In case you’re a hopeful information researcher, you’re curious – continually investigating, learning, and making inquiries. Online instructional exercises and recordings can enable you to set you up for your first job, however the most ideal approach to guarantee that you’re prepared to be an information researcher is by ensuring you’re familiar with the devices individuals use in the business.

All my clients ask me same question that how i grab data so easily, and also making a good statistics reports. Actually it’s very simple i use some Python based tools for data is no news that Python is one of the most popular languages out there and one of the reasons for this success is that it offers and extensive coverage for scientific computing.

let’s take a closer look at the top tools for data science.

IPython is a command shell for interactive computing in multiple programming languages, originally developed for the Python programming language, that offers enhanced introspection, rich media, additional shell syntax, tab completion, and rich history. IPython provides the following features:

Powerful interactive shells (terminal and Qt-based)
A browser-based notebook with support for code, text, mathematical expressions, inline plots, and other rich media
Support for interactive data visualization and use of GUI toolkits
Flexible, embeddable interpreters to load into one’s own projects
Easy to use, high-performance tools for parallel computing

matplotlib is a python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. matplotlib can be used in python scripts, the python and ipython shell (ala MATLAB® or Mathematica®), web application servers, and six graphical user interface toolkits.

matplotlib tries to make easy things easy and hard things possible. You can generate plots, histograms, power spectra, bar charts, error charts, scatterplots, etc, with just a few lines of code.

For simple plotting the pyplot interface provides a MATLAB-like interface, particularly when combined with IPython. For the power user, you have full control of line styles, font properties, axes properties, etc, via an object oriented interface or via a set of functions familiar to MATLAB users.

pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. Python has long been great for data munging and preparation, but less so for data analysis and modeling. pandas helps fill this gap, enabling you to carry out your entire data analysis workflow in Python without having to switch to a more domain specific language like R.

Combined with the excellent IPython toolkit and other libraries, the environment for doing data analysis in Python excels in performance, productivity, and the ability to collaborate. pandas does not implement significant modeling functionality outside of linear and panel regression; for this, look to stats models and scikit-learn. More work is still needed to make Python a first class statistical modeling environment, but we are well on our way toward that goal.

SciPy uses various packages like NumPy, This is a Python-based ecosystem of open-source software for mathematics, science, and engineering. IPython or Pandas to provide libraries for common math- and science-oriented programming tasks. This tool is a great option when you want to manipulate numbers on a computer and display or publish the results and it is free as well.

When working with math-heavy code or code that runs in tight loops, Cython is your best choice. Cython is a source code translator based on Pyrex that allows you to easily write C extensions for Python. What’s more, with the addition of support for integration with IPython/Jupyter notebooks, code compiled with Cython can be used in Jupyter notebooks via inline annotations just like any other Python code.

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