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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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3 SQL Query Examples to Create Views Quickly

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There are three kinds of Views in SQL. The three views are Read-only, Force, and Updatable. Views real usage is to hide data. And you need to ensure base tables are present before you create a View. You can call views as logical tables. The advantage of Views is you can show only some of the fields of base tables. What is a View in SQL A view can be constructed with another view so it is called a nested view. You can create or replace an existing view A view can be created without having base tables. This is possible with the FORCE option. #1: Read-Only Views The standard syntax for the view is as follows: CREATE OR replace VIEW invoice_summary AS SELECT vendor_name count(*) AS invoice_count, SUM(invoice_total) AS invoice_total_sum FROM vendor JOIN invoices ON vendors.vendor_id*invoices.vendor_id GROUP BY vendor_name; Notes: You cannot update Read-only Views #2: Force Views CREATE FORCE VIEW products_list AS SELECT product_description, product_price FROM products;
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