Seaborn should respect categorical order when sorting pd.Categorical objects

[shoyer]

For example, this adaption of the "Grouped boxplots" example should work (if using pandas 0.15 or higher) even without specifying x_order:

import seaborn as sns
sns.set(style="ticks")

tips = sns.load_dataset("tips")
days = ["Thur", "Fri", "Sat", "Sun"]
tips['days'] = pd.Categorical(tips['day'], days)

g = sns.factorplot("day", "total_bill", "sex", tips, kind="box",
                   palette="PRGn", aspect=1.25)
g.despine(offset=10, trim=True)
g.set_axis_labels("Day", "Total Bill")

If you using a pandas method to do the sorting, then this is a pandas bug.

[wrobstory]

I think it should probably respect the index order by default, e.g. if I have a sorted-ascending DataFrame, I should have a sorted-ascending bar chart.

[shoyer]

Generally I agree, but I was assuming @mwaskom had his reasons. If this is just a side effect of the fact that he's using np.unique, he should try the pandas unique method instead, which does not sort (and is also a little faster).

On Mon, Nov 10, 2014 at 10:10 PM, Rob Story notifications@github.com
wrote:

I think it should probably respect the index order by default, e.g. if I have a sorted-ascending DataFrame, I should have a sorted-ascending bar chart.

Reply to this email directly or view it on GitHub:
#361 (comment)

[mwaskom]

I definitely agree with @shoyer about categorical types. I'm not sure I feel all that strongly about alphabetical sorting (though to @wrobstory's point, without having any way to know whether a column is intentionally sorted, it seemed best for the default to do something predictable). But it sounds easy to just change np.unique(Series) to Series.unique() and pick up the main goal here (categorical columns) and maybe also reduce some surprise.

To go along with this I think the test datasets should be updated to use Categorical where appropriate to demonstrate the functionality.

[mwaskom]

I'm in favor of this, but I'm gonna kick it to the 0.6 cycle because it will take a little thinking. I want to figure out the best way to make this behavior as consistent as possible across the package.

[olgabot]

Just wanted to chime in here in support of sticking to the categorical order, for example here:

The alphabetical sort doesn't make sense because then the length bins are out of order, putting the bins with 0's and 5's first, rather than the natural size ordering.

[shoyer]

@olgabot The sorting order for bins will also be fixed upstream when I finish adding an Interval type to pandas (which will sort properly), instead of using string labels.

[yoshiserry]

hi all, is there a workaround for this currently? i love the power of the col= parameter, where you can create a graph for all instances of a column but I want to be able to plot Jan, Feb, Mar in order.

[shoyer]

@yoshiserry Yes, use the col_order argument.

[jseabold]

Do the changes in #409 look reasonable?

It provides a compatibility function that uses pandas sort and unique, which 1) handles NA in a consistent manner because np.sort will fail for object dtype Series with NA. and 2) it handles ordered and unordered Categorical. If an ordered factor/Categorical is passed in then it sorts on that order. If not, it does a lexicographical sort bc. of how unique works.

Want to get some feedback before adding a few more tests and touching the code base in other parts. Should this approach replace all the np.sort calls right now. I don't have a good sense if this would ever not be desired.

[mwaskom]

Thanks for taking a crack at it.

The open issue is really whether things should be lexicographically sorted or in the order that they appear in the dataframe (so just what the straight pandas .unique() method returns). I think that @wrobstory finding the sorting surprising was what originally motivated this chain of issues.

I originally had a mild preference for consistent behavior (sorts are at least predictable once you expect them), but if it requires a fair amount of complexity to make sorting work correctly across a range of pandas versions, it's possible that might weigh in favor of not sorting.

[mwaskom]

I'd been letting this issue fester as it required a hard decision so thanks for poking at it :]