1598366640

NumPy is a python library used for working with arrays. It also has functions for working in the domain of linear algebra, fourier transform, and matrices. In this article, I hope to teach you the fundamentals of NumPy.

If you want to learn about Python first you can check out my beginners guide.

You need to have Python installed and PIP. If you have already done this you can start working with NumPy. Before you can use NumPy we have to install with the following command line:

```
C:\Users\Your Name>pip install numpy
```

Then open up a python script or file and import the library:

```
import numpy
```

Now, NumPy is imported and ready for you to use. NumPy usually is important as np instead of NumPy:

```
import numpy as np
```

We can check if NumPy works by adding two simple lines of code:

```
arr = np.array([1, 2, 3, 4, 5])
print(arr)
#output: [1 2 3 4 5]
```

```
NumPy was developed to work with Arrays, so let’s create one with NumPy. An important thing to know is that NumPy uses the ‘ndarray’ object to create an array. You can create one as follows:import numpy as np #import the library
arr = np.array([1, 2, 3, 4, 5]) #initializing an ndarray
print(arr) #print the array
print(type(arr)) #output: <class 'numpy.ndarray'>
```

A dimension in arrays is one level of array depth.

#numpy #programming #guide #tech #python

1594753020

Multiple vulnerabilities in the Citrix Application Delivery Controller (ADC) and Gateway would allow code injection, information disclosure and denial of service, the networking vendor announced Tuesday. Four of the bugs are exploitable by an unauthenticated, remote attacker.

The Citrix products (formerly known as NetScaler ADC and Gateway) are used for application-aware traffic management and secure remote access, respectively, and are installed in at least 80,000 companies in 158 countries, according to a December assessment from Positive Technologies.

Other flaws announced Tuesday also affect Citrix SD-WAN WANOP appliances, models 4000-WO, 4100-WO, 5000-WO and 5100-WO.

Attacks on the management interface of the products could result in system compromise by an unauthenticated user on the management network; or system compromise through cross-site scripting (XSS). Attackers could also create a download link for the device which, if downloaded and then executed by an unauthenticated user on the management network, could result in the compromise of a local computer.

“Customers who have configured their systems in accordance with Citrix recommendations [i.e., to have this interface separated from the network and protected by a firewall] have significantly reduced their risk from attacks to the management interface,” according to the vendor.

Threat actors could also mount attacks on Virtual IPs (VIPs). VIPs, among other things, are used to provide users with a unique IP address for communicating with network resources for applications that do not allow multiple connections or users from the same IP address.

The VIP attacks include denial of service against either the Gateway or Authentication virtual servers by an unauthenticated user; or remote port scanning of the internal network by an authenticated Citrix Gateway user.

“Attackers can only discern whether a TLS connection is possible with the port and cannot communicate further with the end devices,” according to the critical Citrix advisory. “Customers who have not enabled either the Gateway or Authentication virtual servers are not at risk from attacks that are applicable to those servers. Other virtual servers e.g. load balancing and content switching virtual servers are not affected by these issues.”

A final vulnerability has been found in Citrix Gateway Plug-in for Linux that would allow a local logged-on user of a Linux system with that plug-in installed to elevate their privileges to an administrator account on that computer, the company said.

#vulnerabilities #adc #citrix #code injection #critical advisory #cve-2020-8187 #cve-2020-8190 #cve-2020-8191 #cve-2020-8193 #cve-2020-8194 #cve-2020-8195 #cve-2020-8196 #cve-2020-8197 #cve-2020-8198 #cve-2020-8199 #denial of service #gateway #information disclosure #patches #security advisory #security bugs

1595235240

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

1595235180

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

1598308620

NumPy is a python library used for working with arrays. It also has functions for working in domain of linear algebra, fourier transform, and matrices. In this article, I hope to teach you the fundamentals of NumPy.

You need to have Python installed and PIP. If you have already done this you can start working with NumPy. Before you can use NumPy we have to install with the following command line:

```
C:\Users\Your Name>pip install numpy
```

Then open up a python script or file and import the library:

```
import numpy
```

Now, NumPy is imported and ready for you to use. NumPy usually is important as np instead of NumPy:

```
import numpy as np
```

We can check if NumPy works by adding two simple lines of code:

```
arr = np.array([1, 2, 3, 4, 5])
print(arr)
#output: [1 2 3 4 5]
```

NumPy was developed to work with Arrays, so let’s create one with NumPy. An important thing to know is that NumPy uses the ‘ndarray’ object to create an array. You can create one as follows:

```
import numpy as np #import the library
arr = np.array([1, 2, 3, 4, 5]) #initializing an ndarray
print(arr) #print the array
print(type(arr)) #output: <class 'numpy.ndarray'>
```

#numpy #programming #guide #tech #python

1598366640

NumPy is a python library used for working with arrays. It also has functions for working in the domain of linear algebra, fourier transform, and matrices. In this article, I hope to teach you the fundamentals of NumPy.

If you want to learn about Python first you can check out my beginners guide.

You need to have Python installed and PIP. If you have already done this you can start working with NumPy. Before you can use NumPy we have to install with the following command line:

```
C:\Users\Your Name>pip install numpy
```

Then open up a python script or file and import the library:

```
import numpy
```

Now, NumPy is imported and ready for you to use. NumPy usually is important as np instead of NumPy:

```
import numpy as np
```

We can check if NumPy works by adding two simple lines of code:

```
arr = np.array([1, 2, 3, 4, 5])
print(arr)
#output: [1 2 3 4 5]
```

```
NumPy was developed to work with Arrays, so let’s create one with NumPy. An important thing to know is that NumPy uses the ‘ndarray’ object to create an array. You can create one as follows:import numpy as np #import the library
arr = np.array([1, 2, 3, 4, 5]) #initializing an ndarray
print(arr) #print the array
print(type(arr)) #output: <class 'numpy.ndarray'>
```

A dimension in arrays is one level of array depth.

#numpy #programming #guide #tech #python