How to download csv file from wunderground

EllisR8 EllisR8 1 1 silver badge 8 8 bronze badges. The problem might be that you are saving the same filename each time. Presumably it should depend on i. AndrewGustar Yes, I did think this but was unsure of how to incorporate i into the file name. Preferably the file names would have each date on it if saved as individual files. Add a comment. Active Oldest Votes. I have sorted it out!

Improve this answer. Well done. Actually, inside the loop, you can just use myurl and myfile i. Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password. Post as a guest Name.

In this post, I will provide the Python code to replicate the work and analyse information for your own city. If you want to skip data downloading and scraping, all of the data I used is available to download here. However, CSV format data with hourly rainfall, temperature, and pressure information can be downloaded from the website with some simple Python scripts.

The data downloaded from Wunderground needs a little bit of work. Ultimately, we want to work out when its raining at certain times of the day and aggregate this result to daily, monthly, and yearly levels. As such, we use Pandas to add month, year, and date columns. Simple stuff in preparation, and we can then output plots as required. At this point, the dataset is relatively clean, and ready for analysis.

If you are not familiar with grouping and aggregation procedures in Python and Pandas, here is another blog post on the topic. With the data cleansed, we now have non-uniform samples of the weather at a given station throughout the year, at a sub-hour level.

To make meaningful plots on this data, we can aggregate over the days and months to gain an overall view and to compare across stations.

At this point, we have two basic data frames which we can use to visualise patterns for the city being analysed. You may select a month, a few months, an entire year, or more depending upon your needs and account level.

When we run the query the default view is the weather calendar. This view provides a simple overview of the result data. It is very useful for comparing data from multiple locations side-by-side. This view shows a single row for each day of the requested range and at the location we selected earlier. You can now see the various weather metrics that are provided in the output data. These include common values such as temperature, precipitation, and wind as well as less common value such as heat index, cloud cover, and wind gusts.

For more information on the details and how to use our various weather metrics, see our Weather Data Documentation. So, if you only need the weather history for a single location, you can stop reading this tutorial now and begin using your weather data immediately. However, often you need historical weather data for more than one location. So, we can continue with an additional step to show that working with multiple locations is as easy as working with one.

To add a second location, we need to click on the Locations button at the top of the window and then follow the steps from earlier to add an additional location manually. For this exercise, we can add any location that interests us, including international locations such as Paris France. Then, when we rerun the weather query, we can see that the calendar view now shows the two locations together for comparison. If we gave each location a friendly name earlier, that name will be shown in the calendar view to allow us to easily differentiate them.