我无法使用FacetGrid绘制QQ-plots 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")
expr变量是一个pandas DataFrame，有m行（观察）和n列（要素）。
我得到的例外情况如下：
---------------------------------------------------------------------------
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
答案 0 :(得分：3)
我实现了这一点，并且还改变了使用Seaborn调色板的颜色，使用以下代码：
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")