I don't want to install unnecessary packages on my Windows setup so I have tested this on Posit Cloud but it should be very similar for you:

```
library(reticulate)
library(fitdistrplus)
#> Loading required package: MASS
#> Loading required package: survival
data(groundbeef)
serving <- groundbeef$serving
# You only need to do this once
#conda_create("my_env")
#conda_install("my_env", "distfit")
use_condaenv("my_env")
np <- import("numpy")
distfit <- import("distfit")
X <- np_array(serving, dtype='int64')
dfit <- distfit$distfit(method='parametric', todf=TRUE)
dfit$fit_transform(X)
#> $model
#> $model$name
#> [1] "gamma"
#>
#> $model$score
#> [1] 0.0003573497
#>
#> $model$loc
#> [1] -2.20644
#>
#> $model$scale
#> [1] 17.63285
#>
#> $model$arg
#> $model$arg[[1]]
#> [1] 4.301304
#>
#>
#> $model$params
#> $model$params[[1]]
#> [1] 4.301304
#>
#> $model$params[[2]]
#> [1] -2.20644
#>
#> $model$params[[3]]
#> [1] 17.63285
#>
#>
#> $model$model
#> <scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x7f726d698eb0>
#>
#> $model$bootstrap_score
#> [1] 0
#>
#> $model$bootstrap_pass
#> NULL
#>
#> $model$color
#> [1] "#e41a1c"
#>
#> $model$CII_min_alpha
#> [1] 25.00746
#>
#> $model$CII_max_alpha
#> [1] 142.0396
#>
#>
#> $summary
#> name score loc scale arg
#> 1 gamma 0.0003573497 -2.20644 17.63285 4.301304
#> 2 beta 0.0003606702 5.400327 336.2081 2.670049, 10.487716
#> 3 genextreme 0.0003626564 57.57065 29.49865 0.03931055
#> 4 dweibull 0.0003783728 67.92109 32.61583 1.487238
#> 5 lognorm 0.000392554 9.963838 52.78125 0.8290418
#> 6 t 0.0004097055 72.87649 35.60593 37.17807
#> 7 norm 0.0004128576 73.6378 35.82408 NULL
#> 8 loggamma 0.0004144338 -8538.13 1221.852 1151.153
#> 9 pareto 0.00055225 -34359738358 34359738368 539926600
#> 10 expon 0.0005522501 10 63.6378 NULL
#> 11 uniform 0.0005753646 10 190 NULL
#> params model
#> 1 4.301304, -2.206440, 17.632847 <environment: 0x55d9afd77a00>
#> 2 2.670049, 10.487716, 5.400327, 336.208112 <environment: 0x55d9afd7ad78>
#> 3 0.03931055, 57.57065027, 29.49864839 <environment: 0x55d9afd7a2c0>
#> 4 1.487238, 67.921091, 32.615829 <environment: 0x55d9afd7d638>
#> 5 0.8290418, 9.9638375, 52.7812468 <environment: 0x55d9afd7cb80>
#> 6 37.17807, 72.87649, 35.60593 <environment: 0x55d9afd7c0c8>
#> 7 73.63780, 35.82408 <environment: 0x55d9afd7f440>
#> 8 1151.153, -8538.130, 1221.852 <environment: 0x55d9afd7e988>
#> 9 539926600, -34359738358, 34359738368 <environment: 0x55d9afd7ded0>
#> 10 10.0000, 63.6378 <environment: 0x55d9afd81248>
#> 11 10, 190 <environment: 0x55d9afd80790>
#> bootstrap_score bootstrap_pass color
#> 1 0 NULL #e41a1c
#> 2 0 NULL #e41a1c
#> 3 0 NULL #377eb8
#> 4 0 NULL #4daf4a
#> 5 0 NULL #984ea3
#> 6 0 NULL #ff7f00
#> 7 0 NULL #ffff33
#> 8 0 NULL #a65628
#> 9 0 NULL #f781bf
#> 10 0 NULL #999999
#> 11 0 NULL #999999
#>
#> $histdata
#> $histdata[[1]]
#> [1] 0.0035018649 0.0045793618 0.0215499378 0.0021549938 0.0175093245
#> [6] 0.0002693742 0.0086199751 0.0013468711 0.0070037298 0.0008081227
#> [11] 0.0002693742 0.0000000000 0.0008081227
#>
#> $histdata[[2]]
#> [1] 17.30769 31.92308 46.53846 61.15385 75.76923 90.38462 105.00000
#> [8] 119.61538 134.23077 148.84615 163.46154 178.07692 192.69231
#>
#>
#> $size
#> [1] 254
#>
#> $alpha
#> [1] 0.05
#>
#> $stats
#> [1] "RSS"
#>
#> $bins
#> [1] "auto"
#>
#> $bound
#> [1] "both"
#>
#> $name
#> [1] "popular"
#>
#> $method
#> [1] "parametric"
#>
#> $multtest
#> [1] "fdr_bh"
#>
#> $n_perm
#> [1] 10000
#>
#> $smooth
#> NULL
#>
#> $weighted
#> [1] TRUE
#>
#> $f
#> [1] 1.5
#>
#> $n_boots
#> NULL
#>
#> $random_state
#> NULL
```

^{Created on 2023-02-12 with reprex v2.0.2}