NumPy Filter Array
Filtering Arrays
Getting some elements out of an existing array and creating a new array out of them is called filtering.
In NumPy, you filter an array using a boolean index list.
A boolean index list is a list of booleans corresponding to indexes in the array.
If the value at an index is True that element is contained in the filtered array, if the value at that index is
False that element is excluded from the filtered array.
Example
Create an array from the elements on index 0 and 2:
import numpy as np
arr = np.array([41, 42, 43, 44])
x = [True,
False, True, False]
newarr = arr[x]
print(newarr)
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The example above will return [41, 43], why?
Because the new filter contains only the values where the filter array had the value
True, in this case, index
0 and 2.
Creating the Filter Array
In the example above we hard-coded the True
and False values, but the common use is to create a filter array based on conditions.
Example
Create a filter array that will return only values higher than 42:
import numpy as np
arr = np.array([41, 42, 43, 44])
#
Create an empty list
filter_arr = []
# go through each element in
arr
for element in arr:
# if the element is higher than 42, set
the value to True, otherwise False:
if element > 42:
filter_arr.append(True)
else:
filter_arr.append(False)
newarr = arr[filter_arr]
print(filter_arr)
print(newarr)
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Example
Create a filter array that will return only even elements from the original array:
import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6, 7])
#
Create an empty list
filter_arr = []
# go through each element in
arr
for element in arr:
# if the element is completely divisble
by 2, set the value to True, otherwise False
if element % 2 == 0:
filter_arr.append(True)
else:
filter_arr.append(False)
newarr = arr[filter_arr]
print(filter_arr)
print(newarr)
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Creating Filter Directly From Array
The above example is quite a common task in NumPy and NumPy provides a nice way to tackle it.
We can directly substitute the array instead of the iterable variable in our condition and it will work just as we expect it to.
Example
Create a filter array that will return only values higher than 42:
import numpy as np
arr = np.array([41, 42, 43, 44])
filter_arr = arr
> 42
newarr = arr[filter_arr]
print(filter_arr)
print(newarr)
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Example
Create a filter array that will return only even elements from the original array:
import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6, 7])
filter_arr = arr
% 2 == 0
newarr = arr[filter_arr]
print(filter_arr)
print(newarr)
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Example
Create a boolean index list for perfect squares:
import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6, 7])
#
Square root of number - square root's integer part should be equal to zero (
ie. there should be nothing in decimal part)
arr = (arr ** 0.5 - ( arr **
0.5 ).astype('i')) == 0
print(arr)
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perfect square: A number that is square of an integer.
Example
Filter the array for even elements:
import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6, 7])
#
arr % 2 == 0 creates a boolean array and we pass it like boolean indexing
arr = arr[arr % 2 == 0]
print(arr)
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