Adding values of two Pandas series with different column names
I have two pandas series of the same length but with different column names. How can one add the values in them?
series.add(other, fill_value=0, axis=0) does avoid NaN-values, but the values are not added. Instead, the result is a concatenation of the two series.
Is there a way to obtain a new series consisting of the sum of the values in two series?
python pandas indexing series
edited Nov 23 '18 at 20:27
jpp
100k2161111
asked Nov 23 '18 at 20:12
Sebastian AllardSebastian Allard
84
add a comment |
I have two pandas series of the same length but with different column names. How can one add the values in them?
series.add(other, fill_value=0, axis=0) does avoid NaN-values, but the values are not added. Instead, the result is a concatenation of the two series.
Is there a way to obtain a new series consisting of the sum of the values in two series?
python pandas indexing series
edited Nov 23 '18 at 20:27
jpp
100k2161111
asked Nov 23 '18 at 20:12
Sebastian AllardSebastian Allard
84
add a comment |
I have two pandas series of the same length but with different column names. How can one add the values in them?
series.add(other, fill_value=0, axis=0) does avoid NaN-values, but the values are not added. Instead, the result is a concatenation of the two series.
Is there a way to obtain a new series consisting of the sum of the values in two series?
python pandas indexing series
edited Nov 23 '18 at 20:27
jpp
100k2161111
asked Nov 23 '18 at 20:12
Sebastian AllardSebastian Allard
84
I have two pandas series of the same length but with different column names. How can one add the values in them?
series.add(other, fill_value=0, axis=0) does avoid NaN-values, but the values are not added. Instead, the result is a concatenation of the two series.
Is there a way to obtain a new series consisting of the sum of the values in two series?
python pandas indexing series
python pandas indexing series
edited Nov 23 '18 at 20:27
jpp
100k2161111
asked Nov 23 '18 at 20:12
Sebastian AllardSebastian Allard
84
edited Nov 23 '18 at 20:27
jpp
100k2161111
asked Nov 23 '18 at 20:12
Sebastian AllardSebastian Allard
84
edited Nov 23 '18 at 20:27
jpp
100k2161111
edited Nov 23 '18 at 20:27
jpp
100k2161111
edited Nov 23 '18 at 20:27
jpp
100k2161111
100k2161111
asked Nov 23 '18 at 20:12
Sebastian AllardSebastian Allard
84
asked Nov 23 '18 at 20:12
Sebastian AllardSebastian Allard
84
asked Nov 23 '18 at 20:12
Sebastian AllardSebastian Allard
84
84
add a comment |
add a comment |
2 Answers
2
active
oldest
votes
The values attribute lets you access the underlying raw numpy arrays. You can add those.
raw_sum = series.values + other.values
series2 = Series(raw_sum, index=series.index)
This also works:
series2 = series + other.values
answered Nov 23 '18 at 20:18
shx2shx2
40.7k679110
It's worth noting that in general indices have meanings, even if they just boil down to "row numbers". If your 2 series are meant to be added but you have mismatched indices, it's worth finding out why.
– jpp
Nov 23 '18 at 20:28
Thank you! Simple solution :) The series consisted of a sport teams average home and away statistics. The columns for home statistics were named statistic_home and the corresponding away statistics were named away_statistic.
– Sebastian Allard
Nov 23 '18 at 20:28
add a comment |
Mismatched indidces
This issue is your 2 series have different indices. Here's an example:
s1 = pd.Series([1, np.nan, 3, np.nan, 5], index=np.arange(5))
s2 = pd.Series([np.nan, 7, 8, np.nan, np.nan], index=np.arange(5)+10)
print(s1.add(s2, fill_value=0, axis=0))
0 1.0
1 NaN
2 3.0
3 NaN
4 5.0
10 NaN
11 7.0
12 8.0
13 NaN
14 NaN
dtype: float64
You have 2 options: reindex via, for example, a dictionary or disregard indices and add your series positionally.
Map index of one series to align with the other
You can use a dictionary to realign. The mapping below is arbitrary. NaN values occur where, after reindexing, values in both series are NaN:
index_map = dict(zip(np.arange(5) + 10, [3, 2, 4, 0, 1]))
s2.index = s2.index.map(index_map)
print(s1.add(s2, fill_value=0, axis=0))
0 1.0
1 NaN
2 10.0
3 NaN
4 13.0
dtype: float64
Disregard indices; use positional location only
In this case, you can either construct a new series with the regular pd.RangeIndex as index (i.e. 0, 1, 2, ...), or use an index from one of the input series:
# normalized index
res = pd.Series(s1.values + s2.values)
# take index from s1
res = pd.Series(s1.values + s2.values, index=s1.index)
answered Nov 23 '18 at 20:19
jppjpp
100k2161111
Thank you for a detailed solution :)
– Sebastian Allard
Nov 23 '18 at 20:35
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
The values attribute lets you access the underlying raw numpy arrays. You can add those.
raw_sum = series.values + other.values
series2 = Series(raw_sum, index=series.index)
This also works:
series2 = series + other.values
answered Nov 23 '18 at 20:18
shx2shx2
40.7k679110
It's worth noting that in general indices have meanings, even if they just boil down to "row numbers". If your 2 series are meant to be added but you have mismatched indices, it's worth finding out why.
– jpp
Nov 23 '18 at 20:28
Thank you! Simple solution :) The series consisted of a sport teams average home and away statistics. The columns for home statistics were named statistic_home and the corresponding away statistics were named away_statistic.
– Sebastian Allard
Nov 23 '18 at 20:28
add a comment |
The values attribute lets you access the underlying raw numpy arrays. You can add those.
raw_sum = series.values + other.values
series2 = Series(raw_sum, index=series.index)
This also works:
series2 = series + other.values
answered Nov 23 '18 at 20:18
shx2shx2
40.7k679110
It's worth noting that in general indices have meanings, even if they just boil down to "row numbers". If your 2 series are meant to be added but you have mismatched indices, it's worth finding out why.
– jpp
Nov 23 '18 at 20:28
Thank you! Simple solution :) The series consisted of a sport teams average home and away statistics. The columns for home statistics were named statistic_home and the corresponding away statistics were named away_statistic.
– Sebastian Allard
Nov 23 '18 at 20:28
add a comment |
The values attribute lets you access the underlying raw numpy arrays. You can add those.
raw_sum = series.values + other.values
series2 = Series(raw_sum, index=series.index)
This also works:
series2 = series + other.values
answered Nov 23 '18 at 20:18
shx2shx2
40.7k679110
The values attribute lets you access the underlying raw numpy arrays. You can add those.
raw_sum = series.values + other.values
series2 = Series(raw_sum, index=series.index)
This also works:
series2 = series + other.values
answered Nov 23 '18 at 20:18
shx2shx2
40.7k679110
answered Nov 23 '18 at 20:18
shx2shx2
40.7k679110
answered Nov 23 '18 at 20:18
shx2shx2
40.7k679110
answered Nov 23 '18 at 20:18
shx2shx2
40.7k679110
40.7k679110
It's worth noting that in general indices have meanings, even if they just boil down to "row numbers". If your 2 series are meant to be added but you have mismatched indices, it's worth finding out why.
– jpp
Nov 23 '18 at 20:28
Thank you! Simple solution :) The series consisted of a sport teams average home and away statistics. The columns for home statistics were named statistic_home and the corresponding away statistics were named away_statistic.
– Sebastian Allard
Nov 23 '18 at 20:28
add a comment |
It's worth noting that in general indices have meanings, even if they just boil down to "row numbers". If your 2 series are meant to be added but you have mismatched indices, it's worth finding out why.
– jpp
Nov 23 '18 at 20:28
Thank you! Simple solution :) The series consisted of a sport teams average home and away statistics. The columns for home statistics were named statistic_home and the corresponding away statistics were named away_statistic.
– Sebastian Allard
Nov 23 '18 at 20:28
It's worth noting that in general indices have meanings, even if they just boil down to "row numbers". If your 2 series are meant to be added but you have mismatched indices, it's worth finding out why.
– jpp
Nov 23 '18 at 20:28
It's worth noting that in general indices have meanings, even if they just boil down to "row numbers". If your 2 series are meant to be added but you have mismatched indices, it's worth finding out why.
– jpp
Nov 23 '18 at 20:28
Thank you! Simple solution :) The series consisted of a sport teams average home and away statistics. The columns for home statistics were named statistic_home and the corresponding away statistics were named away_statistic.
– Sebastian Allard
Nov 23 '18 at 20:28
Thank you! Simple solution :) The series consisted of a sport teams average home and away statistics. The columns for home statistics were named statistic_home and the corresponding away statistics were named away_statistic.
– Sebastian Allard
Nov 23 '18 at 20:28
add a comment |
Mismatched indidces
This issue is your 2 series have different indices. Here's an example:
s1 = pd.Series([1, np.nan, 3, np.nan, 5], index=np.arange(5))
s2 = pd.Series([np.nan, 7, 8, np.nan, np.nan], index=np.arange(5)+10)
print(s1.add(s2, fill_value=0, axis=0))
0 1.0
1 NaN
2 3.0
3 NaN
4 5.0
10 NaN
11 7.0
12 8.0
13 NaN
14 NaN
dtype: float64
You have 2 options: reindex via, for example, a dictionary or disregard indices and add your series positionally.
Map index of one series to align with the other
You can use a dictionary to realign. The mapping below is arbitrary. NaN values occur where, after reindexing, values in both series are NaN:
index_map = dict(zip(np.arange(5) + 10, [3, 2, 4, 0, 1]))
s2.index = s2.index.map(index_map)
print(s1.add(s2, fill_value=0, axis=0))
0 1.0
1 NaN
2 10.0
3 NaN
4 13.0
dtype: float64
Disregard indices; use positional location only
In this case, you can either construct a new series with the regular pd.RangeIndex as index (i.e. 0, 1, 2, ...), or use an index from one of the input series:
# normalized index
res = pd.Series(s1.values + s2.values)
# take index from s1
res = pd.Series(s1.values + s2.values, index=s1.index)
answered Nov 23 '18 at 20:19
jppjpp
100k2161111
Thank you for a detailed solution :)
– Sebastian Allard
Nov 23 '18 at 20:35
add a comment |
Mismatched indidces
This issue is your 2 series have different indices. Here's an example:
s1 = pd.Series([1, np.nan, 3, np.nan, 5], index=np.arange(5))
s2 = pd.Series([np.nan, 7, 8, np.nan, np.nan], index=np.arange(5)+10)
print(s1.add(s2, fill_value=0, axis=0))
0 1.0
1 NaN
2 3.0
3 NaN
4 5.0
10 NaN
11 7.0
12 8.0
13 NaN
14 NaN
dtype: float64
You have 2 options: reindex via, for example, a dictionary or disregard indices and add your series positionally.
Map index of one series to align with the other
You can use a dictionary to realign. The mapping below is arbitrary. NaN values occur where, after reindexing, values in both series are NaN:
index_map = dict(zip(np.arange(5) + 10, [3, 2, 4, 0, 1]))
s2.index = s2.index.map(index_map)
print(s1.add(s2, fill_value=0, axis=0))
0 1.0
1 NaN
2 10.0
3 NaN
4 13.0
dtype: float64
Disregard indices; use positional location only
In this case, you can either construct a new series with the regular pd.RangeIndex as index (i.e. 0, 1, 2, ...), or use an index from one of the input series:
# normalized index
res = pd.Series(s1.values + s2.values)
# take index from s1
res = pd.Series(s1.values + s2.values, index=s1.index)
answered Nov 23 '18 at 20:19
jppjpp
100k2161111
Thank you for a detailed solution :)
– Sebastian Allard
Nov 23 '18 at 20:35
add a comment |
Mismatched indidces
This issue is your 2 series have different indices. Here's an example:
s1 = pd.Series([1, np.nan, 3, np.nan, 5], index=np.arange(5))
s2 = pd.Series([np.nan, 7, 8, np.nan, np.nan], index=np.arange(5)+10)
print(s1.add(s2, fill_value=0, axis=0))
0 1.0
1 NaN
2 3.0
3 NaN
4 5.0
10 NaN
11 7.0
12 8.0
13 NaN
14 NaN
dtype: float64
You have 2 options: reindex via, for example, a dictionary or disregard indices and add your series positionally.
Map index of one series to align with the other
You can use a dictionary to realign. The mapping below is arbitrary. NaN values occur where, after reindexing, values in both series are NaN:
index_map = dict(zip(np.arange(5) + 10, [3, 2, 4, 0, 1]))
s2.index = s2.index.map(index_map)
print(s1.add(s2, fill_value=0, axis=0))
0 1.0
1 NaN
2 10.0
3 NaN
4 13.0
dtype: float64
Disregard indices; use positional location only
In this case, you can either construct a new series with the regular pd.RangeIndex as index (i.e. 0, 1, 2, ...), or use an index from one of the input series:
# normalized index
res = pd.Series(s1.values + s2.values)
# take index from s1
res = pd.Series(s1.values + s2.values, index=s1.index)
answered Nov 23 '18 at 20:19
jppjpp
100k2161111
Mismatched indidces
This issue is your 2 series have different indices. Here's an example:
s1 = pd.Series([1, np.nan, 3, np.nan, 5], index=np.arange(5))
s2 = pd.Series([np.nan, 7, 8, np.nan, np.nan], index=np.arange(5)+10)
print(s1.add(s2, fill_value=0, axis=0))
0 1.0
1 NaN
2 3.0
3 NaN
4 5.0
10 NaN
11 7.0
12 8.0
13 NaN
14 NaN
dtype: float64
You have 2 options: reindex via, for example, a dictionary or disregard indices and add your series positionally.
Map index of one series to align with the other
You can use a dictionary to realign. The mapping below is arbitrary. NaN values occur where, after reindexing, values in both series are NaN:
index_map = dict(zip(np.arange(5) + 10, [3, 2, 4, 0, 1]))
s2.index = s2.index.map(index_map)
print(s1.add(s2, fill_value=0, axis=0))
0 1.0
1 NaN
2 10.0
3 NaN
4 13.0
dtype: float64
Disregard indices; use positional location only
In this case, you can either construct a new series with the regular pd.RangeIndex as index (i.e. 0, 1, 2, ...), or use an index from one of the input series:
# normalized index
res = pd.Series(s1.values + s2.values)
# take index from s1
res = pd.Series(s1.values + s2.values, index=s1.index)
answered Nov 23 '18 at 20:19
jppjpp
100k2161111
edited Nov 23 '18 at 20:24
answered Nov 23 '18 at 20:19
jppjpp
100k2161111
answered Nov 23 '18 at 20:19
jppjpp
100k2161111
answered Nov 23 '18 at 20:19
jppjpp
100k2161111
100k2161111
Thank you for a detailed solution :)
– Sebastian Allard
Nov 23 '18 at 20:35
add a comment |
Thank you for a detailed solution :)
– Sebastian Allard
Nov 23 '18 at 20:35
Thank you for a detailed solution :)
– Sebastian Allard
Nov 23 '18 at 20:35
Thank you for a detailed solution :)
– Sebastian Allard
Nov 23 '18 at 20:35
add a comment |
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