Exercise

What state is the most market-friendly?

While exploring the farmer's market data, you wonder what patterns may show up if you aggregated to the state level. Are some states more market-friendly than other states? To investigate this, you group your data by state and get the log-transformed number of markets (log_markets) and state populations (log_pop).

markets_and_pop = (markets
    .groupby('state', as_index = False)
    .agg({
       'name': lambda d: log(len(d)),
       'state_pop': lambda d: log(d.iloc[0]) })
    .rename(columns = {
        'name': 'log_markets', 
        'state_pop': 'log_pop' }))

To visualize, you decide to use a regression plot to get an idea of the 'normal' relationship between market and population numbers and a text-scatter to quickly identify interesting outliers.

Instructions

100 XP
  • Iterate over the rows of the markets_and_pop DataFrame.
  • Place annotations next to their scatter plot points.
  • Reduce annotation text size to 10 points.