You can list all the available timezones with pytz.all_timezones:

In [40]: import pytz
In [41]: pytz.all_timezones
Out[42]: 
["Africa/Abidjan",
 "Africa/Accra",
 "Africa/Addis_Ababa",
 ...]

There is also pytz.common_timezones:

In [45]: len(pytz.common_timezones)
Out[45]: 403

In [46]: len(pytz.all_timezones)
Out[46]: 563

I recommend using python-dateutil. Its parser has been able to parse every date format I"ve thrown at it so far.

>>> from dateutil import parser
>>> parser.parse("Tue Jun 22 07:46:22 EST 2010")
datetime.datetime(2010, 6, 22, 7, 46, 22, tzinfo=tzlocal())
>>> parser.parse("Fri, 11 Nov 2011 03:18:09 -0400")
datetime.datetime(2011, 11, 11, 3, 18, 9, tzinfo=tzoffset(None, -14400))
>>> parser.parse("Sun")
datetime.datetime(2011, 12, 18, 0, 0)
>>> parser.parse("10-11-08")
datetime.datetime(2008, 10, 11, 0, 0)

and so on. No dealing with strptime() format nonsense... just throw a date at it and it Does The Right Thing.

Update: Oops. I missed in your original question that you mentioned that you used dateutil, sorry about that. But I hope this answer is still useful to other people who stumble across this question when they have date parsing questions and see the utility of that module.

Distribution Fitting with Sum of Square Error (SSE)

This is an update and modification to Saullo"s answer, that uses the full list of the current scipy.stats distributions and returns the distribution with the least SSE between the distribution"s histogram and the data"s histogram.

Example Fitting

Using the El Ni√±o dataset from statsmodels, the distributions are fit and error is determined. The distribution with the least error is returned.

All Distributions

Best Fit Distribution

Example Code

%matplotlib inline

import warnings
import numpy as np
import pandas as pd
import scipy.stats as st
import statsmodels.api as sm
from scipy.stats._continuous_distns import _distn_names
import matplotlib
import matplotlib.pyplot as plt

matplotlib.rcParams["figure.figsize"] = (16.0, 12.0)
matplotlib.style.use("ggplot")

# Create models from data
def best_fit_distribution(data, bins=200, ax=None):
    """Model data by finding best fit distribution to data"""
    # Get histogram of original data
    y, x = np.histogram(data, bins=bins, density=True)
    x = (x + np.roll(x, -1))[:-1] / 2.0

    # Best holders
    best_distributions = []

    # Estimate distribution parameters from data
    for ii, distribution in enumerate([d for d in _distn_names if not d in ["levy_stable", "studentized_range"]]):

        print("{:>3} / {:<3}: {}".format( ii+1, len(_distn_names), distribution ))

        distribution = getattr(st, distribution)

        # Try to fit the distribution
        try:
            # Ignore warnings from data that can"t be fit
            with warnings.catch_warnings():
                warnings.filterwarnings("ignore")
                
                # fit dist to data
                params = distribution.fit(data)

                # Separate parts of parameters
                arg = params[:-2]
                loc = params[-2]
                scale = params[-1]
                
                # Calculate fitted PDF and error with fit in distribution
                pdf = distribution.pdf(x, loc=loc, scale=scale, *arg)
                sse = np.sum(np.power(y - pdf, 2.0))
                
                # if axis pass in add to plot
                try:
                    if ax:
                        pd.Series(pdf, x).plot(ax=ax)
                    end
                except Exception:
                    pass

                # identify if this distribution is better
                best_distributions.append((distribution, params, sse))
        
        except Exception:
            pass

    
    return sorted(best_distributions, key=lambda x:x[2])

def make_pdf(dist, params, size=10000):
    """Generate distributions"s Probability Distribution Function """

    # Separate parts of parameters
    arg = params[:-2]
    loc = params[-2]
    scale = params[-1]

    # Get sane start and end points of distribution
    start = dist.ppf(0.01, *arg, loc=loc, scale=scale) if arg else dist.ppf(0.01, loc=loc, scale=scale)
    end = dist.ppf(0.99, *arg, loc=loc, scale=scale) if arg else dist.ppf(0.99, loc=loc, scale=scale)

    # Build PDF and turn into pandas Series
    x = np.linspace(start, end, size)
    y = dist.pdf(x, loc=loc, scale=scale, *arg)
    pdf = pd.Series(y, x)

    return pdf

# Load data from statsmodels datasets
data = pd.Series(sm.datasets.elnino.load_pandas().data.set_index("YEAR").values.ravel())

# Plot for comparison
plt.figure(figsize=(12,8))
ax = data.plot(kind="hist", bins=50, density=True, alpha=0.5, color=list(matplotlib.rcParams["axes.prop_cycle"])[1]["color"])

# Save plot limits
dataYLim = ax.get_ylim()

# Find best fit distribution
best_distibutions = best_fit_distribution(data, 200, ax)
best_dist = best_distibutions[0]

# Update plots
ax.set_ylim(dataYLim)
ax.set_title(u"El Niño sea temp.
 All Fitted Distributions")
ax.set_xlabel(u"Temp (°C)")
ax.set_ylabel("Frequency")

# Make PDF with best params 
pdf = make_pdf(best_dist[0], best_dist[1])

# Display
plt.figure(figsize=(12,8))
ax = pdf.plot(lw=2, label="PDF", legend=True)
data.plot(kind="hist", bins=50, density=True, alpha=0.5, label="Data", legend=True, ax=ax)

param_names = (best_dist[0].shapes + ", loc, scale").split(", ") if best_dist[0].shapes else ["loc", "scale"]
param_str = ", ".join(["{}={:0.2f}".format(k,v) for k,v in zip(param_names, best_dist[1])])
dist_str = "{}({})".format(best_dist[0].name, param_str)

ax.set_title(u"El Niño sea temp. with best fit distribution 
" + dist_str)
ax.set_xlabel(u"Temp. (°C)")
ax.set_ylabel("Frequency")

You can also write:

[e] * n

You should note that if e is for example an empty list you get a list with n references to the same list, not n independent empty lists.

Performance testing

At first glance it seems that repeat is the fastest way to create a list with n identical elements:

>>> timeit.timeit("itertools.repeat(0, 10)", "import itertools", number = 1000000)
0.37095273281943264
>>> timeit.timeit("[0] * 10", "import itertools", number = 1000000)
0.5577236771712819

But wait - it"s not a fair test...

>>> itertools.repeat(0, 10)
repeat(0, 10)  # Not a list!!!

The function itertools.repeat doesn"t actually create the list, it just creates an object that can be used to create a list if you wish! Let"s try that again, but converting to a list:

>>> timeit.timeit("list(itertools.repeat(0, 10))", "import itertools", number = 1000000)
1.7508119747063233

So if you want a list, use [e] * n. If you want to generate the elements lazily, use repeat.

If your program doesn"t have a event loop already, use the sched module, which implements a general purpose event scheduler.

import sched, time
s = sched.scheduler(time.time, time.sleep)
def do_something(sc): 
    print("Doing stuff...")
    # do your stuff
    s.enter(60, 1, do_something, (sc,))

s.enter(60, 1, do_something, (s,))
s.run()

If you"re already using an event loop library like asyncio, trio, tkinter, PyQt5, gobject, kivy, and many others - just schedule the task using your existing event loop library"s methods, instead.

Lock your time loop to the system clock like this:

import time
starttime = time.time()
while True:
    print "tick"
    time.sleep(60.0 - ((time.time() - starttime) % 60.0))