Overlapping boxplots in python

Since you're using pandas already, why not use the boxplot method on dataframes?

expl_var = 'Av_Temp'
cname = 'Tot_Precip'
df[expl_var+'_Deciles'] = pandas.qcut(df[expl_var], 2)

ax = df.boxplot(by='Av_Temp_Deciles', column='Tot_Precip')
ax.set_yscale('log')

That produces this: https://i.sstatic.net/20KPx.png

If you don't like the labels, throw in a

plt.xlabel('');plt.suptitle('');plt.title('')

If you want a standard boxplot, the above should be fine. My understanding of the separation of boxplot into boxplot_stats and bxp is to allow you to modify or replace the stats generated and fed to the plotting routine. See https://github.com/matplotlib/matplotlib/pull/2643 for some details.

If you need to draw a boxplot with non-standard stats, you can use boxplot_stats on 2D numpy arrays, so you only need to call it once. No loops required.

expl_var = 'Av_Temp'
cname = 'Tot_Precip'
df[expl_var+'_Deciles'] = pandas.qcut(df[expl_var], 2)

# I moved your nan check into the df apply function
grp_df = df.groupby('Av_Temp_Deciles').apply(lambda x: numpy.array(x[cname][~numpy.isnan(x[cname])]))

# boxplot_stats can take a 2D numpy array of data, and a 1D array of labels
# stats is now a list of dictionaries of stats, one dictionary per quantile 
stats = cbook.boxplot_stats(grp_df.values, labels=grp_df.index)

# now it's a one-shot plot, no loops
fig, ax = plt.subplots()
ax.bxp(stats)
ax.set_yscale('log')