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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

4 Built-in functions in SQL Explained Superbly

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Here're four types of built-in functions in SQL. You don't need to create these functions, and these come with your SQL. I am discussing today the  SQL functions List . SQL functions DATE-TIME functions Numeric functions Char functions Null-related functions 1. DATE-TIME functions   These are useful to get DATE and Time. Also, you can covert the DATE and TIME to Char. These are TO_CHAR, CURRENT_DATE, and CURRENT_TIME. 2. Numeric Functions   These functions you can use for numeric calculations. Those are TRUNC, SIGN, ROUND, ABS. 3. Char Functions   To handle string-related tasks you can use CHAR functions. The CHAR functions are like SUBSTR, CONCAT, LENGTH. 4. NULL related functions   To handle null values you can use different functions. Those are COALESCE, NVL, NULLIF This post covered all your answers for the SQL functions list, SQL built-in functions in Oracle, and SQL functions in any DBMS. Also, Read SQL tips to improve Query performance Summar