old_contents: import pandas as pd
  import pytest
  
  from vega_datasets import data, local_data
  from vega_datasets.core import Dataset
  
  
  @pytest.mark.parametrize('name', Dataset.list_local_datasets())
  def test_load_local_dataset(name):
      loader = getattr(data, name.replace('-', '_'))
      local_loader = getattr(local_data, name.replace('-', '_'))
  
      df1 = data(name)
      df2 = loader()  # equivalent to data.dataset_name()
      df3 = local_data(name)
      df4 = local_loader()  # equivalent to local_data.dataset_name()
      assert df1.equals(df2)
      assert df1.equals(df3)
      assert df1.equals(df4)
  
      raw1 = loader.raw()
      raw2 = local_loader.raw()
      raw3 = data(name, return_raw=True)
      raw4 = local_data(name, return_raw=True)
      assert raw1 == raw2 == raw3 == raw4
      assert type(raw1) is type(raw2) is type(raw3) is type(raw4) is bytes
  
  
  def test_iris_column_names():
      iris = data.iris()
      assert type(iris) is pd.DataFrame
      assert tuple(iris.columns) == ('petalLength', 'petalWidth', 'sepalLength',
                                     'sepalWidth', 'species')
  
      iris = data.iris.raw()
      assert type(iris) is bytes
  
  
  def test_stocks_column_names():
      stocks = data.stocks()
      assert type(stocks) is pd.DataFrame
      assert tuple(stocks.columns) == ('symbol', 'date', 'price')
  
      stocks = data.stocks.raw()
      assert type(stocks) is bytes
  
  
  def test_cars_column_names():
      cars = data.cars()
      assert type(cars) is pd.DataFrame
      assert tuple(cars.columns) == ('Acceleration', 'Cylinders', 'Displacement',
                                     'Horsepower', 'Miles_per_Gallon', 'Name',
                                     'Origin', 'Weight_in_lbs', 'Year')
  
      cars = data.cars.raw()
      assert type(cars) is bytes
  
  
  @pytest.mark.parametrize('name,col', [('cars', 'Year',), ('stocks', 'date'),
                                        ('seattle-weather', 'date'),
                                        ('seattle-temps', 'date'),
                                        ('sf-temps', 'date')])
  def test_date_types(name, col):
      assert data(name)[col].dtype == 'datetime64[ns]'
  
new_contents: import pandas as pd
  import pytest
  
  from vega_datasets import data, local_data
  from vega_datasets.core import Dataset
  
  
  @pytest.mark.parametrize('name', Dataset.list_local_datasets())
  def test_load_local_dataset(name):
      loader = getattr(data, name.replace('-', '_'))
      local_loader = getattr(local_data, name.replace('-', '_'))
  
      df1 = data(name)
      df2 = loader()  # equivalent to data.dataset_name()
      df3 = local_data(name)
      df4 = local_loader()  # equivalent to local_data.dataset_name()
      assert df1.equals(df2)
      assert df1.equals(df3)
      assert df1.equals(df4)
  
      raw1 = loader.raw()
      raw2 = local_loader.raw()
      raw3 = data(name, return_raw=True)
      raw4 = local_data(name, return_raw=True)
      assert raw1 == raw2 == raw3 == raw4
      assert type(raw1) is type(raw2) is type(raw3) is type(raw4) is bytes
  
  
  def test_iris_column_names():
      iris = data.iris()
      assert type(iris) is pd.DataFrame
      assert tuple(iris.columns) == ('petalLength', 'petalWidth', 'sepalLength',
                                     'sepalWidth', 'species')
  
      iris = data.iris.raw()
      assert type(iris) is bytes
  
  
  def test_stocks_column_names():
      stocks = data.stocks()
      assert type(stocks) is pd.DataFrame
      assert tuple(stocks.columns) == ('symbol', 'date', 'price')
  
      stocks = data.stocks.raw()
      assert type(stocks) is bytes
  
  
  def test_cars_column_names():
      cars = data.cars()
      assert type(cars) is pd.DataFrame
      assert tuple(cars.columns) == ('Acceleration', 'Cylinders', 'Displacement',
                                     'Horsepower', 'Miles_per_Gallon', 'Name',
                                     'Origin', 'Weight_in_lbs', 'Year')
  
      cars = data.cars.raw()
      assert type(cars) is bytes
  
  
  @pytest.mark.parametrize('name,col', [('cars', 'Year',), ('stocks', 'date'),
                                        ('la-riots', 'death_date'),
                                        ('seattle-weather', 'date'),
                                        ('seattle-temps', 'date'),
                                        ('sf-temps', 'date')])
  def test_date_types(name, col):
      assert data(name)[col].dtype == 'datetime64[ns]'
  
subject: Add la-riots to date parsing test