k-NN imputation
You have set up a DIY home weather station, which uses a bunch of different sensors (for humidity, temperature, etc), for a school project. For the past 4 days you have been collecting the data directly into your computer, but today you realised that some data is missing! This is probably due to sensor failure, but you don't want to spend another 4 days measuring because you will miss the deadline for the project! So you decide to use a value imputation method to fill in the missing data with approximate values.
The data you have collected is inside a Pandas DataFrame and the cells containg the numpy.nan value are the missing data. For all numpy.nan values in data, you must find the k Nearest Neighbors and then replace the numpy.nan with the average of the found neighbors.
Input: A Pandas DataFrame with the data, the number of k neighbors.
Output: A Pandas DataFrame with the imputed data.
CheckiO Extensions allow you to use local files to solve missions. More info in a blog post.
In order to install CheckiO client you'll need installed Python (version at least 3.8)
Install CheckiO Client first:
pip3 install checkio_client
Configure your tool
checkio --domain=py config --key=
Sync solutions into your local folder
checkio sync
(in beta testing) Launch local server so your browser can use it and sync solution between local file end extension on the fly. (doesn't work for safari)
checkio serv -d
Alternatevly, you can install Chrome extension or FF addon
checkio install-plugin
checkio install-plugin --ff
checkio install-plugin --chromium
Read more here about other functionality that the checkio client provides. Feel free to submit an issue in case of any difficulties.
