Flatten numpy array with python

ghz 11hours ago ⋅ 1 views

Here is an example to reproduce my problem:

a = np.array([[1,2], [3,4], [6,7]])
b = np.array([[1,2], [3,4], [6,7,8]])
c = np.array([[1,2], [3,4], [6]])
print(a.flatten())
print(b.flatten())
print(c.flatten())

The problem exist when one of the arrays has an item less or more.

Output:
[1 2 3 4 6 7]
[list([1, 2]) list([3, 4]) list([6, 7, 8])] # Won't work
[list([1, 2]) list([3, 4]) list([6])]       # Also won't work

How I want it:
[1 2 3 4 6 7]
[1 2 3 4 6 7 8]
[1 2 3 4 6]

Does anyone know how to flatten the list properly for example b and c?

Answer

The issue you're facing is that when you flatten arrays with jagged (or irregular) subarrays, the result becomes an array of lists, which is not what you're looking for. To fix this, you need to handle the case where subarrays have different lengths and flatten them in such a way that the resulting output is a single, continuous array.

Solution: Flattening Arrays with Different Lengths

You can use a recursive approach to flatten such jagged arrays. Specifically, you can iterate over each element and check if it is a list or a scalar value. If it is a list, you flatten that sublist; if it's a scalar, you append it directly to the output.

Here is a method to achieve this:

import numpy as np

# Define the custom flatten function
def custom_flatten(arr):
    # Initialize the output list
    result = []
    
    # Iterate over the array
    for elem in arr:
        # If the element is a list or array itself, recursively flatten it
        if isinstance(elem, (list, np.ndarray)):
            result.extend(custom_flatten(elem))  # Recursive flattening
        else:
            result.append(elem)  # Scalar element, just append
    
    return result

# Test cases
a = np.array([[1, 2], [3, 4], [6, 7]])
b = np.array([[1, 2], [3, 4], [6, 7, 8]])
c = np.array([[1, 2], [3, 4], [6]])

print(custom_flatten(a))
print(custom_flatten(b))
print(custom_flatten(c))

Output:

[1, 2, 3, 4, 6, 7]
[1, 2, 3, 4, 6, 7, 8]
[1, 2, 3, 4, 6]

Explanation:

  1. Recursive flattening: We check if an element is a list (or numpy array). If it is, we recursively flatten it. Otherwise, we append the scalar value directly to the result.
  2. extend method: This ensures that each nested list gets flattened into the parent list. It works similarly to appending but allows adding elements from an iterable (like a flattened list).

Handling Numpy Arrays Specifically

If you want to handle both NumPy arrays and lists in a way that keeps your solution more flexible, you could modify the condition to check for NumPy arrays and flatten them accordingly. The recursive flattening works well here because NumPy arrays behave similarly to lists when you're iterating over them.

Alternative Approach: Using np.concatenate

If you want a one-liner and the jagged arrays have a consistent structure (such as arrays of lists with a consistent type), you can also use np.concatenate after ensuring all inner arrays are NumPy arrays.

a = np.array([[1, 2], [3, 4], [6, 7]])
b = np.array([[1, 2], [3, 4], [6, 7, 8]])
c = np.array([[1, 2], [3, 4], [6]])

# Flatten with numpy.concatenate
print(np.concatenate([np.array(x) for x in a]))
print(np.concatenate([np.array(x) for x in b]))
print(np.concatenate([np.array(x) for x in c]))

However, using np.concatenate in this way might not always work if the subarrays are of different lengths, so the recursive method is generally more robust.

Let me know if you need further clarification!

#py