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They approach data science differently:
The language to choose depends on your situation.
Points to consider:
• Scala : Scala was created to address some of Java’s inherent issues. Since then, it has grown in popularity and is widely used globally used by software developers. Scala provides support to both object-oriented and functional programming. It allows concise and compact codes to be written and becomes a more powerful language with elegant syntax.
As already mentioned, debugging is considered a subset of troubleshooting. However, troubleshooting does not always entail solving the problem at that moment in time. There may be procedural constraints or workflow protocols that prevent the issue from being solved immediately. Debugging, on the other hand, is meant to discover and fix a problem all in the same session, whenever possible.
People use the two terms interchangeably, which can add to the confusion
Pandas is a Python language package, which is used for data processing. This is a very common basic programming library when we use Python language for machine learning programming. This article is an introductory tutorial to it. Pandas provide fast, flexible and expressive data structures with the goal of making the work of “relational” or “marking” data simple and intuitive. It is intended to be a high-level building block for actual data analysis in Python.
Pandas is suitable for many different types of data, including:
The Pandas Index object contains metadata describing the axis. When creating a Series or DataFrame, the array or sequence of tags is converted to Index. You can get the Index object of the DataFrame column and row in the following way:
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