Operations with Numpy Arrays III
Joining Arrays in Numpy
-
In Numpy we can join two or more arrays in a single array.
-
Concatenation in Numpy arrays is based on the axis.
-
The joining of arrays is done via concatenate function which takes arrays as parameters and the axis by default axis is set to 0.
Example:
import numpy as np
arr1 = np.array([11, 22, 33])
arr2 = np.array([44, 55, 66])
arr = np.concatenate((arr1, arr2))
print(arr) #prints [11, 22, 33, 44, 55, 66]
Now, we will see an example with an axis parameter
Example:
import numpy as np
arr1 = np.array([[11, 22], [33, 44]])
arr2 = np.array([[55, 66], [77, 88]])
arr = np.concatenate((arr1, arr2), axis=1)
print(arr) #prints [[1 2 5 6]
# [3 4 7 8]]
Example:
import numpy as np
arr1 = np.array([[11, 22], [33, 44]])
arr2 = np.array([[55, 66], [77, 88]])
arr = np.concatenate((arr1, arr2), axis=1)
print(arr) #prints [[1 2 5 6]
# [3 4 7 8]]
Splitting arrays in Numpy
-
In joining we merge multiple arrays into one and in splitting we divide one array into multiples.
-
Splitting is the reverse of joining.
-
The Numpy method used here is array_split, which takes the array and number of splits as the parameters.
Example:
Here, we are splitting an array into 4 parts
import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6, 7, 8])
arr_split = np.array_split(arr, 4)
print(arr_split)
#prints [array([1, 2]), array([3, 4]), array([5, 6]), array([7, 8])]
arr_split2 = np.array_split(arr, 6)
print(arr_split2)
#prints [array([1, 2]), array([3, 4]), array([5]), array([6]), array([7]), array([8])]
We can access the splitter arrays like any element of an array like below:
import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6])
arr1 = np.array_split(arr, 4)
print(arr1)
print(arr1[0])
print(arr1[1])
print(arr1[2])
print(arr[3])
Output:
[array([1, 2]), array([3, 4]), array([5]), array([6])]
[1 2]
[3 4]
[5]
4
Searching Arrays in Numpy
-
In Numpy we can search in arrays for a specific value or element and it returns the indexes of that particular value in the given array.
-
We use the where() method in Numpy for searching.
Example:
import numpy as np
arr = np.array([22, 43,76,0, -45, 45, 22, 32, 65, 22])
print(np.where(arr == 22)) #prints (array([0, 6, 9]),)
We can also use this method to check some mathematical expressions in an array like the below:
import numpy as np
arr = np.array([22, 43, 76, 0, -45, 45, 22, 32, 65, 22])
print(np.where(arr%2 == 0))
#prints (array([0, 2, 3, 6, 7, 9]),)
Sorting Arrays in Numpy
-
In Numpy, we can sort an array using the sort() method.
-
Sorting an array means putting the elements of a particular array into an ordered sequence.
-
Sorting can be done based on ascending or descending, or alphabetically if the array has strings.
Example:
import numpy as np
arr = np.array([22, 43, 76, 0, -45, 45, 22, 32, 65, 22])
print(np.sort(arr)) #this method returns the copy of array which means actual array is unchanged
#prints [-45 0 22 22 22 32 43 45 65 76]
