python pandas调用函数时出现未绑定(bind)本地错误 'df.apply'

Question

我尝试在 pandas 中使用 df.apply() 函数，但出现以下错误。如果每个条目小于“阈值”，该函数会尝试将其转换为 0

from pandas import * 
import numpy as np
def discardValueLessThan(x, threshold):
    if x < threshold : return 0
    else: return x

df = DataFrame(np.random.randn(8, 3), columns=['A', 'B', 'C'])

>>> df
          A         B         C
0 -1.389871  1.362458  1.531723
1 -1.200067 -1.114360 -0.020958
2 -0.064653  0.426051  1.856164
3  1.103067  0.194196  0.077709
4  2.675069 -0.848347  0.152521
5 -0.773200 -0.712175 -0.022908
6 -0.796237  0.016256  0.390068
7 -0.413894  0.190118 -0.521194

df.apply(discardValueLessThan, 0.1)

>>> df.apply(discardValueLessThan, 0.1)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Library/Frameworks/EPD64.framework/Versions/7.3/lib/python2.7/site-packages/pandas-0.8.1-py2.7-macosx-10.5-x86_64.egg/pandas/core/frame.py", line 3576, in apply
    return self._apply_standard(f, axis)
  File "/Library/Frameworks/EPD64.framework/Versions/7.3/lib/python2.7/site-packages/pandas-0.8.1-py2.7-macosx-10.5-x86_64.egg/pandas/core/frame.py", line 3637, in _apply_standard
    e.args = e.args + ('occurred at index %s' % str(k),)
UnboundLocalError: local variable 'k' referenced before assignment

Answer 1

该错误消息对我来说看起来像是一个 pandas bug，但我认为还有其他两个问题。

首先，我认为您必须指定命名参数或使用 args 将其他参数传递给 apply。你的第二个参数可能被解释为一个轴。但如果你使用

df.apply(discardValueLessThan, args=(0.1,))

或

df.apply(discardValueLessThan, threshold=0.1)

然后你会得到

ValueError: ('The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()', 'occurred at index A')

因为 apply 不按元素起作用，它作用于整个 Series 对象。其他方法包括使用 applymap 或 bool 索引，即

In [47]: df = DataFrame(np.random.randn(3, 3), columns=['A', 'B', 'C'])

In [48]: df
Out[48]: 
          A         B         C
0 -0.135336 -0.274687  1.480949
1 -1.079800 -0.618610 -0.321235
2 -0.610420 -0.422112  0.102703

In [49]: df1 = df.applymap(lambda x: discardValueLessThan(x, 0.1))

In [50]: df1
Out[50]: 
   A  B         C
0  0  0  1.480949
1  0  0  0.000000
2  0  0  0.102703

或者简单地

In [51]: df[df < 0.1] = 0

In [52]: df
Out[52]: 
   A  B         C
0  0  0  1.480949
1  0  0  0.000000
2  0  0  0.102703