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Python Data Analysis, Second Edition

Python Data Analysis, Second Edition

By : Idris
4 (4)
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Python Data Analysis, Second Edition

Python Data Analysis, Second Edition

4 (4)
By: 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 (16 chapters)
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13
A. Key Concepts
15
C. Online Resources

Jupyter Notebook

Jupyter Notebook, previously known as IPython Notebooks, provides a tool to create and share web pages with text, charts, and Python code in a special format. Have a look at these notebook collections at the following links:

Often, the notebooks are used as an educational tool, or to demonstrate Python software. We can import or export notebooks either from plain Python code or from the special notebook format. The notebooks can be run locally, or we can make them available online by running a dedicated notebook server. Certain cloud computing solutions, such as Wakari and PiCloud, allow you to run notebooks in the cloud. Cloud computing is one of the topics of Chapter 11, Environments Outside the Python Ecosystem and Cloud Computing.

To start a session with Jupyter Notebook,enter the following instruction on the command line:

$ jupyter-notebook

This will start the notebook server and open a web page showing the contents of the folder from which the command will execute. You can then select New | Python 3 to start a new notebook in Python 3.

You can also open ch-01.ipynb, provided in the code package for this book. The ch-01 notebook file has the code for the simple applications that we will describe shortly.

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