Bringing open data to affordable housing decision makers in Washington DC. A D3/Javascript based website to visualize data related to affordable housing in Washington DC. Data processing with Python.
The project table has 3 boolean fields indicating whether a particular project was found in each of the sources. These need to be populated as a post-processing step.
In the project table the unique_data_id column represents where the project was first found, but if a later source has the same record it won't be reflected in the project data. Since the 'subsidies' table always has a record for each project regarldess of where it was first found, use the 'subsidies' table to identify which data sources each project was found in. Construct a sql query and python code that returns a unique list of nlihc_ids and returns true for each column if there was at least one matching unique_data_id for that data source. Bulk update the project table with this data.
The project table has 3 boolean fields indicating whether a particular project was found in each of the sources. These need to be populated as a post-processing step.
In the project table the
unique_data_idcolumn represents where the project was first found, but if a later source has the same record it won't be reflected in the project data. Since the 'subsidies' table always has a record for each project regarldess of where it was first found, use the 'subsidies' table to identify which data sources each project was found in. Construct a sql query and python code that returns a unique list of nlihc_ids and returns true for each column if there was at least one matching unique_data_id for that data source. Bulk update the project table with this data.