成功解决AttributeError: ‘PathCollection‘ object has no property ‘n_levels‘

成功解决AttributeError: 'PathCollection' object has no property 'n_levels'

目录

解决问题

解决思路

解决方法

解决问题

AttributeError: 'PathCollection' object has no property 'n_levels'

解决思路

属性错误:“PathCollection”对象没有属性“n_levels”

解决方法

def scatter Found at: matplotlib.pyplot中并没有n_levels参数！很可能是代码写的有误，这个参数存在在中，如果必须使用n_levels参数，那么应该加到sns.kdeplot函数中，即可！

 
def kdeplot Found at: seaborn.distributions
 
-meta">@_deprecate_positional_args
def kdeplot(
    x=  Allow positional x, because behavior will not change with reorg
    None, 
    *, y=None, 
    shade=  Note "soft" deprecation, explained below
    None, vertical=  Deprecated
    False, kernel=  Deprecated
    None, bw=  Deprecated
    None, gridsize=  TODO maybe depend on uni/bivariate?
    200, cut=3, clip=None, legend=True, cumulative=False, 
    shade_lowest=  Deprecated, controlled with levels now
    None, cbar=False, cbar_ax=None, cbar_kws=None, 
    ax=
     New params
    None, weights=  TODO note that weights is grouped with 
     semantics
    None, hue=None, palette=None, hue_order=None, 
     hue_norm=None, 
    multiple="layer", common_norm=True, common_grid=False, 
    levels=10, thresh=.05, 
    bw_method="scott", bw_adjust=1, log_scale=None, 
    color=None, fill=
     Renamed params
    None, data=None, data2=None, **
    kwargs):
     Handle deprecation of `data2` as name for y variable
    if data2 is not None:
        y = data2
         If `data2` is present, we need to check for the `data` kwarg being
         used to pass a vector for `x`. We'll reassign the vectors and 
         warn.
         We need this check because just passing a vector to `data` is 
         now
         technically valid.
        x_passed_as_data = x is None and data is not None and np.ndim
         (data) == 1
        if x_passed_as_data:
            msg = "Use `x` and `y` rather than `data` `and `data2`"
            x = data
        else:
            msg = "The `data2` param is now named `y`; please update your 
             code"
        warnings.warn(msg, FutureWarning)
     Handle deprecation of `vertical`
    if vertical:
        msg = "The `vertical` parameter is deprecated and will be 
         removed in a "\
        "future version. Assign the data to the `y` variable instead."
        warnings.warn(msg, FutureWarning)
        x, y = y, x
     Handle deprecation of `bw`
    if bw is not None:
        msg = "The `bw` parameter is deprecated in favor of 
         `bw_method` and "\
        f"`bw_adjust`. Using -subst">{bw} for `bw_method`, but please "\
        "see the docs for the new parameters and update your code."
        warnings.warn(msg, FutureWarning)
        bw_method = bw
     Handle deprecation of `kernel`
    if kernel is not None:
        msg = "Support for alternate kernels has been removed. "\
        "Using Gaussian kernel."
        warnings.warn(msg, UserWarning)
     Handle deprecation of shade_lowest
    if shade_lowest is not None:
        if shade_lowest:
            thresh = 0
        msg = "`shade_lowest` is now deprecated in favor of `thresh`. "\
        f"Setting `thresh=-subst">{thresh}`, but please update your code."
        warnings.warn(msg, UserWarning)
     Handle `n_levels`
     This was never in the formal API but it was processed, and 
     appeared in an
     example. We can treat as an alias for `levels` now and deprecate 
     later.
    levels = kwargs.pop("n_levels", levels)
     Handle "soft" deprecation of shade `shade` is not really the right
     terminology here, but unlike some of the other deprecated 
     parameters it
     is probably very commonly used and much hard to remove. This 
     is therefore
     going to be a longer process where, first, `fill` will be introduced 
     and
     be used throughout the documentation. In 0.12, when kwarg-only
     enforcement hits, we can remove the shade/shade_lowest out of 
     the
     function signature all together and pull them out of the kwargs. 
     Then we
     can actually fire a FutureWarning, and eventually remove.
    if shade is not None:
        fill = shade
     - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
    p = _DistributionPlotter(
        data=data, 
        variables=_DistributionPlotter.get_semantics(locals()))
    p.map_hue(palette=palette, order=hue_order, norm=hue_norm)
    if ax is None:
        ax = plt.gca()
     Check for a specification that lacks x/y data and return early
    if not p.has_xy_data:
        return ax
     Pack the kwargs for statistics.KDE
    estimate_kws = dict(bw_method=bw_method, 
        bw_adjust=bw_adjust, 
        gridsize=gridsize, 
        cut=cut, 
        clip=clip, 
        cumulative=cumulative)
    p._attach(ax, allowed_types=["numeric", "datetime"], 
     log_scale=log_scale)
    if p.univariate:
        plot_kws = kwargs.copy()
        if color is not None:
            plot_kws["color"] = color
        p.plot_univariate_density(multiple=multiple, 
         common_norm=common_norm, common_grid=common_grid, 
         fill=fill, legend=legend, estimate_kws=estimate_kws, **plot_kws)
    else:
        p.plot_bivariate_density(common_norm=common_norm, fill=fill, 
         levels=levels, thresh=thresh, legend=legend, color=color, cbar=cbar, 
         cbar_ax=cbar_ax, cbar_kws=cbar_kws, estimate_kws=estimate_kws, 
         **kwargs)
    return ax