<jupyter_start><jupyter_text>1st plot shows that the closer a city is to 0 Latitued (The equator) the warmer the weather temperature.<jupyter_code># 2nd plot ( humidity vs latitude)
plt.scatter(city_df["Lat"], city_df["Humidity"], marker = "o", color = "blue",edgecolor = "black")
plt.grid()
plt.ylim(0, city_df["Humidity"].max()+50)
plt.xlabel("Latitude")
plt.ylabel("Humidity(%)")
plt.title(f"City Latitude vs. Humidity ({shortened_timestamp})")
plt.savefig("Lat_vs_humidity_plot")
plt.show()<jupyter_output><empty_output><jupyter_text>2nd plot does not clearly shows any relation between humidity and latitude.<jupyter_code>plt.scatter(city_df["Lat"], city_df["Cloudiness"], marker = "o", color = "blue",edgecolor = "black")
plt.grid()
plt.xlabel("Latitude")
plt.ylabel("Cloudiness(%)")
plt.title(f"City Latitude vs. Cloudiness ({shortened_timestamp})")
plt.savefig("Lat_vs_cloudiness_plot")
plt.show()<jupyter_output><empty_output><jupyter_text>3rd plot does not clearly shows any relation between cloudiness and latitude<jupyter_code>plt.scatter(city_df["Lat"], city_df["Wind Speed"], marker = "o", color = "blue",edgecolor = "black")
plt.grid()
plt.xlabel("Latitude")
plt.ylabel("Wind Speed(mph)")
plt.title(f"City Latitude vs. Wind Speed ({shortened_timestamp})")
plt.savefig("Lat_vs_windSpeed_plot")
plt.show()<jupyter_output><empty_output>
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