To get historical weather data in Python, install the Meteostat library using pip install meteostat or run !pip install meteostat with the exclamation mark prefix ! in a Jupyter Notebook.
If you haven’t already, also install the Matplotlib library using pip install matplotlib.
Then, copy the following code into your programming environment and change the highlighted lines to set your own timeframe (start, end) and GPS location:
# Import Meteostat library and dependencies from datetime import datetime import matplotlib.pyplot as plt from meteostat import Point, Daily # Set time period start = datetime(2023, 1, 1) end = datetime(2023, 12, 31) # Create Point for Stuttgart, Germany location = Point(48.787399767583295, 9.205803269767616) # Get daily data for 2023 data = Daily(location, start, end) data = data.fetch() # Plot line chart including average, minimum and maximum temperature data.plot(y=['tavg', 'tmin', 'tmax']) plt.show()
This code fetches and visualizes the average, minimum, and maximum temperatures for Stuttgart, Germany, for the entire year of 2023 using the Meteostat library.
Here’s the output:
Here are the three highlighted lines:
start = datetime(2023, 1, 1): This sets the start date to January 1, 2023.end = datetime(2023, 12, 31): This sets the end date to December 31, 2023. Together, these lines define the time period for which we want to fetch the weather data.location = Point(48.787399767583295, 9.205803269767616): This creates a geographical point for Stuttgart, Germany using its latitude and longitude GPS coordinates. ThePointclass is used to represent a specific location on Earth.
You can use Google Maps to copy the GPS location of your desired location:
You can try it yourself in the interactive Jupyter notebook (Google Colab):
If you want to become a Python master, get free cheat sheets, and coding books, check out the free Finxter email academy with 150,000 coders like you:
The post Python Code for Getting Historical Weather Data appeared first on Be on the Right Side of Change.
