numpy 或 pandas:将数组类型保持为整数，同时具有 nan 值-百家乐凯发k8

问题描述

是否有一种首选方法可以将 numpy 数组的数据类型固定为 int (或 int64 或其他)，同时仍然里面有一个元素列为 numpy.nan?

is there a preferred way to keep the data type of a numpy array fixed as int (or int64 or whatever), while still having an element inside listed as numpy.nan?

特别是，我正在将内部数据结构转换为 pandas dataframe.在我们的结构中，我们有仍然有 nan 的整数类型列(但列的 dtype 是 int).如果我们将其设为 dataframe，似乎会将所有内容重铸为浮点数，但我们真的很想成为 int.

in particular, i am converting an in-house data structure to a pandas dataframe. in our structure, we have integer-type columns that still have nan's (but the dtype of the column is int). it seems to recast everything as a float if we make this a dataframe, but we'd really like to be int.

想法?

尝试过的事情:

我尝试使用 pandas.dataframe 下的 from_records() 函数和 coerce_float=false 但这没有帮助.我还尝试使用 numpy 掩码数组和 nan fill_value，这也不起作用.所有这些都导致列数据类型变为浮点数.

i tried using the from_records() function under pandas.dataframe, with coerce_float=false and this did not help. i also tried using numpy masked arrays, with nan fill_value, which also did not work. all of these caused the column data type to become a float.

numpy 或 pandas:将数组类型保持为整数，同时具有 nan 值-百家乐凯发k8

问题描述

推荐答案