what is difference between python and scala?
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Scala is a statically typed language which allows us to find compile time errors. whereas Python is a dynamically typed language. Python language is highly prone to bugs every time you make changes to the existing code. Hence refactoring the code for Scala is easier than refactoring for Python.
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The biggest difference between the two languages is that Java is a statically typed and Python is a dynamically typed. Python is strongly but dynamically typed.
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Python is not a pure object-oriented language. Python is an interpreted language whereas Java is not an interpreted language, it is a compiled language. Python is a scripting language whereas JAVA is a low-level implementation language.
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As mentioned earlier, Python is an interpreted programming language, whereas Cython is a compiled programming language. Despite being a superset of Python, Cython is much faster than Python. ... Hence, many programmers to opt for Cython to write concise and readable code in Python that perform as faster as C code.
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PyPy is an alternative implementation of the Python programming language to CPython (which is the standard implementation). PyPy often runs faster than CPython because PyPy is a just-in-time compiler while CPython is an interpreter.
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Python is a case-sensitive language. This means, Variable and variable are not the same. Always give the identifiers a name that makes sense.
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The biggest difference between the two languages is that Java is a statically typed and Python is a dynamically typed. Python is strongly but dynamically typed. This means names in code are bound to strongly typed objects at runtime. ... Static type inference in Python is a known hard problem.
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1) Apache Spark is written in Scala and because of its scalability on JVM - Scala programming is most prominently used programming language, by big data developers for working on Spark projects. ... Also, the performance achieved using Scala is better than many other traditional data analysis tools like R or Python.
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NumPy is written in C, and executes very quickly as a result. By comparison, Python is a dynamic language that is interpreted by the CPython interpreter, converted to bytecode, and executed. While it's no slouch, compiled C code is always going to be faster.
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SQL is structured query language; it is used to query relational databases. Python is a general purpose object oriented scripting language. R is a functional programming language used for statistical analysis. None of these things supports a BI platform; they're ways to extract and manipulate data.
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Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. ... Python's simple, easy to learn syntax emphasizes readability and therefore reduces the cost of program maintenance. Python supports modules and packages, which encourages program modularity and code reuse.
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Hadoop is a database framework, which allows users to save, process Big Data in a fault tolerant, low latency ecosystem using programming models. ... On the other hand, Python is a programming language and it has nothing to do with the Hadoop ecosystem.
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Pypy is faster than Cpython (the python implementation of reference). Currently, it's around 5 to 6 times faster on average. ... Pypy is an implementation of the dynamic python language, and as such, it can be compared to similar jitted dynamic language engines, such as the V8 javascript vm.
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Python takes a huge advantage over Perl when it comes to code readability. ... On the other hand, Perl borrows its syntax from various programming languages like C, shell scripting and even awk and sed filters when it comes to regular expressions.
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Python is a widely used general-purpose, high-level programming language. Python is a very simple language yet powerful scripting language, it's open-source and object-oriented and it has great libraries that can be used for both for hacking and for writing very useful normal programs other than hacking programs.
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The MicroPython langauge implements most of the core Python 3 language, however MicroPython can't implement the entire Python 3 standard library. Python is known for having an extensive standard library, but trying to squeeze such a big library onto tiny boards with just kilobytes of memory isn't possible.
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Python is faster than R, when the number of iterations is less than 1000. Below 100 steps, python is up to 8 times faster than R, while if the number of steps is higher than 1000, R beats Python when using lapply function!