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Numpy isscalar() function is used to test if an element is scalar or not. The isscalar() returns Boolean value. The isscalar() function returns True if the input value is scalar and returns False otherwise. It is defined under numpy, which can be imported as import numpy as np, and we can create multidimensional arrays and derive other mathematical statistics with the help of numpy, which is a library in Python.

#python

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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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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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Statistics is concerned with collecting and then analyzing that data. It includes methods for collecting the samples, describing the data, and then concluding that data. NumPy is the fundamental package for scientific calculations and hence goes hand-in-hand for NumPy statistical Functions.

NumPy contains various statistical functions that are used to perform statistical data analysis. These statistical functions are useful when finding a maximum or minimum of elements. It is also used to find basic statistical concepts like standard deviation, variance, etc.

NumPy is equipped with the following statistical functions:

1. np.amin()- This function determines the minimum value of the element along a specified axis.

2. np.amax()- This function determines the maximum value of the element along a specified axis.

3. np.mean()- It determines the mean value of the data set.

4. np.median()- It determines the median value of the data set.

5. np.std()- It determines the standard deviation

6. np.var – It determines the variance.

7. np.ptp()- It returns a range of values along an axis.

8. np.average()- It determines the weighted average

9. np.percentile()- It determines the nth percentile of data along the specified axis.

#numpy tutorials #numpy average function #numpy

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Other then the syntactical differences. The main difference is the way the this keyword behaves? In an arrow function, the this keyword remains the same throughout the life-cycle of the function and is always bound to the value of this in the closest non-arrow parent function. Arrow functions can never be constructor functions so they can never be invoked with the new keyword. And they can never have duplicate named parameters like a regular function not using strict mode.

this.name = "Bob";const person = {

name: “Jon”,`<span style="color: #008000">// Regular function</span> func1: <span style="color: #0000ff">function</span> () { console.log(<span style="color: #0000ff">this</span>); }, <span style="color: #008000">// Arrow function</span> func2: () => { console.log(<span style="color: #0000ff">this</span>); }`

}

person.func1(); // Call the Regular function

// Output: {name:“Jon”, func1:[Function: func1], func2:[Function: func2]}person.func2(); // Call the Arrow function

// Output: {name:“Bob”}

const person = (name) => console.log("Your name is " + name); const bob = new person("Bob"); // Uncaught TypeError: person is not a constructor

#arrow functions #javascript #regular functions #arrow functions vs normal functions #difference between functions and arrow functions

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NumPy library contains the numpy.char module for performing NumPy string functions. It consists of a set of vectorized string operations. The functions available are similar to the string operations in python. We perform these functions on arrays of string type

NumPy string operations are of three types-

- String operations
- String information
- NumPy String comparisons

These are string functions used to return strings after applying functions over the input strings.

#numpy string functions #numpy string operations #numpy