使用seaborn绘制QQ图的FacetGrid

Question

我无法绘制的FacetGrid和seaborn。

我有一个m行（观察）和n列（特征）的矩阵，我想为每个特征（列）绘制一个QQ图以将其与正态分布进行比较。

到目前为止，我的代码是这样的：

import scipy.stats as ss 

def qqplots(fpath, expr, title): 

    def quantile_plot(x, **kwargs): 
     x = ss.zscore(x) 
     qntls, xr = ss.probplot(x, dist="norm") 
     plt.scatter(xr, qntls, **kwargs) 

    expr_m = pd.melt(expr) 
    expr_m.columns = ["Feature", "Value"] 
    n_feat = len(expr_m["Feature"].value_counts().index) 

    n_cols = int(np.sqrt(n_feat)) + 1 

    g = sns.FacetGrid(expr_m, col="Feature", col_wrap=n_cols) 
    g.map(quantile_plot, "Value"); 
    plt.savefig(fpath + ".pdf", bbox_inches="tight") 
    plt.savefig(fpath + ".png", bbox_inches="tight") 
    plt.close() 

qqplots("lognorm_qqplot", np.log2(expr), "Log-normal qqplot") 

的expr变量是一个数据帧的熊猫具有m行（观测）和n列（特征）。

的例外，我得到的是以下几点：

--------------------------------------------------------------------------- 
ValueError        Traceback (most recent call last) 
<ipython-input-52-f9333a55702e> in <module>() 
    39  plt.close() 
    40 
---> 41 qqplots("lognorm_qqplot", np.log2(expr), "Log-normal qqplot") 

<ipython-input-52-f9333a55702e> in qqplots(fpath, expr, title) 
    34 
    35  g = sns.FacetGrid(expr_m, col="Feature", col_wrap=n_cols) 
---> 36  g.map(quantile_plot, "Value"); 
    37  plt.savefig(fpath + ".pdf", bbox_inches="tight") 
    38  plt.savefig(fpath + ".png", bbox_inches="tight") 

/usr/local/lib/python3.5/site-packages/seaborn/axisgrid.py in map(self, func, *args, **kwargs) 
    726 
    727    # Draw the plot 
--> 728    self._facet_plot(func, ax, plot_args, kwargs) 
    729 
    730   # Finalize the annotations and layout 

/usr/local/lib/python3.5/site-packages/seaborn/axisgrid.py in _facet_plot(self, func, ax, plot_args, plot_kwargs) 
    810 
    811   # Draw the plot 
--> 812   func(*plot_args, **plot_kwargs) 
    813 
    814   # Sort out the supporting information 

<ipython-input-52-f9333a55702e> in quantile_plot(y, **kwargs) 
    25   y = ss.zscore(y) 
    26   qntls, xr = ss.probplot(y, dist="norm") 
---> 27   plt.scatter(xr, qntls, **kwargs) 
    28 
    29  expr_m = pd.melt(expr) 

/usr/local/lib/python3.5/site-packages/matplotlib/pyplot.py in scatter(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, hold, data, **kwargs) 
    3249       vmin=vmin, vmax=vmax, alpha=alpha, 
    3250       linewidths=linewidths, verts=verts, 
-> 3251       edgecolors=edgecolors, data=data, **kwargs) 
    3252  finally: 
    3253   ax.hold(washold) 

/usr/local/lib/python3.5/site-packages/matplotlib/__init__.py in inner(ax, *args, **kwargs) 
    1810      warnings.warn(msg % (label_namer, func.__name__), 
    1811         RuntimeWarning, stacklevel=2) 
-> 1812    return func(ax, *args, **kwargs) 
    1813   pre_doc = inner.__doc__ 
    1814   if pre_doc is None: 

/usr/local/lib/python3.5/site-packages/matplotlib/axes/_axes.py in scatter(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, **kwargs) 
    3838   y = np.ma.ravel(y) 
    3839   if x.size != y.size: 
-> 3840    raise ValueError("x and y must be the same size") 
    3841 
    3842   s = np.ma.ravel(s) # This doesn't have to match x, y in size. 

ValueError: x and y must be the same size 

Answer 1

我实现了这一点，并且也改变使用Seaborn调色板的颜色，用下面的代码：

def qqplots(fpath, expr, title): 

    def quantile_plot(x, **kwargs): 
     x = ss.zscore(x) 
     ss.probplot(x, plot=plt) 

    expr_m = pd.melt(expr) 
    expr_m.columns = ["Feature", "Value"] 
    n_feat = len(expr_m["Feature"].value_counts().index) 

    n_cols = int(np.sqrt(n_feat)) + 1 

    g = sns.FacetGrid(expr_m, col="Feature", col_wrap=n_cols) 
    g.map(quantile_plot, "Value"); 
    for ax in g.axes: 
     ax.get_lines()[0].set_markerfacecolor(sns.color_palette()[0]) 
     ax.get_lines()[1].set_color(sns.color_palette()[3]) 
    plt.savefig(fpath + ".pdf", bbox_inches="tight") 
    plt.savefig(fpath + ".png", bbox_inches="tight") 
    plt.close() 

qqplots("lognorm_qqplot", np.log2(expr), "Log-normal qqplot")