通过apply传递给函数时，pandas列的数据类型更改为object? [英] Data type of pandas column changes to object when it's passed to a function via apply?

问题描述

我需要在函数中使用pandas列的dtype，但是由于某些原因，当我使用apply调用函数时，dtype更改为object.有人知道这里发生了什么吗?

I need to use the dtype of a pandas column in a function, but for some reason when I call the function using apply, the dtype is changed to object. Does anyone know what is happening here?

import pandas as pd

df = pd.DataFrame({'stringcol':['a'], 'floatcol': [1.5]})
df.dtypes
Out[1]: 
floatcol     float64
stringcol     object
dtype: object

df.apply(lambda col: col.dtype)
Out[2]: 
floatcol     object
stringcol    object
dtype: object

请注意，如果直接传递该列，则不会发生此问题:

Note that this problem doesn't happen if the column is passed directly:

f = lambda col: col.dtype
f(test.floatcol)
Out[3]: dtype('float64')

推荐答案

它似乎是由于DataFrame._apply_standard中的优化所致.该方法代码中的快速路径"将创建一个输出Series，其dtype为df.values的dtype，在您的情况下为object，因为DataFrame是混合类型.如果将reduce=False传递给apply调用，则结果是正确的:

It appears to be due to an optimization in DataFrame._apply_standard. The "fast path" in the code of that method creates an output Series whose dtype is the dtype of df.values, which in your case is object since the DataFrame is of mixed type. If you pass reduce=False to your apply call, the result is correct:

>>> df.apply(lambda col: col.dtype, reduce=False)
floatcol     float64
stringcol     object
dtype: object

(我必须说，我对文档中reduce吉布斯的这种行为尚不清楚.)

(I must say that it is not clear to me how this behavior of reduce jibes with the documentation.)

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