Rolling multidimensional function in pandas
Let's say, I have the following code.
import numpy as np
import pandas as pd
x = pd.DataFrame(np.random.randn(100, 3)).rolling(window=10, center=True).cov()
For each index, I have a 3x3 matrix. I would like to calculate eigenvalues and then some function of those eigenvalues. Or, perhaps, I might want to compute some function of eigenvalues and eigenvectors. The point is that if I take x.loc[0] then I have no problem to compute anything from that matrix. How do I do it in a rolling fashion for all matrices?
Thanks!
pandas rolling
asked Jan 20 at 3:25
freevillagefreevillage
1
add a comment |
Let's say, I have the following code.
import numpy as np
import pandas as pd
x = pd.DataFrame(np.random.randn(100, 3)).rolling(window=10, center=True).cov()
For each index, I have a 3x3 matrix. I would like to calculate eigenvalues and then some function of those eigenvalues. Or, perhaps, I might want to compute some function of eigenvalues and eigenvectors. The point is that if I take x.loc[0] then I have no problem to compute anything from that matrix. How do I do it in a rolling fashion for all matrices?
Thanks!
pandas rolling
asked Jan 20 at 3:25
freevillagefreevillage
1
add a comment |
Let's say, I have the following code.
import numpy as np
import pandas as pd
x = pd.DataFrame(np.random.randn(100, 3)).rolling(window=10, center=True).cov()
For each index, I have a 3x3 matrix. I would like to calculate eigenvalues and then some function of those eigenvalues. Or, perhaps, I might want to compute some function of eigenvalues and eigenvectors. The point is that if I take x.loc[0] then I have no problem to compute anything from that matrix. How do I do it in a rolling fashion for all matrices?
Thanks!
pandas rolling
asked Jan 20 at 3:25
freevillagefreevillage
1
Let's say, I have the following code.
import numpy as np
import pandas as pd
x = pd.DataFrame(np.random.randn(100, 3)).rolling(window=10, center=True).cov()
For each index, I have a 3x3 matrix. I would like to calculate eigenvalues and then some function of those eigenvalues. Or, perhaps, I might want to compute some function of eigenvalues and eigenvectors. The point is that if I take x.loc[0] then I have no problem to compute anything from that matrix. How do I do it in a rolling fashion for all matrices?
Thanks!
pandas rolling
pandas rolling
asked Jan 20 at 3:25
freevillagefreevillage
1
asked Jan 20 at 3:25
freevillagefreevillage
1
asked Jan 20 at 3:25
freevillagefreevillage
1
asked Jan 20 at 3:25
freevillagefreevillage
1
asked Jan 20 at 3:25
freevillagefreevillage
1
1
add a comment |
add a comment |
1 Answer
1
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You can use the analogous eigenvector/eigenvalue methods in spicy.sparse.linalg.
import numpy as np
import pandas as pd
from scipy import linalg as LA
x = pd.DataFrame(np.random.randn(100, 3)).rolling(window=10, center=True).cov()
for i in range(len(x)):
try:
e_vals,e_vec = LA.eig(x.loc[i])
print(e_vals,e_vec)
except:
continue
If there are no NaN values present then you need not use the try and except instead go for only for loop.
answered Jan 20 at 5:48
SumanthSumanth
2169
What I don't understand is why I can calculate a covariance matrix for each "window x 3" window but I cannot calculate a more general function. Of course, I can do another loop after that but it seems wasteful. Why go over the matrix twice?
– freevillage
Jan 21 at 0:46
add a comment |
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1 Answer
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active
oldest
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1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
You can use the analogous eigenvector/eigenvalue methods in spicy.sparse.linalg.
import numpy as np
import pandas as pd
from scipy import linalg as LA
x = pd.DataFrame(np.random.randn(100, 3)).rolling(window=10, center=True).cov()
for i in range(len(x)):
try:
e_vals,e_vec = LA.eig(x.loc[i])
print(e_vals,e_vec)
except:
continue
If there are no NaN values present then you need not use the try and except instead go for only for loop.
answered Jan 20 at 5:48
SumanthSumanth
2169
What I don't understand is why I can calculate a covariance matrix for each "window x 3" window but I cannot calculate a more general function. Of course, I can do another loop after that but it seems wasteful. Why go over the matrix twice?
– freevillage
Jan 21 at 0:46
add a comment |
You can use the analogous eigenvector/eigenvalue methods in spicy.sparse.linalg.
import numpy as np
import pandas as pd
from scipy import linalg as LA
x = pd.DataFrame(np.random.randn(100, 3)).rolling(window=10, center=True).cov()
for i in range(len(x)):
try:
e_vals,e_vec = LA.eig(x.loc[i])
print(e_vals,e_vec)
except:
continue
If there are no NaN values present then you need not use the try and except instead go for only for loop.
answered Jan 20 at 5:48
SumanthSumanth
2169
What I don't understand is why I can calculate a covariance matrix for each "window x 3" window but I cannot calculate a more general function. Of course, I can do another loop after that but it seems wasteful. Why go over the matrix twice?
– freevillage
Jan 21 at 0:46
add a comment |
You can use the analogous eigenvector/eigenvalue methods in spicy.sparse.linalg.
import numpy as np
import pandas as pd
from scipy import linalg as LA
x = pd.DataFrame(np.random.randn(100, 3)).rolling(window=10, center=True).cov()
for i in range(len(x)):
try:
e_vals,e_vec = LA.eig(x.loc[i])
print(e_vals,e_vec)
except:
continue
If there are no NaN values present then you need not use the try and except instead go for only for loop.
answered Jan 20 at 5:48
SumanthSumanth
2169
You can use the analogous eigenvector/eigenvalue methods in spicy.sparse.linalg.
import numpy as np
import pandas as pd
from scipy import linalg as LA
x = pd.DataFrame(np.random.randn(100, 3)).rolling(window=10, center=True).cov()
for i in range(len(x)):
try:
e_vals,e_vec = LA.eig(x.loc[i])
print(e_vals,e_vec)
except:
continue
If there are no NaN values present then you need not use the try and except instead go for only for loop.
answered Jan 20 at 5:48
SumanthSumanth
2169
edited Jan 21 at 5:09
answered Jan 20 at 5:48
SumanthSumanth
2169
answered Jan 20 at 5:48
SumanthSumanth
2169
answered Jan 20 at 5:48
SumanthSumanth
2169
2169
What I don't understand is why I can calculate a covariance matrix for each "window x 3" window but I cannot calculate a more general function. Of course, I can do another loop after that but it seems wasteful. Why go over the matrix twice?
– freevillage
Jan 21 at 0:46
add a comment |
What I don't understand is why I can calculate a covariance matrix for each "window x 3" window but I cannot calculate a more general function. Of course, I can do another loop after that but it seems wasteful. Why go over the matrix twice?
– freevillage
Jan 21 at 0:46
What I don't understand is why I can calculate a covariance matrix for each "window x 3" window but I cannot calculate a more general function. Of course, I can do another loop after that but it seems wasteful. Why go over the matrix twice?
– freevillage
Jan 21 at 0:46
What I don't understand is why I can calculate a covariance matrix for each "window x 3" window but I cannot calculate a more general function. Of course, I can do another loop after that but it seems wasteful. Why go over the matrix twice?
– freevillage
Jan 21 at 0:46
add a comment |
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