熊猫：转换为数字，必要时创建NaN

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个列,其中包含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,并将其他所有不浮动的列转换为NaN?

When I try:

当我尝试:

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

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

I get ValueError: could not convert string to float: -

我得到ValueError:无法将字符串转换为float: -

4 个解决方案

#1

In pandas 0.17.0 convert_objects raise a warning:

在pandas 0.17.0中,convert_objects会发出警告:

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_numeric method and apply it for the dataframe with arg coerce.

您可以使用pd.to_numeric方法并将其应用于具有arg强制的数据帧。

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

or may be in more appropriate way:

或者可能以更恰当的方式:

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

EDIT

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

该方法仅适用于pandas版本> = 0.17.0,来自docs pandas 0.17.0中的新内容:

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)(GH11133)

#2

Use the convert_objects Series method (and convert_numeric):

使用convert_objects Series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。

#3

First replace all the string values with None, to mark them as missing values and then convert it to float.

首先用None替换所有字符串值,将它们标记为缺失值,然后将其转换为float。

df['foo'][df['foo'] == '-'] = None
df['foo'] = df['foo'].astype(float)

#4

You can simply use pd.to_numeric and setting error to coerce without using apply

您可以简单地使用pd.to_numeric并设置错误来强制而不使用apply

df['foo'] = pd.to_numeric(df['foo'], errors='coerce')

#1