In [1]:
import pandas as pd
pd.set_option('display.mpl_style', 'default')
figsize(15,3)


# Summary¶

By the end of this chapter, we're going to have downloaded all of Canada's weather data for 2012, and saved it to a CSV.

We'll do this by downloading it one month at a time, and then combining all the months together.

Here's the temperature every hour for 2012!

In [2]:
weather_2012_final = pd.read_csv('../data/weather_2012.csv', index_col='Date/Time')
weather_2012_final['Temp (C)'].plot(figsize=(15, 6))

Out[2]:
<matplotlib.axes.AxesSubplot at 0x345b5d0>

When playing with the cycling data, I wanted temperature and precipitation data to find out of people like biking when it's raining. So I went to the site for Canadian historical weather data, and figured out how to get it automatically.

Here we're going to get the data for March 2012, and clean it up

Here's an URL template you can use to get data in Montreal.

In [4]:
url_template = "http://climate.weather.gc.ca/climateData/bulkdata_e.html?format=csv&stationID=5415&Year={year}&Month={month}&timeframe=1&submit=Download+Data"


To get the data for March 2013, we need to format it with month=3, year=2012.

In [5]:
url = url_template.format(month=3, year=2012)
weather_mar2012 = pd.read_csv(url, skiprows=16, index_col='Date/Time', parse_dates=True, encoding='latin1')


This is super great! We can just use the same read_csv function as before, and just give it a URL as a filename. Awesome.

There are 16 rows of metadata at the top of this CSV, but pandas knows CSVs are weird, so there's a skiprows options. We parse the dates again, and set 'Date/Time' to be the index column. Here's the resulting dataframe.

In [6]:
weather_mar2012

Out[6]:
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 744 entries, 2012-03-01 00:00:00 to 2012-03-31 23:00:00
Data columns (total 24 columns):
Year                   744  non-null values
Month                  744  non-null values
Day                    744  non-null values
Time                   744  non-null values
Data Quality           744  non-null values
Temp (°C)              744  non-null values
Temp Flag              0  non-null values
Dew Point Temp (°C)    744  non-null values
Dew Point Temp Flag    0  non-null values
Rel Hum (%)            744  non-null values
Rel Hum Flag           0  non-null values
Wind Dir (10s deg)     715  non-null values
Wind Dir Flag          0  non-null values
Wind Spd (km/h)        744  non-null values
Wind Spd Flag          3  non-null values
Visibility (km)        744  non-null values
Visibility Flag        0  non-null values
Stn Press (kPa)        744  non-null values
Stn Press Flag         0  non-null values
Hmdx                   12  non-null values
Hmdx Flag              0  non-null values
Wind Chill             242  non-null values
Wind Chill Flag        1  non-null values
Weather                744  non-null values
dtypes: float64(14), int64(5), object(5)


Let's plot it!

In [7]:
weather_mar2012[u"Temp (\xb0C)"].plot(figsize=(15, 5))

Out[7]:
<matplotlib.axes.AxesSubplot at 0x34e8990>

Notice how it goes up to 25° C in the middle there? That was a big deal. It was March, and people were wearing shorts outside.

And I was out of town and I missed it. Still sad, humans.

I had to write '\xb0' for that degree character °. Let's get rid of that, to make it easier to type.

In [8]:
weather_mar2012.columns = [s.replace(u'\xb0', '') for s in weather_mar2012.columns]


You'll notice in the summary above that there are a few columns which are are either entirely empty or only have a few values in them. Let's get rid of all of those with dropna.

The argument axis=1 to dropna means "drop columns", not rows", and how='any' means "drop the column if any value is null".

This is much better now -- we only have columns with real data.

In [10]:
weather_mar2012 = weather_mar2012.dropna(axis=1, how='any')
weather_mar2012[:5]

Out[10]:
Year Month Day Time Data Quality Temp (C) Dew Point Temp (C) Rel Hum (%) Wind Spd (km/h) Visibility (km) Stn Press (kPa) Weather
Date/Time
2012-03-01 00:00:00 2012 3 1 00:00 -5.5 -9.7 72 24 4.0 100.97 Snow
2012-03-01 01:00:00 2012 3 1 01:00 -5.7 -8.7 79 26 2.4 100.87 Snow
2012-03-01 02:00:00 2012 3 1 02:00 -5.4 -8.3 80 28 4.8 100.80 Snow
2012-03-01 03:00:00 2012 3 1 03:00 -4.7 -7.7 79 28 4.0 100.69 Snow
2012-03-01 04:00:00 2012 3 1 04:00 -5.4 -7.8 83 35 1.6 100.62 Snow

The Year/Month/Day/Time columns are redundant, though, and the Data Quality column doesn't look too useful. Let's get rid of those.

The axis=1 argument means "Drop columns", like before. The default for operations like dropna and drop is always to operate on rows.

In [11]:
weather_mar2012 = weather_mar2012.drop(['Year', 'Month', 'Day', 'Time', 'Data Quality'], axis=1)
weather_mar2012[:5]

Out[11]:
Temp (C) Dew Point Temp (C) Rel Hum (%) Wind Spd (km/h) Visibility (km) Stn Press (kPa) Weather
Date/Time
2012-03-01 00:00:00 -5.5 -9.7 72 24 4.0 100.97 Snow
2012-03-01 01:00:00 -5.7 -8.7 79 26 2.4 100.87 Snow
2012-03-01 02:00:00 -5.4 -8.3 80 28 4.8 100.80 Snow
2012-03-01 03:00:00 -4.7 -7.7 79 28 4.0 100.69 Snow
2012-03-01 04:00:00 -5.4 -7.8 83 35 1.6 100.62 Snow

Awesome! We now only have the relevant columns, and it's much more manageable.

# 2.3 Plotting the temperature by hour of day¶

This one's just for fun -- we've already done this before, using groupby and aggregate! We will learn whether or not it gets colder at night. Well, obviously. But let's do it anyway.

In [12]:
temperatures = weather_mar2012[[u'Temp (C)']]
temperatures['Hour'] = weather_mar2012.index.hour
temperatures.groupby('Hour').aggregate(np.median).plot()

Out[12]:
<matplotlib.axes.AxesSubplot at 0x34ec610>

So it looks like the time with the highest median temperature is 2pm. Neat.

# 5.3 Getting the whole year of data¶

Okay, so what if we want the data for the whole year? Ideally the API would just let us download that, but I couldn't figure out a way to do that.

First, let's put our work from above into a function that gets the weather for a given month.

I noticed that there's an irritating bug where when I ask for January, it gives me data for the previous year, so we'll fix that too. [no, really. You can check =)]

In [15]:
def download_weather_month(year, month):
if month == 1:
year += 1
url = url_template.format(year=year, month=month)
weather_data = pd.read_csv(url, skiprows=16, index_col='Date/Time', parse_dates=True)
weather_data = weather_data.dropna(axis=1)
weather_data.columns = [col.replace('\xb0', '') for col in weather_data.columns]
weather_data = weather_data.drop(['Year', 'Day', 'Month', 'Time', 'Data Quality'], axis=1)
return weather_data


We can test that this function does the right thing:

In [16]:
download_weather_month(2012, 1)[:5]

Out[16]:
Temp (C) Dew Point Temp (C) Rel Hum (%) Wind Spd (km/h) Visibility (km) Stn Press (kPa) Weather
Date/Time
2012-01-01 00:00:00 -1.8 -3.9 86 4 8.0 101.24 Fog
2012-01-01 01:00:00 -1.8 -3.7 87 4 8.0 101.24 Fog
2012-01-01 02:00:00 -1.8 -3.4 89 7 4.0 101.26 Freezing Drizzle,Fog
2012-01-01 03:00:00 -1.5 -3.2 88 6 4.0 101.27 Freezing Drizzle,Fog
2012-01-01 04:00:00 -1.5 -3.3 88 7 4.8 101.23 Fog

Now we can get all the months at once. This will take a little while to run.

In [37]:
data_by_month = [download_weather_month(2012, i) for i in range(1, 13)]


Once we have this, it's easy to concatenate all the dataframes together into one big dataframe using pd.concat. And now we have the whole year's data!

In [57]:
weather_2012 = pd.concat(data_by_month)
weather_2012

Out[57]:
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 8784 entries, 2012-01-01 00:00:00 to 2012-12-31 23:00:00
Data columns (total 7 columns):
Temp (C)              8784  non-null values
Dew Point Temp (C)    8784  non-null values
Rel Hum (%)           8784  non-null values
Wind Spd (km/h)       8784  non-null values
Visibility (km)       8784  non-null values
Stn Press (kPa)       8784  non-null values
Weather               8784  non-null values
dtypes: float64(4), int64(2), object(1)


# 5.4 Saving to a CSV¶

It's slow and unnecessary to download the data every time, so let's save our dataframe:

In [58]:
weather_2012.to_csv('../data/weather_2012.csv')


And we're done!