更改 Pandas 数据帧类型时的异常处理
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Exception handling when changing Pandas dataframe type
提问by lmart999
I have a Pandas dataframe with a single column of strings. I want to convert the column data to float. Some of the values cannot be converted to float due to their format. I want to omit these "illegal strings" from the result and only extract values that can be legally re-cast as floats. The starting data:
我有一个带有单列字符串的 Pandas 数据框。我想将列数据转换为浮动。由于格式的原因，某些值无法转换为浮点数。我想从结果中省略这些“非法字符串”，只提取可以合法地重新转换为浮点数的值。起始数据：
test=pd.DataFrame()
test.loc[0,'Value']='<3'
test.loc[1,'Value']='10'
test.loc[2,'Value']='Detected'
test.loc[3,'Value']=''
The desired output contains only strings that could be re-cast as floats (in this case, 10):
所需的输出仅包含可以重新转换为浮点数的字符串（在本例中为 10）：
cleanDF=test['Value'].astype(float)
cleanDF
0 10
Name: Value, dtype: float64
Of course, this throws an error as expected on the illegal string for float conversion:
当然，这会在浮点转换的非法字符串上按预期抛出错误：
ValueError: could not convert string to float: <3
Is there a simple way to solve this if the dataframe is large and contains many illegal strings in 'Value'?
如果数据框很大并且“值”中包含许多非法字符串，是否有一种简单的方法可以解决这个问题？
Thanks.
谢谢。
采纳答案by Phil
You could try using DataFrame's apply. Write a function that includes an exception handler and apply it to the DataFrame.
您可以尝试使用 DataFrame 的apply. 编写一个包含异常处理程序的函数并将其应用于 DataFrame。
def test_apply(x):
try:
return float(x)
except ValueError:
return None
cleanDF = test['Value'].apply(test_apply).dropna()
