You can use nones = not all(d.values()) If all values are not None, nones would be set to False, else True. It is just an abstraction though, internally it must iterate over values list.
Based on the other answers, I think the cleanest solutions are #Handles None return from get_list for item in get_list() or []: pass #do something or the comprehension equiv result = [item*item for item in get_list() or []]
It means the value of data is None.
Yes, it is fine to use None, as its behavior is specified by the documentation: The slice of s from i to j is defined as the sequence of items with index k such that i <= k < j. If i or j is greater than len(s), use len(s). If i is omitted or … Read more
Just use sum checking if each object is not None which will be True or False so 1 or 0. lst = [‘hey’,’what’,0,False,None,14] print(sum(x is not None for x in lst)) Or using filter with python2: print(len(filter(lambda x: x is not None, lst))) # py3 -> tuple(filter(lambda x: x is not None, lst)) With python3 … Read more
The documentation suggests that what you want is not possible: To make it as easy as possible to interface with modules which implement the DB API, the value None is written as the empty string. This is in the documentation for the writer class, suggesting it is true for all dialects and is an intrinsic … Read more
I think the cleanest way to do this would be: #lis = some list with NoneType’s filter(None, lis)
How about this: obj = Model.objects.filter(id=1).first() now if no object with id=1 exists obj would be None Ref: https://docs.djangoproject.com/en/3.2/ref/models/querysets/#django.db.models.query.QuerySet.first
You can use type(None) to get the type object, but you want to use isinstance() here, not type() in {…}: assert isinstance(value, (str, type(None))) The NoneType object is not otherwise exposed anywhere in Python versions older than 3.10*. I’d not use type checking for that at all really, I’d use: assert value is None or … Read more
Use fillna on one column with the fill values being the other column: df[‘foodstuff’].fillna(df[‘type’]) The resulting output: 0 apple-martini 1 apple-pie 2 strawberry-tart 3 dessert 4 None