Overview

Dataset statistics

Number of variables7
Number of observations36
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 KiB
Average record size in memory66.7 B

Variable types

Numeric7

Dataset

Description국가 에너지(천연가스) 수급 관련 통계정보(국가 전체기준, 1988~) 자료 및 천연가스 공급 및 수요 항목의 데이터(가스공사 기준)를 제공하고 있습니다.
Author산업통상자원부
URLhttps://www.data.go.kr/data/3053100/fileData.do

Alerts

연도 is highly overall correlated with 공급_국내생산(천ton) and 5 other fieldsHigh correlation
공급_국내생산(천ton) is highly overall correlated with 연도 and 5 other fieldsHigh correlation
공급_수입(천ton) is highly overall correlated with 연도 and 5 other fieldsHigh correlation
수입(백만달러) is highly overall correlated with 연도 and 5 other fieldsHigh correlation
수요_발전(천ton) is highly overall correlated with 연도 and 5 other fieldsHigh correlation
수요_지역난방(천ton) is highly overall correlated with 연도 and 5 other fieldsHigh correlation
수요_도시가스(천ton) is highly overall correlated with 연도 and 5 other fieldsHigh correlation
연도 has unique valuesUnique
공급_수입(천ton) has unique valuesUnique
수입(백만달러) has unique valuesUnique
수요_발전(천ton) has unique valuesUnique
수요_도시가스(천ton) has unique valuesUnique
공급_국내생산(천ton) has 18 (50.0%) zerosZeros
수요_지역난방(천ton) has 4 (11.1%) zerosZeros

Reproduction

Analysis started2024-04-06 08:13:18.204723
Analysis finished2024-04-06 08:13:28.655116
Duration10.45 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2005.5
Minimum1988
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2024-04-06T17:13:28.822273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1988
5-th percentile1989.75
Q11996.75
median2005.5
Q32014.25
95-th percentile2021.25
Maximum2023
Range35
Interquartile range (IQR)17.5

Descriptive statistics

Standard deviation10.535654
Coefficient of variation (CV)0.0052533801
Kurtosis-1.2
Mean2005.5
Median Absolute Deviation (MAD)9
Skewness0
Sum72198
Variance111
MonotonicityStrictly increasing
2024-04-06T17:13:29.069498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
1988 1
 
2.8%
2007 1
 
2.8%
2009 1
 
2.8%
2010 1
 
2.8%
2011 1
 
2.8%
2012 1
 
2.8%
2013 1
 
2.8%
2014 1
 
2.8%
2015 1
 
2.8%
2016 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
1988 1
2.8%
1989 1
2.8%
1990 1
2.8%
1991 1
2.8%
1992 1
2.8%
1993 1
2.8%
1994 1
2.8%
1995 1
2.8%
1996 1
2.8%
1997 1
2.8%
ValueCountFrequency (%)
2023 1
2.8%
2022 1
2.8%
2021 1
2.8%
2020 1
2.8%
2019 1
2.8%
2018 1
2.8%
2017 1
2.8%
2016 1
2.8%
2015 1
2.8%
2014 1
2.8%

공급_국내생산(천ton)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.44444
Minimum0
Maximum415
Zeros18
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2024-04-06T17:13:29.456940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median21
Q3250.5
95-th percentile386.75
Maximum415
Range415
Interquartile range (IQR)250.5

Descriptive statistics

Standard deviation150.14915
Coefficient of variation (CV)1.1781538
Kurtosis-1.1478909
Mean127.44444
Median Absolute Deviation (MAD)21
Skewness0.66282889
Sum4588
Variance22544.768
MonotonicityNot monotonic
2024-04-06T17:13:29.776055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 18
50.0%
355 2
 
5.6%
247 1
 
2.8%
42 1
 
2.8%
142 1
 
2.8%
198 1
 
2.8%
234 1
 
2.8%
261 1
 
2.8%
118 1
 
2.8%
144 1
 
2.8%
Other values (8) 8
22.2%
ValueCountFrequency (%)
0 18
50.0%
42 1
 
2.8%
118 1
 
2.8%
142 1
 
2.8%
144 1
 
2.8%
163 1
 
2.8%
181 1
 
2.8%
198 1
 
2.8%
234 1
 
2.8%
247 1
 
2.8%
ValueCountFrequency (%)
415 1
2.8%
398 1
2.8%
383 1
2.8%
355 2
5.6%
347 1
2.8%
334 1
2.8%
271 1
2.8%
261 1
2.8%
247 1
2.8%
234 1
2.8%

공급_수입(천ton)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23219.778
Minimum1898
Maximum46394
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2024-04-06T17:13:30.020987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1898
5-th percentile2181.5
Q19956.25
median23786.5
Q336790.5
95-th percentile44570.75
Maximum46394
Range44496
Interquartile range (IQR)26834.25

Descriptive statistics

Standard deviation14890.267
Coefficient of variation (CV)0.64127515
Kurtosis-1.4113714
Mean23219.778
Median Absolute Deviation (MAD)13459
Skewness-0.0056234052
Sum835912
Variance2.2172004 × 108
MonotonicityNot monotonic
2024-04-06T17:13:30.261556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
1898 1
 
2.8%
25569 1
 
2.8%
25822 1
 
2.8%
32603 1
 
2.8%
36685 1
 
2.8%
36184 1
 
2.8%
39876 1
 
2.8%
37107 1
 
2.8%
33366 1
 
2.8%
33453 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
1898 1
2.8%
2015 1
2.8%
2237 1
2.8%
2494 1
2.8%
2994 1
2.8%
4459 1
2.8%
5996 1
2.8%
6756 1
2.8%
9258 1
2.8%
10189 1
2.8%
ValueCountFrequency (%)
46394 1
2.8%
45932 1
2.8%
44117 1
2.8%
44015 1
2.8%
40748 1
2.8%
39982 1
2.8%
39876 1
2.8%
37537 1
2.8%
37107 1
2.8%
36685 1
2.8%

수입(백만달러)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12535
Minimum323
Maximum50022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2024-04-06T17:13:30.661628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum323
5-th percentile431.75
Q11795.75
median10286
Q319996.25
95-th percentile32564.5
Maximum50022
Range49699
Interquartile range (IQR)18200.5

Descriptive statistics

Standard deviation12393.223
Coefficient of variation (CV)0.98868948
Kurtosis0.83845833
Mean12535
Median Absolute Deviation (MAD)8874
Skewness1.0634138
Sum451260
Variance1.5359197 × 108
MonotonicityNot monotonic
2024-04-06T17:13:31.025487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
323 1
 
2.8%
12653 1
 
2.8%
13875 1
 
2.8%
17006 1
 
2.8%
23859 1
 
2.8%
27364 1
 
2.8%
30645 1
 
2.8%
31403 1
 
2.8%
18779 1
 
2.8%
12170 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
323 1
2.8%
347 1
2.8%
460 1
2.8%
485 1
2.8%
562 1
2.8%
781 1
2.8%
961 1
2.8%
1275 1
2.8%
1549 1
2.8%
1878 1
2.8%
ValueCountFrequency (%)
50022 1
2.8%
36049 1
2.8%
31403 1
2.8%
30645 1
2.8%
27364 1
2.8%
25453 1
2.8%
23859 1
2.8%
23189 1
2.8%
20567 1
2.8%
19806 1
2.8%

수요_발전(천ton)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6886.3889
Minimum1661
Maximum14876
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2024-04-06T17:13:31.433072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1661
5-th percentile1677.5
Q12456.25
median6778
Q311202.25
95-th percentile13230.5
Maximum14876
Range13215
Interquartile range (IQR)8746

Descriptive statistics

Standard deviation4449.5557
Coefficient of variation (CV)0.64613773
Kurtosis-1.5540374
Mean6886.3889
Median Absolute Deviation (MAD)4377
Skewness0.18093477
Sum247910
Variance19798546
MonotonicityNot monotonic
2024-04-06T17:13:32.234747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
1905 1
 
2.8%
8716 1
 
2.8%
6903 1
 
2.8%
10555 1
 
2.8%
11851 1
 
2.8%
12893 1
 
2.8%
14876 1
 
2.8%
13403 1
 
2.8%
11266 1
 
2.8%
11181 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
1661 1
2.8%
1670 1
2.8%
1680 1
2.8%
1709 1
2.8%
1741 1
2.8%
1800 1
2.8%
1905 1
2.8%
2019 1
2.8%
2427 1
2.8%
2466 1
2.8%
ValueCountFrequency (%)
14876 1
2.8%
13403 1
2.8%
13173 1
2.8%
12893 1
2.8%
12789 1
2.8%
11851 1
2.8%
11434 1
2.8%
11285 1
2.8%
11266 1
2.8%
11181 1
2.8%

수요_지역난방(천ton)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2569.1389
Minimum0
Maximum5392
Zeros4
Zeros (%)11.1%
Negative0
Negative (%)0.0%
Memory size456.0 B
2024-04-06T17:13:32.552755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11873
median2212
Q34114
95-th percentile4985.5
Maximum5392
Range5392
Interquartile range (IQR)2241

Descriptive statistics

Standard deviation1558.2082
Coefficient of variation (CV)0.60650993
Kurtosis-0.81832796
Mean2569.1389
Median Absolute Deviation (MAD)1149
Skewness0.029514812
Sum92489
Variance2428012.9
MonotonicityNot monotonic
2024-04-06T17:13:32.851548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 4
 
11.1%
4171 1
 
2.8%
3126 1
 
2.8%
3465 1
 
2.8%
4095 1
 
2.8%
4201 1
 
2.8%
3591 1
 
2.8%
3257 1
 
2.8%
4257 1
 
2.8%
206 1
 
2.8%
Other values (23) 23
63.9%
ValueCountFrequency (%)
0 4
11.1%
206 1
 
2.8%
857 1
 
2.8%
1620 1
 
2.8%
1723 1
 
2.8%
1846 1
 
2.8%
1882 1
 
2.8%
1909 1
 
2.8%
1933 1
 
2.8%
1954 1
 
2.8%
ValueCountFrequency (%)
5392 1
2.8%
5029 1
2.8%
4971 1
2.8%
4793 1
2.8%
4575 1
2.8%
4257 1
2.8%
4201 1
2.8%
4187 1
2.8%
4171 1
2.8%
4095 1
2.8%

수요_도시가스(천ton)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11801.5
Minimum184
Maximum19832
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2024-04-06T17:13:33.109792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum184
5-th percentile519.25
Q15467.75
median14017
Q318249
95-th percentile19650.25
Maximum19832
Range19648
Interquartile range (IQR)12781.25

Descriptive statistics

Standard deviation7019.2827
Coefficient of variation (CV)0.59477886
Kurtosis-1.3347672
Mean11801.5
Median Absolute Deviation (MAD)4647
Skewness-0.47663837
Sum424854
Variance49270330
MonotonicityNot monotonic
2024-04-06T17:13:33.414464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
184 1
 
2.8%
14596 1
 
2.8%
15634 1
 
2.8%
17522 1
 
2.8%
18255 1
 
2.8%
19558 1
 
2.8%
19596 1
 
2.8%
18180 1
 
2.8%
16929 1
 
2.8%
17384 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
184 1
2.8%
349 1
2.8%
576 1
2.8%
879 1
2.8%
1256 1
2.8%
1848 1
2.8%
2451 1
2.8%
3417 1
2.8%
4561 1
2.8%
5770 1
2.8%
ValueCountFrequency (%)
19832 1
2.8%
19813 1
2.8%
19596 1
2.8%
19558 1
2.8%
19331 1
2.8%
18822 1
2.8%
18390 1
2.8%
18328 1
2.8%
18255 1
2.8%
18247 1
2.8%

Interactions

2024-04-06T17:13:27.006587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:18.556933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:20.393357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:21.629171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:22.996779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:24.506558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:25.832676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:27.170729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:18.790490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:20.545753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:21.843449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:23.176178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:24.681192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:26.011821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:27.309276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:18.961161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:20.684442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:21.992398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:23.419564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:24.846800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:26.170962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:27.478906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:19.128204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:20.817138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:22.165493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:23.658069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:25.007226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:26.304540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:27.644631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:19.285995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:20.954315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:22.395673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:23.898817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:25.213880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:26.476683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:27.826371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:19.476410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:21.153240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:22.641666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:24.138878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:25.379734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:26.683607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:28.060851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:19.671752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:21.322623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:22.800934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:24.302010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:25.606662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:26.837364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:13:33.620572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도공급_국내생산(천ton)공급_수입(천ton)수입(백만달러)수요_발전(천ton)수요_지역난방(천ton)수요_도시가스(천ton)
연도1.0000.4270.9570.7550.6220.7660.947
공급_국내생산(천ton)0.4271.0000.4420.8210.8580.6990.000
공급_수입(천ton)0.9570.4421.0000.7400.8360.8600.936
수입(백만달러)0.7550.8210.7401.0000.9140.7060.000
수요_발전(천ton)0.6220.8580.8360.9141.0000.5920.603
수요_지역난방(천ton)0.7660.6990.8600.7060.5921.0000.692
수요_도시가스(천ton)0.9470.0000.9360.0000.6030.6921.000
2024-04-06T17:13:33.836892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도공급_국내생산(천ton)공급_수입(천ton)수입(백만달러)수요_발전(천ton)수요_지역난방(천ton)수요_도시가스(천ton)
연도1.0000.5480.9900.9380.9140.9590.960
공급_국내생산(천ton)0.5481.0000.5740.6140.6460.5250.636
공급_수입(천ton)0.9900.5741.0000.9630.9360.9670.982
수입(백만달러)0.9380.6140.9631.0000.9630.9300.963
수요_발전(천ton)0.9140.6460.9360.9631.0000.9080.948
수요_지역난방(천ton)0.9590.5250.9670.9300.9081.0000.951
수요_도시가스(천ton)0.9600.6360.9820.9630.9480.9511.000

Missing values

2024-04-06T17:13:28.316834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:13:28.552587image/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

연도공급_국내생산(천ton)공급_수입(천ton)수입(백만달러)수요_발전(천ton)수요_지역난방(천ton)수요_도시가스(천ton)
019880189832319050184
119890201534716700349
219900223746017410576
319910249448518000879
419920299456220192061256
519930445978116618571848
6199405996961170916202451
71995067561275168018823417
81996092581878242721954561
919970114712296314722295770
연도공급_국내생산(천ton)공급_수입(천ton)수입(백만달러)수요_발전(천ton)수요_지역난방(천ton)수요_도시가스(천ton)
262014247371073140313403359118180
272015144333661877911266325716929
282016118334531217011181417117384
29201726137537156169500425718390
302018234440152318911434497119813
312019198407482056710199457518822
32202014239982157169932418718247
33202142459322545312789479319331
3420220463945002213173539219832
3520230441173604911285502918328