Is there an "ungroup by" operation opposite to .groupby in pandas?
Question:
Suppose we take a pandas dataframe…
name age family
0 john 1 1
1 jason 36 1
2 jane 32 1
3 jack 26 2
4 james 30 2
Then do a groupby() …
group_df = df.groupby('family')
group_df = group_df.aggregate({'name': name_join, 'age': pd.np.mean})
Then do some aggregate/summarize operation (in my example, my function name_join aggregates the names):
def name_join(list_names, concat='-'):
return concat.join(list_names)
The grouped summarized output is thus:
age name
family
1 23 john-jason-jane
2 28 jack-james
Question:
Is there a quick, efficient way to get to the following from the aggregated table?
name age family
0 john 23 1
1 jason 23 1
2 jane 23 1
3 jack 28 2
4 james 28 2
(Note: the age column values are just examples, I don’t care for the information I am losing after averaging in this specific example)
The way I thought I could do it does not look too efficient:
- create empty dataframe
- from every line in
group_df, separate the names
- return a dataframe with as many rows as there are names in the starting row
- append the output to the empty dataframe
Answers:
The rough equivalent is .reset_index(), but it may not be helpful to think of it as the “opposite” of groupby().
You are splitting a string in to pieces, and maintaining each piece’s association with ‘family’. This old answer of mine does the job.
Just set ‘family’ as the index column first, refer to the link above, and then reset_index() at the end to get your desired result.
There are a few ways to undo DataFrame.groupby, one way is to do DataFrame.groupby.filter(lambda x:True), this gets back to the original DataFrame.
Here’s a complete example that recovers the original dataframe from the grouped object
def name_join(list_names, concat='-'):
return concat.join(list_names)
print('create dataframen')
df = pandas.DataFrame({'name':['john', 'jason', 'jane', 'jack', 'james'], 'age':[1,36,32,26,30], 'family':[1,1,1,2,2]})
df.index.name='indexer'
print(df)
print('create group_by object')
group_obj_df = df.groupby('family')
print(group_obj_df)
print('nrecover grouped df')
group_joined_df = group_obj_df.aggregate({'name': name_join, 'age': 'mean'})
group_joined_df
create dataframe
name age family
indexer
0 john 1 1
1 jason 36 1
2 jane 32 1
3 jack 26 2
4 james 30 2
create group_by object
<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7fbfdd9dd048>
recover grouped df
name age
family
1 john-jason-jane 23
2 jack-james 28
print('nRecover the original dataframe')
print(pandas.concat([group_obj_df.get_group(key) for key in group_obj_df.groups]))
Recover the original dataframe
name age family
indexer
0 john 1 1
1 jason 36 1
2 jane 32 1
3 jack 26 2
4 james 30 2
It turns out that pd.groupby() returns an object with the original data stored in obj. So ungrouping is just pulling out the original data.
group_df = df.groupby('family')
group_df.obj
Example
>>> dat_1 = df.groupby("category_2")
>>> dat_1
<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7fce78b3dd00>
>>> dat_1.obj
order_date category_2 value
1 2011-02-01 Cross Country Race 324400.0
2 2011-03-01 Cross Country Race 142000.0
3 2011-04-01 Cross Country Race 498580.0
4 2011-05-01 Cross Country Race 220310.0
5 2011-06-01 Cross Country Race 364420.0
.. ... ... ...
535 2015-08-01 Triathalon 39200.0
536 2015-09-01 Triathalon 75600.0
537 2015-10-01 Triathalon 58600.0
538 2015-11-01 Triathalon 70050.0
539 2015-12-01 Triathalon 38600.0
[531 rows x 3 columns]
Suppose we take a pandas dataframe…
name age family
0 john 1 1
1 jason 36 1
2 jane 32 1
3 jack 26 2
4 james 30 2
Then do a groupby() …
group_df = df.groupby('family')
group_df = group_df.aggregate({'name': name_join, 'age': pd.np.mean})
Then do some aggregate/summarize operation (in my example, my function name_join aggregates the names):
def name_join(list_names, concat='-'):
return concat.join(list_names)
The grouped summarized output is thus:
age name
family
1 23 john-jason-jane
2 28 jack-james
Question:
Is there a quick, efficient way to get to the following from the aggregated table?
name age family
0 john 23 1
1 jason 23 1
2 jane 23 1
3 jack 28 2
4 james 28 2
(Note: the age column values are just examples, I don’t care for the information I am losing after averaging in this specific example)
The way I thought I could do it does not look too efficient:
- create empty dataframe
- from every line in
group_df, separate the names - return a dataframe with as many rows as there are names in the starting row
- append the output to the empty dataframe
The rough equivalent is .reset_index(), but it may not be helpful to think of it as the “opposite” of groupby().
You are splitting a string in to pieces, and maintaining each piece’s association with ‘family’. This old answer of mine does the job.
Just set ‘family’ as the index column first, refer to the link above, and then reset_index() at the end to get your desired result.
There are a few ways to undo DataFrame.groupby, one way is to do DataFrame.groupby.filter(lambda x:True), this gets back to the original DataFrame.
Here’s a complete example that recovers the original dataframe from the grouped object
def name_join(list_names, concat='-'):
return concat.join(list_names)
print('create dataframen')
df = pandas.DataFrame({'name':['john', 'jason', 'jane', 'jack', 'james'], 'age':[1,36,32,26,30], 'family':[1,1,1,2,2]})
df.index.name='indexer'
print(df)
print('create group_by object')
group_obj_df = df.groupby('family')
print(group_obj_df)
print('nrecover grouped df')
group_joined_df = group_obj_df.aggregate({'name': name_join, 'age': 'mean'})
group_joined_df
create dataframe
name age family
indexer
0 john 1 1
1 jason 36 1
2 jane 32 1
3 jack 26 2
4 james 30 2
create group_by object
<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7fbfdd9dd048>
recover grouped df
name age
family
1 john-jason-jane 23
2 jack-james 28
print('nRecover the original dataframe')
print(pandas.concat([group_obj_df.get_group(key) for key in group_obj_df.groups]))
Recover the original dataframe
name age family
indexer
0 john 1 1
1 jason 36 1
2 jane 32 1
3 jack 26 2
4 james 30 2
It turns out that pd.groupby() returns an object with the original data stored in obj. So ungrouping is just pulling out the original data.
group_df = df.groupby('family')
group_df.obj
Example
>>> dat_1 = df.groupby("category_2")
>>> dat_1
<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7fce78b3dd00>
>>> dat_1.obj
order_date category_2 value
1 2011-02-01 Cross Country Race 324400.0
2 2011-03-01 Cross Country Race 142000.0
3 2011-04-01 Cross Country Race 498580.0
4 2011-05-01 Cross Country Race 220310.0
5 2011-06-01 Cross Country Race 364420.0
.. ... ... ...
535 2015-08-01 Triathalon 39200.0
536 2015-09-01 Triathalon 75600.0
537 2015-10-01 Triathalon 58600.0
538 2015-11-01 Triathalon 70050.0
539 2015-12-01 Triathalon 38600.0
[531 rows x 3 columns]