1. Read in the Texas cities dataset, and identify the variables:

>>> cities = pd.read_csv('data/texas_cities.csv')
>>> cities

2. The City column looks good and contains exactly one value. The Geolocation column, on the other hand, contains four variables: latitude, latitude direction, longitude, and longitude direction. Let's split the Geolocation column into four separate columns:

>>> geolocations = cities.Geolocation.str.split(pat='. ',
                                                expand=True)
>>> geolocations.columns = ['latitude', 'latitude direction',
                            'longitude', 'longitude direction']
>>> geolocations

3. Because the original data type for the Geolocation was an object, all the new columns are also objects. Let's change latitude and longitude into floats:

>>> geolocations = geolocations.astype({'latitude':'float',
                                        'longitude':'float'})
>>> geolocations.dtypes
latitude               float64
latitude direction      object
longitude              float64
longitude direction     object
dtype: object

4. Concatenate these new columns with the City column from the original:

>>> cities_tidy = pd.concat([cities['City'], geolocations],
                            axis='columns')
>>> cities_tidy