Python Data Visualization Solutions
0.0 (0 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
23 students enrolled
Wishlisted Wishlist

Please confirm that you want to add Python Data Visualization Solutions to your Wishlist.

Add to Wishlist

Python Data Visualization Solutions

Create attractive visualizations using Python’s most popular libraries
0.0 (0 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
23 students enrolled
Created by Packt Publishing
Last updated 12/2016
English
Current price: $12 Original price: $95 Discount: 87% off
3 days left at this price!
30-Day Money-Back Guarantee
Includes:
  • 3.5 hours on-demand video
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion

Training 5 or more people?

Get your team access to Udemy's top 2,000 courses anytime, anywhere.

Try Udemy for Business
What Will I Learn?
  • Explore your data using the capabilities of standard Python Data 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
  • Get to know the matplotlib’s hidden gems
View Curriculum
Requirements
  • Some understanding of Python programming will be useful.
  • This course follows a step-by-step, recipe-based approach so you understand various aspects of data visualization. The topics are explained sequentially through a code snippet and the resulting visualization.
Description

Effective visualization can help you get better insights from your data, and help you make better and more informed business decisions.

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 will go through the course, you will 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, we’ll take you on a thorough walkthrough of incorporating matplotlib into various environments and how to create Gantt charts using Python.

With practical, precise, and reproducible videos, you will get a better understanding of the data visualization concepts, how to apply them, and how you can overcome any challenge while implementing them.

About The Author

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.

Who is the target audience?
  • If you are an 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.
Compare to Other Python Courses
Curriculum For This Course
50 Lectures
03:24:16
+
Knowing Your Data
11 Lectures 52:04

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
+
Drawing Your First Plots and Customizing Them
11 Lectures 32:33

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.

There are different kinds of audiences to whom the data is presented. Having lin
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
01:21

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
+
More Plots and Customizations
8 Lectures 24:44

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

Preview 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
02:01

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
+
Making 3D Visualizations
3 Lectures 14:45

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!

Preview 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
+
Plotting Charts with Images and Maps
4 Lectures 22:07

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!

Preview 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
+
Using Right Plots to Understand Data
7 Lectures 30:24

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!

Preview 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
+
More on matplotlib Gems
6 Lectures 27:39

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!

Preview 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
About the Instructor
Packt Publishing
3.9 Average rating
8,274 Reviews
59,218 Students
687 Courses
Tech Knowledge in Motion

Packt has been committed to developer learning since 2004. A lot has changed in software since then - but Packt has remained responsive to these changes, continuing to look forward at the trends and tools defining the way we work and live. And how to put them to work.

With an extensive library of content - more than 4000 books and video courses -Packt's mission is to help developers stay relevant in a rapidly changing world. From new web frameworks and programming languages, to cutting edge data analytics, and DevOps, Packt takes software professionals in every field to what's important to them now.

From skills that will help you to develop and future proof your career to immediate solutions to every day tech challenges, Packt is a go-to resource to make you a better, smarter developer.

Packt Udemy courses continue this tradition, bringing you comprehensive yet concise video courses straight from the experts.