Time-series plotting inconsistencies
Say I have a dataframe `df` where `df.index` consists of `datetime` objects, e.g. 

```
> df.index[0]
datetime.date(2014, 5, 5)
```

If I plot it, Pandas nicely preserves the `datetime` type in the plot, which allows the user to change the time-series sampling as well formatting options of the plot:

```
  # Plot the dataframe:
  f     = plt.figure(figsize=(8,8))
  ax    = f.add_subplot(1,1,1)
  lines = df.plot(ax=ax)

  # Choose the sampling rate in terms of dates:
  ax.xaxis.set_major_locator(matplotlib.dates.WeekdayLocator(byweekday=(0,1,2,3,4,5,6),
                                                            interval=1))

  # We can also re-sample the X axis numerically if we want (e.g. every 4 steps):
  N = 4

  ticks      = ax.xaxis.get_ticklocs()
  ticklabels = [l.get_text() for l in ax.xaxis.get_ticklabels()]

  ax.xaxis.set_ticks(ticks[-1::-N][::-1])
  ax.xaxis.set_ticklabels(ticklabels[-1::-N][::-1])

  # Choose a date formatter using a date-friendly syntax:
  ax.xaxis.set_major_formatter(matplotlib.dates.DateFormatter('%b\n%d'))

  plt.show()
```

However, the above does **not** work for a `boxplot` (the tick labels for the x axis are rendered empty):

```
df2.boxplot(column='A', by='created_dt',ax=ax, sym="k.")

# same code as above ...
```

It looks like in the last example Pandas converts the x-axis labels into **string** type, so the formatter and locators don't work anymore.

This post re-uses solutions from the following threads:
1. Accepted answer to [Pandas timeseries plot setting x-axis major and minor ticks and labels](http://stackoverflow.com/a/13674286/283296) 
2. Accepted answer to http://stackoverflow.com/questions/19143857

Why? How can I use `boxplot` in a way that allows me to use `matplotlib` date locators and formatters?

The original post in SO is [here](http://stackoverflow.com/questions/24665990/time-series-plotting-inconsistencies-in-pandas)
