There are many factors that go into the poverty levels of a given area. One of these factors is the education level of the population. Statistically, people with a high school or college degree are far less likely to live in poverty [1].
Being able to see the relationship between poverty and education levels has two major benefits. Firstly, it can serve as further evidence as to weather there is a correlation between poverty and education. Furthermore, it can allow us to see the states that are most likely lacking in both fields. This can potentially allow for government funding and resources to be allocated to those areas, thereby improving the overall education system, and, as a result, the poverty levels.
https://data.ers.usda.gov/reports.aspx?ID=17829
https://data.ers.usda.gov/reports.aspx?ID=17826
import pandas as pd
df_data = pd.read_csv('simplified_data.csv')
data_dict = df_data.to_dict()
df_data
| State | Poverty Rate (17 and under) | % of pop that completed HS | |
|---|---|---|---|
| 0 | Alabama | 20.9 | 31.60% |
| 1 | Alaska | 12.3 | 30.30% |
| 2 | Arizona | 17.6 | 28.40% |
| 3 | Arkansas | 20.8 | 23.80% |
| 4 | California | 14.6 | 33.90% |
| 5 | Colorado | 10.6 | 20.40% |
| 6 | Connecticut | 11.8 | 21.20% |
| 7 | Delaware | 15.1 | 26.30% |
| 8 | District of Columbia | 22.3 | 31.00% |
| 9 | Florida | 17.2 | 16.60% |
| 10 | Georgia | 19.5 | 28.20% |
| 11 | Hawaii | 10.4 | 27.50% |
| 12 | Idaho | 11.6 | 27.30% |
| 13 | Illinois | 13.9 | 26.70% |
| 14 | Indiana | 14.9 | 25.60% |
| 15 | Iowa | 12.0 | 33.20% |
| 16 | Kansas | 13.0 | 30.80% |
| 17 | Kentucky | 19.4 | 25.80% |
| 18 | Louisiana | 24.3 | 32.60% |
| 19 | Maine | 12.8 | 33.30% |
| 20 | Maryland | 11.2 | 31.30% |
| 21 | Massachusetts | 10.9 | 24.20% |
| 22 | Michigan | 16.8 | 23.50% |
| 23 | Minnesota | 9.5 | 28.50% |
| 24 | Mississippi | 26.0 | 24.20% |
| 25 | Missouri | 15.5 | 30.10% |
| 26 | Montana | 14.6 | 30.50% |
| 27 | National | 15.7 | 28.10% |
| 28 | Nebraska | 10.1 | 25.70% |
| 29 | Nevada | 16.7 | 27.90% |
| 30 | New Hampshire | 8.4 | 27.30% |
| 31 | New Jersey | 12.6 | 26.70% |
| 32 | New Mexico | 21.6 | 26.20% |
| 33 | New York | 16.7 | 25.50% |
| 34 | North Carolina | 17.9 | 25.50% |
| 35 | North Dakota | 10.9 | 26.10% |
| 36 | Ohio | 16.6 | 32.80% |
| 37 | Oklahoma | 18.6 | 31.10% |
| 38 | Oregon | 12.2 | 22.70% |
| 39 | Pennsylvania | 14.3 | 34.20% |
| 40 | Rhode Island | 14.5 | 28.00% |
| 41 | South Carolina | 18.7 | 28.90% |
| 42 | South Dakota | 13.9 | 30.20% |
| 43 | Tennessee | 18.4 | 31.80% |
| 44 | Texas | 18.8 | 24.70% |
| 45 | Utah | 7.5 | 22.80% |
| 46 | Vermont | 10.0 | 28.30% |
| 47 | Virginia | 12.2 | 23.90% |
| 48 | Washington | 11.0 | 21.80% |
| 49 | West Virginia | 20.3 | 40.00% |
| 50 | Wisconsin | 12.4 | 30.30% |
| 51 | Wyoming | 10.3 | 28.60% |
Since this data has both the poverty and education percentages, it should be relatively easy to make a scatterplot that is able to plot points for all 50 states (+DC), using the poverty levels as the x axis, the education percentages as the y axis, and the state name as a label.