Overview

Dataset statistics

Number of variables5
Number of observations24
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 KiB
Average record size in memory50.5 B

Variable types

Numeric5

Dataset

Description연간 천연가스 재고 통계현황 데이터로 최소운영재고(Ton), 최대운영재고(Ton), 재고(Ton), 재고율(%)의 속성정보를 포함하고 있습니다.
Author한국가스공사
URLhttps://www.data.go.kr/data/15040824/fileData.do

Alerts

연도 is highly overall correlated with 최소운영재고(Ton) and 2 other fieldsHigh correlation
최소운영재고(Ton) is highly overall correlated with 연도 and 2 other fieldsHigh correlation
최대운영재고(Ton) is highly overall correlated with 연도 and 2 other fieldsHigh correlation
재고(Ton) is highly overall correlated with 연도 and 3 other fieldsHigh correlation
재고율(퍼센트) is highly overall correlated with 재고(Ton)High correlation
연도 has unique valuesUnique
최소운영재고(Ton) has unique valuesUnique
최대운영재고(Ton) has unique valuesUnique
재고(Ton) has unique valuesUnique

Reproduction

Analysis started2024-01-06 13:10:49.616869
Analysis finished2024-01-06 13:10:58.797853
Duration9.18 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2010.5
Minimum1999
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-01-06T13:10:59.309698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1999
5-th percentile2000.15
Q12004.75
median2010.5
Q32016.25
95-th percentile2020.85
Maximum2022
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation7.0710678
Coefficient of variation (CV)0.0035170693
Kurtosis-1.2
Mean2010.5
Median Absolute Deviation (MAD)6
Skewness0
Sum48252
Variance50
MonotonicityStrictly increasing
2024-01-06T13:10:59.833950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1999 1
 
4.2%
2012 1
 
4.2%
2022 1
 
4.2%
2021 1
 
4.2%
2020 1
 
4.2%
2019 1
 
4.2%
2018 1
 
4.2%
2017 1
 
4.2%
2016 1
 
4.2%
2015 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
1999 1
4.2%
2000 1
4.2%
2001 1
4.2%
2002 1
4.2%
2003 1
4.2%
2004 1
4.2%
2005 1
4.2%
2006 1
4.2%
2007 1
4.2%
2008 1
4.2%
ValueCountFrequency (%)
2022 1
4.2%
2021 1
4.2%
2020 1
4.2%
2019 1
4.2%
2018 1
4.2%
2017 1
4.2%
2016 1
4.2%
2015 1
4.2%
2014 1
4.2%
2013 1
4.2%

최소운영재고(Ton)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean244597.83
Minimum56000
Maximum365439
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-01-06T13:11:00.271896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum56000
5-th percentile63955
Q1188160.25
median278013.5
Q3319194.5
95-th percentile360270.75
Maximum365439
Range309439
Interquartile range (IQR)131034.25

Descriptive statistics

Standard deviation97339.402
Coefficient of variation (CV)0.39795693
Kurtosis-0.68074873
Mean244597.83
Median Absolute Deviation (MAD)58589.5
Skewness-0.71301457
Sum5870348
Variance9.4749592 × 109
MonotonicityNot monotonic
2024-01-06T13:11:00.809995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
56000 1
 
4.2%
308921 1
 
4.2%
365439 1
 
4.2%
362535 1
 
4.2%
347440 1
 
4.2%
319044 1
 
4.2%
313243 1
 
4.2%
310211 1
 
4.2%
320415 1
 
4.2%
330527 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
56000 1
4.2%
61300 1
4.2%
79000 1
4.2%
104396 1
4.2%
160432 1
4.2%
164746 1
4.2%
195965 1
4.2%
203721 1
4.2%
213348 1
4.2%
233637 1
4.2%
ValueCountFrequency (%)
365439 1
4.2%
362535 1
4.2%
347440 1
4.2%
330527 1
4.2%
320415 1
4.2%
319646 1
4.2%
319044 1
4.2%
313243 1
4.2%
310211 1
4.2%
308921 1
4.2%

최대운영재고(Ton)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3234094.3
Minimum892000
Maximum5379971
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-01-06T13:11:01.332555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum892000
5-th percentile959150
Q12006631.5
median3492343.5
Q34562054.2
95-th percentile5316952.6
Maximum5379971
Range4487971
Interquartile range (IQR)2555422.8

Descriptive statistics

Standard deviation1512204
Coefficient of variation (CV)0.46758192
Kurtosis-1.4251828
Mean3234094.3
Median Absolute Deviation (MAD)1197615
Skewness-0.18570616
Sum77618263
Variance2.286761 × 1012
MonotonicityNot monotonic
2024-01-06T13:11:01.736184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
892000 1
 
4.2%
4015976 1
 
4.2%
5379971 1
 
4.2%
5344383 1
 
4.2%
5161514 1
 
4.2%
4691391 1
 
4.2%
4617499 1
 
4.2%
4579737 1
 
4.2%
4527366 1
 
4.2%
4556160 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
892000 1
4.2%
941000 1
4.2%
1062000 1
4.2%
1201533 1
4.2%
1587748 1
4.2%
1905206 1
4.2%
2040440 1
4.2%
2074927 1
4.2%
2296161 1
4.2%
2744859 1
4.2%
ValueCountFrequency (%)
5379971 1
4.2%
5344383 1
4.2%
5161514 1
4.2%
4691391 1
4.2%
4617499 1
4.2%
4579737 1
4.2%
4556160 1
4.2%
4527366 1
4.2%
4240501 1
4.2%
4015976 1
4.2%

재고(Ton)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2011979.6
Minimum425484
Maximum3865254.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-01-06T13:11:02.144279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum425484
5-th percentile536332.6
Q11072688.8
median2016441.5
Q32830707.2
95-th percentile3657854.8
Maximum3865254.7
Range3439770.7
Interquartile range (IQR)1758018.4

Descriptive statistics

Standard deviation1041275.7
Coefficient of variation (CV)0.5175379
Kurtosis-1.0374403
Mean2011979.6
Median Absolute Deviation (MAD)886757.5
Skewness0.10785379
Sum48287511
Variance1.0842551 × 1012
MonotonicityNot monotonic
2024-01-06T13:11:02.522999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
529759.0 1
 
4.2%
1723821.0 1
 
4.2%
3687438.0 1
 
4.2%
2821924.867 1
 
4.2%
2311876.0 1
 
4.2%
3490216.8 1
 
4.2%
3865254.7 1
 
4.2%
2110703.0 1
 
4.2%
1547636.0 1
 
4.2%
2857054.0 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
425484.0 1
4.2%
529759.0 1
4.2%
573583.0 1
4.2%
724017.0 1
4.2%
880868.0 1
4.2%
1040138.0 1
4.2%
1083539.0 1
4.2%
1547636.0 1
4.2%
1588058.0 1
4.2%
1700681.0 1
4.2%
ValueCountFrequency (%)
3865254.7 1
4.2%
3687438.0 1
4.2%
3490216.8 1
4.2%
3142698.0 1
4.2%
3051097.0 1
4.2%
2857054.0 1
4.2%
2821924.867 1
4.2%
2554120.0 1
4.2%
2525804.0 1
4.2%
2311876.0 1
4.2%

재고율(퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.620833
Minimum34.2
Maximum91.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-01-06T13:11:02.883792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.2
5-th percentile36.525
Q150.675
median62.35
Q375
95-th percentile84.805
Maximum91.5
Range57.3
Interquartile range (IQR)24.325

Descriptive statistics

Standard deviation16.634118
Coefficient of variation (CV)0.26563234
Kurtosis-1.0042916
Mean62.620833
Median Absolute Deviation (MAD)13.25
Skewness-0.025320006
Sum1502.9
Variance276.69389
MonotonicityNot monotonic
2024-01-06T13:11:03.406304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
83.7 2
 
8.3%
59.4 1
 
4.2%
42.9 1
 
4.2%
68.5 1
 
4.2%
52.8 1
 
4.2%
44.8 1
 
4.2%
74.4 1
 
4.2%
46.1 1
 
4.2%
34.2 1
 
4.2%
62.7 1
 
4.2%
Other values (13) 13
54.2%
ValueCountFrequency (%)
34.2 1
4.2%
35.4 1
4.2%
42.9 1
4.2%
43.2 1
4.2%
44.8 1
4.2%
46.1 1
4.2%
52.2 1
4.2%
52.8 1
4.2%
53.5 1
4.2%
59.4 1
4.2%
ValueCountFrequency (%)
91.5 1
4.2%
85.0 1
4.2%
83.7 2
8.3%
83.4 1
4.2%
76.8 1
4.2%
74.4 1
4.2%
72.0 1
4.2%
68.5 1
4.2%
68.2 1
4.2%
65.5 1
4.2%

Interactions

2024-01-06T13:10:56.620124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:10:49.852769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:10:50.773921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:10:52.211252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:10:54.168984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:10:56.950535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:10:50.023299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:10:51.019311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:10:52.520881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:10:54.722713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:10:57.295649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:10:50.220532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:10:51.289022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:10:53.026661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:10:55.170165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:10:57.540107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:10:50.362605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:10:51.644536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:10:53.372540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:10:55.593466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:10:57.891506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:10:50.537882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:10:51.910263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:10:53.705958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:10:56.256504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-06T13:11:03.720657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도최소운영재고(Ton)최대운영재고(Ton)재고(Ton)재고율(퍼센트)
연도1.0000.8290.9340.5910.000
최소운영재고(Ton)0.8291.0000.8890.6380.000
최대운영재고(Ton)0.9340.8891.0000.4830.366
재고(Ton)0.5910.6380.4831.0000.000
재고율(퍼센트)0.0000.0000.3660.0001.000
2024-01-06T13:11:04.135178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도최소운영재고(Ton)최대운영재고(Ton)재고(Ton)재고율(퍼센트)
연도1.0000.9660.9970.811-0.022
최소운영재고(Ton)0.9661.0000.9700.789-0.039
최대운영재고(Ton)0.9970.9701.0000.8290.007
재고(Ton)0.8110.7890.8291.0000.524
재고율(퍼센트)-0.022-0.0390.0070.5241.000

Missing values

2024-01-06T13:10:58.289242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-06T13:10:58.628971image/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

연도최소운영재고(Ton)최대운영재고(Ton)재고(Ton)재고율(퍼센트)
0199956000892000529759.059.4
1200061300941000573583.061.0
22001790001062000724017.068.2
320021043961201533425484.035.4
4200316043215877481040138.065.5
5200420372119052061588058.083.4
620052133482040440880868.043.2
7200619596522961611922180.083.7
8200716474620749271083539.052.2
9200823596727911672554120.091.5
연도최소운영재고(Ton)최대운영재고(Ton)재고(Ton)재고율(퍼센트)
14201330838839820372129561.053.5
15201431964642405013051097.072.0
16201533052745561602857054.062.7
17201632041545273661547636.034.2
18201731021145797372110703.046.1
19201831324346174993865254.783.7
20201931904446913913490216.874.4
21202034744051615142311876.044.8
22202136253553443832821924.86752.8
23202236543953799713687438.068.5