Book Image

Learning IPython for Interactive Computing and Data Visualization, Second Edition

By : Cyrille Rossant
Book Image

Learning IPython for Interactive Computing and Data Visualization, Second Edition

By: Cyrille Rossant

Overview of this book

Python is a user-friendly and powerful programming language. IPython offers a convenient interface to the language and its analysis libraries, while the Jupyter Notebook is a rich environment well-adapted to data science and visualization. Together, these open source tools are widely used by beginners and experts around the world, and in a huge variety of fields and endeavors. This book is a beginner-friendly guide to the Python data analysis platform. After an introduction to the Python language, IPython, and the Jupyter Notebook, you will learn how to analyze and visualize data on real-world examples, how to create graphical user interfaces for image processing in the Notebook, and how to perform fast numerical computations for scientific simulations with NumPy, Numba, Cython, and ipyparallel. By the end of this book, you will be able to perform in-depth analyses of all sorts of data.

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Learning IPython for Interactive Computing and Data Visualization, Second Edition
Cyrille Rossant
Oct, 2015 | 200 pages
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Table of Contents (13 chapters)
Learning IPython for Interactive Computing and Data Visualization Second Edition
Learning IPython for Interactive Computing and Data Visualization Second Edition
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Getting Started with IPython
Getting Started with IPython
What are Python, IPython, and Jupyter?
Installing Python with Anaconda
Introducing the Notebook
A crash course on Python
Ten Jupyter/IPython essentials
Summary
2
Interactive Data Analysis with pandas
Interactive Data Analysis with pandas
Exploring a dataset in the Notebook
Manipulating data
Complex operations
Summary
3
Numerical Computing with NumPy
Numerical Computing with NumPy
A primer to vector computing
Creating and loading arrays
Basic array manipulations
Computing with NumPy arrays
Summary
4
Interactive Plotting and Graphical Interfaces
Interactive Plotting and Graphical Interfaces
Choosing a plotting backend
matplotlib and seaborn essentials
Image processing
Further plotting and visualization libraries
Summary
5
High-Performance and Parallel Computing
High-Performance and Parallel Computing
Accelerating Python code with Numba
Writing C in Python with Cython
Distributing tasks on several cores with IPython.parallel
Further high-performance computing techniques
Summary
6
Customizing IPython
Customizing IPython
Creating a custom magic command in an IPython extension
Writing a new Jupyter kernel
Displaying rich HTML elements in the Notebook
Customizing the Notebook interface with JavaScript
Summary
Index
Index

Complex operations

We've seen how to load, select, filter, and operate on data with pandas. In this section, we will show more complex manipulations that are typically done on full-blown databases based on SQL.

Tip

SQL

Structured Query Language is a domain-specific language widely used to manage data in relational database management systems (RDBMS). pandas is somewhat inspired by SQL, which is familiar to many data analysts. Additionally, pandas can connect to SQL databases. You will find more information about the links between pandas and SQL at http://pandas.pydata.org/pandas-docs/stable/comparison_with_sql.html.

Let's first import our NYC taxi dataset as in the previous sections.

In [1]: import numpy as np
        import pandas as pd
        import matplotlib.pyplot as plt
        import seaborn
        %matplotlib inline
        data = pd.read_csv('data/nyc_data.csv',
                           parse_dates=['pickup_datetime',
                                        'dropoff_datetime...
Previous Section
End of Section 4
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