Pandas：将带有空字符串的列转换为浮动
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Pandas: convert column with empty strings to float
提问by LateCoder
In my application, I receive a pandas DataFrame (say, block), that has a column called est. This column can contain a mix of strings or floats. I need to convert all values in the column to floats and have the column type be float64. I do so using the following code:
在我的应用程序中，我收到一个 Pandas DataFrame（比如block），它有一列名为est. 此列可以包含字符串或浮点数的混合。我需要将列中的所有值转换为浮点数并将列类型设为float64. 我使用以下代码执行此操作：
block[est].convert_objects(convert_numeric=True)
block[est].astype('float')
This works for most cases. However, in one case, estcontains all empty strings. In this case, the first statement executes without error, but the empty strings in the column remain empty strings. The second statement then causes an error: ValueError: could not convert string to float:.
这适用于大多数情况。但是，在一种情况下，est包含所有空字符串。在这种情况下，第一条语句执行没有错误，但列中的空字符串仍然是空字符串。然后第二个语句导致错误：ValueError: could not convert string to float:。
How can I modify my code to handle a column with all empty strings?
如何修改我的代码以处理包含所有空字符串的列？
Edit: I know I can just do block[est].replace("", np.NaN), but I was wondering if there's some way to do it with just convert_objectsor astypethat I'm missing.
编辑：我知道我可以做到block[est].replace("", np.NaN)，但我想知道是否有某种方法可以做到这一点，convert_objects或者astype我错过了。
Clarification: For project-specific reasons, I need to use pandas 0.16.2.
说明：由于项目特定的原因，我需要使用 Pandas 0.16.2。
Here's an interaction with some sample data that demonstrates the failure:
这是与一些演示失败的示例数据的交互：
>>> block = pd.DataFrame({"eps":["", ""]})
>>> block = block.convert_objects(convert_numeric=True)
>>> block["eps"]
0
1
Name: eps, dtype: object
>>> block["eps"].astype('float')
...
ValueError: could not convert string to float:
回答by mcrrnz
It's easier to do it using:
使用以下方法更容易：
pandas.to_numeric
http://pandas.pydata.org/pandas-docs/version/0.17.0/generated/pandas.to_numeric.html
pandas.to_numeric
http://pandas.pydata.org/pandas-docs/version/0.17.0/generated/pandas.to_numeric.html
import pandas as pd
df = pd.DataFrame({'eps': ['1', 1.6, '1.6', 'a', '', 'a1']})
df['eps'] = pd.to_numeric(df['eps'], errors='coerce')
'coerce' will convert any value error to NaN
'coerce' 会将任何值错误转换为 NaN
df['eps'].astype('float')
0 1.0
1 1.6
2 1.6
3 NaN
4 NaN
5 NaN
Name: eps, dtype: float64
Then you can apply other functions without getting errors :
然后您可以应用其他功能而不会出错：
df['eps'].round()
0 1.0
1 2.0
2 2.0
3 NaN
4 NaN
5 NaN
Name: eps, dtype: float64
回答by Alexander
def convert_float(val):
try:
return float(val)
except ValueError:
return np.nan
df = pd.DataFrame({'eps': ['1', 1.6, '1.6', 'a', '', 'a1']})
>>> df.eps.apply(lambda x: convert_float(x))
0 1.0
1 1.6
2 1.6
3 NaN
4 NaN
5 NaN
Name: eps, dtype: float64
