Overview

Dataset statistics

Number of variables6
Number of observations31
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 KiB
Average record size in memory57.3 B

Variable types

DateTime1
Numeric5

Dataset

Description한국동서발전(주) 연료도입 현황이 담긴 자료입니다. 유연탄, 무연탄, 저유황유, 등유, LNG의 정보가 담겨 있습니다.
URLhttps://www.data.go.kr/data/15083318/fileData.do

Alerts

등유 is highly overall correlated with 저유황유High correlation
저유황유 is highly overall correlated with 등유High correlation
연월일 has unique valuesUnique
무연탄 has 2 (6.5%) zerosZeros
등유 has 8 (25.8%) zerosZeros
저유황유 has 21 (67.7%) zerosZeros

Reproduction

Analysis started2023-12-12 15:23:24.473739
Analysis finished2023-12-12 15:23:27.410269
Duration2.94 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연월일
Date

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size380.0 B
Minimum2021-01-20 00:00:00
Maximum2023-07-20 00:00:00
2023-12-13T00:23:27.471052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:27.601541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)

유연탄
Real number (ℝ)

Distinct30
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean961.19355
Minimum420
Maximum1424
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T00:23:27.737034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum420
5-th percentile681
Q1834.5
median1003
Q31074.5
95-th percentile1330.5
Maximum1424
Range1004
Interquartile range (IQR)240

Descriptive statistics

Standard deviation207.28908
Coefficient of variation (CV)0.215658
Kurtosis0.90525432
Mean961.19355
Median Absolute Deviation (MAD)94
Skewness-0.10494395
Sum29797
Variance42968.761
MonotonicityNot monotonic
2023-12-13T00:23:27.860773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1074 2
 
6.5%
1350 1
 
3.2%
735 1
 
3.2%
1061 1
 
3.2%
845 1
 
3.2%
646 1
 
3.2%
814 1
 
3.2%
835 1
 
3.2%
1089 1
 
3.2%
1004 1
 
3.2%
Other values (20) 20
64.5%
ValueCountFrequency (%)
420 1
3.2%
646 1
3.2%
716 1
3.2%
735 1
3.2%
740 1
3.2%
801 1
3.2%
814 1
3.2%
834 1
3.2%
835 1
3.2%
838 1
3.2%
ValueCountFrequency (%)
1424 1
3.2%
1350 1
3.2%
1311 1
3.2%
1097 1
3.2%
1089 1
3.2%
1083 1
3.2%
1079 1
3.2%
1075 1
3.2%
1074 2
6.5%
1061 1
3.2%

무연탄
Real number (ℝ)

ZEROS 

Distinct28
Distinct (%)90.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.354839
Minimum0
Maximum176
Zeros2
Zeros (%)6.5%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T00:23:27.975045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q128
median82
Q3116.5
95-th percentile147
Maximum176
Range176
Interquartile range (IQR)88.5

Descriptive statistics

Standard deviation49.659875
Coefficient of variation (CV)0.66787685
Kurtosis-1.0527532
Mean74.354839
Median Absolute Deviation (MAD)41
Skewness0.072897398
Sum2305
Variance2466.1032
MonotonicityNot monotonic
2023-12-13T00:23:28.110732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 2
 
6.5%
120 2
 
6.5%
118 2
 
6.5%
105 1
 
3.2%
30 1
 
3.2%
82 1
 
3.2%
20 1
 
3.2%
98 1
 
3.2%
176 1
 
3.2%
131 1
 
3.2%
Other values (18) 18
58.1%
ValueCountFrequency (%)
0 2
6.5%
6 1
3.2%
9 1
3.2%
15 1
3.2%
20 1
3.2%
21 1
3.2%
26 1
3.2%
30 1
3.2%
39 1
3.2%
40 1
3.2%
ValueCountFrequency (%)
176 1
3.2%
152 1
3.2%
142 1
3.2%
131 1
3.2%
120 2
6.5%
118 2
6.5%
115 1
3.2%
107 1
3.2%
105 1
3.2%
98 1
3.2%

등유
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)67.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1387.3226
Minimum0
Maximum4026
Zeros8
Zeros (%)25.8%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T00:23:28.293171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1369.5
median1199
Q32169.5
95-th percentile3750
Maximum4026
Range4026
Interquartile range (IQR)1800

Descriptive statistics

Standard deviation1229.3716
Coefficient of variation (CV)0.88614691
Kurtosis-0.21084298
Mean1387.3226
Median Absolute Deviation (MAD)984
Skewness0.76782971
Sum43007
Variance1511354.6
MonotonicityNot monotonic
2023-12-13T00:23:28.446840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 8
25.8%
1325 2
 
6.5%
1231 2
 
6.5%
918 2
 
6.5%
4026 1
 
3.2%
2183 1
 
3.2%
2362 1
 
3.2%
2565 1
 
3.2%
3711 1
 
3.2%
2541 1
 
3.2%
Other values (11) 11
35.5%
ValueCountFrequency (%)
0 8
25.8%
739 1
 
3.2%
864 1
 
3.2%
918 2
 
6.5%
985 1
 
3.2%
1110 1
 
3.2%
1119 1
 
3.2%
1199 1
 
3.2%
1231 2
 
6.5%
1325 2
 
6.5%
ValueCountFrequency (%)
4026 1
3.2%
3789 1
3.2%
3711 1
3.2%
3696 1
3.2%
2565 1
3.2%
2541 1
3.2%
2362 1
3.2%
2183 1
3.2%
2156 1
3.2%
1619 1
3.2%

저유황유
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)35.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7166.2903
Minimum0
Maximum75494
Zeros21
Zeros (%)67.7%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T00:23:28.560120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32067
95-th percentile46293
Maximum75494
Range75494
Interquartile range (IQR)2067

Descriptive statistics

Standard deviation18268.178
Coefficient of variation (CV)2.5491819
Kurtosis6.9215427
Mean7166.2903
Median Absolute Deviation (MAD)0
Skewness2.7392172
Sum222155
Variance3.3372631 × 108
MonotonicityNot monotonic
2023-12-13T00:23:28.670122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 21
67.7%
51163 1
 
3.2%
39137 1
 
3.2%
41423 1
 
3.2%
75494 1
 
3.2%
1702 1
 
3.2%
1907 1
 
3.2%
2814 1
 
3.2%
3330 1
 
3.2%
2958 1
 
3.2%
ValueCountFrequency (%)
0 21
67.7%
1702 1
 
3.2%
1907 1
 
3.2%
2227 1
 
3.2%
2814 1
 
3.2%
2958 1
 
3.2%
3330 1
 
3.2%
39137 1
 
3.2%
41423 1
 
3.2%
51163 1
 
3.2%
ValueCountFrequency (%)
75494 1
3.2%
51163 1
3.2%
41423 1
3.2%
39137 1
3.2%
3330 1
3.2%
2958 1
3.2%
2814 1
3.2%
2227 1
3.2%
1907 1
3.2%
1702 1
3.2%

액화천연가스
Real number (ℝ)

Distinct27
Distinct (%)87.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.967742
Minimum62
Maximum149
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T00:23:28.808098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum62
5-th percentile62.5
Q175.5
median98
Q3118
95-th percentile134.5
Maximum149
Range87
Interquartile range (IQR)42.5

Descriptive statistics

Standard deviation25.077857
Coefficient of variation (CV)0.25598076
Kurtosis-1.0754413
Mean97.967742
Median Absolute Deviation (MAD)21
Skewness0.15322213
Sum3037
Variance628.89892
MonotonicityNot monotonic
2023-12-13T00:23:28.963609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
70 2
 
6.5%
62 2
 
6.5%
67 2
 
6.5%
98 2
 
6.5%
133 1
 
3.2%
103 1
 
3.2%
63 1
 
3.2%
77 1
 
3.2%
74 1
 
3.2%
79 1
 
3.2%
Other values (17) 17
54.8%
ValueCountFrequency (%)
62 2
6.5%
63 1
3.2%
67 2
6.5%
70 2
6.5%
74 1
3.2%
77 1
3.2%
79 1
3.2%
84 1
3.2%
87 1
3.2%
91 1
3.2%
ValueCountFrequency (%)
149 1
3.2%
136 1
3.2%
133 1
3.2%
129 1
3.2%
126 1
3.2%
125 1
3.2%
122 1
3.2%
119 1
3.2%
117 1
3.2%
113 1
3.2%

Interactions

2023-12-13T00:23:26.870382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:24.690384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:25.233678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:25.820857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:26.378843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:26.946599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:24.782511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:25.346130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:25.930711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:26.482813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:27.023823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:24.904539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:25.463147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:26.050851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:26.592172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:27.101746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:25.029993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:25.605085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:26.180449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:26.700758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:27.180946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:25.139067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:25.711078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:26.285597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:26.790679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:23:29.058162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연월일유연탄무연탄등유저유황유액화천연가스
연월일1.0001.0001.0001.0001.0001.000
유연탄1.0001.0000.2370.0000.2450.000
무연탄1.0000.2371.0000.3440.0000.566
등유1.0000.0000.3441.0000.4830.583
저유황유1.0000.2450.0000.4831.0000.352
액화천연가스1.0000.0000.5660.5830.3521.000
2023-12-13T00:23:29.166312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
유연탄무연탄등유저유황유액화천연가스
유연탄1.0000.044-0.0760.204-0.128
무연탄0.0441.0000.309-0.296-0.032
등유-0.0760.3091.000-0.6330.076
저유황유0.204-0.296-0.6331.000-0.087
액화천연가스-0.128-0.0320.076-0.0871.000

Missing values

2023-12-13T00:23:27.271851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:23:27.373137image/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

연월일유연탄무연탄등유저유황유액화천연가스
02021-01-20135039051163133
12021-02-208341529180110
22021-03-209741516190106
32021-04-2074088918070
42021-05-201079841199062
52021-06-209061153696067
62021-07-2014241421325062
72021-08-201074411325091
82021-09-201003732156098
92021-10-20109797393913792
연월일유연탄무연탄등유저유황유액화천연가스
212022-10-208382637110113
222022-11-209287425650126
232022-12-2080113123620149
242023-01-20100417621830117
252023-02-20108911801702112
262023-03-20835980190798
272023-04-20814200281479
282023-05-2064600333074
292023-06-20845820295877
302023-07-201061300222763