Basic Plotting in Matplotlib Part 2 How to Use Matplotlib to Analyze CO2 Emissions and GDP Per Capita in the US Using Matplotlib, this article will investigate the connection between CO2 emissions and GDP per capita in the US. With the help of the robust data visualization tool Matplotlib, we can easily build line charts and scatter plots that help us spot patterns and trends in our data. First, we need to pull the GDP per capita data for the US over time, which we will compare to CO2 emissions per capita data we analyzed previously. We will use masks to pull the data we need, and then run the head method to ensure that we have the data we want. The GDP per capita data is based on the value of US dollars in 2005, which is similar to the data we used for CO2 emissions. We'll begin by using a line plot to examine the trend in the GDP per capita for the United States. The data demonstrates consistent growth throughout time, with sporadic dips and a notable decrease in 2008 during the recession. Yet by 2010, the tendency has returned. We will make a scatter plot to examine whether there is any association between the two since the information on its own doesn't provide much insight into the relationship between CO2 emissions and GDP per capita. Before we create the scatter plot, we need to make sure that the time frames for both datasets are the same. We do this by calling min and max in these columns. We notice that both datasets start in the same year, 1960, but we have more data for GDP per capita than we do for CO2 emissions. To create a scatter plot, we need to have the same number of data points for both datasets. To do this, we trim off the extra years and ask for the years before 2012. Now, both GDP per person and CO2 emissions have the same number of values. We call out the fig and the axis, just like we did before, and then use the scatter function to plot the two arrays to generate a scatter plot. The resulting graphic reveals that there is little correlation between the two variables. There seems to have been a correlation between GDP growth and CO2 emissions in the 1960s, but there isn't much of a correlation after that. Using NumPy's correlation coefficient function, which yields a weak correlation coefficient of 0.077, we can verify this. From our initial data analysis, we can conclude that there is a weak relationship between CO2 emissions and GDP per capita in the United States. Therefore, if someone were to argue that we need more CO2 production per capita to boost our economy, this analysis would not support that claim. While this is just a preliminary analysis, it demonstrates how we can use Matplotlib to explore complex datasets and identify patterns and relationships. Matplotlib is a potent tool for data analysis and visualization, to sum up. We can easily examine and evaluate complex datasets using line charts and scatter plots. In this post, we used Matplotlib to examine the connection between US GDP per capita and CO2 emissions. Little correlation between the two variables, as revealed by our preliminary investigation, refutes the idea that increasing CO2 production per person will help the economy. However, additional research could be required to verify these results. Ultimately, Matplotlib is a crucial tool for anyone analyzing data and looking for patterns and insights.