将包含 NaN 的 Pandas 列转换为 dtype `int` [英] Convert Pandas column containing NaNs to dtype `int`

问题描述

我将数据从 .csv 文件读取到 Pandas 数据框，如下所示.对于其中一列，即id，我想将列类型指定为int.问题是 id 系列缺少/空值.

I read data from a .csv file to a Pandas dataframe as below. For one of the columns, namely id, I want to specify the column type as int. The problem is the id series has missing/empty values.

当我在读取 .csv 时尝试将 id 列转换为整数时，我得到:

When I try to cast the id column to integer while reading the .csv, I get:

df= pd.read_csv("data.csv", dtype={'id': int}) 
error: Integer column has NA values

或者，我在阅读以下内容后尝试转换列类型，但这次我得到:

Alternatively, I tried to convert the column type after reading as below, but this time I get:

df= pd.read_csv("data.csv") 
df[['id']] = df[['id']].astype(int)
error: Cannot convert NA to integer

我该如何解决这个问题?

How can I tackle this?

推荐答案

整数列中缺少 NaN 代表是一个 熊猫gotcha".

The lack of NaN rep in integer columns is a pandas "gotcha".

通常的解决方法是简单地使用浮动.

The usual workaround is to simply use floats.

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