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Showing posts with the label PL/SQL error handling

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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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PL/SQL Sample code and error handling mechanism

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SAMPLE PL/SQL CREATE TABLE dummy ( dummy_value VARCHAR2(1)); DECLARE -- Define local variable. my_string VARCHAR2(1) := ' '; my_number NUMBER; BEGIN -- Select a white space into a local variable. SELECT ' ' INTO my_string FROM dummy; -- Attempt to assign a single white space to a number. my_number := TO_NUMBER(my_string); EXCEPTION WHEN no_data_found THEN dbms_output.put_line('SELECT-INTO'||CHR(10)||SQLERRM); END; / Output and Error: The program returns the following output, which illustrates formatting user- defined exceptions.  The CHR(10) inserts a line return and provides a clean break between the program's SQLCODE and SQLERRM messages: RAISE my_error SQLCODE [1]  SQLERRM [User-Defined Exception]
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