Python for Finance: Analyze Big Financial Data
Out[57]: array([‘2016-01-01’, ‘2016-01-02’, ‘2016-01-03’], dtype=‘datetime64[D]’) In [ 58 ]: np.arange(‘2016-01-01’, ‘ ...
‘2016-01-01T00:00:08+0100’, ‘2016-01-01T00:00:09+0100’], dtype=’ ...
pandas The pandas library was specifically designed with time series data in mind. Therefore, the librar ...
Using the DatetimeIndex constructor also allows the opposite operation: In [ 74 ]: pd.DatetimeIndex(pdi) Out[74]: ...
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