Issue 1:
I'm working in Jupyter/IPython to plot an amount of Words per Day, but am having trouble using datetimes with Regplot. The issues seems to be somewhat akin to #257, though I'm not quite sure of the difference between lmplot and regplot in this regard.

A minimal working example, without datetimes but simple timestamps:

%matplotlib inline
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.dates as dates
import seaborn as sns
import time
import datetime
import radar
sns.set(style="whitegrid", color_codes=True)

data = pd.DataFrame([])

for i in np.arange(1, 10):
date = radar.random_datetime(start='2016-05-20', stop='2016-05-25')
data = data.append(pd.DataFrame({'Date': time.mktime(date.timetuple()), 'Words': i + 100}, index=[0]), ignore_index=True)

points = plt.scatter(x = data['Date'], y = data["Words"], c=data["Words"], s=75, cmap="BrBG")
plt.colorbar(points)
sns.regplot(x = data['Date'], y = data["Words"], data=data, scatter=False, color='r')

Which renders a scatterplot with an overlaid regression-line. With the dates as datetimes:

points = plt.scatter(x = pd.to_datetime(data['Date'], unit='s').dt.to_pydatetime(), y = data["Words"], c=data["Words"], s=75, cmap="BrBG")
plt.colorbar(points)
sns.regplot(x = pd.to_datetime(data['Date'], unit='s').dt.to_pydatetime(), y = data["Words"], data=data, scatter=False, color='r')

However, this latter code fails with the following:

---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-7-d6488afe3dcb> in <module>()
1 points = plt.scatter(x = pd.to_datetime(data['Date'], unit='s').dt.to_pydatetime(), y = data["Words"], c=data["Words"], s=75, cmap="BrBG")
2 plt.colorbar(points)
----> 3 sns.regplot(x = pd.to_datetime(data['Date'], unit='s').dt.to_pydatetime(), y = data["Words"], data=data, scatter=False, color='r')

C:\Python\WinPython-64bit-3.5.2.2Qt5\python-3.5.2.amd64\lib\site-packages\seaborn\linearmodels.py in regplot(x, y, data, x_estimator, x_bins, x_ci, scatter, fit_reg, ci, n_boot, units, order, logistic, lowess, robust, logx, x_partial, y_partial, truncate, dropna, x_jitter, y_jitter, label, color, marker, scatter_kws, line_kws, ax)
777 scatter_kws["marker"] = marker
778 line_kws = {} if line_kws is None else copy.copy(line_kws)
--> 779 plotter.plot(ax, scatter_kws, line_kws)
780 return ax
781

C:\Python\WinPython-64bit-3.5.2.2Qt5\python-3.5.2.amd64\lib\site-packages\seaborn\linearmodels.py in plot(self, ax, scatter_kws, line_kws)
330 self.scatterplot(ax, scatter_kws)
331 if self.fit_reg:
--> 332 self.lineplot(ax, line_kws)
333
334 # Label the axes

C:\Python\WinPython-64bit-3.5.2.2Qt5\python-3.5.2.amd64\lib\site-packages\seaborn\linearmodels.py in lineplot(self, ax, kws)
375
376 # Fit the regression model
--> 377 grid, yhat, err_bands = self.fit_regression(ax)
378
379 # Get set default aesthetics

C:\Python\WinPython-64bit-3.5.2.2Qt5\python-3.5.2.amd64\lib\site-packages\seaborn\linearmodels.py in fit_regression(self, ax, x_range, grid)
207 yhat, yhat_boots = self.fit_logx(grid)
208 else:
--> 209 yhat, yhat_boots = self.fit_fast(grid)
210
211 # Compute the confidence interval at each grid point

C:\Python\WinPython-64bit-3.5.2.2Qt5\python-3.5.2.amd64\lib\site-packages\seaborn\linearmodels.py in fit_fast(self, grid)
222 grid = np.c_[np.ones(len(grid)), grid]
223 reg_func = lambda _x, _y: np.linalg.pinv(_x).dot(_y)
--> 224 yhat = grid.dot(reg_func(X, y))
225 if self.ci is None:
226 return yhat, None

C:\Python\WinPython-64bit-3.5.2.2Qt5\python-3.5.2.amd64\lib\site-packages\seaborn\linearmodels.py in <lambda>(_x, _y)
221 X, y = np.c_[np.ones(len(self.x)), self.x], self.y
222 grid = np.c_[np.ones(len(grid)), grid]
--> 223 reg_func = lambda _x, _y: np.linalg.pinv(_x).dot(_y)
224 yhat = grid.dot(reg_func(X, y))
225 if self.ci is None:

C:\Python\WinPython-64bit-3.5.2.2Qt5\python-3.5.2.amd64\lib\site-packages\numpy\linalg\linalg.py in pinv(a, rcond)
1614 a, wrap = _makearray(a)
1615 _assertNoEmpty2d(a)
-> 1616 a = a.conjugate()
1617 u, s, vt = svd(a, 0)
1618 m = u.shape[0]

AttributeError: 'datetime.datetime' object has no attribute 'conjugate'

Though the scatterplot does render with the datetimes well-formatted, also on its own. Is there some special method for using datetimes with regplot, or a way to use the timestamps but still format the labels on the x-axis?

Issue 2:
I would like to use lmplot to visualize polling data for multiple candidates as demonstrated in this notebook, but I get TypeError: invalid type promotion.

I can convert the dates to a Days integer, but then I don't get the date-based axis labels I want. This kind of use of dates is handled transparently when I use R and ggplot2.

Here is the code inline:

import pandas as pd
import seaborn as sns
import StringIO

DATA = """Date,Candidate,Percent
2014-06-01,Smith,43
2014-06-01,Perkins,33
2014-06-05,Smith,44
2014-06-05,Perkins,31
2014-06-10,Smith,49
2014-06-10,Perkins,28
2014-06-12,Smith,49
2014-06-12,Perkins,25
2014-06-19,Smith,49
2014-06-19,Perkins,27"""
df = pd.read_csv(StringIO.StringIO(DATA), parse_dates=["Date"])

# This gives the TypeError
sns.lmplot("Date", "Percent", df, hue="Candidate")

# Converting the date objects to an integer works, but gives
# an innapropriately formatted table.
df["Day"] = df["Date"].map(lambda d: d.day)
sns.lmplot("Day", "Percent", df, hue="Candidate")