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ENGINE Dataset

Overview

Brought to you by YData

Dataset statistics

Number of variables9
Number of observations57
Missing cells0
Missing cells (%)0.0%
Duplicate rows3
Duplicate rows (%)5.3%
Total size in memory4.1 KiB
Average record size in memory74.2 B

Variable types

Numeric9

Alerts

Dataset has 3 (5.3%) duplicate rowsDuplicates
Effisiensi Panas (%) is highly overall correlated with Energi Panas Pembakaran/Jam (Kj/kg) and 2 other fieldsHigh correlation
Effisiensi Volumetrik (%) is highly overall correlated with Manometer H (m)High correlation
Energi Panas Pembakaran/Jam (Kj/kg) is highly overall correlated with Effisiensi Panas (%) and 5 other fieldsHigh correlation
Manometer H (m) is highly overall correlated with Effisiensi Volumetrik (%) and 4 other fieldsHigh correlation
Massa Bahan Bakar (gram) is highly overall correlated with Energi Panas Pembakaran/Jam (Kj/kg) and 4 other fieldsHigh correlation
Massa Bhn.Bakar/det (Kg/det) is highly overall correlated with Effisiensi Panas (%) and 2 other fieldsHigh correlation
Pemakaian Bhn. Bakar Spesifik (SFC) is highly overall correlated with Massa Bahan Bakar (gram)High correlation
Putaran (rpm) is highly overall correlated with Energi Panas Pembakaran/Jam (Kj/kg) and 3 other fieldsHigh correlation
Volume Bahan Bakar (cc) is highly overall correlated with Effisiensi Panas (%) and 5 other fieldsHigh correlation

Reproduction

Analysis started2025-07-26 07:22:12.677298
Analysis finished2025-07-26 07:22:16.804454
Duration4.13 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Putaran (rpm)
Real number (ℝ)

High correlation 

Distinct24
Distinct (%)42.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2785.7018
Minimum2000
Maximum3500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size584.0 B
2025-07-26T14:22:16.834956image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2421.2
Q12643
median2786
Q32929
95-th percentile3264.2
Maximum3500
Range1500
Interquartile range (IQR)286

Descriptive statistics

Standard deviation255.85647
Coefficient of variation (CV)0.091846326
Kurtosis2.0229491
Mean2785.7018
Median Absolute Deviation (MAD)143
Skewness-0.063227327
Sum158785
Variance65462.534
MonotonicityNot monotonic
2025-07-26T14:22:16.893844image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2786 9
15.8%
2643 6
 
10.5%
3000 5
 
8.8%
2714 4
 
7.0%
2893 3
 
5.3%
2857 3
 
5.3%
2821 3
 
5.3%
2571 3
 
5.3%
2929 3
 
5.3%
2964 2
 
3.5%
Other values (14) 16
28.1%
ValueCountFrequency (%)
2000 1
 
1.8%
2214 1
 
1.8%
2250 1
 
1.8%
2464 1
 
1.8%
2500 1
 
1.8%
2536 1
 
1.8%
2571 3
5.3%
2607 2
 
3.5%
2643 6
10.5%
2679 1
 
1.8%
ValueCountFrequency (%)
3500 1
 
1.8%
3357 1
 
1.8%
3321 1
 
1.8%
3250 1
 
1.8%
3071 1
 
1.8%
3000 5
8.8%
2964 2
 
3.5%
2929 3
5.3%
2893 3
5.3%
2857 3
5.3%

Volume Bahan Bakar (cc)
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.701754
Minimum7
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size584.0 B
2025-07-26T14:22:16.957110image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile9.8
Q111
median12
Q312
95-th percentile14
Maximum15
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3088287
Coefficient of variation (CV)0.11184893
Kurtosis3.4007391
Mean11.701754
Median Absolute Deviation (MAD)1
Skewness-0.85559789
Sum667
Variance1.7130326
MonotonicityNot monotonic
2025-07-26T14:22:17.006795image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
12 26
45.6%
11 16
28.1%
13 6
 
10.5%
14 3
 
5.3%
10 2
 
3.5%
8 1
 
1.8%
15 1
 
1.8%
9 1
 
1.8%
7 1
 
1.8%
ValueCountFrequency (%)
7 1
 
1.8%
8 1
 
1.8%
9 1
 
1.8%
10 2
 
3.5%
11 16
28.1%
12 26
45.6%
13 6
 
10.5%
14 3
 
5.3%
15 1
 
1.8%
ValueCountFrequency (%)
15 1
 
1.8%
14 3
 
5.3%
13 6
 
10.5%
12 26
45.6%
11 16
28.1%
10 2
 
3.5%
9 1
 
1.8%
8 1
 
1.8%
7 1
 
1.8%

Massa Bahan Bakar (gram)
Real number (ℝ)

High correlation 

Distinct22
Distinct (%)38.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.0259649
Minimum5.25
Maximum11.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size584.0 B
2025-07-26T14:22:17.055346image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum5.25
5-th percentile7.734
Q18.68
median9
Q39.43
95-th percentile10.5
Maximum11.25
Range6
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.90701996
Coefficient of variation (CV)0.10049008
Kurtosis5.1212271
Mean9.0259649
Median Absolute Deviation (MAD)0.32
Skewness-1.1838133
Sum514.48
Variance0.82268521
MonotonicityNot monotonic
2025-07-26T14:22:17.106250image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
9 9
15.8%
9.21 6
 
10.5%
9.32 5
 
8.8%
9.43 4
 
7.0%
8.68 4
 
7.0%
8.25 3
 
5.3%
8.57 3
 
5.3%
8.89 2
 
3.5%
8.79 2
 
3.5%
9.96 2
 
3.5%
Other values (12) 17
29.8%
ValueCountFrequency (%)
5.25 1
 
1.8%
6.86 1
 
1.8%
7.39 1
 
1.8%
7.82 1
 
1.8%
8.04 2
3.5%
8.25 3
5.3%
8.57 3
5.3%
8.68 4
7.0%
8.79 2
3.5%
8.89 2
3.5%
ValueCountFrequency (%)
11.25 1
 
1.8%
10.61 1
 
1.8%
10.5 2
 
3.5%
10.18 1
 
1.8%
9.96 2
 
3.5%
9.75 2
 
3.5%
9.64 2
 
3.5%
9.43 4
7.0%
9.32 5
8.8%
9.21 6
10.5%

Manometer H (m)
Real number (ℝ)

High correlation 

Distinct36
Distinct (%)63.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2859649
Minimum2.5
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size584.0 B
2025-07-26T14:22:17.159132image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile3
Q14.5
median5
Q36.1
95-th percentile8.12
Maximum10
Range7.5
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation1.4888201
Coefficient of variation (CV)0.28165531
Kurtosis1.2227516
Mean5.2859649
Median Absolute Deviation (MAD)0.9
Skewness0.78740376
Sum301.3
Variance2.2165852
MonotonicityNot monotonic
2025-07-26T14:22:17.215506image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
4.6 7
 
12.3%
5 3
 
5.3%
4.5 3
 
5.3%
6.1 2
 
3.5%
6.5 2
 
3.5%
5.1 2
 
3.5%
4.1 2
 
3.5%
5.6 2
 
3.5%
3.9 2
 
3.5%
6.6 2
 
3.5%
Other values (26) 30
52.6%
ValueCountFrequency (%)
2.5 1
1.8%
2.7 1
1.8%
3 2
3.5%
3.3 1
1.8%
3.4 1
1.8%
3.8 1
1.8%
3.9 2
3.5%
4.1 2
3.5%
4.3 1
1.8%
4.4 1
1.8%
ValueCountFrequency (%)
10 1
1.8%
8.9 1
1.8%
8.6 1
1.8%
8 1
1.8%
7 1
1.8%
6.9 1
1.8%
6.7 2
3.5%
6.6 2
3.5%
6.5 2
3.5%
6.4 1
1.8%

Massa Bhn.Bakar/det (Kg/det)
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)17.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00060526316
Minimum0.00052
Maximum0.00067
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size584.0 B
2025-07-26T14:22:17.267120image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.00052
5-th percentile0.00058
Q10.00059
median0.00061
Q30.00062
95-th percentile0.00064
Maximum0.00067
Range0.00015
Interquartile range (IQR)3 × 10-5

Descriptive statistics

Standard deviation2.3230619 × 10-5
Coefficient of variation (CV)0.038381023
Kurtosis2.9735213
Mean0.00060526316
Median Absolute Deviation (MAD)1 × 10-5
Skewness-0.61038143
Sum0.0345
Variance5.3966165 × 10-10
MonotonicityNot monotonic
2025-07-26T14:22:17.319788image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.0006 12
21.1%
0.00061 11
19.3%
0.00062 8
14.0%
0.00059 7
12.3%
0.00058 7
12.3%
0.00063 6
10.5%
0.00064 3
 
5.3%
0.00055 1
 
1.8%
0.00067 1
 
1.8%
0.00052 1
 
1.8%
ValueCountFrequency (%)
0.00052 1
 
1.8%
0.00055 1
 
1.8%
0.00058 7
12.3%
0.00059 7
12.3%
0.0006 12
21.1%
0.00061 11
19.3%
0.00062 8
14.0%
0.00063 6
10.5%
0.00064 3
 
5.3%
0.00067 1
 
1.8%
ValueCountFrequency (%)
0.00067 1
 
1.8%
0.00064 3
 
5.3%
0.00063 6
10.5%
0.00062 8
14.0%
0.00061 11
19.3%
0.0006 12
21.1%
0.00059 7
12.3%
0.00058 7
12.3%
0.00055 1
 
1.8%
0.00052 1
 
1.8%

Pemakaian Bhn. Bakar Spesifik (SFC)
Real number (ℝ)

High correlation 

Distinct30
Distinct (%)52.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0046964912
Minimum0.0024
Maximum0.0076
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size584.0 B
2025-07-26T14:22:17.376565image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.0024
5-th percentile0.00288
Q10.0041
median0.0046
Q30.0053
95-th percentile0.00644
Maximum0.0076
Range0.0052
Interquartile range (IQR)0.0012

Descriptive statistics

Standard deviation0.0011431418
Coefficient of variation (CV)0.24340337
Kurtosis0.15042819
Mean0.0046964912
Median Absolute Deviation (MAD)0.0006
Skewness0.41566768
Sum0.2677
Variance1.3067732 × 10-6
MonotonicityNot monotonic
2025-07-26T14:22:17.431186image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0.0041 6
 
10.5%
0.005 4
 
7.0%
0.0048 4
 
7.0%
0.0046 4
 
7.0%
0.0043 4
 
7.0%
0.0061 3
 
5.3%
0.0042 2
 
3.5%
0.0076 2
 
3.5%
0.0064 2
 
3.5%
0.0035 2
 
3.5%
Other values (20) 24
42.1%
ValueCountFrequency (%)
0.0024 1
1.8%
0.0027 1
1.8%
0.0028 1
1.8%
0.0029 1
1.8%
0.0032 1
1.8%
0.0033 2
3.5%
0.0035 2
3.5%
0.0036 1
1.8%
0.0037 1
1.8%
0.0038 2
3.5%
ValueCountFrequency (%)
0.0076 2
3.5%
0.0066 1
 
1.8%
0.0064 2
3.5%
0.0062 2
3.5%
0.0061 3
5.3%
0.0057 1
 
1.8%
0.0056 1
 
1.8%
0.0055 1
 
1.8%
0.0054 1
 
1.8%
0.0053 1
 
1.8%

Energi Panas Pembakaran/Jam (Kj/kg)
Real number (ℝ)

High correlation 

Distinct38
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean286801.9
Minimum177409.5
Maximum369603
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size584.0 B
2025-07-26T14:22:17.485336image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum177409.5
5-th percentile240347.56
Q1272450
median289346
Q3296386
95-th percentile344962.84
Maximum369603
Range192193.5
Interquartile range (IQR)23936

Descriptive statistics

Standard deviation31224.646
Coefficient of variation (CV)0.10887182
Kurtosis3.0432351
Mean286801.9
Median Absolute Deviation (MAD)14080
Skewness-0.56890624
Sum16347708
Variance9.7497849 × 108
MonotonicityNot monotonic
2025-07-26T14:22:17.622607image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
295682 5
 
8.8%
292866 4
 
7.0%
289346 3
 
5.3%
285826 3
 
5.3%
285122 2
 
3.5%
265410 2
 
3.5%
299202 2
 
3.5%
295682.4 2
 
3.5%
261186 2
 
3.5%
261890 2
 
3.5%
Other values (28) 30
52.6%
ValueCountFrequency (%)
177409.5 1
1.8%
206978 1
1.8%
221761.8 1
1.8%
244994 1
1.8%
259074 1
1.8%
261186 2
3.5%
261890 2
3.5%
264002 1
1.8%
265410 2
3.5%
271042.2 1
1.8%
ValueCountFrequency (%)
369603 1
1.8%
348483 1
1.8%
344963 1
1.8%
344962.8 1
1.8%
320323 1
1.8%
317507 1
1.8%
316803 2
3.5%
313283 1
1.8%
309763 1
1.8%
303426 1
1.8%

Effisiensi Panas (%)
Real number (ℝ)

High correlation 

Distinct30
Distinct (%)52.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.627193
Minimum3.25
Maximum4.16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size584.0 B
2025-07-26T14:22:17.679514image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum3.25
5-th percentile3.416
Q13.54
median3.62
Q33.69
95-th percentile3.802
Maximum4.16
Range0.91
Interquartile range (IQR)0.15

Descriptive statistics

Standard deviation0.14358614
Coefficient of variation (CV)0.039586022
Kurtosis3.0107574
Mean3.627193
Median Absolute Deviation (MAD)0.08
Skewness0.73390375
Sum206.75
Variance0.02061698
MonotonicityNot monotonic
2025-07-26T14:22:17.734038image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
3.69 4
 
7.0%
3.48 4
 
7.0%
3.75 4
 
7.0%
3.64 3
 
5.3%
3.67 3
 
5.3%
3.58 3
 
5.3%
3.54 3
 
5.3%
3.53 3
 
5.3%
3.61 2
 
3.5%
3.4 2
 
3.5%
Other values (20) 26
45.6%
ValueCountFrequency (%)
3.25 1
 
1.8%
3.4 2
3.5%
3.42 1
 
1.8%
3.46 1
 
1.8%
3.48 4
7.0%
3.49 1
 
1.8%
3.53 3
5.3%
3.54 3
5.3%
3.55 1
 
1.8%
3.57 2
3.5%
ValueCountFrequency (%)
4.16 1
 
1.8%
3.98 1
 
1.8%
3.81 1
 
1.8%
3.8 2
3.5%
3.78 1
 
1.8%
3.76 1
 
1.8%
3.75 4
7.0%
3.74 2
3.5%
3.7 1
 
1.8%
3.69 4
7.0%

Effisiensi Volumetrik (%)
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)21.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.245614
Minimum22
Maximum39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size584.0 B
2025-07-26T14:22:17.779427image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile29.8
Q131
median33
Q335
95-th percentile37.2
Maximum39
Range17
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.8553252
Coefficient of variation (CV)0.085885772
Kurtosis3.0078526
Mean33.245614
Median Absolute Deviation (MAD)2
Skewness-0.85799105
Sum1895
Variance8.1528822
MonotonicityNot monotonic
2025-07-26T14:22:17.827444image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
34 9
15.8%
33 9
15.8%
31 7
12.3%
35 6
10.5%
32 6
10.5%
36 5
8.8%
30 5
8.8%
37 4
7.0%
29 2
 
3.5%
38 2
 
3.5%
Other values (2) 2
 
3.5%
ValueCountFrequency (%)
22 1
 
1.8%
29 2
 
3.5%
30 5
8.8%
31 7
12.3%
32 6
10.5%
33 9
15.8%
34 9
15.8%
35 6
10.5%
36 5
8.8%
37 4
7.0%
ValueCountFrequency (%)
39 1
 
1.8%
38 2
 
3.5%
37 4
7.0%
36 5
8.8%
35 6
10.5%
34 9
15.8%
33 9
15.8%
32 6
10.5%
31 7
12.3%
30 5
8.8%

Interactions

2025-07-26T14:22:16.286882image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:12.717100image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:13.224034image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:13.756396image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:14.163059image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:14.603609image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:15.041458image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:15.426152image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:15.909887image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:16.330916image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:12.799296image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:13.267285image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:13.801629image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:14.205772image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:14.649323image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:15.081507image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:15.471973image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:15.949143image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:16.377174image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:12.875827image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:13.311080image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:13.859691image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:14.247602image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:14.695532image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:15.124764image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:15.520314image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:15.989491image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:16.420171image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:12.950335image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:13.352946image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:13.904638image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:14.287759image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:14.743150image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:15.164191image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:15.564573image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:16.028459image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:16.464779image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:13.019382image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:13.395686image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:13.949697image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:14.341588image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:14.798910image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:15.209858image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:15.611287image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:16.066794image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:16.522243image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:13.061759image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:13.439553image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:13.996763image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:14.416659image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:14.851754image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:15.257191image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:15.738490image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:16.108767image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:16.563704image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:13.101701image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:13.514657image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:14.039191image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:14.459401image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:14.900622image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:15.303357image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:15.779574image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:16.144047image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:16.609293image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:13.145508image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:13.574718image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:14.085041image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:14.523236image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:14.955366image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:15.349597image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:15.825207image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:16.187310image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:16.647636image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:13.183813image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:13.713156image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:14.123152image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:14.561968image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:14.997597image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:15.387217image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:15.867269image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-26T14:22:16.231614image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2025-07-26T14:22:17.869185image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Effisiensi Panas (%)Effisiensi Volumetrik (%)Energi Panas Pembakaran/Jam (Kj/kg)Manometer H (m)Massa Bahan Bakar (gram)Massa Bhn.Bakar/det (Kg/det)Pemakaian Bhn. Bakar Spesifik (SFC)Putaran (rpm)Volume Bahan Bakar (cc)
Effisiensi Panas (%)1.0000.415-0.5400.062-0.466-0.9850.097-0.257-0.523
Effisiensi Volumetrik (%)0.4151.0000.2760.7420.245-0.3720.0260.4630.287
Energi Panas Pembakaran/Jam (Kj/kg)-0.5400.2761.0000.7000.8660.577-0.3800.9240.940
Manometer H (m)0.0620.7420.7001.0000.676-0.018-0.3040.8760.720
Massa Bahan Bakar (gram)-0.4660.2450.8660.6761.0000.478-0.6020.8360.858
Massa Bhn.Bakar/det (Kg/det)-0.985-0.3720.577-0.0180.4781.000-0.1250.2900.554
Pemakaian Bhn. Bakar Spesifik (SFC)0.0970.026-0.380-0.304-0.602-0.1251.000-0.367-0.372
Putaran (rpm)-0.2570.4630.9240.8760.8360.290-0.3671.0000.909
Volume Bahan Bakar (cc)-0.5230.2870.9400.7200.8580.554-0.3720.9091.000

Missing values

2025-07-26T14:22:16.704702image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-26T14:22:16.772894image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Putaran (rpm)Volume Bahan Bakar (cc)Massa Bahan Bakar (gram)Manometer H (m)Massa Bhn.Bakar/det (Kg/det)Pemakaian Bhn. Bakar Spesifik (SFC)Energi Panas Pembakaran/Jam (Kj/kg)Effisiensi Panas (%)Effisiensi Volumetrik (%)
02786128.895.60.000610.0050293570.03.6035
12571107.824.60.000590.0052259074.03.7534
22929129.216.70.000590.0048292866.03.7436
32607118.043.80.000600.0045265410.03.6730
42893139.645.80.000640.0046317507.03.4234
52964129.436.60.000600.0033299906.03.6935
62857129.325.80.000610.0043296386.03.6234
72571118.253.90.000600.0061261186.03.6632
83000139.436.10.000610.0056309763.03.5834
92714129.435.00.000620.0038289346.03.5433
Putaran (rpm)Volume Bahan Bakar (cc)Massa Bahan Bakar (gram)Manometer H (m)Massa Bhn.Bakar/det (Kg/det)Pemakaian Bhn. Bakar Spesifik (SFC)Energi Panas Pembakaran/Jam (Kj/kg)Effisiensi Panas (%)Effisiensi Volumetrik (%)
472964129.117.00.000580.0046289346.03.8137
48221486.863.40.000550.0057206978.03.9835
493071139.966.50.000610.0028316803.03.5833
5035001410.5010.00.000590.0024344962.83.7539
5132501511.258.00.000670.0038369603.03.2537
523000129.006.50.000580.0076295682.43.7536
532750129.002.50.000640.0027295682.43.4022
542500118.253.00.000640.0076271042.23.4030
55225099.002.70.000580.0029221761.83.7531
56200075.253.00.000520.0066177409.54.1637

Duplicate rows

Most frequently occurring

Putaran (rpm)Volume Bahan Bakar (cc)Massa Bahan Bakar (gram)Manometer H (m)Massa Bhn.Bakar/det (Kg/det)Pemakaian Bhn. Bakar Spesifik (SFC)Energi Panas Pembakaran/Jam (Kj/kg)Effisiensi Panas (%)Effisiensi Volumetrik (%)# duplicates
02714119.005.10.000590.0041275266.03.69332
12786129.004.60.000630.0064295682.03.48322
22929129.216.70.000590.0048292866.03.74362

Report generated by YData.

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