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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

The Exclusive Way to Declare Variables in Oracle Procedure

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There are four data types in PLSQL. Those are Numeric, Char, Boolean, and Date/Time. Each data type and its features are demonstrated. And explained how to declare variables in PLSQL procedure. Data types Here are the four popular data types in PLSQL. 1. Numeric DEC, DECIMAL, and NUMERIC are used to declare fixed-point numbers with a precision of a maximum of 38 decimal digits. INTEGER, INT , and SMALLINT declare integers with a maximum precision of 38 digits. 2. Char Char and Varchar data types support storing data of 1 t0 2000 bytes. The VARCHAR2 supports 1 to 4000 bytes of data. The VARCHAR and VARCHAR2 release the unused space in memory,  3. Date/Time The range for the Date is from 01-Jan-4712 BC to 31-DEC-9999. It stores the data in date format DD-MON-YYYY. The value is written in single quotes. 4. Boolean BOOLEAN datatype stores logical values and can be either TRUE or FALSE. Declare variables The Declare block in PL/SQL is reserved for variable declaration. The code between beg

Python Supports These 5 Native Data types

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Python supports five native data types. The Data types are such that a Programmer can use to write the logic and get the output. Many beginners may not aware of native data types. So I am adding a short note on that here. Native means as is Python supported data types. Python Five Key Native Data Types Python Native Data Types In Python, you can find five types of native data types. Here is a quick list for you. Those are Number, String, List, Tuple, Set, and Dictionary. 1. Number For all the numeric values,  you can use this data type. 2. String It handles all Characters, Special-symbols, and Alphanumeric values. 3. List It is something like sequential data. A program can do Sort, Merge, etc. on this data. 4. Tuple Data is a little different from the List. 5. Set This kind of Data-type helps you to do set operations. Those are like Intersection, Difference, etc. 6. Dictionary Here, the Dictionary something like a group of List kinds of data. But, each value has a key associated with i