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# scipy.stats._result_classes.FitResult.plot

FitResult.plot(ax=None, *, plot_type='hist')

  Visually compare the data against the fitted distribution.

  Available only if `matplotlib` is installed.

  Parameters:
    ax `matplotlib.axes.Axes`

      Axes object to draw the plot onto, otherwise uses the current Axes.

    plot_type{“hist”, “qq”, “pp”, “cdf”}

      Type of plot to draw. Options include:

      - “hist”: Superposes the PDF/PMF of the fitted distribution over a normalized histogram of the data.

      - “qq”: Scatter plot of theoretical quantiles against the empirical quantiles. Specifically, the x-coordinates are the values of the fitted distribution PPF evaluated at the percentiles `(np.arange(1, n) - 0.5)/n`, where `n` is the number of data points, and the y-coordinates are the sorted data points.

      - “pp”: Scatter plot of theoretical percentiles against the observed percentiles. Specifically, the x-coordinates are the percentiles `(np.arange(1, n) - 0.5)/n`, where `n` is the number of data points, and the y-coordinates are the values of the fitted distribution CDF evaluated at the sorted data points.

      - “cdf”: Superposes the CDF of the fitted distribution over the empirical CDF. Specifically, the x-coordinates of the empirical CDF are the sorted data points, and the y-coordinates are the percentiles `(np.arange(1, n) - 0.5)/n`, where `n` is the number of data points.

  Returns:
    ax `matplotlib.axes.Axes`

      The matplotlib Axes object on which the plot was drawn.

  Examples

      >>> import numpy as np
      >>> from scipy import stats
      >>> import matplotlib.pyplot as plt  # matplotlib must be installed
      >>> rng = np.random.default_rng()
      >>> data = stats.nbinom(5, 0.5).rvs(size=1000, random_state=rng)
      >>> bounds = [(0, 30), (0, 1)]
      >>> res = stats.fit(stats.nbinom, data, bounds)
      >>> ax = res.plot()  # save matplotlib Axes object

  The `matplotlib.axes.Axes` object can be used to customize the plot. See `matplotlib.axes.Axes` documentation for details.

      >>> ax.set_xlabel('number of trials')  # customize axis label
      >>> ax.get_children()[0].set_linewidth(5)  # customize line widths
      >>> ax.legend()
      >>> plt.show()