ValueError: all the input array dimensions for the concatenation axis must match exactly

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# ValueError: all the input array dimensions for the concatenation axis must match exactly

The NumPy "ValueError: all the input array dimensions for the concatenation axis must match exactly" occurs when the arrays you've passed to numpy.concatenate() don't have the same dimensions for the concatenation axis.

To solve the error, use the numpy.column_stack method to stack the arrays as columns into a 2-D array.

all-the-input-array-dimensions-for-the-concatenation-must-match-exactly

Here is an example of how the error occurs.

main.py

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import numpy as np

arr1 = np.array([[1, 2, 3]])
arr2 = np.array([[4, 5, 6, 7]])

print(arr1.shape)  # (1, 3)
print(arr2.shape)  # (1, 4)


# ⛔️ ValueError: all the input array dimensions for the concatenation axis must match exactly, but along dimension 1, the array at index 0 has size 3 and the array at index 1 has size 4
new_arr = np.concatenate((arr1, arr2))

The first array we passed to numpy.concatenate() has 3 columns and the second one has 4 columns.

Both arrays have only 1 row.

The numpy.concatenate method joins a sequence of arrays along an existing axis.

The error is raised because the dimensions of the arrays for the concatenation axis don't match.

If both arrays had a size of 3 (or a size of 4) along dimension 1, everything would work as expected.

main.py

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import numpy as np

arr1 = np.array([[1, 2, 3]])
arr2 = np.array([[4, 5, 6]])

print(arr1.shape)  # (1, 3)
print(arr2.shape)  # (1, 3)


new_arr = np.concatenate((arr1, arr2))

# [[1 2 3]
#  [4 5 6]]
print(new_arr)

both arrays have same size along axis 1

The code for this article is available on GitHub

If you need to stack 1-D arrays as columns into a 2-D array, use the numpy.column_stack method.

main.py

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import numpy as np

arr1 = np.array([[1, 2, 3]])
arr2 = np.array([[4, 5, 6, 7]])


# [[1 2 3 4 5 6 7]]
print(np.column_stack([arr1, arr2]))

Here is an example that better illustrates how this works.

main.py

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import numpy as np

arr1 = np.array([[1, 2, 3], [4, 5, 6]])
arr2 = np.array([7, 8])

print(arr1.shape)  # (2, 3)
print(arr2.shape)  # (2,)

# [[1 2 3 7]
#  [4 5 6 8]]
print(np.column_stack([arr1, arr2]))

using column stack to solve the error

The code for this article is available on GitHub

The arrays you pass to the column_stack method must all have the same first dimension.

The column_stack() method returns the two-dimensional array that is formed by stacking the supplied arrays.

# Using the `numpy.c_` attribute to solve the error

You can also use the numpy.c_ attribute to solve the error.

main.py

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import numpy as np

arr1 = np.array([[1, 2, 3], [4, 5, 6]])
arr2 = np.array([7, 8])

print(arr1.shape)  # (2, 3)
print(arr2.shape)  # (2,)

# [[1 2 3 7]
#  [4 5 6 8]]
print(np.c_[arr1, arr2])

using numpy c attribute to solve error

The code for this article is available on GitHub

The numpy.c_ attribute translates slice objects to concatenation along the second axis.

main.py

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import numpy as np

# [[1 4]
#  [2 5]
#  [3 6]]
print(
    np.c_[
        np.array([1, 2, 3]),
        np.array([4, 5, 6])
    ]
)

The supplied arrays are stacked along their last axis after being upgraded to at least a 2-D array with column vectors made out of 1-D arrays.

# Using the `numpy.reshape()` method to solve the error

In some cases, you might have to use the numpy.reshape method to solve the error.

Suppose we have the following 2 arrays.

main.py

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import numpy as np

arr1 = np.array([[0, 1], [2, 3], [4, 5]])
arr2 = np.array([[6, 7, 8, 9, 10, 11]])

print(arr1.shape)  # (3, 2)
print(arr2.shape)  # (1, 6)


# ⛔️ ValueError: all the input array dimensions for the concatenation axis must match exactly, but along dimension 1, the array at index 0 has size 2 and the array at index 1 has size 6
new_arr = np.concatenate((arr1, arr2))

The first array has 3 rows and 2 columns and the second array has 1 row and 6 columns.

We can solve the error by reshaping the second array to 3 rows and 2 columns.

main.py

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import numpy as np

arr1 = np.array([[0, 1], [2, 3], [4, 5]])
arr2 = np.array([[6, 7, 8, 9, 10, 11]])

print(arr1.shape)  # (3, 2)
print(arr2.shape)  # (1, 6)

arr2 = arr2.reshape((3, 2))


new_arr = np.concatenate((arr1, arr2))

# [[ 0  1]
#  [ 2  3]
#  [ 4  5]
#  [ 6  7]
#  [ 8  9]
#  [10 11]]
print(new_arr)

The code for this article is available on GitHub

The numpy.reshape() method gives a new shape to an array without changing its data.

main.py

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import numpy as np

arr1 = np.array([[0, 1], [2, 3], [4, 5]])
arr2 = np.array([[6, 7, 8, 9, 10, 11]])

print(arr1.shape)  # (3, 2)
print(arr2.shape)  # (1, 6)

arr2 = arr2.reshape((3, 2))

# [[ 6  7]
#  [ 8  9]
#  [10 11]]
print(arr2)

The two arrays are compatible after calling reshape(), so we can safely pass them to the numpy.concatenate() method.

# Using the `numpy.hstack()` method to solve the error

You can also use the numpy.hstack() method to solve the error.

main.py

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import numpy as np

arr1 = np.array([[1, 2, 3]])
arr2 = np.array([[4, 5, 6, 7]])

new_array = np.hstack((arr1, arr2))

# 👇️ [[1 2 3 4 5 6 7]]
print(new_array)

The code for this article is available on GitHub

The numpy.hstack() method takes a sequence of arrays and stacks the arrays in the sequence horizontally (column-wise).

This is equivalent to concatenation along the second axis, except for one-dimensional arrays where it concatenates along the first axis.

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ValueError: all the input array dimensions for the concatenation axis must match exactly

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