This package aims to carry out a preprocessing of conventional weather stations
Project description
Onestation 0.1.0
Pre-processing data from conventional weather stations
Topics
Project description
The project under development is a library/package in Python with the purpose of pre-processing conventional weather station data. One station is a Python package that has the main objective of helping developers or professionals who seek to treat and clean weather station data and apply it to a Machine Learning model aimed exclusively at precision irrigation.
The package is available on [Pypisistema](https://pypi.org/project/onestation/) it will allow the daily, continuous and remote pre-processing of the collections carried out by conventional meteorological stations, allowing professionals and developers to apply them in their work in a fast and applied way.
Application
Understand data pre-processing concepts and techniques used to transform raw data into an applicable format. Visit the link and learn more https://brain-mentors.com/concepts-of-data-pre-processing.
☑️Note: This work is being finalized and will facilitate the process of processing data from conventional meteorological stations and can also be applied or converted to automatic meteorological stations.
Functionalities
:heavy_check_mark: Functionalities 1:
Perform data cleaning.
:heavy_check_mark: Functionalities 2:
Perform data transformation.
:heavy_check_mark: Functionalities 3:
Apply data reduction.
Tools used
Project access
You may access the project source code or access the project on PyPi at the link https://pypi.org/project/onestation/
Install and run the package
To install the package you must use Pycharm or Jupyter Notebook
. For this, you must have the latest version of Python installed on your machine:
User installation
The easiest way to install onestation
is with pip
pip install onestation
If you are having difficulty installing the correct package versions, you can set up a virtual environment like this:
1- pip install virtualenv
2- virtualenv env
3- source env/bin/activate
4- curl https://bootstrap.pypa.io/get-pip.py | python3
5- pip install onestation
6- pip install ipykernel
7- ipython kernel install --user --name=env
8- jupyter notebook
9- When finished, deactivate your virtualenv with deactivate
Use
For a complete tutorial on how the package is used in Jupyter notebooks, see our Jupyter notebook demo https://colab.research.google.com/github/IRkernel/IRkernel/blob/master/example-notebooks/Demo.ipynb
For more information about the database used in the tests and to be able to download the dataset, see the link: https://www.kaggle.com/datasets/rogerioifpr/brazil-weather-conventional-stations-19612019
Help and Support
For help with using or installing the package, contact Rogerio Pereira do Santos rogerio.dosantos@ifpr.edu.br
Developer
Project details
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