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Groupby, apply custom function to data, return results in new columns

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0

$begingroup$

I want to group by id, apply a custom function to the data, and create a new column with the results. It seems there must be a faster/more efficient way to do this than to pass the data to the function, make the changes, and return the data. Here is an example.

Example

dat = pd.DataFrame('id': ['a', 'a', 'a', 'b', 'b', 'b'], 'x': [4, 8, 12, 25, 30, 50])

def my_func(data):
 data['diff'] = (data['x'] - data['x'].shift(1, fill_value=data['x'].iat[0]))
 return data

dat.groupby('id').apply(my_func)

Output

> print(dat)


id x diff
0 a 4 0
1 a 8 4
2 a 12 4
3 b 25 0
4 b 30 5
5 b 50 20

Is there a more efficient way to do this?

python pandas

share

asked 1 min ago

AmstellAmstell

7611

$endgroup$

add a comment | 

0

$begingroup$

I want to group by id, apply a custom function to the data, and create a new column with the results. It seems there must be a faster/more efficient way to do this than to pass the data to the function, make the changes, and return the data. Here is an example.

Example

dat = pd.DataFrame('id': ['a', 'a', 'a', 'b', 'b', 'b'], 'x': [4, 8, 12, 25, 30, 50])

def my_func(data):
 data['diff'] = (data['x'] - data['x'].shift(1, fill_value=data['x'].iat[0]))
 return data

dat.groupby('id').apply(my_func)

Output

> print(dat)


id x diff
0 a 4 0
1 a 8 4
2 a 12 4
3 b 25 0
4 b 30 5
5 b 50 20

Is there a more efficient way to do this?

python pandas

share

asked 1 min ago

AmstellAmstell

7611

$endgroup$

add a comment | 

$begingroup$

I want to group by id, apply a custom function to the data, and create a new column with the results. It seems there must be a faster/more efficient way to do this than to pass the data to the function, make the changes, and return the data. Here is an example.

Example

dat = pd.DataFrame('id': ['a', 'a', 'a', 'b', 'b', 'b'], 'x': [4, 8, 12, 25, 30, 50])

def my_func(data):
 data['diff'] = (data['x'] - data['x'].shift(1, fill_value=data['x'].iat[0]))
 return data

dat.groupby('id').apply(my_func)

Output

> print(dat)


id x diff
0 a 4 0
1 a 8 4
2 a 12 4
3 b 25 0
4 b 30 5
5 b 50 20

Is there a more efficient way to do this?

python pandas

share

asked 1 min ago

AmstellAmstell

7611

$endgroup$

dat = pd.DataFrame('id': ['a', 'a', 'a', 'b', 'b', 'b'], 'x': [4, 8, 12, 25, 30, 50])

def my_func(data):
 data['diff'] = (data['x'] - data['x'].shift(1, fill_value=data['x'].iat[0]))
 return data

dat.groupby('id').apply(my_func)

> print(dat)


id x diff
0 a 4 0
1 a 8 4
2 a 12 4
3 b 25 0
4 b 30 5
5 b 50 20

share

asked 1 min ago

AmstellAmstell

7611

share

asked 1 min ago

AmstellAmstell

7611

asked 1 min ago

AmstellAmstell

7611

asked 1 min ago

AmstellAmstell

7611