Eradicating or Modifying the Exceptions throughout runtime


Modifying Exceptions Throughout Runtime: Suppose there’s a Registration Type. In case you obtained distinctive knowledge within the kind like within the place of Title you bought telephone numbers to take away that ambiguity from the info we use the next logical abstraction with none specific coding(if-else ladder) not just for registration kind could be utilized to DBMS Information and likewise numerous Datasets.

Distinctive Information: The Information is claimed to be distinctive when knowledge is inaccurate or doesn’t belong to the identical group. this knowledge can be known as ambiguous knowledge to take away this ambiguity within the knowledge some operations wanted to be carried out. eradicating distinctive knowledge from the checklist reduces errors or exceptions whereas performing operations. 

Instance: String operations can’t be carried out on the whole checklist, as a result of it additionally has numerical. If we accomplish that exceptions can be raised. So, to keep away from exceptions, partition the info within the array into their respective knowledge sort.

Diagram for Distinctive knowledge

Convert parts of an inventory to uppercase.

Enter: names[] = [‘geeks’, ‘for’, ‘geeks’, ‘is’, ‘best’, 9876543210, 3.142, 5 + 8j, True], convert strings within the checklist to uppercase and take away undesirable knowledge.
Output: [‘GEEKS’, ‘FOR’, ‘GEEKS’, ‘IS’, ‘BEST’][9876543210, 3.142, 5 + 8j, True]

Recursive Strategy: By eradicating the distinctive knowledge from the checklist utilizing exception dealing with.

Logic Skeleton:




     #replace/take away distinctive knowledge
     #replace args

return end result


  •  Enter: Arr[]
  •  Intilize exceptional_data = [], Index = 0.
  • Iterate over arr and apply uppercase operation on the info in attempt block to seek out distinctive knowledge.

             i.e attempt:

            whereas(index < len(names)):
                 names[index] = names[index].higher()
                 index += 1

  • If distinctive knowledge discovered, retailer it in exceptional_data checklist and take away that knowledge from checklist. and once more name perform with new checklist and index

               i.e besides:
                        exception = names.pop(index)
                        case_changer(names, index)

Beneath is the Implementation of the above strategy:


# Python Program to take away Distinctive
# knowledge from a knowledge container

# Contemplate names checklist wanted to have solely
# string knowledge sort
names = ['geeks', 'for', 'geeks', 'is', 'best', 9876543210, 3.142, 5 + 8j, True]

# Listing to catch distinctive knowledge.
exceptional_data = []

# Distinctive knowledge Exists i.e int,
# complicated and Boolean.
print('Distinctive knowledge:', names)

# Intilize index has worth to 0
index = 0

# case_changer converts string to
# uppercase strings

def case_changer(names, index):

    # Iterate over all parts to seek out
    # distinctive knowledge index.
        whereas(index < len(names)):

            # Carry out operations on
            # non-exceptional knowledge.
            names[index] = names[index].higher()
            index += 1

# After discovering distinctive knowledge index
# attempt to change or take away it.
        exception = names.pop(index)

# After eradicating distinctive knowledge proceed
# operations on non-exceptional by
# calling the perform.
        case_changer(names, index)
    return names

print('Uppercase knowledge  :', case_changer(names, index))
print("Distinctive knowledge:", exceptional_data)

Distinctive knowledge: ['geeks', 'for', 'geeks', 'is', 'best', 9876543210, 3.142, (5+8j), True]
Uppercase knowledge  : ['GEEKS', 'FOR', 'GEEKS', 'IS', 'BEST']
Distinctive knowledge: [9876543210, 3.142, (5+8j), True]

Time Complexity: O(n), the place n is the size of the arr.
Auxiliary House: O(1).

Benefits of Modifying the Exceptions throughout runtime:

  • Respective operations on the respective checklist are carried out simply with out specific coding.
  • Information is partitioned simply.
  • Exceptions are dealt with simply and likewise decreased.
  • The Time complexity can be environment friendly.
  • It additionally can be utilized to deal with bigger knowledge in datasets.


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