Overview

Dataset statistics

Number of variables13
Number of observations68
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.8 KiB
Average record size in memory116.9 B

Variable types

Numeric11
Categorical2

Dataset

Description한국서부발전의 전력생산을 위한 소비탄 성상 정보입니다. 연도,호기,석탄사용량(톤),총수분율,수분(IM),휘발분(VM),회분(ASH),고정탄소(FC),건식유황률,인수식발열량,인수식발열량단위,미연탄소분율(질량백분율),인수유황률 입니다.
URLhttps://www.data.go.kr/data/15069251/fileData.do

Alerts

인수식발열량단위 has constant value ""Constant
연도 is highly overall correlated with 고정탄소(FC) and 3 other fieldsHigh correlation
석탄사용량(톤) is highly overall correlated with 총수분율 and 4 other fieldsHigh correlation
총수분율 is highly overall correlated with 석탄사용량(톤) and 5 other fieldsHigh correlation
수분(IM) is highly overall correlated with 석탄사용량(톤) and 5 other fieldsHigh correlation
휘발분(VM) is highly overall correlated with 석탄사용량(톤) and 5 other fieldsHigh correlation
회분(ASH) is highly overall correlated with 총수분율 and 2 other fieldsHigh correlation
고정탄소(FC) is highly overall correlated with 연도 and 6 other fieldsHigh correlation
건식유황률 is highly overall correlated with 연도 and 2 other fieldsHigh correlation
인수식발열량 is highly overall correlated with 연도 and 5 other fieldsHigh correlation
인수유황률 is highly overall correlated with 연도 and 1 other fieldsHigh correlation
석탄사용량(톤) has unique valuesUnique

Reproduction

Analysis started2023-12-12 22:29:52.757124
Analysis finished2023-12-12 22:30:06.340664
Duration13.58 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.1618
Minimum2015
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size744.0 B
2023-12-13T07:30:06.403461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2015
5-th percentile2015
Q12016.75
median2018
Q32020
95-th percentile2021
Maximum2021
Range6
Interquartile range (IQR)3.25

Descriptive statistics

Standard deviation1.989602
Coefficient of variation (CV)0.00098584864
Kurtosis-1.2148585
Mean2018.1618
Median Absolute Deviation (MAD)2
Skewness-0.078095865
Sum137235
Variance3.9585162
MonotonicityDecreasing
2023-12-13T07:30:06.525710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2021 11
16.2%
2020 10
14.7%
2019 10
14.7%
2018 10
14.7%
2017 10
14.7%
2016 9
13.2%
2015 8
11.8%
ValueCountFrequency (%)
2015 8
11.8%
2016 9
13.2%
2017 10
14.7%
2018 10
14.7%
2019 10
14.7%
2020 10
14.7%
2021 11
16.2%
ValueCountFrequency (%)
2021 11
16.2%
2020 10
14.7%
2019 10
14.7%
2018 10
14.7%
2017 10
14.7%
2016 9
13.2%
2015 8
11.8%

호기
Categorical

Distinct11
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Memory size676.0 B
1호기
2호기
3호기
4호기
5호기
Other values (6)
33 

Length

Max length4
Median length3
Mean length3.0882353
Min length3

Unique

Unique1 ?
Unique (%)1.5%

Sample

1st row1호기
2nd row2호기
3rd row3호기
4th row4호기
5th row5호기

Common Values

ValueCountFrequency (%)
1호기 7
10.3%
2호기 7
10.3%
3호기 7
10.3%
4호기 7
10.3%
5호기 7
10.3%
6호기 7
10.3%
7호기 7
10.3%
8호기 7
10.3%
9호기 6
8.8%
10호기 5
7.4%

Length

2023-12-13T07:30:06.680585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1호기 7
10.3%
2호기 7
10.3%
3호기 7
10.3%
4호기 7
10.3%
5호기 7
10.3%
6호기 7
10.3%
7호기 7
10.3%
8호기 7
10.3%
9호기 6
8.8%
10호기 5
7.4%

석탄사용량(톤)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct68
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1373166.5
Minimum212484
Maximum2835282
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size744.0 B
2023-12-13T07:30:06.822729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum212484
5-th percentile618395.9
Q11079961.8
median1357327.5
Q31570427
95-th percentile2273979.9
Maximum2835282
Range2622798
Interquartile range (IQR)490465.25

Descriptive statistics

Standard deviation504335.14
Coefficient of variation (CV)0.36727895
Kurtosis1.3147107
Mean1373166.5
Median Absolute Deviation (MAD)243981
Skewness0.78498299
Sum93375320
Variance2.5435393 × 1011
MonotonicityNot monotonic
2023-12-13T07:30:06.961443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1028529 1
 
1.5%
1245056 1
 
1.5%
2108652 1
 
1.5%
2835282 1
 
1.5%
1353242 1
 
1.5%
1375439 1
 
1.5%
1218714 1
 
1.5%
1417800 1
 
1.5%
858016 1
 
1.5%
1274953 1
 
1.5%
Other values (58) 58
85.3%
ValueCountFrequency (%)
212484 1
1.5%
590563 1
1.5%
592593 1
1.5%
613540 1
1.5%
627414 1
1.5%
805681 1
1.5%
807470 1
1.5%
820691 1
1.5%
837698 1
1.5%
858016 1
1.5%
ValueCountFrequency (%)
2835282 1
1.5%
2748353 1
1.5%
2698753 1
1.5%
2295116 1
1.5%
2234727 1
1.5%
2195163 1
1.5%
2166002 1
1.5%
2108652 1
1.5%
1895922 1
1.5%
1750731 1
1.5%

총수분율
Real number (ℝ)

HIGH CORRELATION 

Distinct59
Distinct (%)86.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.214853
Minimum11.67
Maximum19.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size744.0 B
2023-12-13T07:30:07.092373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11.67
5-th percentile13.6035
Q114.155
median14.985
Q315.7975
95-th percentile18.2195
Maximum19.6
Range7.93
Interquartile range (IQR)1.6425

Descriptive statistics

Standard deviation1.4921531
Coefficient of variation (CV)0.098072134
Kurtosis0.51048597
Mean15.214853
Median Absolute Deviation (MAD)0.875
Skewness0.75716643
Sum1034.61
Variance2.2265209
MonotonicityNot monotonic
2023-12-13T07:30:07.240821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.56 3
 
4.4%
15.1 3
 
4.4%
14.46 2
 
2.9%
15.16 2
 
2.9%
16.94 2
 
2.9%
13.69 2
 
2.9%
14.02 2
 
2.9%
18.41 1
 
1.5%
18.2 1
 
1.5%
13.71 1
 
1.5%
Other values (49) 49
72.1%
ValueCountFrequency (%)
11.67 1
1.5%
13.35 1
1.5%
13.54 1
1.5%
13.6 1
1.5%
13.61 1
1.5%
13.69 2
2.9%
13.7 1
1.5%
13.71 1
1.5%
13.73 1
1.5%
13.76 1
1.5%
ValueCountFrequency (%)
19.6 1
1.5%
18.41 1
1.5%
18.25 1
1.5%
18.23 1
1.5%
18.2 1
1.5%
17.75 1
1.5%
17.68 1
1.5%
17.21 1
1.5%
16.94 2
2.9%
16.89 1
1.5%

수분(IM)
Real number (ℝ)

HIGH CORRELATION 

Distinct62
Distinct (%)91.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4645588
Minimum4.19
Maximum11.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size744.0 B
2023-12-13T07:30:07.385781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.19
5-th percentile6.0405
Q16.845
median7.335
Q37.7925
95-th percentile9.764
Maximum11.17
Range6.98
Interquartile range (IQR)0.9475

Descriptive statistics

Standard deviation1.1731479
Coefficient of variation (CV)0.15716239
Kurtosis1.459942
Mean7.4645588
Median Absolute Deviation (MAD)0.505
Skewness0.6072727
Sum507.59
Variance1.3762759
MonotonicityNot monotonic
2023-12-13T07:30:07.533142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.76 2
 
2.9%
7.12 2
 
2.9%
7.0 2
 
2.9%
7.24 2
 
2.9%
7.56 2
 
2.9%
7.32 2
 
2.9%
6.29 1
 
1.5%
9.66 1
 
1.5%
7.69 1
 
1.5%
7.72 1
 
1.5%
Other values (52) 52
76.5%
ValueCountFrequency (%)
4.19 1
1.5%
5.31 1
1.5%
6.02 1
1.5%
6.03 1
1.5%
6.06 1
1.5%
6.1 1
1.5%
6.19 1
1.5%
6.24 1
1.5%
6.28 1
1.5%
6.29 1
1.5%
ValueCountFrequency (%)
11.17 1
1.5%
10.03 1
1.5%
10.01 1
1.5%
9.82 1
1.5%
9.66 1
1.5%
9.26 1
1.5%
9.23 1
1.5%
9.11 1
1.5%
9.03 1
1.5%
8.64 1
1.5%

휘발분(VM)
Real number (ℝ)

HIGH CORRELATION 

Distinct63
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.175735
Minimum27.2
Maximum37.11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size744.0 B
2023-12-13T07:30:07.919402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27.2
5-th percentile30.767
Q132.935
median33.44
Q334.0125
95-th percentile34.673
Maximum37.11
Range9.91
Interquartile range (IQR)1.0775

Descriptive statistics

Standard deviation1.5770247
Coefficient of variation (CV)0.047535486
Kurtosis4.7514241
Mean33.175735
Median Absolute Deviation (MAD)0.55
Skewness-1.6524139
Sum2255.95
Variance2.4870069
MonotonicityNot monotonic
2023-12-13T07:30:08.045551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.59 3
 
4.4%
33.14 2
 
2.9%
34.43 2
 
2.9%
33.07 2
 
2.9%
33.08 1
 
1.5%
33.22 1
 
1.5%
33.71 1
 
1.5%
34.58 1
 
1.5%
34.52 1
 
1.5%
31.73 1
 
1.5%
Other values (53) 53
77.9%
ValueCountFrequency (%)
27.2 1
1.5%
28.07 1
1.5%
28.18 1
1.5%
30.69 1
1.5%
30.91 1
1.5%
31.14 1
1.5%
31.44 1
1.5%
31.58 1
1.5%
31.64 1
1.5%
31.73 1
1.5%
ValueCountFrequency (%)
37.11 1
1.5%
34.95 1
1.5%
34.92 1
1.5%
34.68 1
1.5%
34.66 1
1.5%
34.65 1
1.5%
34.58 1
1.5%
34.56 1
1.5%
34.52 1
1.5%
34.46 1
1.5%

회분(ASH)
Real number (ℝ)

HIGH CORRELATION 

Distinct59
Distinct (%)86.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.174265
Minimum8.84
Maximum12.85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size744.0 B
2023-12-13T07:30:08.176369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.84
5-th percentile10.1435
Q110.49
median11.14
Q311.79
95-th percentile12.476
Maximum12.85
Range4.01
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation0.82078651
Coefficient of variation (CV)0.07345329
Kurtosis-0.3267019
Mean11.174265
Median Absolute Deviation (MAD)0.65
Skewness-0.034968838
Sum759.85
Variance0.6736905
MonotonicityNot monotonic
2023-12-13T07:30:08.309843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.32 3
 
4.4%
11.08 2
 
2.9%
10.25 2
 
2.9%
10.57 2
 
2.9%
10.53 2
 
2.9%
11.16 2
 
2.9%
10.49 2
 
2.9%
10.24 2
 
2.9%
11.3 1
 
1.5%
10.31 1
 
1.5%
Other values (49) 49
72.1%
ValueCountFrequency (%)
8.84 1
 
1.5%
9.91 1
 
1.5%
10.06 1
 
1.5%
10.14 1
 
1.5%
10.15 1
 
1.5%
10.24 2
2.9%
10.25 2
2.9%
10.27 1
 
1.5%
10.31 1
 
1.5%
10.32 3
4.4%
ValueCountFrequency (%)
12.85 1
1.5%
12.81 1
1.5%
12.67 1
1.5%
12.49 1
1.5%
12.45 1
1.5%
12.35 1
1.5%
12.21 1
1.5%
12.18 1
1.5%
12.11 1
1.5%
12.07 1
1.5%

고정탄소(FC)
Real number (ℝ)

HIGH CORRELATION 

Distinct64
Distinct (%)94.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.003382
Minimum42.88
Maximum54.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size744.0 B
2023-12-13T07:30:08.442796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum42.88
5-th percentile45.335
Q147.0375
median48.14
Q349.2125
95-th percentile50.5845
Maximum54.17
Range11.29
Interquartile range (IQR)2.175

Descriptive statistics

Standard deviation1.8522633
Coefficient of variation (CV)0.0385861
Kurtosis1.0979671
Mean48.003382
Median Absolute Deviation (MAD)1.09
Skewness0.1867696
Sum3264.23
Variance3.4308794
MonotonicityNot monotonic
2023-12-13T07:30:08.564389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.23 2
 
2.9%
48.29 2
 
2.9%
48.21 2
 
2.9%
48.14 2
 
2.9%
47.25 1
 
1.5%
47.84 1
 
1.5%
48.06 1
 
1.5%
48.82 1
 
1.5%
48.92 1
 
1.5%
50.28 1
 
1.5%
Other values (54) 54
79.4%
ValueCountFrequency (%)
42.88 1
1.5%
45.09 1
1.5%
45.17 1
1.5%
45.3 1
1.5%
45.4 1
1.5%
45.42 1
1.5%
45.46 1
1.5%
45.47 1
1.5%
45.67 1
1.5%
45.74 1
1.5%
ValueCountFrequency (%)
54.17 1
1.5%
51.33 1
1.5%
50.77 1
1.5%
50.63 1
1.5%
50.5 1
1.5%
50.49 1
1.5%
50.28 1
1.5%
50.19 1
1.5%
50.06 1
1.5%
50.01 1
1.5%

건식유황률
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)42.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.57117647
Minimum0.42
Maximum0.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size744.0 B
2023-12-13T07:30:08.670148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.42
5-th percentile0.4435
Q10.48
median0.56
Q30.66
95-th percentile0.72
Maximum0.74
Range0.32
Interquartile range (IQR)0.18

Descriptive statistics

Standard deviation0.097514322
Coefficient of variation (CV)0.17072538
Kurtosis-1.468083
Mean0.57117647
Median Absolute Deviation (MAD)0.09
Skewness0.12891079
Sum38.84
Variance0.009509043
MonotonicityNot monotonic
2023-12-13T07:30:08.774306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0.47 6
 
8.8%
0.66 5
 
7.4%
0.64 5
 
7.4%
0.49 4
 
5.9%
0.45 4
 
5.9%
0.48 4
 
5.9%
0.72 3
 
4.4%
0.69 3
 
4.4%
0.6 3
 
4.4%
0.63 3
 
4.4%
Other values (19) 28
41.2%
ValueCountFrequency (%)
0.42 1
 
1.5%
0.43 1
 
1.5%
0.44 2
 
2.9%
0.45 4
5.9%
0.46 2
 
2.9%
0.47 6
8.8%
0.48 4
5.9%
0.49 4
5.9%
0.5 2
 
2.9%
0.52 3
4.4%
ValueCountFrequency (%)
0.74 1
 
1.5%
0.73 1
 
1.5%
0.72 3
4.4%
0.71 1
 
1.5%
0.7 3
4.4%
0.69 3
4.4%
0.68 1
 
1.5%
0.67 1
 
1.5%
0.66 5
7.4%
0.64 5
7.4%

인수식발열량
Real number (ℝ)

HIGH CORRELATION 

Distinct64
Distinct (%)94.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5673.1029
Minimum5224
Maximum6011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size744.0 B
2023-12-13T07:30:08.896185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5224
5-th percentile5300.1
Q15597.75
median5644.5
Q35815.75
95-th percentile5939.65
Maximum6011
Range787
Interquartile range (IQR)218

Descriptive statistics

Standard deviation190.17872
Coefficient of variation (CV)0.033522874
Kurtosis-0.0048236549
Mean5673.1029
Median Absolute Deviation (MAD)80
Skewness-0.39601699
Sum385771
Variance36167.944
MonotonicityNot monotonic
2023-12-13T07:30:09.035423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5604 3
 
4.4%
5609 2
 
2.9%
5608 2
 
2.9%
5939 1
 
1.5%
5639 1
 
1.5%
5691 1
 
1.5%
5716 1
 
1.5%
5613 1
 
1.5%
5298 1
 
1.5%
5271 1
 
1.5%
Other values (54) 54
79.4%
ValueCountFrequency (%)
5224 1
1.5%
5237 1
1.5%
5271 1
1.5%
5298 1
1.5%
5304 1
1.5%
5314 1
1.5%
5317 1
1.5%
5480 1
1.5%
5486 1
1.5%
5563 1
1.5%
ValueCountFrequency (%)
6011 1
1.5%
5965 1
1.5%
5959 1
1.5%
5940 1
1.5%
5939 1
1.5%
5935 1
1.5%
5933 1
1.5%
5927 1
1.5%
5923 1
1.5%
5915 1
1.5%

인수식발열량단위
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size676.0 B
kcal/kg
68 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowkcal/kg
2nd rowkcal/kg
3rd rowkcal/kg
4th rowkcal/kg
5th rowkcal/kg

Common Values

ValueCountFrequency (%)
kcal/kg 68
100.0%

Length

2023-12-13T07:30:09.162804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:30:09.252422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kcal/kg 68
100.0%
Distinct62
Distinct (%)91.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3832353
Minimum0.42
Maximum4.16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size744.0 B
2023-12-13T07:30:09.358897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.42
5-th percentile0.983
Q11.8375
median2.46
Q32.9125
95-th percentile3.939
Maximum4.16
Range3.74
Interquartile range (IQR)1.075

Descriptive statistics

Standard deviation0.85982025
Coefficient of variation (CV)0.36077858
Kurtosis-0.1936933
Mean2.3832353
Median Absolute Deviation (MAD)0.55
Skewness-0.08036978
Sum162.06
Variance0.73929087
MonotonicityNot monotonic
2023-12-13T07:30:09.497634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.27 2
 
2.9%
2.68 2
 
2.9%
2.39 2
 
2.9%
3.15 2
 
2.9%
2.54 2
 
2.9%
3.01 2
 
2.9%
2.45 1
 
1.5%
4.15 1
 
1.5%
3.96 1
 
1.5%
1.95 1
 
1.5%
Other values (52) 52
76.5%
ValueCountFrequency (%)
0.42 1
1.5%
0.64 1
1.5%
0.66 1
1.5%
0.92 1
1.5%
1.1 1
1.5%
1.19 1
1.5%
1.21 1
1.5%
1.22 1
1.5%
1.3 1
1.5%
1.39 1
1.5%
ValueCountFrequency (%)
4.16 1
1.5%
4.15 1
1.5%
4.13 1
1.5%
3.96 1
1.5%
3.9 1
1.5%
3.5 1
1.5%
3.49 1
1.5%
3.39 1
1.5%
3.2 1
1.5%
3.15 2
2.9%

인수유황률
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)39.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.48470588
Minimum0.35
Maximum0.62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size744.0 B
2023-12-13T07:30:09.646871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.35
5-th percentile0.3735
Q10.41
median0.485
Q30.55
95-th percentile0.6
Maximum0.62
Range0.27
Interquartile range (IQR)0.14

Descriptive statistics

Standard deviation0.079522279
Coefficient of variation (CV)0.16406295
Kurtosis-1.4524206
Mean0.48470588
Median Absolute Deviation (MAD)0.075
Skewness0.046827184
Sum32.96
Variance0.0063237928
MonotonicityNot monotonic
2023-12-13T07:30:09.790337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.4 6
 
8.8%
0.55 5
 
7.4%
0.54 4
 
5.9%
0.42 4
 
5.9%
0.41 4
 
5.9%
0.6 4
 
5.9%
0.57 3
 
4.4%
0.59 3
 
4.4%
0.53 3
 
4.4%
0.43 3
 
4.4%
Other values (17) 29
42.6%
ValueCountFrequency (%)
0.35 1
 
1.5%
0.36 1
 
1.5%
0.37 2
 
2.9%
0.38 2
 
2.9%
0.39 3
4.4%
0.4 6
8.8%
0.41 4
5.9%
0.42 4
5.9%
0.43 3
4.4%
0.44 2
 
2.9%
ValueCountFrequency (%)
0.62 1
 
1.5%
0.61 1
 
1.5%
0.6 4
5.9%
0.59 3
4.4%
0.58 2
 
2.9%
0.57 3
4.4%
0.56 2
 
2.9%
0.55 5
7.4%
0.54 4
5.9%
0.53 3
4.4%

Interactions

2023-12-13T07:30:04.848992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:53.156751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:54.297836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:55.434423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:56.843352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:57.870882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:58.866184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:00.117519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:01.196106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:02.566177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:03.632991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:04.961680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:53.245392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:54.401347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:55.547916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:56.932635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:57.951384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:58.966676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:00.217206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:01.295480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:02.696888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:03.751487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:05.058564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:53.330854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:54.497957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:55.636791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:57.017252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:58.034319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:59.057878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:00.310712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:01.374591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:02.788083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:03.838315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:05.165257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:53.424486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:54.608158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:56.046381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:57.106522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:58.153211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:59.155315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:00.423324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:01.468198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:02.881968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:03.958035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:05.269732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:53.528836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:54.699301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:56.163137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:57.200561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:58.256875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:59.260068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:00.511247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:01.551499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:02.980066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:04.094832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:05.369163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:53.629014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:54.789727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:56.259658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:57.288489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:58.331912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:59.347234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:00.596291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:01.628732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:03.064137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:04.181496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:05.474447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:53.762028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:54.883973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:56.362172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:57.401742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:58.418882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:59.465801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:00.720324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:01.736345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:03.166199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:04.315499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:05.588153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:53.859090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:54.979550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:56.464152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:57.488241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:58.495118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:59.593187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:00.817861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:01.853926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:03.248848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:04.408832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:05.682680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:53.969450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:55.095881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:56.566217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:57.583939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:58.591830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:59.708273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:00.923786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:02.263110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:03.336292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:04.542144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:05.790428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:54.075925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:55.225478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:56.660432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:57.680738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:58.670273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:59.832864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:01.016159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:02.359762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:03.434742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:04.661616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:05.909305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:54.194908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:55.336700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:56.752679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:57.771886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:58.770691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:59.962331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:01.104317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:02.462122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:03.545523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:30:04.753262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:30:09.928705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도호기석탄사용량(톤)총수분율수분(IM)휘발분(VM)회분(ASH)고정탄소(FC)건식유황률인수식발열량미연탄소분율(질량백분율)인수유황률
연도1.0000.0000.4690.7010.4370.5400.7260.5710.7920.8310.4910.803
호기0.0001.0000.5060.6800.6890.3110.2070.7060.0000.5170.6570.000
석탄사용량(톤)0.4690.5061.0000.6620.7160.4490.3770.7940.4860.5900.2360.490
총수분율0.7010.6800.6621.0000.9150.7310.8350.9380.5910.9380.6610.644
수분(IM)0.4370.6890.7160.9151.0000.8030.7290.9030.6930.7720.7110.628
휘발분(VM)0.5400.3110.4490.7310.8031.0000.7660.8250.6560.5930.5990.497
회분(ASH)0.7260.2070.3770.8350.7290.7661.0000.8440.5900.7250.5780.481
고정탄소(FC)0.5710.7060.7940.9380.9030.8250.8441.0000.5340.8630.6270.615
건식유황률0.7920.0000.4860.5910.6930.6560.5900.5341.0000.6780.7530.960
인수식발열량0.8310.5170.5900.9380.7720.5930.7250.8630.6781.0000.6310.706
미연탄소분율(질량백분율)0.4910.6570.2360.6610.7110.5990.5780.6270.7530.6311.0000.658
인수유황률0.8030.0000.4900.6440.6280.4970.4810.6150.9600.7060.6581.000
2023-12-13T07:30:10.089112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도석탄사용량(톤)총수분율수분(IM)휘발분(VM)회분(ASH)고정탄소(FC)건식유황률인수식발열량미연탄소분율(질량백분율)인수유황률호기
연도1.000-0.473-0.453-0.417-0.488-0.0090.611-0.8510.509-0.208-0.8140.000
석탄사용량(톤)-0.4731.0000.7190.6630.501-0.111-0.7130.418-0.6970.1340.3550.238
총수분율-0.4530.7191.0000.9550.763-0.530-0.8730.380-0.8630.2150.2800.383
수분(IM)-0.4170.6630.9551.0000.772-0.629-0.8500.349-0.8430.2280.2520.374
휘발분(VM)-0.4880.5010.7630.7721.000-0.522-0.8470.335-0.6260.1020.2670.146
회분(ASH)-0.009-0.111-0.530-0.629-0.5221.0000.2830.1370.268-0.0300.2120.079
고정탄소(FC)0.611-0.713-0.873-0.850-0.8470.2831.000-0.5230.877-0.213-0.4400.407
건식유황률-0.8510.4180.3800.3490.3350.137-0.5231.000-0.4710.4190.9870.000
인수식발열량0.509-0.697-0.863-0.843-0.6260.2680.877-0.4711.000-0.277-0.3740.264
미연탄소분율(질량백분율)-0.2080.1340.2150.2280.102-0.030-0.2130.419-0.2771.0000.4320.345
인수유황률-0.8140.3550.2800.2520.2670.212-0.4400.987-0.3740.4321.0000.000
호기0.0000.2380.3830.3740.1460.0790.4070.0000.2640.3450.0001.000

Missing values

2023-12-13T07:30:06.059231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:30:06.261726image/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

연도호기석탄사용량(톤)총수분율수분(IM)휘발분(VM)회분(ASH)고정탄소(FC)건식유황률인수식발열량인수식발열량단위미연탄소분율(질량백분율)인수유황률
020211호기102852913.716.2931.7311.6950.280.495939kcal/kg2.450.43
120212호기97528613.616.2431.4411.750.630.495935kcal/kg3.150.43
220213호기91544313.816.5131.9711.3350.190.485959kcal/kg2.410.42
320214호기98973013.76.2831.6411.5850.490.495923kcal/kg2.390.42
420215호기59056313.355.3130.6912.6751.330.455965kcal/kg1.710.4
520216호기104667613.736.0331.1412.0650.770.475933kcal/kg1.390.41
620217호기111082413.776.1931.5811.7450.50.475927kcal/kg2.540.41
720218호기83769813.766.6132.2211.1650.010.475940kcal/kg2.030.41
820219호기229511617.217.9633.3111.4847.260.455570kcal/kg2.630.37
9202110호기216600216.817.7332.7312.3547.180.465563kcal/kg1.870.38
연도호기석탄사용량(톤)총수분율수분(IM)휘발분(VM)회분(ASH)고정탄소(FC)건식유황률인수식발열량인수식발열량단위미연탄소분율(질량백분율)인수유황률
5820168호기162808315.567.6334.1611.1947.030.645633kcal/kg2.820.55
5920169호기87811619.611.1737.118.8442.880.55237kcal/kg0.420.4
6020151호기139290616.668.6434.9510.9545.460.745572kcal/kg2.90.62
6120152호기169526616.418.6234.3710.5746.440.725569kcal/kg2.750.6
6220153호기147368716.898.333.3510.1448.210.695568kcal/kg2.470.57
6320154호기163999516.848.6334.4610.5646.350.715566kcal/kg3.020.59
6420155호기165329515.277.1334.0511.8247.00.695722kcal/kg1.10.59
6520156호기144603715.367.1734.011.5547.270.675729kcal/kg1.190.57
6620157호기167085016.067.9234.6611.1246.30.735650kcal/kg2.690.61
6720158호기157038516.277.9134.3811.045.420.725630kcal/kg2.890.6