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Python: Data Analytics and Visualization

You're reading from   Python: Data Analytics and Visualization Perform data processing and analysis with the help of python libraries, gain practical insights into predictive modeling and generate effective results in a variety of visually appealing charts using the plotting packages in Python

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Product type Course
Published in Mar 2017
Publisher Packt
ISBN-13 9781788290098
Length 866 pages
Edition 1st Edition
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Authors (4):
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Martin Czygan Martin Czygan
Author Profile Icon Martin Czygan
Martin Czygan
Ashish Kumar Ashish Kumar
Author Profile Icon Ashish Kumar
Ashish Kumar
Kirthi Raman Kirthi Raman
Author Profile Icon Kirthi Raman
Kirthi Raman
Phuong Vo.T.H Phuong Vo.T.H
Author Profile Icon Phuong Vo.T.H
Phuong Vo.T.H
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What you need for this learning path

You will need a Python programming environment installed on your system. The first module uses a recent Python 2, but many examples will work with Python 3 as well.b The versions of the libraries used in the first module are: NumPy 1.9.2, Pandas 0.16.2, matplotlib 1.4.3, tables 3.2.2, pymongo 3.0.3, redis 2.10.3, and scikit-learn 0.16.1. As these packages are all hosted on PyPI, the Python package index, they can be easily installed with pip. To install NumPy, you would write:

$ pip install numpy If you are not using them already, we suggest you take a look at virtual environments for managing isolating Python environment on your computer. For Python 2, there are two packages of interest there: virtualenv and virtualenvwrapper. Since Python 3.3, there is a tool in the standard library called pyvenv (https://docs.python.org/3/library/venv.html), which serves the same purpose. Most libraries will have an attribute for the version, so if you already have a library installed, you can quickly check its version:

>>> import redis

>>> redis.__version__

‘2.10.3’).

While all the examples in second module can be run interactively in a Python shell. We used IPython 4.0.0 with Python 2.7.10.

For the third module, you need Python 2.7.6 or a later version installed on your operating system. For the examples in this module, Mac OS X 10.10.5’s Python default version (2.7.6) has been used. Install the prepackaged scientific Python distributions, such as Anaconda from Continuum or Enthought Python Distribution if possible

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