Скачать или смотреть How to Determine If One Date is Smaller Than Another in a Pandas DataFrame and Create a New Column

Learn how to compare dates in a Pandas DataFrame and assign values to a new column based on your comparison. This guide covers step-by-step methods to achieve your goal effectively.
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How to Determine If One Date is Smaller Than Another in a Pandas DataFrame and Create a New Column

Working with dates in a Pandas DataFrame can sometimes be tricky, especially when you need to compare two dates and create a new column based on the results of that comparison. In this guide, we will explore how to check if one date (referred to as Shipment Dt) is smaller or equal to another date (labeled as theoretical ship date). We will also provide a solution to create a new column labeled on time that will contain a value of "1" if the condition is satisfied and "0" otherwise.

Problem Statement

Suppose you have a DataFrame containing shipment details, and you need to assess whether the Shipment Dt is on time compared to the theoretical ship date. If the shipment date is before or on the theoretical ship date, you want to assign a value of "1" to a new column called on time. Conversely, if the shipment date is after the theoretical ship date, you want to assign a value of "0".

For example, your DataFrame might look like this:

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

The Solution

Step 1: Create a Boolean Mask

The simplest way to compare dates in Pandas is to make use of boolean indexing. Here is how you can achieve this:

Use a boolean condition to compare the Shipment Dt and theoretical ship date.

Convert the boolean result to integers (where True becomes 1 and False becomes 0).

You can do this in just one line of code:

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

Step 2: Understanding the Code

df['Shipment Dt'] <= df['theoretical ship date']: This line checks each element of Shipment Dt against theoretical ship date and returns a Series of boolean values (True or False).

.astype(int): This method converts the boolean values in the Series into integers (1 for True, 0 for False). The result is then assigned to the new column on time in your DataFrame.

Step 3: Alternative Condition

If you wish to determine whether the Shipment Dt is before or equal to the sum of the Creation date and Lead time in days without creating a column for theoretical ship date, you can modify your method as follows:

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

Here, we're adding the lead time (converted to a timedelta) to the Creation date, and then making a similar comparison with Shipment Dt.

Conclusion

Comparing dates in a Pandas DataFrame is straightforward with the right code. By utilizing boolean indexing and conversion techniques, you can create informative columns that help evaluate shipping times effectively. Whether you need to check against a theoretical date or adjust your assessment based on lead times, the methods discussed here will streamline your data analysis processes.

If you have any questions or need further assistance, feel free to leave a comment below!