pydict | DICT

as you can see in the code below, I'm having troubles adding new rows to a Table saved in a memory mapped file. I just want to write the file again with the new rows.

I have some code that is using PyDict internally to store data. A user sets the data like you would with a normal python dictionary, and the data is stored by the C code in the PyDict object. My question is this: Do I need to reference count (Py_INCREF) the value AND the key? Just the value? The documentation indicates that calling PyDict_SetItem does NOT steal a reference for the value. My understanding is that this means the PyDict object is not responsible for the value PyObject, so the reference count needs to be incremented. No mention is made of the key in the docs - does it need to be incremented as well?

A related question involves the deallocator for my code - these reference counts need to be cleaned up - is there any "short cut" to iterate on iterables in the C API and decrement all references, or will I need to manually iterate on the PyDict object and decrement all the values individually (and possibly the keys, depending on the response to the above?)

The code in question would look something like this (with questions in the comments):

I am using a library in Python called trendln

I am trying to call it through C# form. For that I have used Pythonnet Python.Runtime.Dll. My Python version is 3.7.8.

In python, it was easy to fetch the function using:

I was writing a python file and I am getting this error when pressing the tab in insert mode in vim.

I am new to vim and I just copied someone's vimrc file. this is the vimrc file

In the __enter__ method I want to return an object which is accessible in Rust and Python, so that Rust is able to update values in the object and Python can read the updated values.

I would like to have something like this:

I'm chasing a memory leak that seems to come from a long-running process which contains a C extension that I wrote. I've been poring over the code and the Extensions docs and I'm sure it's correct but I'd like to make sure regarding the reference handling of PyList and PyDict.

From the docs I gather that PyDict_SetItem() borrows references to both key and value, hence I have to DECREF them after inserting. PyList_SetItem() and PyTuple_SetItem() steal a reference to the inserted item so I don't have to DECREF. Correct?

Creating a dict:

I've got a use case I'm testing where, for performance, it's convenient to write a Cython wrapper class for C++'s unordered_map type up front, then pass the wrapper to a bunch of Cython functions that use it, rather than always passing around Python dict (keyed and valued by Python ints that fit in C int) and converting on the fly over and over. I'm also trying to bake in some basic functionality to make it behave like a dict at the Python layer, so it can be worked with without converting en masse from Python dict to Cython unordered_map wrapper and back.

My problem occurs in trying to implement __eq__ efficiently in the simplest case (comparing one wrapper instance to another). AFAICT, in theory Cython's libcpp.unordered_map pxd definition pretends unordered_map has a member operator==. This answer claims it should be fine, even though the operator== is actually a non-member function, since the definition only needs to exist so Cython knows it can just put a literal == in the code; the C++ compiler will look up the real overload when compiling the extension module.

But in practice, I can't seem to get it to work without additional manual (and hacky) intervention. For experimenting, I've just been using ipython %%cython magic. Right now what I'm doing is (minimized as much as possible while still exhibiting problem):

I'm generating Parquet files via two methods: a Kinesis Firehose and a Spark job. They are both written into the same partition structure on S3. Both sets of data can be queried using the same Athena table definition. Both use gzip compression.

I'm noticing, however, that the Parquet files generated by Spark are about 3x as large as those from Firehose. Any reason this should be the case? I do notice some schema and metadata differences when I load them using Pyarrow:

I am struggling to get kwargs to work. I understand that just like #[args(args="*")] is for args, #[args(kwargs="**")] is for kwargs.

However when I pack the following using pyo3-pack and try to call test1 with any arguments I get the error MyClass.test1()() takes at most 0 argument (1 given).

What am I doing wrong?