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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

MongoDB Basics Tutorial top Resources

Mongo DB has rapidly grown to become a popular database for web applications and is a perfect fit for Node.JS applications, letting you write Javascript for the client, backend and database layer. Its schemaless nature is a better match to our constantly evolving data structures in web applications, and the integrated support for location queries is a bonus that’s hard to ignore.

Throw in Replica Sets for scaling, and we’re looking at really nice platform to grow your storage needs now and in the future. Now to shamelessly plug my driver. It can be downloaded via npm, or fetched from the github repository. To install via npm, do the following:

npm install mongodb

or go fetch it from github at https://github.com/mongodb/node-mongodb-native
Once this business is taken care of, let’s move through the types available for the driver and then how to connect to your Mongo DB instance before facing the usage of some CRUD operations.

Mongo DB data types

So there is an important thing to keep in mind when working with Mongo DB, and that is the slight mapping difference between types Mongo DB supports and native Javascript data types. Let’s have a look at the types supported out of the box and then how types are promoted by the driver to fit as close to native Javascript types as possible.
  • Float is a 8 byte and is directly convertible to the Javascript type Number 
  • Double class a special class representing a float value, this is especially useful when using capped collections where you need to ensure your values are always floats. 
  • Integers is a bit trickier due to the fact that Javascript represents all Numbers as 64 bit floats meaning that the maximum integer value is at a 53 bit. Mongo has two types for integers, a 32 bit and a 64 bit. The driver will try to fit the value into 32 bits if it can and promote it to 64 bits if it has to. Similarly it will deserialize attempting to fit it into 53 bits if it can. If it cannot it will return an instance of Long to avoid losing precision. 
  • Long class a special class that lets you store 64 bit integers and also lets you operate on the 64 bit integers. 
  • Date maps directly to a Javascript Date 
  • RegExp maps directly to a Javascript RegExp 
  • String maps directly to a Javascript String (encoded in utf8) 
  • Binary class a special class that lets you store data in Mongo DB 
  • Code class a special class that lets you store javascript functions in Mongo DB, can also provide a scope to run the method in 
  • ObjectID class a special class that holds a MongoDB document identifier (the equivalent to a Primary key) 
  • DbRef class a special class that lets you include a reference in a document pointing to another object 
  • Symbol class a special class that lets you specify a symbol, not really relevant for javascript but for languages that supports the concept of symbols.
As we see the number type can be a little tricky due to the way integers are implemented in Javascript. The latest driver will do correct conversion up to 53 bits of complexity. If you need to handle big integers the recommendation is to use the Long class to operate on the numbers.

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