NumPy 或 Pandas:将数组类型保持为整数，同时具有 NaN 值

NumPy or Pandas: Keeping array type as integer while having a NaN value(NumPy 或 Pandas:将数组类型保持为整数，同时具有 NaN 值)

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.

推荐答案

此功能已添加到 pandas(从 0.24 版本开始):https://pandas.pydata.org/pandas-docs/version/0.24/whatsnew/v0.24.0.html#optional-integer-na-support

This capability has been added to pandas (beginning with version 0.24): https://pandas.pydata.org/pandas-docs/version/0.24/whatsnew/v0.24.0.html#optional-integer-na-support

此时，它需要使用扩展dtype Int64(大写)，而不是默认dtype int64(小写).

At this point, it requires the use of extension dtype Int64 (capitalized), rather than the default dtype int64 (lowercase).

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