Time series usually comes with a fixed frequency, for example, every microsecond, every second, every minute, and so on. These frequencies can be changed from one to another. 

We can use the asfreq function to change frequencies, as shown in the following snippet:

    In [131]: # Frequency conversion using asfreq
              ibmTS.asfreq('BM')
    Out[131]: 1959-06-30    448
        1959-07-31    428
        1959-08-31    425
        1959-09-30    411
        1959-10-30    411
        1959-11-30    428
        1959-12-31    439
        1960-01-29    418
        1960-02-29    419
        1960-03-31    445
        1960-04-29    453
        1960-05-31    504
        1960-06-30    522
        Freq: BM, Name: closingPrice, dtype: float64
  

In this case, we just obtain the values corresponding to the last day of the month from the ibmTS time series. Here, bm stands for business month end frequency. For a list of all possible frequency aliases, go to http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases.

If we specify a frequency that is smaller than the granularity of the data, the gaps will be filled in with NaN values:

    In [132]: ibmTS.asfreq('H')
    Out[132]: 1959-06-29 00:00:00    445
        1959-06-29 01:00:00    NaN
        1959-06-29 02:00:00    NaN
        1959-06-29 03:00:00    NaN
        ...
        1960-06-29 23:00:00    NaN
        1960-06-30 00:00:00    522
        Freq: H, Name: closingPrice, Length: 8809
  

We can also apply the asfreq method to the Period and PeriodIndex objects, similar to how we do it for the datetime and Timestamp objects. Period and PeriodIndex are introduced later and are used to represent time intervals.

The asfreq method accepts a method argument that allows you to forward fill (ffill) or back fill the gaps, similar to fillna:

    In [140]: ibmTS.asfreq('H', method='ffill') Out[140]: 1959-06-29 00:00:00 445 1959-06-29 01:00:00 445 1959-06-29 02:00:00 445 1959-06-29 03:00:00 445 ... 1960-06-29 23:00:00 522 1960-06-30 00:00:00 522 Freq: H, Name: closingPrice, Length: 8809