By Igor Milovanovic, Dimitry Foures, Giuseppe Vettigli

Over 70 recipes to get you began with well known Python libraries in response to the relevant options of information visualization

About This Book
Learn the right way to organize an optimum Python setting for information visualization
Understand the best way to import, fresh and manage your data
Determine varied techniques to information visualization and the way to decide on the main acceptable on your needs
Who This ebook Is For
If you realize approximately Python programming and need to appreciate facts, facts codecs, information visualization, and the way to exploit Python to imagine info then this publication is for you.

What you are going to Learn
Introduce your self to the fundamental tooling to establish your operating environment
Explore your info utilizing the features of ordinary Python information Library and Panda Library
Draw your first chart and customise it
Use the most well-liked info visualization Python libraries
Make 3D visualizations ordinarily utilizing mplot3d
Create charts with photographs and maps
Understand the main applicable charts to explain your data
Know the matplotlib hidden gems
Use plot.ly to percentage your visualization online

In Detail
Python facts Visualization Cookbook will growth the reader from the purpose of putting in and establishing a Python surroundings for info manipulation and visualization the entire solution to 3D animations utilizing Python libraries. Readers will reap the benefits of over 60 specific and reproducible recipes that may consultant the reader in the direction of a greater figuring out of knowledge strategies and the development blocks for next and infrequently extra complex concepts.
Python information Visualization Cookbook begins by way of exhibiting tips to arrange matplotlib and the similar libraries which are required for many components of the e-book, earlier than relocating directly to talk about a number of the lesser-used diagrams and charts reminiscent of Gantt Charts or Sankey diagrams. at the beginning it makes use of easy plots and charts to extra complex ones, to make it effortless to appreciate for readers. because the readers will battle through the booklet, they're going to get to grasp concerning the 3D diagrams and animations. Maps are irreplaceable for exhibiting geo-spatial information, so this booklet also will express how one can construct them. within the final bankruptcy, it contains clarification on the right way to contain matplotlib into assorted environments, reminiscent of a writing method, LaTeX, or find out how to create Gantt charts utilizing Python.

Style and approach
A step by step recipe dependent method of information visualization. the subjects are defined sequentially as cookbook recipes including a code snippet and the ensuing visualization.

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Additional info for Python Data Visualization Cookbook (2nd Edition)

Sample text

After reading everything, print the header and the rest of the rows. exit(-1) if header: print header print '==================' for datarow in data: print datarow How it works... First, we import the csv module in order to enable access to the required methods. Then, we open the file with data using the with compound statement and bind it to the object f. The context manager with statement releases us of care about the closing resource after we are finished manipulating those resources. It is a very handy way of working with resource-like files because it makes sure that the resource is freed (for example, that the file is closed) after the block of code is executed over it.

The main part of the program accepts the input and calls appropriate functions to transform data. We will walk through separate sections of code explaining its purpose, as shown here: 1. Import the required modules: import os import sys 29 Knowing Your Data import argparse try: import cStringIO as StringIO except: import StringIO import struct import json import csv 2. Then, define the appropriate functions for reading and writing data: def import_data(import_file): ''' Imports data from import_file.

Columns >>>Index([u'day', u'amount'], dtype='object') Also, the function read_csv that we used to import the data has many parameters that we make use of to deal with messy files and parse particular data formats. For example, if the values of our files are delimited by spaces instead of commas, we can use the parameter delimiter to correctly parse the data. tab', skiprows=1, delimiter=' *', names=['day','amount']) Importing data from a database Very often, our work on data analysis and visualization is at the consumer end of the data pipeline.

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