numpy add


NumPy add()



One of the fundamental operations in NumPy is array addition, which allows you to add two or more arrays element-wise. In this blog, we'll explore NumPy's array addition capabilities through step-by-step examples to help you understand how to use this powerful feature effectively. 
 
 
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Step 1: Installing NumPy



Before we can get started with NumPy, it's essential to make sure you have it installed. You can use the following pip command to install NumPy:



pip install numpy


Step 2: Importing NumPy



Once NumPy is installed, you need to import it into your Python script or Jupyter Notebook. This is typically done using the import statement:



import numpy as np


Step 3: Creating Arrays



To perform array addition, we first need to create arrays. You can create NumPy arrays from Python lists or by using NumPy functions. Let's create a couple of arrays for demonstration purposes:



array1 = np.array([1, 2, 3])
array2 = np.array([4, 5, 6])


Step 4: Element-Wise Addition



Now that we have our arrays, we can perform element-wise addition. This means adding corresponding elements from both arrays together. NumPy makes this operation simple and efficient and Use the '+' operator or the 'add()' function to add two equal-sized arrays:



result = array1 + array2

(or)

result = np.add(array1, array2)


The result array now contains the element-wise addition of array1 and array2.
 

Step 5: Printing the Result



To see the result of the addition, you can use the print statement:



print(result)


The output will be:



[5 7 9]


How it's works:




[1+4, 2+5, 3+6] = [5 7 9]


NumPy also allows you to add a scalar value to an entire array or add arrays of different shapes through broadcasting. Broadcasting automatically expands the smaller array to match the shape of the larger array, making the addition operation possible. Here's an example:



import numpy as np

array1 = np.array([1, 2, 3])
array3 = np.array([10, 20, 30])

result = array1 + array3

print(result)


The output will be:



[11 22 33]


In this example, array3 is broadcasted to match the shape of array1, and then element-wise addition is performed.
 
 
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Multidimensional Arrays



NumPy is not limited to one-dimensional arrays. You can perform array addition on multidimensional arrays as well. Let's create a 2D array and perform addition:



import numpy as np

matrix1 = np.array([[1, 2], [3, 4]])
matrix2 = np.array([[5, 6], [7, 8]])

result = matrix1 + matrix2

print(result)


The output will be:



[[ 6  8]
 [10 12]]


NumPy's array addition capabilities make it a powerful tool for numerical and scientific computing. This blog provided step-by-step examples to help you get started with NumPy's array addition, from installation to performing element-wise addition and broadcasting. With NumPy, you can efficiently work with arrays of various shapes and sizes, making it a must-have library for data analysis, machine learning, and scientific research in Python.


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