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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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MongoDB and HCL on Bigdata

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MongoDB and HCL Infosystems have announced a global partnership which will allow HCL Infosystems to further broaden its solution offerings in the emerging big data segment by developing services around MongoDB.

Commenting on the partnership, Kamal Brar, Vice President APAC, MongoDB, said: "Big Data is more than just addressing increasing volume; organizations need solutions that can manage increasing data variety and velocity. Today, businesses need to quickly ingest, store, and access useful information from massive pools of multi-structured data.This is where MongoDB excels. HCL Infosystems will offer innovative solutions and services for organizations to gain value from their data and make smarter and faster business decisions. Partnering with HCL will further strengthen our footprint globally."

Since its inception MongoDB has collaborated with organizations worldwide and now has a thriving global community. Common MongoDB use cases include single view, Internet o ..

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