NumPy's arrays are more compact than Python lists -- a list of lists as you describe, in Python, would take at least 20 MB or so, while a NumPy 3D array with single-precision floats in the cells would fit in 4 MB. Access in reading and writing items is also faster with NumPy. Maybe you don't care that much for just a million cells, but you definitely would for a billion cells -- neither ...
The colon, :, is what tells Python you're giving it a slice and not a regular index. That's why the idiomatic way of making a shallow copy of lists in Python 2 is
In Python, when should you use lists and when tuples? Sometimes you don't have a choice, for example if you have "hello %s you are %s years old" % x then x must be a tuple. But if I am the one who
Closed 3 years ago. I'm basically looking for a python version of Combination of List<List<int>> Given a list of lists, I need a new list that gives all the possible combinations of items between the lists.
One common application for this technique is to avoid deeply nested loops. See Avoiding nested for loops for a more specific duplicate. Similarly, this technique might be used to "explode" a dictionary with list values; see Combine Python Dictionary Permutations into List of Dictionaries . If you want a Cartesian product of the same list with itself multiple times, itertools.product can handle ...
1 Dictionary: A python dictionary is used like a hash table with key as index and object as value. List: A list is used for holding objects in an array indexed by position of that object in the array. Set: A set is a collection with functions that can tell if an object is present or not present in the set.
