Python | Index of Non-Zero elements in Python list

Python | Index of Non-Zero elements in Python list

Finding the indices of non-zero elements in a Python list is a common task, especially in areas like numerical computing. Let's explore this step by step.

1. Problem Definition

Given a list of numbers, find the indices of all non-zero elements.

2. Simple Approach

Loop through the list and collect the indices of elements that are not zero.

3. Pythonic Approach

Use Python's list comprehension to achieve this in a concise way.

4. Implementation

Let's explore both the simple and Pythonic approaches:

4.1. Simple Approach

def indices_of_non_zero(lst):
    result = []
    for idx, value in enumerate(lst):
        if value != 0:
            result.append(idx)
    return result

# Test
lst = [0, 2, 0, 3, 4, 0, 5]
print(indices_of_non_zero(lst))  # Expected output: [1, 3, 4, 6]

4.2. Pythonic Approach

def indices_of_non_zero_pythonic(lst):
    return [idx for idx, value in enumerate(lst) if value != 0]

# Test
lst = [0, 2, 0, 3, 4, 0, 5]
print(indices_of_non_zero_pythonic(lst))  # Expected output: [1, 3, 4, 6]

In this Pythonic approach:

  • We use list comprehension to iterate over the list and its indices simultaneously using enumerate.
  • We then filter out indices for which the corresponding value is zero.

5. Advanced Usage with NumPy

If you frequently deal with numerical operations and arrays, NumPy is a library that can make many tasks more efficient and concise:

import numpy as np

def indices_of_non_zero_numpy(array):
    return np.nonzero(array)[0].tolist()

# Test
array = np.array([0, 2, 0, 3, 4, 0, 5])
print(indices_of_non_zero_numpy(array))  # Expected output: [1, 3, 4, 6]

In this approach:

  • We use NumPy's nonzero function which returns a tuple of indices for each dimension of the input array. Since we're working with a 1D array, we only need the first element of this tuple.

6. Conclusion

Finding the indices of non-zero elements in a Python list can be done using traditional loops, list comprehensions, or specialized libraries like NumPy, depending on the specific use case and performance requirements.

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