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# Embarking on a 100-day adventure: Day 5 – No.Of Occurrences In An Array

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Welcome to Day 5 of our coding journey! Today, we dive deep into the world of programming to tackle a common, yet curious problem: finding the number of occurrences of a specific value within an array. Brace yourself as we unravel the magic behind an elegant solution utilizing the power of HashMaps.

### The Journey for Occurrence Count:

In the middle of our array adventures, a common challenge surfacesâ€”how often does a particular value appear within an array? This simple question unfolds into a journey that starts with developers across various domains. In data analysis, understanding the distribution of values becomes paramount. In algorithmic problem-solving, decoding the frequency of the key elements is often the key to optimization.

### Introducing the HashMap:

In our search for an efficient solution, we turn to the flexible HashMap, a key-value pair storage mechanism within the extensive Collections framework. The HashMap proves to be our trusted partner in effortlessly tallying the occurrences of each element in an array.

### The Code Unveiled:

Let’s take a peek at the code that makes this magic happen:

### Breaking Down The Code:

The code begins by initializing an integer array ‘a’ containing a sequence of numbers. The variable ‘v’ represents the value whose occurrences we want to count within the array. A ‘HashMap’ named ‘map’ is created to store element-frequency pairs. The key represents the array element, and the value represents the frequency (number of occurrences). The code uses a ‘for each’ loop to iterate through each element of the array ‘a’. For each element ‘i’, the code checks whether it is already in the ‘HashMap ‘ using ‘map.containsKey(i)’.

If present, it increments the frequency by 1. If not present, it adds the element to the ‘ HashMap ‘ with a frequency of 1. Finally, the code prints the number of occurrences of the target value ‘ v ‘ by retrieving the frequency from the ‘ HashMap ‘ using ‘ map.get(v) ‘.

### Time Complexity:

The time complexity is O(N) because we are iterating through each element from the given array.

### Space Complexity:

Space complexity is how much memory we need. We use a HashMap to remember numbers and their counts. If there are 10 different numbers, we use memory for 10 things; if there are 100, we use memory for 100 things. So, space complexity is O(N), where N is the number of unique elements in the array.

### Wrapping Up:

In this blog post, we’ve explored a simple and effective way to find the number of occurrences of a specific value in an array using Java. The use of a HashMap allows us to efficiently track the frequency of each element, providing a clear and concise solution to the problem. Happy Coding!

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