How to set series frequency?
Having two time series like:

```python
import pandas
from numpy import nan


# 1-minute period
index = pandas.date_range('1/1/2010', periods=8, freq='T')
series1 = pandas.Series([1., nan, nan, nan, nan, 2., nan, nan], index=index)

# 5-minutes period
index = pandas.date_range('1/1/2010', periods=2, freq='5T')
series5 = pandas.Series([1., 2.], index=index)
```

One of them is 1-minute based, the other is 5-minutes based. They both have the same values (excluding nan) at the same timestamps.

I do not want to keep `nan` values, so I drop them. However, dropping them also drops the frequency information from the DatetimeIndex:

```python
series1 = series1.dropna()
print(series1.index.freq)
```

Now, I would really like to be able to differenciate two series like these: same timestamps, same values, but originating from different base frequencies.

In order to do so, I was able to set the frequency back "by hand":

```python
from pandas.tseries.frequencies import to_offset


series1.index.freq = to_offset('T')
```

But since 0.23 it seems I can no longer do that:

```
ValueError: Inferred frequency None from passed values does not conform to passed frequency T
```

What can I do now to keep that frequency information?

Also, is it incorrect to set a frequency "by hand" for an index that has that frequency but some missing values?