Python Data Visualization Solutions

Create attractive visualizations using Python’s most popular libraries Set up an optimal Python environment for data visualization Import, organize, and visualize your data with the popular open source Python libraries such as matplotlib, NumPy, plot.ly and more A practical tutorial to help you determine different approaches to data visualization, and how to choose the most appropriate one for your needs Learning 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 About 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. Style and Approach 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. Knowing Your Data The Course Overview Importing Data from CSV Importing Data from Microsoft Excel Files Importing Data from Fix-Width Files Importing Data from Tab Delimited Files Importing Data from a JSON Resource Importing Data from a Database Cleaning Up Data from Outliers Importing Image Data into NumPy Arrays Generating Controlled Random Datasets Smoothing Noise in Real-World Data Drawing Your First Plots and Customizing Them Defining Plot Types and Drawing 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 Center Making Histograms Making Bar Charts with Error Bars Making Pie Charts Count Plotting with Filled Areas Drawing Scatter Plots with Colored Markers More Plots and Customizations 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 Making 3D Visualizations Creating 3D Bars Creating 3D Histograms Animating with OpenGL Plotting Charts with Images and Maps Plotting with Images Displaying Images with Other Plots in the Figure Plotting Data on a Map Using Basemap Generating CAPTCHA Using Right Plots to Understand Data Understanding Logarithmic Plots Creating a Stem Plot Drawing Streamlines of Vector Flow Using Colormaps Using Scatter Plots and Histograms Plotting the Cross Correlation Between Two Variables The Importance of Autocorrelation More on matplotlib Gems Drawing Barbs Making a Box-and-Whisker Plot Making Gantt Charts Making Error Bars Making Use of Text and Font Properties Understanding the Difference between pyplot and OO API