0.8%
457 1
0.8%
267 1
0.8%
224 1
0.8%
197 1
0.8%
186 1
0.8%
170 1
0.8%
168 1
0.8%
166 1
0.8%
158 1
0.8%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
Minimum2023-05-19 00:00:00
Maximum2023-05-19 00:00:00
2024-04-17T20:39:42.008270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:39:42.088969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-04-17T20:39:40.427187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:39:40.287922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:39:40.500562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:39:40.357515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T20:39:42.151582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호읍면동설치수량
번호1.0000.9690.573
읍면동0.9691.0000.532
설치수량0.5730.5321.000
2024-04-17T20:39:42.226272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호설치수량읍면동
번호1.000-0.3700.867
설치수량-0.3701.0000.299
읍면동0.8670.2991.000

Missing values

2024-04-17T20:39:40.598562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T20:39:40.685227image/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

번호읍면동설치수량데이터기준일자
01영암읍회문리1972023-05-19
12영암읍교동리1862023-05-19
23영암읍서남리2242023-05-19
34영암읍동무리2672023-05-19
45영암읍역리4572023-05-19
56영암읍남풍리1582023-05-19
67영암읍춘양리1682023-05-19
78영암읍망호리1702023-05-19
89영암읍송평리1662023-05-19
910영암읍대신리1072023-05-19
번호읍면동설치수량데이터기준일자
113114삼호읍산호리632023-05-19
114115삼호읍용앙리7592023-05-19
115116삼호읍서호리102023-05-19
116117삼호읍동호리32023-05-19
117118삼호읍나불리122023-05-19
118119삼호읍용당리512023-05-19
119120삼호읍삼포리102023-05-19
120121삼호읍난전리202023-05-19
121122삼호읍나불리292023-05-19
122123삼호읍난전리152023-05-19