Question

我想使用pandas和statsmodels在数据帧的子集上拟合线性模型并返回预测值。但是，我无法弄清楚使用正确的熊猫成语。这是我想要做的：

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)

错误是有道理的，我认为.transform想要将DataFrame映射到DataFrame。但是有没有办法在DataFrame上进行groupby操作，将每个块传递给一个函数，将其缩减为一个Series（具有相同的索引），然后将得到的Series组合成可插入原始数据帧的东西？

Answer 1

这里的顶部是相同的，我只是使用玩具数据集b / c我在防火墙后面。

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)

如果您将'transform'更改为'apply'，您将获得：

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

这不是你想要的，但如果你降低等级= 0，你可以按照需要继续：

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

Answer 2

编辑：

Scanner sc = new Scanner(System.in); System.out.println("Enter number: "); int number = sc.nextInt(); int five = 5; int seven = 7; boolean a = (number % five == 0) && (number % seven == 0); if (a == true) { System.out.println(number + " divides on both 5 and 7."); } else { System.out.println(number + " doesn't divide on both 5 and 7."); } if (number % five == 0) { System.out.println(number + " divides successfuly by 5."); } else { System.out.println(number + " can't be divided successfuly by 5."); } if (number % seven == 0) { System.out.println(number +" divides successfuly by 7."); } else { System.out.println(number + " can't be divided successfuly by 7."); }

我必须修改您的q.gps.apply(lambda df: df.join(q.fit_predict(df)))函数以命名fit_predict。

Series

pandas groupby可以将DataFrame转换为系列吗？

2 个答案: