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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
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Data Vault Top benefits Useful to Your Project

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Data Vault 2.0 (DV2) is a system of business intelligence that includes: modeling, methodology, architecture, and implementation best practices.
The benefits of Data Vault
The components, also known as the pillars of DV2 are identified as follows:
data vault
  • DV2 Modeling (changes to the model for performance and scalability)
  • DV2 Methodology (following Scrum and agile best practices)
  • DV2 Architecture (including NoSQL systems and Big Data systems)
  • DV2 Implementation (pattern-based, automation, generation Capability Maturity Model Integration [CMMI] level 5)
There are many special aspects of Data Vault, including the modeling style for the enterprise data warehouse. The methodology takes commonsense lessons from software development best practices such as CMMI, Six Sigma, total quality management (TQM), Lean initiatives, and cycle-time reduction and applies these notions for repeatability, consistency, automation, and error reduction.

Each of these components plays a key role in the overall success of an enterprise data warehousing project. These components are combined with industry-known and time-tested best practices ranging from CMMI to Six Sigma, TQM (total quality management) to Project Management Professional (PMP).

Data Vault 1.0

Data Vault 1.0 is highly focused on just the data modeling section, while DV2 encompasses the effort of business intelligence. The evolution of Data Vault extends beyond the data model and enables teams to execute in parallel while leveraging Scrum agile best practices.

Data Vault 2.0

DV2 architecture is designed to include NoSQL (think: Big Data, unstructured, multistructured, and structured data sets). Seamless integration points in the model and well-defined standards for implementation offer guidance to the project teams.

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