My application involves dealing with data (contained in a CSV) which is of the following form:
Epoch (number of seconds since Jan 1, 1970), Value 1368431149,20.3 1368431150,21.4 ..
Currently i read the CSV using numpy loadtxt method (can easily use read_csv from Pandas). Currently for my series i am converting the timestamps field as follows:
timestamp_date=[datetime.datetime.fromtimestamp(timestamp_column[i]) for i in range(len(timestamp_column))]
I follow this by setting timestamp_date as the Datetime index for my DataFrame. I tried searching at several places to see if there is a quicker (inbuilt) way of using these Unix epoch timestamps, but could not find any. A lot of applications make use of such timestamp terminology.
Convert them to
np.array([1368431149, 1368431150]).astype('datetime64[s]') # array([2013-05-13 07:45:49, 2013-05-13 07:45:50], dtype=datetime64[s])
You can also use pandas to_datetime:
df['datetime'] = pd.to_datetime(df["timestamp"], unit='s')
This method requires Pandas 0.18 or later.