How to Convert a Timezone-Aware DateTimeIndex to Naive Timestamps While Preserving Local Time?

Converting Timezone-Aware DateTimeIndex to Naive Timestamps

Question:

How can you convert a timezone-aware DateTimeIndex to a naive one while preserving its timezone?

Importance:

• To avoid timezone complexities while working with timezone-aware timeseries.
• To represent timeseries in the local timezone, but without explicit timezone information.

Original Problem:

Setting the timezone to None converts the timestamp to UTC, losing the local time information.

Solution:

Starting with Pandas 0.15.0, you can use the tz_localize(None) function to remove timezone information. This retains the local time without converting to UTC. The tz_convert(None) function converts to naive UTC time.

Examples:

<code class="python"># Create a timezone-aware DateTimeIndex
t = pd.date_range(start="2013-05-18 12:00:00", periods=2, freq='H', tz="Europe/Brussels")

# Remove timezone, resulting in naive local time
t_local = t.tz_localize(None)  # Output: ['2013-05-18 12:00:00', '2013-05-18 13:00:00']

# Convert to naive UTC time
t_utc = t.tz_convert(None)  # Output: ['2013-05-18 10:00:00', '2013-05-18 11:00:00']</code>

Performance:

tz_localize(None) is significantly faster than using the datetime.replace method to remove timezone information.

The above is the detailed content of How to Convert a Timezone-Aware DateTimeIndex to Naive Timestamps While Preserving Local Time?. For more information, please follow other related articles on the PHP Chinese website!