I have a DataFrame with repeating values in the index. I would like to filter this dataset down to only show me one instance of each index by selecting the row within the index with the greatest value in a different column. For example, my DataFrame looks like this:
df:
Product ID     Store     Sales
    1            A         50
    1            B        200
    1            C         20
    2            A        400
    2            B         10
    3            A        200
    4            A         50
    4            B        100
    4            C        500
I would like to filter this data down to this:
df2:
Product ID     Store     Sales
    1            B        200
    2            A        400
    3            A        200
    4            C        500
Any thoughts on how best to approach this issue in pandas?
Thanks very much for your time -
You can use df[df["Courses"] == 'Spark'] to filter rows by a condition in pandas DataFrame. Not that this expression returns a new DataFrame with selected rows.
The max() method returns a Series with the maximum value of each column. By specifying the column axis ( axis='columns' ), the max() method searches column-wise and returns the maximum value for each row.
You can perform a groupby on 'Product ID', then apply idxmax on 'Sales' column.
This will create a series with the index of the highest values.
We can then use the index values to index into the original dataframe using iloc
In [201]:
df.iloc[df.groupby('Product ID')['Sales'].agg(pd.Series.idxmax)]
Out[201]:
   Product_ID Store  Sales
1           1     B    200
3           2     A    400
5           3     A    200
8           4     C    500
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