How I use Python to map the global spread of COVID-19
======
Create a color coded geographic map of the potential spread of the virus
using these open source scripts.
![Globe up in the clouds][1]
The spread of disease is a real concern for a world in which global travel is commonplace. A few organizations track significant epidemics (and any pandemic), and fortunately, they publish their work as open data. The raw data can be difficult for humans to process, though, and that's why data science is so vital. For instance, it could be useful to visualize the worldwide spread of COVID-19 with Python and Pandas.
It can be hard to know where to start when you're faced with large amounts of raw data. The more you do it, however, the more patterns begin to emerge. Here's a common scenario, applied to COVID-19 data:
1. Download COVID-19 country spread daily data into a Pandas DataFrame object from GitHub. For this, you need the Python Pandas library.
2. Process and clean the downloaded data and make it suitable for visualizing. The downloaded data (as you will see for yourself) is in quite good condition. The one problem with this data is that it uses the names of countries, but it's better to use three-digit ISO 3 codes. To generate the three-digit ISO 3 codes, use a small Python library called pycountry. Having generated these codes, you can add an extra column to our DataFrame and populate it with these codes.
3. Finally, for the visualization, use the **express** module of a library called Plotly. This article uses what are called choropleth maps (available in Plotly) to visualize the worldwide spread of the disease.
We will load the data directly into a Pandas DataFrame. Pandas provides a function, **read_csv()**, which can take a URL and return a DataFrame object as shown below:
print(df1.head(3)) # Get first 3 entries in the dataframe
print(df1.tail(3)) # Get last 3 entries in the dataframe
```
The screenshot of output (on Jupyter) is:
![Jupyter screenshot][2]
From output, you can see that the DataFrame (df1) has the following columns:
1. Date
2. Country
3. Confirmed
4. Recovered
5. Dead
Further, you can see that the **Date** column has entries starting from January 22 to March 31. This database is updated daily, so you will get the current values.
### Step 2: Cleaning and modifying the data frame
We need to add another column to this DataFrame, which has the three-letter ISO alpha-3 codes. To do this, I followed these steps:
1. Create a list of all countries in the database. This was required because in the **df**, in the column **Country**, each country was figuring for each date. So in effect, the **Country** column had multiple entries for each country. To do this, I used the **unique().tolist()** functions.
2. Then I took a dictionary **d_country_code** (initially empty) and populated it with keys consisting of country names and values consisting of their three-letter ISO codes.
3. To generate the three-letter ISO code for a country, I used the function **pycountry.countries.search_fuzzy(country)**. You need to understand that the return value of this function is a "list of **Country** objects." I passed the return value of this function to a name country_data. Further, in this list of objects, the first object i.e., at index 0, is the best fit. Further, this **\** object has an attribute **alpha_3**. So, I can "access" the 3 letter ISO code by using **country_data[0].alpha_3**. However, it is possible that some country names in the DataFrame may not have a corresponding ISO code (For example, disputed territories). So, for such countries, I gave an ISO code of "i.e. a blank string. Further, you need to wrap this code in a try-except block. The statement: **print(_‘could not add ISO 3 code for ->'_, country)** will give a printout of those countries for which the ISO 3 codes could not be found. In fact, you will find such countries as shown with white color in the final output.
4. Having got the three-letter ISO code for each country (or an empty string for some), I added the country name (as key) and its corresponding ISO code (as value) to the dictionary **d_country_code**. For adding these, I used the **update()** method of the Python dictionary object.
5. Having created a dictionary of country names and their codes, I added them to the DataFrame using a simple for loop.
### Step 3: Visualizing the spread using Plotly
A choropleth map is a map composed of colored polygons. It is used to represent spatial variations of a quantity. We will use the express module of Plotly conventionally called **px**. Here we show you how to create a choropleth map using the function: **px.choropleth**.
The noteworthy points are that the **choropleth()** function needs the following things:
1. A geometry in the form of a **geojson** object. This is where things are a bit confusing and not clearly mentioned in its documentation. You may or may not provide a **geojson** object. If you provide a **geojson** object, then that object will be used to plot the earth features, but if you don't provide a **geojson** object, then the function will, by default, use one of the built-in geometries. (In our example here, we will use a built-in geometry, so we won't provide any value for the **geojson** argument)
2. A pandas DataFrame object for the attribute **data_frame**. Here we provide our DataFrame ie **df1** we created earlier.
3. We will use the data of **Confirmed** column to decide the color of each country polygon.
4. Further, we will use the **Date** column to create the **animation_frame**. Thus as we slide across the dates, the colors of the countries will change as per the values in the **Confirmed** column.
