Discretizing a Datetime Column into 10-Minute Bins

Overview

In this article, we will explore how to discretize a datetime column in pandas DataFrames into 10-minute bins. We will discuss different approaches and provide code examples to help you achieve this.

Problem Statement

Given a DataFrame with a datetime column, we want to divide it into two blocks (day and night or am/pm) and then discretize the time in each block into 10-minute bins. This can be achieved by adding an indicator column for every 10-minute interval.

Solution Approach

We will use lambda expressions to loop through each observation from the series and create a new column with indicator values based on the 10-minute intervals.

Step 1: Import Required Libraries and Load Data

import pandas as pd
from datetime import datetime

# make dataframe
df = pd.DataFrame({
    'started_at': ['14:20:56', 
                   '00:13:24', 
                   '16:01:33']
})

print(df)

Step 2: Convert Datetime Column to Pandas Timestamp

We will use the pd.to_datetime() function to convert the datetime column into pandas timestamps.

df['started_at'] = pd.to_datetime(df['started_at'])

Step 3: Create Day Indicator Column

To create a day indicator column, we can use a lambda expression that checks if the hour is greater than 12. This will give us an indicator value of 1 for days after 12 PM and 0 otherwise.

df['day'] = df['started_at'].apply(lambda ts: 1 if ts.hour > 12 else 0)

Step 4: Create Indicator Columns for Every 10-Minute Interval

We will create new columns with indicator values based on the 10-minute intervals. We can use nested loops to achieve this.

for i in range(24):
    for j in range(6):
        col = 'hour_' + str(i) + '_min_' + str(j) + '0'
        df[col] = df['started_at'].apply(lambda ts: 1 if int(ts.minute/10) == j and ts.hour == i else 0)

Step 5: Print the DataFrame

After creating all the indicator columns, we can print the DataFrame to verify our results.

print(df)

Example Output

The output of the code will be a DataFrame with additional columns indicating the 10-minute intervals.

| started_at | day | hour_0_min_00 | hour_0_min_10 | hour_0_min_20 | … | | — | — | — | — | — | … | | 2021-11-21 14:20:56 | 1 | 0 | 0 | 0 | … | | 2021-11-21 00:13:24 | 0 | 0 | 1 | 0 | … | | 2021-11-21 16:01:33 | 1 | 0 | 0 | 0 | … |

Conclusion

In this article, we demonstrated how to discretize a datetime column in pandas DataFrames into 10-minute bins. We used lambda expressions to create new columns with indicator values based on the time intervals. This approach can be applied to various datasets and use cases where time interval discretization is necessary.

Additional Considerations

• Timezone Handling: If your dataset contains dates from different timezones, you may need to handle timezone conversions before discretizing the datetime column.
• Date Range Limitation: The above approach assumes that the date range is within a single year. If you need to handle larger date ranges, you can use pandas’ built-in functions for date manipulation and grouping.

Best Practices

• Code Readability: Use clear and concise variable names and function descriptions to improve code readability.
• Testing: Test your code thoroughly before applying it to large datasets to avoid errors and unexpected results.

By following these guidelines and using the provided code examples, you should be able to discretize your datetime column into 10-minute bins effectively.

Last modified on 2023-08-13