IPython notebook illustrating issue with Matplotlib's errorbar and Seaborn's FacetGrid

Seaborn_FacetGrid_issue.ipynb

seaborn_mod.py

from seaborn import *
from seaborn.axisgrid import FacetGrid as FacetGrid_orig

class FacetGrid(FacetGrid_orig):

    def __init__(self, data, *args, **kwargs):

        super(FacetGrid, self).__init__(data, *args, **kwargs)

        # The original palette isn't preserved by Seaborn, which seems unfortunate
        self._palette = kwargs['palette'] if 'palette' in kwargs.keys() else None

        # col_order and hue_order also aren't preserved
        self._col_order = kwargs['col_order'] if 'col_order' in kwargs.keys() else None
        self._hue_order = kwargs['hue_order'] if 'hue_order' in kwargs.keys() else None

        return

    def set_data(self, data):

        # Update the data
        self.data = data


        # Update the hue and color information since these are done at intilization
        # instead of when a plotting function is called by 'map'
        hue = self._hue_var
        palette = self._palette
        hue_order = self._hue_order

        if hue is None:
            hue_names = None
        else:
            hue_names = utils.categorical_order(data[hue], hue_order)

        colors = self._get_palette(data, hue, hue_order, palette)

        self.hue_names = hue_names
        self._colors = colors


        # Update the boolean mask
        row = self._row_var
        col = self._col_var
        dropna = self._dropna

        none_na = np.zeros(len(data), np.bool)
        if dropna:
            row_na = none_na if row is None else data[row].isnull()
            col_na = none_na if col is None else data[col].isnull()
            hue_na = none_na if hue is None else data[hue].isnull()
            not_na = ~(row_na | col_na | hue_na)
        else:
            not_na = ~none_na

        self._not_na = not_na


        # Update the column names
        col = self._col_var
        col_order = self._col_order

        if col is None:
            col_names = []
        else:
            col_names = utils.categorical_order(data[col], col_order)

        self.col_names = col_names

        return

test_data.csv