Loading repository data…
neo4j-graph-analytics / repository
Jupyter notebooks showing how to use Neo4j Graph Algorithms
= Neo4j Graph Algorithms Jupyter Notebooks
This repository contains Jupyter Notebooks for each of the https://neo4j-contrib.github.io/neo4j-graph-algorithms/[Neo4j graph algorithms^].
== Path finding
== Centrality
== Community Detection
== Run the notebooks locally
If we want to run the notebooks locally we need to setup Python and Neo4j environments.
=== Python
I use https://virtualenv.pypa.io/en/stable/[virtualenv^] but that's just one option. We can run the following set of commands to create a Python environment with the libraries installed:
virtualenv a
. a/bin/activate
pip install -r requirements.txt
=== Neo4j
We'll also need to have a Neo4j server, with the Graph Algorithms library installed, running locally. The easiest way to do this is to download the Neo4j Desktop from http://neo4j.com/download[neo4j.com/download^].
Once we've done that we can create a project and then install Graph Algorithms from the Plugins section.
image::images/installation.png[]
=== Launching Jupyter
We're now ready to launch the Jupyter server.
The notebooks assume that there's a Neo4j server running at localhost:7687 with username neo4j and password neo.
You can override these values with environment variables.
NEO4J_HOST="bolt://localhost:7687" NEO4J_USER="neo4j" NEO4J_PASSWORD="neo" jupyter notebook
Navigate to http://localhost:8888/notebooks - the notebooks will be waiting!
Selected from shared topics, language and repository description—not editorial ratings.
Living-with-machines /
Tutorial code showing how to put a D3 JavaScript visualisation in a Python Jupyter notebook.
vunturi /
Project2_Grp5 To run Flask App in command prompt use $python app.py Heroku Link: https://salty-reaches-91530.herokuapp.com/ Not working, could not find log on Heroku Dashboard with failed/ error message DashApp_V2 folder data folder raw data folder csv files from API calls dataMathNaional.csv dataReadNational.csv jupyter notebook file for cleaning called data creating new dataframes and connecting new dataframes to postgres Clean csv files: female_math_data.csv female_reading_data.csv male_math_data.csv male_reading_data.csv schema.sql creating tables for new database in postgres db folder store the sqlite db file for running flask app (app.py) static folder css folder style.css file contains styling for index.html js folder app.js - Javascript functions that will call data from the app.py file to render visuals for Metadata webpage template folder chart.html - Chart.js bar charts showing the percent change in test scores for all states by gender math.html - National Math Scores Data Table. Uses jquery read.html - National Reading Scoes Data Table. Uses jquery index.html - Metadata tables showing boys vs girls test scores for each subject Flask App (app.py) routes to call data and html files Source Data Math URLS - 4th Grade Boys & Girls Math Scores from 2009 and 2017 National Mean https://www.nationsreportcard.gov/Dataservice/GetAdhocData.aspx?type=sigacrossyear&subject=mathematics&grade=4&subscale=MRPCM&variable=GENDER&jurisdiction=NT,AL,AZ,AK,AR,CA,CO,CT,DE,DC,FL,GA,HI,ID,IL,IN,IA,KS,KY,LA,ME,MD,MA,MI,MN,MS,MO,MT,NE,NV,NH,NJ,NM,NY,NC,ND,OH,OK,OR,PA,RI,SC,SD,TN,TX,UT,VT,VA,WA,WV,WI,WY&stattype=MN:MN&Year=2017,2009 Reading URLS - 4th Grade Boys & Girls Reading Scores from 2009 and 2017 National Mean https://www.nationsreportcard.gov/Dataservice/GetAdhocData.aspx?type=sigacrossyear&subject=reading&grade=4&subscale=RRPCM&variable=GENDER&jurisdiction=NT,AL,AZ,AK,AR,CA,CO,CT,DE,DC,FL,GA,HI,ID,IL,IN,IA,KS,KY,LA,ME,MD,MA,MI,MN,MS,MO,MT,NE,NV,NH,NJ,NM,NY,NC,ND,OH,OK,OR,PA,RI,SC,SD,TN,TX,UT,VT,VA,WA,WV,WI,WY&stattype=MN:MN&Year=2017,2009 API URL LEGEND baseURL = https://www.nationsreportcard.gov/Dataservice/GetAdhocData.aspx? type=sigacrossyear (difference in test scores between 2009 & 2017) &subject=reading &subject=mathematics &grade=4 &subscale=RRPCM (reading) &subscale=MWPCM (math) &variable=GENDER &jurisdiction=NT (National); &jurisdiction=NP (National public); &jurisdiction=NR (National private); (add states after juridicition) &stattype=MN:MN (Mean) &Year=2009, 2017 Data cleaned using Jupyter Notebook Data transformed into sqlite db file using pandas in Jupyter Notebook