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This is a detailed tutorial of the NumPy Set Operations Universal Functions. Learn the usage of these functions with the help of examples. Table of Contents: Creating Sets, Finding Union, Finding Intersection, Finding Difference...

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NumPy includes a package to perform bitwise operations on the array elements. These NumPy bitwise operators perform bit by bit operations. It performs the function of two-bit values to produce a new value. There are functions to convert the elements into their binary representation and then apply operations on the bits.

This is a specific package that applies bitwise operations on the binary format of elements. These functions compare the binary value of elements and then produce output. There are 6 basic bitwise operations available in NumPy

1. bitwise_and()- It calculates the bit-wise AND operation between two array elements.

2. bitwise_or()- It calculates the bit-wise OR operation between two array elements.

3. invert()- It calculates the bit-wise NOT operation between two array elements.

4. bitwise_xor()- It calculates the bit-wise OR operation between two array elements.

5. left_shift()- This operator shifts the bits of the binary representation of the element towards left.

6. right_shift()- This operator shifts the bits of the binary representation of the element towards the right.

The function performs bitwise AND on two array elements. The bitwise function performs an operation on the corresponding bits of the binary representation of the operands i.e. elements. The output of the operation depends on the AND truth table. If both the corresponding values are 1 only then the output will be 1, otherwise 0. Here 1 can also is equivalent to True and 0 as False. Hence the result will be True only if both the values are True, otherwise, it will result to be False.

#numpy tutorials #numpy binary operators #numpy bitwise operators #numpy

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

Table of Contents

A set is a collection of unique values on which we can perform various operations. The operation which we usually perform on sets is union, intersection and difference operations. These operations help us in data manipulation and then this data we can use anywhere.

In NumPy, we have a method with the help of which we can create a set that will help us in finding unique values. This method is` unique() `

which will help us in finding unique values. When we are creating the set array, it should always be in one dimension only.

Let us take an example to understand it better:

In order to find a union, we use the `union1d()`

method. With the help of this method, we will be able to combine all the values of the two arrays we have given which are unique.

#programming #python #numpy #numpy universal functions #ufuncs #function

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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.

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.

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.

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.

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.

#numpy tutorials #applications of numpy #numpy applications #uses of numpy #numpy

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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.

These are the important features of NumPy:

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.

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

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.

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.

#numpy tutorials #features of numpy #numpy features #why use numpy #numpy

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**Ternary Operator in Python**

What is a ternary operator: The ternary operator is a conditional expression that means this is a comparison operator and results come on a true or false condition and it is the shortest way to writing an if-else statement. It is a **condition** in a single line replacing the multiline if-else code.

**syntax : condition ? value_if_true : value_if_false**

**condition**: A boolean expression evaluates true or false

**value_if_true**: a value to be assigned if the expression is evaluated to true.

**value_if_false**: A value to be assigned if the expression is evaluated to false.

How to use ternary operator in python here are some examples of **Python ternary operator if-else**.

Brief description of examples we have to take two variables a and b. The value of a is 10 and b is 20. find the minimum number using a ternary operator with one line of code. ( **min = a if a < b else b ) **. if a less than b then print a otherwise print b and second examples are the same as first and the third example is check number is even or odd.

#python #python ternary operator #ternary operator #ternary operator in if-else #ternary operator in python #ternary operator with dict #ternary operator with lambda