Skip to content
geeksforgeeks
  • Tutorials
    • Python
    • Java
    • Data Structures & Algorithms
    • ML & Data Science
    • Interview Corner
    • Programming Languages
    • Web Development
    • CS Subjects
    • DevOps And Linux
    • School Learning
    • Practice Coding Problems
  • Courses
    • DSA to Development
    • Get IBM Certification
    • Newly Launched!
      • Master Django Framework
      • Become AWS Certified
    • For Working Professionals
      • Interview 101: DSA & System Design
      • Data Science Training Program
      • JAVA Backend Development (Live)
      • DevOps Engineering (LIVE)
      • Data Structures & Algorithms in Python
    • For Students
      • Placement Preparation Course
      • Data Science (Live)
      • Data Structure & Algorithm-Self Paced (C++/JAVA)
      • Master Competitive Programming (Live)
      • Full Stack Development with React & Node JS (Live)
    • Full Stack Development
    • Data Science Program
    • All Courses
  • Numpy exercise
  • pandas
  • Matplotlib
  • Data visulisation
  • EDA
  • Machin Learning
  • Deep Learning
  • NLP
  • Data science
  • ML Tutorial
  • Computer Vision
  • ML project
Open In App
Next Article:
NumPy Array in Python
Next article icon

Python NumPy

Last Updated : 26 Mar, 2025
Comments
Improve
Suggest changes
Like Article
Like
Report

Numpy is a general-purpose array-processing package. It provides a high-performance multidimensional array object, and tools for working with these arrays. It is the fundamental package for scientific computing with Python.

Besides its obvious scientific uses, Numpy can also be used as an efficient multi-dimensional container of generic data.

Arrays in Numpy

Array in Numpy is a table of elements (usually numbers), all of the same type, indexed by a tuple of positive integers. In Numpy, number of dimensions of the array is called rank of the array. A tuple of integers giving the size of the array along each dimension is known as shape of the array. An array class in Numpy is called as ndarray. Elements in Numpy arrays are accessed by using square brackets and can be initialized by using nested Python Lists.

Creating a Numpy Array

Arrays in Numpy can be created by multiple ways, with various number of Ranks, defining the size of the Array. Arrays can also be created with the use of various data types such as lists, tuples, etc. The type of the resultant array is deduced from the type of the elements in the sequences.
Note: Type of array can be explicitly defined while creating the array.

Python 
import numpy as np   # Creating a rank 1 Array arr = np.array([1, 2, 3]) print(arr)   # Creating a rank 2 Array arr = np.array([[1, 2, 3],                 [4, 5, 6]]) print(arr)   # Creating an array from tuple arr = np.array((1, 3, 2)) print(arr)

Output
Array with Rank 1:   [1 2 3] Array with Rank 2:   [[1 2 3]  [4 5 6]]  Array created using passed tuple:  [1 3 2]

Accessing the array Index

In a numpy array, indexing or accessing the array index can be done in multiple ways. To print a range of an array, slicing is done. Slicing of an array is defining a range in a new array which is used to print a range of elements from the original array. Since, sliced array holds a range of elements of the original array, modifying content with the help of sliced array modifies the original array content.

Python 
import numpy as np   arr = np.array([[-1, 2, 0, 4],                 [4, -0.5, 6, 0],                 [2.6, 0, 7, 8],                 [3, -7, 4, 2.0]])  # Printing a range of Array # with the use of slicing method arr2 = arr[:2, ::2] print ("first 2 rows and alternate columns(0 and 2):\n", arr2)   # Printing elements at # specific Indices arr3 = arr[[1, 1, 0, 3],                  [3, 2, 1, 0]] print ("\nElements at indices (1, 3), "     "(1, 2), (0, 1), (3, 0):\n", arr3)

Output
first 2 rows and alternate columns(0 and 2):  [[-1.  0.]  [ 4.  6.]]  Elements at indices (1, 3), (1, 2), (0, 1), (3, 0):  [0. 6. 2. 3.]

Basic Array Operations

In numpy, arrays allow a wide range of operations which can be performed on a particular array or a combination of Arrays. These operation include some basic Mathematical operation as well as Unary and Binary operations.

Python 
import numpy as np   # Defining Array 1 a = np.array([[1, 2],               [3, 4]])   # Defining Array 2 b = np.array([[4, 3],               [2, 1]])                 # Adding 1 to every element print ("Adding 1 to every element:", a + 1)   # Subtracting 2 from each element print ("\nSubtracting 2 from each element:", b - 2)   # sum of array elements # Performing Unary operations print ("\nSum of all array elements: ", a.sum())   # Adding two arrays # Performing Binary operations print ("\nArray sum:\n", a + b)

Output
Adding 1 to every element: [[2 3]  [4 5]]  Subtracting 2 from each element: [[ 2  1]  [ 0 -1]]  Sum of all array elements:  10  Array sum:  [[5 5]  [5 5]]

More on Numpy Arrays

  • Basic Array Operations in Numpy
  • Advanced Array Operations in Numpy
  • Basic Slicing and Advanced Indexing in NumPy Python

Data Types in Numpy

Every Numpy array is a table of elements (usually numbers), all of the same type, indexed by a tuple of positive integers. Every ndarray has an associated data type (dtype) object. This data type object (dtype) provides information about the layout of the array. The values of an ndarray are stored in a buffer which can be thought of as a contiguous block of memory bytes which can be interpreted by the dtype object. Numpy provides a large set of numeric datatypes that can be used to construct arrays. At the time of Array creation, Numpy tries to guess a datatype, but functions that construct arrays usually also include an optional argument to explicitly specify the datatype.

Constructing a Datatype Object

In Numpy, datatypes of Arrays need not to be defined unless a specific datatype is required. Numpy tries to guess the datatype for Arrays which are not predefined in the constructor function.

Python 
import numpy as np   # Integer datatype x = np.array([1, 2])   print(x.dtype)            # Float datatype x = np.array([1.0, 2.0])  print(x.dtype)     # Forced Datatype x = np.array([1, 2], dtype = np.int64)    print(x.dtype)

Output
int64 float64 int64

Math Operations on DataType array

In Numpy arrays, basic mathematical operations are performed element-wise on the array. These operations are applied both as operator overloads and as functions. Many useful functions are provided in Numpy for performing computations on Arrays such as sum: for addition of Array elements, T: for Transpose of elements, etc.

Python 
import numpy as np   # First Array arr1 = np.array([[4, 7], [2, 6]],                   dtype = np.float64)                    # Second Array arr2 = np.array([[3, 6], [2, 8]],                   dtype = np.float64)    # Addition of two Arrays Sum = np.add(arr1, arr2) print(Sum)   # Addition of all Array elements Sum1 = np.sum(arr1) print(Sum1)   # Square root of Array Sqrt = np.sqrt(arr1) print(Sqrt)   # Transpose of Array Trans_arr = arr1.T print(Trans_arr)

Output
Addition of Two Arrays:  [[ 7. 13.]  [ 4. 14.]]  Addition of Array elements:  19.0  Square root of Array1 elements:  [[2.         2.64575131]  [1.41421356 2.44948974]]  Transpose of Array:  [[4. 2.]  ...

More on Numpy Data Type

  • Data type Object (dtype) in NumPy

Methods in Numpy:

all()diag()hypot()ones_like()
any()diagflat()absolute()full_like()
take()diag_indices()ceil()sin()
put()asmatrix()floor()cos()
apply_along_axis()bmat()degrees()tan()
apply_over_axes()eye()radians()sinh()
argmin()roll()npv()cosh()
argmax()identity()fv()tanh()
nanargmin()arange()pv()arcsin()
nanargmax()place()power()arccos()
amax()extract()float_power()exp()
amin()compress()log()exp2()
insert()rot90()log1()fix()
delete()tile()log2()logical_or()
append()reshape()log10()logical_and()
around()ravel()dot()logical_not()
flip()isinf()vdot()logical_xor()
fliplr()isrealobj()trunc()array_equal()
flipud()isscalar()divide()array_equiv()
triu()isneginf()floor_divide()arctan2()
tril()isposinf()true_divide()equal()
tri()iscomplex()random.rand()not_equal()
empty()isnan()random.randn()less()
empty_like()iscomplexobj()ndarray.flat()less_equal()
zeros()isreal()expm1()greater()
zeros_like()isfinite()bincount()greater_equal()
ones()isfortran()rint()prod()
arctan()cbrt()square()

Programs on Numpy

  • Python | Check whether a list is empty or not
  • Python | Get unique values from a list
  • Python | Multiply all numbers in the list (3 different ways)
  • Transpose a matrix in Single line in Python
  • Multiplication of two Matrices in Single line using Numpy in Python
  • Python program to print checkerboard pattern of nxn using numpy
  • Graph Plotting in Python | Set 1, Set 2, Set 3

Useful Numpy Articles

  • Matrix manipulation in Python
  • Basic Slicing and Advanced Indexing in NumPy Python
  • Differences between Flatten() and Ravel()
  • rand vs normal in Numpy.random in Python

Next Article
NumPy Array in Python

K
kumar_satyam
Improve
Article Tags :
  • Numpy
  • Python-numpy

Similar Reads

    NumPy Tutorial - Python Library
    NumPy (short for Numerical Python ) is one of the most fundamental libraries in Python for scientific computing. It provides support for large, multi-dimensional arrays and matrices along with a collection of mathematical functions to operate on arrays.At its core it introduces the ndarray (n-dimens
    3 min read

    Introduction

    NumPy Introduction
    NumPy(Numerical Python) is a fundamental library for Python numerical computing. It provides efficient multi-dimensional array objects and various mathematical functions for handling large datasets making it a critical tool for professionals in fields that require heavy computation.Table of ContentK
    7 min read
    Python NumPy
    Numpy is a general-purpose array-processing package. It provides a high-performance multidimensional array object, and tools for working with these arrays. It is the fundamental package for scientific computing with Python.Besides its obvious scientific uses, Numpy can also be used as an efficient m
    6 min read
    NumPy Array in Python
    NumPy (Numerical Python) is a powerful library for numerical computations in Python. It is commonly referred to multidimensional container that holds the same data type. It is the core data structure of the NumPy library and is optimized for numerical and scientific computation in Python. Table of C
    2 min read
    Basics of NumPy Arrays
    NumPy stands for Numerical Python and is used for handling large, multi-dimensional arrays and matrices. Unlike Python's built-in lists NumPy arrays provide efficient storage and faster processing for numerical and scientific computations. It offers functions for linear algebra and random number gen
    4 min read
    Numpy - ndarray
    ndarray is a short form for N-dimensional array which is a important component of NumPy. It’s allows us to store and manipulate large amounts of data efficiently. All elements in an ndarray must be of same type making it a homogeneous array. This structure supports multiple dimensions which makes it
    3 min read
    Data type Object (dtype) in NumPy Python
    Every ndarray has an associated data type (dtype) object. This data type object (dtype) informs us about the layout of the array. This means it gives us information about: Type of the data (integer, float, Python object, etc.)Size of the data (number of bytes)The byte order of the data (little-endia
    3 min read

    Creating NumPy Array

    Numpy - Array Creation
    Numpy Arrays are grid-like structures similar to lists in Python but optimized for numerical operations. The most straightforward way to create a NumPy array is by converting a regular Python list into an array using the np.array() function.Let's understand this with the help of an example:Pythonimp
    5 min read
    numpy.arange() in Python
    numpy.arange() function creates an array of evenly spaced values within a given interval. It is similar to Python's built-in range() function but returns a NumPy array instead of a list. Let's understand with a simple example:Pythonimport numpy as np #create an array arr= np.arange(5 , 10) print(arr
    2 min read
    numpy.zeros() in Python
    numpy.zeros() function creates a new array of specified shapes and types, filled with zeros. It is beneficial when you need a placeholder array to initialize variables or store intermediate results. We can create 1D array using numpy.zeros().Let's understand with the help of an example:Pythonimport
    2 min read
    NumPy - Create array filled with all ones
    To create an array filled with all ones, given the shape and type of array, we can use numpy.ones() method of NumPy library in Python.Pythonimport numpy as np array = np.ones(5) print(array) Output:[1. 1. 1. 1. 1.] 2D Array of OnesWe can also create a 2D array (matrix) filled with ones by passing a
    2 min read
    NumPy - linspace() Function
    linspace() function in NumPy returns an array of evenly spaced numbers over a specified range. Unlike the range() function in Python that generates numbers with a specific step size. linspace() allows you to specify the total number of points you want in the array, and NumPy will calculate the spaci
    2 min read
    numpy.eye() in Python
    numpy.eye() is a function in the NumPy library that creates a 2D array with ones on the diagonal and zeros elsewhere. This function is often used to generate identity matrices with ones along the diagonal and zeros in all other positions.Let's understand with the help of an example:Pythonimport nump
    2 min read
    Creating a one-dimensional NumPy array
    One-dimensional array contains elements only in one dimension. In other words, the shape of the NumPy array should contain only one value in the tuple. We can create a 1-D array in NumPy using the array() function, which converts a Python list or iterable object. Pythonimport numpy as np # Create a
    2 min read
    How to create an empty and a full NumPy array?
    Creating arrays is a basic operation in NumPy. Empty array: This array isn’t initialized with any specific values. It’s like a blank page, ready to be filled with data later. However, it will contain random leftover values in memory until you update it.Full array: This is an array where all the elem
    2 min read
    Create a Numpy array filled with all zeros - Python
    In this article, we will learn how to create a Numpy array filled with all zeros, given the shape and type of array. We can use Numpy.zeros() method to do this task. Let's understand with the help of an example:Pythonimport numpy as np # Create a 1D array of zeros with 5 elements array_1d = np.zeros
    2 min read
    How to generate 2-D Gaussian array using NumPy?
    In this article, let us discuss how to generate a 2-D Gaussian array using NumPy. To create a 2 D Gaussian array using the Numpy python module.Functions used:numpy.meshgrid()- It is used to create a rectangular grid out of two given one-dimensional arrays representing the Cartesian indexing or Matri
    2 min read
    How to create a vector in Python using NumPy
    NumPy is a general-purpose array-processing package. It provides a high-performance multidimensional array object, and tools for working with these arrays. It is the fundamental package for scientific computing with Python. Numpy is basically used for creating array of n dimensions. Vector are built
    4 min read
    Python - Numpy fromrecords() method
    numpy.fromrecords() method is a powerful tool in the NumPy library that allows you to create structured arrays from a sequence of tuples or other array-like objects. Let's understand the help of an example:Pythonimport numpy as np # Define a list of records records = [(1, 'Alice', 25.5), (2, 'Bob',
    2 min read

    NumPy Array Manipulation

    NumPy Copy and View of Array
    While working with NumPy, you may notice that some operations return a copy, while others return a view. A copy creates a new, independent array with its own memory, while a view shares the same memory as the original array. As a result, changes made to a view also affect the original and vice versa
    4 min read
    How to Copy NumPy array into another array?
    Many times there is a need to copy one array to another. Numpy provides the facility to copy array using different methods. In this Copy NumPy Array into Another ArrayThere are various ways to copies created in NumPy arrays in Python, here we are discussing some generally used methods for copies cre
    2 min read
    Appending values at the end of an NumPy array
    Let us see how to append values at the end of a NumPy array. Adding values at the end of the array is a necessary task especially when the data is not fixed and is prone to change. For this task, we can use numpy.append() and numpy.concatenate(). This function can help us to append a single value as
    4 min read
    How to swap columns of a given NumPy array?
    Swapping columns of a NumPy array means exchanging the positions of two specified columns across all rows. For example, if you have a 3x3 array with values like [[0, 1, 2], [3, 4, 5], [6, 7, 9]] and you swap column 0 with column 2, the array becomes [[2, 1, 0], [5, 4, 3], [9, 7, 6]]. Let’s explore d
    4 min read
    Insert a new axis within a NumPy array
    This post deals with the ways to increase the dimension of an array in NumPy. NumPy provides us with two different built-in functions to increase the dimension of an array i.e., 1D array will become 2D array2D array will become 3D array3D array will become 4D array4D array will become 5D arrayAdd a
    4 min read
    numpy.hstack() in Python
    numpy.hstack() function stacks arrays in sequence horizontally (column-wise). It joins arrays along their second axis for 2D arrays or flattens and joins them for 1D arrays. This is useful for combining arrays side by side. Example:Pythonimport numpy as np a = np.array([1, 2, 3]) b = np.array([4, 5,
    2 min read
    numpy.vstack() in python
    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()Pythonimport numpy as geek a = geek.array(
    2 min read
    Joining NumPy Array
    Joining NumPy arrays means combining multiple arrays into one larger array. For example, joining two arrays [1, 2] and [3, 4] results in a combined array [1, 2, 3, 4]. Let’s explore some common ways to join arrays using NumPy.1. Using numpy.concatenate()numpy.concatenate() joins two or more arrays a
    3 min read
    Combining a one and a two-dimensional NumPy Array
    Sometimes we need to combine 1-D and 2-D arrays and display their elements. Numpy has a function named as numpy.nditer(), which provides this facility. Syntax: numpy.nditer(op, flags=None, op_flags=None, op_dtypes=None, order='K', casting='safe', op_axes=None, itershape=None, buffersize=0) Example 1
    2 min read
    Numpy np.ma.concatenate() method-Python
    np.ma.concatenate() method joins two or more masked arrays along an existing axis. It works similarly to np.concatenate() but it is specifically designed for masked arrays which may contain missing or invalid data that is "masked" i.e marked to be ignored in computations. Example:Pythonimport numpy
    2 min read
    Numpy dstack() method-Python
    numpy.dstack() stacks arrays depth-wise along the third axis (axis=2). For 1D arrays, it promotes them to (1, N, 1) before stacking. For 2D arrays, it stacks them along axis=2 to form a 3D array. Example: Pythonimport numpy as np a = np.array([1, 2, 3]) b = np.array([4, 5, 6]) res = np.dstack((a, b)
    2 min read
    Splitting Arrays in NumPy
    NumPy arrays are an essential tool for scientific computing. However, at times, it becomes necessary to manipulate and analyze specific parts of the data. This is where array splitting comes into play, allowing you to break down an array into smaller sub-arrays, making the data more manageable. It i
    6 min read
    How to compare two NumPy arrays?
    Here we will be focusing on the comparison done using NumPy on arrays. Comparing two NumPy arrays determines whether they are equivalent by checking if every element at each corresponding index is the same.  Method 1: We generally use the == operator to compare two NumPy arrays to generate a new arr
    2 min read
    Find the union of two NumPy arrays
    Finding the union of two NumPy arrays means combining both arrays into one while removing any duplicate values. For example, if you have two arrays [10, 20, 30] and [20, 30, 40], the union would be [10, 20, 30, 40]. Let's explore different ways to do this efficiently. Using np.union1d()If you want t
    2 min read
    Find unique rows in a NumPy array
    Finding unique rows means removing duplicate rows from a 2D array so that each row appears only once. For example, given [[1, 2], [3, 4], [1, 2], [5, 6]], the unique rows would be [[1, 2], [3, 4], [5, 6]]. Let’s explore efficient ways to achieve this using NumPy.Using np.unique(axis=0)np.unique(axis
    3 min read
    Numpy np.unique() method-Python
    numpy.unique() finds the unique elements of an array. It is often used in data analysis to eliminate duplicate values and return only the distinct values in sorted order. Example:Pythonimport numpy as np a = np.array([1, 2, 2, 3, 4, 4, 4]) res = np.unique(a) print(res)Output[1 2 3 4] Explanation: nu
    2 min read
    numpy.trim_zeros() in Python
    numpy.trim_zeros() removes the leading and trailing zeros from a 1-D array. It is often used to clean up data by trimming unnecessary zeros from the beginning or end of the array. Example:Pythonimport numpy as np a = np.array([0, 0, 3, 4, 0, 5, 0, 0]) res = np.trim_zeros(a) print(res)Output[3 4 0 5]
    2 min read

    Matrix in NumPy

    Matrix manipulation in Python
    In python matrix can be implemented as 2D list or 2D Array. Forming matrix from latter, gives the additional functionalities for performing various operations in matrix. These operations and array are defines in module "numpy". Operation on Matrix : 1. add() :- This function is used to perform eleme
    4 min read
    numpy matrix operations | empty() function
    numpy.matlib.empty() is another function for doing matrix operations in numpy.It returns a new matrix of given shape and type, without initializing entries. Syntax : numpy.matlib.empty(shape, dtype=None, order='C') Parameters : shape : [int or tuple of int] Shape of the desired output empty matrix.
    1 min read
    numpy matrix operations | zeros() function
    numpy.matlib.zeros() is another function for doing matrix operations in numpy. It returns a matrix of given shape and type, filled with zeros. Syntax : numpy.matlib.zeros(shape, dtype=None, order='C') Parameters : shape : [int, int] Number of rows and columns in the output matrix.If shape has length
    2 min read
    numpy matrix operations | ones() function
    numpy.matlib.ones() is another function for doing matrix operations in numpy. It returns a matrix of given shape and type, filled with ones. Syntax : numpy.matlib.ones(shape, dtype=None, order='C') Parameters : shape : [int, int] Number of rows and columns in the output matrix.If shape has length on
    2 min read
    numpy matrix operations | eye() function
    numpy.matlib.eye() is another function for doing matrix operations in numpy. It returns a matrix with ones on the diagonal and zeros elsewhere. Syntax : numpy.matlib.eye(n, M=None, k=0, dtype='float', order='C') Parameters : n : [int] Number of rows in the output matrix. M : [int, optional] Number o
    2 min read
    numpy matrix operations | identity() function
    numpy.matlib.identity() is another function for doing matrix operations in numpy. It returns a square identity matrix of given input size. Syntax : numpy.matlib.identity(n, dtype=None) Parameters : n : [int] Number of rows and columns in the output matrix. dtype : [optional] Desired output data-type
    1 min read
    Adding and Subtracting Matrices in Python
    In this article, we will discuss how to add and subtract elements of the matrix in Python.  Example: Suppose we have two matrices A and B. A = [[1,2],[3,4]] B = [[4,5],[6,7]] then we get A+B = [[5,7],[9,11]] A-B = [[-3,-3],[-3,-3]] Now let us try to implement this using Python  1. Adding elements of
    4 min read
    Matrix Multiplication in NumPy
    Let us see how to compute matrix multiplication with NumPy. We will be using the numpy.dot() method to find the product of 2 matrices. For example, for two matrices A and B. A = [[1, 2], [2, 3]] B = [[4, 5], [6, 7]] So, A.B = [[1*4 + 2*6, 2*4 + 3*6], [1*5 + 2*7, 2*5 + 3*7] So the computed answer wil
    2 min read
    Numpy ndarray.dot() function | Python
    The numpy.ndarray.dot() function computes the dot product of two arrays. It is widely used in linear algebra, machine learning and deep learning for operations like matrix multiplication and vector projections.Example:Pythonimport numpy as np a = np.array([1, 2, 3]) b = np.array([4, 5, 6]) result =
    2 min read
    NumPy | Vector Multiplication
    NumPy is a Python library used for performing numerical computations. It provides an efficient way to work with vectors and matrices especially when performing vector multiplication operations. It is used in various applications such as data science, machine learning, physics simulations and many mo
    4 min read
    How to calculate dot product of two vectors in Python?
    dot product or also known as the scalar product is an algebraic operation that takes two equal-length sequences of numbers and returns a single number. Let us given two vectors A and B, and we have to find the dot product of two vectors.Given that, A = a_1i + a_2j + a_3k B = b_1i + b_2j + b_3k Where
    3 min read
    Multiplication of two Matrices in Single line using Numpy in Python
    Matrix multiplication is an operation that takes two matrices as input and produces single matrix by multiplying rows of the first matrix to the column of the second matrix.In matrix multiplication make sure that the number of columns of the first matrix should be equal to the number of rows of the
    3 min read
    Python | Numpy np.eigvals() method
    With the help of np.eigvals() method, we can get the eigen values of a matrix by using np.eigvals() method. Syntax : np.eigvals(matrix) Return : Return the eigen values of a matrix. Example #1 : In this example we can see that by using np.eigvals() method, we are able to get the eigen values of a ma
    1 min read
    How to Calculate the determinant of a matrix using NumPy?
    The determinant of a square matrix is a special number that helps determine whether the matrix is invertible and how it transforms space. It is widely used in linear algebra, geometry and solving equations. NumPy provides built-in functions to easily compute the determinant of a matrix, let's explor
    2 min read
    Python | Numpy matrix.transpose()
    With the help of Numpy matrix.transpose() method, we can find the transpose of the matrix by using the matrix.transpose()method in Python. Numpy matrix.transpose() Syntax Syntax : matrix.transpose() Parameter: No parameters; transposes the matrix it is called on. Return : Return transposed matrix Wh
    3 min read
    Python | Numpy matrix.var()
    With the help of Numpy matrix.var() method, we can find the variance of a matrix by using the matrix.var() method. Syntax : matrix.var() Return : Return variance of a matrix Example #1 : In this example we can see that by using matrix.var() method we are able to find the variance of a given matrix.
    1 min read
    Compute the inverse of a matrix using NumPy
    The inverse of a matrix is just a reciprocal of the matrix as we do in normal arithmetic for a single number which is used to solve the equations to find the value of unknown variables. The inverse of a matrix is that matrix which when multiplied with the original matrix will give as an identity mat
    2 min read

    Operations on NumPy Array

    Numpy | Binary Operations
    Binary operators acts on bits and performs bit by bit operation. Binary operation is simply a rule for combining two values to create a new value. numpy.bitwise_and() : This function is used to Compute the bit-wise AND of two array element-wise. This function computes the bit-wise AND of the underly
    8 min read
    Numpy | Mathematical Function
    NumPy contains a large number of various mathematical operations. NumPy provides standard trigonometric functions, functions for arithmetic operations, handling complex numbers, etc. Trigonometric Functions –NumPy has standard trigonometric functions which return trigonometric ratios for a given ang
    9 min read
    Numpy - String Functions & Operations
    NumPy String functions belong to the numpy.char module and are designed to perform element-wise operations on arrays. These functions can help to handle and manipulate string data efficiently.Table of ContentString OperationsString Information String Comparison In this article, we’ll explore the var
    5 min read

    Reshaping NumPy Array

    Reshape NumPy Array
    NumPy is a general-purpose array-processing package. It provides a high-performance multidimensional array object, and tools for working with these arrays. It is the fundamental package for scientific computing with Python. Numpy is basically used for creating array of n dimensions.Reshaping numpy a
    5 min read
    Python | Numpy matrix.resize()
    With the help of Numpy matrix.resize() method, we are able to resize the shape of the given matrix. Remember all elements should be covered after resizing the given matrix. Syntax : matrix.resize(shape) Return: new resized matrix Example #1 : In the given example we are able to resize the given matr
    1 min read
    Python | Numpy matrix.reshape()
    With the help of Numpy matrix.reshape() method, we are able to reshape the shape of the given matrix. Remember all elements should be covered after reshaping the given matrix. Syntax : matrix.reshape(shape) Return: new reshaped matrix Example #1 : In the given example we are able to reshape the give
    1 min read
    NumPy Array Shape
    The shape of an array can be defined as the number of elements in each dimension. Dimension is the number of indices or subscripts, that we require in order to specify an individual element of an array. How can we get the Shape of an Array?In NumPy, we will use an attribute called shape which return
    2 min read
    Change the dimension of a NumPy array
    Let's discuss how to change the dimensions of an array. In NumPy, this can be achieved in many ways. Let's discuss each of them.  Method #1: Using Shape() Syntax : array_name.shape()Python3 # importing numpy import numpy as np def main(): # initialising array print('Initialised array') gfg = np.arra
    3 min read
    numpy.ndarray.resize() function - Python
    numpy.ndarray.resize() function change shape and size of array in-place. Syntax : numpy.ndarray.resize(new_shape, refcheck = True) Parameters : new_shape :[tuple of ints, or n ints] Shape of resized array. refcheck :[bool, optional] If False, reference count will not be checked. Default is True. Ret
    1 min read
    Flatten a Matrix in Python using NumPy
    Let's discuss how to flatten a Matrix using NumPy in Python. By using ndarray.flatten() function we can flatten a matrix to one dimension in python. Syntax:numpy_array.flatten(order='C') order:'C' means to flatten in row-major.'F' means to flatten in column-major.'A' means to flatten in column-major
    1 min read
    numpy.moveaxis() function | Python
    numpy.moveaxis() function allows you to rearrange axes of an array. It is used when you need to shift dimensions of an array to different positions without altering the actual data. The syntax for the numpy.moveaxis() function is as follows:numpy.moveaxis(array, source, destination)Parameters:array:
    2 min read
    numpy.swapaxes() function - Python
    numpy.swapaxes() function allow us to interchange two axes of a multi-dimensional NumPy array. It focuses on swapping only two specified axes while leaving the rest unchanged. It is used to rearrange the structure of an array without altering its actual data. The syntax of numpy.swapaxes() is:numpy.
    2 min read
    Python | Numpy matrix.swapaxes()
    With the help of matrix.swapaxes() method, we are able to swap the axes a matrix by using the same method. Syntax : matrix.swapaxes() Return : Return matrix having interchanged axes Example #1 : In this example we are able to swap the axes of a matrix by using matrix.swapaxes() method. Python3 1=1 #
    1 min read
    numpy.vsplit() function | Python
    numpy.vsplit() function split an array into multiple sub-arrays vertically (row-wise). vsplit is equivalent to split with axis=0 (default), the array is always split along the first axis regardless of the array dimension. Syntax : numpy.vsplit(arr, indices_or_sections) Parameters : arr : [ndarray] A
    2 min read
    numpy.hsplit() function | Python
    The numpy.hsplit() function is used to split a NumPy array into multiple sub-arrays horizontally (column-wise). It is equivalent to using the numpy.split() function with axis=1. Regardless of the dimensionality of the input array, numpy.hsplit() always divides the array along its second axis (column
    2 min read
    Numpy MaskedArray.reshape() function | Python
    numpy.MaskedArray.reshape() function is used to give a new shape to the masked array without changing its data.It returns a masked array containing the same data, but with a new shape. The result is a view on the original array; if this is not possible, a ValueError is raised. Syntax : numpy.ma.resh
    3 min read
    Python | Numpy matrix.squeeze()
    With the help of matrix.squeeze() method, we are able to squeeze the size of a matrix by using the same method. But remember one thing we use this method on Nx1 size of matrix which gives out as 1xN matrix. Syntax : matrix.squeeze() Return : Return a squeezed matrix Example #1 : In this example we a
    1 min read

    Indexing NumPy Array

    Basic Slicing and Advanced Indexing in NumPy
    Indexing a NumPy array means accessing the elements of the NumPy array at the given index.There are two types of indexing in NumPy: basic indexing and advanced indexing.Slicing a NumPy array means accessing the subset of the array. It means extracting a range of elements from the data.In this tutori
    5 min read
    numpy.compress() in Python
    The numpy.compress() function returns selected slices of an array along mentioned axis, that satisfies an axis. Syntax: numpy.compress(condition, array, axis = None, out = None) Parameters : condition : [array_like]Condition on the basis of which user extract elements. Applying condition on input_ar
    2 min read
    Accessing Data Along Multiple Dimensions Arrays in Python Numpy
    NumPy (Numerical Python) is a Python library that comprises of multidimensional arrays and numerous functions to perform various mathematical and logical operations on them. NumPy also consists of various functions to perform linear algebra operations and generate random numbers. NumPy is often used
    3 min read
    How to access different rows of a multidimensional NumPy array?
    Let us see how to access different rows of a multidimensional array in NumPy. Sometimes we need to access different rows of multidimensional NumPy array-like first row, the last two rows, and even the middle two rows, etc. In NumPy , it is very easy to access any rows of a multidimensional array. Al
    3 min read
    numpy.tril_indices() function | Python
    numpy.tril_indices() function return the indices for the lower-triangle of an (n, m) array. Syntax : numpy.tril_indices(n, k = 0, m = None) Parameters : n : [int] The row dimension of the arrays for which the returned indices will be valid. k : [int, optional] Diagonal offset. m : [int, optional] Th
    1 min read

    Arithmetic operations on NumPyArray

    NumPy Array Broadcasting
    Broadcasting in NumPy allows us to perform arithmetic operations on arrays of different shapes without reshaping them. It automatically adjusts the smaller array to match the larger array's shape by replicating its values along the necessary dimensions. This makes element-wise operations more effici
    5 min read
    Estimation of Variable | set 1
    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
    3 min read
    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
    3 min read
    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
    4 min read
    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
    3 min read
    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 : Python3 1== # Python Program illustrating # numpy.inner() method import numpy as geek # Scalars product = geek.inner(5, 4) print(
    1 min read
    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
    3 min read
    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
    2 min read
    numpy.gcd() in Python
    numpy.gcd() function computes the greatest common divisor (GCD) of two integers element-wise. The GCD of two numbers is the largest positive integer that divides both numbers without leaving a remainder.Pythonimport numpy as np res = np.gcd(36, 60) print(res)Output12 The GCD of 36 and 60 is 12, whic
    2 min read

    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
    6 min read
    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
    3 min read
    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
geeksforgeeks-footer-logo
Corporate & Communications Address:
A-143, 7th Floor, Sovereign Corporate Tower, Sector- 136, Noida, Uttar Pradesh (201305)
Registered Address:
K 061, Tower K, Gulshan Vivante Apartment, Sector 137, Noida, Gautam Buddh Nagar, Uttar Pradesh, 201305
GFG App on Play Store GFG App on App Store
Advertise with us
  • Company
  • About Us
  • Legal
  • Privacy Policy
  • In Media
  • Contact Us
  • Advertise with us
  • GFG Corporate Solution
  • Placement Training Program
  • Languages
  • Python
  • Java
  • C++
  • PHP
  • GoLang
  • SQL
  • R Language
  • Android Tutorial
  • Tutorials Archive
  • DSA
  • Data Structures
  • Algorithms
  • DSA for Beginners
  • Basic DSA Problems
  • DSA Roadmap
  • Top 100 DSA Interview Problems
  • DSA Roadmap by Sandeep Jain
  • All Cheat Sheets
  • Data Science & ML
  • Data Science With Python
  • Data Science For Beginner
  • Machine Learning
  • ML Maths
  • Data Visualisation
  • Pandas
  • NumPy
  • NLP
  • Deep Learning
  • Web Technologies
  • HTML
  • CSS
  • JavaScript
  • TypeScript
  • ReactJS
  • NextJS
  • Bootstrap
  • Web Design
  • Python Tutorial
  • Python Programming Examples
  • Python Projects
  • Python Tkinter
  • Python Web Scraping
  • OpenCV Tutorial
  • Python Interview Question
  • Django
  • Computer Science
  • Operating Systems
  • Computer Network
  • Database Management System
  • Software Engineering
  • Digital Logic Design
  • Engineering Maths
  • Software Development
  • Software Testing
  • DevOps
  • Git
  • Linux
  • AWS
  • Docker
  • Kubernetes
  • Azure
  • GCP
  • DevOps Roadmap
  • System Design
  • High Level Design
  • Low Level Design
  • UML Diagrams
  • Interview Guide
  • Design Patterns
  • OOAD
  • System Design Bootcamp
  • Interview Questions
  • Inteview Preparation
  • Competitive Programming
  • Top DS or Algo for CP
  • Company-Wise Recruitment Process
  • Company-Wise Preparation
  • Aptitude Preparation
  • Puzzles
  • School Subjects
  • Mathematics
  • Physics
  • Chemistry
  • Biology
  • Social Science
  • English Grammar
  • Commerce
  • World GK
  • GeeksforGeeks Videos
  • DSA
  • Python
  • Java
  • C++
  • Web Development
  • Data Science
  • CS Subjects
@GeeksforGeeks, Sanchhaya Education Private Limited, All rights reserved
We use cookies to ensure you have the best browsing experience on our website. By using our site, you acknowledge that you have read and understood our Cookie Policy & Privacy Policy
Lightbox
Improvement
Suggest Changes
Help us improve. Share your suggestions to enhance the article. Contribute your expertise and make a difference in the GeeksforGeeks portal.
geeksforgeeks-suggest-icon
Create Improvement
Enhance the article with your expertise. Contribute to the GeeksforGeeks community and help create better learning resources for all.
geeksforgeeks-improvement-icon
Suggest Changes
min 4 words, max Words Limit:1000

Thank You!

Your suggestions are valuable to us.

What kind of Experience do you want to share?

Interview Experiences
Admission Experiences
Career Journeys
Work Experiences
Campus Experiences
Competitive Exam Experiences