Pandas merge handling duplicates in join output
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1
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Is there a nice way to bring only one row, preferably random in one-to-many matching during left join in Pandas?
e.g
left = [[1,1,1], [2,2,2],[3,3,3], [9,9,9], [1,3,2]]
right = [[1,2,2],[1,2,3],[3,2,2], [3,2,9], [3,2,2]]
left = np.asarray(left)
right = np.asarray(right)
left = pd.DataFrame(left)
right = pd.DataFrame(right)
joined_left = left.merge(right, how="left", left_on=[0], right_on=[0])
So this is what we get
0 1 2
0 1 1 1
1 2 2 2
2 3 3 3
3 9 9 9
4 1 3 2
0 1 2
0 1 2 2
1 1 2 3
2 3 2 2
3 3 2 9
4 3 2 2
0 1_x 2_x 1_y 2_y
0 1 1 1 2.0 2.0
1 1 1 1 2.0 3.0
2 2 2 2 NaN NaN
3 3 3 3 2.0 2.0
4 3 3 3 2.0 9.0
5 3 3 3 2.0 2.0
6 9 9 9 NaN NaN
7 1 3 2 2.0 2.0
8 1 3 2 2.0 3.0
So now I want to have output to be of the same size as my left dataframe and when there are more than one match in right dataframe I want to bring only single random column.
Is there a nice way of doing it using pandas short cut tricks?
thank you!
python pandas dataframe random merge
edited Nov 11 at 1:26
coldspeed
111k1799169
asked Nov 11 at 0:36
YohanRoth
8861819
add a comment |
up vote
1
down vote
favorite
Is there a nice way to bring only one row, preferably random in one-to-many matching during left join in Pandas?
e.g
left = [[1,1,1], [2,2,2],[3,3,3], [9,9,9], [1,3,2]]
right = [[1,2,2],[1,2,3],[3,2,2], [3,2,9], [3,2,2]]
left = np.asarray(left)
right = np.asarray(right)
left = pd.DataFrame(left)
right = pd.DataFrame(right)
joined_left = left.merge(right, how="left", left_on=[0], right_on=[0])
So this is what we get
0 1 2
0 1 1 1
1 2 2 2
2 3 3 3
3 9 9 9
4 1 3 2
0 1 2
0 1 2 2
1 1 2 3
2 3 2 2
3 3 2 9
4 3 2 2
0 1_x 2_x 1_y 2_y
0 1 1 1 2.0 2.0
1 1 1 1 2.0 3.0
2 2 2 2 NaN NaN
3 3 3 3 2.0 2.0
4 3 3 3 2.0 9.0
5 3 3 3 2.0 2.0
6 9 9 9 NaN NaN
7 1 3 2 2.0 2.0
8 1 3 2 2.0 3.0
So now I want to have output to be of the same size as my left dataframe and when there are more than one match in right dataframe I want to bring only single random column.
Is there a nice way of doing it using pandas short cut tricks?
thank you!
python pandas dataframe random merge
edited Nov 11 at 1:26
coldspeed
111k1799169
asked Nov 11 at 0:36
YohanRoth
8861819
add a comment |
up vote
1
down vote
favorite
up vote
1
down vote
favorite
Is there a nice way to bring only one row, preferably random in one-to-many matching during left join in Pandas?
e.g
left = [[1,1,1], [2,2,2],[3,3,3], [9,9,9], [1,3,2]]
right = [[1,2,2],[1,2,3],[3,2,2], [3,2,9], [3,2,2]]
left = np.asarray(left)
right = np.asarray(right)
left = pd.DataFrame(left)
right = pd.DataFrame(right)
joined_left = left.merge(right, how="left", left_on=[0], right_on=[0])
So this is what we get
0 1 2
0 1 1 1
1 2 2 2
2 3 3 3
3 9 9 9
4 1 3 2
0 1 2
0 1 2 2
1 1 2 3
2 3 2 2
3 3 2 9
4 3 2 2
0 1_x 2_x 1_y 2_y
0 1 1 1 2.0 2.0
1 1 1 1 2.0 3.0
2 2 2 2 NaN NaN
3 3 3 3 2.0 2.0
4 3 3 3 2.0 9.0
5 3 3 3 2.0 2.0
6 9 9 9 NaN NaN
7 1 3 2 2.0 2.0
8 1 3 2 2.0 3.0
So now I want to have output to be of the same size as my left dataframe and when there are more than one match in right dataframe I want to bring only single random column.
Is there a nice way of doing it using pandas short cut tricks?
thank you!
python pandas dataframe random merge
edited Nov 11 at 1:26
coldspeed
111k1799169
asked Nov 11 at 0:36
YohanRoth
8861819
Is there a nice way to bring only one row, preferably random in one-to-many matching during left join in Pandas?
e.g
left = [[1,1,1], [2,2,2],[3,3,3], [9,9,9], [1,3,2]]
right = [[1,2,2],[1,2,3],[3,2,2], [3,2,9], [3,2,2]]
left = np.asarray(left)
right = np.asarray(right)
left = pd.DataFrame(left)
right = pd.DataFrame(right)
joined_left = left.merge(right, how="left", left_on=[0], right_on=[0])
So this is what we get
0 1 2
0 1 1 1
1 2 2 2
2 3 3 3
3 9 9 9
4 1 3 2
0 1 2
0 1 2 2
1 1 2 3
2 3 2 2
3 3 2 9
4 3 2 2
0 1_x 2_x 1_y 2_y
0 1 1 1 2.0 2.0
1 1 1 1 2.0 3.0
2 2 2 2 NaN NaN
3 3 3 3 2.0 2.0
4 3 3 3 2.0 9.0
5 3 3 3 2.0 2.0
6 9 9 9 NaN NaN
7 1 3 2 2.0 2.0
8 1 3 2 2.0 3.0
So now I want to have output to be of the same size as my left dataframe and when there are more than one match in right dataframe I want to bring only single random column.
Is there a nice way of doing it using pandas short cut tricks?
thank you!
python pandas dataframe random merge
python pandas dataframe random merge
edited Nov 11 at 1:26
coldspeed
111k1799169
asked Nov 11 at 0:36
YohanRoth
8861819
edited Nov 11 at 1:26
coldspeed
111k1799169
asked Nov 11 at 0:36
YohanRoth
8861819
edited Nov 11 at 1:26
coldspeed
111k1799169
edited Nov 11 at 1:26
coldspeed
111k1799169
edited Nov 11 at 1:26
coldspeed
111k1799169
111k1799169
asked Nov 11 at 0:36
YohanRoth
8861819
asked Nov 11 at 0:36
YohanRoth
8861819
asked Nov 11 at 0:36
YohanRoth
8861819
8861819
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
up vote
1
down vote
accepted
You can shuffle right and drop_duplicates(...[, keep='first']) before merging.
right2 = right.sample(frac=1).drop_duplicates(subset=[0])
left.merge(right2, how='left', left_on=[0], right_on=[0])
0 1_x 2_x 1_y 2_y
0 1 1 1 2.0 2.0
1 2 2 2 NaN NaN
2 3 3 3 2.0 2.0
3 9 9 9 NaN NaN
4 1 3 2 2.0 2.0
We shuffle right first, and then drop every duplicate except the first row (considering only column #0), which is the same as randomly selecting a row.
answered Nov 11 at 0:39
coldspeed
111k1799169
1
I see, so you drop duplicates for a merge key column right. Ingenious! Thank you
– YohanRoth
Nov 11 at 0:43
@YohanRoth - in this case - if your first row of the output is1 1 1 2.0 2.0, I think that guarantees the last row is also1 3 2 2.0 2.0since you've dropped1 2 3. From your question asking for arandomchoice, I'm a bit concerned that this may not be the behavior you want. Perhaps it's fine, but worth making sure it's consistent with what you want.
– Joel
Nov 11 at 4:47
add a comment |
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
1
down vote
accepted
You can shuffle right and drop_duplicates(...[, keep='first']) before merging.
right2 = right.sample(frac=1).drop_duplicates(subset=[0])
left.merge(right2, how='left', left_on=[0], right_on=[0])
0 1_x 2_x 1_y 2_y
0 1 1 1 2.0 2.0
1 2 2 2 NaN NaN
2 3 3 3 2.0 2.0
3 9 9 9 NaN NaN
4 1 3 2 2.0 2.0
We shuffle right first, and then drop every duplicate except the first row (considering only column #0), which is the same as randomly selecting a row.
answered Nov 11 at 0:39
coldspeed
111k1799169
1
I see, so you drop duplicates for a merge key column right. Ingenious! Thank you
– YohanRoth
Nov 11 at 0:43
@YohanRoth - in this case - if your first row of the output is1 1 1 2.0 2.0, I think that guarantees the last row is also1 3 2 2.0 2.0since you've dropped1 2 3. From your question asking for arandomchoice, I'm a bit concerned that this may not be the behavior you want. Perhaps it's fine, but worth making sure it's consistent with what you want.
– Joel
Nov 11 at 4:47
add a comment |
up vote
1
down vote
accepted
You can shuffle right and drop_duplicates(...[, keep='first']) before merging.
right2 = right.sample(frac=1).drop_duplicates(subset=[0])
left.merge(right2, how='left', left_on=[0], right_on=[0])
0 1_x 2_x 1_y 2_y
0 1 1 1 2.0 2.0
1 2 2 2 NaN NaN
2 3 3 3 2.0 2.0
3 9 9 9 NaN NaN
4 1 3 2 2.0 2.0
We shuffle right first, and then drop every duplicate except the first row (considering only column #0), which is the same as randomly selecting a row.
answered Nov 11 at 0:39
coldspeed
111k1799169
1
I see, so you drop duplicates for a merge key column right. Ingenious! Thank you
– YohanRoth
Nov 11 at 0:43
@YohanRoth - in this case - if your first row of the output is1 1 1 2.0 2.0, I think that guarantees the last row is also1 3 2 2.0 2.0since you've dropped1 2 3. From your question asking for arandomchoice, I'm a bit concerned that this may not be the behavior you want. Perhaps it's fine, but worth making sure it's consistent with what you want.
– Joel
Nov 11 at 4:47
add a comment |
up vote
1
down vote
accepted
up vote
1
down vote
accepted
You can shuffle right and drop_duplicates(...[, keep='first']) before merging.
right2 = right.sample(frac=1).drop_duplicates(subset=[0])
left.merge(right2, how='left', left_on=[0], right_on=[0])
0 1_x 2_x 1_y 2_y
0 1 1 1 2.0 2.0
1 2 2 2 NaN NaN
2 3 3 3 2.0 2.0
3 9 9 9 NaN NaN
4 1 3 2 2.0 2.0
We shuffle right first, and then drop every duplicate except the first row (considering only column #0), which is the same as randomly selecting a row.
answered Nov 11 at 0:39
coldspeed
111k1799169
You can shuffle right and drop_duplicates(...[, keep='first']) before merging.
right2 = right.sample(frac=1).drop_duplicates(subset=[0])
left.merge(right2, how='left', left_on=[0], right_on=[0])
0 1_x 2_x 1_y 2_y
0 1 1 1 2.0 2.0
1 2 2 2 NaN NaN
2 3 3 3 2.0 2.0
3 9 9 9 NaN NaN
4 1 3 2 2.0 2.0
We shuffle right first, and then drop every duplicate except the first row (considering only column #0), which is the same as randomly selecting a row.
answered Nov 11 at 0:39
coldspeed
111k1799169
answered Nov 11 at 0:39
coldspeed
111k1799169
answered Nov 11 at 0:39
coldspeed
111k1799169
answered Nov 11 at 0:39
coldspeed
111k1799169
111k1799169
1
I see, so you drop duplicates for a merge key column right. Ingenious! Thank you
– YohanRoth
Nov 11 at 0:43
@YohanRoth - in this case - if your first row of the output is1 1 1 2.0 2.0, I think that guarantees the last row is also1 3 2 2.0 2.0since you've dropped1 2 3. From your question asking for arandomchoice, I'm a bit concerned that this may not be the behavior you want. Perhaps it's fine, but worth making sure it's consistent with what you want.
– Joel
Nov 11 at 4:47
add a comment |
1
I see, so you drop duplicates for a merge key column right. Ingenious! Thank you
– YohanRoth
Nov 11 at 0:43
@YohanRoth - in this case - if your first row of the output is1 1 1 2.0 2.0, I think that guarantees the last row is also1 3 2 2.0 2.0since you've dropped1 2 3. From your question asking for arandomchoice, I'm a bit concerned that this may not be the behavior you want. Perhaps it's fine, but worth making sure it's consistent with what you want.
– Joel
Nov 11 at 4:47
1
1
I see, so you drop duplicates for a merge key column right. Ingenious! Thank you
– YohanRoth
Nov 11 at 0:43
I see, so you drop duplicates for a merge key column right. Ingenious! Thank you
– YohanRoth
Nov 11 at 0:43
@YohanRoth - in this case - if your first row of the output is
1 1 1 2.0 2.0, I think that guarantees the last row is also 1 3 2 2.0 2.0 since you've dropped 1 2 3. From your question asking for a random choice, I'm a bit concerned that this may not be the behavior you want. Perhaps it's fine, but worth making sure it's consistent with what you want.– Joel
Nov 11 at 4:47
@YohanRoth - in this case - if your first row of the output is
1 1 1 2.0 2.0, I think that guarantees the last row is also 1 3 2 2.0 2.0 since you've dropped 1 2 3. From your question asking for a random choice, I'm a bit concerned that this may not be the behavior you want. Perhaps it's fine, but worth making sure it's consistent with what you want.– Joel
Nov 11 at 4:47
add a comment |
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