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Python pandas: calculate rolling mean based on multiple criteria

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0

I have a dataframe that shows the closing value for a bunch of stocks for the last 10 days. It has a datetime index & stocks can be identified by their name/code. Can you help me figure out how to calculate the 4 day moving average for each separate stock in the dataframe?

I've tried to use the pd.DataFrame.rolling().mean() method but it just gives the rolling mean for the whole dataset. Not sure where to go next...

sampleData = hundredDayData['2019-03-11':'2019-03-20']
sampleData['Close: 4 day mean'] = sampleData['Close'].rolling(window=4).mean()
sampleData.head(24)

python python-3.x pandas

share|improve this question

asked Mar 24 at 19:45

AbhayAbhay

519

add a comment | 

0

I have a dataframe that shows the closing value for a bunch of stocks for the last 10 days. It has a datetime index & stocks can be identified by their name/code. Can you help me figure out how to calculate the 4 day moving average for each separate stock in the dataframe?

I've tried to use the pd.DataFrame.rolling().mean() method but it just gives the rolling mean for the whole dataset. Not sure where to go next...

sampleData = hundredDayData['2019-03-11':'2019-03-20']
sampleData['Close: 4 day mean'] = sampleData['Close'].rolling(window=4).mean()
sampleData.head(24)

python python-3.x pandas

share|improve this question

asked Mar 24 at 19:45

AbhayAbhay

519

add a comment | 

I have a dataframe that shows the closing value for a bunch of stocks for the last 10 days. It has a datetime index & stocks can be identified by their name/code. Can you help me figure out how to calculate the 4 day moving average for each separate stock in the dataframe?

I've tried to use the pd.DataFrame.rolling().mean() method but it just gives the rolling mean for the whole dataset. Not sure where to go next...

sampleData = hundredDayData['2019-03-11':'2019-03-20']
sampleData['Close: 4 day mean'] = sampleData['Close'].rolling(window=4).mean()
sampleData.head(24)

python python-3.x pandas

share|improve this question

asked Mar 24 at 19:45

AbhayAbhay

519

share|improve this question

asked Mar 24 at 19:45

AbhayAbhay

519

share|improve this question

asked Mar 24 at 19:45

AbhayAbhay

519

asked Mar 24 at 19:45

AbhayAbhay

519

asked Mar 24 at 19:45

AbhayAbhay

519

1 Answer
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1

df = pd.DataFrame('code': ['a']*10+['b']*10, 'Close': [1]*20)
df.groupby('code')['Close'].rolling(window=4).mean().reset_index()

• Group by code and calculate rolling mean with in the group

Note: If your code's are all jumbled (as shown below), then you should be using

df = pd.DataFrame('code': ['a']*10+['b']*10+['a']*10, 'Close': [1]*30)
rolling = df.groupby('code')['Close'].rolling(window=4).mean().reset_index().set_index('level_1').rename(
 columns='Close':'rolling')[['rolling']]
df.merge(rolling, left_index=True, right_index=True)

Calculate rolling mean at group level but use the index to merge it back into the main dataframe as a new column.

share|improve this answer

edited Mar 24 at 20:22

answered Mar 24 at 19:54

mujjigamujjiga

4,06011321

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  This works, thanks for your help!
  
  – Abhay
  Mar 25 at 7:08
  

  
  
  
  

add a comment | 

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1

df = pd.DataFrame('code': ['a']*10+['b']*10, 'Close': [1]*20)
df.groupby('code')['Close'].rolling(window=4).mean().reset_index()

• Group by code and calculate rolling mean with in the group

Note: If your code's are all jumbled (as shown below), then you should be using

df = pd.DataFrame('code': ['a']*10+['b']*10+['a']*10, 'Close': [1]*30)
rolling = df.groupby('code')['Close'].rolling(window=4).mean().reset_index().set_index('level_1').rename(
 columns='Close':'rolling')[['rolling']]
df.merge(rolling, left_index=True, right_index=True)

Calculate rolling mean at group level but use the index to merge it back into the main dataframe as a new column.

share|improve this answer

edited Mar 24 at 20:22

answered Mar 24 at 19:54

mujjigamujjiga

4,06011321

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  This works, thanks for your help!
  
  – Abhay
  Mar 25 at 7:08
  

  
  
  
  

add a comment | 

1

df = pd.DataFrame('code': ['a']*10+['b']*10, 'Close': [1]*20)
df.groupby('code')['Close'].rolling(window=4).mean().reset_index()

• Group by code and calculate rolling mean with in the group

Note: If your code's are all jumbled (as shown below), then you should be using

df = pd.DataFrame('code': ['a']*10+['b']*10+['a']*10, 'Close': [1]*30)
rolling = df.groupby('code')['Close'].rolling(window=4).mean().reset_index().set_index('level_1').rename(
 columns='Close':'rolling')[['rolling']]
df.merge(rolling, left_index=True, right_index=True)

Calculate rolling mean at group level but use the index to merge it back into the main dataframe as a new column.

share|improve this answer

edited Mar 24 at 20:22

answered Mar 24 at 19:54

mujjigamujjiga

4,06011321

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  This works, thanks for your help!
  
  – Abhay
  Mar 25 at 7:08
  

  
  
  
  

add a comment | 

df = pd.DataFrame('code': ['a']*10+['b']*10, 'Close': [1]*20)
df.groupby('code')['Close'].rolling(window=4).mean().reset_index()

• Group by code and calculate rolling mean with in the group

Note: If your code's are all jumbled (as shown below), then you should be using

df = pd.DataFrame('code': ['a']*10+['b']*10+['a']*10, 'Close': [1]*30)
rolling = df.groupby('code')['Close'].rolling(window=4).mean().reset_index().set_index('level_1').rename(
 columns='Close':'rolling')[['rolling']]
df.merge(rolling, left_index=True, right_index=True)

Calculate rolling mean at group level but use the index to merge it back into the main dataframe as a new column.

share|improve this answer

edited Mar 24 at 20:22

answered Mar 24 at 19:54

mujjigamujjiga

4,06011321

df = pd.DataFrame('code': ['a']*10+['b']*10, 'Close': [1]*20)
df.groupby('code')['Close'].rolling(window=4).mean().reset_index()

df = pd.DataFrame('code': ['a']*10+['b']*10+['a']*10, 'Close': [1]*30)
rolling = df.groupby('code')['Close'].rolling(window=4).mean().reset_index().set_index('level_1').rename(
 columns='Close':'rolling')[['rolling']]
df.merge(rolling, left_index=True, right_index=True)

share|improve this answer

edited Mar 24 at 20:22

answered Mar 24 at 19:54

mujjigamujjiga

4,06011321

answered Mar 24 at 19:54

mujjigamujjiga

4,06011321

answered Mar 24 at 19:54

mujjigamujjiga

4,06011321