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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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Scala: Unique features you need to know

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scala features

Let us start it in layman terms...

Why you need Scala...

The name Scala stands for “scalable language.” The language is so named because it was designed to grow with the demands of its users.

Where Scala can be applied...

You can apply Scala to a wide range of programming tasks, from writing small scripts to
building large systems. 

The real use of Scala...

  • Scala is easy to get into. It runs on the standard Java platform and interoperates seamlessly with all Java libraries. It’s quite a good language for writing scripts that pull together Java components.
  • But it can apply its strengths even more when used for building large systems and frameworks of reusable components.
Technically, Scala is a blend of object-oriented and functional programming
concepts in a statically typed language.
The fusion of object-oriented and functional programming shows up in many different aspects of Scala;
It is probably more pervasive than in any other widely used language. The two programming styles have complementary strengths when it comes to scalability.

Also read: The real Quiz on Scala for developers 

What are the Scala unique strengths...
  • Scala’s functional programming constructs make it easy to build interesting things quickly from simple parts.
  • Its object-oriented constructs make it easy to structure larger systems and to adapt them to new demands.
The combination of both styles in Scala makes it possible to express new kinds of programming patterns and component abstractions. It also leads to a legible and concise programming style. And because it is so malleable, programming in Scala can be a lot of fun. 

The sample Scala program...

Scala program:

var capital = Map("US" -> "Washington", "France" -> "Paris")
capital += ("Japan" -> "Tokyo")
println(capital("France"))

This program sets up a map from countries to their capitals, modifies the map by adding a new binding ("Japan" -> "Tokyo"), and prints the capital associated with the country France.

The notation in this example is high-level, to the point, and not cluttered with extraneous semicolons or type annotations.

Indeed, the feel is that of a modern “scripting” language like Perl, Python, or Ruby.

One common characteristic of these languages, which is relevant for the example above, is that they each support an “associative map” construct in the syntax of the language

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