Hourly Weather Data

class Hourly:
    def __init__(self, loc: Union[pd.DataFrame, Point, list, str], start=datetime(1890, 1, 1, 0, 0, 0), end=datetime.combine(datetime.today().date() + timedelta(days=10), datetime.max.time()), timezone: Optional[str] = None, model=True, flags=False) -> None:
        """
        Initialize hourly weather data retrieval.
        
        Parameters:
        - loc: Union[pd.DataFrame, Point, list, str]
            - pd.DataFrame: Station data from Stations.fetch()
            - Point: Geographic point for automatic station selection
            - list: List of station IDs  
            - str: Single station ID
        - start: datetime, start of time period (default: 1890-01-01)
        - end: datetime, end of time period (default: 10 days from today)
        - timezone: str, optional IANA timezone name for localization
        - model: bool, whether to include model/forecast data (default: True)
        - flags: bool, whether to include data source flags (default: False)
        """

def expected_rows(self) -> int:
    """
    Return expected number of hourly rows for the date range.
    
    Returns:
    int, expected number of hourly observations
    """

# Temperature measurements (°C)
temp: float       # Air temperature 
dwpt: float       # Dew point temperature

# Humidity and precipitation
rhum: float       # Relative humidity (%)
prcp: float       # Precipitation amount (mm)

# Snow and winter weather  
snow: float       # Snow depth (mm)

# Wind measurements
wdir: float       # Wind direction (degrees, 0-360)
wspd: float       # Wind speed (km/h)
wpgt: float       # Wind gust speed (km/h)

# Atmospheric pressure
pres: float       # Sea level pressure (hPa)

# Solar and weather conditions
tsun: float       # Sunshine duration (minutes)
coco: float       # Weather condition code (1-27)

# Data quality flags (when flags=True)
temp_flag: str    # Temperature data source flag
dwpt_flag: str    # Dew point data source flag
# ... (additional _flag columns for each parameter)

from datetime import datetime
from meteostat import Point, Hourly

# Set time period
start = datetime(2020, 1, 1)
end = datetime(2020, 1, 31)

# Create point for Vancouver, BC
vancouver = Point(49.2497, -123.1193, 70)

# Get hourly data
data = Hourly(vancouver, start, end)
hourly_data = data.fetch()

print(f"Retrieved {len(hourly_data)} hourly observations")
print(hourly_data[['temp', 'rhum', 'prcp', 'wspd']].head())

from datetime import datetime
from meteostat import Stations, Hourly

# Select stations in New York area
stations = Stations().nearby(40.7128, -74.0060, 50000).fetch(5)

# Get hourly data for multiple stations
start = datetime(2020, 6, 1)
end = datetime(2020, 6, 7)

data = Hourly(stations, start, end)
hourly_data = data.fetch()

# Data includes station column for multi-station queries
print(hourly_data.groupby('station')['temp'].mean())

from datetime import datetime
from meteostat import Point, Hourly

# Create point for Tokyo
tokyo = Point(35.6762, 139.6503)

# Get hourly data with Tokyo timezone
start = datetime(2020, 7, 1, 0, 0, 0)
end = datetime(2020, 7, 1, 23, 59, 59)

data = Hourly(tokyo, start, end, timezone='Asia/Tokyo')
hourly_data = data.fetch()

print("Hourly temperatures in Tokyo (JST):")
print(hourly_data[['temp']].head(10))

from datetime import datetime
from meteostat import Point, Hourly

# Get hourly data with source quality flags
location = Point(52.5200, 13.4050)  # Berlin
start = datetime(2020, 12, 1)  
end = datetime(2020, 12, 7)

data = Hourly(location, start, end, flags=True)
hourly_data = data.fetch()

# Examine data sources
print("Data source flags:")
print(hourly_data[['temp', 'temp_flag', 'prcp', 'prcp_flag']].head())

from datetime import datetime
from meteostat import Point, Hourly

# Get only observational data (no model/forecast data)
location = Point(41.8781, -87.6298)  # Chicago
start = datetime(2019, 1, 1)
end = datetime(2019, 1, 31)

data = Hourly(location, start, end, model=False)
observational_data = data.fetch()

print(f"Observational data points: {len(observational_data)}")

# Source flag meanings
"A": str  # High-quality government weather service data
"B": str  # ISD Lite observational data  
"C": str  # SYNOP observational data
"D": str  # METAR aviation weather reports
"E": str  # Model/forecast data (reanalysis, numerical weather prediction)

# Condition code ranges
1-4:   Clear to overcast conditions
5-6:   Fog conditions  
7-13:  Rain and freezing rain
14-16: Snow conditions
17-22: Shower conditions
23-27: Thunderstorm and severe weather

from meteostat import units

# Convert condition code to description
condition_desc = hourly_data['coco'].apply(units.condition)
print(condition_desc.value_counts())

# Data retrieval and analysis
def fetch(self) -> pd.DataFrame: ...
def count(self) -> int: ...
def coverage(self): ...

# Data processing
def normalize(self): ...
def interpolate(self, limit: int = 3): ...
def aggregate(self, freq: str, spatial: bool = False): ...
def convert(self, units: dict): ...

# Utility methods
def stations(self) -> pd.Index: ...
def clear_cache(self): ...