![]() The course goes on to explore the other library, Folium, used in visualizing geospatial data. In Module 3, we learn about the more advanced visuals of waffle charts and revisit them by using Seaborn to observe how this library simplifies the process of creating plots and visuals. In the second module, we learn the basic data visualizations such as area plots, histograms, and bar charts, as well as the more specialized ones like pie charts, box plots, scatter plots, and bubble plots. The dataset used through the course is also presented which is about immigration from different countries to Canada from 1980 to 2013.The data itself is stored in pandas dataframes so, before we get to building visualizations and plots, a brief crash course on pandas is provided to learn how to read data from csv files into dataframes. ![]() In the first module, data visualization and some of the best practices are briefly introduced along with learning about Matplotlib's history and architecture. The course requires around 19 hours of efort and is spread over five modules. All lessons are accompanied with hands-on labs in Jupyter notebooks where you write the Python code. That includes line plots, area plots, histograms, bar charts, box plots, pie charts, maps and dashboards ![]() With those tools under your belt you will learn how to create interesting graphics and charts and customize them to make them more effective and more pleasing to your audience. The course also introduces four other libraries, Seaborn, Folium, Plotly and Dash. While there's many software libraries that visualize data, the main advantages of Matplotlib is that it gives you complete control over the properties of your plot as you can customize all the properties of your charts. It does this mainly by utilizing Matplotlib and with good reason. Having completed it you will be able to take data that at first glance has little meaning and present that data in a form that conveys insights.ĭisclosure: When you make a purchase having followed a link from this article, we may earn an affiliate commission.ĭata Visualization With Python teaches you to work with many data visualization tools and techniques and create various types of basic and advanced graphs and charts like: Waffle Charts, Area Plots, Histograms, Bar Charts, Pie Charts, Scatter Plots, Word Clouds, Choropleth Maps, and many more! You will also create interactive dashboards that allow even those without any Data Science experience to better understand data, and make more effective and informed decisions. This course, which makes use of videos and hands-on exercises in Jupyter notebooks, aims to teach many ways to effectively visualize both small and large-scale data. On Coursera, it forms part of two IBM Professional Certificates.Ī picture is worth a thousand words - data visualizations let you derive insights from data and communicate it to others. This is a free and self-paced course by the IBM Developer Skills Network Team that demonstrates the principles of Data Visualization, with Python of course. This means that numeric strings must be parsed to be used for continuous color, and conversely, numbers used as category codes must be converted to strings.IBM's Visualizing Data with Python Course If the data is numeric, the color will automatically be considered continuous. Most Plotly Express functions accept a color argument which automatically assigns data values to discrete colors if the data is non-numeric. Legends are the discrete equivalent of continuous color bars Legends are configurable under the layout.legend attribute. Legend markers also change shape when used with various kinds of traces, such as symbols or lines for scatter-like traces. legends are visible representations of the mapping between colors and data values.Color sequence defaults depend on the lorway attribute of the active template, and can be explicitly specified using the color_discrete_sequence argument for many Plotly Express functions. No interpolation occurs when using color sequences, unlike with continuous color scales, and each color is used as-is. color sequences are lists of colors to be mapped onto discrete data values.This document explains the following discrete-color-related concepts: This page is about using color to represent categorical data using discrete colors, but Plotly can also represent continuous values with color. ![]() labels), color can be used to represent continuous or discrete data. amounts or moments in time) or categories (i.e. In the same way as the X or Y position of a mark in cartesian coordinates can be used to represent continuous values (i.e.
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