pandas .apply()函数中的异常处理 [英] Exception Handling in Pandas .apply() function

问题描述

如果我有一个DataFrame:

If I have a DataFrame:

myDF = DataFrame(data=[[11,11],[22,'2A'],[33,33]], columns = ['A','B'])

给出以下数据框(从stackoverflow开始，并且对于该DataFrame的图像没有足够的信誉)

Gives the following dataframe (Starting out on stackoverflow and don't have enough reputation for an image of the DataFrame)

   | A  | B  |

0  | 11 | 11 |

1  | 22 | 2A |

2  | 33 | 33 |

如果我想将B列转换为int值并删除无法转换的值，我必须这样做:

If i want to convert column B to int values and drop values that can't be converted I have to do:

def convertToInt(cell):
    try:
        return int(cell)
    except:
        return None
myDF['B'] = myDF['B'].apply(convertToInt)

如果我只做:

myDF ['B'].apply(int)

myDF['B'].apply(int)

错误显然是:

C:\ WinPython-32bit-2.7.5.3 \ python-2.7.5 \ lib \ site-packages \ pandas \ lib.pyd在pandas.lib.map_infer(pandas \ lib.c:42840)()

C:\WinPython-32bit-2.7.5.3\python-2.7.5\lib\site-packages\pandas\lib.pyd in pandas.lib.map_infer (pandas\lib.c:42840)()

ValueError:以10为底的int()无效文字:'2A'

ValueError: invalid literal for int() with base 10: '2A'

是否可以将异常处理添加到myDF ['B'].apply()

Is there a way to add exception handling to myDF['B'].apply()

提前谢谢！

推荐答案

做得更好/更快:

In [1]: myDF = DataFrame(data=[[11,11],[22,'2A'],[33,33]], columns = ['A','B'])

In [2]: myDF.convert_objects(convert_numeric=True)
Out[2]: 
    A   B
0  11  11
1  22 NaN
2  33  33

[3 rows x 2 columns]

In [3]: myDF.convert_objects(convert_numeric=True).dtypes
Out[3]: 
A      int64
B    float64
dtype: object

这是执行此操作的向量化方法. coerce 标志说可以将所有无法转换为数字的内容标记为 nan .

This is a vectorized method of doing just this. The coerce flag say to mark as nan anything that cannot be converted to numeric.

当然，您可以根据需要在单个列中执行此操作.

You can of course do this to a single column if you'd like.

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