Pandas: Outer join with a specified range of difference between the keys
I want to perform outer join on two data frames where the keys are id: int and date: pd.Timestamp objects. On top of that, I want the keys to be considered as equal if the ids are the same (the normal behaviour) and the dates are either equal (the normal behaviour) or the difference between the dates is maximum 30 days. Then, when the outer join is performed, the date from the right data frame should be taken. An example is included below:
left = pd.DataFrame({"id": [1, 2, 3], "date": [pd.Timestamp(2002, 3, 25), pd.Timestamp(2003, 4, 4), pd.Timestamp(2004, 6, 6)], "val_3": [77, 88, 11]})
right = pd.DataFrame({"id": [1, 2, 3], "date": [pd.Timestamp(2002, 3, 10), pd.Timestamp(2003, 4, 27), pd.Timestamp(2004, 5, 5)], "val_1": [99, 66, 33], "val_2": [101, 102, 103]})
And the result after the join should be:
result = pd.DataFrame({"id": [1, 2, 3, 3], "date": [pd.Timestamp(2002, 3, 10), pd.Timestamp(2003, 4, 27), pd.Timestamp(2004, 6, 6), pd.Timestamp(2004, 5, 5)], "val_3": [77, 88, 11, np.nan], "val_1": [99, 66, np.nan, 33], "val_2": [101, 102, np.nan, 103]})
Looking forward to your answers!
python pandas outer-join
asked Jan 3 at 19:30
gorjangorjan
1,450615
add a comment |
I want to perform outer join on two data frames where the keys are id: int and date: pd.Timestamp objects. On top of that, I want the keys to be considered as equal if the ids are the same (the normal behaviour) and the dates are either equal (the normal behaviour) or the difference between the dates is maximum 30 days. Then, when the outer join is performed, the date from the right data frame should be taken. An example is included below:
left = pd.DataFrame({"id": [1, 2, 3], "date": [pd.Timestamp(2002, 3, 25), pd.Timestamp(2003, 4, 4), pd.Timestamp(2004, 6, 6)], "val_3": [77, 88, 11]})
right = pd.DataFrame({"id": [1, 2, 3], "date": [pd.Timestamp(2002, 3, 10), pd.Timestamp(2003, 4, 27), pd.Timestamp(2004, 5, 5)], "val_1": [99, 66, 33], "val_2": [101, 102, 103]})
And the result after the join should be:
result = pd.DataFrame({"id": [1, 2, 3, 3], "date": [pd.Timestamp(2002, 3, 10), pd.Timestamp(2003, 4, 27), pd.Timestamp(2004, 6, 6), pd.Timestamp(2004, 5, 5)], "val_3": [77, 88, 11, np.nan], "val_1": [99, 66, np.nan, 33], "val_2": [101, 102, np.nan, 103]})
Looking forward to your answers!
python pandas outer-join
asked Jan 3 at 19:30
gorjangorjan
1,450615
What should happen if 2 dates in the right frame fall within 30 days of one date in the left frame for the same id? Should we get 2 rows for the date in the left frame, or do we take only the first or last date within that 30 day window?
– ALollz
Jan 3 at 19:41
add a comment |
I want to perform outer join on two data frames where the keys are id: int and date: pd.Timestamp objects. On top of that, I want the keys to be considered as equal if the ids are the same (the normal behaviour) and the dates are either equal (the normal behaviour) or the difference between the dates is maximum 30 days. Then, when the outer join is performed, the date from the right data frame should be taken. An example is included below:
left = pd.DataFrame({"id": [1, 2, 3], "date": [pd.Timestamp(2002, 3, 25), pd.Timestamp(2003, 4, 4), pd.Timestamp(2004, 6, 6)], "val_3": [77, 88, 11]})
right = pd.DataFrame({"id": [1, 2, 3], "date": [pd.Timestamp(2002, 3, 10), pd.Timestamp(2003, 4, 27), pd.Timestamp(2004, 5, 5)], "val_1": [99, 66, 33], "val_2": [101, 102, 103]})
And the result after the join should be:
result = pd.DataFrame({"id": [1, 2, 3, 3], "date": [pd.Timestamp(2002, 3, 10), pd.Timestamp(2003, 4, 27), pd.Timestamp(2004, 6, 6), pd.Timestamp(2004, 5, 5)], "val_3": [77, 88, 11, np.nan], "val_1": [99, 66, np.nan, 33], "val_2": [101, 102, np.nan, 103]})
Looking forward to your answers!
python pandas outer-join
asked Jan 3 at 19:30
gorjangorjan
1,450615
I want to perform outer join on two data frames where the keys are id: int and date: pd.Timestamp objects. On top of that, I want the keys to be considered as equal if the ids are the same (the normal behaviour) and the dates are either equal (the normal behaviour) or the difference between the dates is maximum 30 days. Then, when the outer join is performed, the date from the right data frame should be taken. An example is included below:
left = pd.DataFrame({"id": [1, 2, 3], "date": [pd.Timestamp(2002, 3, 25), pd.Timestamp(2003, 4, 4), pd.Timestamp(2004, 6, 6)], "val_3": [77, 88, 11]})
right = pd.DataFrame({"id": [1, 2, 3], "date": [pd.Timestamp(2002, 3, 10), pd.Timestamp(2003, 4, 27), pd.Timestamp(2004, 5, 5)], "val_1": [99, 66, 33], "val_2": [101, 102, 103]})
And the result after the join should be:
result = pd.DataFrame({"id": [1, 2, 3, 3], "date": [pd.Timestamp(2002, 3, 10), pd.Timestamp(2003, 4, 27), pd.Timestamp(2004, 6, 6), pd.Timestamp(2004, 5, 5)], "val_3": [77, 88, 11, np.nan], "val_1": [99, 66, np.nan, 33], "val_2": [101, 102, np.nan, 103]})
Looking forward to your answers!
python pandas outer-join
python pandas outer-join
asked Jan 3 at 19:30
gorjangorjan
1,450615
asked Jan 3 at 19:30
gorjangorjan
1,450615
asked Jan 3 at 19:30
gorjangorjan
1,450615
asked Jan 3 at 19:30
gorjangorjan
1,450615
asked Jan 3 at 19:30
gorjangorjan
1,450615
1,450615
What should happen if 2 dates in the right frame fall within 30 days of one date in the left frame for the same id? Should we get 2 rows for the date in the left frame, or do we take only the first or last date within that 30 day window?
– ALollz
Jan 3 at 19:41
add a comment |
What should happen if 2 dates in the right frame fall within 30 days of one date in the left frame for the same id? Should we get 2 rows for the date in the left frame, or do we take only the first or last date within that 30 day window?
– ALollz
Jan 3 at 19:41
What should happen if 2 dates in the right frame fall within 30 days of one date in the left frame for the same id? Should we get 2 rows for the date in the left frame, or do we take only the first or last date within that 30 day window?
– ALollz
Jan 3 at 19:41
What should happen if 2 dates in the right frame fall within 30 days of one date in the left frame for the same id? Should we get 2 rows for the date in the left frame, or do we take only the first or last date within that 30 day window?
– ALollz
Jan 3 at 19:41
add a comment |
1 Answer
1
active
oldest
votes
I think merge on 'id' and then split the DataFrame as needed if the date doesn't fall within 30 days
import pandas as pd
# Rename so it's easier to split columns later
left = left.rename(columns={'date': 'date_l'})
m = left.merge(right, on='id', how='outer')
mask = m.date >= m.date_l - pd.Timedelta(days=30)
pd.concat([
m[mask].drop(columns='date_l'),
m.loc[~mask, left.columns].rename(columns={'date_l': 'date'}),
m.loc[~mask, right.columns]],
ignore_index=True, sort=False)
Output:
id val_3 date val_1 val_2
0 1 77.0 2002-03-10 99.0 101.0
1 2 88.0 2003-04-27 66.0 102.0
2 3 11.0 2004-06-06 NaN NaN
3 3 NaN 2004-05-05 33.0 103.0
answered Jan 3 at 19:52
ALollzALollz
16k31738
Although I concluded that my use-case is a lot more complex, this answer achieves what I said i want so I will mark it as correct.
– gorjan
Jan 5 at 2:18
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
I think merge on 'id' and then split the DataFrame as needed if the date doesn't fall within 30 days
import pandas as pd
# Rename so it's easier to split columns later
left = left.rename(columns={'date': 'date_l'})
m = left.merge(right, on='id', how='outer')
mask = m.date >= m.date_l - pd.Timedelta(days=30)
pd.concat([
m[mask].drop(columns='date_l'),
m.loc[~mask, left.columns].rename(columns={'date_l': 'date'}),
m.loc[~mask, right.columns]],
ignore_index=True, sort=False)
Output:
id val_3 date val_1 val_2
0 1 77.0 2002-03-10 99.0 101.0
1 2 88.0 2003-04-27 66.0 102.0
2 3 11.0 2004-06-06 NaN NaN
3 3 NaN 2004-05-05 33.0 103.0
answered Jan 3 at 19:52
ALollzALollz
16k31738
Although I concluded that my use-case is a lot more complex, this answer achieves what I said i want so I will mark it as correct.
– gorjan
Jan 5 at 2:18
add a comment |
I think merge on 'id' and then split the DataFrame as needed if the date doesn't fall within 30 days
import pandas as pd
# Rename so it's easier to split columns later
left = left.rename(columns={'date': 'date_l'})
m = left.merge(right, on='id', how='outer')
mask = m.date >= m.date_l - pd.Timedelta(days=30)
pd.concat([
m[mask].drop(columns='date_l'),
m.loc[~mask, left.columns].rename(columns={'date_l': 'date'}),
m.loc[~mask, right.columns]],
ignore_index=True, sort=False)
Output:
id val_3 date val_1 val_2
0 1 77.0 2002-03-10 99.0 101.0
1 2 88.0 2003-04-27 66.0 102.0
2 3 11.0 2004-06-06 NaN NaN
3 3 NaN 2004-05-05 33.0 103.0
answered Jan 3 at 19:52
ALollzALollz
16k31738
Although I concluded that my use-case is a lot more complex, this answer achieves what I said i want so I will mark it as correct.
– gorjan
Jan 5 at 2:18
add a comment |
I think merge on 'id' and then split the DataFrame as needed if the date doesn't fall within 30 days
import pandas as pd
# Rename so it's easier to split columns later
left = left.rename(columns={'date': 'date_l'})
m = left.merge(right, on='id', how='outer')
mask = m.date >= m.date_l - pd.Timedelta(days=30)
pd.concat([
m[mask].drop(columns='date_l'),
m.loc[~mask, left.columns].rename(columns={'date_l': 'date'}),
m.loc[~mask, right.columns]],
ignore_index=True, sort=False)
Output:
id val_3 date val_1 val_2
0 1 77.0 2002-03-10 99.0 101.0
1 2 88.0 2003-04-27 66.0 102.0
2 3 11.0 2004-06-06 NaN NaN
3 3 NaN 2004-05-05 33.0 103.0
answered Jan 3 at 19:52
ALollzALollz
16k31738
I think merge on 'id' and then split the DataFrame as needed if the date doesn't fall within 30 days
import pandas as pd
# Rename so it's easier to split columns later
left = left.rename(columns={'date': 'date_l'})
m = left.merge(right, on='id', how='outer')
mask = m.date >= m.date_l - pd.Timedelta(days=30)
pd.concat([
m[mask].drop(columns='date_l'),
m.loc[~mask, left.columns].rename(columns={'date_l': 'date'}),
m.loc[~mask, right.columns]],
ignore_index=True, sort=False)
Output:
id val_3 date val_1 val_2
0 1 77.0 2002-03-10 99.0 101.0
1 2 88.0 2003-04-27 66.0 102.0
2 3 11.0 2004-06-06 NaN NaN
3 3 NaN 2004-05-05 33.0 103.0
answered Jan 3 at 19:52
ALollzALollz
16k31738
edited Jan 3 at 20:19
answered Jan 3 at 19:52
ALollzALollz
16k31738
answered Jan 3 at 19:52
ALollzALollz
16k31738
answered Jan 3 at 19:52
ALollzALollz
16k31738
16k31738
Although I concluded that my use-case is a lot more complex, this answer achieves what I said i want so I will mark it as correct.
– gorjan
Jan 5 at 2:18
add a comment |
Although I concluded that my use-case is a lot more complex, this answer achieves what I said i want so I will mark it as correct.
– gorjan
Jan 5 at 2:18
Although I concluded that my use-case is a lot more complex, this answer achieves what I said i want so I will mark it as correct.
– gorjan
Jan 5 at 2:18
Although I concluded that my use-case is a lot more complex, this answer achieves what I said i want so I will mark it as correct.
– gorjan
Jan 5 at 2:18
add a comment |
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What should happen if 2 dates in the right frame fall within 30 days of one date in the left frame for the same id? Should we get 2 rows for the date in the left frame, or do we take only the first or last date within that 30 day window?
– ALollz
Jan 3 at 19:41