psmattas/covid19-dashboard
Archived
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Error Checks Cleared

Browse Source
This commit is contained in:
Puranjay Savar Mattas
2021-05-14 20:20:36 +05:30
parent c82cbbb542
commit 6d8081dc55
1 changed files with 1 additions and 1 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
README.md
View File

@@ -149,7 +149,7 @@ Date
* [Django:](https://www.djangoproject.com/) Django is a high-level Web framework that encourages rapid development and clean, pragmatic design. * [Django:](https://www.djangoproject.com/) Django is a high-level Web framework that encourages rapid development and clean, pragmatic design.
* [gunicorn:](https://pypi.org/project/gunicorn/) Gunicorn ‘Green Unicorn’ is a Python WSGI HTTP Server for UNIX. It’s a pre-fork worker model ported from Ruby’s Unicorn project. The Gunicorn server is broadly compatible with various web frameworks, simply implemented, light on server resource usage, and fairly speedy. * [gunicorn:](https://pypi.org/project/gunicorn/) Gunicorn ‘Green Unicorn’ is a Python WSGI HTTP Server for UNIX. It’s a pre-fork worker model ported from Ruby’s Unicorn project. The Gunicorn server is broadly compatible with various web frameworks, simply implemented, light on server resource usage, and fairly speedy.
* [numpy:](https://pypi.org/project/numpy/) Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases. * [numpy:](https://pypi.org/project/numpy/) Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.
*[pandas:](https://pypi.org/project/pandas/) pandas is a Python package that provides fast, flexible, and expressive data structures designed to make working with structured (tabular, multidimensional, potentially heterogeneous) and time series data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way toward this goal. * [pandas:](https://pypi.org/project/pandas/) pandas is a Python package that provides fast, flexible, and expressive data structures designed to make working with structured (tabular, multidimensional, potentially heterogeneous) and time series data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way toward this goal.
* [Plotly:](https://plotly.com/) The leading front-end for ML & data science models in Python, R, and Julia. * [Plotly:](https://plotly.com/) The leading front-end for ML & data science models in Python, R, and Julia.
* [psycopg2-binary:](https://pypi.org/project/psycopg2-binary/) Psycopg 2 is mostly implemented in C as a libpq wrapper, resulting in being both efficient and secure. It features client-side and server-side cursors, asynchronous communication and notifications, “COPY TO/COPY FROM” support. Many Python types are supported out-of-the-box and adapted to matching PostgreSQL data types; adaptation can be extended and customized thanks to a flexible objects adaptation system. * [psycopg2-binary:](https://pypi.org/project/psycopg2-binary/) Psycopg 2 is mostly implemented in C as a libpq wrapper, resulting in being both efficient and secure. It features client-side and server-side cursors, asynchronous communication and notifications, “COPY TO/COPY FROM” support. Many Python types are supported out-of-the-box and adapted to matching PostgreSQL data types; adaptation can be extended and customized thanks to a flexible objects adaptation system.
* [pycodestyle:](https://pypi.org/project/pycodestyle/) pycodestyle is a tool to check your Python code against some of the style conventions in PEP 8. * [pycodestyle:](https://pypi.org/project/pycodestyle/) pycodestyle is a tool to check your Python code against some of the style conventions in PEP 8.