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# Tutorial Of The NumPy LCM Universal Functions

This is a detailed tutorial of the NumPy LCM Universal Functions. Learn the usage of these functions with the help of examples.

In the number theory, the lowest common multiple of any two integers is the smallest integer that is divisible by both a and b. Where a and b cannot be zero in number, these are helpful in the fractions which we can add, subtract or compare with other values. So in very simple words, it is the least number which can be a common multiple of any two numbers.

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## NumPy Applications - Uses of Numpy

In this Numpy tutorial, we will learn Numpy applications.

NumPy is a basic level external library in Python used for complex mathematical operations. NumPy overcomes slower executions with the use of multi-dimensional array objects. It has built-in functions for manipulating arrays. We can convert different algorithms to can into functions for applying on arrays.NumPy has applications that are not only limited to itself. It is a very diverse library and has a wide range of applications in other sectors. Numpy can be put to use along with Data Science, Data Analysis and Machine Learning. It is also a base for other python libraries. These libraries use the functionalities in NumPy to increase their capabilities. #### 1. An alternative for lists and arrays in Python

Arrays in Numpy are equivalent to lists in python. Like lists in python, the Numpy arrays are homogenous sets of elements. The most important feature of NumPy arrays is they are homogenous in nature. This differentiates them from python arrays. It maintains uniformity for mathematical operations that would not be possible with heterogeneous elements. Another benefit of using NumPy arrays is there are a large number of functions that are applicable to these arrays. These functions could not be performed when applied to python arrays due to their heterogeneous nature.

#### 2. NumPy maintains minimal memory

Arrays in NumPy are objects. Python deletes and creates these objects continually, as per the requirements. Hence, the memory allocation is less as compared to Python lists. NumPy has features to avoid memory wastage in the data buffer. It consists of functions like copies, view, and indexing that helps in saving a lot of memory. Indexing helps to return the view of the original array, that implements reuse of the data. It also specifies the data type of the elements which leads to code optimization.

#### 3. Using NumPy for multi-dimensional arrays

We can also create multi-dimensional arrays in NumPy.These arrays have multiple rows and columns. These arrays have more than one column that makes these multi-dimensional. Multi-dimensional array implements the creation of matrices. These matrices are easy to work with. With the use of matrices the code also becomes memory efficient. We have a matrix module to perform various operations on these matrices.

#### 4. Mathematical operations with NumPy

Working with NumPy also includes easy to use functions for mathematical computations on the array data set. We have many modules for performing basic and special mathematical functions in NumPy. There are functions for Linear Algebra, bitwise operations, Fourier transform, arithmetic operations, string operations, etc.

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## NumPy Features - Why we should use Numpy?

Welcome to DataFlair!!! In this tutorial, we will learn Numpy Features and its importance.

NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays

NumPy (Numerical Python) is an open-source core Python library for scientific computations. It is a general-purpose array and matrices processing package. Python is slower as compared to Fortran and other languages to perform looping. To overcome this we use NumPy that converts monotonous code into the compiled form. #### NumPy Features

These are the important features of NumPy:

#### 1. High-performance N-dimensional array object

This is the most important feature of the NumPy library. It is the homogeneous array object. We perform all the operations on the array elements. The arrays in NumPy can be one dimensional or multidimensional.

#### a. One dimensional array

The one-dimensional array is an array consisting of a single row or column. The elements of the array are of homogeneous nature.

#### b. Multidimensional array

In this case, we have various rows and columns. We consider each column as a dimension. The structure is similar to an excel sheet. The elements are homogenous.

#### 2. It contains tools for integrating code from C/C++ and Fortran

We can use the functions in NumPy to work with code written in other languages. We can hence integrate the functionalities available in various programming languages. This helps implement inter-platform functions.

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## LCM - NumPy uFuncs - WTMatter

This is a detailed tutorial of the NumPy LCM Universal Functions. Learn the usage of these functions with the help of examples.

### Lowest Common Multiple (LCM)

In the number theory, the lowest common multiple of any two integers is the smallest integer that is divisible by both a and b. Where a and b cannot be zero in number, these are helpful in the fractions which we can add, subtract or compare with other values. So in very simple words, it is the least number which can be a common multiple of any two numbers.

Let us take an example:

#### Finding LCM in Arrays

In order to find the Lowest Common Multiple of all the elements in an array, we have a method in NumPy. With the help of this, we can find the lcm of all the elements in an array. The method we can use to fulfil this purpose is `reduce()`. This method will use the Unfunc and will help in reducing the array by one dimension.

Let us take an example to understand it better:

I hope you found this guide useful. If so, do share it with others who are willing to learn Numpy and Python. If you have any questions related to this article, feel free to ask us in the comments section.

And do not forget to subscribe to WTMatter!

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## Difference - NumPy uFuncs (Python Tutorial)

This is a detailed tutorial of the NumPy Difference Universal Functions. Learn the usage of these functions with the help of examples.

### NumPy Difference

When we are trying to find the difference between two values, we are actually subtracting them from each other. When we subtract these two values, we get the difference between them. So we get a discrete difference by subtracting two successive elements with each other.

And in order to find the difference between the values, we use `diff()` the function. so let us take an example to understand it better:

So here in result, we are getting [2 2 2] which is because of 3-1=2, 5-3=2 and 7-3=2. So this is how we get the discrete difference between the values. Let us take another example :

Here in this example, we are getting the output as [1 5 6] because 3-2=1, 8-3=5 and 14-8=6.

We can aloe perform the operation of difference for n number of times. We can do this by giving parameter. `n`.

Let us take an example to understand it better:

So here in this example, we have given a parameter n which defines that the difference will be taken two times. So when the difference is to take for the first time, we get [2 2 2] which is because of 3-1=2, 5-3=2 and 7-3=2. But the second time we get [0 0] because 2-2=0 and 2-2=0 . So we can have n number of differences to get our results.

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## Products - NumPy uFuncs (Python Tutorial) - WTMatter

### NumPy Products

In the process of finding the product of a certain set of values, we are multiplying them with each other and what we receive at the end is the product. In order to find the product of elements in an array we can use the` prod()` function.

Let us take an example:

Here in this example, we are getting the product of the values present in the array. Now we will take another example where we will take two arrays to find their product.

#### Product Over an Axis

Now in order to get the product of arrays individually, we will use the axis. If we specify the axis to be one, then it will give us a product of each array as a result.

let us take an example to understand it better:

#### Cumulative Product

In order to take the product of the elements in the array partially, we will use the cumulative product. As a result of this product, every value will be the product of the value itself and the values behind it. We will use the `cumprod()` function of this product.

let us take an example to understand in a better manner:

Here in this example, we see at the end we get the partial product of the elements in the array. Let take another example where we will use the axis.

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