Overview

Dataset statistics

Number of variables5
Number of observations414
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.9 KiB
Average record size in memory44.3 B

Variable types

Categorical2
Numeric3

Dataset

Description각 년도별 지역본부의 용도별(발전공급,도시가스공급)가스 공급량을 보여주는 자료이며, 각 지역본부 별 가스공급 점유율을 표시한 자료 입니다.
Author한국가스공사
URLhttps://www.data.go.kr/data/15049902/fileData.do

Alerts

발전 공급량 is highly overall correlated with 도시가스 공급량 and 1 other fieldsHigh correlation
도시가스 공급량 is highly overall correlated with 발전 공급량High correlation
지역본부 is highly overall correlated with 발전 공급량High correlation
발전 공급량 has unique valuesUnique
도시가스 공급량 has unique valuesUnique

Reproduction

Analysis started2023-12-12 01:21:29.093909
Analysis finished2023-12-12 01:21:30.480692
Duration1.39 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년도
Categorical

Distinct4
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
2021
108 
2020
108 
2019
108 
2022
90 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2021 108
26.1%
2020 108
26.1%
2019 108
26.1%
2022 90
21.7%

Length

2023-12-12T10:21:30.583668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:21:30.768182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 108
26.1%
2020 108
26.1%
2019 108
26.1%
2022 90
21.7%

월
Real number (ℝ)

Distinct12
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2826087
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2023-12-12T10:21:30.899468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.3640799
Coefficient of variation (CV)0.53545908
Kurtosis-1.165447
Mean6.2826087
Median Absolute Deviation (MAD)3
Skewness0.046970455
Sum2601
Variance11.317033
MonotonicityNot monotonic
2023-12-12T10:21:31.051747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 36
8.7%
2 36
8.7%
3 36
8.7%
4 36
8.7%
5 36
8.7%
6 36
8.7%
7 36
8.7%
8 36
8.7%
9 36
8.7%
10 36
8.7%
Other values (2) 54
13.0%
ValueCountFrequency (%)
1 36
8.7%
2 36
8.7%
3 36
8.7%
4 36
8.7%
5 36
8.7%
6 36
8.7%
7 36
8.7%
8 36
8.7%
9 36
8.7%
10 36
8.7%
ValueCountFrequency (%)
12 27
6.5%
11 27
6.5%
10 36
8.7%
9 36
8.7%
8 36
8.7%
7 36
8.7%
6 36
8.7%
5 36
8.7%
4 36
8.7%
3 36
8.7%

지역본부
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
서울
46 
인천
46 
경기
46 
강원
46 
대전.충청
46 
Other values (4)
184 

Length

Max length5
Median length2
Mean length3.3333333
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울
2nd row인천
3rd row경기
4th row강원
5th row대전.충청

Common Values

ValueCountFrequency (%)
서울 46
11.1%
인천 46
11.1%
경기 46
11.1%
강원 46
11.1%
대전.충청 46
11.1%
전북 46
11.1%
광주.전남 46
11.1%
대구.경북 46
11.1%
부산.경남 46
11.1%

Length

2023-12-12T10:21:31.188543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:21:31.335406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울 46
11.1%
인천 46
11.1%
경기 46
11.1%
강원 46
11.1%
대전.충청 46
11.1%
전북 46
11.1%
광주.전남 46
11.1%
대구.경북 46
11.1%
부산.경남 46
11.1%

발전 공급량
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct414
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean208993.67
Minimum0
Maximum707668
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2023-12-12T10:21:31.820088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile31331.6
Q169948.75
median142794
Q3330896.5
95-th percentile519086.55
Maximum707668
Range707668
Interquartile range (IQR)260947.75

Descriptive statistics

Standard deviation164593.93
Coefficient of variation (CV)0.78755461
Kurtosis-0.23415958
Mean208993.67
Median Absolute Deviation (MAD)105142
Skewness0.79694918
Sum86523380
Variance2.7091161 × 1010
MonotonicityNot monotonic
2023-12-12T10:21:31.992991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
536013 1
 
0.2%
35563 1
 
0.2%
15950 1
 
0.2%
15751 1
 
0.2%
344254 1
 
0.2%
314389 1
 
0.2%
402715 1
 
0.2%
319631 1
 
0.2%
91597 1
 
0.2%
134315 1
 
0.2%
Other values (404) 404
97.6%
ValueCountFrequency (%)
0 1
0.2%
2431 1
0.2%
4213 1
0.2%
7290 1
0.2%
9011 1
0.2%
15751 1
0.2%
15950 1
0.2%
18461 1
0.2%
18705 1
0.2%
21560 1
0.2%
ValueCountFrequency (%)
707668 1
0.2%
695456 1
0.2%
690412 1
0.2%
679305 1
0.2%
663121 1
0.2%
642568 1
0.2%
634403 1
0.2%
624553 1
0.2%
598908 1
0.2%
582009 1
0.2%

도시가스 공급량
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct414
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean171040.6
Minimum29644
Maximum762786
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2023-12-12T10:21:32.191971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum29644
5-th percentile44232.2
Q180535.75
median133280
Q3206956
95-th percentile456945.65
Maximum762786
Range733142
Interquartile range (IQR)126420.25

Descriptive statistics

Standard deviation132706.83
Coefficient of variation (CV)0.7758791
Kurtosis3.5977045
Mean171040.6
Median Absolute Deviation (MAD)58806.5
Skewness1.826818
Sum70810809
Variance1.7611102 × 1010
MonotonicityNot monotonic
2023-12-12T10:21:32.346134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
740240 1
 
0.2%
33813 1
 
0.2%
110936 1
 
0.2%
45369 1
 
0.2%
218985 1
 
0.2%
83266 1
 
0.2%
217425 1
 
0.2%
181090 1
 
0.2%
103100 1
 
0.2%
61981 1
 
0.2%
Other values (404) 404
97.6%
ValueCountFrequency (%)
29644 1
0.2%
29813 1
0.2%
30474 1
0.2%
30624 1
0.2%
30787 1
0.2%
31199 1
0.2%
32453 1
0.2%
33343 1
0.2%
33813 1
0.2%
33929 1
0.2%
ValueCountFrequency (%)
762786 1
0.2%
740240 1
0.2%
715200 1
0.2%
658929 1
0.2%
653209 1
0.2%
640475 1
0.2%
617690 1
0.2%
610990 1
0.2%
577470 1
0.2%
550733 1
0.2%

Interactions

2023-12-12T10:21:29.929531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:21:29.302215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:21:29.601696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:21:30.043342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:21:29.391778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:21:29.712976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:21:30.143991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:21:29.496398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:21:29.824478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T10:21:32.451171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도월지역본부발전 공급량도시가스 공급량
년도1.0000.0000.0000.0610.000
월0.0001.0000.0000.0000.535
지역본부0.0000.0001.0000.8090.577
발전 공급량0.0610.0000.8091.0000.740
도시가스 공급량0.0000.5350.5770.7401.000
2023-12-12T10:21:32.551914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도지역본부
년도1.0000.000
지역본부0.0001.000
2023-12-12T10:21:32.653761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
월발전 공급량도시가스 공급량년도지역본부
월1.000-0.024-0.2090.0000.000
발전 공급량-0.0241.0000.5600.0360.540
도시가스 공급량-0.2090.5601.0000.0000.307
년도0.0000.0360.0001.0000.000
지역본부0.0000.5400.3070.0001.000

Missing values

2023-12-12T10:21:30.305645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T10:21:30.430758image/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

년도월지역본부발전 공급량도시가스 공급량
020221서울536013740240
120221인천315530205830
220221경기679305515973
320221강원37660101392
420221대전.충청60836260380
520221전북142693183006
620221광주.전남142895167422
720221대구.경북92435288622
820221부산.경남300606497094
920222서울521331640475
년도월지역본부발전 공급량도시가스 공급량
404201911부산.경남253400321868
405201912서울548381617690
406201912인천392322171463
407201912경기663121425593
408201912강원3236879168
409201912대전.충청51232227829
410201912전북118902135803
411201912광주.전남154075134686
412201912대구.경북102552245961
413201912부산.경남285271429269