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I have a Python Pandas Data Frame. The df has 2 columns, I would like to sort the df by the second column.
Kappa_prod Angle
0 0.004511 -5.457840
1 0.003977 -5.312861
2 0.004476 -5.311292
3 0.003644 -117.579594
4 0.003306 -117.542817
I would like to sort the df by Angle (ascending order).
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To find duplicate columns we need to iterate through all columns of a DataFrame and for each and every column it will search if any other column exists in DataFrame with the same contents already. If yes then that column name will be stored in the duplicate column set.
To sort the DataFrame based on the values in a single column, you'll use . sort_values() . By default, this will return a new DataFrame sorted in ascending order. It does not modify the original DataFrame.
To remove duplicates of only one or a subset of columns, specify subset as the individual column or list of columns that should be unique. To do this conditional on a different column's value, you can sort_values(colname) and specify keep equals either first or last .
We can use Pandas built-in method drop_duplicates() to drop duplicate rows. Note that we started out as 80 rows, now it's 77. By default, this method returns a new DataFrame with duplicate rows removed. We can set the argument inplace=True to remove duplicates from the original DataFrame.
You can use this:
df.sort_values("Angle", inplace=True)
By default ascending=True is passed to the above call. For more information check the documentation here.
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