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Home > Data > Data Visualization > Python Data Visualization Cookbook (Second Edition)
Python Data Visualization Cookbook (Second Edition)
Python Data Visualization Cookbook (Second Edition)

Python Data Visualization Cookbook (Second Edition): Visualize data using Python's most popular libraries

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Profile Icon Igor Milovanovic Profile Icon Foures Profile Icon Giuseppe Vettigli
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Profile Icon Igor Milovanovic Profile Icon Foures Profile Icon Giuseppe Vettigli
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Python Data Visualization Cookbook (Second Edition)

Chapter 2. Knowing Your Data

In this chapter, we'll cover the following topics:

  • Importing data from CSV
  • Importing data from Microsoft Excel files
  • Importing data from fixed-width data files
  • Importing data from tab-delimited files
  • Importing data from a JSON resource
  • Exporting data to JSON, CSV, and Excel
  • Importing and manipulating data with Pandas
  • Importing data from a database
  • Cleaning up data from outliers
  • Reading files in chunks
  • Reading streaming data sources
  • Importing image data into NumPy arrays
  • Generating controlled random datasets
  • Smoothing the noise in real-world data

Introduction

This chapter covers basics about importing and exporting data from various formats. We first introduce how to import data by just using only the capabilities of the Python standard library; then we introduce the powerful Pandas library which is becoming the de facto standard in data manipulation in Python. Also we've covered the ways of cleaning data such as normalizing values, adding missing data, live data inspection, and usage of some similar tricks to get data correctly prepared for visualization.

Importing data from CSV

In this recipe, we'll work with the most common file format that you will encounter in the wild world of data—CSV. It stands for Comma Separated Values, which almost explains all the formatting there is. (There is also a header part of the file, but those values are also comma separated.)

Python has a module called csv that supports reading and writing CSV files in various dialects. Dialects are important because there is no standard CSV, and different applications implement CSV in slightly different ways. A file's dialect is almost always recognizable by the first look into the file.

Getting ready

What we need for this recipe is the CSV file itself. We'll use sample CSV data that you can download from ch02-data.csv.

We assume that sample data files are in the same folder as the code reading them.

How to do it...

The following code example demonstrates how to import data from a CSV file. We will perform the following steps for this:

  1. Open the ch02-data...

Importing data from Microsoft Excel files

Although Microsoft Excel supports some charting, sometimes you need more flexible and powerful visualization and need to export data from existing spreadsheets into Python for further use.

A common approach to importing data from Excel files is to export data from Excel into CSV-formatted files and use the tools described in the previous recipe to import data using Python from the CSV file. This is a fairly easy process if we have one or two files (and have Microsoft Excel or OpenOffice.org installed), but if we are automating a data pipe for many files (as part of an ongoing data processing effort), we are not in a position to manually convert every Excel file into CSV. So, we need a way to read any Excel file.

Python has decent support for reading and writing Excel files through the project www.python-excel.org. This support is available in the form of different modules for reading and writing and is platform-independent; in other words, we don...

Importing data from fixed-width data files

Log files from events and time series data files are common sources for data visualizations. Sometimes, we can read them using CSV dialect for tab-separated data, but sometimes they are not separated by any specific character. Instead, fields are of fixed widths and we can infer the format to match and extract data.

One way to approach this is to read a file line by line and then use string manipulation functions to split a string into separate parts. This approach seems straightforward, and if performance is not an issue, it should be tried first.

If performance is more important or the file to parse is large (hundreds of megabytes), using the Python module struct (http://docs.python.org/library/struct.html) can speed us up as the module is implemented in C rather than in Python.

Getting ready

As the module struct is part of the Python Standard Library, we don't need to install any additional software to implement this recipe.

How to do it...

We...

Importing data from tab-delimited files

Another very common format of flat datafile is the tab-delimited file. This can also come from an Excel export but can be the output of some custom software we must get our input from.

The good thing is that usually this format can be read in almost the same way as CSV files as the Python module csv supports the so-called dialects that enable us to use the same principles to read variations of similar file formats, one of them being the tab- delimited format.

Getting ready

Now you're already able to read CSV files. If not, please refer to the Importing data from CSV recipe first.

How to do it...

We will reuse the code from the Importing data from CSV recipe, where all we need to change is the dialect we are using as shown in the following code:

import csv

filename = 'ch02-data.tab'

data = []
try:
    with open(filename) as f:
        reader = csv.reader(f, dialect=csv.excel_tab)
       header = reader.next()
       data = [row for row in...

Importing data from a JSON resource

This recipe will show us how we can read the JSON data format. Moreover, we'll be using a remote resource in this recipe. It will add a tiny level of complexity to the recipe, but it will also make it much more useful because in real life we will encounter more remote resources than local ones.

JavaScript Object Notation (JSON) is widely used as a platform-independent format to exchange data between systems or applications.

A resource, in this context, is anything we can read, be it a file or a URL endpoint (which can be the output of a remote process/program or just a remote static file). In short, we don't care who produced a resource and how they did it; we just need it to be in a known format like JSON.

Getting ready

In order to get started with this recipe, we need the requests module installed and importable (in PYTHONPATH) in our virtual environment. We have installed this module in Chapter 1, Preparing Your Working Environment.

We also...

Introduction

This chapter covers basics about importing and exporting data from various formats. We first introduce how to import data by just using only the capabilities of the Python standard library; then we introduce the powerful Pandas library which is becoming the de facto standard in data manipulation in Python. Also we've covered the ways of cleaning data such as normalizing values, adding missing data, live data inspection, and usage of some similar tricks to get data correctly prepared for visualization.

Importing data from CSV

In this recipe, we'll work with the most common file format that you will encounter in the wild world of data—CSV. It stands for Comma Separated Values, which almost explains all the formatting there is. (There is also a header part of the file, but those values are also comma separated.)

Python has a module called csv that supports reading and writing CSV files in various dialects. Dialects are important because there is no standard CSV, and different applications implement CSV in slightly different ways. A file's dialect is almost always recognizable by the first look into the file.

Getting ready

What we need for this recipe is the CSV file itself. We'll use sample CSV data that you can download from ch02-data.csv.

We assume that sample data files are in the same folder as the code reading them.

How to do it...

The following code example demonstrates how to import data from a CSV file. We will perform the following steps for this:

  1. Open the ch02-data.csv file for reading...

Importing data from Microsoft Excel files

Although Microsoft Excel supports some charting, sometimes you need more flexible and powerful visualization and need to export data from existing spreadsheets into Python for further use.

A common approach to importing data from Excel files is to export data from Excel into CSV-formatted files and use the tools described in the previous recipe to import data using Python from the CSV file. This is a fairly easy process if we have one or two files (and have Microsoft Excel or OpenOffice.org installed), but if we are automating a data pipe for many files (as part of an ongoing data processing effort), we are not in a position to manually convert every Excel file into CSV. So, we need a way to read any Excel file.

Python has decent support for reading and writing Excel files through the project www.python-excel.org. This support is available in the form of different modules for reading and writing and is platform-independent; in other words, we don't...

Importing data from fixed-width data files

Log files from events and time series data files are common sources for data visualizations. Sometimes, we can read them using CSV dialect for tab-separated data, but sometimes they are not separated by any specific character. Instead, fields are of fixed widths and we can infer the format to match and extract data.

One way to approach this is to read a file line by line and then use string manipulation functions to split a string into separate parts. This approach seems straightforward, and if performance is not an issue, it should be tried first.

If performance is more important or the file to parse is large (hundreds of megabytes), using the Python module struct (http://docs.python.org/library/struct.html) can speed us up as the module is implemented in C rather than in Python.

Getting ready

As the module struct is part of the Python Standard Library, we don't need to install any additional software to implement this recipe.

How to do it...

We will...

Importing data from tab-delimited files

Another very common format of flat datafile is the tab-delimited file. This can also come from an Excel export but can be the output of some custom software we must get our input from.

The good thing is that usually this format can be read in almost the same way as CSV files as the Python module csv supports the so-called dialects that enable us to use the same principles to read variations of similar file formats, one of them being the tab- delimited format.

Getting ready

Now you're already able to read CSV files. If not, please refer to the Importing data from CSV recipe first.

How to do it...

We will reuse the code from the Importing data from CSV recipe, where all we need to change is the dialect we are using as shown in the following code:

import csv

filename = 'ch02-data.tab'

data = []
try:
    with open(filename) as f:
        reader = csv.reader(f, dialect=csv.excel_tab)
       header = reader.next()
       data = [row for row in reader]
except...

Importing data from a JSON resource

This recipe will show us how we can read the JSON data format. Moreover, we'll be using a remote resource in this recipe. It will add a tiny level of complexity to the recipe, but it will also make it much more useful because in real life we will encounter more remote resources than local ones.

JavaScript Object Notation (JSON) is widely used as a platform-independent format to exchange data between systems or applications.

A resource, in this context, is anything we can read, be it a file or a URL endpoint (which can be the output of a remote process/program or just a remote static file). In short, we don't care who produced a resource and how they did it; we just need it to be in a known format like JSON.

Getting ready

In order to get started with this recipe, we need the requests module installed and importable (in PYTHONPATH) in our virtual environment. We have installed this module in Chapter 1, Preparing Your Working Environment.

We also need Internet...

Exporting data to JSON, CSV, and Excel

While as producers of data visualization, we are mostly using other people's data, importing and reading data are our major activities. We do need to write or export data that we produced or processed, whether it is for our or others' current or future use.

We will demonstrate how to use the previously mentioned Python modules to import, export, and write data to various formats such as JSON, CSV, and XLSX.

For demonstration purposes, we are using the pregenerated dataset from the Importing data from fixed-width data files recipe.

Getting ready

For the Excel writing part, we will need to install the xlwt module (inside our virtual environment) by executing the following command:

$ pip install xlwt

How to do it...

We will present one code sample that contains all the formats that we want to demonstrate: CSV, JSON, and XLSX. The main part of the program accepts the input and calls appropriate functions to transform data. We will walk through separate sections...

Importing and manipulating data with Pandas

Until now we have seen how to import and export data using mostly the tools provided in the Python standard library. Now, we'll see how to do some of the operations shown above in just few lines using the Pandas library. Pandas is an open source, BSD-licensed library that simplifies the process of data import and manipulation thus providing data structures and parsing functions.

We will demonstrate how to import, manipulate and export data using Pandas.

Getting ready

To be able to use the code in this section, we need to install Pandas.This can be done again using pip as shown here:

pip install pandas

How to do it...

Here, we will import again the data ch2-data.csv, add a new column to the original data and export the result in csv, as shown in the following code snippet:

data = pd.read_csv('ch02-data.csv')
data['amount_x_2'] = data['amount']*2
data.to_csv('ch02-data_more.csv)

How it works...

First, we import Pandas in our environment and then we use...

Importing data from a database

Very often, our work on data analysis and visualization is at the consumer end of the data pipeline. We most often use the already produced data rather than producing the data ourselves. A modern application, for example, holds different datasets inside relational databases (or other databases like MongoDB), and we use these databases to produce beautiful graphs.

This recipe will show you how to use SQL drivers from Python to access data.

We will demonstrate this recipe using a SQLite database because it requires the least effort to set up, but the interface is similar to most other SQL-based database engines (MySQL and PostgreSQL). There are, however, differences in the SQL dialect that those database engines support. This example uses simple SQL language and should be reproducible on most common SQL database engines.

Getting ready

To be able to execute this recipe, we need to install the SQLite library as shown here:

$ sudo apt-get install sqlite3

Python support...

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Page 1 of 16

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Key benefits

  • Learn how to set up an optimal Python environment for data visualization
  • Understand how to import, clean and organize your data
  • Determine different approaches to data visualization and how to choose the most appropriate for your needs

Description

Python Data Visualization Cookbook will progress the reader from the point of installing and setting up a Python environment for data manipulation and visualization all the way to 3D animations using Python libraries. Readers will benefit from over 60 precise and reproducible recipes that will guide the reader towards a better understanding of data concepts and the building blocks for subsequent and sometimes more advanced concepts. Python Data Visualization Cookbook starts by showing how to set up matplotlib and the related libraries that are required for most parts of the book, before moving on to discuss some of the lesser-used diagrams and charts such as Gantt Charts or Sankey diagrams. Initially it uses simple plots and charts to more advanced ones, to make it easy to understand for readers. As the readers will go through the book, they will get to know about the 3D diagrams and animations. Maps are irreplaceable for displaying geo-spatial data, so this book will also show how to build them. In the last chapter, it includes explanation on how to incorporate matplotlib into different environments, such as a writing system, LaTeX, or how to create Gantt charts using Python.

Who is this book for?

If you already know about Python programming and want to understand data, data formats, data visualization, and how to use Python to visualize data then this book is for you.

What you will learn

  • Introduce yourself to the essential tooling to set up your working environment.
  • Explore your data using the capabilities of standard Python Data Library and Panda Library
  • Draw your first chart and customize it
  • Use the most popular data visualization Python libraries
  • Make 3D visualizations mainly using mplot3d
  • Create charts with images and maps
  • Understand the most appropriate charts to describe your data
  • Know the matplotlib hidden gems
  • Use plot.ly to share your visualization online
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Length: 302 pages
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ISBN-13 : 9781784396695
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Tools :
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Length: 302 pages
Edition : 1st
Language : English
ISBN-13 : 9781784396695
Category :
Data
Languages :
Python
Concepts :
Data Visualization
Tools :
Matplotlib

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Table of Contents

10 Chapters
1. Preparing Your Working Environment Chevron down icon Chevron up icon
1. Preparing Your Working Environment
Introduction
Installing matplotlib, NumPy, and SciPy
Installing virtualenv and virtualenvwrapper
Installing matplotlib on Mac OS X
Installing matplotlib on Windows
Installing Python Imaging Library (PIL) for image processing
Installing a requests module
Customizing matplotlib's parameters in code
Customizing matplotlib's parameters per project
2. Knowing Your Data Chevron down icon Chevron up icon
2. Knowing Your Data
Introduction
Importing data from CSV
Importing data from Microsoft Excel files
Importing data from fixed-width data files
Importing data from tab-delimited files
Importing data from a JSON resource
Exporting data to JSON, CSV, and Excel
Importing and manipulating data with Pandas
Importing data from a database
Cleaning up data from outliers
Reading files in chunks
Reading streaming data sources
Importing image data into NumPy arrays
Generating controlled random datasets
Smoothing the noise in real-world data
3. Drawing Your First Plots and Customizing Them Chevron down icon Chevron up icon
3. Drawing Your First Plots and Customizing Them
Introduction
Defining plot types – bar, line, and stacked charts
Drawing simple sine and cosine plots
Defining axis lengths and limits
Defining plot line styles, properties, and format strings
Setting ticks, labels, and grids
Adding legends and annotations
Moving spines to the center
Making histograms
Making bar charts with error bars
Making pie charts count
Plotting with filled areas
Making stacked plots
Drawing scatter plots with colored markers
4. More Plots and Customizations Chevron down icon Chevron up icon
4. More Plots and Customizations
Introduction
Setting the transparency and size of axis labels
Adding a shadow to the chart line
Adding a data table to the figure
Using subplots
Customizing grids
Creating contour plots
Filling an under-plot area
Drawing polar plots
Visualizing the filesystem tree using a polar bar
Customizing matplotlib with style
5. Making 3D Visualizations Chevron down icon Chevron up icon
5. Making 3D Visualizations
Introduction
Creating 3D bars
Creating 3D histograms
Animating in matplotlib
Animating with OpenGL
6. Plotting Charts with Images and Maps Chevron down icon Chevron up icon
6. Plotting Charts with Images and Maps
Introduction
Processing images with PIL
Plotting with images
Displaying images with other plots in the figure
Plotting data on a map using Basemap
Plotting data on a map using the Google Map API
Generating CAPTCHA images
7. Using the Right Plots to Understand Data Chevron down icon Chevron up icon
7. Using the Right Plots to Understand Data
Introduction
Understanding logarithmic plots
Understanding spectrograms
Creating stem plot
Drawing streamlines of vector flow
Using colormaps
Using scatter plots and histograms
Plotting the cross correlation between two variables
Importance of autocorrelation
8. More on matplotlib Gems Chevron down icon Chevron up icon
8. More on matplotlib Gems
Introduction
Drawing barbs
Making a box-and-whisker plot
Making Gantt charts
Making error bars
Making use of text and font properties
Rendering text with LaTeX
Understanding the difference between pyplot and OO API
9. Visualizations on the Clouds with Plot.ly Chevron down icon Chevron up icon
9. Visualizations on the Clouds with Plot.ly
Introduction
Creating line charts
Creating bar charts
Plotting a 3D trefoil knot
Visualizing maps and bubbles
Index Chevron down icon Chevron up icon
Index

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Oleg Okun Jan 16, 2016
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The title of this book includes the word "cookbook" and as a cookbook the book contains a plenty of practical recipes of data visualization in Python. It presents not a mere description of Python packages and commands related to visualization, but embeds these tools into real-world scenarios. Not only visualization itself but also data manipulation enabling insightful visualization are discussed in detail. Needless to say, the discussion of every topic is accompanied by ready-to-use Python code.
Amazon Verified review Amazon
Amazon Customer Dec 07, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The book helped me to understand how to visualize data with python anf find a good solution to implement own little datamart at home for home automation project.
Amazon Verified review Amazon
Amazon Customer Dec 31, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I received a free copy of this book in exchange for my review. I think this is a great book. The examples for the different plotting methods and customizations all worked. The first chapter describe set-up and code samples for using data in different formats. I remember when I was first given the task to add a chart to a report to represent data and how it took me a minute to ensure I was doing things correctly. This book would helped me a great deal at that time. Many questions I had previously about plotting and correctly coding solutions for charts I haven't been asked to make yet, were answered. I have been creating reports and charts for a University Research team and this book has been a godsend. I think this book would have helped me when I was working using java for reports and charts. I just this is a great book.
Amazon Verified review Amazon
Jonathan Jul 14, 2017
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
Please don"t get me wrong, the book is quite useful and it"s quite frankly more handy for me to look things up in a book than on the internet.But in essence, all the information is freely available on the internet and therefore the book is very, very expensive for a black-and-white handbook!
Amazon Verified review Amazon

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Profile icon Igor Milovanovic
Igor Milovanovic
Igor Milovanović is an experienced developer, with strong background in Linux system knowledge and software engineering education. He is skilled in building scalable data-driven distributed software rich systems. An evangelist for high-quality systems design, he has a strong interest in software architecture and development methodologies. Igor is always committed to advocating methodologies that promote high-quality software, such as test-driven development, one-step builds, and continuous integration. He also possesses solid knowledge of product development. With field experience and official training, he is capable of transferring knowledge and communication flow from business to developers and vice versa. Igor is most grateful to his girlfriend for letting him spend hours on work instead with her and being an avid listener to his endless book monologues. He thanks his brother for being the strongest supporter. He is also thankful to his parents for letting him develop in various ways to become a person he is today.
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Profile icon Foures
Foures
Dimitry is a data scientist with a background in applied mathematics and theoretical physics. After completing his physics undergraduate studies in ENS Lyon (France), he studied fluid mechanics at cole Polytechnique in Paris where he obtained first Class class Masters degree. He holds a PhD in applied mathematics from the University of Cambridge. He currently works as a data-scientist for a smart-energy start-up in Cambridge, in close collaboration with the university.
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Profile icon Giuseppe Vettigli
Giuseppe Vettigli
Giuseppe Vettigli is a data scientist who has worked in the research industry and academia for many years. His work is focused on the development of machine learning models and applications to use information from structured and unstructured data. He also writes about scientific computing and data visualisation in Python on his blog at http://glowingpython.blogspot.com.
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