当数据帧存在NaN时使用类型错误
问题说明
df
A B
0 a=10 b=20.10
1 a=20 NaN
2 NaN b=30.10
3 a=40 b=40.10
我尝试过:
df['A'] = df['A'].str.extract('(\d )').astype(int)
df['B'] = df['B'].str.extract('(\d )').astype(float)
但是出现以下错误:
ValueError:无法将float NaN转换为整数
ValueError: cannot convert float NaN to integer
并且:
AttributeError:只能将.str访问器与字符串值一起使用，后者在熊猫中使用np.object_ dtype
AttributeError: Can only use .str accessor with string values, which use np.object_ dtype in pandas
我该如何解决?
正确答案
如果缺少列中的某些值(NaN)，然后将其转换为数字，则dtype始终为float.您不能将值转换为int.仅限于float，因为NaN的type是float.
If some values in column are missing (NaN) and then converted to numeric, always dtype is float. You cannot convert values to int. Only to float, because type of NaN is float.
print (type(np.nan))
<class 'float'>
请参见 docs 如何在以下情况下转换值:至少一个NaN:
See docs how convert values if at least one NaN:
整数>转换为float64
integer > cast to float64
如果需要int值，则需要将NaN替换为某些int，例如0通过 fillna 效果很好:
If need int values you need replace NaN to some int, e.g. 0 by fillna and then it works perfectly:
df['A'] = df['A'].str.extract('(\d )', expand=False)
df['B'] = df['B'].str.extract('(\d )', expand=False)
print (df)
A B
0 10 20
1 20 NaN
2 NaN 30
3 40 40
df1 = df.fillna(0).astype(int)
print (df1)
A B
0 10 20
1 20 0
2 0 30
3 40 40
print (df1.dtypes)
A int32
B int32
dtype: object
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