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Python Data Analysis

You're reading from   Python Data Analysis Learn how to apply powerful data analysis techniques with popular open source Python modules

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Product type Paperback
Published in Oct 2014
Publisher
ISBN-13 9781783553358
Length 348 pages
Edition 1st Edition
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Author (1):
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Ivan Idris Ivan Idris
Author Profile Icon Ivan Idris
Ivan Idris
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Toc

Table of Contents (17) Chapters Close

Preface 1. Getting Started with Python Libraries 2. NumPy Arrays FREE CHAPTER 3. Statistics and Linear Algebra 4. pandas Primer 5. Retrieving, Processing, and Storing Data 6. Data Visualization 7. Signal Processing and Time Series 8. Working with Databases 9. Analyzing Textual Data and Social Media 10. Predictive Analytics and Machine Learning 11. Environments Outside the Python Ecosystem and Cloud Computing 12. Performance Tuning, Profiling, and Concurrency A. Key Concepts
B. Useful Functions C. Online Resources
Index

Installing and exploring pandas


The minimal dependency set requirements for pandas is given as follows:

  • NumPy: This is the fundamental numerical array package that we installed and covered extensively in the preceding chapters

  • python-dateutil: This is a date-handling library

  • pytz: This handles time zone definitions

This list is the bare minimum; a longer list of optional dependencies can be located at http://pandas.pydata.org/pandas-docs/stable/install.html. We can install pandas via PyPI with pip or easy_install, using a binary installer, with the aid of our operating system package manager, or from the source by checking out the code. The binary installers can be downloaded from http://pandas.pydata.org/getpandas.html.

The command to install pandas with pip is as follows:

$ pip install pandas

You may have to prepend the preceding command with sudo if your user account doesn't have sufficient rights. For most, if not all, Linux distributions, the pandas package name is python-pandas. Please...

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