Note: to do something like what I am about to describe, you will need an account with plotly as well as mapbox. Particularly because you will need an access token, that can be created here, and a username and API key that can be generated here. All this is free. I would also recommend that you are very well versed with Python dictionaries, since they are fundamental to create map plots like these, as you will notice.

I gathered data from the National Climatic Data Center about storm events in the US from year 2000 up to now. As usual I had to do a lot of cleaning, deal with missing data and engineer some features. At this link you can find the .csv file used for this purpose, and for my capstone project in general. The data has information about the latitude and longitude of the phenomenon, which is fundamental for us in order to plot on a map. It also indicates whether it was a thunderstorm, hail, flash flood, etc for a total of seven different categories. The magnitude of the storm event, in which State, and when it happened

Before working on the actual plot, we need to import and assign a few things. Here is where we need our access token from Mapbox, as well as username and api key from Plotly.

import plotly.plotly as pyimport plotly.graph_objs as goimport plotlyimport pandas as pd

# setting user, api key and access tokenplotly.tools.set_credentials_file(username='your_username_here', api_key='your_api_key_here')mapbox_access_token = 'your_access_token_here'

In order to generate our figure, we will need two things: data and layout. Our variable data will be a list of seven dictionaries (one per event type), where each one contains the latitude and longitude values, as well as the name of the storm type and the marker characteristics for the plot. The variable layout is a dictionary containing everything concerning the image that is no directly connected with the data: the drop-down menus, the annotations, how we want to format them and where we want to place them.

Data

After reading and assigning the dataframe to a df variable, I created a list containing the names of the seven different storm categories and named it event_types. I for-looped over this list and used it to filter the dataframe, creating a dictionary for each event type and appending it to the data list. This way I have my list of dictionaries, data.

The reason why I assigned an opacity value of 0.5 to the markers is because, most likely, there will be areas with more points than others. Giving some sort of transparency to the points, will allow us to see different shades of the same color on the map, corresponding to different frequencies of the same phenomenon in different areas.

We are done with data, and now it’s layout’s turn, which will definitively require more work.

Layout

Let’s set the basic settings of the map.

Now we can start adding things to the map to customize it and make interactive as we like.

Layout[‘annotation’]

Let’s say I want to put a black box with white text on it, that specifies that my points represent only storm events from year 2000 up to now, that caused more than $50k of economic damage. Some static prompt like this, will fall under the annotation key of the layout dictionary: the value corresponding to annotation will need to be a list of dictionaries and in this specific case, a list containing only one dictionary since I want to put only one annotation on my map. It seems cumbersome in words, but doing it is definitely easier than explaining it.

Now it’s time for the drop-down menus, which will need to be assigned to the updatemenus key of the layout dictionary.

Layout[‘updatemenus’]

I want to add two drop-down menus: one to select the type of storm event to visualize, in case we want to look at only of type instead of all of them, while the other drop-down will allow us to change the type of map on the background, which is dark by default. The name and the styles of the available maps can be found on Mapbox.

Every drop-down menu is defined again through a dictionary, and all these dictionaries will be contained into a list that we will assign to layout['updatemenus'].

Layout[‘title’]

If you want, you can easily assign a title to your plot. I did so just by doing layout['title'] = 'Storm Events'.

Generate the plot!

Once we have our data and layout in order, we can generate the plot just by running:

figure = dict(data = data, layout = layout)py.iplot(figure, filename = 'put_your_name_here')

Et voilà!

You can zoom in, zoom out, select what you want to visualize from the top menu, and change the map type from the menu on the bottom. Pretty cool! I look forward to work on some other plots like this, now that I learned how to do it. Hopefully you found this useful as well!