NumPy, the powerhouse of numerical computing in Python, empowers users with an array of functions for efficient data manipulation. Among its arsenal lies the versatile tool of concatenation, allowing for seamless merging of arrays along specified axes. In this article, we delve into NumPy concatenation, exploring its functionalities, applications, and best practices.
np.concatenate()
: np.concatenate()
function concatenates arrays along a specified axis.np.vstack()
and np.hstack()
: np.vstack()
stacks arrays vertically, i.e., along the first axis (axis=0), to increase the number of rows.np.hstack()
stacks arrays horizontally, i.e., along the second axis (axis=1), to increase the number of columns.arr1 = np.array([[1, 2], [3, 4]])
arr2 = np.array([[5, 6, 7], [8, 9, 10]])
concatenated_arr = np.concatenate((arr1, arr2), axis=0)
print("Concatenated Array along axis 0:")
print(concatenated_arr)
[[ 1 2]
[ 3 4]
[ 5 6 7]
[ 8 9 10]]
In this example, arr1
has a shape of (2, 2) and arr2
has a shape of (2, 3). By concatenating along axis 0, the arrays are stacked vertically, resulting in a new array with a shape of (4, 3).
arr1 = np.array([[1, 2], [3, 4]])
arr2 = np.array([[5, 6], [7, 8]])
concatenated_arr = np.concatenate((arr1, arr2), axis=1)
print(concatenated_arr)
[[1 2 5 6]
[3 4 7 8]]
In this example:
arr1
and arr2
, with the same number of rows are created.np.concatenate()
function is used to concatenate the arrays along axis 1 (columns).concatenated_arr
contains the elements of arr1
followed by the elements of arr2
, forming a new array with an increased number of columns.For details of hstack and vstack click here
NumPy concatenation is a powerful tool for efficiently merging arrays in Python. By understanding its functionalities and applications, users can streamline data integration tasks, manipulate arrays with ease, and unlock new possibilities in numerical computing. Incorporate NumPy concatenation into your workflows to enhance productivity and accelerate data-driven insights.
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