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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

Python Matrix Vs COBOL Arrays Top Differences

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Your most looking information where Python matrix and COBOL arrays differ, in this post, I am giving complete information. The Logic is different in both the languages. The way of definition and accessing element in an array or matrix is different. Python Matrix Vs COBOL Array. In reality both Array and Matrix are the same What are Arrays  Arrays are storing data structure to store data in one or more dimensional form. You can access the data for further processing in your application program. One Dimensional Array  In general, one dimensional array is a row of elements either numeric or Strings separated by commas. Here, each element is separated by comma. This is key concept. >>> a = ['Srini',25,33,42] Two Dimensional Arrays  In the case of Two dimensional array data stored in Tabular form and you can access whichever tuple you want. Real use of multi dimensional array is to give input in Tabular form and can access particular tuple as you want. >>>