Plot histogram of epoch list, x axis by month-year in PyPlot

In order to do this you have to pick out the timestamps at which the beginning of each month begins. Dates/Times are always a lot trickier than just regular numbers so while this code looks a bit cumbersome, it does work.

import numpy as np
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import random

data = [int(random.randint(1293836400, 1356994800)) for _ in range(1000)]

# create your bins as timestamps marked at the beginning of each month, using datetime objects to increment
import datetime as d
mindate = d.datetime.fromtimestamp(min(data))
maxdate = d.datetime.fromtimestamp(max(data))
bindate = d.datetime(year=mindate.year, month=mindate.month, day=1)
bins = [bindate.timestamp()]
while bindate < maxdate:
    if bindate.month == 12:
        bindate = d.datetime(year=bindate.year + 1, month=1, day=1)
    else:
        bindate = d.datetime(year=bindate.year, month=bindate.month + 1, day=1)
    bins.append(bindate.timestamp())
bins = mdates.epoch2num(bins)

mpl_data = mdates.epoch2num(data)
fig, ax = plt.subplots(1,1, figsize=(16, 4), facecolor='white')
ax.hist(mpl_data, bins=bins, ec='black')
ax.xaxis.set_major_locator(mdates.MonthLocator())
ax.xaxis.set_major_formatter(mdates.DateFormatter('%d.%m.%y'))
fig.autofmt_xdate()

Another approach is to use pandas to group data by month, and then counting them. The code is much shorter, and you can make a quick bar plot. To re-create your plot above would take more work, but this gives you a feel for things you can do with other tools:

srs = pd.DatetimeIndex(pd.Series(data) * 1e9)  # convert sec to nsec
df = pd.DataFrame({'count': np.ones(shape=len(srs))}, index=srs)
fig, ax = plt.subplots(1, 1, figsize=(16,4), facecolor='white')
df.groupby(pd.Grouper(freq='M')).count().plot.bar(ax=ax)