The US Census Bureau's (https://www.census.gov/) serves as the nation's leading provider of data about its people and economy. This data is provided at different geographical levels namely: state, county, city, census tract, and census block group. Census Block Groups (CBG) are the highest resolution that the census data is provided at. Each CBG is defined by a unique 11 digits ID (e.g., 10010201002). The file census_sample.csv Download census_sample.csv includes the following columns: 1- CBG ID (cbg_id) 2- Median Income (median_income) 3- Median Age (median_age) 4- Percentage of population from White race (white_ppl) 5- Percentage of population from Asian race (asian_ppl) 6- Percentage of population from Black-AfricanAmerican race (black_ppl) 7- Percentage of population from Histpanic-Latino race (hispanic_ppl) 8- Percentage of population with Professional College education degree (edu_college) 9- Percentage of population with Bachelors degree (edu_bachelors) 10- Percentage of population with Masters degree (edu_masters) 11- Percentage of population with a Ph.D. degree (edu_phd) Download the file and use Python or R to read the file and perform the following: 1- Read the data and view the first 5 rows of the data (use head function). 2- Provide details about the data type in each column. 3- Remove the rows that have missing values (NA). 4- Keep only first 1000 rows for the rest of analysis. 5- Create a new columns named 'edu_degree' that is sum of all degree owners (i.e., Professional College, Bachelors, Masters, and PhD) 6- Remove columns edu_college, edu_bachelors, edu_masters, and edu_phd. 7- Use visualization methods you learned in class to explore the data. 8- Use pair plots and correlation plots to find patterns in the data. You should write a report that covers all the steps above including the code, results, and your comments. Finally, you should have a section of maximum one-page where you explain what interesting patterns you've observed in the data. Submit your report file.

English Version of "Análise População Carcerária de 2016 a 2019 no Brasil"

Análise do baco de dados do INFOPEN de 2016 a 2019