Python Hands-On Course: Interactive Maps and Bar Chart Races

learn to create interactive maps in python with the volume library, initialize a map using coordinates and zoom, customize tiles, and save the result as an HTML file.

Learn to create markers and add them to maps using marker objects, popups, and tooltips. Customize markers with icons, colors, and circle markers, then visualize them on the map.

Employ GeoJson files, an open standard for geographical features and attributes, with the volume library to build a map, add a GeoJson layer, and enable layer control for US states.

Build an interactive United States map in Python that colors states by population. The map shows markers for each capital with its population, guiding you through a hands-on intro.

Create a two-part US map project: part one builds a population-based model of each state, and part two adds markers for state capitals.

Connect a population data frame to a geojson file by loading the geojson, extracting state names, replacing D.C. with District of Columbia, and preparing for a map.

Create a choropleth map by joining the population data frame to the state geometry, configure a color scale and legend, and save the final map.

Dynamically add markers to the choropleth map by loading a capitals data set with coordinates and population, then iterate over lat/long and population to place city markers with popups.

Explore an interactive map with a top slider that color-codes countries by covid-19 confirmed cases, highlighting China, the United States of America, Brazil, India, and Russia by August 9, 2020.

Learn to load and align two datasets—a global covid-19 confirmation map and a world shapefile—clean columns, unify country names, and prepare a country-by-day data frame for visualization.

Merge the shapes and datasets with an inner join on country, then create a geo data frame with geometry and export boundaries to json for mapping.

Prepare the country data frame for visualization by dropping geometry, stacking to long form, renaming columns, and applying a log scale after filtering out observations with fewer than 10 cases.

Map confirmed cases to a nine-shade color scale from yellow to red by creating a color column in the dataframe, linking colors to case counts.

Convert the date column from string to a time object and then to a number of milliseconds after the first January 97, and cast to strings for the upcoming lessons.

Build a style_dict for the times slider plugin by formatting data as a year-indexed dictionary with color, opacity, and country IDs from Afghanistan to Zimbabwe.

Create the final interactive map by configuring the map object, importing the times slider plugin, and applying data and style layers to visualize covid-19 confirmed cases with a legend.

Create a bar chart animation in this new course section, learning to recreate the exact same animation in just three lessons and under 50 minutes.

Install the new bar chart races library and FFmpeg, meet Python 3.6 requirements, and follow setup steps to configure PATH and environment variables on Windows and macOS.

Import pandas, load and clean the dataset from GitHub, group by country to one entry per country, transpose, convert the index to datetime, and format for bar chart races.

Create a bar chart race animation using the bar trace function, inputting a data frame and showing top 10 covid-19 countries while tuning title, period formatting, speed, and total cases.