Overview

Dataset statistics

Number of variables5
Number of observations27
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 KiB
Average record size in memory47.9 B

Variable types

Numeric3
Text1
DateTime1

Dataset

Description강원도 원주시의 원격 및 디지털 수도 검침기 현황에 대한 데이터입니다. 법정읍면동별 원격수도검침기와 디지털수도검침기 설치 수 정보를 제공하고 있습니다. 디지털수도검침기는 계량기 형식이 디지털인 기기이며, 원격수도검침기는 원격검침이 가능한 기기를 의미합니다.
URLhttps://www.data.go.kr/data/15113765/fileData.do

Alerts

데이터기준일 has constant value ""Constant
원격수도검침기수 is highly overall correlated with 디지털수도검침기수High correlation
디지털수도검침기수 is highly overall correlated with 원격수도검침기수High correlation
연번 has unique valuesUnique
법정읍면동 has unique valuesUnique
원격수도검침기수 has unique valuesUnique
디지털수도검침기수 has unique valuesUnique

Reproduction

Analysis started2023-12-12 20:03:20.653753
Analysis finished2023-12-12 20:03:21.995915
Duration1.34 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14
Minimum1
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T05:03:22.057558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.3
Q17.5
median14
Q320.5
95-th percentile25.7
Maximum27
Range26
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.9372539
Coefficient of variation (CV)0.56694671
Kurtosis-1.2
Mean14
Median Absolute Deviation (MAD)7
Skewness0
Sum378
Variance63
MonotonicityStrictly increasing
2023-12-13T05:03:22.186524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1 1
 
3.7%
2 1
 
3.7%
27 1
 
3.7%
26 1
 
3.7%
25 1
 
3.7%
24 1
 
3.7%
23 1
 
3.7%
22 1
 
3.7%
21 1
 
3.7%
20 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
1 1
3.7%
2 1
3.7%
3 1
3.7%
4 1
3.7%
5 1
3.7%
6 1
3.7%
7 1
3.7%
8 1
3.7%
9 1
3.7%
10 1
3.7%
ValueCountFrequency (%)
27 1
3.7%
26 1
3.7%
25 1
3.7%
24 1
3.7%
23 1
3.7%
22 1
3.7%
21 1
3.7%
20 1
3.7%
19 1
3.7%
18 1
3.7%

법정읍면동
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-13T05:03:22.368651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9259259
Min length2

Characters and Unicode

Total characters79
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st row가현동
2nd row개운동
3rd row관설동
4th row귀래면
5th row단계동
ValueCountFrequency (%)
가현동 1
 
3.7%
우산동 1
 
3.7%
호저면 1
 
3.7%
행구동 1
 
3.7%
학성동 1
 
3.7%
평원동 1
 
3.7%
판부면 1
 
3.7%
태장동 1
 
3.7%
지정면 1
 
3.7%
중앙동 1
 
3.7%
Other values (17) 17
63.0%
2023-12-13T05:03:22.668995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 18
22.8%
면 8
 
10.1%
산 3
 
3.8%
부 2
 
2.5%
원 2
 
2.5%
단 2
 
2.5%
구 2
 
2.5%
정 1
 
1.3%
림 1
 
1.3%
우 1
 
1.3%
Other values (39) 39
49.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 79
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 18
22.8%
면 8
 
10.1%
산 3
 
3.8%
부 2
 
2.5%
원 2
 
2.5%
단 2
 
2.5%
구 2
 
2.5%
정 1
 
1.3%
림 1
 
1.3%
우 1
 
1.3%
Other values (39) 39
49.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 79
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 18
22.8%
면 8
 
10.1%
산 3
 
3.8%
부 2
 
2.5%
원 2
 
2.5%
단 2
 
2.5%
구 2
 
2.5%
정 1
 
1.3%
림 1
 
1.3%
우 1
 
1.3%
Other values (39) 39
49.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 79
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 18
22.8%
면 8
 
10.1%
산 3
 
3.8%
부 2
 
2.5%
원 2
 
2.5%
단 2
 
2.5%
구 2
 
2.5%
정 1
 
1.3%
림 1
 
1.3%
우 1
 
1.3%
Other values (39) 39
49.4%

원격수도검침기수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1714.6667
Minimum34
Maximum6882
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T05:03:22.779832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34
5-th percentile90
Q1461.5
median1147
Q32018
95-th percentile5684.5
Maximum6882
Range6848
Interquartile range (IQR)1556.5

Descriptive statistics

Standard deviation1771.0693
Coefficient of variation (CV)1.0328943
Kurtosis2.8327841
Mean1714.6667
Median Absolute Deviation (MAD)779
Skewness1.7271421
Sum46296
Variance3136686.6
MonotonicityNot monotonic
2023-12-13T05:03:22.893283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
143 1
 
3.7%
2820 1
 
3.7%
2082 1
 
3.7%
857 1
 
3.7%
1085 1
 
3.7%
368 1
 
3.7%
125 1
 
3.7%
1003 1
 
3.7%
1754 1
 
3.7%
1644 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
34 1
3.7%
75 1
3.7%
125 1
3.7%
143 1
3.7%
269 1
3.7%
368 1
3.7%
441 1
3.7%
482 1
3.7%
709 1
3.7%
857 1
3.7%
ValueCountFrequency (%)
6882 1
3.7%
6328 1
3.7%
4183 1
3.7%
3599 1
3.7%
2820 1
3.7%
2781 1
3.7%
2082 1
3.7%
1954 1
3.7%
1754 1
3.7%
1729 1
3.7%

디지털수도검침기수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1830.2593
Minimum70
Maximum6910
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T05:03:23.007440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile162.6
Q1697.5
median1437
Q32134
95-th percentile5798.7
Maximum6910
Range6840
Interquartile range (IQR)1436.5

Descriptive statistics

Standard deviation1747.3227
Coefficient of variation (CV)0.95468589
Kurtosis3.0607941
Mean1830.2593
Median Absolute Deviation (MAD)744
Skewness1.743601
Sum49417
Variance3053136.6
MonotonicityNot monotonic
2023-12-13T05:03:23.117154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
162 1
 
3.7%
2830 1
 
3.7%
2087 1
 
3.7%
862 1
 
3.7%
1090 1
 
3.7%
692 1
 
3.7%
164 1
 
3.7%
1437 1
 
3.7%
1849 1
 
3.7%
1721 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
70 1
3.7%
162 1
3.7%
164 1
3.7%
244 1
3.7%
309 1
3.7%
445 1
3.7%
692 1
3.7%
703 1
3.7%
862 1
3.7%
914 1
3.7%
ValueCountFrequency (%)
6910 1
3.7%
6534 1
3.7%
4083 1
3.7%
3615 1
3.7%
2830 1
3.7%
2784 1
3.7%
2181 1
3.7%
2087 1
3.7%
1849 1
3.7%
1803 1
3.7%

데이터기준일
Date

CONSTANT 

Distinct1
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size348.0 B
Minimum2023-05-16 00:00:00
Maximum2023-05-16 00:00:00
2023-12-13T05:03:23.224872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:03:23.317427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-13T05:03:21.423216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:03:20.819354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:03:21.118320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:03:21.552310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:03:20.911810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:03:21.222694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:03:21.667855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:03:21.022853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:03:21.320307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:03:23.380352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번법정읍면동원격수도검침기수디지털수도검침기수
연번1.0001.0000.6100.606
법정읍면동1.0001.0001.0001.000
원격수도검침기수0.6101.0001.0000.980
디지털수도검침기수0.6061.0000.9801.000
2023-12-13T05:03:23.465166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번원격수도검침기수디지털수도검침기수
연번1.000-0.326-0.314
원격수도검침기수-0.3261.0000.984
디지털수도검침기수-0.3140.9841.000

Missing values

2023-12-13T05:03:21.822218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:03:21.955087image/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

연번법정읍면동원격수도검침기수디지털수도검침기수데이터기준일
01가현동1431622023-05-16
12개운동282028302023-05-16
23관설동195421812023-05-16
34귀래면4414452023-05-16
45단계동688269102023-05-16
56단구동632865342023-05-16
67명륜동114713962023-05-16
78무실동278127842023-05-16
89문막읍418340832023-05-16
910반곡동139418032023-05-16
연번법정읍면동원격수도검침기수디지털수도검침기수데이터기준일
1718일산동48212372023-05-16
1819중앙동2693092023-05-16
1920지정면164417212023-05-16
2021태장동175418492023-05-16
2122판부면100314372023-05-16
2223평원동1251642023-05-16
2324학성동3686922023-05-16
2425행구동108510902023-05-16
2526호저면8578622023-05-16
2627흥업면208220872023-05-16