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Python: Data Visualization using Python
Rating: 4.2 out of 5(39 ratings)
255 students

Python: Data Visualization using Python

Learn Python for data visualization with bqplot, NetworkX, and Bokeh
Created byPackt Publishing
Last updated 11/2018
English

What you'll learn

  • Learn Data analysis using Pandas
  • Explore different plots and how to apply these plots to different datasets
  • Applying data visualization on large datasets
  • Visualize a social network with NetworkX
  • Turn dash plots into interactive visualizations for major Android version releases

Course content

2 sections • 46 lectures • 4h 15m total length

  • The Course Overview3:45

    This video gives an overview of the entire course.

  • Introduction to Python and Data Analysis4:49

    In this video, we will learn why data analysis is useful and why is Python a good tool for it.

    • Learn about the value of data analysis

    • Learn why Python is a great tool for it

    • Explore what we will be working with

  • Installing Visualization Tools5:02

    In this video, we will understand what tools do we need to use in Python for data analysis andhow can we install these tools.

    • Fetch tools using Linux package managers

    • Fetch tools with Python’s pip

    • Check that our install works

  • Getting Started With NumPy8:09

    In this video, we are going to cover the benefits of NumPy.

    • Learn how to import NumPy

    • Learn how to use NumPy to create and manipulate arrays

    • See NumPy’s performance benefit

  • Using NumPy on Alcohol Consumption Data7:44

    In this video, we will learn toload real data into NumPy andwhat can we do with it.

    • Read csv files into NumPy

    • Select items in NumPy arrays

    • Calculate basic statistics with NumPy

  • Basics of the Pandas Library5:58

    In this video, we will cover Pandas DataFrame.

    • Load data into a DataFrame

    • See the difference between DataFrames and series

    • Learn DataFrame methods for basic statistics

  • Manipulating Data in Pandas7:03

    In this video, we will learn to apply functions to Pandas DataFrames and make new DataFrames.

    • Apply functions on DataFrames

    • Concatenate DataFrames together

    • Select subsets of DataFrames

  • Why Visualize Data?5:25

    In this video, we shall see how we can visualize raw numbers.

    • Learn the limitations of human memory

    • See how data can be plotted on different dimensions

    • Learn some important caveats

  • Line Charts to Understand US Unemployment Data6:47

    In this video, we will learn to load up simple time-series data and see it evolve.

    • Load data into Pandas

    • Plot different values as line curves

    • Sort by date to clean up lines

  • Multiple Plots for US Unemployment Data5:55

    In this video, we shall show multiple values together.

    • Generate multiple curves on the same plot

    • Add a legend for clarity

    • Add multiple panels to a figure

  • More on Multiple Plots8:34

    In this video, wewill learn to make more sophisticated plots than simple rows or columns.

    • Make non-uniform subplots with subplot2grid

    • Add twinned axes for different ranges

    • Learn about color axes labels for clarity

  • Investigating Fandango Scores with Bar Plots8:07

    In this video, wecansee how different groups/websites rate a handful of movies.

    • Load data from Fandango with Pandas

    • Select movies from the data set

    • Make bar plots comparing ratings

  • Using Scatter Plots on Fandango Scores5:53

    In this video, we can plot the difference between critics and viewers opinions on movies.

    • Generate scatter plots comparing two different sets of ratings

    • Add points from multiple ratings sites

    • Tweak the appearance for greater clarity

  • Applying Histograms5:32

    In this video, we examine the distribution of ratings that critics and viewers give to movies.

    • Generate histograms to show the distribution of ratings

    • Add multiple groups to compare distributions

    • Tweak appearance to show all details

  • Box Plots to Find Distributions and Medians9:30

    In this video, we can explore the different statistical properties of ratings given by different groups/websites.

    • Generate box plot to show statistical information

    • Add multiple groups to compare statistics

    • Annotate box plots with additional information

  • Improving Plot Aesthetics10:08

    In this video, we improve the appearance of our plots for the medium we are using.

    • Use matplotlib style sheets

    • Explain LaTeX and rcParams for fonts

    • Configure the legend

  • Using Color for Better Clarity10:20

    In this video, we will learn to use color to better convey information in our data sets.

    • See the basic color theory

    • Select colormaps

    • Choose colors from a colormap

  • Using Layouts and Annotations9:11

    In this video, we shall learn to use annotations to point out important bits of data.

    • Explore horizontal and vertical line annotations

    • Add text annotations

    • Use the matplotlib annotate() method

  • Using the Seaborn Package9:30

    In this video, we shall learn about Seaborn and see what it can do for us beyond basic matplotlib.

    • Plot distributions with Seaborn

    • Plot 2D/Joint distributions in Seaborn

    • Understand Seaborn’s smart theming

  • Regressions and Statistics with Seaborn7:23

    In this video, we derive fits to our data automatically in Seaborn.

    • Learn to make heat maps with Seaborn

    • Generate regression plots

    • Explore different options and varieties of regression plots

  • Comparisons with Conditional Plots9:25

    In this video, we will compare relationships between points and distributions.

    • Compare distributions using violin and swarm plots

    • Make conditional plots with pair plot

    • Add regressions to conditional plots

  • Introduction to Geographical Data8:25

    In this video, we will learn to plot geographic data.

    • Explore the matplotlib Basemap class

    • Load map data

    • Plot maps

  • Working with Latitudes and Longitudes7:57

    In this video, we will see how latitudes and longitudes differ from Cartesian coordinates and learn to plot them.

    • Translation between spherical and Cartesian coordinates

    • Map projections for projecting into a plane

    • Understand what map projections Basemap provides

  • Introduction to Great Circles9:00

    In this video, we will learn to display points and lines on a map and understand the difference between them.

    • Plot points and lines

    • See the distance on a sphere

    • Plot great circles on a map

  • Adding Extra Information to Maps9:15

    In this video, we will learn about additional information on maps.

    • Plot parallels and meridians

    • Add a scale to the maps

    • Change the background and resolution of maps

  • Test Your Knowledge

Requirements

  • A basic knowledge of Python and basic data visualization is required.

Description

Python is a straightforward, powerful, easy programing language. Python’s elegant syntax and dynamic typing, along with its interpreted nature, makes it a perfect language for data visualization that may be a wise investment for your future big-data needs.If you are a Python user who desires to enter the field of data visualization or enhance your data visualization skills to become more effective visual communicator, then this learning path is for you.

With this easy to follow, hands-on course you will initially begin with introduction to data visualization, and the techniques and libraries which can be leveraged with the Python language. Ten you will learn to program stunning & interactive Data Visualizations using bqplot, an open source Python library developed by Bloomberg. Furthermore, you will gain knowledge on how to programmatically create interactive network graphs and visualizations & then visualize data with the interactive Python visualization library, Bokeh. Finally, you will build interactive web visualizations of data using Python: you will choose a number of inputs your users can control, then use any Python graphing library to create plots based on those inputs.

By the end of this course you will be able to demonstrate visualizations with interesting, real-world data sets. Also you’ll be able to create effective visualizations for your data sets using tools: matplotlib, bqplot, NetworkX, Bokeh, and Dash in Python.

Contents and Overview

This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.

The first course, Learning Python Data Visualization begins with  visualization concepts so viewers can analyze large and small sets of data using libraries such as Matplotlib, IPython, and so on. This course primarily employs the IPython environment and matplotlib, with the following structure: Introduce key data visualization libraries (matplotlib and so on.) and cover data importing/exporting (CSV, Excel, JSON and so on), Introduce real-world data sets (to be visualized in the video), Visualization types/techniques (bar chart, histogram, scatter plot, geospatial, and so on); demonstrate how to customize visualizations. Introduce intermediate topics to create more advanced visualizations and using complex techniques, such as real-time data visualization. By the end of the course, you will be able to demonstrate visualizations with interesting, real-world data sets.

In the second course, Data Visualization Projects in Python you will start by programming stunning interactive Data Visualizations using bqplot, an open source Python library developed by Bloomberg. Then you will learn how to programmatically create interactive network graphs and visualizations. You will then programmatically visualize data with the interactive Python visualization library, Bokeh. Finally, you will build interactive web visualizations of data using Python: you will choose a number of inputs your users can control, then use any Python graphing library to create plots based on those inputs.

About the Authors

  • Benjamin Keller is a postdoctoral researcher in the MUSTANG group at Universität Heidelberg's Astronomisches Rechen-Institut. He obtained his PhD at McMaster University and got his BSc in Physics with a minor in Computer Science from the University of Calgary in 2011. His current research involves numerical modeling of the interstellar medium over cosmological timescales. As an undergraduate at the U of C, he worked with Dr. Jeroen Stil on stacking radio polarization to examine faint extragalactic sources. He also worked in POSSUM Working Group 2 to determine the requirements for stacking applications for the Australian SKA Pathfinder (ASKAP) radio telescope. At McMaster, he worked with Dr. James Wadsley in the Physics & Astronomy department. His current research is focused on understanding how the energy released from supernovae explosions regulate the flow of gas through galaxies, and how that gas is converted into stars.

  • Harish Garg is a Data Scientist and a Lead Software Developer with 17 years' software industry experience. He worked for McAfee\Intel for 11+ years before starting his own software consultancy. He is an expert in creating data visualizations using R, Python, and web-based visualization libraries.

Who this course is for:

  • Python users who wish to enter the field of data visualization or enhance their data visualization skills.
  • Big Data analysts, web developers.
  • Budding data scientist who wants to know how to use Python to visualize your data to get effective insights from it.

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