Python: Data Visualization with Python: 2-in-1
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Python: Data Visualization with Python: 2-in-1

Build powerful visualizations for your data sets using Python’s data visualization tools: matplotlib, ggplot, seaborn an
3.7 (3 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
43 students enrolled
Created by Packt Publishing
Last updated 4/2018
English
English [Auto]
Current price: $139.99 Original price: $199.99 Discount: 30% off
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This course includes
  • 4.5 hours on-demand video
  • 1 downloadable resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Learn the best ways to visualize data on the most interesting data sets to create your own plots
  • Explore appropriate charts to describe your data and get to know the matplotlib’s techniques
  • Use the most popular data visualization Python libraries to make 3D visualizations mainly using mplot3d
  • Add impact to data analysis by visualizing the interpretation
  • Manipulate and interact directly with data
  • Manipulate and interact directly with data
Course content
Expand all 71 lectures 04:42:26
+ Data Visualization in Python by Examples
21 lectures 01:17:33

This video gives glimpse of the entire course.

Preview 04:00

Learn how to setup your computer for Data Visualization with Python Matplotlib.

Setting Up and Getting Started with Python Data Visualization
03:09

Read in the disaster data to analyze and visualize to find most affected states.

Analyzing Effects of Tornadoes in the US – Most Affected States
05:28
Read in the disaster data to analyze and visualize to find least affected states.
Analyzing Effects of Tornadoes in the US – Least Affected States
03:21

Use Matplotlib to plot and analyze impact of North Korean tests on Stock prices.

Plots – Impact of North Korean Atomic Test on Global Stock Markets
04:34
Learn to use Matplotlib to visualize and analyze Forex data.
Analyzing Forex Performance Using Custom Charts
03:32

Introduce ggplot and setup your computer for creating visualizing with it.

Preview 02:47

Analyze and visualize BRICS economies GDP data using ggplot.

Plotting a Comparison of BRICS Market Economies – GDP Numbers
02:40

Visualize and compare growth rates using ggplot and various plot types.

Plotting a Comparison of BRICS Market Economies – GDP Growth Trends
02:13

Read in, explore and visualize crude price data.

Crude Prices Representation Through Plots with ggplot
02:40

Use ggplot to visualize crude price change, price and 50 days moving average and customize it using a ggplot theme.

Customizing Representation of Crude Prices with ggplot
03:29

Introduce Seaborn module and how to get started with start using it.

Setting Up and Getting Started with Seaborn Python Library
02:37
Use Seaborn’s plotting features to visualize dataset for most unstable areas in the world.
Plotting the Most Unstable Areas in the World Using Seaborn
04:51

Customize the visualizations created from most unstable areas data set.

Plotting the Most Unstable Areas – Advanced Customizations
03:12
Use Seaborn’s advanced plotting features to showcase visualization of Hollywood releases performance data set.
Visualizing Performance of Recent Hollywood Releases in Seaborn
04:19
Applying more Seaborn customizations to the Hollywood release performance data.
Visualizing Performance of Hollywood Releases in Seaborn Using Custom Plots
04:45
Introduce Plotly, create Plotly account, and install and setup Plotly Python module.
Setting Up and Getting Started with Plotly
03:20
Read and in explore and visualize Apple iPhone release data using plotl.y.
Plotting the Data for Apple iPhone Launches with Plotly
04:38
Create advanced plots with Plotly on the dataset from Apple iPhone release data.
Plotting the Data for Apple iPhone Launches – Customizations
03:04
Read in and visualize game consoles data using Plotly.
Various Plots Showing Performance of Game Consoles Sales
05:27

Build and explore Dashboards from the plots created on game console data.

Performance of Game Consoles Sales – Building Online Dashboards
03:27
+ Python Data Visualization Solutions
50 lectures 03:24:53

This section gives an overview of the entire course.

Preview 03:38
Importing data from csv into Python can be a bit tricky. It needs careful inspection and appropriate functions. Let's see how we can do that.
Importing Data from CSV
04:32

When we are automating a data pipe for many files, we are not in a position to convert an Excel file into CSV and then import it. This video shows us how to import data directly from an Excel file.

Importing Data from Microsoft Excel Files
04:45

We've learned how to import data from CSV and Excel. But how do we do that with a file that has fixed-width data? Let's explore.

Importing Data from Fix-Width Files
03:05
Although tab-delimited format is simple to read as csv files, we need to ensure that certain parameters are there to keep the reading process accurate. Let's explore how we can do that.
Importing Data from Tab Delimited Files
02:23

Let's explore how we can import data from a JSON resource like GitHub, and How to get it and process it later.

Importing Data from a JSON Resource
05:17

Modern applications often hold different datasets inside relational databases (or other databases like MongoDB), and we have to use these databases to produce beautiful graphs. This video will show us how to use SQL drivers from Python to access data.

Importing Data from a Database
05:08
Data coming from the real world needs cleaning before processing or even visualization. It's not fully automated and we need to understand outliers in order to clean the data. Let's see how we can do that.
Cleaning Up Data from Outliers
05:54

In scientific computing, images are often represented as NumPy array data structures. We can import images using various techniques. In this video, we will take a look at using image processing in Python, mainly related to scientific processing and less on the artistic side of image manipulation.

Importing Image Data into NumPy Arrays
06:01

In this video, we will see different ways of generating random number sequences and word sequences. Some of the examples use standard Python modules, and others use NumPy/SciPy functions.

Generating Controlled Random Datasets
06:36

Data that comes from different real-life sensors is not smooth; it contains some noise that we don't want to show on diagrams and plots. In this video, we introduce a few advanced algorithms to help with cleaning of data coming from real-world sources.

Smoothing Noise in Real-World Data
04:45

There are different plots used for representing data differently. In this video, we'll compare them and understand advanced concepts in data visualization. We would also plot sine and cosine plots and customize them.

Preview 07:53

Now that we've learned the concepts of basic plotting and customizing, this video will show us a variety of useful axis properties that we can configure in matplotlib to define axis lengths and limits.

Defining Axis Lengths and Limits
05:16

There are different kinds of audiences to whom the data is presented. Having lines set up distinct enough for target audiences (for example, vivid colors for young audience) leaves a great impact on the viewer. This video shows how we can change various line properties such as styles, colors, or width.

Defining Plot Line Styles, Properties, and Format Strings
01:58
As we now know how to change various line properties such as styles, colors, and width, this video will guide us with adding more data to our figure and charts by setting axis and line properties.
Setting Ticks, Labels, and Grids
02:42
Legends and annotations explain data plots clearly and in context. By assigning each plot a short description about what data it represents, we enable an easier model for the viewer. This video will show how to annotate specific points on our figures and how to create and position data legends.
Adding Legends and Annotations
02:33
Spines define data area boundaries; they connect the axis tick marks. There are four spines. We can place them wherever we want. As they are placed on the border of the axis, we see a box around our data plot. This video will demonstrate how to move spines to the center.
Moving Spines to Center
01:21

Histograms are often used in image manipulation software as a way to visualize image properties such as distribution of light in a particular color channel. This video will help us create histograms in 2D.

Making Histograms
03:59
To visualize the uncertainty of measurement in our dataset or to indicate the error, we can use error bars. Error bars can easily give an idea of how error free the dataset is. In this video, we will see how to create bar charts and how to draw error bars.
Making Bar Charts with Error Bars
03:23

Pie charts are special in many ways, the most important being that the dataset they display must sum up to 100 percent or they are just not valid. Let's explore how we can create pie charts to represent data in a better way.

Making Pie Charts Count
01:58
The matplotlib library allows us to fill areas in between and under the curves with color so that we can display the value of that area to the viewer. In this video, we will learn how to fill the area under a curve or in between two different curves.
Plotting with Filled Areas
01:56

If you have two variables and want to spot the correlation between those, a scatter plot may be the solution to spot patterns. This type of plot is also very useful as a start for more advanced visualizations of multidimensional data. Let's see how to create a scatter plot.

Drawing Scatter Plots with Colored Markers
02:12

To be able to distinguish one particular plot line in the figure, we need to add a shadow effect.

Adding a Shadow to the Chart Line
03:55

Adding a data table beside our chart helps to visualize information.

Adding a Data Table to the Figure
02:26

You can create custom subplot configurations on your plots in this video.

Using Subplots
03:57

To spot differences in patterns and compare plots visually in the figure, we need to customize our grids.

Customizing Grids
03:04

To display isolines, we create contour plots.

Creating Contour Plots
03:23
To distinguish clearly between two different plots, we fill the areas with different patterns.
Filling an Under-Plot Area
Processing..
When the information is radial in nature, we need a polar plot to display information.
Drawing Polar Plots
02:56
You will learn how to visualize a real-world task in this video.
Visualizing the filesystem Tree Using a Polar Bar
03:02

You must be curious to plot 3D data after getting your hands on 2D. Python provides a toolkit called mplot3d in matplotlib for this. Let's go ahead and explore its working!

Creating 3D Bars
05:32

Similar to 3D bars, you might want to create 3D histograms since these are useful for easily spotting correlations between three independent variables. Let us now dive into it!

Creating 3D Histograms
03:12

This video will walk you through graphics rendering with OpenGL. So let's go ahead and do it!

Animating with OpenGL
06:01

Images can be used to highlight the strengths of your visualization in addition to pure data values. It maps deeper into the viewer's mental model, thereby helping the viewer to remember the visualizations better and for a longer time. Let's see how we could use them in Python!

Plotting with Images
06:17

This video will walk you through how you can make simple yet effective usage of the Python matplotlib library to process image channels and display the per-channel histogram of an external image.

Displaying Images with Other Plots in the Figure
03:52

The best geospatial visualizations are done by overlaying data on the map. This video will show you how to project data on a map using matplotlib's Basemap toolkit. Let's dive into it!

Plotting Data on a Map Using Basemap
05:22

This video will take you through the generation of random images to tell humans and computers apart. Let's do it!

Generating CAPTCHA
06:36

With the logarithmic scale, the ratio of consecutive values is constant. This is important when we are trying to read log plots. Let us step ahead and see how to perform it!

Understanding Logarithmic Plots
05:18

In this video we will discuss how to create a stem plot which will display data as lines extending from a baseline along the x-axis.

Creating a Stem Plot
04:17

In this video we will visualize wind patterns or liquid flow, and we will use uniform representation of the vector field for this. So, let's go ahead and do it!

Drawing Streamlines of Vector Flow
03:27
Color-coding the data can have great impact on how your visualizations are perceived by the viewer, as they come with assumptions about colors and what colors represent. This video will walk you through the steps showing the use of colormaps!
Using Colormaps
05:16
If we want to take a quick look at the data and see if there is any correlation, we would draw a quick scatter plot.Iin this video, you will understand scatter plots.
Using Scatter Plots and Histograms
04:28

If you have two different datasets from two different observations, you want to know if those two event sets are correlated. You want to cross-correlate them and see if they match in any way. This video will let you achieve this goal!

Plotting the Cross Correlation Between Two Variables
03:27

How you could predict the growth of stock dividends? In this video we will dive into some interesting steps which will let you understand the importance of autocorrelation for this prediction!

The Importance of Autocorrelation
04:11

Let's look into how to visualize two-dimensional vector quantities such as speed and direction of wind!

Drawing Barbs
06:23
How will you visually compare several similar data series? This video will walk you through making a box-and-whisker plot which achieves this goal!
Making a Box-and-Whisker Plot
03:36
One form of very widely used visualization of time-based data is a Gantt chart. Let us see how to work with it!
Making Gantt Charts
03:49

Error bars are useful to display the dispersion of data on a plot. So, let's explore their use in Python for data visualization.

Making Error Bars
04:40

This video will let you explore more features of text manipulation in matplotlib, giving a powerful toolkit for even advanced typesetting needs. Let's dive into it.

Making Use of Text and Font Properties
03:59

This video will explain some of the programming interfaces in matplotlib and make a comparison of pyplot and object-oriented API. Let us now explore it!

Understanding the Difference between pyplot and OO API
05:12
Requirements
  • Prior experience in Python programming is assumed
  • Familiarity with the basics of data visualization will be useful
Description

Effective visualization helps you get better insights from your data, make better and more informed business decisions. Python is a favorite tool for programmers and data scientists because it’s easy to learn, and the extensive list of built-in features and importable libraries contribute to increased productivity. To do this, we will focus on the following very popular libraries in Python: matplotlib, ggplot, seaborn, and plotly.

This comprehensive 2-in-1 course is a practical tutorial to help you determine different approaches to data visualization, and how to choose the most appropriate one for your needs. It will help you use data visualization as your preferred business reporting tool. Adds impact to your data by representing information in the form of a chart, diagram, pictures, and so on. This will also help you deploy plots and charts using various data visualization tools 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, Data Visualization in Python by Examples, covers Data visualization with matplotlib, ggplot, and seaborn in Python. In this course, you will walk through some of the fundamentals of data visualization, sharing many examples of how to handle different types of data and how best to present your insights. Finally, you’ll use Plotly to plot comparative graphs of Apple iPhone version releases and compare the performance of gaming consoles such as Xbox and PlayStation.

The second course, Python Data Visualization Solutions, covers creation of attractive visualizations using Python’s most popular libraries. This video starts by showing you how to set up matplotlib and other Python libraries that are required for most parts of the course, before moving on to discuss various widely used diagrams and charts such as Gantt Charts. As you’ll go through the course, you’ll get to know about various 3D diagrams and animations. As maps are irreplaceable to display geo-spatial data, this course will show you how to build them. In the last section, you’ll take you on a thorough walkthrough of incorporating matplotlib into various environments and how to create Gantt charts using Python.

By the end of this training program you’ll be able to create effective visualizations for your data sets using tools: matplotlib, ggplot, seaborn and plotly in Python.

About the Authors
  • 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.
  • 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 École Polytechnique in Paris where he obtained first Class class Master’s 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.
  • 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.
  • 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.
Who this course is for:
  • Python users who wish to enter the field of data visualization or enhance their data visualization skills to become more effective visual communicators. The target audience includes business analysts; data analysts; web developers; product managers; program managers; decision makers
  • Analyst or a budding data scientist who wants to know how to use Python to visualize your data to get effective insights from it, then this book is for you.