WeatherPy.py

#!/usr/bin/env python
# coding: utf-8

# # WeatherPy
# ----
# 
# #### Note
# * Instructions have been included for each segment. You do not have to follow them exactly, but they are included to help you think through the steps.

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# Dependencies and Setup
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import requests
import time
from scipy.stats import linregress

# Import API key
from api_keys import weather_api_key

# Incorporated citipy to determine city based on latitude and longitude
from citipy import citipy

# Output File (CSV)
output_data_file = "output_data/cities.csv"

# Range of latitudes and longitudes
lat_range = (-90, 90)
lng_range = (-180, 180)


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weather_api_key


# ## Generate Cities List

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# List for holding lat_lngs and cities
lat_lngs = []
cities = []

# Create a set of random lat and lng combinations
lats = np.random.uniform(low=-90.000, high=90.000, size=1500)
lngs = np.random.uniform(low=-180.000, high=180.000, size=1500)
lat_lngs = zip(lats, lngs)

# Identify nearest city for each lat, lng combination
for lat_lng in lat_lngs:
    city = citipy.nearest_city(lat_lng[0], lat_lng[1]).city_name
    
    # If the city is unique, then add it to a our cities list
    if city not in cities:
        cities.append(city)

# Print the city count to confirm sufficient count
len(cities)


# ### Perform API Calls
# * Perform a weather check on each city using a series of successive API calls.
# * Include a print log of each city as it'sbeing processed (with the city number and city name).
# 

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url = 'http://api.openweathermap.org/data/2.5/weather?units=Imperial&APPID=' + weather_api_key
number = 1
city_name = []
lat = []
lng = []
temp = []
humid = []
clouds = []
wind = []

print('------------------------------')
print('Beginning Data Retrieval')
print('------------------------------')

for city in cities:
    try:
        city_data = (requests.get(url + '&q=' + city)).json()
        city_name.append(city_data['name'])
        lat.append(city_data['coord']['lat'])
        lng.append(city_data['coord']['lon'])
        temp.append(city_data['main']['temp'])
        humid.append(city_data['main']['humidity'])
        clouds.append(city_data['clouds']['all'])
        wind.append(city_data['wind']['speed'])
        print(f'Processing record {number} of set {len(cities)} | {city}')
        number = number + 1
    
    except KeyError:
        print(f'Missing data in city number {number} of {len(cities)}. | Skipping {city}')
        number = number + 1
        
print('------------------------------')
print('Data Retrieval Complete')
print('------------------------------')


# ### Convert Raw Data to DataFrame
# * Export the city data into a .csv.
# * Display the DataFrame

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cdf = pd.DataFrame({'City': city_name,
                            'Latitude': lat,
                            'Longitude': lng,
                            'Temperature': temp,
                            'Humidity': humid,
                            'Cloudiness': clouds,
                            'Wind Speed': wind})

pd.DataFrame.to_csv(cdf, 'city_data.csv')

cdf


# ### 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

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plt.scatter(cdf['Latitude'], cdf['Temperature'])
plt.title(f'Latitude vs. Temperature')
plt.xlabel('Latitude')
plt.ylabel('Temperature (F)')
plt.grid(True)
plt.savefig('latvstemp.png')


# #### Latitude vs. Humidity Plot

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#Plot latitude vs humidity and save as .png
plt.scatter(cdf['Latitude'], cdf['Humidity'])
plt.title(f'Latitude vs. Humidity')
plt.xlabel('Latitude')
plt.ylabel('Humidity (%)')
plt.grid(True)
plt.savefig('latvshumid.png')


# #### Latitude vs. Cloudiness Plot

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#Plot latitude vs cloudiness and save as .png
plt.scatter(cdf['Latitude'], cdf['Cloudiness'])
plt.title('Latitude vs. Cloudiness')
plt.xlabel('Latitude')
plt.ylabel('Cloudiness (%)')
plt.grid(True)
plt.savefig('latvscloud.png')


# #### Latitude vs. Wind Speed Plot

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plt.scatter(cdf['Latitude'], cdf['Wind Speed'])
plt.title('Latitude vs. Wind Speed')
plt.xlabel('Latitude')
plt.ylabel('Wind Speed (mph)')
plt.grid(True)
plt.savefig('latvswind.png')


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