如何将权重传递给Seaborn FacetGrid [英] How to pass weights to a Seaborn FacetGrid
问题描述
我有一组数据，我正在尝试使用Seaborn中的FacetGrid进行绘制.每个数据点都有一个与之关联的权重，我想在网格的每个面上绘制一个加权的直方图.
I have a set of data that I'm trying to plot using a FacetGrid in seaborn. Each data point has a weight associated with it, and I want to plot a weighted histogram in each of the facets of the grid.
例如，假设我有以下(随机创建的)数据集:
For example, say I had the following (randomly created) data set:
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'))
此数据的结构如下:
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，如果我没有重量，我会像这样绘制它:
Normally, if I didn't have weights, I would plot it like this:
fg = sns.FacetGrid(d, col='cat', col_wrap=3)
fg.map(plt.hist, 'val')
这将构成一个直方图网格，其中每个直方图显示类别cat的一个值的变量val的分布.
This makes a grid of histograms where each histogram shows the distribution of the variable val for one value of the category cat.
我想做的是加权每个直方图.如果我要使用Matplotlib制作单个直方图，则可以执行以下操作:
What I would like to do is to weight each of the histograms. If I were making a single histogram with Matplotlib, I would do this:
plt.hist(d.val, weights=d.weight)
我尝试将weights参数传递给FacetGrid.map，但是由于seaborn在内部对数据进行切片以形成网格的方式而导致了错误:
I tried passing the weights argument to FacetGrid.map, but it raises an error due to the way seaborn slices the data internally to make the grid:
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，有什么办法制作这样的情节吗?
So, is there any way to make a plot like this?
推荐答案
您需要在plt.hist周围编写一个小的包装函数，该函数接受权重向量作为位置参数.像
You'll need to write a little wrapper function around plt.hist that accepts a vector of weights as a positional argument. Something like
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")
这篇关于如何将权重传递给Seaborn FacetGrid的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
