Use array.prototype.map and array.prototype.some: var values = [ { name: ‘someName1’ }, { name: ‘someName2’ }, { name: ‘someName4’ }, { name: ‘someName2’ } ]; var valueArr = values.map(function(item){ return item.name }); var isDuplicate = valueArr.some(function(item, idx){ return valueArr.indexOf(item) != idx }); console.log(isDuplicate);
A better approach would be: new Date().valueOf(); instead of new Date().getUTCMilliseconds(); valueOf() is “most likely” a unique number. http://www.w3schools.com/jsref/jsref_valueof_date.asp.
.NET 4 and later only Good news, everyone! The perfect tool for this job is built in .NET 4 and it’s called ConditionalWeakTable<TKey, TValue>. This class: can be used to associate arbitrary data with managed object instances much like a dictionary (although it is not a dictionary) does not depend on memory addresses, so is … Read more
Not the most efficient, but straight forward and concise: if len(x) > len(set(x)): pass # do something Probably won’t make much of a difference for short lists.
Yes, you can. Since GUIDs are 128 bits long, there is admittedly a minute possibility of a clash—but the word “minute” is nowhere near strong enough. There are so many GUIDs that if you generate several trillion of them randomly, you’re still more likely to get hit by a meteorite than to have even one … Read more
A very simple answer if you say you don’t care which address is used. SELECT CName, MIN(AddressLine) FROM MyTable GROUP BY CName If you want the first according to, say, an “inserted” column then it’s a different query SELECT M.CName, M.AddressLine, FROM ( SELECT CName, MIN(Inserted) AS First FROM MyTable GROUP BY CName ) foo … Read more
Use Array#uniq with a block: @photos = @photos.uniq { |p| p.album_id }
You need nunique: df = df.groupby(‘domain’)[‘ID’].nunique() print (df) domain ‘facebook.com’ 1 ‘google.com’ 1 ‘twitter.com’ 2 ‘vk.com’ 3 Name: ID, dtype: int64 If you need to strip ‘ characters: df = df.ID.groupby([df.domain.str.strip(“‘”)]).nunique() print (df) domain facebook.com 1 google.com 1 twitter.com 2 vk.com 3 Name: ID, dtype: int64 Or as Jon Clements commented: df.groupby(df.domain.str.strip(“‘”))[‘ID’].nunique() You can retain … Read more
You can put the values as the table’s keys. For example: function addToSet(set, key) set[key] = true end function removeFromSet(set, key) set[key] = nil end function setContains(set, key) return set[key] ~= nil end There’s a more fully-featured example here.
You need to do the subselect based on a column name, not *. For example, if you had an id field common to both tables, you could do: SELECT * FROM Table1 WHERE id NOT IN (SELECT id FROM Table2) Refer to the MySQL subquery syntax for more examples.