# Finding the First Non-repeating Character from a Stream of Characters:

Suppose we are given a stream of characters and we need to find the first non-repeating character from the stream. We need to tell the first non-repeating character in O(1) time at any moment in python for data science.

We will use a DLL (doubly linked list) for efficiently getting the first non-repeating character from the stream. DLL will contain all the non-repeating characters in order.

We will maintain two arrays. One array will maintain the characters which we have already visited two or more times; we will call it repeated[]. The other array consists of pointers to linked list nodes, we will call it DLL[]. Both the arrays will be of equal size, i.e., the size of the alphabet, which is 256.

###### Steps for finding the character –
1. First, we will create an empty DLL. We will also create two arrays in DLL[] and repeated[] of size 256. repeated[x] will be true in case x is repeated two or more times. In DLL[x] we have pointers to a DLL node in case the character x is present in DLL otherwise, it will be NULL.
2. We will next initialize all the entries of inDLL[] as Null and repeated[] as false.
3. To obtain the first non-repeating character, we need to return the character at the head of the DLL.
4. We need to take the following steps in order to process a new character ‘x’ in a stream.
• In case the repeated[x] is true, we need to ignore the character.
• If repeated[x] is false and inDLL[x] is NULL, we will append x to DLL and then store the address of new DLL node in the inDLL[x].
• In case repeated[x] is false and inDLL[x] is not NULL, we will get the DLL node of x by using inDLL[x] and removing the node. We will also mark inDLL[x] as NULL and repeated[x] as true.

In case we maintain a tail pointer, we will append a new node to DLL is O(1). If we remove a node from DLL also it will be O(1). So, in both the operations addition of new character and finding the first noon-repeating character will take O(1) time.

###### Below we will see the implementation of this in Python.

So, to learn more about it in python for data science, you can check this and this as well.

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