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R Vs Python ⏩Swipe Left⏩ R and Python are both open-source programming languages with a large community. New libraries or tools are added continuously to their respective catalog. R is mainly used for statistical analysis while Python provides a more general approach to data science. Choice between R or Python depends on: 1. Statistical analysis or deployment 2. The amount of time you can invest 3. Your company/industry most-used tool #data#science#datascience#datascientist#engineer#datascienceengineer#python#dataanalyst#computerscience#business#hotdatascience#R#Rvspython https://www.instagram.com/p/Bt3uyfQAhUX/?utm_source=ig_tumblr_share&igshid=uec7isl87155
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Identifier is the name given to entities like class, functions, variables, etc in python.
Identifier helps in differentiating one entity from another.
Identifiers can be a combination of letters in lowercase (a to z) or uppercase (A to Z) or digits (0 to 9) or an underscore (_).
An identifier cannot start with a digit. 1_variable is invalid, but variable_1 is perfectly fine.
Keywords cannot be used as identifiers.
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Keywords used in python
Keywords are reserved words in Python.
We can't use a keyword as variable name, function name or any other identifier
Keywords are case sentive
Below are the keywords used:
['and', 'as', 'assert', 'break', 'class', 'continue', 'def', 'del', 'elif', 'else', 'except', 'exec', 'finally', 'for', 'from', 'global', 'if', 'import', 'in', 'is', 'lambda', 'not', 'or', 'pass', 'print', 'raise', 'return', 'try', 'while', 'with', 'yield']
We can print whole list of keywords in python in the following way:
import keyword print(keyword.kwlist)
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Why Python for Data Science??
There are many reasons for using python for data science, some of them are :1. Very simple language to learn. 2. Best packages and libraries like matplotlib, numpy, scipy, scikit-learn, tensorflow, pandas,       seaborn, keras, pytorch and many more. 3. iPython notebooks for interactive data analysis and modelling. 4. Extensively used in industry.
Python is a very powerful tool, which is also open sourced and flexible, adding more to its popularity. It is known to have massive libraries for manipulation of data and is extremely easy to learn and use for all data analysts. Anyone who is familiar with programming languages such as, Java, Visual Basic, C++ or C, will find this tool to be very accessible and easy to work with. Apart from being an independent platform, this tool has the ability to easily integrate with the existing Infrastructure and can also solve the most difficult of problems. It is said, that this tool is powerful, friendly, easy and plays well with others, apart from running everywhere. A lot of banks use this tool for the purpose of crunching data, some institutions use it for analyzing and visualization. This tool offers the great benefit of using one programming language, across multiple application platforms.
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