Pandas:转换为数字，必要时创建 NaN [英] Pandas: Converting to numeric, creating NaNs when necessary

问题描述

假设我在数据框中有一列有一些数字和一些非数字

Say I have a column in a dataframe that has some numbers and some non-numbers

>> 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

如何将此列转换为 np.float，并将其他所有非浮动列转换为 NaN?

How can I convert this column to np.float, and have everything else that is not float convert it to NaN?

当我尝试时:

>> df['foo'].astype(np.float)

或

>> df['foo'].apply(np.float)

我得到 ValueError: could not convert string to float: -

推荐答案

In pandas 0.17.0 convert_objects 引发警告:

In pandas 0.17.0 convert_objects raises a warning:

FutureWarning:convert_objects 已弃用.使用数据类型特定转换器 pd.to_datetime、pd.to_timedelta 和 pd.to_numeric.

FutureWarning: convert_objects is deprecated. Use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.

您可以使用 pd.to_numeric 方法并将其应用于带有 arg coerce 的数据帧.

You could use pd.to_numeric method and apply it for the dataframe with arg coerce.

df1 = df.apply(pd.to_numeric, args=('coerce',))

或者更恰当:

df1 = df.apply(pd.to_numeric, errors='coerce')

编辑

以上方法只对pandas版本>=0.17.0有效，来自文档 Pandas 0.17.0 的新功能:

The above method is only valid for pandas version >= 0.17.0, from docs what's new in pandas 0.17.0:

pd.to_numeric 是一个将字符串强制转换为数字的新函数(可能使用强制转换)(GH11133)

pd.to_numeric is a new function to coerce strings to numbers (possibly with coercion) (GH11133)

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