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Practical Data Science Cookbook, Second Edition

You're reading from   Practical Data Science Cookbook, Second Edition Data pre-processing, analysis and visualization using R and Python

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Product type Paperback
Published in Jun 2017
Publisher Packt
ISBN-13 9781787129627
Length 434 pages
Edition 2nd Edition
Languages
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Authors (5):
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Anthony Ojeda Anthony Ojeda
Author Profile Icon Anthony Ojeda
Anthony Ojeda
Prabhanjan Narayanachar Tattar Prabhanjan Narayanachar Tattar
Author Profile Icon Prabhanjan Narayanachar Tattar
Prabhanjan Narayanachar Tattar
ABHIJIT DASGUPTA ABHIJIT DASGUPTA
Author Profile Icon ABHIJIT DASGUPTA
ABHIJIT DASGUPTA
Sean P Murphy Sean P Murphy
Author Profile Icon Sean P Murphy
Sean P Murphy
Bhushan Purushottam Joshi Bhushan Purushottam Joshi
Author Profile Icon Bhushan Purushottam Joshi
Bhushan Purushottam Joshi
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Toc

Table of Contents (12) Chapters Close

Preface 1. Preparing Your Data Science Environment 2. Driving Visual Analysis with Automobile Data with R FREE CHAPTER 3. Creating Application-Oriented Analyses Using Tax Data and Python 4. Modeling Stock Market Data 5. Visually Exploring Employment Data 6. Driving Visual Analyses with Automobile Data 7. Working with Social Graphs 8. Recommending Movies at Scale (Python) 9. Harvesting and Geolocating Twitter Data (Python) 10. Forecasting New Zealand Overseas Visitors 11. German Credit Data Analysis

Installing extra Python packages

There are a few additional Python libraries that you will need throughout this book. Just as R provides a central repository for community-built packages, so does Python in the form of the Python Package Index (PyPI). As of August 28, 2014, there were 48,054 packages in PyPI.

Getting ready

A reasonable Internet connection is all that is needed for this recipe. Unless otherwise specified, these directions assume that you are using the default Python distribution that came with your system, and not Anaconda.

How to do it...

The following steps will show you how to download a Python package and install it from the command line:

  1. Download the source code for the package in the place you like to keep
    your downloads.
  2. Unzip the package.
  3. Open a terminal window.
  4. Navigate to the base directory of the source code.
  5. Type in the following command:
python setup.py install 
  1. If you need root access, type in the following command:
sudo python setup.py install 

To use pip, the contemporary and easiest way to install Python packages, follow these steps:

  1. First, let's check whether you have pip already installed by opening a terminal and launching the Python interpreter. At the interpreter, type:
>>>import pip 
  1. If you don't get an error, you have pip installed and can move on to step 5. If you see an error, let's quickly install pip.
  2. Download the get-pip.py file from https://raw.github.com/pypa/pip/master/contrib/get-pip.py onto your machine.
  3. Open a terminal window, navigate to the downloaded file, and type:
python get-pip.py 

Alternatively, you can type in the following command:

sudo python get-pip.py 
  1. Once pip is installed, make sure you are at the system command prompt.
  2. If you are using the default system distribution of Python, type in the following:
pip install networkx 

Alternatively, you can type in the following command:

sudo pip install networkx 
  1. If you are using the Anaconda distribution, type in the following command:
conda install networkx 
  1. Now, let's try to install another package, ggplot. Regardless of your distribution, type in the following command:
pip install ggplot 

Alternatively, you can type in the following command:

sudo pip install ggplot 

How it works...

You have at least two options to install Python packages. In the preceding old fashioned way, you download the source code and unpack it on your local computer. Next, you run the included setup.py script with the install flag. If you want, you can open the setup.py script in a text editor and take a more detailed look at exactly what the script is doing. You might need the sudo command, depending on the current user's system privileges.

As the second option, we leverage the pip installer, which automatically grabs the package from the remote repository and installs it to your local machine for use by the system-level Python installation. This is the preferred method, when available.

There's more...

The pip is capable, so we suggest taking a look at the user guide online. Pay special attention to the very useful pip freeze > requirements.txt functionality so that you can communicate about external dependencies with your colleagues.

Finally, conda is the package manager and pip replacement for the Anaconda Python distribution or, in the words of its home page, a cross-platform, Python-agnostic binary package manager. Conda has some very lofty aspirations that transcend the Python language. If you are using Anaconda, we encourage you to read further on what conda can do and use it, and not pip, as your default package manager.

See also

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