I have no clear opinion on this since I never used pandas.
I would tend to agree with Gael saying that if pandas should become
popular, it has to start becoming now, thanks to its acceptance within
packages such as nilearn.

But I remember that there have been many pandas-related issued on
statsmodels list. So I am a bit afraid of it now.
Le 20 mai 2014 19:04, "Gael Varoquaux" <notifications@github.com> a écrit :

> In nilearn/datasets.py:
>
> >
> >      # Actual data fetching
> >      files = _fetch_files('brainomics_localizer', filenames, data_dir=data_dir)
> >      anats = None
> >      masks = None
> >      tmaps = None
> > +    # combine data from both covariates files into one single recarray
> > +    from numpy.lib.recfunctions import join_by
> > +    ext_vars_file2 = files[-1]
> > +    csv_data2 = np.recfromcsv(ext_vars_file2, delimiter=';')
> > +    files = files[:-1]
> > +    ext_vars_file = files[-1]
> > +    csv_data = np.recfromcsv(ext_vars_file, delimiter=';')
> > +    files = files[:-1]
> > +    # join_by sorts the output along the key
> > +    csv_data = join_by('subject_id', csv_data, csv_data2,
> > +                       usemask=False, asrecarray=True)[:n_subjects]
>
> Open question to all the developers: should we be using a pandas dataframe
> here?
>
> I think that in the long run, we should be using more and more pandas
> datasframes in the example code and for behavioral variables. Indeed, when
> they are well used, they make the code more readable. Thus to foster
> adoption this could be something important.
>
> Right now, pandas is a package that has enough adoption and install basis
> that I feel it is safe to depend on it. Nonetheless we could delay the
> import to inside the function, so that if pandas is not installed, the
> whole import of nilearn does not fail.
>
> —
> Reply to this email directly or view it on GitHub<https://github.com/nilearn/nilearn/pull/192/files#r12855394>
> .
>