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0

For 1D I can use:

a=np.array([1,2,3,4])
b=pandas.Series(a).rolling(window=3,center=True).mean()

But the problem is, if I have array a, in 3D then using this method gives error

Exception: Data must be 1-dimensional

The code which I used is:

t[:,:,0]=(pd.Series(imgg[:,:,0:4]).rolling(window=[1,1,3],center=True).mean())

Here imgg is 3D numpy array.

What else I tried:

I also tried the old function rolling_mean i.e. pd.rolling_mean(a,4,center=True), but it is also not working, it gives error:

AssertionError: cannot support ndim > 2 for ndarray compat

python-3.x pandas rolling-average

share|improve this question

edited Mar 26 at 6:50

Prvt_Yadv

asked Mar 26 at 4:40

Prvt_YadvPrvt_Yadv

11322 silver badges1313 bronze badges

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  3d input array, lot of way to show a 3d array... could you precise by an example the initial 3d array and the expected output..
  
  – Frenchy
  Mar 26 at 5:42
  
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  I took one 3D image and converted it into np array using np.array(img.dataobj).
  
  – Prvt_Yadv
  Mar 26 at 6:03
  
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  the rolling number is the same for all Dimensions?
  
  – Frenchy
  Mar 26 at 6:28
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  I used different values in window i just want to reduce depth of array. So height width are 1 1 in widow .
  
  – Prvt_Yadv
  Mar 26 at 6:29
  
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  If i'm not getting it wrong, you want to compute the rolling mean only on the the 3rd dimension of imgg?
  
  – kerwei
  Mar 26 at 6:52
  

  
  
  
  

 | 
show 1 more comment

0

For 1D I can use:

a=np.array([1,2,3,4])
b=pandas.Series(a).rolling(window=3,center=True).mean()

But the problem is, if I have array a, in 3D then using this method gives error

Exception: Data must be 1-dimensional

The code which I used is:

t[:,:,0]=(pd.Series(imgg[:,:,0:4]).rolling(window=[1,1,3],center=True).mean())

Here imgg is 3D numpy array.

What else I tried:

I also tried the old function rolling_mean i.e. pd.rolling_mean(a,4,center=True), but it is also not working, it gives error:

AssertionError: cannot support ndim > 2 for ndarray compat

python-3.x pandas rolling-average

share|improve this question

edited Mar 26 at 6:50

Prvt_Yadv

asked Mar 26 at 4:40

Prvt_YadvPrvt_Yadv

11322 silver badges1313 bronze badges

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  3d input array, lot of way to show a 3d array... could you precise by an example the initial 3d array and the expected output..
  
  – Frenchy
  Mar 26 at 5:42
  
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  I took one 3D image and converted it into np array using np.array(img.dataobj).
  
  – Prvt_Yadv
  Mar 26 at 6:03
  
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  the rolling number is the same for all Dimensions?
  
  – Frenchy
  Mar 26 at 6:28
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  I used different values in window i just want to reduce depth of array. So height width are 1 1 in widow .
  
  – Prvt_Yadv
  Mar 26 at 6:29
  
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  If i'm not getting it wrong, you want to compute the rolling mean only on the the 3rd dimension of imgg?
  
  – kerwei
  Mar 26 at 6:52
  

  
  
  
  

 | 
show 1 more comment

For 1D I can use:

a=np.array([1,2,3,4])
b=pandas.Series(a).rolling(window=3,center=True).mean()

But the problem is, if I have array a, in 3D then using this method gives error

Exception: Data must be 1-dimensional

The code which I used is:

t[:,:,0]=(pd.Series(imgg[:,:,0:4]).rolling(window=[1,1,3],center=True).mean())

Here imgg is 3D numpy array.

What else I tried:

I also tried the old function rolling_mean i.e. pd.rolling_mean(a,4,center=True), but it is also not working, it gives error:

AssertionError: cannot support ndim > 2 for ndarray compat

python-3.x pandas rolling-average

share|improve this question

edited Mar 26 at 6:50

Prvt_Yadv

asked Mar 26 at 4:40

Prvt_YadvPrvt_Yadv

11322 silver badges1313 bronze badges

a=np.array([1,2,3,4])
b=pandas.Series(a).rolling(window=3,center=True).mean()

Exception: Data must be 1-dimensional

t[:,:,0]=(pd.Series(imgg[:,:,0:4]).rolling(window=[1,1,3],center=True).mean())

AssertionError: cannot support ndim > 2 for ndarray compat

share|improve this question

edited Mar 26 at 6:50

Prvt_Yadv

asked Mar 26 at 4:40

Prvt_YadvPrvt_Yadv

11322 silver badges1313 bronze badges

share|improve this question

edited Mar 26 at 6:50

Prvt_Yadv

asked Mar 26 at 4:40

Prvt_YadvPrvt_Yadv

11322 silver badges1313 bronze badges

asked Mar 26 at 4:40

Prvt_YadvPrvt_Yadv

11322 silver badges1313 bronze badges

asked Mar 26 at 4:40

Prvt_YadvPrvt_Yadv

11322 silver badges1313 bronze badges

1 Answer
1

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oldest

votes

1

Okay, hopefully this is what you need.

I think you can try to split up the arrays first, instead of trying to work on a 3-dimensional array - since we know that it works on 1D.

import pandas as pd

imgg = [(1,2,1),(2,3,3),(4,1,2),(5,3,2),(6,2,1),(2,3,4),(5,6,2)]

>>>imgg
 0 1 2
0 1 2 1
1 2 3 3
2 4 1 2
3 5 3 2
4 6 2 1
5 2 3 4
6 5 6 2

x = []
y = []
d = []

# Split into components
for img in imgg:
 x.append(img[0])
 y.append(img[1])
 d.append(img[2])

# Compute rolling mean
dm = pd.Series(d).rolling(window=3,center=True).mean()

# Stitch them back to form your desired dataframe
data = [k for k in zip(x,y,dm)]
df = pd.DataFrame(data)

>>>df
 0 1 2
0 1 2 NaN
1 2 3 2.000000
2 4 1 2.333333
3 5 3 1.666667
4 6 2 2.333333
5 2 3 2.333333
6 5 6 NaN

share|improve this answer

answered Mar 26 at 7:29

kerweikerwei

1,52111 gold badge99 silver badges2020 bronze badges

add a comment | 

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1

Okay, hopefully this is what you need.

I think you can try to split up the arrays first, instead of trying to work on a 3-dimensional array - since we know that it works on 1D.

import pandas as pd

imgg = [(1,2,1),(2,3,3),(4,1,2),(5,3,2),(6,2,1),(2,3,4),(5,6,2)]

>>>imgg
 0 1 2
0 1 2 1
1 2 3 3
2 4 1 2
3 5 3 2
4 6 2 1
5 2 3 4
6 5 6 2

x = []
y = []
d = []

# Split into components
for img in imgg:
 x.append(img[0])
 y.append(img[1])
 d.append(img[2])

# Compute rolling mean
dm = pd.Series(d).rolling(window=3,center=True).mean()

# Stitch them back to form your desired dataframe
data = [k for k in zip(x,y,dm)]
df = pd.DataFrame(data)

>>>df
 0 1 2
0 1 2 NaN
1 2 3 2.000000
2 4 1 2.333333
3 5 3 1.666667
4 6 2 2.333333
5 2 3 2.333333
6 5 6 NaN

share|improve this answer

answered Mar 26 at 7:29

kerweikerwei

1,52111 gold badge99 silver badges2020 bronze badges

add a comment | 

1

Okay, hopefully this is what you need.

I think you can try to split up the arrays first, instead of trying to work on a 3-dimensional array - since we know that it works on 1D.

import pandas as pd

imgg = [(1,2,1),(2,3,3),(4,1,2),(5,3,2),(6,2,1),(2,3,4),(5,6,2)]

>>>imgg
 0 1 2
0 1 2 1
1 2 3 3
2 4 1 2
3 5 3 2
4 6 2 1
5 2 3 4
6 5 6 2

x = []
y = []
d = []

# Split into components
for img in imgg:
 x.append(img[0])
 y.append(img[1])
 d.append(img[2])

# Compute rolling mean
dm = pd.Series(d).rolling(window=3,center=True).mean()

# Stitch them back to form your desired dataframe
data = [k for k in zip(x,y,dm)]
df = pd.DataFrame(data)

>>>df
 0 1 2
0 1 2 NaN
1 2 3 2.000000
2 4 1 2.333333
3 5 3 1.666667
4 6 2 2.333333
5 2 3 2.333333
6 5 6 NaN

share|improve this answer

answered Mar 26 at 7:29

kerweikerwei

1,52111 gold badge99 silver badges2020 bronze badges

add a comment | 

Okay, hopefully this is what you need.

I think you can try to split up the arrays first, instead of trying to work on a 3-dimensional array - since we know that it works on 1D.

import pandas as pd

imgg = [(1,2,1),(2,3,3),(4,1,2),(5,3,2),(6,2,1),(2,3,4),(5,6,2)]

>>>imgg
 0 1 2
0 1 2 1
1 2 3 3
2 4 1 2
3 5 3 2
4 6 2 1
5 2 3 4
6 5 6 2

x = []
y = []
d = []

# Split into components
for img in imgg:
 x.append(img[0])
 y.append(img[1])
 d.append(img[2])

# Compute rolling mean
dm = pd.Series(d).rolling(window=3,center=True).mean()

# Stitch them back to form your desired dataframe
data = [k for k in zip(x,y,dm)]
df = pd.DataFrame(data)

>>>df
 0 1 2
0 1 2 NaN
1 2 3 2.000000
2 4 1 2.333333
3 5 3 1.666667
4 6 2 2.333333
5 2 3 2.333333
6 5 6 NaN

share|improve this answer

answered Mar 26 at 7:29

kerweikerwei

1,52111 gold badge99 silver badges2020 bronze badges

import pandas as pd

imgg = [(1,2,1),(2,3,3),(4,1,2),(5,3,2),(6,2,1),(2,3,4),(5,6,2)]

>>>imgg
 0 1 2
0 1 2 1
1 2 3 3
2 4 1 2
3 5 3 2
4 6 2 1
5 2 3 4
6 5 6 2

x = []
y = []
d = []

# Split into components
for img in imgg:
 x.append(img[0])
 y.append(img[1])
 d.append(img[2])

# Compute rolling mean
dm = pd.Series(d).rolling(window=3,center=True).mean()

# Stitch them back to form your desired dataframe
data = [k for k in zip(x,y,dm)]
df = pd.DataFrame(data)

>>>df
 0 1 2
0 1 2 NaN
1 2 3 2.000000
2 4 1 2.333333
3 5 3 1.666667
4 6 2 2.333333
5 2 3 2.333333
6 5 6 NaN

share|improve this answer

answered Mar 26 at 7:29

kerweikerwei

1,52111 gold badge99 silver badges2020 bronze badges

answered Mar 26 at 7:29

kerweikerwei

1,52111 gold badge99 silver badges2020 bronze badges

answered Mar 26 at 7:29

kerweikerwei

1,52111 gold badge99 silver badges2020 bronze badges