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The Quick and Easy Way to Analyze Numpy Arrays

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

20 IT Job Trends in 2020

Rapidly Growing IT Jobs analysis from major job boards. Keep watch on these skills to boost your pasy scale. SAP+HANA Jobs Mainframe+DB2+CICS+Jobs Hadoop+Jobs ControlM+Mainframe+Jobs Teradata+Data+warehousing+Jobs CICS+DB2+IMSDB+Jobs Data+Warehousing+Jobs MS+BI+Jobs Cloud+Computing+Jobs NOSql+Jobs Android+Development+Jobs IOS+Development+Jobs Puppet+Labs+Jobs Python+Bigdata+Jobs Java+Jobs Data+Analysis+Jobs Cloud+Bigadata+Jobs UX+UI+Design+jobs .NET+Development+Jobs Virtualization+Jobs