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The Quick and Easy Way to Analyze Numpy Arrays

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The quickest and easiest way to analyze NumPy arrays is by using the numpy.array() method. This method allows you to quickly and easily analyze the values contained in a numpy array. This method can also be used to find the sum, mean, standard deviation, max, min, and other useful analysis of the value contained within a numpy array. Sum You can find the sum of Numpy arrays using the np.sum() function.  For example:  import numpy as np  a = np.array([1,2,3,4,5])  b = np.array([6,7,8,9,10])  result = np.sum([a,b])  print(result)  # Output will be 55 Mean You can find the mean of a Numpy array using the np.mean() function. This function takes in an array as an argument and returns the mean of all the values in the array.  For example, the mean of a Numpy array of [1,2,3,4,5] would be  result = np.mean([1,2,3,4,5])  print(result)  #Output: 3.0 Standard Deviation To find the standard deviation of a Numpy array, you can use the NumPy std() function. This function takes in an array as a par

Differences: Data Center Vs. Telecom Networking

Data Center Networking


Data Center (DC)-based services are emerging as a relevant source of network capacity demand for service providers and telecom operators. Cloud computing services, Content Distribution Networks (CDNs), and, generally, networked applications have a huge impact on the telecom operator infrastructure.

New trends

The Cloud computing paradigm provides a new model for service delivery where computing resources can be provided on-demand across the network. This elasticity permits the sharing of resources among users, thus reducing costs and maximizing utilization while posing a challenge towards an efficient cloud-aware network.

The computing resources can be provided on-demand depending on the user requests. Such resources can be allocated on distinct servers into a data center, or through data centers distributed in the network. Under this new model, the users access their assigned resources, as well as the applications and services using them, through telecom operator networks. 



Differences: Data Center Vs. Telecom Networking
Networking


Tradition telecom networks


Traditional telecom networks have been built on the concept of totally managed services, with an end-to-end approach, where the telco operator is in charge, not only, of providing the necessary connectivity to the end-user and the final service itself, but also of providing total control of the service provision, including tasks such as subscription management, billing, network operation and troubleshooting, quality of service guarantee, customer care, etc.

Such an approach mandates a tight control of the service path and a comprehensive understanding of the service and its implications. The telco operator offers those services to its customers, which merely consume them (even, in some cases, composing some of them) in a controlled manner, within the limits provided by the telco operator. 

New Trends


These services can be seen as building blocks, which at the same time are supported by network building blocks, both at transport and control level, monolithically. The telco services are typically provided by centralized nodes located deep in the network.

These service nodes are under the sole control of the network operator. Such a controlled environment tends to remain stable where the innovation in technology and services is gradual and modulated by the network operator.

However, during the last decades, the technology fundamentals of computer networking have been influencing the telecom networks, mainly due to the hegemony of the Internet Protocol (IP), which has been emerged as the technology substrate for every kind of service, also for the traditional services offered by telco operators.


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