NumPy或Pandas：在具有NaN值的同时将数组类型保持为整数 [英] NumPy or Pandas: Keeping array type as integer while having a NaN value

问题描述

是否有一种首选的方法可以将 numpy 数组的数据类型固定为 int （或 int64 或者其他什么），同时仍然有一个元素列在 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，我们有整数类型的列仍然具有NaN（但列的dtype是int）。如果我们把它变成一个DataFrame，它似乎将所有内容重新设置为float，但我们真的希望 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？

尝试的事情：

我尝试使用 from_records（） pandas.DataFrame下的函数， coerce_float = False ，这没有帮助。我也尝试使用带有NaN fill_value的NumPy蒙版数组，这也没有用。所有这些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

NaN 不能存储在整数数组中。这是目前大熊猫的一个已知限制;我一直在等待NumPy中NA值的进展（类似于R中的NAs），但是在NumPy获得这些功能之前至少需要6个月到一年，似乎：

NaN can't be stored in an integer array. This is a known limitation of pandas at the moment; I have been waiting for progress to be made with NA values in NumPy (similar to NAs in R), but it will be at least 6 months to a year before NumPy gets these features, it seems:

http：// pandas.pydata.org/pandas-docs/stable/gotchas.html#support-for-integer-na

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