is there a method to skip unconvertible rows when casting a pandas series from str to float?

is there a method to skip unconvertible rows when casting a pandas series
from str to float?

I have a pandas datagframe created from a csv file. One column of this
dataframe contains numeric data that is initially cast as a string. Most
entries are numeric-like, but some contain various error codes that are
non-numeric. I do not know beforehand what all the error codes might be or
how many there are. So, for instance, the dataframe might look like:
[In 1]: df
[Out 1]:
data OtherAttr
MyIndex
0 1.4 aaa
1 error1 foo
2 2.2 bar
3 0.8 bar
4 xxx bbb
...
743733 BadData ccc
743734 7.1 foo
I want to cast df.data as a float and throw out any values that don't
convert properly. Is there a built-in functionality for this? Something
like:
df.data = df.data.astype(float, skipbad = True)
(Although I know that specifically will not work and I don't see any
kwargs within astype that do what I want)
I guess I could write a function using try and then use pandas apply or
map, but that seems like an inelegant solution. This must be a fairly
common problem, right?