Post-Graduate Challenge in Data Analysis using Seaborn

The script pairwise_relationships.py generates a paired relationship plot that crosses information on the number of bedrooms, number of bathrooms, parking spaces, and total rent. The data is colored by the city of the property.

From the analysis of these relationships, it is observed, for example, in the fourth graph of the first row, that the city with the highest rent for a one-bedroom property is São Paulo.

The script faceted_graphic.py constructs a faceted graph by city mapping the number of bedrooms each city has. For this, the sns.countplot function was used as visualization for the subplots.

One result observed in the graph is the most frequent number of bedrooms in the apartments available in each of the cities. There are 3 bedrooms in São Paulo, Campinas, and Belo Horizonte, and 2 bedrooms in Porto Alegre and Rio de Janeiro.

The script animal_scatter.py constructs a faceted graph by cities (columns) and by the animal variable (rows) with the distribution of the total rent value.

The graph allows us to analyze if there is any relationship between the rent price and whether the condominium accepts pets, but the lack of density hinders the analysis.

Therefore, the script animal_swarm.py was created to observe the density of the values.

The graph with the data densities allows for a better conclusion of the relationship between the rent price and whether the condominium accepts pets. It is concluded that the number of apartments with higher rent values that accept pets is greater than the number that does not.