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Joining NumPy Array
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numpy.vstack() in python

Last Updated : 25 Apr, 2025
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numpy.vstack() is a function in NumPy used to stack arrays vertically (row-wise). It takes a sequence of arrays as input and returns a single array by stacking them along the vertical axis (axis 0).

Example: Vertical Stacking of 1D Arrays Using numpy.vstack()

Python 
import numpy as geek  a = geek.array([ 1, 2, 3] ) print ("1st Input array : \n", a)   b = geek.array([ 4, 5, 6] ) print ("2nd Input array : \n", b)   res = geek.vstack((a, b)) print ("Output vertically stacked array:\n ", res)

Output
1st Input array :   [1 2 3] 2nd Input array :   [4 5 6] Output vertically stacked array:   [[1 2 3]  [4 5 6]]

Explanation: The two 1D arrays a and b are stacked vertically using np.vstack(), combining them into a 2D array where each input array forms a row.

Syntax

numpy.vstack(tup)

Parameters:

  • tup: [sequence of ndarrays] Tuple containing arrays to be stacked. The arrays must have the same shape along all but the first axis.

Return: [stacked ndarray] The stacked array of the input arrays.

Vertical Stacking of 2D Arrays Using numpy.vstack()

This code shows how to vertically stack two 2D arrays using numpy.vstack(), resulting in a combined 2D array.

Python 
import numpy as geek  a = geek.array([[ 1, 2, 3], [ -1, -2, -3]] ) print ("1st Input array : \n", a)   b = geek.array([[ 4, 5, 6], [ -4, -5, -6]] ) print ("2nd Input array : \n", b)   res = geek.vstack((a, b)) print ("Output stacked array :\n ", res)

Output
1st Input array :   [[ 1  2  3]  [-1 -2 -3]] 2nd Input array :   [[ 4  5  6]  [-4 -5 -6]] Output stacked array :   [[ 1  2  3]  [-1 -2 -3]  [ 4  5  6]  [-4 -5 -6]]

Explanation: Two 2D arrays a and b are vertically stacked, creating a new 2D array where each original array becomes a set of rows in the resulting array.


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      Variability: It is the import dimension that measures the data variation i.e. whether the data is spread out or tightly clustered. Also known as Dispersion When working on data sets in Machine Learning or Data Science, involves many steps - variance measurement, reduction, and distinguishing random
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    • Python: Operations on Numpy Arrays
      NumPy is a Python package which means 'Numerical Python'. It is the library for logical computing, which contains a powerful n-dimensional array object, gives tools to integrate C, C++ and so on. It is likewise helpful in linear based math, arbitrary number capacity and so on. NumPy exhibits can lik
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    • How to use the NumPy sum function?
      NumPy's sum() function is extremely useful for summing all elements of a given array in Python. In this article, we'll be going over how to utilize this function and how to quickly use this to advance your code's functionality. Let's go over how to use these functions and the benefits of using this
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    • numpy.divide() in Python
      numpy.divide(arr1, arr2, out = None, where = True, casting = 'same_kind', order = 'K', dtype = None) : Array element from first array is divided by elements from second element (all happens element-wise). Both arr1 and arr2 must have same shape and element in arr2 must not be zero; otherwise it will
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    • numpy.inner() in python
      numpy.inner(arr1, arr2): Computes the inner product of two arrays. Parameters : arr1, arr2 : array to be evaluated. Return: Inner product of the two arrays. Code #1 : # Python Program illustrating # numpy.inner() method import numpy as geek # Scalars product = geek.inner(5, 4) print("inner Prod
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    • Absolute Deviation and Absolute Mean Deviation using NumPy | Python
      Absolute value: Absolute value or the modulus of a real number x is the non-negative value of x without regard to its sign. For example absolute value of 7 is 7 and the absolute value of -7 is also 7. Deviation: Deviation is a measure of the difference between the observed value of a variable and so
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    • Calculate standard deviation of a Matrix in Python
      In this article we will learn how to calculate standard deviation of a Matrix using Python. Standard deviation is used to measure the spread of values within the dataset. It indicates variations or dispersion of values in the dataset and also helps to determine the confidence in a model’s statistica
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    • numpy.gcd() in Python
      numpy.gcd(arr1, arr2, out = None, where = True, casting = ‘same_kind’, order = ‘K’, dtype = None) : This mathematical function helps user to calculate GCD value of |arr1| and |arr2| elements. Greatest Common Divisor (GCD) of two or more numbers, which are not all zero, is the largest positive number
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    Linear Algebra in NumPy Array

    • Numpy | Linear Algebra
      The Linear Algebra module of NumPy offers various methods to apply linear algebra on any numpy array.One can find: rank, determinant, trace, etc. of an array.eigen values of matricesmatrix and vector products (dot, inner, outer,etc. product), matrix exponentiationsolve linear or tensor equations and
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    • Get the QR factorization of a given NumPy array
      In this article, we will discuss QR decomposition or QR factorization of a matrix. QR factorization of a matrix is the decomposition of a matrix say 'A' into 'A=QR' where Q is orthogonal and R is an upper-triangular matrix. We factorize the matrix using numpy.linalg.qr() function. Syntax : numpy.lin
      2 min read
    • How to get the magnitude of a vector in NumPy?
      The fundamental feature of linear algebra are vectors, these are the objects having both direction and magnitude. In Python, NumPy arrays can be used to depict a vector. There are mainly two ways of getting the magnitude of vector: By defining an explicit function which computes the magnitude of a g
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    • How to compute the eigenvalues and right eigenvectors of a given square array using NumPY?
      In this article, we will discuss how to compute the eigenvalues and right eigenvectors of a given square array using NumPy library. Example: Suppose we have a matrix as: [[1,2], [2,3]] Eigenvalue we get from this matrix or square array is: [-0.23606798 4.23606798] Eigenvectors of this matrix are: [[
      2 min read
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