Group two Dataframes with MultiIndex columns
I have two dataframes and I would like to create a new one, which will include all the unique columns of the two source dataframes and the aggregation of the common columns.
These are two samples:
And this is the result:
All the column indexes should match in order to be aggregated.
I have written the following code:
df_all = pd.DataFrame
for dfColumn in df_1:
if dfColumn in df_2.columns:
df_all[dfColumn] = df_1.loc[:, dfColumn].add(df_2.loc[:, dfColumn])
else:
df_all[dfColumn] = df_1[dfColumn]
for dfColumn in df_2:
if dfColumn not in df_all.columns:
df_all[dfColumn] = df_2[dfColumn]
However, I get an error on the following line:
df_all[dfColumn] = df_1.loc[:, dfColumn].add(df_2.loc[:, dfColumn])
when I am trying to assign the value to df_all[dfColumn]
It drives me crazy all the different possibilities that you have with Python.
But I cannot find one to make it work.
Thanks for your help and time.
python-3.x pandas dataframe
asked Nov 16 '18 at 10:16
ThanasisThanasis
1821112
add a comment |
I have two dataframes and I would like to create a new one, which will include all the unique columns of the two source dataframes and the aggregation of the common columns.
These are two samples:
And this is the result:
All the column indexes should match in order to be aggregated.
I have written the following code:
df_all = pd.DataFrame
for dfColumn in df_1:
if dfColumn in df_2.columns:
df_all[dfColumn] = df_1.loc[:, dfColumn].add(df_2.loc[:, dfColumn])
else:
df_all[dfColumn] = df_1[dfColumn]
for dfColumn in df_2:
if dfColumn not in df_all.columns:
df_all[dfColumn] = df_2[dfColumn]
However, I get an error on the following line:
df_all[dfColumn] = df_1.loc[:, dfColumn].add(df_2.loc[:, dfColumn])
when I am trying to assign the value to df_all[dfColumn]
It drives me crazy all the different possibilities that you have with Python.
But I cannot find one to make it work.
Thanks for your help and time.
python-3.x pandas dataframe
asked Nov 16 '18 at 10:16
ThanasisThanasis
1821112
add a comment |
I have two dataframes and I would like to create a new one, which will include all the unique columns of the two source dataframes and the aggregation of the common columns.
These are two samples:
And this is the result:
All the column indexes should match in order to be aggregated.
I have written the following code:
df_all = pd.DataFrame
for dfColumn in df_1:
if dfColumn in df_2.columns:
df_all[dfColumn] = df_1.loc[:, dfColumn].add(df_2.loc[:, dfColumn])
else:
df_all[dfColumn] = df_1[dfColumn]
for dfColumn in df_2:
if dfColumn not in df_all.columns:
df_all[dfColumn] = df_2[dfColumn]
However, I get an error on the following line:
df_all[dfColumn] = df_1.loc[:, dfColumn].add(df_2.loc[:, dfColumn])
when I am trying to assign the value to df_all[dfColumn]
It drives me crazy all the different possibilities that you have with Python.
But I cannot find one to make it work.
Thanks for your help and time.
python-3.x pandas dataframe
asked Nov 16 '18 at 10:16
ThanasisThanasis
1821112
I have two dataframes and I would like to create a new one, which will include all the unique columns of the two source dataframes and the aggregation of the common columns.
These are two samples:
And this is the result:
All the column indexes should match in order to be aggregated.
I have written the following code:
df_all = pd.DataFrame
for dfColumn in df_1:
if dfColumn in df_2.columns:
df_all[dfColumn] = df_1.loc[:, dfColumn].add(df_2.loc[:, dfColumn])
else:
df_all[dfColumn] = df_1[dfColumn]
for dfColumn in df_2:
if dfColumn not in df_all.columns:
df_all[dfColumn] = df_2[dfColumn]
However, I get an error on the following line:
df_all[dfColumn] = df_1.loc[:, dfColumn].add(df_2.loc[:, dfColumn])
when I am trying to assign the value to df_all[dfColumn]
It drives me crazy all the different possibilities that you have with Python.
But I cannot find one to make it work.
Thanks for your help and time.
python-3.x pandas dataframe
python-3.x pandas dataframe
asked Nov 16 '18 at 10:16
ThanasisThanasis
1821112
asked Nov 16 '18 at 10:16
ThanasisThanasis
1821112
asked Nov 16 '18 at 10:16
ThanasisThanasis
1821112
asked Nov 16 '18 at 10:16
ThanasisThanasis
1821112
asked Nov 16 '18 at 10:16
ThanasisThanasis
1821112
1821112
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
Actually,
I fixed it only with the following:
df_all = pd.concat([df_1, df_2], axis=1)
df_all = df_all.groupby(level=[0, 1, 2], axis=1).sum()
Is there a way to replace level=[0, 1, 2] with something like level=df_all.columns.levels ?
answered Nov 16 '18 at 11:02
ThanasisThanasis
1821112
Okay @Thanasis I edited my answer to do what you wanted. In this case I keep the unique columns and add the duplicated ones.
– yatu
Nov 16 '18 at 11:37
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
Actually,
I fixed it only with the following:
df_all = pd.concat([df_1, df_2], axis=1)
df_all = df_all.groupby(level=[0, 1, 2], axis=1).sum()
Is there a way to replace level=[0, 1, 2] with something like level=df_all.columns.levels ?
answered Nov 16 '18 at 11:02
ThanasisThanasis
1821112
Okay @Thanasis I edited my answer to do what you wanted. In this case I keep the unique columns and add the duplicated ones.
– yatu
Nov 16 '18 at 11:37
add a comment |
Actually,
I fixed it only with the following:
df_all = pd.concat([df_1, df_2], axis=1)
df_all = df_all.groupby(level=[0, 1, 2], axis=1).sum()
Is there a way to replace level=[0, 1, 2] with something like level=df_all.columns.levels ?
answered Nov 16 '18 at 11:02
ThanasisThanasis
1821112
Okay @Thanasis I edited my answer to do what you wanted. In this case I keep the unique columns and add the duplicated ones.
– yatu
Nov 16 '18 at 11:37
add a comment |
Actually,
I fixed it only with the following:
df_all = pd.concat([df_1, df_2], axis=1)
df_all = df_all.groupby(level=[0, 1, 2], axis=1).sum()
Is there a way to replace level=[0, 1, 2] with something like level=df_all.columns.levels ?
answered Nov 16 '18 at 11:02
ThanasisThanasis
1821112
Actually,
I fixed it only with the following:
df_all = pd.concat([df_1, df_2], axis=1)
df_all = df_all.groupby(level=[0, 1, 2], axis=1).sum()
Is there a way to replace level=[0, 1, 2] with something like level=df_all.columns.levels ?
answered Nov 16 '18 at 11:02
ThanasisThanasis
1821112
answered Nov 16 '18 at 11:02
ThanasisThanasis
1821112
answered Nov 16 '18 at 11:02
ThanasisThanasis
1821112
answered Nov 16 '18 at 11:02
ThanasisThanasis
1821112
1821112
Okay @Thanasis I edited my answer to do what you wanted. In this case I keep the unique columns and add the duplicated ones.
– yatu
Nov 16 '18 at 11:37
add a comment |
Okay @Thanasis I edited my answer to do what you wanted. In this case I keep the unique columns and add the duplicated ones.
– yatu
Nov 16 '18 at 11:37
Okay @Thanasis I edited my answer to do what you wanted. In this case I keep the unique columns and add the duplicated ones.
– yatu
Nov 16 '18 at 11:37
Okay @Thanasis I edited my answer to do what you wanted. In this case I keep the unique columns and add the duplicated ones.
– yatu
Nov 16 '18 at 11:37
add a comment |
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