Overview

Dataset statistics

Number of variables6
Number of observations174
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.0 KiB
Average record size in memory52.8 B

Variable types

Numeric4
Categorical2

Dataset

Description김해시에서 통계기반 도시현황 파악을 위해 개발한 통계지수 중 하나로서, 통계연도, 시도명, 시군구명, 도시가스보급률(퍼센트), 공급권내 총가구수(가구), 수요가구수(가구)로 구성되어 있습니다. 김해시 중심의 통계지수로서, 데이터 수집, 가공 등의 어려움으로 김해시 외 지역의 정보는 누락될 수 있습니다.
Author경상남도 김해시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15110144

Alerts

시군구명 is highly overall correlated with 도시가스보급률(퍼센트) and 3 other fieldsHigh correlation
시도명 is highly overall correlated with 도시가스보급률(퍼센트) and 3 other fieldsHigh correlation
도시가스보급률(퍼센트) is highly overall correlated with 수요가구수(가구) and 2 other fieldsHigh correlation
공급권내 총가구수(가구) is highly overall correlated with 수요가구수(가구) and 2 other fieldsHigh correlation
수요가구수(가구) is highly overall correlated with 도시가스보급률(퍼센트) and 3 other fieldsHigh correlation
공급권내 총가구수(가구) has unique valuesUnique
수요가구수(가구) has unique valuesUnique

Reproduction

Analysis started2023-12-11 00:57:10.052544
Analysis finished2023-12-11 00:57:11.849411
Duration1.8 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

통계연도
Real number (ℝ)

Distinct10
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2016.5402
Minimum2012
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-11T09:57:11.898251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2012
5-th percentile2012
Q12014
median2017
Q32019
95-th percentile2021
Maximum2021
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8378311
Coefficient of variation (CV)0.0014072772
Kurtosis-1.1904769
Mean2016.5402
Median Absolute Deviation (MAD)2
Skewness-0.015712405
Sum350878
Variance8.0532855
MonotonicityNot monotonic
2023-12-11T09:57:11.993353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2019 18
10.3%
2015 18
10.3%
2018 18
10.3%
2016 18
10.3%
2017 18
10.3%
2013 17
9.8%
2021 17
9.8%
2014 17
9.8%
2020 17
9.8%
2012 16
9.2%
ValueCountFrequency (%)
2012 16
9.2%
2013 17
9.8%
2014 17
9.8%
2015 18
10.3%
2016 18
10.3%
2017 18
10.3%
2018 18
10.3%
2019 18
10.3%
2020 17
9.8%
2021 17
9.8%
ValueCountFrequency (%)
2021 17
9.8%
2020 17
9.8%
2019 18
10.3%
2018 18
10.3%
2017 18
10.3%
2016 18
10.3%
2015 18
10.3%
2014 17
9.8%
2013 17
9.8%
2012 16
9.2%

시도명
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
경상남도
15 
전라남도
 
10
인천광역시
 
10
경기도
 
10
충청남도
 
10
Other values (12)
119 

Length

Max length7
Median length5
Mean length4.6149425
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row세종특별자치시
2nd row전라남도
3rd row인천광역시
4th row경기도
5th row충청남도

Common Values

ValueCountFrequency (%)
경상남도 15
 
8.6%
전라남도 10
 
5.7%
인천광역시 10
 
5.7%
경기도 10
 
5.7%
충청남도 10
 
5.7%
울산광역시 10
 
5.7%
대구광역시 10
 
5.7%
경상북도 10
 
5.7%
서울특별시 10
 
5.7%
광주광역시 10
 
5.7%
Other values (7) 69
39.7%

Length

2023-12-11T09:57:12.126669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경상남도 15
 
8.6%
광주광역시 10
 
5.7%
충청북도 10
 
5.7%
강원도 10
 
5.7%
제주특별자치도 10
 
5.7%
전라북도 10
 
5.7%
부산광역시 10
 
5.7%
대전광역시 10
 
5.7%
서울특별시 10
 
5.7%
전라남도 10
 
5.7%
Other values (7) 69
39.7%

시군구명
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
충청북도
 
10
전라남도
 
10
인천광역시
 
10
경기도
 
10
충청남도
 
10
Other values (13)
124 

Length

Max length7
Median length5
Mean length4.5862069
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row세종특별자치시
2nd row전라남도
3rd row인천광역시
4th row경기도
5th row충청남도

Common Values

ValueCountFrequency (%)
충청북도 10
 
5.7%
전라남도 10
 
5.7%
인천광역시 10
 
5.7%
경기도 10
 
5.7%
충청남도 10
 
5.7%
울산광역시 10
 
5.7%
대구광역시 10
 
5.7%
경상북도 10
 
5.7%
광주광역시 10
 
5.7%
서울특별시 10
 
5.7%
Other values (8) 74
42.5%

Length

2023-12-11T09:57:12.257449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
충청북도 10
 
5.7%
전라남도 10
 
5.7%
강원도 10
 
5.7%
제주특별자치도 10
 
5.7%
전라북도 10
 
5.7%
경상남도 10
 
5.7%
부산광역시 10
 
5.7%
대전광역시 10
 
5.7%
서울특별시 10
 
5.7%
광주광역시 10
 
5.7%
Other values (8) 74
42.5%

도시가스보급률(퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct156
Distinct (%)89.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.25977
Minimum6.2
Maximum100.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-11T09:57:12.447409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile16.205
Q161.3
median79.55
Q393.15
95-th percentile98.305
Maximum100.2
Range94
Interquartile range (IQR)31.85

Descriptive statistics

Standard deviation22.578382
Coefficient of variation (CV)0.30404595
Kurtosis1.0043426
Mean74.25977
Median Absolute Deviation (MAD)15.25
Skewness-1.1232125
Sum12921.2
Variance509.78334
MonotonicityNot monotonic
2023-12-11T09:57:12.572159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99.7 2
 
1.1%
94.0 2
 
1.1%
96.6 2
 
1.1%
94.8 2
 
1.1%
71.8 2
 
1.1%
92.8 2
 
1.1%
70.0 2
 
1.1%
90.7 2
 
1.1%
49.7 2
 
1.1%
100.0 2
 
1.1%
Other values (146) 154
88.5%
ValueCountFrequency (%)
6.2 1
0.6%
9.2 1
0.6%
9.3 1
0.6%
10.1 1
0.6%
11.6 1
0.6%
12.6 1
0.6%
14.0 1
0.6%
14.3 1
0.6%
15.1 1
0.6%
16.8 1
0.6%
ValueCountFrequency (%)
100.2 1
0.6%
100.0 2
1.1%
99.9 1
0.6%
99.7 2
1.1%
99.3 1
0.6%
98.6 1
0.6%
98.5 1
0.6%
98.2 2
1.1%
98.0 1
0.6%
97.6 2
1.1%

공급권내 총가구수(가구)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct174
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1227325.7
Minimum53012
Maximum5834570
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-11T09:57:12.739601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum53012
5-th percentile162683.35
Q1592585.5
median793731
Q31200325.2
95-th percentile4635475.4
Maximum5834570
Range5781558
Interquartile range (IQR)607739.75

Descriptive statistics

Standard deviation1317038.3
Coefficient of variation (CV)1.073096
Kurtosis3.6400539
Mean1227325.7
Median Absolute Deviation (MAD)329866.5
Skewness2.1742364
Sum2.1355467 × 108
Variance1.7345898 × 1012
MonotonicityNot monotonic
2023-12-11T09:57:12.916641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
135408 1
 
0.6%
1226159 1
 
0.6%
584877 1
 
0.6%
680960 1
 
0.6%
4426007 1
 
0.6%
4614341 1
 
0.6%
1178843 1
 
0.6%
902294 1
 
0.6%
1177029 1
 
0.6%
575600 1
 
0.6%
Other values (164) 164
94.3%
ValueCountFrequency (%)
53012 1
0.6%
62807 1
0.6%
81806 1
0.6%
94343 1
0.6%
109490 1
0.6%
123762 1
0.6%
135408 1
0.6%
144275 1
0.6%
153649 1
0.6%
167548 1
0.6%
ValueCountFrequency (%)
5834570 1
0.6%
5660814 1
0.6%
5461923 1
0.6%
5296906 1
0.6%
5112281 1
0.6%
4997602 1
0.6%
4868605 1
0.6%
4781299 1
0.6%
4674725 1
0.6%
4614341 1
0.6%

수요가구수(가구)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct174
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1007687.1
Minimum12239
Maximum4955986
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-11T09:57:13.349065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12239
5-th percentile30659.9
Q1402208.25
median572323.5
Q31013163.8
95-th percentile4217222.5
Maximum4955986
Range4943747
Interquartile range (IQR)610955.5

Descriptive statistics

Standard deviation1232646.1
Coefficient of variation (CV)1.223243
Kurtosis3.3189475
Mean1007687.1
Median Absolute Deviation (MAD)259952.5
Skewness2.1513703
Sum1.7533755 × 108
Variance1.5194165 × 1012
MonotonicityNot monotonic
2023-12-11T09:57:13.518812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
108412 1
 
0.6%
828780 1
 
0.6%
542674 1
 
0.6%
428544 1
 
0.6%
4357591 1
 
0.6%
3875132 1
 
0.6%
776786 1
 
0.6%
552170 1
 
0.6%
1092226 1
 
0.6%
530299 1
 
0.6%
Other values (164) 164
94.3%
ValueCountFrequency (%)
12239 1
0.6%
15599 1
0.6%
17559 1
0.6%
20607 1
0.6%
23455 1
0.6%
25556 1
0.6%
27762 1
0.6%
27846 1
0.6%
29005 1
0.6%
31551 1
0.6%
ValueCountFrequency (%)
4955986 1
0.6%
4870428 1
0.6%
4790907 1
0.6%
4669015 1
0.6%
4493608 1
0.6%
4357591 1
0.6%
4350098 1
0.6%
4309684 1
0.6%
4249965 1
0.6%
4199592 1
0.6%

Interactions

2023-12-11T09:57:11.315270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:10.287553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:10.620843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:10.977342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:11.428243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:10.364288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:10.707309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:11.054366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:11.511890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:10.436692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:10.780264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:11.132874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:11.591899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:10.531036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:10.883351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:11.217534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:57:13.631697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명시군구명도시가스보급률(퍼센트)공급권내 총가구수(가구)수요가구수(가구)
통계연도1.0000.0000.0000.0000.0000.000
시도명0.0001.0001.0000.8940.9200.872
시군구명0.0001.0001.0000.8970.9720.880
도시가스보급률(퍼센트)0.0000.8940.8971.0000.5830.497
공급권내 총가구수(가구)0.0000.9200.9720.5831.0000.830
수요가구수(가구)0.0000.8720.8800.4970.8301.000
2023-12-11T09:57:13.731179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명시도명
시군구명1.0000.997
시도명0.9971.000
2023-12-11T09:57:13.815748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도도시가스보급률(퍼센트)공급권내 총가구수(가구)수요가구수(가구)시도명시군구명
통계연도1.0000.2340.0930.1500.0000.000
도시가스보급률(퍼센트)0.2341.0000.2000.5370.6370.644
공급권내 총가구수(가구)0.0930.2001.0000.9140.7250.743
수요가구수(가구)0.1500.5370.9141.0000.6220.629
시도명0.0000.6370.7250.6221.0000.997
시군구명0.0000.6440.7430.6290.9971.000

Missing values

2023-12-11T09:57:11.694185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:57:11.806392image/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

통계연도시도명시군구명도시가스보급률(퍼센트)공급권내 총가구수(가구)수요가구수(가구)
02019세종특별자치시세종특별자치시80.1135408108412
12015전라남도전라남도48.4739282357704
22015인천광역시인천광역시91.211427521042413
32019경기도경기도87.754619234790907
42015충청남도충청남도57.7885968511511
52019울산광역시울산광역시95.0468659445116
62018경기도경기도88.152969064669015
72013대구광역시대구광역시84.6960265812574
82021경상북도경상북도68.61248060856443
92016광주광역시광주광역시97.6586464572660
통계연도시도명시군구명도시가스보급률(퍼센트)공급권내 총가구수(가구)수요가구수(가구)
1642019강원도강원도54.0672097362805
1652017대전광역시대전광역시94.6614639581453
1662013강원도강원도49.7522170259433
1672013경상남도경상남도61.81189546734674
1682012충청남도충청남도48.8889023434186
1692016경상북도경상북도61.61153060710828
1702019부산광역시부산광역시94.814979081419384
1712016대전광역시대전광역시94.4606137571987
1722012서울특별시서울특별시93.042159523921173
1732015경상북도경상북도59.11141784674713