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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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SQL Tester Ideas DATE, Numeric, CHAR, NULL

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I have shared top SQL Date functions along with Numeric, CHAR and NULL functions. List of Built-in Functions DATE-TIME functions Numeric functions Char functions Null-related functions 1. DATE/TIME functions SELECT p_code, launch_dt, CURRENT_DATE FROM product; CURRENT_DATE returns the CURRENT date. SELECT p_code, To_char(launch_dt, 'DD MONTH YYYY') reformatted_dt FROM product; TO_CHAR FUNCTION returns date IN char format. 2. Numeric functions SELECT p_code, price, (price - 20), Abs(price - 20.00) FROM product; ABS - FUNCTION returns the absolute value SELECT p_code, price, Round (price, 1) FROM product; ROUND FUNCTION - round TO 1 digit. SELECT p_code, price, Sign(price - 15) FROM product; SIGN FUNCTION - it returns the sign SELECT p_code, price, Trunc(price, 1) FROM product; TRUNC FUNCTION - truncates TO a certain number OF decimal places. 3. CHAR functions SELE
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