传递什么Pandas数据类型以转换或应用于groupby

Question

在尝试调试groupby函数应用程序时，someone suggested我使用虚函数来“查看正在传递的内容”到每个组的函数中。当然，我是游戏：

import numpy as np
import pandas as pd

np.random.seed(0) # so we can all play along at home

categories = list('abc')
categories = categories * 4
data_1 = np.random.randn(len(categories))
data_2 = np.random.randn(len(categories))

df = pd.DataFrame({'category': categories, 'data_1': data_1, 'data_2': data_2})

def f(x):
    print type(x)
    return x

print 'single column transform'
df.groupby(['category'])['data_1'].transform(f)
print '\n'

print 'single column (nested) transform'
df.groupby(['category'])[['data_1']].transform(f)
print '\n'

print 'multiple column transform'
df.groupby(['category'])[['data_1', 'data_2']].transform(f)

print '\n'
print '\n'

print 'single column apply'
df.groupby(['category'])['data_1'].apply(f)
print '\n'

print 'single column (nested) apply'
df.groupby(['category'])[['data_1']].apply(f)
print '\n'

print 'multiple column apply'
df.groupby(['category'])[['data_1', 'data_2']].apply(f)

这将以下内容放入我的标准输出中：

single column transform
<class 'pandas.core.series.Series'>
<class 'pandas.core.series.Series'>
<class 'pandas.core.series.Series'>


single column (nested) transform
<class 'pandas.core.series.Series'>
<class 'pandas.core.series.Series'>
<class 'pandas.core.frame.DataFrame'>
<class 'pandas.core.frame.DataFrame'>
<class 'pandas.core.frame.DataFrame'>


multiple column transform
<class 'pandas.core.series.Series'>
<class 'pandas.core.series.Series'>
<class 'pandas.core.series.Series'>
<class 'pandas.core.frame.DataFrame'>
<class 'pandas.core.frame.DataFrame'>
<class 'pandas.core.frame.DataFrame'>




single column apply
<class 'pandas.core.series.Series'>
<class 'pandas.core.series.Series'>
<class 'pandas.core.series.Series'>


single column (nested) apply
<class 'pandas.core.frame.DataFrame'>
<class 'pandas.core.frame.DataFrame'>
<class 'pandas.core.frame.DataFrame'>
<class 'pandas.core.frame.DataFrame'>


multiple column apply
<class 'pandas.core.frame.DataFrame'>
<class 'pandas.core.frame.DataFrame'>
<class 'pandas.core.frame.DataFrame'>
<class 'pandas.core.frame.DataFrame'>

所以看起来像是：

• 变换
  • 单列：3 Series
  • 单列（嵌套）：2 Series和3 DataFrame
  • 多列：3 Series和3 DataFrame
• 应用
  • 单列：3 Series
  • 单列（嵌套）：4 DataFrame
  • 多列：4 DataFrame

这里发生了什么？任何人都可以解释为什么这6个调用中的每一个都导致上面描述的一系列对象被传递给指定的函数？

Answer 1

GroupBy.transform将为您的函数尝试fast_path和slow_path。

• fast_path：使用DataFrame对象调用您的函数
• slow_path：使用DataFrame.apply函数
调用您的函数

当fast_path的结果与slow_path相同时，它将选择fast_path。

以下输出表示它最终选择了fast_path：

multiple column transform
<class 'pandas.core.series.Series'>
<class 'pandas.core.series.Series'>
<class 'pandas.core.series.Series'>
<class 'pandas.core.frame.DataFrame'>
<class 'pandas.core.frame.DataFrame'>
<class 'pandas.core.frame.DataFrame'>

以下是代码链接：

https://github.com/pydata/pandas/blob/master/pandas/core/groupby.py#L2277

修改

检查调用堆栈：

import numpy as np
import pandas as pd

np.random.seed(0) # so we can all play along at home

categories = list('abc')
categories = categories * 4
data_1 = np.random.randn(len(categories))
data_2 = np.random.randn(len(categories))

df = pd.DataFrame({'category': categories, 'data_1': data_1, 'data_2': data_2})

import traceback
import inspect
import itertools

def f(x):
    flag = True
    stack = itertools.dropwhile(lambda x:"#stop here" not in x, 
                                traceback.format_stack(inspect.currentframe().f_back))
    print "*"*20
    print x
    print type(x)
    print
    print "\n".join(stack)
    return x

df.groupby(['category'])[['data_1', 'data_2']].transform(f) #stop here