intervaltree | A java interval tree implementation

applaunchservices appears to be Apple-only:

Simple package for registering an app with apple Launch Services to handle UTI and URL. See Apple documentations for details.

I suspect you don't need that, either. Did you create your requirements.txt from a pip freeze? There's likely a bunch of stuff in there you don't need.

I suggest you review that file and remove anything you aren't directly depending on. pip will find transitive dependencies (dependencies your dependencies depend on) and install them automatically.

Prune that file, commit, and redeploy.

Build tags in you environment.yml are quite strict requirements to satisfy and most often not needed. In your case, changing the yml file to

So, given the following dataframe:

The data structure you're looking for is a binary search tree (BST), and preferably a balanced BST. Your dictionary keys are the keys of the BST, and each node would just have an additional field to store the corresponding value. Then your lookup is just a lower-bound / bisect-left on the keys. Looking up Python implementations for Red-Black trees or AVL trees returns many possible packages.

There is no builtin library for always-sorted data. If you never need to add or delete keys, you can use bisect with (key, value) tuples in a sorted list.

For a pure Python implementation that allows modification, I would recommend checking out SortedDict from the SortedContainers library. It's built to be a drop-in replacement for BST's, is very usable and tested, and claims to outperform pointer-based BST's in memory and speed on reasonably sized datasets (but does not have the same asymptotic guarantees as a BST). You can also provide a custom key for comparing objects of different types.

I am not sure a DataFrame is the best structure to solve this problem - but here is one approach

geopandas 0.10.1

• have noted that your data is on kaggle, so start by sourcing it
• there really is only one issue shapely.geometry.MultiPoint() constructor does not work with a filtered series. Pass it a numpy array instead and it works.
• full code below, have randomly selected a point to serve as gpdPoint

It's because you are using Codable, and it is trying to decode id from the JSON. The id isn't in your JSON, and I can see you want each new instance to create its own ID.

If you printed an error in a catch statement, by using do-try-catch rather than try?, you would have seen the following:

keyNotFound(CodingKeys(stringValue: "id", intValue: nil), Swift.DecodingError.Context(codingPath: [_JSONKey(stringValue: "Index 0", intValue: 0)], debugDescription: "No value associated with key CodingKeys(stringValue: "id", intValue: nil) ("id").", underlyingError: nil))

Basically, they is no value associated with the key id, because the key is not in the JSON.

You need to create custom CodingKeys to exclude id from being encoded/decoded:

No need for a complex custom function, you can use pandas vector functions for that:

get the name of columns with at least five 1:

I have solved my issue by adding tf.compat.v1.enable_eager_execution() to import and it works fine