Maciek, thanks for the note via private message!
To all of you: Here comes the solution to just skip entries inside a column
that contain a combination of float and “>” :
pd.read_csv('test_mw_r2.csv', sep=';', converters={'r2': lambda x: np.NaN if x
and x[0] == '>' else x}).dropna(axis=0)
Paul
Von: Maciek Wójcikowski [mailto:mac...@wojcikowski.pl]
Gesendet: Freitag, 11. März 2016 12:29
An: Paul Czodrowski <paul.czodrow...@merckgroup.com>
Cc: rdkit <rdkit-discuss@lists.sourceforge.net>
Betreff: Re: [Rdkit-discuss] Pandas dataframe manipulation
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<mailto:mac...@wojcikowski.pl>
2016-03-11 11:12 GMT+01:00 Paul Czodrowski
<paul.czodrow...@merckgroup.com<mailto: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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