<jupyter_start><jupyter_text>WeatherPy---- <NAME>* This file explores relationships between weather and lat/long positioning through an API  Analysis----* Temperature shows a correlation with latitude whereby temperature increases as it approaches a latitude of zero from both positive and negative latitudes* Wind speed demonstrates low correlation with latitude from our data* Wind speed concentrates in the band of 0mph and 10mph regardless of latitude, yet there are still a significant number of cities with wind speeds in the range of 10mph to 20mph with a few cities skewing even higher* Humidity shows ow correlation wiht latitude from our data* The bulk of cities show humidities between 60% and above, regardless of latitude* Cloudiness does not appear to be correlated to latitude from our data* Of the data in the dataset, only temperature appears to be correlated with latitude with wind speed, humidity, and cloudiness showing low correlation.<jupyter_code># Dependencies and Setup
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
# Read in CSV
weather_df = pd.read_csv('Resources/cities.csv')

# Check counts to make sure even
print(weather_df.count())

weather_df = weather_df.set_index('City_ID')
weather_df.head()<jupyter_output>City_ID       547
City          547
Cloudiness    547
Country       545
Date          547
Humidity      547
Lat           547
Lng           547
Max Temp      547
Wind Speed    547
dtype: int64<jupyter_text>Plotting the Data* Use proper labeling of the plots using plot titles (including date of analysis) and axes labels.* Save the plotted figures as .pngs.  Latitude vs. Temperature Plot<jupyter_code># Create latitude vs. max temp scatter plot
plt.scatter(weather_df['Lat'], weather_df['Max Temp'], edgecolors='black', facecolors = 'lightblue')
plt.title('City Latitude vs. Max Temperature')
plt.xlabel('Latitude')
plt.ylabel('Max Temperature (F)')
plt.grid(b = True, which = 'Major', axis = 'both', linestyle = '-', color = 'lightgrey')
plt.savefig("Resources/assets/images/latvtemp.png")
plt.savefig('latvtemp2.png')
plt.show()<jupyter_output><empty_output><jupyter_text>Latitude vs. Humidity Plot<jupyter_code># Create latitude vs humidity scatter plot
plt.scatter(weather_df['Lat'], weather_df['Humidity'], edgecolors='black', facecolors='lightblue')
plt.title('City Latitude vs. Humidity')
plt.xlabel('Latitude')
plt.ylabel('Humidity (%)')
plt.ylim(0,100)
plt.grid(b=True, which='major', axis='both', linestyle='-', color='lightgrey')
plt.savefig('Resources/assets/images/latvhumidity.png')
plt.show()<jupyter_output><empty_output><jupyter_text>Latitude vs. Cloudiness Plot<jupyter_code># Create latitude vs. cloudiness scatterplot
plt.scatter(weather_df['Lat'],weather_df['Cloudiness'], edgecolors='black', facecolors='lightblue')
plt.title('City Latitude vs. Cloudiness')
plt.xlabel('Latitude')
plt.ylabel('Cloudiness (%)')
plt.grid(b = True, which='Major', axis='both', linestyle='-',color='lightgrey')
plt.savefig('Resources/assets/images/latvcloud')
plt.show()<jupyter_output><empty_output><jupyter_text>Latitude vs. Wind Speed Plot<jupyter_code># Create latitude vs. wind speed scatter plot
plt.scatter(weather_df['Lat'], weather_df['Wind Speed'], edgecolors='black', facecolors='lightblue')
plt.title('City Latitude vs. Wind Speed')
plt.xlabel('Latitude')
plt.ylabel('Wind Speed (mph)')
plt.grid(b= True, which='major', axis='both', linestyle='-', color='lightgrey')
plt.ylim(-2, 34)
plt.savefig('Resources/assets/images/latvwind.png')
plt.show()<jupyter_output><empty_output>
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