# 5 Best Ways to Convert Python Complex Numbers to Arrays

π‘ Problem Formulation: In Python, complex numbers are a built-in data type used for computations requiring real and imaginary components. Sometimes it’s necessary to convert a complex number or a collection of complex numbers into a list or array. An example input could be a single complex number like `3 + 4j`, and the desired output would be an array containing the real and imaginary parts, such as `[3, 4]`.

## Method 1: Using list()

You can convert a complex number into an array by passing the real and imaginary parts to the `list()` method. This gives you a list that contains the real part as the first element and the imaginary part as the second element, which is straightforward and easily readable.

Here’s an example:

```complex_num = 3 + 4j
array_from_complex = list((complex_num.real, complex_num.imag))
print(array_from_complex)```

Output: [3.0, 4.0]

This code creates a tuple with the real and imaginary parts of a complex number and then converts it into a list. This method is clean and explicit, making the operations clear to anyone reading the code.

## Method 2: Using NumPy library

For those working in scientific computing, NumPy provides a convenient and efficient way to handle arrays. You can create an array directly from a complex number using NumPy’s `array()` function.

Here’s an example:

```import numpy as np
complex_num = 3 + 4j
array_from_complex = np.array([complex_num.real, complex_num.imag])
print(array_from_complex)```

Output: [3. 4.]

This snippet uses NumPy’s `array()` function to create an array from the real and imaginary parts of the complex number. NumPy arrays are preferable when performing heavy numerical computations and offer additional functionalities over standard Python lists.

## Method 3: Using a generator expression

A generator expression can be used for this conversion, providing a concise way to iterate over the complex number’s attributes and create a list. This method is particularly useful when working with a sequence of complex numbers.

Here’s an example:

```complex_num = 3 + 4j
array_from_complex = [attr for attr in (complex_num.real, complex_num.imag)]
print(array_from_complex)```

Output: [3.0, 4.0]

Here, we make use of a generator expression to create a list in a single line. While this is as readable as the tuple/list method, it has the added benefit of being easily extendable when dealing with multiple complex numbers.

## Method 4: Using a custom function

For reusability and abstraction, you can define a custom function that takes a complex number as an argument and returns a list of its real and imaginary components. This method encapsulates logic, making your codebase cleaner and more maintainable.

Here’s an example:

```def complex_to_array(c_num):
return [c_num.real, c_num.imag]

complex_num = 3 + 4j
array_from_complex = complex_to_array(complex_num)
print(array_from_complex)```

Output: [3.0, 4.0]

This code defines a function, `complex_to_array()`, which converts the complex number to an array of its real and imaginary parts. This method is clean and abstracts the conversion, allowing for easy reuse in other parts of your program.

## Bonus One-Liner Method 5: Using a lambda function

Lambda functions offer a quick, inline method for converting complex numbers to arrays without the need for defining a standalone function. Best used for simple tasks that are unlikely to need reuse.

Here’s an example:

```complex_num = 3 + 4j
array_from_complex = (lambda c_num: [c_num.real, c_num.imag])(complex_num)
print(array_from_complex)```

Output: [3.0, 4.0]

The above snippet demonstrates a one-liner lambda function that receives a complex number and returns a list of its real and imaginary parts. While concise, lambda functions can reduce readability when overused and are best kept to simple operations.

## Summary/Discussion

• Method 1: Using list(). Strengths: Simple and explicit, no dependencies. Weaknesses: Not as efficient in batch operations.
• Method 2: Using NumPy library. Strengths: Fast and efficient for large datasets or numerical computations. Weaknesses: Requires an external library, NumPy.
• Method 3: Using a generator expression. Strengths: Concise, easily extendable to multiple complex numbers. Weaknesses: Might be less intuitive for beginners.
• Method 4: Using a custom function. Strengths: Reusability, abstraction, maintainability. Weaknesses: Might be an overkill for one-time conversions.
• Bonus Method 5: Using a lambda function. Strengths: Extremely concise. Weaknesses: Can hurt readability, not suitable for more complex operations.