A series of tools using Jupyter Notebooks that utilize the AerisWeather API to download weather data and load into a pandas DataFrame for data science purposes.
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This notebook will allow the user to fetch current conditions for a list of locations. An optional feature allows the user to specify a list of weather attributes to include which will remove irrelevant data for that specific use case. The user also has the option to utilize a simple snippet that converts the DataFrame into a csv.
Use this notebook when historical data for a list of locations is needed. Building on the previous example, users can provide a date range, or collection of random dates, and generate CSVs with hourly conditions for each day.
Last modified: September 27, 2022