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

Love The Way You Lie 2022-11-02 00:55 100阅读 0赞

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
    
    @_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 {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={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