python - Pandas - 如何避免将 map 转换为 float

Question

I have a dictionary:

matches = {282: 285,
 266: 277,
 276: 293,
 263: 264,
 286: 280,
 356: 1371,
 373: 262,
 314: 327,
 294: 290,
 285: 282,
 277: 266,
 293: 276,
 264: 263,
 280: 286,
 1371: 356,
 262: 373,
 327: 314,
 290: 294}

还有一个 df，像这样:

    team_id 
0   327 
1   293 
2   373 
3   282 
4   314 
5   263 
6   280
7   354 
8   264 
9   294 
10  1371    
11  262 
12  266 
13  356 
14  290 
15  285 
16  286 
17  275 
18  277 
19  276 

---

现在我正在尝试创建一个“adversary_id”列，从字典映射，如下所示:

df['adversary_id'] = df['team_id'].map(matches)

但这个新列 adversary_id 正在转换为 type float，并且两行以 NaN 结尾:

为什么，如果所有数据都是 int 类型？

我该如何解决这个问题？

Answer 1

这是因为您在数据框中看到的 np.nan 或 NaN(它们并不完全相同)值是 float 类型。遗憾的是，只要您的代码中有 NaN 值，就无法避免这种限制。

请阅读 pandas 文档中的更多信息 here .

Because NaN is a float, a column of integers with even one missing values is cast to floating-point dtype (see Support for integer NA for more). pandas provides a nullable integer array, which can be used by explicitly requesting the dtype:

建议的解决方案是强制使用 type:

df['team_id'] = pd.Series(df['team_id'],dtype=pd.Int64Dtype())

返回:

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Data columns (total 1 columns):
 #   Column   Non-Null Count  Dtype
---  ------   --------------  -----
 0   Example  4 non-null      Int64
dtypes: Int64(1)
memory usage: 173.0 bytes