numpy.append() in Python Last Updated : 14 Apr, 2025 Comments Improve Suggest changes Like Article Like Report numpy.append() function is used to add new values at end of existing NumPy array. This is useful when we have to add more elements or rows in existing numpy array. It can also combine two arrays into a bigger one. Syntax: numpy.append(array, values, axis = None)array: Input array. values: The values to append the input array. If axis is specified the shape of values must match the dimensions of the input array along the specified axis. Otherwise values will be flattened before appending.axis: along which we want to insert the values. By default array is flattened. 1. Appending 1D ArraysIn this example, we append two 1D arrays without specifying an axis. By default the arrays are flattened before appending. Python import numpy as geek arr1 = geek.arange(5) print("1D arr1 :", arr1) print("Shape:", arr1.shape) arr2 = geek.arange(8, 12) print("1D arr2 :", arr2) print("Shape :", arr2.shape) arr3 = geek.append(arr1, arr2) print("Appended arr3:", arr3) Output : Appending 1d ArrayFrom the above output we can see that the numpy.append() function does not modify the original arrays (arr1 and arr2). Instead it creates a new array (arr3) with the combined elements.2. Appending 2D Arrays with Axis SpecificationWhen working with multi-dimensional arrays you can specify the axis parameter to control how the values are appended. Python import numpy as geek arr1 = geek.arange(8).reshape(2, 4) print("2D arr1 :", arr1) print("Shape :", arr1.shape) arr2 = geek.arange(8, 16).reshape(2, 4) print("2D arr2:", arr2) print("Shape :", arr2.shape) arr3 = geek.append(arr1, arr2) print("Appended arr3 by flattened :", arr3) arr3 = geek.append(arr1, arr2, axis = 0) print("Appended arr3 with axis 0 :", arr3) arr3 = geek.append(arr1, arr2, axis = 1) print("Appended arr3 with axis 1 :", arr3) Output : Appending 2D ArrayWhen no axis is given both arrays are flattened into a single long 1D array and then joined.When axis=0 rows from the second array are added below the first. With axis=1 columns are added to the right side of the first array.numpy.append() function is used to extend a array and it creates a new array rather than modifying the original one. Comment More info M Mohit Gupta Improve Article Tags : Python Python-numpy Python numpy-arrayManipulation Explore Python FundamentalsPython Introduction 3 min read Input and Output in Python 4 min read Python Variables 5 min read Python Operators 5 min read Python Keywords 2 min read Python Data Types 7 min read Conditional Statements in Python 3 min read Loops in Python - For, While and Nested Loops 6 min read Python Functions 5 min read Recursion in Python 4 min read Python Lambda Functions 5 min read Python Data StructuresPython String 5 min read Python Lists 4 min read Python Tuples 4 min read Python Dictionary 3 min read Python Sets 6 min read Python Arrays 7 min read List Comprehension in Python 4 min read Advanced PythonPython OOP Concepts 11 min read Python Exception Handling 5 min read File Handling in Python 4 min read Python Database Tutorial 4 min read Python MongoDB Tutorial 2 min read Python MySQL 9 min read Python Packages 12 min read Python Modules 7 min read Python DSA Libraries 15 min read List of Python GUI Library and Packages 3 min read Data Science with PythonNumPy Tutorial - Python Library 3 min read Pandas Tutorial 6 min read Matplotlib Tutorial 5 min read Python Seaborn Tutorial 15+ min read StatsModel Library- Tutorial 4 min read Learning Model Building in Scikit-learn 8 min read TensorFlow Tutorial 2 min read PyTorch Tutorial 7 min read Web Development with PythonFlask Tutorial 8 min read Django Tutorial | Learn Django Framework 7 min read Django ORM - Inserting, Updating & Deleting Data 4 min read Templating With Jinja2 in Flask 6 min read Django Templates 7 min read Python | Build a REST API using Flask 3 min read How to Create a basic API using Django Rest Framework ? 4 min read Python PracticePython Quiz 3 min read Python Coding Practice 1 min read Python Interview Questions and Answers 15+ min read Like