semvec/pypi-packages huggingface dataset:
Nummobility 0.3.Beta
A Mobility-data Preprocessing Library using parallel computation.
License: new BSD
Requires Python: >=3.6

<!---------------------- Introduction Section ------------------->
<h1> NumMobility: A Mobility Data PreProcessing Library </h1>

<h2> Introduction </h2>

<p align='justify'>
NumMobility is a state-of-the art Mobility Data Preprocessing Library that mainly deals with filtering data, generating features and interpolation of Trajectory Data.

<b><i> The main features of NumMobility are: </i></b>
</p>

<ol align='justify'>
<li> NumMobility uses primarily parallel computation based on
     python Pandas and numpy which makes it very fast as compared
     to other libraries available.
</li>

<li> NumMobility harnesses the full power of the machine that
     it is running on by using all the cores available in the
     computer.
</li>

<li> NumMobility uses a customized DataFrame built on top of python
     pandas for representation and storage of Trajectory Data.
</li>

<li> NumMobility also provides several Temporal and spatial features
     which are calculated mostly using parallel computation for very
     fast and accurate calculations.
</li>

<li> Moreover, NumMobility also provides several filteration and
     outlier detection methods for cleaning and noise reduction of
     the Trajectory Data.
</li>

<li> Apart from the features mentioned above, <i><b> four </b></i>
     different kinds of Trajectory Interpolation techniques are
     offered by NumMobility which is a first in the community.
</li>
</ol>

<!------------------------- Documentation Link ----------------->
<h2> Documentation </h2>

<span> &#8618; </span>
<a href='https://nummobility.readthedocs.io/en/latest/' target='_blank'> <i> NumMobility Documentation </i> </a>

<!-------------------- Pip Installation ------------------------->
<h2> Pip Installation </h2>

1. `pip install Nummobility`

<!------------------------ Usage Examples ----------------------->
<h2> Examples </h2>

<span> &#8618; </span>
<a href='https://github.com/YakshHaranwala/NumMobility/tree/main/examples' target='_blank'> <i> NumMobility Examples </i> </a>

<!--------------------- Binder Link ---------------------------->
<h2> Examples </h2>

<span> &#8618; </span>
<a href='https://mybinder.org/v2/gh/YakshHaranwala/NumMobility/ef4be1ed4c535e0dc9bb40226659ac9f9cecffc5' target='_blank'> <i> Binder Link </i> </a>