python-dateutil

I wish to get the total duration of a relativedelta in terms of days. Expected: dateutil.timedelta(1 month, 24 days) -> dateutil.timedelta(55 days) What I tried: dateutil.timedelta(1 month, 24 days).days -> 24 (WRONG) Is there a simple way to do this? Thanks! This one bothered me as well. There isn't a very clean way to get the span of time in a particular unit. This is partly because of the date-range dependency on units. relativedelta() takes an argument for months. But when you think about how long a month is, the answer is "it depends". With that said, it's technically impossible to

Pandas internals question: I've been surprised to find a few times that explicitly passing a callable to date_parser within pandas.read_csv results in much slower read time than simply using infer_datetime_format=True . Why is this? Will timing differences between these two options be date-format-specific, or what other factors will influence their relative timing? In the below case, infer_datetime_format=True takes one-tenth the time of passing a date parser with a specified format. I would have naively assumed the latter would be faster because it's explicit. The docs do note, [if True,]

I have several CSV files with the format: Year,Day,Hour,Min,Sec.,P1'S1 2003, 1, 0, 0,12.22, 0.541 2003, 1, 1, 0,20.69, 0.708 2003, 1, 2, 0, 4.95, 0.520 2003, 1, 3, 0,13.42, 0.539 ... (where day , is the day of the year ) and I'm trying to read them using the pandas library (seems a fantastic lib so far). There is a built-in function to read CSV in pandas, and even better, that function supposedly checks the columns for a date type. and automatically uses that as an index (which would be exactly perfect for what I'm doing). The thing is, I cannot get it to work with date data in this format. I

When I try to compile the hello.py file with cx_freeze, I get the following error with pandas 0.15.2 but no error with 0.15.1. Has someone any idea on how to correct it ? I have include the dateutil/zoneinfo files with the include_files function but the problem still occurs. hello.py: import numpy as np import pandas as pd import matplotlib import matplotlib.pyplot as plt print 'hello world' df_dates = pd.date_range(start='01/01/2013', freq='H', periods=100) df = pd.DataFrame(range(len(df_dates)), index=df_dates) print df print df.info() df.plot() plt.savefig('test.png') plt.show(block=False)

I am trying to dateutil.parser.parse() to parse the default str(datetime.datetime.now()) output using timezone-aware datetimes. However, parse() seems to lose the timezone information and replace it with the local time timezone. Below is the IPython output: In [1]: from django.utils.timezone import now In [3]: import dateutil In [4]: t = now() In [6]: print t 2014-07-14 08:51:49.123342+00:00 In [7]: st = unicode(t) In [8]: print dateutil.parser.parse(st) 2014-07-14 08:51:49.123342+02:00 As far I understood dateutil does some heurestics when guessing the date format and it might go wrong here.

sudo pip install pandas Installing collected packages: numpy, six, python-dateutil, pandas Found existing installation: numpy 1.8.0rc1 Cannot uninstall 'numpy'. It is a distutils installed project and thus we cannot accurately determine which files belong to it which would lead to only a partial uninstall. You are using pip version 18.1, however version 19.1.1 is available. You should consider upgrading via the 'pip install --upgrade pip' command. sudo pip install --upgrade pip pip list|grep numpy numpy 1.8.0rc1 要求1.12版本，但是1.8版本 cd /System/Library/Frameworks/Python.framework/Versions/2.7