Methods to Deal with Time Zones and Timestamps Precisely with Pandas

Time-based information could be distinctive once we face totally different time-zones. Nonetheless, decoding timestamps could be onerous due to these variations. This information will enable you to handle time zones and timestamps with the Pandas library in Python.

Preparation

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

Now, we’ll discover the right way to work with time-based information in Pandas with sensible examples.
 

Dealing with Time Zones and Timestamps with Pandas

Time information is a novel dataset that gives a time-specific reference for occasions. Probably the most correct time information is the timestamp, which incorporates detailed details about time from yr to millisecond.

Let’s begin by making a pattern dataset.

import pandas as pd

information = {
    '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(information)
df['timestamp'] = pd.to_datetime(df['timestamp'])

The ‘timestamp’ column within the instance above incorporates time information with second-level precision. To transform this column to a datetime format, we must always use the pd.to_datetime perform.”

Afterward, we will make the datetime information 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 prevailing time-zone to a different one. For instance, I used the UTC column and adjusted 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 response to 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 information would permit us to carry out time-series resampling. Let us take a look at an instance of knowledge resampling hourly for every column in our dataset.

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

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

Extra Sources

Cornellius Yudha Wijaya is an information science assistant supervisor and information author. Whereas working full-time at Allianz Indonesia, he likes to share Python and information ideas through social media and writing media. Cornellius writes on quite a lot of AI and machine studying subjects.