rexdeng Goto Github PK

national-toxic-pollutants

Exercises related to data science examining the 2018 TRI Preliminary Dataset.

nicolelook_drinking-water-contaminant-classification

The California Dept of Public Health estimates that 85 percent of California’s community public water systems, supplying more than 30 million residents, rely on groundwater for at least part of their drinking water supply. Because many community water systems are entirely reliant on groundwater for their drinking water supply, contamination of this resource can have far-reaching consequences. Many groundwater basins throughout California are contaminated with either naturally occurring or anthropogenic pollutants, or both. When a groundwater source is contaminated, community water systems must use costly treatment systems to ensure that the water is safe to drink. Where treatment and alternative water supplies are not available, some community water systems serve contaminated groundwater until a solution is implemented. Contamination of the state’s groundwater resources results in higher costs for ratepayers and consumers due to the necessity of additional treatment and can pose a threat to public health for community water systems that cannot afford the necessary treatment systems. The identification of a relationship between ambient groundwater quality and community water systems that rely on a given groundwater source may help focus available efforts and resources to ensure the provision of safe drinking water. Due to the enormity of the data available, the scope of this project was selected to pertain to samples collected from Kern County from 2012 - present. This is a follow up to a legislative report published in 2013 about communities in California that rely on contaminated groundwater as a water source, identifying Kern County as having the most community water systems that are 100% reliant on groundwater.

niwa_python_seminars

A series of IPython notebooks on Python for data analysis geared towards environmental sciences

pointpolygon

Repo for Simulating and Testing Point Polygon Strategies

pubtrends-nature-reviews

The source code for PubTrends topics optimization

pubtrends-review

Automatic generation of reviews of scientific papers

pydata-book

Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media

pydata-book-1

机器学习之数据分析《利用Python进行数据分析》Jupyter notebook

pyexam

A python-django based online exam system

python

All Algorithms implemented in Python

python-1

最良心的 Python 教程：

python-100-days

Python - 100天从新手到大师

python-100-days-1

出处：https://github.com/jackfrued/Python-100-Days.git

python-basic

老齐（qiwsir）的Python基础教程Gitbook版

python-for-data-analysis

An introduction to data science using Python and Pandas with Jupyter notebooks.

python-for-environmental-science

Repo for the Jupyter notebooks for a programming course at the Justus-Liebig-University Giessen

python-for-environmental-science-1

python-language-programming

电子科技大学2020年《Python语言程序设计》的平时作业和期末课设。平时作业共4次，每次有25道编程题和100道单项选择题；课设题目是自动组卷评卷考试系统。

python-practices

Project(s) derived from "Python for Environmental Engineering" Graduate course, taken during 2018.1 trimester at Federal University of Santa Catarina. Advisor: Konrad Ziemowit Miotlinski.

python_data_analysis

Introduction to Data Analysis with Python for UW Foster School of Business

python_data_structures_and_algorithms

Python 中文数据结构和算法教程

python_for_data_analysis_2nd_chinese_version

《利用Python进行数据分析·第2版》

pythondatasciencehandbook

Python Data Science Handbook: full text in Jupyter Notebooks

pythonfordataanalysis

利用Python进行数据分析学习笔记以及自己寻找数据集做的练习

pythonscience

由机械工业出版社出版的python金融大数据分析，python学习手册三本经典书籍以及利用python进行数据分析机器代码。

qsar-tools

Scripts for assisting in modeling quantitative structure activity relationships from 2D chemical data

release

Deep Reinforcement Learning for de-novo Drug Design

shiny-gallery

Code and other documentation for apps in the Shiny Gallery ✨

simstrat

Simstrat - 1D lake model

smiles-enumeration

SMILES enumeration for QSAR modelling using LSTM recurrent neural networks