成功解决TypeError: distplot() got an unexpected keyword argument ‘y‘

╰半橙微兮° 2023-01-11 03:50 109阅读 0赞

成功解决TypeError: distplot() got an unexpected keyword argument 'y'

**目录**

解决问题

解决思路

解决方法

--------------------

## 解决问题 ##

TypeError: distplot() got an unexpected keyword argument 'y'

## 解决思路 ##

类型错误:distplot()得到了一个意外的关键字参数'y'

## 解决方法 ##

fg=sns.JointGrid(x=cols[0],y=cols[1],data=data_frame,)
    fg.plot_marginals(sns.distplot)

distplot()函数中，只接受一个输入数据，即有x，没有y

def distplot Found at: seaborn.distributions
    
    def distplot(a=None, bins=None, hist=True, kde=True, rug=False, fit=None, 
        hist_kws=None, kde_kws=None, rug_kws=None, fit_kws=None, 
        color=None, vertical=False, norm_hist=False, axlabel=None, 
        label=None, ax=None, x=None):
        """DEPRECATED: Flexibly plot a univariate distribution of observations.
        
        .. warning::
        This function is deprecated and will be removed in a future version.
        Please adapt your code to use one of two new functions:
        
        - :func:`displot`, a figure-level function with a similar flexibility
        over the kind of plot to draw
        - :func:`histplot`, an axes-level function for plotting histograms,
        including with kernel density smoothing
        
        This function combines the matplotlib ``hist`` function (with automatic
        calculation of a good default bin size) with the seaborn :func:`kdeplot`
        and :func:`rugplot` functions. It can also fit ``scipy.stats``
        distributions and plot the estimated PDF over the data.
        
        Parameters
        ----------
        a : Series, 1d-array, or list.
        Observed data. If this is a Series object with a ``name`` attribute,
        the name will be used to label the data axis.
        bins : argument for matplotlib hist(), or None, optional
        Specification of hist bins. If unspecified, as reference rule is used
        that tries to find a useful default.
        hist : bool, optional
        Whether to plot a (normed) histogram.
        kde : bool, optional
        Whether to plot a gaussian kernel density estimate.
        rug : bool, optional
        Whether to draw a rugplot on the support axis.
        fit : random variable object, optional
        An object with `fit` method, returning a tuple that can be passed to a
        `pdf` method a positional arguments following a grid of values to
        evaluate the pdf on.
        hist_kws : dict, optional
        Keyword arguments for :meth:`matplotlib.axes.Axes.hist`.
        kde_kws : dict, optional
        Keyword arguments for :func:`kdeplot`.
        rug_kws : dict, optional
        Keyword arguments for :func:`rugplot`.
        color : matplotlib color, optional
        Color to plot everything but the fitted curve in.
        vertical : bool, optional
        If True, observed values are on y-axis.
        norm_hist : bool, optional
        If True, the histogram height shows a density rather than a count.
        This is implied if a KDE or fitted density is plotted.
        axlabel : string, False, or None, optional
        Name for the support axis label. If None, will try to get it
        from a.name if False, do not set a label.
        label : string, optional
        Legend label for the relevant component of the plot.
        ax : matplotlib axis, optional
        If provided, plot on this axis.
        
        Returns
        -------
        ax : matplotlib Axes
        Returns the Axes object with the plot for further tweaking.
        
        See Also
        --------
        kdeplot : Show a univariate or bivariate distribution with a kernel
        density estimate.
        rugplot : Draw small vertical lines to show each observation in a
        distribution.
        
        Examples
        --------
        
        Show a default plot with a kernel density estimate and histogram with bin
        size determined automatically with a reference rule:
        
        .. plot::
        :context: close-figs
        
        >>> import seaborn as sns, numpy as np
        >>> sns.set_theme(); np.random.seed(0)
        >>> x = np.random.randn(100)
        >>> ax = sns.distplot(x)
        
        Use Pandas objects to get an informative axis label:
        
        .. plot::
        :context: close-figs
        
        >>> import pandas as pd
        >>> x = pd.Series(x, name="x variable")
        >>> ax = sns.distplot(x)
        
        Plot the distribution with a kernel density estimate and rug plot:
        
        .. plot::
        :context: close-figs
        
        >>> ax = sns.distplot(x, rug=True, hist=False)
        
        Plot the distribution with a histogram and maximum likelihood gaussian
        distribution fit:
        
        .. plot::
        :context: close-figs
        
        >>> from scipy.stats import norm
        >>> ax = sns.distplot(x, fit=norm, kde=False)
        
        Plot the distribution on the vertical axis:
        
        .. plot::
        :context: close-figs
        
        >>> ax = sns.distplot(x, vertical=True)
        
        Change the color of all the plot elements:
        
        .. plot::
        :context: close-figs
        
        >>> sns.set_color_codes()
        >>> ax = sns.distplot(x, color="y")
        
        Pass specific parameters to the underlying plot functions:
        
        .. plot::
        :context: close-figs
        
        >>> ax = sns.distplot(x, rug=True, rug_kws={"color": "g"},
        ...                   kde_kws={"color": "k", "lw": 3, "label": "KDE"},
        ...                   hist_kws={"histtype": "step", "linewidth": 3,
        ...                             "alpha": 1, "color": "g"})
        
        """
        if kde and not hist:
            axes_level_suggestion = "`kdeplot` (an axes-level function for kernel 
             density plots)."
        else:
            axes_level_suggestion = "`histplot` (an axes-level function for 
             histograms)."
        msg = "`distplot` is a deprecated function and will be removed in a future 
         version. "\
        "Please adapt your code to use either `displot` (a figure-level function 
         with "\
        "similar flexibility) or " + axes_level_suggestion
        warnings.warn(msg, FutureWarning)
        if ax is None:
            ax = plt.gca()
        # Intelligently label the support axis
        label_ax = bool(axlabel)
        if axlabel is None and hasattr(a, "name"):
            axlabel = a.name
            if axlabel is not None:
                label_ax = True
        # Support new-style API
        if x is not None:
            a = x
        # Make a a 1-d float array
        a = np.asarray(a, float)
        if a.ndim > 1:
            a = a.squeeze()
        # Drop null values from array
        a = remove_na(a)
        # Decide if the hist is normed
        norm_hist = norm_hist or kde or fit is not None
        # Handle dictionary defaults
        hist_kws = {} if hist_kws is None else hist_kws.copy()
        kde_kws = {} if kde_kws is None else kde_kws.copy()
        rug_kws = {} if rug_kws is None else rug_kws.copy()
        fit_kws = {} if fit_kws is None else fit_kws.copy()
        # Get the color from the current color cycle
        if color is None:
            if vertical:
                line, = ax.plot(0, a.mean())
            else:
                line, = ax.plot(a.mean(), 0)
            color = line.get_color()
            line.remove()
        # Plug the label into the right kwarg dictionary
        if label is not None:
            if hist:
                hist_kws["label"] = label
            elif kde:
                kde_kws["label"] = label
            elif rug:
                rug_kws["label"] = label
            elif fit:
                fit_kws["label"] = label
        if hist:
            if bins is None:
                bins = min(_freedman_diaconis_bins(a), 50)
            hist_kws.setdefault("alpha", 0.4)
            hist_kws.setdefault("density", norm_hist)
            orientation = "horizontal" if vertical else "vertical"
            hist_color = hist_kws.pop("color", color)
            ax.hist(a, bins, orientation=orientation, color=hist_color, **hist_kws)
            if hist_color != color:
                hist_kws["color"] = hist_color
        if kde:
            kde_color = kde_kws.pop("color", color)
            kdeplot(a, vertical=vertical, ax=ax, color=kde_color, **kde_kws)
            if kde_color != color:
                kde_kws["color"] = kde_color
        if rug:
            rug_color = rug_kws.pop("color", color)
            axis = "y" if vertical else "x"
            rugplot(a, axis=axis, ax=ax, color=rug_color, **rug_kws)
            if rug_color != color:
                rug_kws["color"] = rug_color
        if fit is not None:
            def pdf(x):
                return fit.pdf(x, *params)
            
            fit_color = fit_kws.pop("color", "#282828")
            gridsize = fit_kws.pop("gridsize", 200)
            cut = fit_kws.pop("cut", 3)
            clip = fit_kws.pop("clip", (-np.inf, np.inf))
            bw = stats.gaussian_kde(a).scotts_factor() * a.std(ddof=1)
            x = _kde_support(a, bw, gridsize, cut, clip)
            params = fit.fit(a)
            y = pdf(x)
            if vertical:
                x, y = y, x
            ax.plot(x, y, color=fit_color, **fit_kws)
            if fit_color != "#282828":
                fit_kws["color"] = fit_color
        if label_ax:
            if vertical:
                ax.set_ylabel(axlabel)
            else:
                ax.set_xlabel(axlabel)
        return ax