LancasterAQ 1.0.0

Python package developed for the Lancaster Air Quality project

LancasterAQ Package

Python package developed for the Lancaster Air Quality project.

Included are tools to:

• Import the dataset
• Convert the dataset to different formats

Installation Instructions

PyPI Installation

$ pip install LancasterAQ

Manual Installation

Local install

# clone from github
$ git clone https://github.com/lgouldsbrough/LancasterAQ.git
# change directory into project root
$ cd LancasterAQ

# regular install
$ pip install .
# or 
# development install 
# $ pip install -e .

Directly install from GitHub

# pip install from github
pip install git+https://github.com/lgouldsbrough/LancasterAQ.git
# or `python -m pip ...` for environment safety 

Example notebook

An introductory notebook can be found within the examples folder.

_{Note: requires Matplotlib and Seaborn packages.}

Loading in the Lancaster AQ dataset

import LancasterAQ as laq

# load tabular data
data = laq.TabularObject()

# OR load the graph object
data = laq.GraphObject()

A helper function is also available:

import LancasterAQ as laq
# load tabular data
data = laq.dataset('TabularObject')
# OR load the graph object
data = laq.dataset('GraphObject')

Convert the tabular data to different formats

To avoid implicit data copies replace the data object with the dataset function call.

For example: replace data.to_numpy() with laq.TabularObject().to_numpy()

Convert to a pandas dataframe

data = data.to_pandas()

Convert to a numpy array

data = data.to_numpy()

Convert the graph object to different formats

To avoid implicit data copies replace the data object with the dataset function call.

For example: replace data.to_numpy() with laq.GraphObject().to_numpy()

Convert to a numpy sparse array

# returns the graph adjacency matrix as a numpy array
numpy_array = data.to_numpy()

Convert to a dictionary of dictionaries

# returns adjacency representation of graph as a dictionary of dictionaries
dict_of_dicts = data.to_dict()

Convert to an edge list

# returns a list of edges in the graph
edge_list = data.to_edgelist()

Convert to a dictionary of lists

# returns adjacency representation of graph as a dictionary of lists
dict_of_lists = data.to_dict_of_lists()

Convert to a scipy sparse array

# returns the graph adjacency matrix as a scipy sparse array
scipy_sparse_array = data.to_scipy()

Convert to JSON

# returns json object of graph
data_json = data.to_json()