Book Image

Python Data Analysis - Second Edition

By : Ivan Idris
Book Image

Python Data Analysis - Second Edition

By: Ivan Idris

Overview of this book

Data analysis techniques generate useful insights from small and large volumes of data. Python, with its strong set of libraries, has become a popular platform to conduct various data analysis and predictive modeling tasks. With this book, you will learn how to process and manipulate data with Python for complex analysis and modeling. We learn data manipulations such as aggregating, concatenating, appending, cleaning, and handling missing values, with NumPy and Pandas. The book covers how to store and retrieve data from various data sources such as SQL and NoSQL, CSV fies, and HDF5. We learn how to visualize data using visualization libraries, along with advanced topics such as signal processing, time series, textual data analysis, machine learning, and social media analysis. The book covers a plethora of Python modules, such as matplotlib, statsmodels, scikit-learn, and NLTK. It also covers using Python with external environments such as R, Fortran, C/C++, and Boost libraries.
Table of Contents (22 chapters)
Python Data Analysis - Second Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Key Concepts
Online Resources

Reading manual pages


When the libraries are imported in IPython, we can open manual pages for library functions with the help command. It is not necessary to know the name of a function. We can type a few characters and then let the tab completion do its work. Let's, for instance, browse the available information for the arange() function.

We can browse the available information in either of the following two ways:

  • Calling the help function: Type in help( followed by a few characters of the function and press the Tab key. A list of functions will appear. Select the function from the list using the arrow keys and press the Enter key. Close the help function call with )  and press the Enter key.

  • Querying with a question mark: Another option is to append a question mark to the function name. You will then, of course, need to know the function name, but you don't have to type help, for example:

    In [3]: numpy.arange?
    

    Tab completion is dependent on readline, so you need to make sure that it is installed. It can be installed with pip by typing the following command:

    $ pip3 install readline
    

    The question mark gives you information from docstrings.