PandasColumn.integer_column("temperatureHighTime", min_value=0),
PandasColumn.float_column("temperatureLow",
min_value=30.0,
max_value=100.0),
PandasColumn.integer_column("temperatureLowTime", min_value=0),
PandasColumn.float_column("dewPoint", min_value=10.0, max_value=70.0),
PandasColumn.float_column("humidity", min_value=0.0, max_value=1.0),
PandasColumn.float_column("pressure", min_value=900.0, max_value=1200.0),
PandasColumn.float_column("windSpeed", min_value=0.0, max_value=100.0),
PandasColumn.float_column("windGust", min_value=0.0, max_value=40.0),
PandasColumn.integer_column("windGustTime", min_value=0),
PandasColumn.integer_column("windBearing", min_value=0),
PandasColumn.float_column("cloudCover", min_value=0.0, max_value=1.0),
PandasColumn.integer_column("uvIndex", min_value=0, max_value=12),
PandasColumn.integer_column("uvIndexTime", min_value=0),
PandasColumn.numeric_column("visibility", min_value=0.0, max_value=10.0),
PandasColumn.float_column("ozone", min_value=200.0, max_value=500.0),
]
WeatherDataFrame = create_dagster_pandas_dataframe_type(
name="WeatherDataFrame",
columns=WeatherDataFrameSchema,
event_metadata_fn=compute_weather_dataframe_event_metadata,
)
def validate_snapshot_timeseries(_, training_set_data):
if not isinstance(training_set_data, tuple):
return TypeCheck(False)
if len(training_set_data) != 2:
PandasColumn.float_column('temperatureLow',
min_value=30.0,
max_value=100.0),
PandasColumn.integer_column('temperatureLowTime', min_value=0),
PandasColumn.float_column('dewPoint', min_value=10.0, max_value=70.0),
PandasColumn.float_column('humidity', min_value=0.0, max_value=1.0),
PandasColumn.float_column('pressure', min_value=900.0, max_value=1200.0),
PandasColumn.float_column('windSpeed', min_value=0.0, max_value=100.0),
PandasColumn.float_column('windGust', min_value=0.0, max_value=40.0),
PandasColumn.integer_column('windGustTime', min_value=0),
PandasColumn.integer_column('windBearing', min_value=0),
PandasColumn.float_column('cloudCover', min_value=0.0, max_value=1.0),
PandasColumn.integer_column('uvIndex', min_value=0, max_value=12),
PandasColumn.integer_column('uvIndexTime', min_value=0),
PandasColumn.numeric_column('visibility',
min_value=0.0,
max_value=10.0,
expected_dtypes={'int64', 'float64'}),
PandasColumn.float_column('ozone', min_value=200.0, max_value=500.0),
PandasColumn.boolean_column('didRain', non_nullable=True),
]
WeatherDataFrame = create_dagster_pandas_dataframe_type(
name='WeatherDataFrame',
columns=WeatherDataFrameSchema,
event_metadata_fn=compute_weather_dataframe_event_metadata,
)
def validate_snapshot_timeseries(_, training_set_data):
if not isinstance(training_set_data, tuple):
return TypeCheck(False)
