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# scipy.stats.rv_continuous.fit

`rv_continuous.``fit`(data, *args, **kwds)

  Return MLEs for shape, location, and scale parameters from data.

  MLE stands for Maximum Likelihood Estimate. Starting estimates for the fit are given by input arguments; for any arguments not provided with starting estimates, `self._fitstart(data)` is called to generate such.

  One can hold some parameters fixed to specific values by passing in keyword arguments `f0`..[is this supposed to be an ellipsis?] `fn` (for shape parameters) and `floc` and `fscale` (for location and scale parameters, respectively).

  Parameters : | data : array_like Data to use in calculating the MLEs args : floats, optional Starting value(s) for any shape-characterizing arguments (those not provided will be determined by a call to `_fitstart(data)`). No default value. kwds : floats, optional Starting values for the location and scale parameters; no default. Special keyword arguments are recognized as holding certain parameters fixed: f0..fn : hold respective shape parameters fixed. floc : hold location parameter fixed to specified value. fscale : hold scale parameter fixed to specified value. optimizer : The optimizer to use. The optimizer must take func, and starting position as the first two arguments, plus args (for extra arguments to pass to the function to be optimized) and disp=0 to suppress output as keyword arguments.
  Returns : | shape, loc, scale : tuple of floats MLEs for any shape statistics, followed by those for location and scale.

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