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06-Sep-2020 01:39

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To install dask and its requirements, open a terminal and type (you need pip for this): Now, let’s write some code to load csv data and and start analyzing it.For this example, I’m using the 311 Service Requests dataset from NYC’s Open Data portal.But Postgre SQL does The amount of data (200m records per year) is not really big and should go with any standard database engine.The case is yet easier if you do not need live reports on it. Lott suggested, you might like to read up on data warehousing.Our database requires pretty regular maintenance (VACUUM/ANALYZE) to keep it running smoothly.I could avoid some of this by more carefully optimizing autovacuum and other settings, and it's not so much of an issue if you're not doing many DELETEs.

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Other than out-of-core manipulation, dask’s dataframe uses the pandas API, which makes things extremely easy for those of us who use and love pandas.Unlike pandas, the data isn’t read into memory…we’ve just set up the dataframe to be ready to do some compute functions on the data in the csv file using familiar functions from pandas.