Matplotlib showing x-tick labels overlapping

Question

Have a look at the graph below:

It's a subplot of this larger figure:

I see two problems with it. First, the x-axis labels overlap with one another (this is my major issue). Second. the location of the x-axis minor gridlines seems a bit wonky. On the left of the graph, they look properly spaced. But on the right, they seem to be crowding the major gridlines...as if the major gridline locations aren't proper multiples of the minor tick locations.

My setup is that I have a DataFrame called df which has a DatetimeIndex on the rows and a column called value which contains floats. I can provide an example of the df contents in a gist if necessary. A dozen or so lines of df are at the bottom of this post for reference.

Here's the code that produces the figure:

now = dt.datetime.now()  fig, axes = plt.subplots(2, 2, figsize=(15, 8), dpi=200) for i, d in enumerate([360, 30, 7, 1]):     ax = axes.flatten()[i]     earlycut = now - relativedelta(days=d)     data = df.loc[df.index>=earlycut, :]     ax.plot(data.index, data['value'])     ax.xaxis_date()      ax.get_xaxis().set_minor_locator(mpl.ticker.AutoMinorLocator())     ax.get_yaxis().set_minor_locator(mpl.ticker.AutoMinorLocator())      ax.grid(b=True, which='major', color='w', linewidth=1.5)     ax.grid(b=True, which='minor', color='w', linewidth=0.75) 

What is my best option here to get the x-axis labels to stop overlapping each other (in each of the four subplots)? Also, separately (but less urgently), what's up with the minor tick issue in the top-left subplot?

I am on Pandas 0.13.1, numpy 1.8.0, and matplotlib 1.4.x.

Here's a small snippet of df for reference:

                                    id scale  tempseries_id    value timestamp                                                            2014-11-02 14:45:10.302204+00:00  7564     F              1  68.0000 2014-11-02 14:25:13.532391+00:00  7563     F              1  68.5616 2014-11-02 14:15:12.102229+00:00  7562     F              1  68.9000 2014-11-02 14:05:13.252371+00:00  7561     F              1  69.0116 2014-11-02 13:55:11.792191+00:00  7560     F              1  68.7866 2014-11-02 13:45:10.782227+00:00  7559     F              1  68.6750 2014-11-02 13:35:10.972248+00:00  7558     F              1  68.4500 2014-11-02 13:25:10.362213+00:00  7557     F              1  68.1116 2014-11-02 13:15:10.822247+00:00  7556     F              1  68.2250 2014-11-02 13:05:10.102200+00:00  7555     F              1  68.5616 2014-11-02 12:55:10.292217+00:00  7554     F              1  69.0116 2014-11-02 12:45:10.382226+00:00  7553     F              1  69.3500 2014-11-02 12:35:10.642245+00:00  7552     F              1  69.2366 2014-11-02 12:25:12.642255+00:00  7551     F              1  69.1250 2014-11-02 12:15:11.122382+00:00  7550     F              1  68.7866 2014-11-02 12:05:11.332224+00:00  7549     F              1  68.5616 2014-11-02 11:55:11.662311+00:00  7548     F              1  68.2250 2014-11-02 11:45:11.122193+00:00  7547     F              1  68.4500 2014-11-02 11:35:11.162271+00:00  7546     F              1  68.7866 2014-11-02 11:25:12.102211+00:00  7545     F              1  69.2366 2014-11-02 11:15:10.422226+00:00  7544     F              1  69.4616 2014-11-02 11:05:11.412216+00:00  7543     F              1  69.3500 2014-11-02 10:55:10.772212+00:00  7542     F              1  69.1250 2014-11-02 10:45:11.332220+00:00  7541     F              1  68.7866 2014-11-02 10:35:11.332232+00:00  7540     F              1  68.5616 2014-11-02 10:25:11.202411+00:00  7539     F              1  68.2250 2014-11-02 10:15:11.932326+00:00  7538     F              1  68.5616 2014-11-02 10:05:10.922229+00:00  7537     F              1  68.9000 2014-11-02 09:55:11.602357+00:00  7536     F              1  69.3500 

Edit: Trying fig.autofmt_xdate(): I don't think this going to do the trick. This seems to use the same x-tick labels for both graphs on the left and also for both graphs on the right. Which is not correct given my data. Please see the problematic output below:

Answer 1

Ok, finally got it working. The trick was to use plt.setp to manually rotate the tick labels. Using fig.autofmt_xdate() did not work as it does some unexpected things when you have multiple subplots in your figure. Here's the working code with its output:

for i, d in enumerate([360, 30, 7, 1]):     ax = axes.flatten()[i]     earlycut = now - relativedelta(days=d)     data = df.loc[df.index>=earlycut, :]     ax.plot(data.index, data['value'])      ax.get_xaxis().set_minor_locator(mpl.ticker.AutoMinorLocator())     ax.get_yaxis().set_minor_locator(mpl.ticker.AutoMinorLocator())      ax.grid(b=True, which='major', color='w', linewidth=1.5)     ax.grid(b=True, which='minor', color='w', linewidth=0.75)      plt.setp(ax.get_xticklabels(), rotation=30, horizontalalignment='right')  fig.tight_layout() 

By the way, the comment earlier about some matplotlib things taking forever is very interesting here. I'm using a raspberry pi to act as a weather station at a remote location. It's collecting the data and serving the results via the web. And boy oh boy, it's really wheezing trying to put out these graphics.