In this section I will be making the actual requests to the API and collecting the successful responses using the function defined below. "mintempm", "maxdewptm", "mindewptm", "maxpressurem", "minpressurem", "precipm"]ĭailySummary = namedtuple( "DailySummary", features) target_date = datetime( 2016, 5, 16)įeatures = [ "date", "meantempm", "meandewptm", "meanpressurem", "maxhumidity", "minhumidity", "maxtempm", Those features are used to define a namedtuple called DailySummary which I'll use to organize the individual request's data in a list of DailySummary tuples.
The features are simply the keys present in the history -> dailysummary portion of the JSON response.
Then I will specify the features that I would like to parse from the responses returned from the API. Next I will initialize the target date to the first day of the year in 2015. By the time this article is published I will have deactivated this one.īASE_URL is a string with two place holders represented by curly brackets. Note you will need to signup for an account with Weather Underground and receive your own API_KEY. Now I will define a couple of constants representing my API_KEY and the BASE_URL of the API endpoint I will be requesting. Let us get started by importing these libraries: from datetime import datetime, timedelta Used to make networked requests to the API Used to process, organize and clean the data Use namedtuples for structured collection of data Used to delay requests to stay under 10 per minute For installation instructions please refer to the listed documentation. Below is a table of the libraries I will be using and their description. To make requests to the Weather Underground history API and process the returned data I will make use of a few standard libraries as well as some popular third party libraries. CITY: The name of the city associated with the state you requested.STATE: The two letter state abbreviation in the United States.YYYYMMDD: A string representing the target date of your request.API_KEY: The API_KEY that Weather Underground provides with your account.The format of the request for the history API resource is as follows: The history API provides a summary of various weather measurements for a city and state on a specific day. Weather Underground provides many different web service API's to access data from but, the one we will be concerned with is their history API. This account provides an API key to access the web service at a rate of 10 requests per minute and up to a total of 500 requests in a day.
Easy weather python code free#
If you would like to follow along with the tutorial you will want to sign up for their free developer account here.
Easy weather python code how to#
In this article, I will describe how to programmatically pull daily weather data from Weather Underground using their free tier of service available for non-commercial purposes. The company provides a swath of API's that are available for both commercial and non-commercial uses. Weather Underground is a company that collects and distributes data on various weather measurements around the globe. Getting Familiar with Weather Underground I will compare the process of building a Neural Network model, interpreting the results and, overall accuracy between the Linear Regression model built in the prior article and the Neural Network model. The final article will focus on using Neural Networks. This article will conclude with a discussion of Linear Regression model testing and validation. I will discuss the importance of understanding the assumptions necessary for using a Linear Regression model and demonstrate how to evaluate the features to build a robust model. The second article will focus on analyzing the trends in the data with the goal of selecting appropriate features for building a Linear Regression model using the statsmodels and scikit-learn Python libraries. Once collected, the data will need to be process and aggregated into a format that is suitable for data analysis, and then cleaned.
I will be using the requests library to interact with the API to pull in weather data since 2015 for the city of Lincoln, Nebraska.
Easy weather python code series#
The data used in this series will be collected from Weather Underground's free tier API web service.
Data collection and processing (this article).The series will be comprised of three different articles describing the major aspects of a Machine Learning project. This is the first article of a multi-part series on using Python and Machine Learning to build models to predict weather temperatures based off data collected from Weather Underground. Part 1: Collecting Data From Weather Underground