How to Convert a Timezone-Aware Timestamp to Naive Local Time in Pandas?

How to Convert a Timezone-Aware Timestamp to Naive Local Time in Pandas

In pandas, the tz_localize function is used to create a timestamp or DateTimeIndex that is timezone-aware. However, when dealing with data that is already timezone-aware, there may be a need to convert it back to a naive timestamp while preserving the timezone information for local time.

One option is to set the timezone to None, but this results in the time being converted to UTC. To avoid this, pandas provides the tz_localize(None) function, which effectively removes the timezone information while preserving the user-visible time in the local timezone.

For example, consider the following timezone-aware DateTimeIndex:

t = pd.date_range(start="2013-05-18 12:00:00", periods=2, freq='H', tz="Europe/Brussels")

Using tz_localize(None), we can convert it to a naive local time:

t_naive_local = t.tz_localize(None)

The resulting index will have the same times as before, but without the timezone information:

t_naive_local

DatetimeIndex(['2013-05-18 12:00:00', '2013-05-18 13:00:00'],
               dtype='datetime64[ns]', freq='H')

Additionally, pandas also provides the tz_convert(None) function, which removes the timezone information and converts the time to UTC, resulting in naive UTC time.

This conversion is significantly more efficient than the alternative approach of using the datetime.replace method, as demonstrated by the following timings:

%timeit t.tz_localize(None)
1000 loops, best of 3: 233 µs per loop

%timeit pd.DatetimeIndex([i.replace(tzinfo=None) for i in t])
10 loops, best of 3: 99.7 ms per loop

By leveraging these functions, it is easy to convert timezone-aware pandas data to naive local time or UTC, preserving the user-visible time and enhancing the efficiency of data handling.

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