How to plot timestamps in python using matplotlib?

Question

I have been searching about this on entire google, but it looks like I am not able to find exactly what I am looking for.

So, basically, I have two lists: one list consists of timestamp data and a second list consists of values that correspond to that.

Now my issue is: my timestamps are in a following format

['Mon Sep 1 16:40:20 2015', 'Mon Sep 1 16:45:20 2015',
 'Mon Sep 1 16:50:20 2015', 'Mon Sep 1 16:55:20 2015'] 

So, which time format is used in matplotlib? I tried to plot this straightaway but it gives me:

ValueError: invalid literal 

Can I use datetime.datetime.strptime to convert it? If not, then what is the other way of doing it?

After converting the timestamp in the proper format, how should I plot the new converted timestamp with it's corresponding value?

Can I use matplotlib.pyplot.plot(time, data) or do I have to use plot_date method to plot it?

Answer 1

Yup, use strptime

import datetime
import matplotlib.pyplot as plt

x = ['Mon Sep 1 16:40:20 2015', 'Mon Sep 1 16:45:20 2015',
    'Mon Sep 1 16:50:20 2015', 'Mon Sep 1 16:55:20 2015']
y = range(4)

x = [datetime.datetime.strptime(elem, '%a %b %d %H:%M:%S %Y') for elem in x]

(fig, ax) = plt.subplots(1, 1)
ax.plot(x, y)
fig.show()

Answer 2

Well, a two-step story _{to get 'em PLOT really nice}

step 1: from a string to a datetime instance
step 2: from a datetime to a matplotlib convention compatible float for dates/times

---

As usual, devil is hidden in detail.

matplotlib dates are almost equal, but not equal:

#  mPlotDATEs.date2num.__doc__
#                  
#     *d* is either a class `datetime` instance or a sequence of datetimes.
#
#     Return value is a floating point number (or sequence of floats)
#     which gives the number of days (fraction part represents hours,
#     minutes, seconds) since 0001-01-01 00:00:00 UTC, *plus* *one*.
#     The addition of one here is a historical artifact.  Also, note
#     that the Gregorian calendar is assumed; this is not universal
#     practice.  For details, see the module docstring.

So, highly recommended to re-use their "own" tool:

from matplotlib import dates as mPlotDATEs   # helper functions num2date()
#                                            #              and date2num()
#                                            #              to convert to/from.

---

Managing axis-labels & formatting & scale (min/max) is a separate issue

Nevertheless, matplotlib brings you arms for this part too:

from matplotlib.dates   import  DateFormatter,    \
                                AutoDateLocator,   \
                                HourLocator,        \
                                MinuteLocator,       \
                                epoch2num
from matplotlib.ticker  import  ScalarFormatter, FuncFormatter

and may for example do:

    aPlotAX.set_xlim( x_min, x_MAX )               # X-AXIS LIMITs ------------------------------------------------------------------------------- X-LIMITs

    #lt.gca().xaxis.set_major_locator(      matplotlib.ticker.FixedLocator(  secs ) )
    #lt.gca().xaxis.set_major_formatter(    matplotlib.ticker.FuncFormatter( lambda pos, _: time.strftime( "%d-%m-%Y %H:%M:%S", time.localtime( pos ) ) ) )

    aPlotAX.xaxis.set_major_locator(   AutoDateLocator() )

    aPlotAX.xaxis.set_major_formatter( DateFormatter( '%Y-%m-%d %H:%M' ) )  # ----------------------------------------------------------------------------------------- X-FORMAT

    #--------------------------------------------- # 90-deg x-tick-LABELs

    plt.setp( plt.gca().get_xticklabels(),  rotation            = 90,
                                            horizontalalignment = 'right'
                                            )

    #------------------------------------------------------------------