ValueError: object too deep for desired array [Solved]

# ValueError: object too deep for desired array [Solved]

The NumPy "ValueError: object too deep for desired array" occurs when you pass a multidimensional array where a one-dimensional array is expected.

You can solve the error by removing the extra dimension or converting the array to one-dimensional.

Here is an example of how the error occurs.

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import numpy as np

x = np.array([1, 2, 3])
y = np.array([[0.5, 1, 0.3], [0.2, 0.7, 0.8]])

# ⛔️ ValueError: object too deep for desired array
arr = np.convolve(x, y, 'same')

The numpy.convolve() method takes two one-dimensional input arrays and returns the discrete, linear convolution of two one-dimensional sequences.

The error is raised because the second argument we passed to numpy.convolve is a two-dimensional array.

You can use the numpy.shape() attribute to verify that the array is not one-dimensional.

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import numpy as np

x = np.array([1, 2, 3])
print(x.shape)  # (3,)

y = np.array([[0.5, 1, 0.3], [0.2, 0.7, 0.8]])
print(y.shape)  # (2, 3)

The second array is two-dimensional and has 2 rows and 3 columns.

# Removing the extra dimension to solve the error

One way to solve the error is to remove the extra dimension by slicing the array.

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import numpy as np

x = np.array([1, 2, 3])

y = np.array([[0.5, 1, 0.3], [0.2, 0.7, 0.8]])

print(y[:, 0])  # 👉️ [0.5 0.2]

arr = np.convolve(x, y[:, 0], 'same')

print(arr)  # 👉️ [0.5 1.2 1.9]

We used array slicing to select the first element of each subarray.

Now both arrays we passed to numpy.convolve() are one-dimensional, so the error is resolved.

# Converting a multidimensional array to one-dimensional to solve the error

You can also solve the error by converting the multidimensional array to one-dimensional.

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import numpy as np

x = np.array([1, 2, 3])

y = np.array([[0.5, 1, 0.3], [0.2, 0.7, 0.8]])

# ✅ convert the array to one-dimensional
y = np.reshape(y, y.size)
print(y)  # 👉️ [0.5 1. 0.3 0.2 0.7 0.8]

arr = np.convolve(x, y, 'same')

print(arr)  # 👉️ [2.  3.8 3.8 2.  2.8 3.7]

We used the numpy.reshape() method to convert the multidimensional array to one-dimensional.

The two arguments we passed to numpy.reshape are:

1. The array we want to reshape.
2. The new shape of the array. If an integer is supplied, then the result is a one-dimensional array of that length.

# Flattening the array to solve the error

You can also flatten the array to solve the error.

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import numpy as np

x = np.array([1, 2, 3])

y = np.array([[0.5, 1, 0.3], [0.2, 0.7, 0.8]])

y = y.flatten()

print(y)  # 👉️ [0.5 1.  0.3 0.2 0.7 0.8]

arr = np.convolve(x, y, 'same')

print(arr)  # 👉️ [2.  3.8 3.8 2.  2.8 3.7]

We used the numpy.ndarray.flatten() method to get a copy of the array collapsed into one dimension.

The flatten() method doesn't flatten the array in place, so make sure to assign the result of calling flatten() to a variable.

# Using the numpy.ravel() method to solve the error

You can also use the numpy.ravel() method to flatten the array and solve the error.

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import numpy as np

x = np.array([1, 2, 3])

y = np.array([[0.5, 1, 0.3], [0.2, 0.7, 0.8]])

y = np.ravel(y)

print(y)  # 👉️ [0.5 1.  0.3 0.2 0.7 0.8]

arr = np.convolve(x, y, 'same')

print(arr)  # 👉️ [2.  3.8 3.8 2.  2.8 3.7]

The numpy.ravel() method returns a 1-D array containing the elements of the input.

The method makes a copy of the array only if needed.

# Additional Resources

You can learn more about the related topics by checking out the following tutorials:

• Convert a NumPy array to 0 or 1 based on threshold in Python
• How to get the length of a 2D Array in Python
• TypeError: 'numpy.ndarray' object is not callable in Python
• TypeError: Object of type ndarray is not JSON serializable
• IndexError: too many indices for array in Python [Solved]
• How to filter a JSON array in Python
• AttributeError module 'numpy' has no attribute array or int
• How to check if a NumPy Array is multidimensional or 1D
• Numpy: How to extract a Submatrix from an array