Weather Data Visualization

Visit Open-Meteo: Go to Open-Meteo Documentation.
Select Location: Choose your desired location for which you want to retrieve weather data.
Select Date Range: Specify the start and end dates for the data you need.
Select Hourly Weather Variables: Ensure you select the following variables to match the configuration shown below:
- Temperature (°C)
- Relative Humidity (%)
- Rain (mm)
- Snowfall (cm)
- Snow Depth (cm)
- Surface Pressure (hPa)
- Cloud Cover (%)
- Wind Speed (m/s)
- Wind Direction (°)
- Wind Gusts (m/s)
Generate Python Code: Use the provided Python script to fetch and generate the hourly_weather_data.csv file.
Upload CSV File: Once you have the CSV file, upload it using the "Upload hourly_weather_data.csv" button below to visualize the data in graphs.

Python Script to Fetch Weather Data


import requests
import pandas as pd

# Replace with your desired parameters
latitude = 52.52  # Example: Berlin
longitude = 13.405
start_date = '2023-01-01'
end_date = '2023-01-31'
hourly_vars = [
    'temperature_2m', 'relative_humidity_2m', 'rain', 'snowfall',
    'snow_depth', 'surface_pressure', 'cloud_cover', 'wind_speed_10m',
    'wind_direction_10m', 'wind_gusts_10m'
]

url = 'https://api.open-meteo.com/v1/forecast'
params = {
    'latitude': latitude,
    'longitude': longitude,
    'start_date': start_date,
    'end_date': end_date,
    'hourly': ','.join(hourly_vars),
    'timezone': 'UTC'
}

response = requests.get(url, params=params)
data = response.json()

# Convert to DataFrame
df = pd.DataFrame(data['hourly'])
df['date'] = pd.to_datetime(df['time'])

# Reorder columns
cols = ['date'] + [var for var in hourly_vars]
df = df[cols]

# Save to CSV
df.to_csv('hourly_weather_data.csv', index=False)
print('CSV file generated successfully.')

Upload hourly_weather_data.csv