# fibonacci series using recursion in python with code examples

The Fibonacci series is a classic example of a recursive algorithm. In this article, we will explore how to implement the Fibonacci series using recursion in Python. We will provide code examples throughout the article to help explain the concepts behind this algorithm.

What is the Fibonacci Series?

The Fibonacci series is a mathematical sequence in which each number is the sum of the two preceding numbers. The series is defined as follows:

0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, …

Notice that the first two numbers in the series are 0 and 1. After that, each number is calculated by adding the two preceding numbers.

Implementing the Fibonacci Series using Recursion

Recursion is a programming technique that allows a function to call itself in order to solve a problem. It is particularly useful for solving problems that can be broken down into smaller subproblems of the same type.

In order to calculate the Fibonacci series using recursion, we can define a function that takes an integer as input and returns the corresponding value in the series. The function will call itself twice with slightly different inputs in order to calculate the sum of the two preceding numbers.

Here is an example of a Python function that implements the Fibonacci series using recursion:

```def fibonacci(n):
if n == 0:
return 0
elif n == 1:
return 1
else:
return fibonacci(n-1) + fibonacci(n-2)
```

The function takes an integer argument `n` and returns the `n`th number in the Fibonacci series. Here is what the function does:

1. If `n` is equal to 0, the function returns 0.
2. If `n` is equal to 1, the function returns 1.
3. Otherwise, the function calls itself twice with inputs `n-1` and `n-2`, and returns the sum of the two results.

When the function is called with `n=0` or `n=1`, it returns the corresponding value in the series. Otherwise, it enters into a recursive loop and calls itself twice with inputs `n-1` and `n-2`. This continues until `n` is either 0 or 1, at which point the function returns the corresponding value in the series.

Here is an example of how to call the `fibonacci` function:

```>>> fibonacci(10)
55
>>> fibonacci(20)
6765
>>> fibonacci(30)
832040
```

The function correctly calculates the corresponding value in the Fibonacci series for each input value.

Performance Considerations

Recursive algorithms can have performance issues if they are called excessively. In the case of the Fibonacci series, the same calculations are repeated many times for different inputs. This can lead to an exponential growth in the number of function calls and can make the algorithm slow.

One way to improve the performance of the Fibonacci series using recursion is to use memoization. Memoization is a technique that involves storing the results of expensive function calls and returning the cached result when the same inputs occur again.

Here is an example of a Python function that uses memoization to improve the performance of the Fibonacci series:

```cache = {}

def fibonacci_memo(n):
if n == 0:
return 0
elif n == 1:
return 1
elif n in cache:
return cache[n]
else:
result = fibonacci_memo(n-1) + fibonacci_memo(n-2)
cache[n] = result
return result
```

The function uses a global `cache` dictionary to store the results of expensive function calls. Here is what the function does:

1. If `n` is equal to 0, the function returns 0.
2. If `n` is equal to 1, the function returns 1.
3. If `n` is in the `cache` dictionary, the function returns the cached result.
4. Otherwise, the function calculates the result by calling itself twice with inputs `n-1` and `n-2` and adds the results together.
5. The function stores the result in the `cache` dictionary and returns it.

When the function is called with `n=0` or `n=1`, it returns the corresponding value in the series. If the result for `n` is already in the `cache` dictionary, the function returns the cached result. Otherwise, it calculates the result by calling itself twice with inputs `n-1` and `n-2` and adds the results together. The result is then stored in the `cache` dictionary for future use.

Here is an example of how to call the `fibonacci_memo` function:

```>>> fibonacci_memo(10)
55
>>> fibonacci_memo(20)
6765
>>> fibonacci_memo(30)
832040
```

The function correctly calculates the corresponding value in the Fibonacci series for each input value. However, because of the memoization, the results are calculated much more quickly than with the original `fibonacci` function.

Conclusion

In this article, we explored how to implement the Fibonacci series using recursion in Python. We also discussed how memoization can be used to improve the performance of the algorithm. The Fibonacci series is a classic example of a recursive algorithm and is a great way to learn about recursion and memoization in Python.

here is some additional information about the topics covered in the previous article.

Recursion in Python

Recursion is a powerful programming technique that allows a function to call itself in order to solve a problem. It is particularly useful for solving problems that can be broken down into smaller subproblems of the same type. In Python, recursion is widely used in algorithms such as the Fibonacci series, binary tree traversal, and others.

When using recursion in Python, it is important to ensure that the function has a terminating condition, known as the base case. The base case is a condition under which the function stops calling itself and returns a value. Without a proper base case, the function may enter an infinite loop and crash the program.

Memoization in Python

Memoization is a technique that involves storing the results of expensive function calls and returning the cached result when the same inputs occur again. Memoization can dramatically improve the performance of certain algorithms, especially those that involve repeated calculations of the same function with the same input.

When using memoization in Python, it is important to choose an appropriate data structure to store the cached results. The most commonly used data structure for memoization is a dictionary, but other data structures such as lists, sets, or even custom classes may also be used.

The Fibonacci Series

The Fibonacci series is a mathematical sequence in which each number is the sum of the two preceding numbers. The series is defined as follows: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, …

The Fibonacci series is a classic example of a recursive algorithm. By defining a function that takes an integer as input and returns the corresponding value in the series, we can recursively calculate each number in the series.

The Fibonacci series has many interesting properties and is found in numerous areas of mathematics, science, and art. It is also used in computer science, particularly in the analysis of algorithms and data structures.

Conclusion

Recursion and memoization are powerful techniques that can greatly enhance the performance and functionality of Python programs. The Fibonacci series is a particularly interesting example of a recursive algorithm that has many real-world applications. By understanding these concepts and applying them to programming problems, developers can write more efficient, elegant, and robust code.

## Popular questions

1. What is recursion in Python programming?

Recursion in Python programming is a technique that allows a function to call itself in order to solve a problem. It is particularly useful for solving problems that can be broken down into smaller subproblems of the same type.

1. What is the Fibonacci series, and how is it defined?

The Fibonacci series is a mathematical sequence in which each number is the sum of the two preceding numbers. The series is defined as follows: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, …

1. What is the base case, and why is it important when using recursion?

The base case is a terminating condition in a recursive function that stops the function from calling itself and returns a value. It is important to include a proper base case to prevent the function from entering an infinite loop, crashing the program, and consuming unnecessary resources.

1. What is memoization, and how is it used to improve the performance of algorithms?

Memoization is a technique that involves storing the results of expensive function calls and returning the cached result when the same inputs occur again. Memoization can improve the performance of algorithms that involve repeated calculations of the same function with the same input by eliminating the need to perform those calculations repeatedly.

1. How can you implement the Fibonacci series using recursion in Python?

To implement the Fibonacci series using recursion in Python, you can define a function that takes an integer as input and returns the corresponding value in the series. The function calls itself twice with slightly different inputs in order to calculate the sum of the two preceding numbers. Here is an example code:

```def fibonacci(n):
if n == 0:
return 0
elif n == 1:
return 1
else:
return fibonacci(n-1) + fibonacci(n-2)
```

The function takes an integer argument `n` and returns the `n`th number in the Fibonacci series. The function checks for the base cases – if `n` is 0 or 1, it returns the corresponding value in the series. Otherwise, it enters into a recursive loop and calls itself twice with inputs `n-1` and `n-2`. This continues until `n` is either 0 or 1, at which point the function returns the corresponding value in the series.

### Tag

recursion-fibonacci-python ##### Abdelmonem Aly
Cloud Computing and DevOps Engineering have always been my driving passions, energizing me with enthusiasm and a desire to stay at the forefront of technological innovation. I take great pleasure in innovating and devising workarounds for complex problems. Drawing on over 8 years of professional experience in the IT industry, with a focus on Cloud Computing and DevOps Engineering, I have a track record of success in designing and implementing complex infrastructure projects from diverse perspectives, and devising strategies that have significantly increased revenue. I am currently seeking a challenging position where I can leverage my competencies in a professional manner that maximizes productivity and exceeds expectations.
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