根据另一列Python，Pandas中的值删除一列的重复项

I have a dataframe like this:

我有一个这样的数据帧：

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 duplicates dates and keep the with the smallest distance.

我试图保持最小距离的日期，所以删除重复日期并保持最小距离。

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

有没有办法在pandas的df.drop_duplicates中实现这一点，还是我坚持使用if语句来找到最小的距离？

3 个解决方案

#1

Sort by distances and drop by dates:

按距离排序并按日期排序：

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

#2

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

这些方法的优点是它不需要排序。

Option 1
You can identify the index values for the minimum values with idxmin and you can use it within a groupby. Use these results to slice your dataframe.

选项1您可以使用idxmin标识最小值的索引值，并且可以在groupby中使用它。使用这些结果来切割数据帧。

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

Option 2
You can use pd.DataFrame.nsmallest to return the rows associated with the smallest distance.

选项2您可以使用pd.DataFrame.nsmallest返回与最小距离关联的行。

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

#3

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

我会说先排序数据然后删除重复的日期：

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

#1