Combine two Pandas series?
I have two Pandas Series, s1 and s2 that I would like to combine.
s1 = pd.Series([2,5,5], index=['a','b','c'])
s2 = pd.Series([2,4,7], index=['a','b','d'])
This is the result I would like:
s3 = pd.Series([4,9,5,7], index=['a','b','c','d'])
a 4
b 9
c 5
d 7
dtype: int64
I've tried the + operator, s1.append(s2), and pd.join([s1,s2]) but these don't give the result I'm looking for.
python pandas
asked Nov 20 '18 at 17:04
maxmax
480113
add a comment |
I have two Pandas Series, s1 and s2 that I would like to combine.
s1 = pd.Series([2,5,5], index=['a','b','c'])
s2 = pd.Series([2,4,7], index=['a','b','d'])
This is the result I would like:
s3 = pd.Series([4,9,5,7], index=['a','b','c','d'])
a 4
b 9
c 5
d 7
dtype: int64
I've tried the + operator, s1.append(s2), and pd.join([s1,s2]) but these don't give the result I'm looking for.
python pandas
asked Nov 20 '18 at 17:04
maxmax
480113
1
How about s1.add(s2)?
– Anton vBR
Nov 20 '18 at 17:07
add a comment |
I have two Pandas Series, s1 and s2 that I would like to combine.
s1 = pd.Series([2,5,5], index=['a','b','c'])
s2 = pd.Series([2,4,7], index=['a','b','d'])
This is the result I would like:
s3 = pd.Series([4,9,5,7], index=['a','b','c','d'])
a 4
b 9
c 5
d 7
dtype: int64
I've tried the + operator, s1.append(s2), and pd.join([s1,s2]) but these don't give the result I'm looking for.
python pandas
asked Nov 20 '18 at 17:04
maxmax
480113
I have two Pandas Series, s1 and s2 that I would like to combine.
s1 = pd.Series([2,5,5], index=['a','b','c'])
s2 = pd.Series([2,4,7], index=['a','b','d'])
This is the result I would like:
s3 = pd.Series([4,9,5,7], index=['a','b','c','d'])
a 4
b 9
c 5
d 7
dtype: int64
I've tried the + operator, s1.append(s2), and pd.join([s1,s2]) but these don't give the result I'm looking for.
python pandas
python pandas
asked Nov 20 '18 at 17:04
maxmax
480113
asked Nov 20 '18 at 17:04
maxmax
480113
asked Nov 20 '18 at 17:04
maxmax
480113
asked Nov 20 '18 at 17:04
maxmax
480113
asked Nov 20 '18 at 17:04
maxmax
480113
480113
1
How about s1.add(s2)?
– Anton vBR
Nov 20 '18 at 17:07
add a comment |
1
How about s1.add(s2)?
– Anton vBR
Nov 20 '18 at 17:07
1
1
How about s1.add(s2)?
– Anton vBR
Nov 20 '18 at 17:07
How about s1.add(s2)?
– Anton vBR
Nov 20 '18 at 17:07
add a comment |
5 Answers
5
active
oldest
votes
Seems like add will work here
s1.add(s2,fill_value=0)
Out[145]:
a 4.0
b 9.0
c 5.0
d 7.0
dtype: float64
answered Nov 20 '18 at 17:07
W-BW-B
106k83165
add a comment |
Numpy np.add.at
This is kind of ridiculous but I wanted to show something different
import numpy as np
import pandas as pd
from itertools import chain
k, v = zip(*chain(*map(pd.Series.items, [s1, s2])))
i, r = pd.factorize(k)
out = np.zeros(len(r), dtype=int)
np.add.at(out, i, v)
pd.Series(out, r)
a 4
b 9
c 5
d 7
dtype: int64
answered Nov 20 '18 at 17:23
piRSquaredpiRSquared
153k22144287
1
This is good but seems way lengthy when pandas DataFrame.add is there :-)
– pygo
Nov 20 '18 at 17:29
@pygo hence the comment ridiculous. I personally believe it serves to show how it could be done differently. If people see these alternative answers, they may incorporate the knowledge and find better uses for it later.
– piRSquared
Nov 20 '18 at 17:33
of course that's good for ppls over SO for looking such logics, thnx.
– pygo
Nov 20 '18 at 17:35
add a comment |
Just use pandas.Series.add
s3 = s1.add(s2, fill_value=0)
#result
a 4.0
b 9.0
c 5.0
d 7.0
dtype: float64
answered Nov 20 '18 at 17:11
Drew NicoletteDrew Nicolette
1063
add a comment |
Alternative to add
pd.concat((s1,s2), axis=1, sort=True).sum(1, min_count=1)
Output:
a 4.0
b 9.0
c 5.0
d 7.0
dtype: float64
answered Nov 20 '18 at 17:11
Srce CdeSrce Cde
1,144511
add a comment |
There is simple answer Directly from pandas.Series.add , you will get it from here
For you Use case here..
First dataframe:
>>> s1
a 2
b 4
c 5
dtype: int64
Second Dataframe:
>>> s2
a 2
b 4
d 7
dtype: int64
Simple DataFrame.add + fill_value which is explained in docs:
>>> s1.add(s2, fill_value=0)
a 4.0
b 8.0
c 5.0
d 7.0
dtype: float64
Document :
fill_value : None or float value, default None
Fill existing missing (NaN) values, and any new element needed for
successful DataFrame alignment, with this value before computation. If
data in both corresponding DataFrame locations is missing the result
will be missing
answered Nov 20 '18 at 17:24
pygopygo
2,6681619
These areSeriesso the correct method ispandas.Series.add. Also, this is addressed by @W-B
– piRSquared
Nov 20 '18 at 17:35
Thnx for pointing that out , link is just stand corrected , i Know its addressed by @W-B and Drew Nicolette but thought to link that with original docs while showing that for the use case here, i'm firm believer in getting & putting the explanation and like that style because it helps newbies to understand under the hood :).
– pygo
Nov 20 '18 at 17:43
add a comment |
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5 Answers
5
active
oldest
votes
5 Answers
5
active
oldest
votes
active
oldest
votes
active
oldest
votes
Seems like add will work here
s1.add(s2,fill_value=0)
Out[145]:
a 4.0
b 9.0
c 5.0
d 7.0
dtype: float64
answered Nov 20 '18 at 17:07
W-BW-B
106k83165
add a comment |
Seems like add will work here
s1.add(s2,fill_value=0)
Out[145]:
a 4.0
b 9.0
c 5.0
d 7.0
dtype: float64
answered Nov 20 '18 at 17:07
W-BW-B
106k83165
add a comment |
Seems like add will work here
s1.add(s2,fill_value=0)
Out[145]:
a 4.0
b 9.0
c 5.0
d 7.0
dtype: float64
answered Nov 20 '18 at 17:07
W-BW-B
106k83165
Seems like add will work here
s1.add(s2,fill_value=0)
Out[145]:
a 4.0
b 9.0
c 5.0
d 7.0
dtype: float64
answered Nov 20 '18 at 17:07
W-BW-B
106k83165
answered Nov 20 '18 at 17:07
W-BW-B
106k83165
answered Nov 20 '18 at 17:07
W-BW-B
106k83165
answered Nov 20 '18 at 17:07
W-BW-B
106k83165
106k83165
add a comment |
add a comment |
Numpy np.add.at
This is kind of ridiculous but I wanted to show something different
import numpy as np
import pandas as pd
from itertools import chain
k, v = zip(*chain(*map(pd.Series.items, [s1, s2])))
i, r = pd.factorize(k)
out = np.zeros(len(r), dtype=int)
np.add.at(out, i, v)
pd.Series(out, r)
a 4
b 9
c 5
d 7
dtype: int64
answered Nov 20 '18 at 17:23
piRSquaredpiRSquared
153k22144287
1
This is good but seems way lengthy when pandas DataFrame.add is there :-)
– pygo
Nov 20 '18 at 17:29
@pygo hence the comment ridiculous. I personally believe it serves to show how it could be done differently. If people see these alternative answers, they may incorporate the knowledge and find better uses for it later.
– piRSquared
Nov 20 '18 at 17:33
of course that's good for ppls over SO for looking such logics, thnx.
– pygo
Nov 20 '18 at 17:35
add a comment |
Numpy np.add.at
This is kind of ridiculous but I wanted to show something different
import numpy as np
import pandas as pd
from itertools import chain
k, v = zip(*chain(*map(pd.Series.items, [s1, s2])))
i, r = pd.factorize(k)
out = np.zeros(len(r), dtype=int)
np.add.at(out, i, v)
pd.Series(out, r)
a 4
b 9
c 5
d 7
dtype: int64
answered Nov 20 '18 at 17:23
piRSquaredpiRSquared
153k22144287
1
This is good but seems way lengthy when pandas DataFrame.add is there :-)
– pygo
Nov 20 '18 at 17:29
@pygo hence the comment ridiculous. I personally believe it serves to show how it could be done differently. If people see these alternative answers, they may incorporate the knowledge and find better uses for it later.
– piRSquared
Nov 20 '18 at 17:33
of course that's good for ppls over SO for looking such logics, thnx.
– pygo
Nov 20 '18 at 17:35
add a comment |
Numpy np.add.at
This is kind of ridiculous but I wanted to show something different
import numpy as np
import pandas as pd
from itertools import chain
k, v = zip(*chain(*map(pd.Series.items, [s1, s2])))
i, r = pd.factorize(k)
out = np.zeros(len(r), dtype=int)
np.add.at(out, i, v)
pd.Series(out, r)
a 4
b 9
c 5
d 7
dtype: int64
answered Nov 20 '18 at 17:23
piRSquaredpiRSquared
153k22144287
Numpy np.add.at
This is kind of ridiculous but I wanted to show something different
import numpy as np
import pandas as pd
from itertools import chain
k, v = zip(*chain(*map(pd.Series.items, [s1, s2])))
i, r = pd.factorize(k)
out = np.zeros(len(r), dtype=int)
np.add.at(out, i, v)
pd.Series(out, r)
a 4
b 9
c 5
d 7
dtype: int64
answered Nov 20 '18 at 17:23
piRSquaredpiRSquared
153k22144287
answered Nov 20 '18 at 17:23
piRSquaredpiRSquared
153k22144287
answered Nov 20 '18 at 17:23
piRSquaredpiRSquared
153k22144287
answered Nov 20 '18 at 17:23
piRSquaredpiRSquared
153k22144287
153k22144287
1
This is good but seems way lengthy when pandas DataFrame.add is there :-)
– pygo
Nov 20 '18 at 17:29
@pygo hence the comment ridiculous. I personally believe it serves to show how it could be done differently. If people see these alternative answers, they may incorporate the knowledge and find better uses for it later.
– piRSquared
Nov 20 '18 at 17:33
of course that's good for ppls over SO for looking such logics, thnx.
– pygo
Nov 20 '18 at 17:35
add a comment |
1
This is good but seems way lengthy when pandas DataFrame.add is there :-)
– pygo
Nov 20 '18 at 17:29
@pygo hence the comment ridiculous. I personally believe it serves to show how it could be done differently. If people see these alternative answers, they may incorporate the knowledge and find better uses for it later.
– piRSquared
Nov 20 '18 at 17:33
of course that's good for ppls over SO for looking such logics, thnx.
– pygo
Nov 20 '18 at 17:35
1
1
This is good but seems way lengthy when pandas DataFrame.add is there :-)
– pygo
Nov 20 '18 at 17:29
This is good but seems way lengthy when pandas DataFrame.add is there :-)
– pygo
Nov 20 '18 at 17:29
@pygo hence the comment ridiculous. I personally believe it serves to show how it could be done differently. If people see these alternative answers, they may incorporate the knowledge and find better uses for it later.
– piRSquared
Nov 20 '18 at 17:33
@pygo hence the comment ridiculous. I personally believe it serves to show how it could be done differently. If people see these alternative answers, they may incorporate the knowledge and find better uses for it later.
– piRSquared
Nov 20 '18 at 17:33
of course that's good for ppls over SO for looking such logics, thnx.
– pygo
Nov 20 '18 at 17:35
of course that's good for ppls over SO for looking such logics, thnx.
– pygo
Nov 20 '18 at 17:35
add a comment |
Just use pandas.Series.add
s3 = s1.add(s2, fill_value=0)
#result
a 4.0
b 9.0
c 5.0
d 7.0
dtype: float64
answered Nov 20 '18 at 17:11
Drew NicoletteDrew Nicolette
1063
add a comment |
Just use pandas.Series.add
s3 = s1.add(s2, fill_value=0)
#result
a 4.0
b 9.0
c 5.0
d 7.0
dtype: float64
answered Nov 20 '18 at 17:11
Drew NicoletteDrew Nicolette
1063
add a comment |
Just use pandas.Series.add
s3 = s1.add(s2, fill_value=0)
#result
a 4.0
b 9.0
c 5.0
d 7.0
dtype: float64
answered Nov 20 '18 at 17:11
Drew NicoletteDrew Nicolette
1063
Just use pandas.Series.add
s3 = s1.add(s2, fill_value=0)
#result
a 4.0
b 9.0
c 5.0
d 7.0
dtype: float64
answered Nov 20 '18 at 17:11
Drew NicoletteDrew Nicolette
1063
answered Nov 20 '18 at 17:11
Drew NicoletteDrew Nicolette
1063
answered Nov 20 '18 at 17:11
Drew NicoletteDrew Nicolette
1063
answered Nov 20 '18 at 17:11
Drew NicoletteDrew Nicolette
1063
1063
add a comment |
add a comment |
Alternative to add
pd.concat((s1,s2), axis=1, sort=True).sum(1, min_count=1)
Output:
a 4.0
b 9.0
c 5.0
d 7.0
dtype: float64
answered Nov 20 '18 at 17:11
Srce CdeSrce Cde
1,144511
add a comment |
Alternative to add
pd.concat((s1,s2), axis=1, sort=True).sum(1, min_count=1)
Output:
a 4.0
b 9.0
c 5.0
d 7.0
dtype: float64
answered Nov 20 '18 at 17:11
Srce CdeSrce Cde
1,144511
add a comment |
Alternative to add
pd.concat((s1,s2), axis=1, sort=True).sum(1, min_count=1)
Output:
a 4.0
b 9.0
c 5.0
d 7.0
dtype: float64
answered Nov 20 '18 at 17:11
Srce CdeSrce Cde
1,144511
Alternative to add
pd.concat((s1,s2), axis=1, sort=True).sum(1, min_count=1)
Output:
a 4.0
b 9.0
c 5.0
d 7.0
dtype: float64
answered Nov 20 '18 at 17:11
Srce CdeSrce Cde
1,144511
answered Nov 20 '18 at 17:11
Srce CdeSrce Cde
1,144511
answered Nov 20 '18 at 17:11
Srce CdeSrce Cde
1,144511
answered Nov 20 '18 at 17:11
Srce CdeSrce Cde
1,144511
1,144511
add a comment |
add a comment |
There is simple answer Directly from pandas.Series.add , you will get it from here
For you Use case here..
First dataframe:
>>> s1
a 2
b 4
c 5
dtype: int64
Second Dataframe:
>>> s2
a 2
b 4
d 7
dtype: int64
Simple DataFrame.add + fill_value which is explained in docs:
>>> s1.add(s2, fill_value=0)
a 4.0
b 8.0
c 5.0
d 7.0
dtype: float64
Document :
fill_value : None or float value, default None
Fill existing missing (NaN) values, and any new element needed for
successful DataFrame alignment, with this value before computation. If
data in both corresponding DataFrame locations is missing the result
will be missing
answered Nov 20 '18 at 17:24
pygopygo
2,6681619
These areSeriesso the correct method ispandas.Series.add. Also, this is addressed by @W-B
– piRSquared
Nov 20 '18 at 17:35
Thnx for pointing that out , link is just stand corrected , i Know its addressed by @W-B and Drew Nicolette but thought to link that with original docs while showing that for the use case here, i'm firm believer in getting & putting the explanation and like that style because it helps newbies to understand under the hood :).
– pygo
Nov 20 '18 at 17:43
add a comment |
There is simple answer Directly from pandas.Series.add , you will get it from here
For you Use case here..
First dataframe:
>>> s1
a 2
b 4
c 5
dtype: int64
Second Dataframe:
>>> s2
a 2
b 4
d 7
dtype: int64
Simple DataFrame.add + fill_value which is explained in docs:
>>> s1.add(s2, fill_value=0)
a 4.0
b 8.0
c 5.0
d 7.0
dtype: float64
Document :
fill_value : None or float value, default None
Fill existing missing (NaN) values, and any new element needed for
successful DataFrame alignment, with this value before computation. If
data in both corresponding DataFrame locations is missing the result
will be missing
answered Nov 20 '18 at 17:24
pygopygo
2,6681619
These areSeriesso the correct method ispandas.Series.add. Also, this is addressed by @W-B
– piRSquared
Nov 20 '18 at 17:35
Thnx for pointing that out , link is just stand corrected , i Know its addressed by @W-B and Drew Nicolette but thought to link that with original docs while showing that for the use case here, i'm firm believer in getting & putting the explanation and like that style because it helps newbies to understand under the hood :).
– pygo
Nov 20 '18 at 17:43
add a comment |
There is simple answer Directly from pandas.Series.add , you will get it from here
For you Use case here..
First dataframe:
>>> s1
a 2
b 4
c 5
dtype: int64
Second Dataframe:
>>> s2
a 2
b 4
d 7
dtype: int64
Simple DataFrame.add + fill_value which is explained in docs:
>>> s1.add(s2, fill_value=0)
a 4.0
b 8.0
c 5.0
d 7.0
dtype: float64
Document :
fill_value : None or float value, default None
Fill existing missing (NaN) values, and any new element needed for
successful DataFrame alignment, with this value before computation. If
data in both corresponding DataFrame locations is missing the result
will be missing
answered Nov 20 '18 at 17:24
pygopygo
2,6681619
There is simple answer Directly from pandas.Series.add , you will get it from here
For you Use case here..
First dataframe:
>>> s1
a 2
b 4
c 5
dtype: int64
Second Dataframe:
>>> s2
a 2
b 4
d 7
dtype: int64
Simple DataFrame.add + fill_value which is explained in docs:
>>> s1.add(s2, fill_value=0)
a 4.0
b 8.0
c 5.0
d 7.0
dtype: float64
Document :
fill_value : None or float value, default None
Fill existing missing (NaN) values, and any new element needed for
successful DataFrame alignment, with this value before computation. If
data in both corresponding DataFrame locations is missing the result
will be missing
answered Nov 20 '18 at 17:24
pygopygo
2,6681619
edited Nov 21 '18 at 3:20
answered Nov 20 '18 at 17:24
pygopygo
2,6681619
answered Nov 20 '18 at 17:24
pygopygo
2,6681619
answered Nov 20 '18 at 17:24
pygopygo
2,6681619
2,6681619
These areSeriesso the correct method ispandas.Series.add. Also, this is addressed by @W-B
– piRSquared
Nov 20 '18 at 17:35
Thnx for pointing that out , link is just stand corrected , i Know its addressed by @W-B and Drew Nicolette but thought to link that with original docs while showing that for the use case here, i'm firm believer in getting & putting the explanation and like that style because it helps newbies to understand under the hood :).
– pygo
Nov 20 '18 at 17:43
add a comment |
These areSeriesso the correct method ispandas.Series.add. Also, this is addressed by @W-B
– piRSquared
Nov 20 '18 at 17:35
Thnx for pointing that out , link is just stand corrected , i Know its addressed by @W-B and Drew Nicolette but thought to link that with original docs while showing that for the use case here, i'm firm believer in getting & putting the explanation and like that style because it helps newbies to understand under the hood :).
– pygo
Nov 20 '18 at 17:43
These are
Series so the correct method is pandas.Series.add. Also, this is addressed by @W-B– piRSquared
Nov 20 '18 at 17:35
These are
Series so the correct method is pandas.Series.add. Also, this is addressed by @W-B– piRSquared
Nov 20 '18 at 17:35
Thnx for pointing that out , link is just stand corrected , i Know its addressed by @W-B and Drew Nicolette but thought to link that with original docs while showing that for the use case here, i'm firm believer in getting & putting the explanation and like that style because it helps newbies to understand under the hood :).
– pygo
Nov 20 '18 at 17:43
Thnx for pointing that out , link is just stand corrected , i Know its addressed by @W-B and Drew Nicolette but thought to link that with original docs while showing that for the use case here, i'm firm believer in getting & putting the explanation and like that style because it helps newbies to understand under the hood :).
– pygo
Nov 20 '18 at 17:43
add a comment |
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How about s1.add(s2)?
– Anton vBR
Nov 20 '18 at 17:07