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Learning IPython for Interactive Computing and Data Visualization, Second Edition
Learning IPython for Interactive Computing and Data Visualization, Second Edition

Learning IPython for Interactive Computing and Data Visualization, Second Edition: Get started with Python for data analysis and numerical computing in the Jupyter notebook

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Learning IPython for Interactive Computing and Data Visualization, Second Edition

Chapter 2. Interactive Data Analysis with pandas

In this chapter, we will cover the following topics:

  • Exploring a dataset in the Notebook
  • Manipulating data
  • Complex operations

We'll see how to load, explore, and visualize a real-world dataset with pandas, matplotlib, and seaborn, all in the Notebook. We will also perform data manipulations efficiently.

Exploring a dataset in the Notebook

Here, we will explore a dataset containing the taxi trips made in New York City in 2013. Maintained by the New York City Taxi and Limousine Commission, this 50GB dataset contains the date, time, geographical coordinates of pickup and dropoff locations, fare, and other information for 170 million taxi trips.

To keep the analysis times reasonable, we will analyze a subset of this dataset containing 0.5% of all trips (about 850,000 rides). Compressed, this subset data represents a little less than 100MB. You are free to download and analyze the full dataset (or a larger subset), as explained below.

Provenance of the data

You will find the data subset we will be using in this chapter at https://github.com/ipython-books/minibook-2nd-data.

If you are interested in the original dataset containing all trips, you can refer to https://github.com/ipython-books/minibook-2nd-code/tree/master/chapter2/cleaning. This page contains the code to download the original dataset...

Manipulating data

Visualizing raw data and computing basic statistics is particularly easy with pandas. All we have to do is choose a couple of columns in a DataFrame and use built-in statistical or visualization functions.

However, more sophisticated data manipulations methods quickly become necessary as we explore a dataset. In this section, we will first see how to make selections of a DataFrame. Then, we will see how to efficiently make transformations and computations on columns.

We first import the NYC taxi dataset, as in the previous section.

In [1]: import numpy as np
        import pandas as pd
        import matplotlib.pyplot as plt
        %matplotlib inline
        data = pd.read_csv('data/nyc_data.csv', 
                           parse_dates=['pickup_datetime',
                                        'dropoff_datetime'])
        fare = pd.read_csv('data/nyc_fare.csv',
                           parse_dates=['pickup_datetime']...

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',
                          ...

Summary

In this chapter, we covered the basics of data analysis with pandas: loading a dataset, selecting rows and columns, grouping and aggregating quantities, and performing complex operations efficiently.

The next natural step is to conduct statistical analyses: hypothesis testing, modeling, predictions, and so on. Several Python libraries provide such functionality beyond pandas: SciPy, statsmodels, PyMC, and more. The IPython Cookbook contains many advanced examples of such analyses.

In the next chapter, we will introduce NumPy, the library underlying the entire SciPy ecosystem.

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Key benefits

  • • Learn the basics of Python in the Jupyter Notebook
  • • Analyze and visualize data with pandas, NumPy, matplotlib, and seaborn
  • • Perform highly-efficient numerical computations with Numba, Cython, and ipyparallel

Description

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.

Who is this book for?

This book targets students, teachers, researchers, engineers, analysts, journalists, hobbyists, and all data enthusiasts who are interested in analyzing and visualizing real-world datasets. If you are new to programming and data analysis, this book is exactly for you. If you're already familiar with another language or analysis software, you will also appreciate this introduction to the Python data analysis platform. Finally, there are more technical topics for advanced readers. No prior experience is required; this book contains everything you need to know.

What you will learn

  • • Install Anaconda and code in Python in the Jupyter Notebook
  • • Load and explore datasets interactively
  • • Perform complex data manipulations effectively with pandas
  • • Create engaging data visualizations with matplotlib and seaborn
  • • Simulate mathematical models with NumPy
  • • Visualize and process images interactively in the Jupyter Notebook with scikit-image
  • • Accelerate your code with Numba, Cython, and IPython.parallel
  • • Extend the Notebook interface with HTML, JavaScript, and D3

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Oct 21, 2015
Length: 200 pages
Edition : 1st
Language : English
ISBN-13 : 9781783986996
Category :
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What do you get with eBook?

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Product feature icon Download this book in EPUB and PDF formats
Product feature icon Access this title in our online reader with advanced features
Product feature icon DRM FREE - Read whenever, wherever and however you want

Product Details

Publication date : Oct 21, 2015
Length: 200 pages
Edition : 1st
Language : English
ISBN-13 : 9781783986996
Category :
Languages :

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Table of Contents

7 Chapters
1. Getting Started with IPython Chevron down icon Chevron up icon
2. Interactive Data Analysis with pandas Chevron down icon Chevron up icon
3. Numerical Computing with NumPy Chevron down icon Chevron up icon
4. Interactive Plotting and Graphical Interfaces Chevron down icon Chevron up icon
5. High-Performance and Parallel Computing Chevron down icon Chevron up icon
6. Customizing IPython Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

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Leif C. Ulstrup Nov 29, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This is a great introduction to using the latest version of iPython (aka jupyter notebook). It is well written with a wide range of examples. The examples are at just the right level to get you started and nudge you in the right direction to explore more. I intend to purchase the author's iPython Cookbook with more in depth examples based on the quality of this book.
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J. Rice Feb 15, 2018
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This is a good read. Many good concise examples.
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David E. May 02, 2018
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Good primer on Jupyter, IPython, and data science learning.
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Jerry Moore Jan 10, 2017
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Fun and easy to read. A tutorial.
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ruben Dec 26, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This is a great introduction to using the latest version of iPython. It has many examples all of them start from the beginning to the most dificult ones. The book has a great content very easy to understand. I got this information beause it has the most important thing to apply in development programming and data presentation software.Ruben.
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