Python Pandas：转换为数字，必要时创建 NaN
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Pandas: Converting to numeric, creating NaNs when necessary
提问by Amelio Vazquez-Reina
Say I have a column in a dataframe that has some numbers and some non-numbers
假设我在数据框中有一列有一些数字和一些非数字
>> df['foo']
0 0.0
1 103.8
2 751.1
3 0.0
4 0.0
5 -
6 -
7 0.0
8 -
9 0.0
Name: foo, Length: 9, dtype: object
How can I convert this column to np.float, and have everything else that is not float convert it to NaN?
如何将此列转换为np.float，并将其他所有非浮点数转换为NaN？
When I try:
当我尝试：
>> df['foo'].astype(np.float)
or
或者
>> df['foo'].apply(np.float)
I get ValueError: could not convert string to float: -
我得到 ValueError: could not convert string to float: -
采纳答案by Anton Protopopov
In pandas 0.17.0convert_objectsraises a warning:
在熊猫中0.17.0convert_objects引发警告：
FutureWarning: convert_objects is deprecated. Use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.
FutureWarning：不推荐使用 convert_objects。使用特定于数据类型的转换器 pd.to_datetime、pd.to_timedelta 和 pd.to_numeric。
You could use pd.to_numericmethod and apply it for the dataframe with arg coerce.
您可以使用pd.to_numericmethod 并将其应用于带有 arg 的数据帧coerce。
df1 = df.apply(pd.to_numeric, args=('coerce',))
or maybe more appropriately:
或者也许更合适：
df1 = df.apply(pd.to_numeric, errors='coerce')
EDIT
编辑
The above method is only valid for pandas version >= 0.17.0, from docs what's new in pandas 0.17.0:
上述方法仅适用于 pandas 版本 >= 0.17.0，来自docs what's new in pandas 0.17.0：
pd.to_numeric is a new function to coerce strings to numbers (possibly with coercion) (GH11133)
pd.to_numeric 是一个将字符串强制转换为数字的新函数（可能带有强制转换）（GH11133）
回答by Viktor Kerkez
First replace all the string values with None, to mark them as missing values and then convert it to float.
首先用 替换所有字符串值None，将它们标记为缺失值，然后将其转换为浮点数。
df['foo'][df['foo'] == '-'] = None
df['foo'] = df['foo'].astype(float)
回答by Andy Hayden
Use the convert_objectsSeries method (and convert_numeric):
使用convert_objectsSeries 方法（和convert_numeric）：
In [11]: s
Out[11]:
0 103.8
1 751.1
2 0.0
3 0.0
4 -
5 -
6 0.0
7 -
8 0.0
dtype: object
In [12]: s.convert_objects(convert_numeric=True)
Out[12]:
0 103.8
1 751.1
2 0.0
3 0.0
4 NaN
5 NaN
6 0.0
7 NaN
8 0.0
dtype: float64
Note: this is also available as a DataFrame method.
注意：这也可用作 DataFrame 方法。
回答by Amir Imani
You can simply use pd.to_numericand setting error to coercewithout using apply
您可以简单地使用pd.to_numeric并将错误设置为coerce不使用apply
df['foo'] = pd.to_numeric(df['foo'], errors='coerce')
