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Python is the choice of data scientists these days to perform their day-to-day activities, as it has a diverse range of open-source libraries, and everything is free. The work of data scientists involves several interrelated activities, such as:
If a data scientist wants to do some ad hoc analysis on data, he would certainly not go about writing Java code; the reason being, java is too complicated for a data scientist in order to start programming. It has its own syntax and semantics, and every time there is a chance that while developing a program one might run into a syntax or a semantic error, which no one wants. Hence, Pig and Hive were developed, but thankfully we have Python also in parallel, wherein you do not have to write a lot of lines of code.
The only thing that you need to remember, in Python is indentation. Whenever a code is being written in, one needs to take care of spacing. If the indentation is not proper, the program will fail. If you are running a ‘for loop’, anything within the ‘for loop’ has to come a few inches inside the ‘for loop’. All the lines of code should have same indentation or should be in one line.
SciPy (pronounced as “sigh pie”) is Scientific Python which enables scientific analysis. It is a Python-based ecosystem of open-source software for mathematics, science, and engineering. We all have done differentiation, equation, etc. in mathematics, in school and college. Now, how is it done in computers? It can be done in Octave as well, but Python provides us with SciPy, with which one can perform such operations very easily. The libraries that Python integrates are NumPy, SciPy library, Matplotib, IPython, Sympy, and pandas, and each one has its own role to play.
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