In this example, you’ll be creating a Python script to visualize the weather of 500+ cities across the world of varying distance from the equator. To accomplish this, you’ll be utilizing a , the , and a little common sense to create a representative model of weather across world cities. Your first requirement is to create a series of scatter plots to showcase the following relationships: After each plot add a sentence or too explaining what the code is and analyzing. Your second requirement is to run linear regression on each relationship, only this time separating them into Northern Hemisphere (greater than or equal to 0 degrees latitude) and Southern Hemisphere (less than 0 degrees latitude): After each pair of plots explain what the linear regression is modeling such as any relationships you notice and any other analysis you may have. You will be creating multiple linear regression plots. To optimize your code, write a function that creates the linear regression plots.

In this assignment, we will be creating a Python script to visualize the weather of 500+ cities across the world. The cities selected will have varying distances from the equator. To achieve this, we will be utilizing a few tools – the OpenWeatherMap API, the Matplotlib library, and some common sense.

The first requirement is to create a series of scatter plots that showcase the following relationships. Each plot will be followed by a sentence or two explaining the code and providing an analysis.

The second requirement is to run linear regression on each relationship. This time, we will separate the data into the Northern Hemisphere (cities with latitude greater than or equal to 0 degrees) and the Southern Hemisphere (cities with latitude less than 0 degrees). Again, after each pair of plots, we will explain what the linear regression is modeling, any relationships we notice, and any other analysis we may have.

To optimize our code, we will write a function that creates the linear regression plots. By doing so, we can easily apply the function to multiple sets of data.

Let’s begin by discussing the scatter plots. A scatter plot is a graphical representation of two variables where each data point is represented as a dot on the graph. By plotting two variables against each other, we can observe any potential relationships or patterns in the data. In this case, we will be plotting various weather attributes against the latitude of each city.

For example, one scatter plot could show the relationship between temperature and latitude. We would plot the temperature (in degrees Fahrenheit or Celsius) on the y-axis and the latitude on the x-axis. By examining this plot, we can analyze any trends or correlations between temperature and distance from the equator.

Similarly, we could create scatter plots for other weather attributes such as humidity, cloudiness, and wind speed. Each plot would have its own y-axis representing the specific attribute, while the x-axis remains the latitude.

Moving on to linear regression, it is a statistical technique used to model the relationship between two variables by fitting a linear equation to the observed data. It helps us understand how changes in one variable affect the other. In this case, we will use linear regression to analyze the relationship between each weather attribute and latitude.

We will perform linear regression separately for the Northern Hemisphere and Southern Hemisphere datasets. This allows us to determine if there are any differences in the relationships between the weather attributes and latitude in each hemisphere.

By analyzing the linear regression plots, we can draw conclusions about the strength and direction of the relationships. We may also identify any outliers or anomalies in the data that require further investigation.

To efficiently process these plots, we will write a function that takes in the data for each hemisphere and creates the scatter plot along with the linear regression line. This function will streamline the code and make it more organized and reusable.

Overall, this assignment aims to create visualizations and perform linear regression analysis on weather data from cities around the world. By visualizing the relationships between weather attributes and latitude, we can gain insights into the impact of distance from the equator on different weather conditions. Using linear regression, we can quantify these relationships and compare them between the Northern and Southern Hemispheres.