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python - Replacing Header with Top Row - Stack Overflow Header refer to the Row number (s) to use as the column names Make no mistake, the row number is not the df but from the excel file (0 is the first row, 1 is the second and so on) This way, you will get the column name you want and won't have to write additional codes or create new df Good thing is, it drops the replaced row
Pandas: Get first row value of a given column - Stack Overflow If you select by column first, a view can be returned (which is quicker than returning a copy) and the original dtype is preserved In contrast, if you select by row first, and if the DataFrame has columns of different dtypes, then Pandas copies the data into a new Series of object dtype So selecting columns is a bit faster than selecting rows
How to get First and Last record from a sql query? In SQL Server, if you have a table with columns timestamp and action, your first query generates one row for every row in events On the first row, created_dt is the first timestamp as expected, but last_update_dt is always equal to created_dt That is, it's the last value seen to that point in the data retrieval Perhaps PostGreSQL is different
sql - Best way to get 1st record per partition: FIRST_VALUE vs ROW . . . SELECT * FROM ( SELECT a,b,c, ROW_NUMBER() OVER ( PARTITION by a, b ORDER BY date DESC) as row_num FROM T ) WHERE row_num =1 But it probably does extra work behind the scene - I need only 1st row per partition Approach #2 using FIRST_VALUE () Since FIRST_VALUE () returns expression let pack concatenate a,b,c using some separator into single expression, e g :
Select first row in each GROUP BY group? - Stack Overflow I'd like to select the first row of each set of rows grouped with a GROUP BY Specifically, if I've got a purchases table that looks like this: SELECT * FROM purchases; My Output: id customer tota
Get first row of dataframe in Python Pandas based on criteria Get first row where A > 6 (returns row 4 by ordering it by A desc and get the first one) I was able to do it by iterating on the DataFrame (I know that craps :P) So, what would be a more pythonic way to solve it?