The question was originally asked here as a comment but could not get a proper answer as the question was marked as a duplicate.
For a given pandas.Da
One way to overcome this is to make the 'A'
column an index
and use loc
on the newly generated pandas.DataFrame
. Eventually, the subsampled dataframe's index can be reset.
Here is how:
ret = df.set_index('A').loc[list_of_values].reset_index(inplace=False)
# ret is
# A B
# 0 3 3
# 1 4 5
# 2 6 2
Note that the drawback of this method is that the original indexing has been lost in the process.
More on pandas
indexing: What is the point of indexing in pandas?
1] Generic approach for list_of_values
.
In [936]: dff = df[df.A.isin(list_of_values)]
In [937]: dff.reindex(dff.A.map({x: i for i, x in enumerate(list_of_values)}).sort_values().index)
Out[937]:
A B
2 3 3
3 4 5
1 6 2
2] If list_of_values
is sorted. You can use
In [926]: df[df.A.isin(list_of_values)].sort_values(by='A')
Out[926]:
A B
2 3 3
3 4 5
1 6 2
Use merge with helper DataFrame
created by list and with column name of matched column:
df = pd.DataFrame({'A' : [5,6,3,4], 'B' : [1,2,3,5]})
list_of_values = [3,6,4]
df1 = pd.DataFrame({'A':list_of_values}).merge(df)
print (df1)
A B
0 3 3
1 6 2
2 4 5
For more general solution:
df = pd.DataFrame({'A' : [5,6,5,3,4,4,6,5], 'B':range(8)})
print (df)
A B
0 5 0
1 6 1
2 5 2
3 3 3
4 4 4
5 4 5
6 6 6
7 5 7
list_of_values = [6,4,3,7,7,4]
#create df from list
list_df = pd.DataFrame({'A':list_of_values})
print (list_df)
A
0 6
1 4
2 3
3 7
4 7
5 4
#column for original index values
df1 = df.reset_index()
#helper column for count duplicates values
df1['g'] = df1.groupby('A').cumcount()
list_df['g'] = list_df.groupby('A').cumcount()
#merge together, create index from column and remove g column
df = list_df.merge(df1).set_index('index').rename_axis(None).drop('g', axis=1)
print (df)
A B
1 6 1
4 4 4
3 3 3
5 4 5