Overview

Dataset statistics

Number of variables5
Number of observations180
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.9 KiB
Average record size in memory44.7 B

Variable types

DateTime1
Numeric4

Dataset

Description한국가스공사의 연도별 월별 발전용 천연가스요금에 대한 데이터로, 원료비와 공급비에 대한 정보를 포함하고 있음. 2008년 이전 발전용 천연가스 요금 단가는 원/Nm3로 산정되었으며, GJ 단위는 이해를 돕기 위해 23.21146Nm3/GJ를 기준으로 환산한 것임
Author한국가스공사
URLhttps://www.data.go.kr/data/15052058/fileData.do

Alerts

원(Nm3 원료비) is highly overall correlated with 원(GJ 원료비)High correlation
원(Nm3 공급비) is highly overall correlated with 원(GJ 공급비)High correlation
원(GJ 원료비) is highly overall correlated with 원(Nm3 원료비)High correlation
원(GJ 공급비) is highly overall correlated with 원(Nm3 공급비)High correlation
연월 has unique valuesUnique

Reproduction

Analysis started2023-12-12 15:28:01.841918
Analysis finished2023-12-12 15:28:03.692648
Duration1.85 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연월
Date

UNIQUE 

Distinct180
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
Minimum2008-01-01 00:00:00
Maximum2022-12-01 00:00:00
2023-12-13T00:28:03.756102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:28:03.888562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

원(Nm3 원료비)
Real number (ℝ)

HIGH CORRELATION 

Distinct178
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean631.70106
Minimum275.1
Maximum1587.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T00:28:04.015222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum275.1
5-th percentile396.034
Q1477.065
median576.33
Q3748.32
95-th percentile954.745
Maximum1587.66
Range1312.56
Interquartile range (IQR)271.255

Descriptive statistics

Standard deviation218.4275
Coefficient of variation (CV)0.3457767
Kurtosis4.8169674
Mean631.70106
Median Absolute Deviation (MAD)121.165
Skewness1.7635923
Sum113706.19
Variance47710.574
MonotonicityNot monotonic
2023-12-13T00:28:04.150035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
584.97 2
 
1.1%
793.48 2
 
1.1%
528.53 1
 
0.6%
474.78 1
 
0.6%
467.44 1
 
0.6%
466.14 1
 
0.6%
451.21 1
 
0.6%
463.19 1
 
0.6%
455.82 1
 
0.6%
517.73 1
 
0.6%
Other values (168) 168
93.3%
ValueCountFrequency (%)
275.1 1
0.6%
278.28 1
0.6%
310.42 1
0.6%
320.1 1
0.6%
334.85 1
0.6%
369.73 1
0.6%
372.34 1
0.6%
372.52 1
0.6%
384.9 1
0.6%
396.62 1
0.6%
ValueCountFrequency (%)
1587.66 1
0.6%
1538.01 1
0.6%
1500.46 1
0.6%
1443.31 1
0.6%
1262.85 1
0.6%
1184.76 1
0.6%
1160.25 1
0.6%
1085.72 1
0.6%
979.73 1
0.6%
953.43 1
0.6%

원(Nm3 공급비)
Real number (ℝ)

HIGH CORRELATION 

Distinct37
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.826778
Minimum21.38
Maximum110.44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T00:28:04.272568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21.38
5-th percentile22.71
Q126.19
median33.09
Q369.71
95-th percentile94.22
Maximum110.44
Range89.06
Interquartile range (IQR)43.52

Descriptive statistics

Standard deviation25.926531
Coefficient of variation (CV)0.54209236
Kurtosis-0.89526737
Mean47.826778
Median Absolute Deviation (MAD)10.38
Skewness0.68597962
Sum8608.82
Variance672.18499
MonotonicityNot monotonic
2023-12-13T00:28:04.390060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
23.61 22
 
12.2%
75.9 20
 
11.1%
68.38 12
 
6.7%
49.16 7
 
3.9%
28.03 6
 
3.3%
22.71 6
 
3.3%
27.04 5
 
2.8%
36.89 5
 
2.8%
29.66 5
 
2.8%
69.71 4
 
2.2%
Other values (27) 88
48.9%
ValueCountFrequency (%)
21.38 4
 
2.2%
22.19 4
 
2.2%
22.71 6
 
3.3%
23.61 22
12.2%
23.74 4
 
2.2%
25.03 1
 
0.6%
25.73 3
 
1.7%
26.19 4
 
2.2%
26.85 4
 
2.2%
27.04 5
 
2.8%
ValueCountFrequency (%)
110.44 1
 
0.6%
109.27 3
 
1.7%
99.32 4
 
2.2%
94.22 4
 
2.2%
92.74 4
 
2.2%
81.08 4
 
2.2%
79.29 4
 
2.2%
75.9 20
11.1%
69.71 4
 
2.2%
68.38 12
6.7%

원(GJ 원료비)
Real number (ℝ)

HIGH CORRELATION 

Distinct178
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14559.08
Minimum6385.44
Maximum36852.07
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T00:28:04.514309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6385.44
5-th percentile9095.289
Q111051.297
median13377.36
Q317185.89
95-th percentile21926.642
Maximum36852.07
Range30466.63
Interquartile range (IQR)6134.5925

Descriptive statistics

Standard deviation5053.851
Coefficient of variation (CV)0.34712707
Kurtosis5.053264
Mean14559.08
Median Absolute Deviation (MAD)2815.745
Skewness1.8141287
Sum2620634.5
Variance25541410
MonotonicityNot monotonic
2023-12-13T00:28:04.631981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13578.08 2
 
1.1%
18222.96 2
 
1.1%
12267.93 1
 
0.6%
10903.68 1
 
0.6%
10735.31 1
 
0.6%
10705.34 1
 
0.6%
10362.48 1
 
0.6%
10637.59 1
 
0.6%
10580.22 1
 
0.6%
12017.34 1
 
0.6%
Other values (168) 168
93.3%
ValueCountFrequency (%)
6385.44 1
0.6%
6459.21 1
0.6%
7205.46 1
0.6%
7429.94 1
0.6%
7772.63 1
0.6%
8551.12 1
0.6%
8582.0 1
0.6%
8646.73 1
0.6%
8839.53 1
0.6%
9108.75 1
0.6%
ValueCountFrequency (%)
36852.07 1
0.6%
35699.56 1
0.6%
34828.13 1
0.6%
33501.47 1
0.6%
29312.72 1
0.6%
27500.0 1
0.6%
26931.13 1
0.6%
25201.3 1
0.6%
22500.49 1
0.6%
21896.44 1
0.6%

원(GJ 공급비)
Real number (ℝ)

HIGH CORRELATION 

Distinct36
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1071.4149
Minimum491.09
Maximum2536.38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T00:28:04.744753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum491.09
5-th percentile521.51
Q1601.58
median763.855
Q31570.42
95-th percentile2186.94
Maximum2536.38
Range2045.29
Interquartile range (IQR)968.84

Descriptive statistics

Standard deviation583.13951
Coefficient of variation (CV)0.54427049
Kurtosis-0.52680455
Mean1071.4149
Median Absolute Deviation (MAD)242.345
Skewness0.85350293
Sum192854.68
Variance340051.69
MonotonicityNot monotonic
2023-12-13T00:28:04.857477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
542.24 22
 
12.2%
1761.67 12
 
6.7%
1570.42 12
 
6.7%
1043.69 8
 
4.4%
1128.95 7
 
3.9%
521.51 6
 
3.3%
643.73 6
 
3.3%
688.36 5
 
2.8%
856.19 5
 
2.8%
621.0 5
 
2.8%
Other values (26) 92
51.1%
ValueCountFrequency (%)
491.09 4
 
2.2%
515.08 4
 
2.2%
521.51 6
 
3.3%
542.24 22
12.2%
545.28 4
 
2.2%
574.93 1
 
0.6%
597.27 3
 
1.7%
601.58 4
 
2.2%
616.72 4
 
2.2%
621.0 5
 
2.8%
ValueCountFrequency (%)
2536.38 4
 
2.2%
2280.88 4
 
2.2%
2186.94 4
 
2.2%
2152.74 4
 
2.2%
1862.15 4
 
2.2%
1821.08 4
 
2.2%
1761.67 12
6.7%
1618.04 4
 
2.2%
1570.42 12
6.7%
1400.95 4
 
2.2%

Interactions

2023-12-13T00:28:03.156854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:28:01.983936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:28:02.447389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:28:02.782000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:28:03.259741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:28:02.111399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:28:02.536606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:28:02.859892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:28:03.342588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:28:02.220130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:28:02.614984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:28:02.952627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:28:03.442512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:28:02.345840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:28:02.696333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:28:03.055924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:28:04.944018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
원(Nm3 원료비)원(Nm3 공급비)원(GJ 원료비)원(GJ 공급비)
원(Nm3 원료비)1.0000.3870.9990.473
원(Nm3 공급비)0.3871.0000.3770.999
원(GJ 원료비)0.9990.3771.0000.488
원(GJ 공급비)0.4730.9990.4881.000
2023-12-13T00:28:05.027468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
원(Nm3 원료비)원(Nm3 공급비)원(GJ 원료비)원(GJ 공급비)
원(Nm3 원료비)1.000-0.1671.000-0.210
원(Nm3 공급비)-0.1671.000-0.1650.982
원(GJ 원료비)1.000-0.1651.000-0.207
원(GJ 공급비)-0.2100.982-0.2071.000

Missing values

2023-12-13T00:28:03.559390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:28:03.658592image/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

연월원(Nm3 원료비)원(Nm3 공급비)원(GJ 원료비)원(GJ 공급비)
02008-01-01523.1449.1612014.391128.95
12008-02-01560.8249.1612879.81128.95
22008-03-01559.8149.1612856.521128.95
32008-04-01630.4728.0314479.25643.73
42008-05-01670.7928.0315405.23643.73
52008-06-01700.121.3816078.48491.09
62008-07-01748.5921.3817192.07491.09
72008-08-01770.221.3817688.4491.09
82008-09-01878.6321.3820178.64491.09
92008-10-01912.9328.0320966.22643.73
연월원(Nm3 원료비)원(Nm3 공급비)원(GJ 원료비)원(GJ 공급비)
1702022-03-011085.7275.925201.31761.67
1712022-04-011160.2575.926931.131761.67
1722022-05-01802.0975.918617.681043.69
1732022-06-01754.1975.917505.671043.69
1742022-07-01891.675.920695.361043.69
1752022-08-011262.8575.929312.721043.69
1762022-09-011443.3175.933501.471043.69
1772022-10-011538.0175.935699.561043.69
1782022-11-011500.4675.934828.131043.69
1792022-12-011587.6675.936852.071043.69