EDA Python - 21-Pandas_Geo_1

This tutorial covers plotting COVID-19 data on a map using GeoPandas. It begins with setting up the necessary libraries and downloading data from Johns Hopkins University. The tutorial then guides through creating a world map, filtering data for European countries, and plotting it. It also includes steps for cleaning and optimizing data for better visualization, culminating in an improved map with aggregated data for each country.

Which library is essential for plotting geographical data in this tutorial?

• NumPy

• Seaborn

• GeoPandas

• Matplotlib

Which library is used to handle geometric operations in this tutorial?

• Pandas

• Shapely

• NumPy

• Matplotlib

What type of data is downloaded from Johns Hopkins University for this tutorial?

• Weather data

• COVID-19 data

• Population data

• Economic data

What is the primary focus of the geographical data in this tutorial?

• American countries

• European countries

• Asian countries

• African countries

Which country is specifically excluded from the EU data in the tutorial?

• France

• Germany

• Russia

• Italy

What is the purpose of setting axis values when plotting the map?

• To change the map color

• To focus on a specific region

• To add labels

• To increase map size

What is the main reason for normalizing marker sizes in the plot?

• To fit them on the map

• To make them invisible

• To make them proportional to data

• To make them colorful

How are regional numbers aggregated to improve the map visualization?

• By multiplying

• By summing

• By dividing

• By averaging

What method is used to handle outliers in the dataset?

• Deleting them

• Manually adjusting them

• Averaging them

• Ignoring them

What is the final goal of the map visualization in this tutorial?

• To display global data

• To show regional data

• To show one marker per country

• To highlight continents