pandas groupby可以将DataFrame转换为Series吗? [英] Can pandas groupby transform a DataFrame into a Series?

问题描述

我想使用pandas和statsmodels在数据框的子集上拟合线性模型并返回预测值.但是，我在找出正确的熊猫习惯用法时遇到了麻烦.这是我正在尝试做的事情:

I would like to use pandas and statsmodels to fit a linear model on subsets of a dataframe and return the predicted values. However, I am having trouble figuring out the right pandas idiom to use. Here is what I am trying to do:

import pandas as pd
import statsmodels.formula.api as sm
import seaborn as sns

tips = sns.load_dataset("tips")
def fit_predict(df):
    m = sm.ols("tip ~ total_bill", df).fit()
    return pd.Series(m.predict(df), index=df.index)
tips["predicted_tip"] = tips.groupby("day").transform(fit_predict)

这会引发以下错误:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-139-b3d2575e2def> in <module>()
----> 1 tips["predicted_tip"] = tips.groupby("day").transform(fit_predict)

/Users/mwaskom/anaconda/lib/python2.7/site-packages/pandas/core/groupby.pyc in transform(self, func, *args, **kwargs)
   3033                     return self._transform_general(func, *args, **kwargs)
   3034         except:
-> 3035             return self._transform_general(func, *args, **kwargs)
   3036 
   3037         # a reduction transform

/Users/mwaskom/anaconda/lib/python2.7/site-packages/pandas/core/groupby.pyc in _transform_general(self, func, *args, **kwargs)
   2988                     group.T.values[:] = res
   2989                 else:
-> 2990                     group.values[:] = res
   2991 
   2992                 applied.append(group)

ValueError: could not broadcast input array from shape (62) into shape (62,6)

该错误是有意义的，因为我认为.transform想要将DataFrame映射到DataFrame.但是，是否有一种方法可以对DataFrame进行分组操作，将每个块传递给一个将其缩减为Series(具有相同索引)的函数，然后将生成的Series组合成可以插入到原始Dataframe中的东西?

The error makes sense in that I think .transform wants to map a DataFrame to a DataFrame. But is there a way to do a groupby operation on a DataFrame, pass each chunk into a function that reduces it to a Series (with the same index), and then combine the resulting Series into something that can be inserted into the original dataframe?

推荐答案

这里的顶部是相同的，我只是使用玩具数据集b/c，位于防火墙后面.

The top part here is the same, I'm just using a toy dataset b/c I'm behind a firewall.

tips = pd.DataFrame({ 'day':list('MMMFFF'), 'tip':range(6), 
                      'total_bill':[10,40,20,80,50,40] })

def fit_predict(df):
    m = sm.ols("tip ~ total_bill", df).fit()
    return pd.Series(m.predict(df), index=df.index)

如果将转换"更改为应用"，则会得到:

If you change 'transform' to 'apply', you'll get:

tips.groupby("day").apply(fit_predict)

day   
F    3    2.923077
     4    4.307692
     5    4.769231
M    0    0.714286
     1    1.357143
     2    0.928571

这不是您想要的，但是如果您将level降为0，则可以根据需要进行操作:

That's not quite what you want, but if you drop level=0, you can proceed as desired:

tips['predicted'] = tips.groupby("day").apply(fit_predict).reset_index(level=0,drop=True)

  day  tip  total_bill  predicted
0   M    0          10   0.714286
1   M    1          40   1.357143
2   M    2          20   0.928571
3   F    3          80   2.923077
4   F    4          50   4.307692
5   F    5          40   4.769231

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