Pandas 'isin' with output keeping order of input list

This question is a bit old, but I stumbled into having to do this. This is how I resolved the problem. I believe it's quite a generic and simple solution that hasn't been proposed yet here, and that actually doesn't use the isin() method:

df.set_index('A').loc[[3,6]].reset_index()

With the example provided:

>>> df = pd.DataFrame({'A': [5, 6, 3, 4], 'B': [1, 2, 3, 5]})
>>> df.set_index('A').loc[[3,6]].reset_index()
   A  B
0  3  3
1  6  2

Of course, this has the disadvantage that it loses the original index. To preserve the index you could also:

>>> df.reset_index().set_index('A').loc[[3,6]].reset_index().set_index('index')
       A  B
index      
2      3  3
1      6  2

This is a bit long, but it works. isin(), then sort_values() based on the list.

df = pandas.DataFrame({'A' : [5,6,3,4], 'B' : [1,2,3,5]})
mylist = [3,6]
ndf =  df[df['A'].isin(mylist)]
ndf['sort_cat'] = pandas.Categorical(ndf['A'], categories=mylist, ordered=True)
ndf.sort_values('sort_cat', inplace=True)
ndf.reset_index(inplace=True)
print ndf
   A  B sort_cat
2  3  3        3
1  6  2        6

(I based this answer on sort pandas dataframe based on list)

Another option which filters and sorts in one shot

import pandas as pd
from functools import reduce
reduce(pd.DataFrame.append, map(lambda i: df[df.A == i], [3, 6]))

You can make the input list a dataframe and use the merge function. I've found this particularly useful for large input lists where order matters.

For example:

df = pd.DataFrame({'A': [5, 6, 3, 4], 'B': [1, 2, 3, 5]})
input = pd.DataFrame({'input': [3, 6]})
output = input.merge(df, left_on='input', right_on='A').loc[:, ['A', 'B']]
print(output)

   A  B
0  3  3
1  6  2

There are 2 caveats. First, you have to specify which column of df you are searching for the match using the 'right_on' input to the merge function. Secondly, the indices of the resulting output dataframe are re-indexed.

This is the best solution I found:

 df.iloc[pd.Index(df.A).get_indexer([3,6])]

Result:

>>> df.iloc[pd.Index(df.A).get_indexer([3,6])]
   A  B
2  3  3
1  6  2

Credit: @cs95

isin is a set operation, and pandas aligns the input, so order of the input set is normally in the same order as the reference frame

You could if you REALLY want to do this:

In [15]: df.take(df['A'][df['A'].isin([3,6])].order().index)
Out[15]: 
   A  B
2  3  3
1  6  2

[2 rows x 2 columns]

It is not the same but in my problem this solution provides me the data frame in the same order as the list in the "isin" function which is what I wanted. Take a look here

How to maintain order when selecting rows in pandas dataframe?

Perhaps it could help you.