I’m working on a Python project where I need to process a nested data structure. The structure consists of lists and dictionaries, and the nesting level can vary from a few levels to potentially hundreds. I need to flatten this data structure into a single list while preserving the values. However, I am facing performance issues when dealing with deep nesting.

I’m working on a Python project where I need to process a nested data structure. The structure consists of lists and dictionaries, and the nesting level can vary from a few levels to potentially hundreds. I need to flatten this data structure into a single list while preserving the values. However, I am facing performance issues when dealing with deep nesting.

Closed 3 months ago.

I have two lists where one list is filled with ID’s and the other list is filled with process names. Multiple process names can share an ID. I want to be able to create a data structure where I can use a specific ID and then return the list of processes associated with that ID. I also want to be able to use a specific process name and return the list of ID’s it’s connected with. I know I could create a dictionary for this but the list of ID’s will be very large and will continue to grow but the process names will be fairly short (fewer than 20 most likely) and will not grow as much. I’m not sure how efficient a dictionary will be in the long run. I want to be able to store the connections between the two lists locally on my computer so I can reload them into my program to add new connections for new ID’s.

I have a very large table with 10 columns and around 10,000,000 rows. This table needs to be filtered millions of times based on specific values in one or more columns. The filtering process should return all rows where at least one of the specified values exists in the given column.

I have a segment of code that is dramatically slowing down an optimization procedure to the point where it is basically not useable. I have attached the segment here and am hoping there is some way where it does not need to ‘re-map’ for every tuple I have to give it per optimization step (which is 200 tuples). So each time this function is called it does the remapping. I am hoping to define the mapping once and per each step in the optimizer it only needs to pull the new values that the optimizer changed rather than redo the mapping with this complex scheme I have developed. I have also described the code below this snippet. Any help would be appreciated!

I am working on a program in python to record data of different types. I’m thinking of a hierarchical data structure like below:
dataStructure
/
dataType1 dataType2
/ | / |
instance1 instance2 instance3 instance1 instance2 instance3