plt.scatter() 图的行为类似于 Matplotlib 中的 plt.plot() 图

Question

I'm trying to compare the GDP-per-capita of the world's countries' to each countries' COVID-19 death total. Every time I try to turn it into a scatter plot, it displays the same plot as would be displayed using the plt.plot() command. Here is my code:

import pandas as pd
from matplotlib import pyplot as plt
plt.style.use('seaborn-whitegrid')
data = pd.read_csv(r'/Users/john.smith/covid-data.csv')

gdp = data["gdp_per_capita"]
deaths = data["total_deaths"]

plt.scatter(gdp, deaths)
plt.title('GDP-per-Capita Compared to COVID-19 Death Total')
plt.xlabel('GDP-per-Capita')
plt.ylabel('Confirmed Deaths')

plt.tight_layout()
plt.show()

While running this code, the following graph is produced. This is obviously not the scatter plot I'm trying to get, and it's worth noting that the only thing that changes when I use the plt.scatter() command is that the points on the plot just get very large.

I ran a test of the whole Matplotlib module entirely on a different file. When I use normal variables without importing from a CSV file, like this:

x = [7, 3, 8, 3]
y = [1, 5, 7, 4]
plt.scatter(x, y)

Then the code works perfectly fine and produces a scatter plot. I have been digging for hours online to try and find a solution, and have tried to use other methods of importing CSVs or creating scatter plots but nothing is working. Thank you for any tips.

Answer 1

Answer is courtesy of G. Anderson in the comments above.

As it turns out I just didn't have experience with the xlim() and ylim() commands, so the individual points in the scatter plot just overlapped very tightly in vertical lines. The reason this happened is simply because the original view window was too wide for this large of a dataset.

I did some slight additional research to try and put two plots onto a single figure with one being zoomed in, here's the code:

figs, axs = plt.subplots(2)
figs.suptitle('GDP-per-Capita Compared to COVID-19 Death Total')
axs[0].scatter(gdp, deaths)
axs[1].scatter(gdp, deaths)
plt.axis([10000, 20000, 10000, 20000])

This produced some nice plots I can use:

I'm going to look into ways to make the two plots much more readable.