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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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4 Essential Points on Modern Data Warehouse 2.0

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The new data warehouse often called “Data Warehouse 2.0,” is the fast-growing trend of doing away with the old idea of huge, off-site, mega-warehouses stuffed with hardware and connected to the world through huge trunk lines and big satellite dishes.
modern data warehouse

How Modern Data warehouse You Can Implement

The replacement is very different from that highly controlled, centralized, and inefficient ideal towards a more cloud-based, decentralized preference of varied hardware and widespread connectivity.

In today’s world of instant, varied access by many different users and consumers, data is no longer nicely tucked away in big warehouses.

How Data Will Be Stored in Modern Data Warehouse

Instead, it is often stored in multiple locations (often with redundancy) and overlapping small storage spaces that are often nothing more than large closets in an office building. 

The trend is towards always-on, always-accessible, and very open storage that is fast and friendly for consumers yet complex and deep enough to appease the most intense data junkie.

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