The best way to Deal with Time Zones and Timestamps Precisely with Pandas

Time-based knowledge could be distinctive after we face completely different time-zones. Nonetheless, decoding timestamps could be laborious due to these variations. This information will make it easier to handle time zones and timestamps with the Pandas library in Python.

Preparation

On this tutorial, we’ll use the Pandas package deal. We will set up the package deal utilizing the next code.

Now, we’ll discover how one can work with time-based knowledge in Pandas with sensible examples.
 

Dealing with Time Zones and Timestamps with Pandas

Time knowledge is a singular dataset that gives a time-specific reference for occasions. Probably the most correct time knowledge is the timestamp, which accommodates detailed details about time from 12 months to millisecond.

Let’s begin by making a pattern dataset.

import pandas as pd

knowledge = {
    'transaction_id': [1, 2, 3],
    'timestamp': ['2023-06-15 12:00:05', '2024-04-15 15:20:02', '2024-06-15 21:17:43'],
    'quantity': [100, 200, 150]
}

df = pd.DataFrame(knowledge)
df['timestamp'] = pd.to_datetime(df['timestamp'])

The ‘timestamp’ column within the instance above accommodates time knowledge with second-level precision. To transform this column to a datetime format, we should always use the pd.to_datetime operate.”

Afterward, we will make the datetime knowledge timezone-aware. For instance, we will convert the information to Coordinated Common Time (UTC)

df['timestamp_utc'] = df['timestamp'].dt.tz_localize('UTC')
print(df)

Output>> 
  transaction_id           timestamp  quantity             timestamp_utc
0               1 2023-06-15 12:00:05     100 2023-06-15 12:00:05+00:00
1               2 2024-04-15 15:20:02     200 2024-04-15 15:20:02+00:00
2               3 2024-06-15 21:17:43     150 2024-06-15 21:17:43+00:00

The ‘timestamp_utc’ values include a lot data, together with the time-zone. We will convert the present time-zone to a different one. For instance, I used the UTC column and altered it to the Japan Timezone.

df['timestamp_japan'] = df['timestamp_utc'].dt.tz_convert('Asia/Tokyo')
print(df)

Output>>>
  transaction_id           timestamp  quantity             timestamp_utc  
0               1 2023-06-15 12:00:05     100 2023-06-15 12:00:05+00:00   
1               2 2024-04-15 15:20:02     200 2024-04-15 15:20:02+00:00   
2               3 2024-06-15 21:17:43     150 2024-06-15 21:17:43+00:00   

            timestamp_japan  
0 2023-06-15 21:00:05+09:00  
1 2024-04-16 00:20:02+09:00  
2 2024-06-16 06:17:43+09:00 

We might filter the information in accordance with a specific time-zone with this new time-zone. For instance, we will filter the information utilizing Japan time.

start_time_japan = pd.Timestamp('2024-06-15 06:00:00', tz='Asia/Tokyo')
end_time_japan = pd.Timestamp('2024-06-16 07:59:59', tz='Asia/Tokyo')

filtered_df = df[(df['timestamp_japan'] >= start_time_japan) & (df['timestamp_japan'] <= end_time_japan)]

print(filtered_df)

Output>>>
  transaction_id           timestamp  quantity             timestamp_utc  
2               3 2024-06-15 21:17:43     150 2024-06-15 21:17:43+00:00   

            timestamp_japan  
2 2024-06-16 06:17:43+09:00 

Working with time-series knowledge would permit us to carry out time-series resampling. Let us take a look at an instance of information resampling hourly for every column in our dataset.

resampled_df = df.set_index('timestamp_japan').resample('H').rely()

Leverage Pandas’ time-zone knowledge and timestamps to take full benefit of its options.

Further Assets

Cornellius Yudha Wijaya is an information science assistant supervisor and knowledge author. Whereas working full-time at Allianz Indonesia, he likes to share Python and knowledge suggestions by way of social media and writing media. Cornellius writes on a wide range of AI and machine studying subjects.