Downloading weather data

In this notebook, I’ll write a small illustration on downloading historical weather data using forceast.io. I’ll also illustrate handling timezone issues when using such time series data. I am going to use python-forecastio, which is a Python wrapper around forecast.io service. I’ll be downloading hourly weather data for Austin, Texas.

import datetime
import pandas as pd
import forecastio
import getpass

# Enter your API here
api_key = getpass.getpass()

········

len(api_key)

32

Austin’s Latitude and longitude

lat = 30.25
lng = -97.25

Let us see the forecast for 1 Jan 2015

date = datetime.datetime(2015,1,1)

forecast = forecastio.load_forecast(api_key, lat, lng, time=date, units="us")

forecast

<forecastio.models.Forecast at 0x10319ce50>

hourly = forecast.hourly()

hourly.data

[<forecastio.models.ForecastioDataPoint at 0x1068643d0>,
 <forecastio.models.ForecastioDataPoint at 0x106864bd0>,
 <forecastio.models.ForecastioDataPoint at 0x106864ad0>,
 <forecastio.models.ForecastioDataPoint at 0x106864cd0>,
 <forecastio.models.ForecastioDataPoint at 0x106864fd0>,
 <forecastio.models.ForecastioDataPoint at 0x106864d10>,
 <forecastio.models.ForecastioDataPoint at 0x100734e10>,
 <forecastio.models.ForecastioDataPoint at 0x1061e3450>,
 <forecastio.models.ForecastioDataPoint at 0x1061e3350>,
 <forecastio.models.ForecastioDataPoint at 0x1068b3250>,
 <forecastio.models.ForecastioDataPoint at 0x1068b3110>,
 <forecastio.models.ForecastioDataPoint at 0x1068b3150>,
 <forecastio.models.ForecastioDataPoint at 0x1068b3190>,
 <forecastio.models.ForecastioDataPoint at 0x1068b31d0>,
 <forecastio.models.ForecastioDataPoint at 0x1068b3210>,
 <forecastio.models.ForecastioDataPoint at 0x1068b3fd0>,
 <forecastio.models.ForecastioDataPoint at 0x1068b3dd0>,
 <forecastio.models.ForecastioDataPoint at 0x1068b3e10>,
 <forecastio.models.ForecastioDataPoint at 0x1068b3e50>,
 <forecastio.models.ForecastioDataPoint at 0x1068b3f50>,
 <forecastio.models.ForecastioDataPoint at 0x1068c84d0>,
 <forecastio.models.ForecastioDataPoint at 0x1068c8390>,
 <forecastio.models.ForecastioDataPoint at 0x1068c8510>,
 <forecastio.models.ForecastioDataPoint at 0x1068c8550>]

Extracting data for a single hour.

hourly.data[0].d

{u'apparentTemperature': 32.57,
 u'dewPoint': 33.39,
 u'humidity': 0.79,
 u'icon': u'clear-night',
 u'precipIntensity': 0,
 u'precipProbability': 0,
 u'pressure': 1032.61,
 u'summary': u'Clear',
 u'temperature': 39.46,
 u'time': 1420005600,
 u'visibility': 10,
 u'windBearing': 21,
 u'windSpeed': 10.95}

Let us say that we want to use the temperature and humidity only.

attributes = ["temperature", "humidity"]

times = []
data = {}
for attr in attributes:
    data[attr] = []

Now, let us download hourly data for 30 days staring January 1 this year.

start = datetime.datetime(2015, 1, 1)
for offset in range(1, 60):
    forecast = forecastio.load_forecast(api_key, lat, lng, time=start+datetime.timedelta(offset), units="us")
    h = forecast.hourly()
    d = h.data
    for p in d:
        times.append(p.time)
        for attr in attributes:
            data[attr].append(p.d[attr])

Now, let us create a Pandas data frame for this time series data.

df = pd.DataFrame(data, index=times)

df.head()

	humidity	temperature
2015-01-01 11:30:00	0.73	38.74
2015-01-01 12:30:00	0.74	38.56
2015-01-01 13:30:00	0.75	38.56
2015-01-01 14:30:00	0.79	37.97
2015-01-01 15:30:00	0.80	37.78

Now, we need to fix the timezone.

df = df.tz_localize("Asia/Kolkata").tz_convert("US/Central")

df.head()

	humidity	temperature
2015-01-01 00:00:00-06:00	0.73	38.74
2015-01-01 01:00:00-06:00	0.74	38.56
2015-01-01 02:00:00-06:00	0.75	38.56
2015-01-01 03:00:00-06:00	0.79	37.97
2015-01-01 04:00:00-06:00	0.80	37.78

I’ll now export this file to a CSV to use it for following demonstrations on aggregations on time series.

df.to_csv("weather.csv")

A quick validation of our downloaded data.

%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('ggplot')

df.plot(subplots=True);