semvec/pypi-packages huggingface dataset:
lark2ldap 0.1.0
Django app,to sync feishu contacts to ldap.
License: MIT
Requires Python: >=3.8,<4.0

# Poetry Template

Django app template, using `poetry-python` as dependency manager.

This project is a template that can be cloned and re-used for
redistributable apps.

It includes the following:

* `poetry` for dependency management
* `isort`, `black`, `pyupgrade` and `flake8` linting
* `pre-commit` to run linting
* `mypy` for type checking
* `tox` and Github Actions for builds and CI

There are default config files for the linting and mypy.

## Principles

The motivation for this project is to provide a consistent set of
standards across all YunoJuno public Python/Django projects. The
principles we want to encourage are:

* Simple for developers to get up-and-running
* Consistent style (`black`, `isort`, `flake8`)
* Future-proof (`pyupgrade`)
* Full type hinting (`mypy`)

## Versioning

We currently support Python 3.7+, and Django 3.2+. We will aggressively
upgrade Django versions, and we won't introduce hacks to support
breaking changes - if Django 4 introduces something that 2.2 doesn't
support we'll drop it.

## Tests

#### Tests package

The package tests themselves are _outside_ of the main library code, in
a package that is itself a Django app (it contains `models`, `settings`,
and any other artifacts required to run the tests (e.g. `urls`).) Where
appropriate, this test app may be runnable as a Django project - so that
developers can spin up the test app and see what admin screens look
like, test migrations, etc.

#### Running tests

The tests themselves use `pytest` as the test runner. If you have
installed the `poetry` evironment, you can run them thus:

```
$ poetry run pytest
```

or

```
$ poetry shell
(my_app) $ pytest
```

The full suite is controlled by `tox`, which contains a set of
environments that will format, lint, and test against all
support Python + Django version combinations.

```
$ tox
...
______________________ summary __________________________
  fmt: commands succeeded
  lint: commands succeeded
  mypy: commands succeeded
  py37-django22: commands succeeded
  py37-django32: commands succeeded
  py37-djangomain: commands succeeded
  py38-django22: commands succeeded
  py38-django32: commands succeeded
  py38-djangomain: commands succeeded
  py39-django22: commands succeeded
  py39-django32: commands succeeded
  py39-djangomain: commands succeeded
```

#### CI

There is a `.github/workflows/tox.yml` file that can be used as a
baseline to run all of the tests on Github. This file runs the oldest
(2.2), newest (3.2), and head of the main Django branch.
dask-deltalake 0.0.1
Dask + Deltalake
License: MIT
Requires Python: >=3.8.1,<4

## Dask Deltalake
Reads and write to deltalake from Dask leveraging delta-rs

## Dask Deltalake Reader

Reads data from Deltalake with Dask

To Try out the package:

```
pip install dask_deltalake
```

### Features:
1. Reads the parquet files based on delta logs parallely using dask engine
2. Supports all three filesystem like s3, azurefs, gcsfs
3. Supports some delta features like
   - Time Travel
   - Schema evolution
   - parquet filters
     - row filter
     - partition filter
4. Query Delta commit info - History
5. vacuum the old/ unused parquet files
6. load different versions of data using datetime.

### Usage:

```
import dask_deltalake as ddl

# read delta table
ddl.read_delta("delta_path")

# read delta table for specific version
ddl.read_delta("delta_path",version=3)

# read delta table for specific datetime
ddl.read_delta("delta_path",datetime="2018-12-19T16:39:57-08:00")


# read delta complete history
ddl.read_delta_history("delta_path")

# read delta history upto given limit
ddl.read_delta_history("delta_path",limit=5)

# read delta history to delete the files
ddl.vacuum("delta_path",dry_run=False)

# Can read from S3,azure,gcfs etc.
ddl.read_delta("s3://bucket_name/delta_path",version=3)
# please ensure the credentials are properly configured as environment variable or
# configured as in ~/.aws/credential

# can connect with AWS Glue catalog and read the complete delta table (currently only AWS catalog available)
# will take expilicit AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY from environment
# variables if available otherwise fallback to ~/.aws/credential
ddl.read_delta(catalog=glue,database_name="science",table_name="physics")

```