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What is Dask?
Dask is a Python library used working with really big datasets that don’t fit into computer’s memory or want to process with speed using multithreaded approach.

How is It Different from Pandas?
It’s similar to pandas but for handling large data, it splits it up into smaller parts and processes these parts one by one or all at once across multiple processors or even a network of computers. This makes Dask great for scaling up data work, where pandas would struggle.

Key Differences Between Dask and Pandas:

• Data Size and Memory:
  • Pandas: Works well with smaller datasets that fit into memory.
  • Dask: Works with large datasets by breaking them into chunks to process data that’s bigger than computer’s memory.
• Using Multiple Processors:
  • Pandas: Uses only one processor by default, so it’s slower for big data tasks.
  • Dask: Uses multiple processors or computers to handle tasks faster by spreading them out.
• Lazy Processing:
  • Pandas: Runs each command immediately, which can slow things down if the data is big.
  • Dask: Doesn’t actually do the work until it has a whole plan, so it’s faster with large datasets and saves memory.

When to Use Dask or Pandas?
Pandas: When data is small or medium-sized and fits into memory.
Dask: When dealing with big data or need to speed up work by using multiple processors or a network of computers or in multithreaded approach for faster processing.

Dask DataFrames is wrapper over pandas, so migration to dark is easy with least change.

Difference can be seen by processing a large file using Dask. You will see the difference. For details on Dask, visit the link below:
https://docs.dask.org/en/stable/