如何选择哪个多索引轴将groupby对象中的数据拆分到不同的子图中?

问题描述

我正在使用一个pandas.groupby对象，该对象已对其应用了这样的功能:

I'm working with a pandas.groupby object to which I have applied a function as such:

x = data.groupby(['congruent', 'contrast']).apply(lambda s: s.mean())[['cresp1', 'cresp2']]

print x的输出:

                      cresp1    cresp2
congruent contrast                    
False     1.0       0.423077  0.442308
          2.0       0.537037  0.481481
          2.5       0.576923  0.634615
          3.0       0.568182  0.500000
          3.5       0.675000  0.750000
          4.0       0.687500  0.604167
          5.0       0.687500  0.875000
          10.0      0.869565  0.913043
True      1.0       0.568182  0.386364
          2.0       0.547619  0.500000
          2.5       0.522727  0.477273
          3.0       0.557692  0.634615
          3.5       0.571429  0.928571
          4.0       0.770833  0.937500
          5.0       0.791667  0.937500
          10.0      0.820000  0.920000

我想将这些数据绘制成两个不同的子图，一个用于congruent == False的所有值，另一个用于congruent == True的所有值.

I would like to plot these data into two distinct subplots, one for all values where congruent == False and the other for all values where congruent == True.

我尝试做x.plot(subplots=True)，但这会为每个列创建一个子图(即cresp1与cresp2)，这是我想要的不是:

I tried doing x.plot(subplots=True), but this creates a subplot for each column (i.e. cresp1 vs cresp2), which is not what I want:

我该怎么做?

推荐答案

您可以自己绘制:

import pylab as pl
import io
import pandas as pd

txt = """congruent contrast  cresp1    cresp2
False     1.0       0.423077  0.442308
          2.0       0.537037  0.481481
          2.5       0.576923  0.634615
          3.0       0.568182  0.500000
          3.5       0.675000  0.750000
          4.0       0.687500  0.604167
          5.0       0.687500  0.875000
          10.0      0.869565  0.913043
True      1.0       0.568182  0.386364
          2.0       0.547619  0.500000
          2.5       0.522727  0.477273
          3.0       0.557692  0.634615
          3.5       0.571429  0.928571
          4.0       0.770833  0.937500
          5.0       0.791667  0.937500
          10.0      0.820000  0.920000"""

df = pd.read_csv(io.BytesIO(txt), delim_whitespace=True).ffill()
df = df.set_index(["congruent","contrast"])
levels = df.index.levels[0]
fig, axes = pl.subplots(len(levels))

for level, ax in zip(levels, axes):
    df.loc[level].plot(ax=ax, title=str(level))

输出:

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