Drop duplicates of one column based on the value in another column, Python, Pandas

Question

I have a dataframe like this:

Date                PlumeO      Distance
2014-08-13 13:48:00  754.447905 5.844577 
2014-08-13 13:48:00  754.447905 6.888653
2014-08-13 13:48:00  754.447905 6.938860
2014-08-13 13:48:00  754.447905 6.977284
2014-08-13 13:48:00  754.447905 6.946430 
2014-08-13 13:48:00  754.447905 6.345506
2014-08-13 13:48:00  754.447905 6.133567
2014-08-13 13:48:00  754.447905 5.846046 
2014-08-13 16:59:00  754.447905 6.345506 
2014-08-13 16:59:00  754.447905 6.694847 
2014-08-13 16:59:00  754.447905 5.846046 
2014-08-13 16:59:00  754.447905 6.977284 
2014-08-13 16:59:00  754.447905 6.938860 
2014-08-13 16:59:00  754.447905 5.844577 
2014-08-13 16:59:00  754.447905 6.888653 
2014-08-13 16:59:00  754.447905 6.133567 
2014-08-13 16:59:00  754.447905 6.946430

I'm trying to keep the date with the smallest distance, so drop the dates of the duplicates and keep them with the smallest distance.

Is there a way to achieve this in pandas' df.drop_duplicatesor am I stuck using if statements to find the smallest distance?

Answer 1

Sort by distance and by date:

df.sort_values('Distance').drop_duplicates(subset='Date', keep='first')
Out: 
                   Date      PlumeO  Distance
0   2014-08-13 13:48:00  754.447905  5.844577
13  2014-08-13 16:59:00  754.447905  5.844577

Answer 2

The advantage of these approaches is that it does not require sorting.

1
idxmin, groupby. , .

df.loc[df.groupby('Date').Distance.idxmin()]

                   Date      PlumeO  Distance
0   2014-08-13 13:48:00  754.447905  5.844577
13  2014-08-13 16:59:00  754.447905  5.844577

---

2
pd.DataFrame.nsmallest , .

df.groupby('Date', group_keys=False).apply(
    pd.DataFrame.nsmallest, n=1, columns='Distance'
)

                   Date      PlumeO  Distance
0   2014-08-13 13:48:00  754.447905  5.844577
13  2014-08-13 16:59:00  754.447905  5.844577

Answer 3

I would say to sort the data first and then reset the duplicate dates:

stripped_data = df.sort_values('distance').drop_duplicates('date', keep='first')