Phitter is a phython library for accurately fitting statistical distributions to datasets, offering intuitive usage, comprehensive visualization, and support for multiple distributions to enhance data analysis projects.
Phitter analyzes datasets and determines the best analytical probability distributions that represent them. Phitter studies over 80 probability distributions, both continuous and discrete, 3 goodness-of-fit tests, and interactive visualizations. For each selected probability distribution, a standard modeling guide is provided along with spreadsheets that detail the methodology for using the chosen distribution in data science, operations research, and artificial intelligence.
This repository contains the implementation of the python library and the kernel of Phitter Web
Estimation time parameters continuous distributions
Sample Size / Workers
1
2
4
1K
0.1688
2.6402
2.8719
10K
0.4462
2.4452
3.0471
100K
4.5598
6.3246
7.5869
500K
19.0172
21.8047
19.8420
1M
39.8065
29.8360
30.2334
Estimation time parameters continuous distributions
Distribution / Sample Size
1K
10K
100K
500K
1M
10M
alpha
0.3345
0.4625
2.5933
18.3856
39.6533
362.2951
arcsine
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
argus
0.0559
0.2050
2.2472
13.3928
41.5198
362.2472
beta
0.1880
0.1790
0.1940
0.2110
0.1800
0.3134
beta_prime
0.1766
0.7506
7.6039
40.4264
85.0677
812.1323
beta_prime_4p
0.0720
0.3630
3.9478
20.2703
40.2709
413.5239
bradford
0.0110
0.0000
0.0000
0.0000
0.0000
0.0010
burr
0.0733
0.6931
5.5425
36.7684
79.8269
668.2016
burr_4p
0.1552
0.7981
8.4716
44.4549
87.7292
858.0035
cauchy
0.0090
0.0160
0.1581
1.1052
2.1090
21.5244
chi_square
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
chi_square_3p
0.0510
0.3545
3.0933
14.4116
21.7277
174.8392
dagum
0.3381
0.8278
9.6907
45.5855
98.6691
917.6713
dagum_4p
0.3646
1.3307
13.3437
70.9462
140.9371
1396.3368
erlang
0.0010
0.0000
0.0000
0.0000
0.0000
0.0000
erlang_3p
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
error_function
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
exponential
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
exponential_2p
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
f
0.0592
0.2948
2.6920
18.9458
29.9547
402.2248
fatigue_life
0.0352
0.1101
1.7085
9.0090
20.4702
186.9631
folded_normal
0.0020
0.0020
0.0020
0.0022
0.0033
0.0040
frechet
0.1313
0.4359
5.7031
39.4202
43.2469
671.3343
f_4p
0.3269
0.7517
0.6183
0.6037
0.5809
0.2073
gamma
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
gamma_3p
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
generalized_extreme_value
0.0833
0.2054
2.0337
10.3301
22.1340
243.3120
generalized_gamma
0.0298
0.0178
0.0227
0.0236
0.0170
0.0241
generalized_gamma_4p
0.0371
0.0116
0.0732
0.0725
0.0707
0.0730
generalized_logistic
0.1040
0.1073
0.1037
0.0819
0.0989
0.0836
generalized_normal
0.0154
0.0736
0.7367
2.4831
5.9752
55.2417
generalized_pareto
0.3189
0.8978
8.9370
51.3813
101.6832
1015.2933
gibrat
0.0328
0.0432
0.4287
2.7159
5.5721
54.1702
gumbel_left
0.0000
0.0000
0.0000
0.0000
0.0010
0.0010
gumbel_right
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
half_normal
0.0010
0.0000
0.0000
0.0010
0.0000
0.0000
hyperbolic_secant
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
inverse_gamma
0.0308
0.0632
0.7233
5.0127
10.7885
99.1316
inverse_gamma_3p
0.0787
0.1472
1.6513
11.1161
23.4587
227.6125
inverse_gaussian
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
inverse_gaussian_3p
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
johnson_sb
0.2966
0.7466
4.0707
40.2028
56.2130
728.2447
johnson_su
0.0070
0.0010
0.0010
0.0143
0.0010
0.0010
kumaraswamy
0.0164
0.0120
0.0130
0.0123
0.0125
0.0150
laplace
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
levy
0.0100
0.0314
0.2296
1.1365
2.7211
26.4966
loggamma
0.0085
0.0050
0.0050
0.0070
0.0062
0.0080
logistic
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
loglogistic
0.1402
0.3464
3.9673
12.0310
42.0038
471.0324
loglogistic_3p
0.2558
0.9152
11.1546
56.5524
114.5535
1118.6104
lognormal
0.0000
0.0000
0.0000
0.0000
0.0010
0.0000
maxwell
0.0000
0.0000
0.0000
0.0000
0.0000
0.0010
moyal
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
nakagami
0.0000
0.0030
0.0213
0.1215
0.2649
2.2457
non_central_chi_square
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
non_central_f
0.0190
0.0182
0.0210
0.0192
0.0190
0.0200
non_central_t_student
0.0874
0.0822
0.0862
0.1314
0.2516
0.1781
normal
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
pareto_first_kind
0.0010
0.0030
0.0390
0.2494
0.5226
5.5246
pareto_second_kind
0.0643
0.1522
1.1722
10.9871
23.6534
201.1626
pert
0.0052
0.0030
0.0030
0.0040
0.0040
0.0092
power_function
0.0075
0.0040
0.0040
0.0030
0.0040
0.0040
rayleigh
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
reciprocal
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
rice
0.0182
0.0030
0.0040
0.0060
0.0030
0.0050
semicircular
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
trapezoidal
0.0083
0.0072
0.0073
0.0060
0.0070
0.0060
triangular
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
t_student
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
t_student_3p
0.3892
1.1860
11.2759
71.1156
143.1939
1409.8578
uniform
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
weibull
0.0010
0.0000
0.0000
0.0000
0.0010
0.0010
weibull_3p
0.0061
0.0040
0.0030
0.0040
0.0050
0.0050
Estimation time parameters discrete distributions
Distribution / Sample Size
1K
10K
100K
500K
1M
10M
bernoulli
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
binomial
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
geometric
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
hypergeometric
0.0773
0.0061
0.0030
0.0020
0.0030
0.0051
logarithmic
0.0210
0.0035
0.0171
0.0050
0.0030
0.0756
negative_binomial
0.0293
0.0000
0.0000
0.0000
0.0000
0.0000
poisson
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
uniform
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
Contribution
If you would like to contribute to the Phitter project, please create a pull request with your proposed changes or enhancements. All contributions are welcome!