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.
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.
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.
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.
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.
Let's explore how we can import data from a JSON resource like GitHub, and How to get it and process it later.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
To be able to distinguish one particular plot line in the figure, we need to add a shadow effect.
Adding a data table beside our chart helps to visualize information.
You can create custom subplot configurations on your plots in this video.
To spot differences in patterns and compare plots visually in the figure, we need to customize our grids.
To display isolines, we create contour plots.
To distinguish clearly between two different plots, we fill the areas with different patterns.
When the information is radial in nature, we need a polar plot to display information.
You will learn how to visualize a real-world task in this video.
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!
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!
This video will walk you through graphics rendering with OpenGL. So let's go ahead and do it!
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!
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.
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!
This video will take you through the generation of random images to tell humans and computers apart. Let's do it!
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!
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.
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!
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!
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.
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!
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!
Let's look into how to visualize two-dimensional vector quantities such as speed and direction of wind!
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!
One form of very widely used visualization of time-based data is a Gantt chart. Let us see how to work with it!
Error bars are useful to display the dispersion of data on a plot. So, let's explore their use in Python for data visualization.
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.
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!
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