added reduce,map,filter

docs/python/Advanced Concepts/filter-function.md

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+# Filter Function
+
+## Definition
+The filter function is a built-in Python function used for constructing an iterator from elements of an iterable for which a function returns true.
+
+**Syntax**:
+ ```python
+filter(function, iterable)
+```
+**Parameters**:<br>
+*function*: A function that tests if each element of an iterable returns True or False.<br>
+*iterable*: An iterable like sets, lists, tuples, etc., whose elements are to be filtered.<br>
+*Returns* : An iterator that is already filtered.
+
+## Basic Usage
+**Example 1: Filtering a List of Numbers**:
+```python
+# Define a function that returns True for even numbers
+def is_even(n):
+    return n % 2 == 0
+
+numbers = [1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+even_numbers = filter(is_even, numbers)
+
+# Convert the filter object to a list
+print(list(even_numbers))  # Output: [2, 4, 6, 8, 10]
+```
+
+**Example 2: Filtering with a Lambda Function**:
+```python
+numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+odd_numbers = filter(lambda x: x % 2 != 0, numbers)
+
+print(list(odd_numbers))  # Output: [1, 3, 5, 7, 9]
+```
+
+**Example 3: Filtering Strings**:
+```python
+words = ["apple", "banana", "cherry", "date", "elderberry", "fig", "grape" , "python"]
+long_words = filter(lambda word: len(word) > 5, words)
+
+print(list(long_words))  # Output: ['banana', 'cherry', 'elderberry', 'python']
+```
+
+## Advanced Usage
+**Example 4: Filtering Objects with Attributes**:
+```python
+class Person:
+    def __init__(self, name, age):
+        self.name = name
+        self.age = age
+
+people = [
+    Person("Alice", 30),
+    Person("Bob", 15),
+    Person("Charlie", 25),
+    Person("David", 35)
+]
+
+adults = filter(lambda person: person.age >= 18, people)
+adult_names = map(lambda person: person.name, adults)
+
+print(list(adult_names))  # Output: ['Alice', 'Charlie', 'David']
+```
+
+**Example 5: Using None as the Function**:
+```python
+numbers = [0, 1, 2, 3, 0, 4, 0, 5]
+non_zero_numbers = filter(None, numbers)
+
+print(list(non_zero_numbers))  # Output: [1, 2, 3, 4, 5]
+```
+**NOTE**: When None is passed as the function, filter removes all items that are false.
+
+## Time Complexity:
+- The time complexity of filter() depends on two factors:
+    1. The time complexity of the filtering function (the one you provide as an argument).
+    2. The size of the iterable being filtered.
+- If the filtering function has a constant time complexity (e.g., O(1)), the overall time complexity of filter() is   linear (O(n)), where ‘n’ is the number of elements in the iterable.
+
+## Space Complexity:
+- The space complexity of filter() is also influenced by the filtering function and the size of the iterable.
+- Since filter() returns an iterator, it doesn’t create a new list in memory. Instead, it generates filtered elements on-the-fly as you iterate over it. Therefore, the space complexity is O(1).
+
+## Conclusion:
+Python’s filter() allows you to perform filtering operations on iterables. This kind of operation 
+consists of applying a Boolean function to the items in an iterable and keeping only those values 
+for which the function returns a true result. In general, you can use filter() to process existing 
+iterables and produce new iterables containing the values that you currently need.Both versions of 
+Python support filter(), but Python 3’s approach is more memory-efficient due to the use of 
+iterators.

docs/python/Advanced Concepts/map-function.md

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+# Filter Function
+
+The `map()` function in Python is a built-in function used for applying a given function to each 
+item of an iterable (like a list, tuple, or dictionary) and returning a new iterable with the 
+results. It's a powerful tool for transforming data without the need for explicit loops. Let's break 
+down its syntax, explore examples, and discuss various use cases.
+
+### Syntax:
+
+```python
+map(function, iterable1, iterable2, ...)
+```
+
+- `function`: The function to apply to each item in the iterables.
+- `iterable1`, `iterable2`, ...: One or more iterable objects whose items will be passed as 
+arguments to `function`.
+
+### Examples:
+
+#### Example 1: Doubling the values in a list
+
+```python
+# Define the function
+def double(x):
+    return x * 2
+
+# Apply the function to each item in the list using map
+original_list = [1, 2, 3, 4, 5]
+doubled_list = list(map(double, original_list))
+print(doubled_list)  # Output: [2, 4, 6, 8, 10]
+```
+
+#### Example 2: Converting temperatures from Celsius to Fahrenheit
+
+```python
+# Define the function
+def celsius_to_fahrenheit(celsius):
+    return (celsius * 9/5) + 32
+
+# Apply the function to each Celsius temperature using map
+celsius_temperatures = [0, 10, 20, 30, 40]
+fahrenheit_temperatures = list(map(celsius_to_fahrenheit, celsius_temperatures))
+print(fahrenheit_temperatures)  # Output: [32.0, 50.0, 68.0, 86.0, 104.0]
+```
+
+### Use Cases:
+
+1. **Data Transformation**: When you need to apply a function to each item of a collection and 
+obtain the transformed values, `map()` is very handy.
+
+2. **Parallel Processing**: In some cases, `map()` can be utilized in parallel processing scenarios, 
+especially when combined with `multiprocessing` or `concurrent.futures`.
+
+3. **Cleaning and Formatting Data**: It's often used in data processing pipelines for tasks like 
+converting data types, normalizing values, or applying formatting functions.
+
+4. **Functional Programming**: In functional programming paradigms, `map()` is frequently used along 
+with other functional constructs like `filter()` and `reduce()` for concise and expressive code.
+
+5. **Generating Multiple Outputs**: You can use `map()` to generate multiple outputs simultaneously 
+by passing multiple iterables. The function will be applied to corresponding items in the iterables.
+
+6. **Lazy Evaluation**: In Python 3, `map()` returns an iterator rather than a list. This means it's 
+memory efficient and can handle large datasets without loading everything into memory at once.
+
+Remember, while `map()` is powerful, it's essential to balance its use with readability and clarity. 
+Sometimes, a simple loop might be more understandable than a `map()` call.

docs/python/Advanced Concepts/reduce-function.md

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+# Reduce Function
+
+## Definition:
+The reduce() function is part of the functools module and is used to apply a binary function (a 
+function that takes two arguments) cumulatively to the items of an iterable (e.g., a list, tuple, or 
+string). It reduces the iterable to a single value by successively combining elements.
+
+**Syntax**:
+```python
+from functools import reduce
+reduce(function, iterable, initial=None)
+```
+**Parameters**:<br>
+*function* : The binary function to apply. It takes two arguments and returns a single value.<br>
+*iterable* : The sequence of elements to process.<br>
+*initial (optional)*: An initial value. If provided, the function is applied to the initial value 
+and the first element of the iterable. Otherwise, the first two elements are used as the initial 
+values.
+
+## Working:
+- Intially , first two elements of iterable are picked and the result is obtained.
+- Next step is to apply the same function to the previously attained result and the number just 
+succeeding the second element and the result is again stored.
+- This process continues till no more elements are left in the container.
+- The final returned result is returned and printed on console.
+
+## Examples:
+
+**Example 1:**
+```python
+numbers = [1, 2, 3, 4, 10]
+total = reduce(lambda x, y: x + y, numbers)
+print(total)  # Output: 20
+```
+**Example 2:**
+```python
+numbers = [11, 7, 8, 20, 1]
+max_value = reduce(lambda x, y: x if x > y else y, numbers)
+print(max_value)  # Output: 20
+```
+**Example 3:**
+```python
+# Importing reduce function from functools  
+from functools import reduce  
+
+# Creating a list  
+my_list = [10, 20, 30, 40, 50]  
+
+# Calculating the product of the numbers in my_list  
+# using reduce and lambda functions together  
+product = reduce(lambda x, y: x * y, my_list)  
+
+# Printing output  
+print(f"Product = {product}") # Output : Product = 12000000
+```
+
+## Difference Between reduce() and accumulate():
+- **Behavior:**
+    - reduce() stores intermediate results and only returns the final summation value.
+    - accumulate() returns an iterator containing all intermediate results. The last value in the 
+    iterator is the summation value of the list.
+
+- **Use Cases:**
+    - Use reduce() when you need a single result (e.g., total sum, product) from the iterable.
+    - Use accumulate() when you want to access intermediate results during the reduction process.
+
+- **Initial Value:**
+    - reduce() allows an optional initial value.
+    - accumulate() also accepts an optional initial value since Python 3.8.
+
+- **Order of Arguments:**
+    - reduce() takes the function first, followed by the iterable.
+    - accumulate() takes the iterable first, followed by the function.
+
+## Conclusion:
+Python's Reduce function enables us to apply reduction operations to iterables using lambda and call
+able functions. A function called reduce() reduces the elements of an iterable to a single 
+cumulative value. The reduce function in Python solves various straightforward issues, including 
+adding and multiplying iterables of numbers.