Hi Paul,
I would suggest:
- assigning dtype of dataframe/column to str/np.object
- cleaning up the IC50s
- casting to float/int as dataframe.astype()
Or alternatively you could use "converters" argument:
pd.read_csv('filename.csv', converters={'ic50_colname': lambda x:
x.replace('>', '')})
http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html
----
Pozdrawiam, | Best regards,
Maciek Wójcikowski
mac...@wojcikowski.pl
2016-03-11 11:12 GMT+01:00 Paul Czodrowski <paul.czodrow...@merckgroup.com>:
> Dear RDKitter & Pandas-Dataframes heavy users,
>
>
>
> please find below a question concerning the conversion of pandas
> dataframes:
>
> df = pd.DataFrame({"item": ["a", "b", "c", "d", "e"], "row1":
> [1,2,3,">2",5],
> "row2":[0.1,0.2,0.3,0.4,0.5],"row3":["ab","cd","ed","gh","ij"]})
>
> df_new = df[df[["row1"]].applymap(np.isreal).all(1)]
>
>
>
> I would like to get rid of this nasty ">2" entry in "row1" => This works
> perfect given the snippet above.
>
>
>
> However, when I read in a CSV file containing similar data (see the
> attached CSV) => The conversion does not work: all columns in the IC50
> value are discarded and end up in yielding "NaN".
>
>
>
> What is going wrong?
>
>
>
>
>
> Thanks & Cheers,
>
> Paul
>
>
>
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