How to Flatten and Remove Duplicates from a List of Lists of datetime64 Using Numpy or Pandas

Discover how to effectively flatten and remove duplicates from a list of lists containing `pandas` timestamps using `numpy` or `pandas` in Python.
---

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: How to flatten and remove duplicates from a list of lists of datetime64 using numpy or pandas or Python

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Flattening and Removing Duplicates from Lists of Timestamps in Python

Have you ever found yourself working with a list of lists containing pandas timestamps and needed to flatten that structure while also removing any duplicates? This is a common task in data manipulation, especially when dealing with time series data. In this guide, we'll walk you through a straightforward solution using Python's powerful libraries: Pandas and Numpy.

Problem Overview

Let's start by clarifying the problem statement. You might have a data structure represented as follows:

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

This nested list captures datetime ranges, and for our needs, we want to:

Flatten the input list.

Remove duplicates from the list.

Convert the timestamps to a specific string format (e.g., "%Y/%m/%d/%H").

You might be wondering: should you tackle this using pandas, numpy, or pure Python? Let's explore elegant solutions using both pandas and numpy.

Solution Using Pandas

If you want to capture every hour between two timestamps (assuming a minimum of 2 hours difference), you can follow these steps:

Step 1: Transform Input into DataFrame

First, manage the input by creating a DataFrame:

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

Step 2: Compute Hour Differences

You'll want to subtract the 'left' column from the 'right' column to calculate the hour differences:

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

Step 3: Expand the Left Column Using repeat()

You can use the repeat function to create a long list of timestamps:

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

Step 4: Add Hours and Drop Duplicates

Now, you want to group by the existing timestamps while adding the hours and ensuring no duplicates survive:

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

Result

After executing the above code, you'll get a flattened list of unique hours as follows:

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

Solution Using Numpy

If the hour differences between timestamps are not a concern and you only want to flatten your data and remove duplicates, you can use numpy as follows:

Step 1: Create a DataFrame

Just like before, start with the DataFrame:

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

Step 2: Flatten and Remove Duplicates

Use numpy’s ravel function to flatten your DataFrame and then apply unique to eliminate duplicates:

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

Conclusion

Incorporating the functionalities of pandas and numpy can make your data processing tasks much easier and efficient. Whether you need to carefully track hours or simply flatten your list and remove duplicates, both libraries offer optimized solutions for your needs.

With these methods in your toolkit, you can confidently handle lists of datetime64 in Python and present your data in a clear and organized manner.