Merge and intersect sorted numpy arrays.
Project description
Sortednp
Numpy and Numpy arrays are a really great tool. However, intersecting and merging multiple sorted numpy arrays is rather less performant. The current numpy implementation concatenates the two arrays and sorts the combination. If you want to merge or intersect multiple numpy arrays, there is a much faster way, by using the property, that the resulting array is sorted.
Sortednp (sorted numpy) operates on sorted numpy arrays to calculate the intersection or the union of two numpy arrays in an efficient way. The resulting array is again a sorted numpy array, which can be merged or intersected with the next array. The intended use case is that sorted numpy arrays are sorted as the basic data structure and merged or intersected at request. Typical applications include information retrieval and search engines in particular.
It is also possible to implement a kway merging or intersecting algorithm,
which operates on an arbitrary number of arrays at the same time. This package
is intended to deal with arrays with $10^6
$ or $10^{10}
$ items. Usually, these
arrays are too large to keep more than two of them in memory at the same
time. This package implements methods to merge and intersect multiple arrays,
which can be loaded ondemand.
Installation
There are two different methods to install sortednp
.
Using pip
(recommended)
You can install the package directly from PyPI using pip
(here pip3
). There are
precompiled wheels for linux
32 and 64bit.
$ pip3 install sortednp
Using setuptools
Alternatively, you can clone the git repository and run the setup script.
$ git clone https://gitlab.sauerburger.com/frank/sortednp.git
$ cd sortednp
$ python3 setup.py install
Numpy Dependency
The installation fails in some cases, because of a buildtime dependency on
numpy. Usually, the problem can be solved by manually installing a recent numpy
version via pip3 install U numpy
.
Usage
The package provides two different kinds of methods. The first class is intended to operate on two arrays. The second class operates on two or more arrays and calls the first class of methods internally.
Twoway methods
Two numpy sorted arrays can be merged with the merge
method, which takes two
numpy arrays and returns the sorted union of the two arrays.
## merge.py import numpy as np import sortednp as snp a = np.array([0, 3, 4, 6, 7]) b = np.array([1, 2, 3, 5, 7, 9]) m = snp.merge(a, b) print(m)
If you run this, you should see the union of both arrays as a sorted numpy array.
$ python3 merge.py [0 1 2 3 3 4 5 6 7 7 9]
Two sorted numpy arrays can be intersected with the intersect
method, which takes two
numpy arrays and returns the sorted intersection of the two arrays.
## intersect.py import numpy as np import sortednp as snp a = np.array([0, 3, 4, 6, 7]) b = np.array([1, 2, 3, 5, 7, 9]) i = snp.intersect(a, b) print(i)
If you run this, you should see the intersection of both arrays as a sorted numpy array.
$ python3 intersect.py [3 7]
Since version 0.4.0, the library provides the issubset(a, b)
method which
checks if the array a
is a subset of b
, and the isitem(v, a)
method which
checks if value
is contained in array a
.
## set.py import numpy as np import sortednp as snp a = np.array([2, 4, 5, 10]) b = np.array([1, 2, 3, 4, 5, 6, 10, 11]) print(snp.issubset(a, b)) # a is subset of b print(snp.issubset(b, a)) # b is not a subset of a print(snp.isitem(4, a)) # 4 is an item of a print(snp.isitem(3, a)) # 3 is not an item of a
If you execute this example, you get the expected result: a
is a subset ob
b
, 4
is a member of a
.
$ python3 set.py True False True False
Returning array indices
The intersect
method takes an optional argument indices
which is False
by default. If this is set to True
, the return value consists of the
intersection array and a tuple with the indices of the common values for both
arrays. The index arrays have the length of the output. The indices show the
position in the input from which the value was copied.
## intersect_indices.py import numpy as np import sortednp as snp a = np.array([2,4,6,8,10]) b = np.array([1,2,3,4]) intersection, indices = snp.intersect(a,b, indices=True) print(intersection) print(indices)
The above example gives:
$ python3 intersect_indices.py [2 4] (array([0, 1]), array([1, 3]))
The first line shows the intersection of the two arrays. The second line
prints a tuple with the indices where the common values appeared in the input
arrays. For example, the value 4
is at position 1
in array a
and at position
3
in array b
.
Since version 0.3.0, the merge
has to indices
argument too. The returned
indices have the length of the inputs. The indices show the position in the
output to which an input value was copied.
## merge_indices.py import numpy as np import sortednp as snp a = np.array([2,4]) b = np.array([3,4,5]) merged, indices = snp.merge(a,b, indices=True) print(merged) print(indices)
The above example gives:
$ python3 merge_indices.py [2 3 4 4 5] (array([0, 2]), array([1, 3, 4]))
The first line shows that the two arrays have been merged. The second line
prints a tuple with the indices. For example, the value 3
from array b
can
be found at position 1
in the output.
Duplicate treatment
Since version 0.3.0, sortednp supported multiple different strategies to deal with duplicated entries.
Duplicates during intersecting
There are three different duplicate treatments for the intersect method:

sortednp.DROP
: Ignore any duplicated entries. The output will contain only unique values. 
sortednp.KEEP_MIN_N
: If an entry occursn
times in one input array andm
times in the other input array, the output will contain the entrymin(n, m)
times. 
sortednp.KEEP_MAX_N
: If an entry occursn
times in one input array andm
times in the other input array, the output will contain the entrymax(n, m)
times (assuming the entry occurs at least once in both arrays, i.e.n > 0
andm > 0
).
The strategy can be selected with the optional duplicates
argument of
intersect
. The default is sortednp.KEEP_MIN_N
. Consider the following example.
## intersect_duplicates.py import numpy as np import sortednp as snp a = np.array([2, 4, 4, 5]) # Twice b = np.array([3, 4, 4, 4, 5]) # Three times intersect_drop = snp.intersect(a, b, duplicates=snp.DROP) print(intersect_drop) # Contains a single 4 intersect_min = snp.intersect(a, b, duplicates=snp.KEEP_MIN_N) print(intersect_min) # Contains 4 twice intersect_max = snp.intersect(a, b, duplicates=snp.KEEP_MAX_N) print(intersect_max) # Contains 4 three times
The above example gives:
$ python3 intersect_duplicates.py [4 5] [4 4 5] [4 4 4 5]
Duplicates during merging
The merge
method offers three different duplicates treatment strategies:

sortednp.DROP
: Ignore any duplicated entries. The output will contain only unique values. 
sortednp.DROP_IN_INPUT
: Ignores duplicated entries in the input arrays separately. This is the same as ensuring that each input array unique values. The output contains every value at most twice. 
sortednp.KEEP
: Keep all duplicated entries. If an item occursn
times in one input array andm
times in the other input array, the output contains the itemn + m
times.
The strategy can be selected with the optional duplicates
.
The default is sortednp.KEEP
. Consider the following example.
## merge_duplicates.py import numpy as np import sortednp as snp a = np.array([2, 4, 4, 5]) # Twice b = np.array([3, 4, 4, 4, 5]) # Three times merge_drop = snp.merge(a, b, duplicates=snp.DROP) print(merge_drop) # Contains a single 4 merge_dii = snp.merge(a, b, duplicates=snp.DROP_IN_INPUT) print(merge_dii) # Contains 4 twice merge_keep = snp.merge(a, b, duplicates=snp.KEEP) print(merge_keep) # Contains 4 five times
The above example gives:
$ python3 merge_duplicates.py [2 3 4 5] [2 3 4 4 5 5] [2 3 4 4 4 4 4 5 5]
Duplicates during subset checks
The issubset
method offers two different duplicates treatment strategies:

sortednp.IGNORE
: Ignore any duplications. The method returns True if each value in the first array is contained at least once in the second array. Duplicated entries in the first array do not change the return value. 
sortednp.REPEAT
: For each duplicated item in the first array, require at least as many items in the second array. If for one value the first array contains more duplicated entries than the second array, the method returns False.
The strategy can be selected with the optional duplicates
.
The default is sortednp.IGNORE
. Consider the following example.
## subset_duplicates.py import numpy as np import sortednp as snp a = np.array([3, 4, 4, 5]) # Twice b = np.array([3, 4, 4, 4, 5]) # Three times # Number of occurances ignored print(snp.issubset(a, b, duplicates=snp.IGNORE)) # is subset print(snp.issubset(b, a, duplicates=snp.IGNORE)) # is subset # Number of in subset must be smaller or equal print(snp.issubset(a, b, duplicates=snp.REPEAT)) # is subset # three 4s not subset of two 4s print(snp.issubset(b, a, duplicates=snp.REPEAT))
The above example gives:
$ python3 subset_duplicates.py True True True False
Index tracking and duplicates
Tracking indices with the indices=True
argument is possible while selecting a
nondefault duplicate treatment strategy. For merging the indices point to the
position in the output array. If the input has duplicates that were skipped, the
index is simply repeated. For example with snp.DROP
, if the input is [9, 9, 9, 9]
, the index array for this input contains four times the position where
9
is found in the output.
Similarly, with snp.KEEP_MAX_N
and intersect
, the index of the last item in
the array with less occurrences is duplicates.
## duplicates_index.py import numpy as np import sortednp as snp a = np.array([2, 4, 4, 5]) # Twice b = np.array([3, 4, 4, 4, 5]) # Three times # Merge merge_drop, (index_a, index_b) = snp.merge(a, b, duplicates=snp.DROP, indices=True) print(index_b) # Intersect intersect_max, (index_a, index_b) = snp.intersect(a, b, duplicates=snp.KEEP_MAX_N, indices=True) print(index_a)
The above example gives:
$ python3 duplicates_index.py [1 2 2 2 3] [1 2 2 3]
For merging, this means that the three 4
s from the input all appear at same position
in the output, namely position 2
.
For the intersect, this means that the second and third occurrence of 4
in the
output, both came from item at position 2
in the input.
kway methods
Similarly, the kway intersect and merge methods take two or more arrays and perform the merge or intersect operation on its arguments.
## kway_intersect.py import numpy as np import sortednp as snp a = np.array([0, 3, 4, 6, 7]) b = np.array([0, 3, 5, 7, 9]) c = np.array([1, 2, 3, 5, 7, 9]) d = np.array([2, 3, 6, 7, 8]) i = snp.kway_intersect(a, b, c, d) print(i)
If you run this, you should see the intersection of all four arrays as a sorted numpy array.
$ python3 kway_intersect.py [3 7]
The kway merger sortednp.kway_merge
works analogously. However, the native
numpy
implementation is faster compared to the merge provided by this package.
The kway merger has been added for completeness. The package heapq
provides
efficient methods to merge multiple arrays simultaneously.
The methods kway_merge
and kway_intersect
accept the optional keyword
argument assume_sorted
. By default, it is set to True
. If it is set to False
,
the method calls sort()
on the input arrays before performing the operation.
The default should be kept if the arrays are already sorted to save the time it
takes to sort the arrays.
Since the arrays might be too large to keep all of them in memory at the same
time, it is possible to pass a callable
instead of an array to the methods.
The callable
is expected to return the actual array. It is called immediately
before the array is required. This reduces the memory consumption.
Algorithms
Intersections are calculated by iterating both arrays. For a given element in
one array, the method needs to search the other and check if the element is
contained. In order to make this more efficient, we can use the fact that the
arrays are sorted. There are three search methods, which can be selected via the
optional keyword argument algorithm
.
sortednp.SIMPLE_SEARCH
: Search for an element by linearly iterating over the array elementbyelement. More Information.sortednp.BINARY_SEARCH
: Slice the remainder of the array in halves and repeat the procedure on the slice which contains the searched element. More Information.sortednp.GALLOPING_SEARCH
: First, search for an element linearly, doubling the step size after each step. If a step goes beyond the search element, perform a binary search between the last two positions. More Information.
The default is sortednp.GALLOPING_SEARCH
. The performance of all three
algorithms is compared in the next section. The methods issubset()
and
isitem()
also support the algorithm keyword.
Performance
The performance of the package can be compared with the default implementation of numpy, the intersect1d` method. The ratio of the execution time between sortednp and numpy is shown for various different benchmark tests.
The merge or intersect time can be estimated under two different assumptions. If
the arrays, which are merged or intersected, are already sorted, one should not
consider the time it takes to sort the random arrays in the benchmark. On the
other hand, if one considers a scenario in which the arrays are not sorted, one
should take the sorting time into account. The benchmarks here on this page,
assume that the arrays are already sorted. If you would like to benchmark the
package and include the sorting time, have a look at the methods defined in
ci/benchmark.py
.
The random scattering of the points indicates the uncertainty caused by random load fluctuations on the benchmark machine (Spikes of serveral orders of magnitude usualy mean that there was a shortage of memory and large chunks had to be moved to SWAP.)
Intersect
The performance of the intersection operation depends on the sparseness of the two arrays. For example, if the first element of one of the arrays is larger than all elements in the other array, only the other array has to be searched (linearly, binarily, or exponentially). Similarly, if the common elements are far apart in the arrays (sparseness), large chunks of the arrays can be skipped. The arrays in the benchmark contain random (unique) integers. The sparseness is defined as the average difference between two consecutive elements in one array.
The first set of tests studies the performance dependence on the size of the arrays. The second set of tests studies the dependence on the sparseness of the array for a fixed size of array. Every shows a colorcoded comparison of the performance of intersecting more than two arrays.
Test  Simple Search  Binary Search  Galloping Search 

Intersect  
Sparseness 
Merge
The following chart shows the performance of merging 2 or more arrays as a function of the array size. It is assumed that the arrays are already sorted.
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SHA256  dada9f29b7dfd589e9a3f12cdc8484898a49e9c6e4d182d46798a9e8eabfddea 

MD5  9e9d6835e83a9acd382ec1848f353d0f 

BLAKE2256  af30936172f91d5fec4dfb6433763c54ed6d8ae3b06a3d18a25eb428ed3ab6ad 
Hashes for sortednp0.4.0cp35cp35mmanylinux1_i686.whl
Algorithm  Hash digest  

SHA256  af83cb46c4fa35288d16f1550a6ec40bd865a909640e92b4d3d16c0a7d7d8529 

MD5  a626af2df50c00f3717b690281936c7e 

BLAKE2256  51cbd198456cae06cfcd5e17c7b60929019d6d09516e960de5bf9e0fa0a06238 