Category : filter

So I have an object JSON_1 looks like below: { "data": { "Company": { "meta": { "meta1": "abcd", "date": "2021-06-16T00:00:00.000Z" }, "data": [ { "catagories": { "fruits": [ { "Apples": { "name": "apple", "number": "100", "buy_date": "2021-01-01", "price": "5.55" }, "status": "fresh" }, { "Bananas": { "name": "banana", "number": "100", "buy_date": "2021-01-01", "price": "6.5" }, ..

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The example is in the picture. How could I drop rows with non-unique values in column ‘signal’? cols = [‘signal’, ‘metabolite’, ‘adduct’, ‘s_ind’, ‘m_ind’, ‘a_ind’, ‘distance’] data = [[0.500001, 1.000002, -0.5, 1, 1, 2, 0.000001], [0.500001, 0.000002, 0.5, 1, 2, 1, 0.000001], [0.500002, 1.000002, -0.5, 2, 1, 2, 0.000000], [0.500002, 0.000002, 0.5, 2, 2, 1, ..

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I am attempting to filter my DF to remove entries with counts less than 100. the Data frame results from the "COMBINED" is below: Row(movieID=26, avg(rating)=3.452054794520548, count=73) when I run the code below, I get the following error: TypeError: ‘>=’ not supported between instances of ‘method’ and ‘int’ movieDataset = spark.createDataFrame(movies) movieratings=movieDataset.groupBy("movieID").mean().drop("avg(movieID)") topMovieIDs=movieDataset.groupBy("movieID").count() combined=movieratings.join(topMovieIDs, on=["movieID"], ..

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