python - 如何将权重传递给 Seaborn FacetGrid

Question

我有一组数据，我正在尝试使用 seaborn 中的 FacetGrid 绘制这些数据。每个数据点都有一个与之相关的权重，我想在网格的每个方面绘制一个加权直方图。

例如，假设我有以下(随机创建的)数据集:

import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

d = pd.DataFrame(np.array([np.random.randint(0, 6, 5000), 
                           np.random.normal(0, 1., 5000),
                           np.random.uniform(0, 1, 5000)]).T, 
                 columns=('cat', 'val', 'weight'))

这些数据的结构如下:

   cat       val    weight
0    0 -0.844542  0.668081
1    0 -0.521177  0.521396
2    1 -1.160358  0.788465
3    0 -0.394765  0.115242
4    5  0.735328  0.003495

通常，如果我没有权重，我会这样绘制:

fg = sns.FacetGrid(d, col='cat', col_wrap=3)
fg.map(plt.hist, 'val')

这构成了一个直方图网格，其中每个直方图显示了变量 val 的分布。对于类别的一个值 cat .

我想做的是对每个直方图进行加权。如果我用 Matplotlib 制作单个直方图，我会这样做:

plt.hist(d.val, weights=d.weight)

我尝试将 weights 参数传递给 FacetGrid.map ，但由于 seaborn 在内部对数据进行切片以制作网格的方式，它会引发错误:

fg.map(plt.hist, 'val', weights=d.weight)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-33-1403d26cff86> in <module>()
      9 
     10 fg = sns.FacetGrid(d, col='cat', col_wrap=3)
---> 11 fg.map(plt.hist, 'val', weights=d.weight)

/opt/conda/lib/python3.4/site-packages/seaborn/axisgrid.py in map(self, func, *args, **kwargs)
    443 
    444             # Draw the plot
--> 445             self._facet_plot(func, ax, plot_args, kwargs)
    446 
    447         # Finalize the annotations and layout

/opt/conda/lib/python3.4/site-packages/seaborn/axisgrid.py in _facet_plot(self, func, ax, plot_args, plot_kwargs)
    527 
    528         # Draw the plot
--> 529         func(*plot_args, **plot_kwargs)
    530 
    531         # Sort out the supporting information

/opt/conda/lib/python3.4/site-packages/matplotlib/pyplot.py in hist(x, bins, range, normed, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, hold, **kwargs)
   2894                       histtype=histtype, align=align, orientation=orientation,
   2895                       rwidth=rwidth, log=log, color=color, label=label,
-> 2896                       stacked=stacked, **kwargs)
   2897         draw_if_interactive()
   2898     finally:

/opt/conda/lib/python3.4/site-packages/matplotlib/axes/_axes.py in hist(self, x, bins, range, normed, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, **kwargs)
   5647                 if len(w[i]) != len(x[i]):
   5648                     raise ValueError(
-> 5649                         'weights should have the same shape as x')
   5650         else:
   5651             w = [None]*nx

ValueError: weights should have the same shape as x

那么，有没有办法制作这样的情节呢？

Answer 1

你需要在 plt.hist 周围写一个小包装函数接受一个权重向量作为位置参数。就像是

def weighted_hist(x, weights, **kwargs):
    plt.hist(x, weights=weights, **kwargs)

g = sns.FacetGrid(df, ...)
g.map(weighted_hist, "x_var", "weight_var")
g.set_axis_labels("x_var", "count")