drop-duplicates | 易学教程

Eliminate duplicates in MongoDB with a specific sort

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问题 I have a database composed by entries which correspond to work contracts. In the MongoDB database I have aggregated by specific worker, then the database - in a simplified version - looks like something like that. { "_id" : ObjectId("5ea995662a40c63b14266071"), "worker" : "1070", "employer" : "2116096", "start" : ISODate("2018-01-11T01:00:00.000+01:00"), "ord_id" : 0 }, { "_id" : ObjectId("5ea995662a40c63b14266071"), "worker" : "1070", "employer" : "2116096", "start" : ISODate("2018-01-11T01

Eliminate duplicates in MongoDB with a specific sort

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Drop duplicate if the value in another column is null - Pandas

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问题 What I have: df Name |Vehicle Dave |Car Mark |Bike Steve|Car Dave | Steve| I want to drop duplicates from the Name column but only if the corresponding value in Vehicle column is null. I know I can use df.dropduplicates(subset=['Name']) with either Keep = either 'First' or 'Last' but what I am looking for is a way to drop duplicates from Name column where the corresponding value of Vehicle column is null . So basically, keep the Name if the Vehicle column is NOT null and drop the rest. If a

Drop duplicate if the value in another column is null - Pandas

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How to deduplicate and keep latest based on timestamp field in spark structured streaming?

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问题 Spark dropDuplicates keeps the first instance and ignores all subsequent occurrences for that key. Is it possible to do remove duplicates while keeping the most recent occurrence? For example if below are the micro batches that I get, then I want to keep the most recent record (sorted on timestamp field) for each country. batchId: 0 Australia, 10, 2020-05-05 00:00:06 Belarus, 10, 2020-05-05 00:00:06 batchId: 1 Australia, 10, 2020-05-05 00:00:08 Belarus, 10, 2020-05-05 00:00:03 Then output

Drop duplicate list elements in column of lists

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问题 This is my dataframe: pd.DataFrame({'A':[1, 3, 3, 4, 5, 3, 3], 'B':[0, 2, 3, 4, 5, 6, 7], 'C':[[1,4,4,4], [1,4,4,4], [3,4,4,5], [3,4,4,5], [4,4,2,1], [1,2,3,4,], [7,8,9,1]]}) I want to get set\drop duplicate values of column C per row but not drop duplicate rows. This what I hope to get: pd.DataFrame({'A':[1, 3, 3, 4, 5, 3, 3], 'B':[0, 2, 3, 4, 5, 6, 7], 'C':[[1,4], [1,4], [3,4,5], [3,4,5], [4,2,1], [1,2,3,4,], [7,8,9,1]]}) 回答1: If you're using python 3.7>, you could could map with dict

How to drop rows that are not exact duplicates but contain no new information (more NaN)

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问题 My goal is to collapse the below table into one single column. For this question specifically, I am asking how I can intelligently delete the yellow row because it is a duplicate of the gray row, although with less information. The table has three categorical variables and 6 analysis/quantitative variables. Columns C1 and C2 are the only variables that need to match for a successful join; all of the . All blank cells are NaNs and python code for copying is below. Question 1 . (Yellow) All of

How to join two rows that have the same keys and complementary values

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问题 My goal is to collapse the below table into one single column and this question deals specifically with the blue row below. The table has three categorical variables and 6 analysis/quantitative variables. Columns C1 and C2 are the only variables that need to match for a successful join. All blank cells are NaNs and python code for copying is below. These rows are exported independently because they have information found in other related tables, not included in the export. Question . (Blue)

Keeping the last N duplicates in pandas

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问题 Given a dataframe: >>> import pandas as pd >>> lol = [['a', 1, 1], ['b', 1, 2], ['c', 1, 4], ['c', 2, 9], ['b', 2, 10], ['x', 2, 5], ['d', 2, 3], ['e', 3, 5], ['d', 2, 10], ['a', 3, 5]] >>> df = pd.DataFrame(lol) >>> df.rename(columns={0:'value', 1:'key', 2:'something'}) value key something 0 a 1 1 1 b 1 2 2 c 1 4 3 c 2 9 4 b 2 10 5 x 2 5 6 d 2 3 7 e 3 5 8 d 2 10 9 a 3 5 The goal is to keep the last N rows for the unique values of the key column. If N=1 , I could simply use the .drop

Keeping the last N duplicates in pandas

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Given a dataframe: >>> import pandas as pd >>> lol = [['a', 1, 1], ['b', 1, 2], ['c', 1, 4], ['c', 2, 9], ['b', 2, 10], ['x', 2, 5], ['d', 2, 3], ['e', 3, 5], ['d', 2, 10], ['a', 3, 5]] >>> df = pd.DataFrame(lol) >>> df.rename(columns={0:'value', 1:'key', 2:'something'}) value key something 0 a 1 1 1 b 1 2 2 c 1 4 3 c 2 9 4 b 2 10 5 x 2 5 6 d 2 3 7 e 3 5 8 d 2 10 9 a 3 5 The goal is to keep the last N rows for the unique values of the key column. If N=1 , I could simply use the .drop_duplicates() function as such: >>> df.drop_duplicates(subset='key', keep='last') value key something 2 c 1 4 8