weatherhw.md

# Dependencies
import csv
import matplotlib.pyplot as plt
import requests as req
import pandas as pd

api_key = "25bc90a1196e6f153eece0bc0b0fc9eb"
units = "imperial"

# Import the census data into a pandas DataFrame

cities = pd.read_csv("worldcities.csv")

# Preview the data
cities.head()

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	Country	City	Latitude	Longitude
0	ad	andorra la vella	42.500000	1.516667
1	ad	canillo	42.566667	1.600000
2	ad	encamp	42.533333	1.583333
3	ad	la massana	42.550000	1.516667
4	ad	les escaldes	42.500000	1.533333

#take a sample of cities 
selected_cities = cities.sample(n=500)
selected_cities.head()

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	Country	City	Latitude	Longitude
37617	ru	peskovka	59.044781	52.360571
32182	ro	baia	44.716667	28.666667
15646	hu	janossomorja	47.786207	17.136033
17346	in	burla	21.500000	83.866667
5631	cl	bulnes	-36.733333	-72.300000

selected_cities["Temperature"] = ""
selected_cities["Humidity"] = ""
selected_cities["Cloudiness"] = ""
selected_cities["Wind Speed"] = ""
row_count = 0

for index, row in selected_cities.iterrows():
    
    # Create endpoint URL
    target_url = "http://api.openweathermap.org/data/2.5/weather?appid=%s&q=%s" % (api_key, row["City"])
    
    #THIS IS WHERE THE ERROR IS, SOMETHING WITH THE URL IS WRONG
    
    # Print log to ensure loop is working correctly
    #print("Now retrieving city # " + str(row_count))
    #print(target_url)
    #row_count += 1
    
    w_city = req.get(target_url).json()
    
    try: 
        city_temp = w_city["main"]["temp"]
        city_hum = w_city["main"]["humidity"]
        city_clouds = w_city["clouds"]["all"]
        city_wind = w_city["wind"]["speed"]
        
        selected_cities.set_value(index, "Temperature", city_temp)
        selected_cities.set_value(index, "Humidity", city_hum)
        selected_cities.set_value(index, "Cloudiness", city_clouds)
        selected_cities.set_value(index, "Wind Speed", city_wind)
    except:
        continue
selected_cities.head()

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	Country	City	Latitude	Longitude	Temperature	Humidity	Cloudiness	Wind Speed
37617	ru	peskovka	59.044781	52.360571	264.913	78	64	1.28
32182	ro	baia	44.716667	28.666667	282.15	76	0	4.1
15646	hu	janossomorja	47.786207	17.136033	284.88	76	0	2.1
17346	in	burla	21.500000	83.866667	299.013	83	0	1.83
5631	cl	bulnes	-36.733333	-72.300000	287	72	0	4.1

selected_cities.to_csv("citytemp_data.csv", encoding="utf-8", index=False)

selected_cities['Temperature'] = pd.to_numeric(selected_cities['Temperature'])
selected_cities['Humidity'] = pd.to_numeric(selected_cities['Humidity'])
selected_cities.plot(kind="scatter", x="Latitude", y="Temperature", grid=True, figsize=(20,10),
              title="Latitude v. Temperature")
plt.show()

selected_cities['Cloudiness'] = pd.to_numeric(selected_cities['Cloudiness'])
selected_cities.plot(kind="scatter", x="Latitude", y="Cloudiness", grid=True, figsize=(20,10),
              title="Latitude v. Cloudiness")
plt.show()

selected_cities['Wind Speed'] = pd.to_numeric(selected_cities['Wind Speed'])
selected_cities.plot(kind="scatter", x="Latitude", y="Wind Speed", grid=True, figsize=(20,10),
              title="Latitude v. Wind Speed")
plt.show()

selected_cities.plot(kind="scatter", x="Latitude", y="Humidity", grid=True, figsize=(20,10),
              title="Latitude v. Humidity")
plt.show()