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How can I find nearest neighbors of points in a data frame from another data frame
How do I return multiple values from a function?How to join (merge) data frames (inner, outer, left, right)Peak detection in a 2D arrayhow to combine two data frames in python pandas“Large data” work flows using pandasReplace invalid values with None in Pandas DataFrameHow to reset index in a pandas data frame?Find K nearest neighbors, starting from a distance matrixfinding nearest neighbors of pdb models using kd-treeNearest Neighbor matching with replacement Python
.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty margin-bottom:0;
I want to find k nearest neighbors of all points in dataframe A from a dataframe B. How is that doable?
It seems sklearn.neighbors.NearestNeighbors takes only one set of data, and just one query point.
Like:
samples = [[0., 0., 0.], [0., .5, 0.], [1., 1., .5]]
from sklearn.neighbors import NearestNeighbors
neigh = NearestNeighbors(n_neighbors=1)
neigh.fit(samples)
print(neigh.kneighbors([[1., 1., 1.]]))
from Python documentaion
I want to have a data frame with more than one query point instead of [[1., 1., 1.]]
P.S. I want the metric to be Mahalanobis which sklearn.neighbors.KDTree does not accept, and scipy.spatial.KDTreeeven does not have any metric option.
python pandas dataframe knn
asked Mar 27 at 0:31
No LieNo Lie
3831 silver badge9 bronze badges
add a comment |
I want to find k nearest neighbors of all points in dataframe A from a dataframe B. How is that doable?
It seems sklearn.neighbors.NearestNeighbors takes only one set of data, and just one query point.
Like:
samples = [[0., 0., 0.], [0., .5, 0.], [1., 1., .5]]
from sklearn.neighbors import NearestNeighbors
neigh = NearestNeighbors(n_neighbors=1)
neigh.fit(samples)
print(neigh.kneighbors([[1., 1., 1.]]))
from Python documentaion
I want to have a data frame with more than one query point instead of [[1., 1., 1.]]
P.S. I want the metric to be Mahalanobis which sklearn.neighbors.KDTree does not accept, and scipy.spatial.KDTreeeven does not have any metric option.
python pandas dataframe knn
asked Mar 27 at 0:31
No LieNo Lie
3831 silver badge9 bronze badges
add a comment |
I want to find k nearest neighbors of all points in dataframe A from a dataframe B. How is that doable?
It seems sklearn.neighbors.NearestNeighbors takes only one set of data, and just one query point.
Like:
samples = [[0., 0., 0.], [0., .5, 0.], [1., 1., .5]]
from sklearn.neighbors import NearestNeighbors
neigh = NearestNeighbors(n_neighbors=1)
neigh.fit(samples)
print(neigh.kneighbors([[1., 1., 1.]]))
from Python documentaion
I want to have a data frame with more than one query point instead of [[1., 1., 1.]]
P.S. I want the metric to be Mahalanobis which sklearn.neighbors.KDTree does not accept, and scipy.spatial.KDTreeeven does not have any metric option.
python pandas dataframe knn
asked Mar 27 at 0:31
No LieNo Lie
3831 silver badge9 bronze badges
I want to find k nearest neighbors of all points in dataframe A from a dataframe B. How is that doable?
It seems sklearn.neighbors.NearestNeighbors takes only one set of data, and just one query point.
Like:
samples = [[0., 0., 0.], [0., .5, 0.], [1., 1., .5]]
from sklearn.neighbors import NearestNeighbors
neigh = NearestNeighbors(n_neighbors=1)
neigh.fit(samples)
print(neigh.kneighbors([[1., 1., 1.]]))
from Python documentaion
I want to have a data frame with more than one query point instead of [[1., 1., 1.]]
P.S. I want the metric to be Mahalanobis which sklearn.neighbors.KDTree does not accept, and scipy.spatial.KDTreeeven does not have any metric option.
python pandas dataframe knn
python pandas dataframe knn
asked Mar 27 at 0:31
No LieNo Lie
3831 silver badge9 bronze badges
asked Mar 27 at 0:31
No LieNo Lie
3831 silver badge9 bronze badges
edited Mar 27 at 1:41
No Lie
asked Mar 27 at 0:31
No LieNo Lie
3831 silver badge9 bronze badges
asked Mar 27 at 0:31
No LieNo Lie
3831 silver badge9 bronze badges
asked Mar 27 at 0:31
No LieNo Lie
3831 silver badge9 bronze badges
3831 silver badge9 bronze badges
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
import pandas as pd
from scipy.spatial import KDTree
dataA = pd.DataFrame(pd.np.random.rand(100, 100))
dataB = pd.DataFrame(pd.np.random.rand(100, 100))
kdB = KDTree(dataB.values)
print(kdB.query(dataA.values, k=3)[-1]) # k desired number of neighbors
#returns indices of 3-neighbors for all rows/points in A
answered Mar 27 at 0:44
bubblebubble
1,1808 silver badges13 bronze badges
Thank you @bubble, I need the metric to be Mahalanobis.
– No Lie
Mar 27 at 1:41
May be BallTree would be useful; it supports custom distances
– bubble
Mar 27 at 2:06
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
import pandas as pd
from scipy.spatial import KDTree
dataA = pd.DataFrame(pd.np.random.rand(100, 100))
dataB = pd.DataFrame(pd.np.random.rand(100, 100))
kdB = KDTree(dataB.values)
print(kdB.query(dataA.values, k=3)[-1]) # k desired number of neighbors
#returns indices of 3-neighbors for all rows/points in A
answered Mar 27 at 0:44
bubblebubble
1,1808 silver badges13 bronze badges
Thank you @bubble, I need the metric to be Mahalanobis.
– No Lie
Mar 27 at 1:41
May be BallTree would be useful; it supports custom distances
– bubble
Mar 27 at 2:06
add a comment |
import pandas as pd
from scipy.spatial import KDTree
dataA = pd.DataFrame(pd.np.random.rand(100, 100))
dataB = pd.DataFrame(pd.np.random.rand(100, 100))
kdB = KDTree(dataB.values)
print(kdB.query(dataA.values, k=3)[-1]) # k desired number of neighbors
#returns indices of 3-neighbors for all rows/points in A
answered Mar 27 at 0:44
bubblebubble
1,1808 silver badges13 bronze badges
Thank you @bubble, I need the metric to be Mahalanobis.
– No Lie
Mar 27 at 1:41
May be BallTree would be useful; it supports custom distances
– bubble
Mar 27 at 2:06
add a comment |
import pandas as pd
from scipy.spatial import KDTree
dataA = pd.DataFrame(pd.np.random.rand(100, 100))
dataB = pd.DataFrame(pd.np.random.rand(100, 100))
kdB = KDTree(dataB.values)
print(kdB.query(dataA.values, k=3)[-1]) # k desired number of neighbors
#returns indices of 3-neighbors for all rows/points in A
answered Mar 27 at 0:44
bubblebubble
1,1808 silver badges13 bronze badges
import pandas as pd
from scipy.spatial import KDTree
dataA = pd.DataFrame(pd.np.random.rand(100, 100))
dataB = pd.DataFrame(pd.np.random.rand(100, 100))
kdB = KDTree(dataB.values)
print(kdB.query(dataA.values, k=3)[-1]) # k desired number of neighbors
#returns indices of 3-neighbors for all rows/points in A
answered Mar 27 at 0:44
bubblebubble
1,1808 silver badges13 bronze badges
answered Mar 27 at 0:44
bubblebubble
1,1808 silver badges13 bronze badges
answered Mar 27 at 0:44
bubblebubble
1,1808 silver badges13 bronze badges
answered Mar 27 at 0:44
bubblebubble
1,1808 silver badges13 bronze badges
1,1808 silver badges13 bronze badges
Thank you @bubble, I need the metric to be Mahalanobis.
– No Lie
Mar 27 at 1:41
May be BallTree would be useful; it supports custom distances
– bubble
Mar 27 at 2:06
add a comment |
Thank you @bubble, I need the metric to be Mahalanobis.
– No Lie
Mar 27 at 1:41
May be BallTree would be useful; it supports custom distances
– bubble
Mar 27 at 2:06
Thank you @bubble, I need the metric to be Mahalanobis.
– No Lie
Mar 27 at 1:41
Thank you @bubble, I need the metric to be Mahalanobis.
– No Lie
Mar 27 at 1:41
May be BallTree would be useful; it supports custom distances
– bubble
Mar 27 at 2:06
May be BallTree would be useful; it supports custom distances
– bubble
Mar 27 at 2:06
add a comment |
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