Understanding how Python stores data can lead to more efficient memory usage. For example, using arrays from the 'array' module instead of lists for homogeneous data can save space because arrays ...

Dask is a Python library that allows you to use familiar data structures like Pandas dataframe and Numpy array and process them in parallel. In addition, Dask comes with a set of functionalities that ...

Note that device memory data structures such as rmm:: ... In Python, memory event logging is enabled when the logging parameter of rmm.reinitialize() ... Subsequent allocations will draw from this ...

Selecting the right data structures can significantly impact memory usage. For instance, using sets or dictionaries for membership tests and unique values is more memory-efficient than lists.

Another under-the-hood example: While dataframes can in theory use any kind of under-the-hood data layout, many of them use columnar storage. Data is laid out in a column-wise format, instead of ...

Data scientists and other developers who are frustrated with the inability to see what’s going on with their hybrid Python-C/C++ applications will appreciate Memray, a new open source memory profiler ...