django-postgres-copy californiacivicdata.org

django-postgres-copy django-postgres-copy 0.0.5 documentation

Why and what for? Quickly load comma-delimited data into a Django model using PostgreSQLs COPY command. Why and what for? Who made this library are data journalists. We are often downloading, cleaning and analyzing new data. That means we write a load of loaders. You can usually do this by looping through each row and saving it to the database using the Djangos ORM create method. But if you have a big CSV, Django will rack up database queries and it can take a long time to finish. Give it the model.

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Django CAL-ACCESS documentation django-calaccess documentation

Technical documentation for a collection of applications that make it easier to work with CAL-ACCESS. The jumbled, dirty and difficult government database that tracks campaign finance and lobbying activity in California politics. Built using the Django web framework. This is a work in progress. It is maintained by the California Civic Data Coalition. An open-source team of journalists and computer programmers from news organizations across America. To learn more and get involved, read our backstory in.

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django-postgres-copy django-postgres-copy 0.0.5 documentation

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Why and what for? Quickly load comma-delimited data into a Django model using PostgreSQLs COPY command. Why and what for? Who made this library are data journalists. We are often downloading, cleaning and analyzing new data. That means we write a load of loaders. You can usually do this by looping through each row and saving it to the database using the Djangos ORM create method. But if you have a big CSV, Django will rack up database queries and it can take a long time to finish. Give it the model.

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The domain states the following, "Why and what for? Quickly load comma-delimited data into a Django model using PostgreSQLs COPY command." We observed that the web page stated " Why and what for? Who made this library are data journalists." It also stated " We are often downloading, cleaning and analyzing new data. That means we write a load of loaders. You can usually do this by looping through each row and saving it to the database using the Djangos ORM create method. But if you have a big CSV, Django will rack up database queries and it can take a long time to finish."

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