Fill in NaN values for left join by sampling from right table












0














I cannot figure out a nice panda-ish way to fill in missing NaN values for left join by sampling from right table.



e.g
joined_left = left.merge(right, how="left", left_on=[attr1], right_on=[attr2])
from left and right



   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


produces smth like



   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


How do I sample a row from a right table instead of filling NaNs?



This is what I tried so far playground:



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])

while(joined_left.isnull().values.any()):
right_sample = right.sample().drop(0, axis=1)
joined_left.fillna(value=right_sample, limit=1)

print joined_left


Basically sample randomly and use fillna() for first occurance of NaN value to fill in...but for some reason I get no output.



Thank you!



One of outputs could be



   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 2.0 2.0
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 3.0 2.9
7 1 3 2 2.0 2.0
8 1 3 2 2.0 3.0


with sampled 3 2 2and3 2 9










share|improve this question
























  • What is your expected output?. Please provide a Minimal, Complete, and Verifiable example.
    – Sandeep Kadapa
    Nov 11 at 2:55










  • @SandeepKadapa provided
    – YohanRoth
    Nov 11 at 3:06
















0














I cannot figure out a nice panda-ish way to fill in missing NaN values for left join by sampling from right table.



e.g
joined_left = left.merge(right, how="left", left_on=[attr1], right_on=[attr2])
from left and right



   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


produces smth like



   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


How do I sample a row from a right table instead of filling NaNs?



This is what I tried so far playground:



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])

while(joined_left.isnull().values.any()):
right_sample = right.sample().drop(0, axis=1)
joined_left.fillna(value=right_sample, limit=1)

print joined_left


Basically sample randomly and use fillna() for first occurance of NaN value to fill in...but for some reason I get no output.



Thank you!



One of outputs could be



   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 2.0 2.0
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 3.0 2.9
7 1 3 2 2.0 2.0
8 1 3 2 2.0 3.0


with sampled 3 2 2and3 2 9










share|improve this question
























  • What is your expected output?. Please provide a Minimal, Complete, and Verifiable example.
    – Sandeep Kadapa
    Nov 11 at 2:55










  • @SandeepKadapa provided
    – YohanRoth
    Nov 11 at 3:06














0












0








0







I cannot figure out a nice panda-ish way to fill in missing NaN values for left join by sampling from right table.



e.g
joined_left = left.merge(right, how="left", left_on=[attr1], right_on=[attr2])
from left and right



   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


produces smth like



   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


How do I sample a row from a right table instead of filling NaNs?



This is what I tried so far playground:



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])

while(joined_left.isnull().values.any()):
right_sample = right.sample().drop(0, axis=1)
joined_left.fillna(value=right_sample, limit=1)

print joined_left


Basically sample randomly and use fillna() for first occurance of NaN value to fill in...but for some reason I get no output.



Thank you!



One of outputs could be



   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 2.0 2.0
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 3.0 2.9
7 1 3 2 2.0 2.0
8 1 3 2 2.0 3.0


with sampled 3 2 2and3 2 9










share|improve this question















I cannot figure out a nice panda-ish way to fill in missing NaN values for left join by sampling from right table.



e.g
joined_left = left.merge(right, how="left", left_on=[attr1], right_on=[attr2])
from left and right



   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


produces smth like



   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


How do I sample a row from a right table instead of filling NaNs?



This is what I tried so far playground:



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])

while(joined_left.isnull().values.any()):
right_sample = right.sample().drop(0, axis=1)
joined_left.fillna(value=right_sample, limit=1)

print joined_left


Basically sample randomly and use fillna() for first occurance of NaN value to fill in...but for some reason I get no output.



Thank you!



One of outputs could be



   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 2.0 2.0
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 3.0 2.9
7 1 3 2 2.0 2.0
8 1 3 2 2.0 3.0


with sampled 3 2 2and3 2 9







python pandas






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited Nov 11 at 16:38

























asked Nov 11 at 2:40









YohanRoth

9191919




9191919












  • What is your expected output?. Please provide a Minimal, Complete, and Verifiable example.
    – Sandeep Kadapa
    Nov 11 at 2:55










  • @SandeepKadapa provided
    – YohanRoth
    Nov 11 at 3:06


















  • What is your expected output?. Please provide a Minimal, Complete, and Verifiable example.
    – Sandeep Kadapa
    Nov 11 at 2:55










  • @SandeepKadapa provided
    – YohanRoth
    Nov 11 at 3:06
















What is your expected output?. Please provide a Minimal, Complete, and Verifiable example.
– Sandeep Kadapa
Nov 11 at 2:55




What is your expected output?. Please provide a Minimal, Complete, and Verifiable example.
– Sandeep Kadapa
Nov 11 at 2:55












@SandeepKadapa provided
– YohanRoth
Nov 11 at 3:06




@SandeepKadapa provided
– YohanRoth
Nov 11 at 3:06












1 Answer
1






active

oldest

votes


















1














Using sample with fillna



joined_left = left.merge(right, how="left", left_on=[0], right_on=[0],indicator=True) # adding indicator
joined_left
Out[705]:
0 1_x 2_x 1_y 2_y _merge
0 1 1 1 2.0 2.0 both
1 1 1 1 2.0 3.0 both
2 2 2 2 NaN NaN left_only
3 3 3 3 2.0 2.0 both
4 3 3 3 2.0 9.0 both
5 3 3 3 2.0 2.0 both
6 9 9 9 NaN NaN left_only
7 1 3 2 2.0 2.0 both
8 1 3 2 2.0 3.0 both
nnull=joined_left['_merge'].eq('left_only').sum() # find all many row miss match , at the mergedf
s=right.sample(nnull)# rasmple from the dataframe after dropna
s.index=joined_left.index[joined_left['_merge'].eq('left_only')] # reset the index of the subset fill df to the index of null value show up
joined_left.fillna(s.rename(columns={1:'1_y',2:'2_y'}))
Out[706]:
0 1_x 2_x 1_y 2_y _merge
0 1 1 1 2.0 2.0 both
1 1 1 1 2.0 3.0 both
2 2 2 2 2.0 2.0 left_only
3 3 3 3 2.0 2.0 both
4 3 3 3 2.0 9.0 both
5 3 3 3 2.0 2.0 both
6 9 9 9 2.0 3.0 left_only
7 1 3 2 2.0 2.0 both
8 1 3 2 2.0 3.0 both





share|improve this answer























  • could you pls briefly explain the logic
    – YohanRoth
    Nov 11 at 3:07










  • @YohanRoth added
    – W-B
    Nov 11 at 3:10










  • @YohanRoth you should assign it back df=df.fillna(s)
    – W-B
    Nov 11 at 3:17










  • it's not really sampling from right table, but rather from right table values that we brought in the join
    – YohanRoth
    Nov 11 at 3:34










  • @YohanRoth got you , let me fix
    – W-B
    Nov 11 at 3:43











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1 Answer
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1 Answer
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oldest

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active

oldest

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1














Using sample with fillna



joined_left = left.merge(right, how="left", left_on=[0], right_on=[0],indicator=True) # adding indicator
joined_left
Out[705]:
0 1_x 2_x 1_y 2_y _merge
0 1 1 1 2.0 2.0 both
1 1 1 1 2.0 3.0 both
2 2 2 2 NaN NaN left_only
3 3 3 3 2.0 2.0 both
4 3 3 3 2.0 9.0 both
5 3 3 3 2.0 2.0 both
6 9 9 9 NaN NaN left_only
7 1 3 2 2.0 2.0 both
8 1 3 2 2.0 3.0 both
nnull=joined_left['_merge'].eq('left_only').sum() # find all many row miss match , at the mergedf
s=right.sample(nnull)# rasmple from the dataframe after dropna
s.index=joined_left.index[joined_left['_merge'].eq('left_only')] # reset the index of the subset fill df to the index of null value show up
joined_left.fillna(s.rename(columns={1:'1_y',2:'2_y'}))
Out[706]:
0 1_x 2_x 1_y 2_y _merge
0 1 1 1 2.0 2.0 both
1 1 1 1 2.0 3.0 both
2 2 2 2 2.0 2.0 left_only
3 3 3 3 2.0 2.0 both
4 3 3 3 2.0 9.0 both
5 3 3 3 2.0 2.0 both
6 9 9 9 2.0 3.0 left_only
7 1 3 2 2.0 2.0 both
8 1 3 2 2.0 3.0 both





share|improve this answer























  • could you pls briefly explain the logic
    – YohanRoth
    Nov 11 at 3:07










  • @YohanRoth added
    – W-B
    Nov 11 at 3:10










  • @YohanRoth you should assign it back df=df.fillna(s)
    – W-B
    Nov 11 at 3:17










  • it's not really sampling from right table, but rather from right table values that we brought in the join
    – YohanRoth
    Nov 11 at 3:34










  • @YohanRoth got you , let me fix
    – W-B
    Nov 11 at 3:43
















1














Using sample with fillna



joined_left = left.merge(right, how="left", left_on=[0], right_on=[0],indicator=True) # adding indicator
joined_left
Out[705]:
0 1_x 2_x 1_y 2_y _merge
0 1 1 1 2.0 2.0 both
1 1 1 1 2.0 3.0 both
2 2 2 2 NaN NaN left_only
3 3 3 3 2.0 2.0 both
4 3 3 3 2.0 9.0 both
5 3 3 3 2.0 2.0 both
6 9 9 9 NaN NaN left_only
7 1 3 2 2.0 2.0 both
8 1 3 2 2.0 3.0 both
nnull=joined_left['_merge'].eq('left_only').sum() # find all many row miss match , at the mergedf
s=right.sample(nnull)# rasmple from the dataframe after dropna
s.index=joined_left.index[joined_left['_merge'].eq('left_only')] # reset the index of the subset fill df to the index of null value show up
joined_left.fillna(s.rename(columns={1:'1_y',2:'2_y'}))
Out[706]:
0 1_x 2_x 1_y 2_y _merge
0 1 1 1 2.0 2.0 both
1 1 1 1 2.0 3.0 both
2 2 2 2 2.0 2.0 left_only
3 3 3 3 2.0 2.0 both
4 3 3 3 2.0 9.0 both
5 3 3 3 2.0 2.0 both
6 9 9 9 2.0 3.0 left_only
7 1 3 2 2.0 2.0 both
8 1 3 2 2.0 3.0 both





share|improve this answer























  • could you pls briefly explain the logic
    – YohanRoth
    Nov 11 at 3:07










  • @YohanRoth added
    – W-B
    Nov 11 at 3:10










  • @YohanRoth you should assign it back df=df.fillna(s)
    – W-B
    Nov 11 at 3:17










  • it's not really sampling from right table, but rather from right table values that we brought in the join
    – YohanRoth
    Nov 11 at 3:34










  • @YohanRoth got you , let me fix
    – W-B
    Nov 11 at 3:43














1












1








1






Using sample with fillna



joined_left = left.merge(right, how="left", left_on=[0], right_on=[0],indicator=True) # adding indicator
joined_left
Out[705]:
0 1_x 2_x 1_y 2_y _merge
0 1 1 1 2.0 2.0 both
1 1 1 1 2.0 3.0 both
2 2 2 2 NaN NaN left_only
3 3 3 3 2.0 2.0 both
4 3 3 3 2.0 9.0 both
5 3 3 3 2.0 2.0 both
6 9 9 9 NaN NaN left_only
7 1 3 2 2.0 2.0 both
8 1 3 2 2.0 3.0 both
nnull=joined_left['_merge'].eq('left_only').sum() # find all many row miss match , at the mergedf
s=right.sample(nnull)# rasmple from the dataframe after dropna
s.index=joined_left.index[joined_left['_merge'].eq('left_only')] # reset the index of the subset fill df to the index of null value show up
joined_left.fillna(s.rename(columns={1:'1_y',2:'2_y'}))
Out[706]:
0 1_x 2_x 1_y 2_y _merge
0 1 1 1 2.0 2.0 both
1 1 1 1 2.0 3.0 both
2 2 2 2 2.0 2.0 left_only
3 3 3 3 2.0 2.0 both
4 3 3 3 2.0 9.0 both
5 3 3 3 2.0 2.0 both
6 9 9 9 2.0 3.0 left_only
7 1 3 2 2.0 2.0 both
8 1 3 2 2.0 3.0 both





share|improve this answer














Using sample with fillna



joined_left = left.merge(right, how="left", left_on=[0], right_on=[0],indicator=True) # adding indicator
joined_left
Out[705]:
0 1_x 2_x 1_y 2_y _merge
0 1 1 1 2.0 2.0 both
1 1 1 1 2.0 3.0 both
2 2 2 2 NaN NaN left_only
3 3 3 3 2.0 2.0 both
4 3 3 3 2.0 9.0 both
5 3 3 3 2.0 2.0 both
6 9 9 9 NaN NaN left_only
7 1 3 2 2.0 2.0 both
8 1 3 2 2.0 3.0 both
nnull=joined_left['_merge'].eq('left_only').sum() # find all many row miss match , at the mergedf
s=right.sample(nnull)# rasmple from the dataframe after dropna
s.index=joined_left.index[joined_left['_merge'].eq('left_only')] # reset the index of the subset fill df to the index of null value show up
joined_left.fillna(s.rename(columns={1:'1_y',2:'2_y'}))
Out[706]:
0 1_x 2_x 1_y 2_y _merge
0 1 1 1 2.0 2.0 both
1 1 1 1 2.0 3.0 both
2 2 2 2 2.0 2.0 left_only
3 3 3 3 2.0 2.0 both
4 3 3 3 2.0 9.0 both
5 3 3 3 2.0 2.0 both
6 9 9 9 2.0 3.0 left_only
7 1 3 2 2.0 2.0 both
8 1 3 2 2.0 3.0 both






share|improve this answer














share|improve this answer



share|improve this answer








edited Nov 11 at 16:22

























answered Nov 11 at 3:06









W-B

99.7k73163




99.7k73163












  • could you pls briefly explain the logic
    – YohanRoth
    Nov 11 at 3:07










  • @YohanRoth added
    – W-B
    Nov 11 at 3:10










  • @YohanRoth you should assign it back df=df.fillna(s)
    – W-B
    Nov 11 at 3:17










  • it's not really sampling from right table, but rather from right table values that we brought in the join
    – YohanRoth
    Nov 11 at 3:34










  • @YohanRoth got you , let me fix
    – W-B
    Nov 11 at 3:43


















  • could you pls briefly explain the logic
    – YohanRoth
    Nov 11 at 3:07










  • @YohanRoth added
    – W-B
    Nov 11 at 3:10










  • @YohanRoth you should assign it back df=df.fillna(s)
    – W-B
    Nov 11 at 3:17










  • it's not really sampling from right table, but rather from right table values that we brought in the join
    – YohanRoth
    Nov 11 at 3:34










  • @YohanRoth got you , let me fix
    – W-B
    Nov 11 at 3:43
















could you pls briefly explain the logic
– YohanRoth
Nov 11 at 3:07




could you pls briefly explain the logic
– YohanRoth
Nov 11 at 3:07












@YohanRoth added
– W-B
Nov 11 at 3:10




@YohanRoth added
– W-B
Nov 11 at 3:10












@YohanRoth you should assign it back df=df.fillna(s)
– W-B
Nov 11 at 3:17




@YohanRoth you should assign it back df=df.fillna(s)
– W-B
Nov 11 at 3:17












it's not really sampling from right table, but rather from right table values that we brought in the join
– YohanRoth
Nov 11 at 3:34




it's not really sampling from right table, but rather from right table values that we brought in the join
– YohanRoth
Nov 11 at 3:34












@YohanRoth got you , let me fix
– W-B
Nov 11 at 3:43




@YohanRoth got you , let me fix
– W-B
Nov 11 at 3:43


















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