Numpy Operations

np.insert - To Insert items to an array

np.append - To append items to an array

np.delete - To delete items from an array

np.unique - To get unique items from an array

A np.insert(arr, obj, values, axis)

  • arr: Input array
  • obj : The index before which insertion is to be made
  • values: The array of values to be inserted
  • axis: The axis along insert in given array. If not given, arr is flattened
>>>a = np.random.randint(10,20,(2,3,4)) 
# Axis 0–2 ; Axis 1–3 ; Axis 2–4 Now we will insert in different #indexes using np.insert operation
>>>print(a)>>>print(a.shape)[[[13 13 18 11]
[19 11 18 16]
[15 10 13 18]]
[[12 16 14 14]
[11 14 11 13]
[17 17 15 14]]]
(2, 3, 4)#Insert along axis =0
>>>np.insert(a,1,100,axis=0)
array([[[ 13, 13, 18, 11],
[ 19, 11, 18, 16],
[ 15, 10, 13, 18]],
[[100, 100, 100, 100],
[100, 100, 100, 100],
[100, 100, 100, 100]],
[[ 12, 16, 14, 14],
[ 11, 14, 11, 13],
[ 17, 17, 15, 14]]])
# Insert along axis = 1
>>>np.insert(a,0,50,axis=1)
array([[[50, 50, 50, 50],
[13, 13, 18, 11],
[19, 11, 18, 16],
[15, 10, 13, 18]],
[[50, 50, 50, 50],
[12, 16, 14, 14],
[11, 14, 11, 13],
[17, 17, 15, 14]]])
# Insert along axis=2
>>>np.insert(a,0,200,axis=2)
array([[[200, 13, 13, 18, 11],
[200, 19, 11, 18, 16],
[200, 15, 10, 13, 18]],
[[200, 12, 16, 14, 14],
[200, 11, 14, 11, 13],
[200, 17, 17, 15, 14]]])

B np.append(arr, values, axis)

  • arr: Array
  • values: To be appended to arr. It must be of the same shape as of arr (excluding axis of appending)
  • axis: The axis along which append operation is to be done. If not given, both parameters are flattened
#Example 1:>>>a = np.random.randint(2,6,(2,3,3))
>>>print(a)
>>>print(a.shape)
[[[4 3 2] [3 5 3] [3 5 5]] [[2 4 3] [5 4 5] [5 2 3]]] (2, 3, 3)# Append a value to the array. It flattens the array and append -99 to the end>>>np.append(a, 50)array([ 4, 3, 2, 3, 5, 3, 3, 5, 5, 2, 4, 3, 5, 4, 5, 5, 2, 3, 50])#Example 2:>>>b = np.random.randint(2,6,(2,1,3))>>>print(b)[[[4 5 5]] [[3 5 3]]]>>>np.append(a,b,axis = 1)array([[[4, 3, 2], [3, 5, 3], [3, 5, 5], [4, 5, 5]], [[2, 4, 3], [5, 4, 5], [5, 2, 3], [3, 5, 3]]])

C np.delete(arr, obj, axis)

  • arr: Input array
  • obj: Can be a slice, an integer or array of integers, indicating the subarray to be deleted from the input array
  • axis: The axis along which to delete the given subarray. If not given, arr is flattened
  • Returns a copy of arr with the elements specified by obj removed. Note that delete does not occur in-place. If axis is None, out is a flattened array.
>>>a = np.random.randint(2,6,(2,3,3))>>>print(a)>>>print(a.shape)[[[2 3 3] [2 3 4] [4 5 4]]
[[3 2 4] [5 4 4] [5 4 3]]]
(2, 3, 3)
# Delete 0th element along Axis=0>>>np.delete(a,(0),axis = 0)array([[[3, 2, 4], [5, 4, 4], [5, 4, 3]]])# Delete 0th and 2nd element along axis=1>>>np.delete(a,(0,2),axis = 1)array([[[2, 3, 4]],
[[5, 4, 4]]])
# Delete 0th and 1st element along axis=2>>>np.delete(a,(0,1),axis = 2)array([[[3],
[4],
[4]],
[[4],
[4],
[3]]])

D np.unique(arr, return_index, return_inverse, return_counts)

  • arr: Input array
  • return_index : If True, returns the indices of elements in the input array.
  • return_counts: If True, returns the number of times the element in unique array appears in the original array.
#Initializing array>>>a = np.random.randint(2,20,(2,3,3))>>>print(a)>>>print(a.shape)[[[ 6 2 7] [ 7 9 15] [ 2 7 7]] 
[[ 3 18 15] [ 4 7 9] [ 8 2 19]]]
(2, 3, 3)>>>np.unique(a)array([ 2, 3, 4, 6, 7, 8, 9, 15, 18, 19])# return_counts= True>>>np.unique(a,return_counts=True) # Returns tuple(array([ 2, 3, 4, 6, 7, 8, 9, 15, 18, 19]), array([3, 1, 1, 1, 5, 1, 2, 2, 1, 1]))# return_index = True
#1.unique values. 2. Their indexes. 3. Their counts.
>>>np.unique(a,return_counts=True,return_index=True)(array([ 2, 3, 4, 6, 7, 8, 9, 15, 18, 19]), array([ 1, 9, 12, 0, 2, 15, 4, 5, 10, 17]), array([3, 1, 1, 1, 5, 1, 2, 2, 1, 1]))

Basic Operations & Functions

  • np.add() - Adding two arrays

Arguments are the two arrays to be added.

  • np.subtract() - Subtracting two arrays

Arguments are the two arrays to be added.

  • np.multiply() - Multiplying two arrays

Arguments are the two arrays to be added.

  • np.divide() - Dividing two arrays

Arguments are the two arrays to be added.

  • np.sum() - Sum of items in an array

Argument is the array of which elements are to be added.

  • np.min() - Minimum of items in an array

Argumet is the array from which max value is to be returned.

  • np.max() - Minimum of items in an array

Argumet is the array from which max value is to be returned.

  • np.mean() - Mean of items in an array

Argumet is the array from which mean value is to be returned.

  • np.sort() - Sorting an array

Argument is the array which is to be sorted.

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