根据另一列、Python、Pandas 中的值删除一列的重复项
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Drop duplicates of one column based on value in another column, Python, Pandas
提问by Ahmed
I have a dataframe like this:
我有一个这样的数据框：
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.
我试图保持距离最小的日期，所以删除重复的日期并保持距离最小的日期。
Is there a way to achieve this in pandas' df.drop_duplicatesor am I stuck using if statements to find the smallest distance?
有没有办法在Pandas中实现这一点，df.drop_duplicates还是我坚持使用 if 语句来找到最小距离？
回答by ayhan
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
回答by piRSquared
The advantage of these approaches is that it does not require a sort.
这些方法的优点是不需要排序。
Option 1
You can identify the index values for the minimum values with idxminand you can use it within a groupby. Use these results to slice your dataframe.
选项 1
您可以使用 标识最小值的索引值，idxmin并且可以在groupby. 使用这些结果来切片您的数据框。
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.nsmallestto 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
回答by Zach O
I would say sort the data first and then drop the duplicate dates:
我会说先对数据进行排序，然后删除重复的日期：
stripped_data = df.sort_values('distance').drop_duplicates('date', keep='first')
