I want to turn a list of interspersed values into an array of dictionaries. I'm trying to make a list of prediction values in Python capable to be used with a JSON API. Once I have the dictionary, I'll use json.dumps on it.
I am familiar with the concept of "vectorization", and how pandas employs vectorized techniques to speed up computation. Vectorized functions broadcast operations over the entire series or DataFrame to achieve speedups much greater than conventionally iterating over the data.
Say I have a list [2, 3, 7, 2, 3, 8, 7, 3]I would like to produce lists that contain identical values from the list above.
I've written the following (trivial) function:I'd've expected this to be desugared as:In turn, desugared (ignoring the fail stuff) as:
Lets say I have the following list in python. It is ordered first by Equip, then by Date:What I want to do is collapse the list by each set where a given piece of Equipment's job does not change, and grab the first and last date the equipment was there....
I want to compare each element of a list with corresponding element of another list to see if it is greater or lesser.
I have two lists:I want to return a list with objects only from list1 but if the same important_key1 and important_key2 is in any element in list2 I want this element from list2.
How can I do the following in Python's list comprehension?Essentially in this example, zero out the rest of the list once a 0 occurs.
I have a dict-I want to use list comprehension only to achieve this output-A simple for loop gets it done with -
Suppose we have a list of numbers, l. I need to COUNT all tuples of length 3 from l, (l_i,l_j,l_k) such that l_i evenly divides l_j, and l_j evenly divides l_k. With the stipulation that the indices i,j,k have the relationship i<j<k