I wish did fix the issue. How much down time can you incur? How big are the rows? How many are you deleting? Simply put, deleting rows is one of the most expensive things you can do to a table. It's just a horrible thing overall.
What are the differences between git remote prune, git prune, git fetch --prune, etc
wish of those help I don't blame you for getting frustrated about this. The best way to look at is this. There are potentially three versions of every remote branch: The actual branch on the remote repository
it should still fix some issue Based on the ideas from @Mitch's answer, I created a solution with a slightly different thinking than originally presented in the question. Instead of creating the list (bits_list) of all combinations and then pruning those combinations that do not match the sets listed, I built bits_list from the sets.
all_sets = [[0, 1, 2], [3, 4, 5], [6, 7], , [9, 19, 29], [10, 20, 30],
[11, 21, 31], [12, 22, 32], ...[57, 58], ... , , ]
bits_list = [list(itertools.chain.from_iterable(x)) for y in [1, 2, 3, 4, 5]
for x in itertools.combinations(all_sets, y)]
How to quickly create edge lists (itertools combinations style) from a boolean indexed pandas dataframe (or other fast s
help you fix your problem I'm attempting to create an edgelist (a unique set of a;b, a;c, a;f, etc, where a;b == b;a) from a very large (long) pandas dataframe which has two columns. The edge lists required are between all combinations of rows of one column conditional on the other column having the same value. An example below shows this: , How about this?
In : df1.groupby('A')['B'].apply(lambda x : list(itertools.combinations(x,2)))
Clive [(Apples, Pears), (Apples, Bananas), (Pears, B...
John [(Bananas, Pears)]
Mary [(Apples, Oranges), (Apples, Strawberries), (O...
Name: B, dtype: object
How to efficiently and quickly find valid combinations out of an array of string elements for employee scheduling?
it fixes the issue I think this is an example of the famous Nurse scheduling problem. This problem is NP-hard, i.e. to find an optimal solution you had to create all possible combinations of assignments, and select one that fits best. Since this is of exponential time complexity, it is only feasible for small problems, and yours is apparently already too large. If you only want to find a reasonable (not the optimal) solution, one could apply general-purpose stochastic algorithms, as they are cited in the Wiki post mentioned above, e.g. stochastic optimization, genetic algorithms, and simulated annealing. But such methods have typically long computation times.