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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

SQL: 8 Frequently Used DATE Functions

SQL Date functions
SQL DATE function for all Developers.

DATE Function is powerful in SQL. In SQL projects, people use this Function variety of ways.

All projects depends in DATE, and without it there is no way of existing of project. 

The Quick tour helps you use this function effectively. You can learn Oracle SQL step by step for all Developers.

Below is the list of SQL DATE functions
  • SYSDATE - It returns current local DATE and Time
  • CURRENT_DATE - Return local DATE and TIME and adjusted to current session Time Zone.
  • ROUND (DATE) - Rounds to nearest DAY
  • TRUNC (DATE) - Truncates Time
  • MONTHS_BETWEEN(date1,date2) - Returns number of months between date1 and date2'
  • LAST_DAY(DATE)- Returns last of the month
  • ADD_MONTHS(date,integer_months) - Adds specified number of months to specified date.
  • NEXT_DAY(date, day_of_week) - Returns to next day of the week that comes after the specified DATE
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