frequency of time series read with `read_csv`

I f I read a regular time series (with a fixed frequency) with read_csv, the resulting DataFrame has no freuqency.

In [82]: lines = ['2012-07-27T13:%02d, %d' % (min, index) for index, min in enumerate(range(1, 59))]

In [83]: input = StringIO.StringIO('\n'.join(lines))

In [84]: df = pd.read_csv(input, parse_dates=True, index_col=0, names='A')

In [85]: df.index
Out[85]: 
<class 'pandas.tseries.index.DatetimeIndex'>
[2012-07-27 13:01:00, ..., 2012-07-27 13:58:00]
Length: 58, Freq: None, Timezone: None

What I would like to have, is a possibility to set the frequency without resampling (or maybe read_csv could guess a frequency).

[dacoex]

[Edit: corrected code snippet]

So what is the best way to set the frequency directly from the csv file dates?

df = df.asfreq(pd.infer_freq(df.index))

And why can a infer_freq no be part of the read_csv function

[patricktokeeffe]

This should not implemented using a freq argument if the intent is to pass in some numeric value. If the read_csv function is called with parse_dates=True then the date parsing function should attempt to determine the frequency on it’s own. It’s nonsense to expect the frequency to be known and passed to the parsing function. If the user can specify a value for freq they might as well reindex it after loading.

Maybe I could understand adding a determine_freq boolean argument…

Better still (for the user), parse the dates then try and determine the frequency anyway using pd.infer_freq. It took me quite a while to find this function; since it exists, I’m a little puzzled the date parser doesn’t use it by by default.