luisserota / nba_data_visualization

Messing around with NBA data using flask and d3

forking from https://github.com/adilmoujahid/DonorsChoose_Visualization

Getting started

The dependencies for the project can be installed using

$ pip install -r requirements.txt

Use Vagrant to start a machine with a MongoDB instance running

$ vagrant up

Initialize the database you need to download the data

$ wget http://s3.amazonaws.com/open_data/opendata_projects000.gz && gunzip opendata_projects000.gz

Add a header to the extracted opendata_projects000 file (note, make sure not to use spaces!):

_projectid,_teacher_acctid,_schoolid,school_ncesid,school_latitude,school_longitude,school_city,school_state,school_zip,school_metro,school_district,school_county,school_charter,school_magnet,school_year_round,school_nlns,school_kipp,school_charter_ready_promise,teacher_prefix,teacher_teach_for_america,teacher_ny_teaching_fellow,primary_focus_subject,primary_focus_area,secondary_focus_subject,secondary_focus_area,resource_type,poverty_level,grade_level,vendor_shipping_charges,sales_tax,payment_processing_charges,fulfillment_labor_materials,total_price_excluding_optional_support,total_price_including_optional_support,students_reached,total_donations,num_donors,eligible_double_your_impact_match,eligible_almost_home_match,funding_status,date_posted,date_completed,date_thank_you_packet_mailed,date_expiration

and import it (this will take a long time unless you trim down the file)

$ mongoimport -d donorschoose -c projects --type csv --file ./opendata_projects000 --headerline

Once imported, run app via

$ python app.py

View goodness at:

localhost:5000/