Скачать или смотреть How to Create Conditional Columns in Pandas DataFrames Using Datetime

Learn how to effectively categorize time periods in a Pandas DataFrame by creating new columns based on datetime conditions.
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Handling Datetime Conditions in Pandas DataFrames

Working with datasets often requires you to generate new insights from existing data. One common requirement is to categorize data based on datetime conditions. In this guide, we'll tackle a specific problem where we need to create a new column in a Pandas DataFrame based on defined time periods: 'Night', 'Morning', and 'Afternoon'.

The Problem Statement

Imagine you are working with a DataFrame containing log records, including timestamps, and your goal is to label each record based on the time of day. Specifically, we want:

'Night' for the hours between 20:00 and 06:00,

'Morning' for the time between 06:00 and 14:30,

'Afternoon' for the time between 14:30 and 20:00.

Let's see how we can achieve this in Python using the Pandas library.

Setting Up Your Data

First, we need to set up our DataFrame with datetime data. We'll create a sample DataFrame with various datetime entries:

[[See Video to Reveal this Text or Code Snippet]]

This creates our DataFrame, converting the Date column to a datetime format that Pandas can work with.

Applying Conditional Logic

To classify the times into 'Morning', 'Afternoon', and 'Night', we can use np.select from the NumPy library. Here’s how to structure that logic:

Import the necessary libraries:
Make sure to import numpy (it's common to import it as np):

[[See Video to Reveal this Text or Code Snippet]]

Define your conditions:
We will set up a list of conditions based on the time of the day:

[[See Video to Reveal this Text or Code Snippet]]

Use np.select:
Pass the conditions and the corresponding labels to np.select, providing a default value for 'Night':

[[See Video to Reveal this Text or Code Snippet]]

Output and Result

After applying the above steps, let's examine the transformed DataFrame:

[[See Video to Reveal this Text or Code Snippet]]

The output will look like this:

[[See Video to Reveal this Text or Code Snippet]]

Important Note

When creating conditions, you only need to specify the conditions for 'Morning' and 'Afternoon'. The 'Night' condition is automatically checked by using the default parameter in np.select, which handles all other times.

That's it! You now have a DataFrame that classifies times of the day, helping you analyze time-related data more effectively. By following these simple steps, you can customize datetime conditions to suit your specific needs in any dataset you work with.

With this knowledge, go ahead and categorize your data today! Happy coding!