Data Processing

In part one of this blog series , I explored the motivation behind developing a personal recommendation system. The main goals are to learn how recommendation systems work and to build a tool that helps me find interesting blog posts and articles from feeds where only 1 in 20 posts might match my content interests. If you are interested in the technical implementation, the complete codebase is available in this github repository . ...

Today I came across this brilliant python library called leopards which allows you to do some basic but frequently used filters and aggregations on python lists. I"ve always used pandas for any quick and adhoc work with large lists or CSV files, but leopards sounds a quick and performant alternative when you don"t need to do any fancy data analysis work. Leopards provides following filters: eq: equals and this default filter gt: greater than. gte: greater than or equal. lt: less than lte: less than or equal in: the value in a list of a tuple. e.g. age__in=[10,20,30] contains: contains a substring as in the example. icontains: case-insensitive contains. startswith: checks if a value starts with a query strings. istartswith: case-insensitive startswith. endswith: checks if a value ends with a query strings. iendswith: case-insensitive endswith. isnull: checks if the value matches any of NULL_VALUES which are ("", ".", None, "None", "null", "NULL") e.g. filter__isnull=True or filter__isnull=False A quick example of its usage with filters: ...