group-by

Count unique values per groups with Pandas

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I need to count unique ID values in every domain. I have data: ID, domain 123, 'vk.com' 123, 'vk.com' 123, 'twitter.com' 456, 'vk.com' 456, 'facebook.com' 456, 'vk.com' 456, 'google.com' 789, 'twitter.com' 789, 'vk.com' I try df.groupby([‘domain’, ‘ID’]).count() But I want to get domain, count vk.com 3 twitter.com 2 facebook.com 1 google.com 1 Answers: Thank you … Read more

Pandas Groupby Range of Values

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Is there an easy method in pandas to invoke groupby on a range of values increments? For instance given the example below can I bin and group column B with a 0.155 increment so that for example, the first couple of groups in column B are divided into ranges between ‘0 – 0.155, 0.155 – 0.31 …`