semvec/pypi-packages huggingface dataset:
pathlab 0.2
An object-oriented path interface to archives, images, remote filesystems, etc
Requires Python: >=3.6

=======
Pathlab
=======

|pypi| |docs|

Pathlab provides an object-oriented path interface to archives, images, remote
filesystems, etc. It is built upon pathlib_ and includes built-in support for:

- ``tar`` archives
- ``zip`` archives
- ``iso`` disc images (inc Rock Ridge; exc Joliet and UDF)
- JFrog Artifactory (via ``requests``)

You can also define your own ``Path`` subclass with its own accessor.

Installation
------------

Requires Python 3.6+. Use pip::

    pip install --user pathlab

Usage
-----

These usage examples are adapted from the pathlib_ documentation.

Getting a path type:

    >>> from pathlab import TarAccessor
    >>> TarPath = TarAccessor('project.tgz').TarPath

Listing subdirectories:

    >>> root = TarPath('/')
    >>> [x for x in root.iterdir() if x.is_dir()]
    [TarAccessor('project.tgz').TarPath('/docs')
     TarAccessor('project.tgz').TarPath('/etc'),
     TarAccessor('project.tgz').TarPath('/project')]

Listing Python source files in this directory tree:

    >>> list(root.glob('**/*.py'))
    [TarAccessor('project.tgz').TarPath('/setup.py'),
     TarAccessor('project.tgz').TarPath('/docs/conf.py'),
     TarAccessor('project.tgz').TarPath('/project/__init__.py')]

Navigating inside a directory tree:

    >>> p = TarPath('/etc')
    >>> q = p / 'init.d' / 'reboot'
    >>> q
    TarAccessor('project.tgz').TarPath('/etc/init.d/reboot')
    >>> q.resolve()
    TarAccessor('project.tgz').TarPath('/etc/rc.d/init.d/halt')

Querying path properties:

    >>> q.exists()
    True
    >>> q.is_dir()
    False

Opening a file:

    >>> with q.open() as f: f.readline()
    ...
    '#!/bin/bash\n'


.. _pathlib: https://docs.python.org/3/library/pathlib.html

.. |pypi| image:: https://img.shields.io/pypi/v/pathlab.svg
    :target: https://pypi.python.org/pypi/pathlab
    :alt: Latest version released on PyPi

.. |docs| image:: https://readthedocs.org/projects/pathlab/badge
    :target: http://pathlab.readthedocs.io/en/latest
    :alt: Documentation
arushigupta-101703106-missing1 0.0.1
A small package that handles missing values in pandas dataframe
Requires Python: >=3.6

This is a package to handle missing value in a pd dataframe.
You have to run python file as:
python file.py inputfile.csv output file.csv n,n,c,n
where last argument n,n,nc,n represents type of feature 
that is is it numerical feature or categorical feature,
if you want to correctly get rrsults without any errors please
follow instructions.
aryansindhi-101703110-missing-1 0.0.1
A small package that handles missing values in pandas dataframe
Requires Python: >=3.6

This is a package to handle missing value in a pd dataframe.
You have to run python file as:
python file.py inputfile.csv output file.csv n,n,c,n
where last argument n,n,nc,n represents type of feature 
that is is it numerical feature or categorical feature,
if you want to correctly get rrsults without any errors please
follow instructions.
MissingValues-Arsh 0.0.2
Treating Missing values in a dataset
Requires Python: >=3.6

Data in real world are rarely clean and homogeneous. Data can either be missing during data extraction or collection. Missing values need to be handled because they reduce the quality for any of our performance metric. It can also lead to wrong prediction or classification and can also cause a high bias for any given model being used.
Depending on data sources, missing data are identified differently. Pandas always identify missing values as NaN. However, unless the data has been pre-processed to a degree that an analyst will encounter missing values as NaN. Missing values can appear as a question mark (?) or a zero (0) or minus one (-1) or a blank.