Is there a more pythonic way to implement elementwise addition for named tuples? Using this class that inherits from a namedtuple generated class named “Point I can do elementwise addition for this specific named tuple. If we use this functionality: The result is: Is there a more pythonic way to do this in general? Is there a generalized way to
Tag: namedtuple
Search substring in list of dictionaries of namedtuples keyed with an event type
I have created a list of dictionaries of named tuples, keyed with an event type. What would be the most pythonic method to return/print results that only contain “approved=1”, or “reviewed=1” and “approved=0”? Answer Not sure what output format you want exactly, but here’s a demo for approved == 1.
How to group data from a list of namedtuples
In python, I have the following data in a list of namedtuple in memory: I want to group the data by : cluster cluster and host cluster and host and database cluster and host and database and diskgroup I won’t need the disk details. In each group I want to : sum the values of read_bytes_per_sec and write_bytes_per_sec compute the
namedtuple._source not working in python 3.7
I’ve tried with this code But when I run it it says that there is no attribute _source (for t and therefore also for i). Has it been eliminated since 3.6? Answer Yes, as stated here, the attribute _source has been removed from namedtuples in Python 3.7. Changed in version 3.7: Remove the verbose parameter and the _source attribute.
Data Classes vs typing.NamedTuple primary use cases
Long story short PEP-557 introduced data classes into Python standard library, that basically can fill the same role as collections.namedtuple and typing.NamedTuple. And now I’m wondering how to separate the use cases in which namedtuple is still a better solution. Data classes advantages over NamedTuple Of course, all the credit goes to dataclass if we need: mutable objects inheritance support
How to cut my addiction to Python dictionaries
So, I have a large 30k line program I’ve been writing for a year. It basically gathers non-normalized and non-standardized data from multiple sources and matches everything up after standardizing the sources. I’ve written most everything with ordered dictionaries. This allowed me to keep the columns ordered, named and mutable, which made processing easier as values can be assigned/fixed throughout