So my solution was to use a key to sort on multiple columns with a custom sorting order: import pandas as pdĭef custom_sorting(col: pd.Series) -> pd. Sorting such a vector or any other applicable (mutable input iterator) range of custom objects of type X can be achieved using various methods, especially including the use of standard library algorithms like sort, stablesort, partialsort or partialsortcopy. Also None is not supported as a category and coerced automatically. One of the solutions is to make both columns pd.Categorical and pass the expected order as an argument "categories".īut I had some requirements where I cannot coerce unknown\unexpected values and unfortunately that is what pd.Categorical is doing. I had the same task but with an addition to sort on multiple columns. Any tips on speeding up the code would be appreciated! Not sure how the performance compares to adding, sorting, then deleting a column. Were you able to get it to work with your saved songs. The easiest way to do this is to introduce a static variable currentMonth to hold the current month for all instances of DateObj. Im pretty sure it writes onto the files that allow embedding metadata. 2 Answers Sorted by: 6 A Comparator is the correct approach, you just need to mod the month by the current month before comparing them. The ease at which it goes about its work is brilliant and will save users a lot of time in the long run. It will be of use to DJs for its analysis and playlist-building capabilities. I haven't done any stress testing but I'd imagine this could get slow on very large DataFrames. Anyone have experience using Beatunes on Mac Does the app actually edit metadata directly on the files or is it kept in a database that requires the app to be open/present Im using it. beaTunes5 is a handy tool for anyone with an untamed music library. To me this feels clean, but it uses python operations heavily rather than relying on optimized pandas operations. Sort_by_last_digit = sort_pd(key=lambda x: x) Or, if you have plenty of playlists already, then beaTunes will sort them so that, wherever possible, the best-matched songs always follow each other. Sort_by_month = sort_pd(key=months.index) This also works on multiindex DataFrames and Series objects: months = The pipeline itself can rely on the pumped AudioBuffer s having a sample rate of 44.1kHz, two channels, being signed, and 16 bits/sample (i.e. beaTunes, the app that tidies up your music library and aids in playlist-building, just released a new version that brings improved graphics to PC owners in the form of better HiDPI support (you can think of HiDPI as the Windows equivalent of Apple’s Retina display standard). Sort_by_custom_dict = sort_pd(key=custom_dict.get) You just need to implement the interface, which basically serves as a factory for your pipeline and tells beaTunes which song property to write the result to. The third issue addressed in 5.2.23 deals with the creation of Matchlists. You could create an intermediary series, and set_index on that: df = pd.DataFrame(,], columns=) By clicking on the table header of the Ratingcolumn (not the arrow, but 'Rating'), you can toggle the sort order to first sort by computedor not (note that other columns also offer similar secondary sort orders, for example, the album or artist columns). Note: if a value is not in the list it will be converted to NaN. Now, when you sort the month column it will sort with respect to that list: In : df.sort_values("m") In any case: I hope you enjoy this new feature.Pandas 0.15 introduced Categorical Series, which allows a much clearer way to do this:įirst make the month column a categorical and specify the ordering to use. Of course, after re-ordering like this, you can still fine-tune manually via drag and drop. Another application is workout playlists, when you want to increase intensity one or multiple times (e.g. Whenever the chosen property is circular in nature (mood, key), the selected song is taken as a starting point for a clockwise or counter-clockwise order. Key, mood and others are possible as well. Naturally, BPM is not the only property you can use for this. Creating a playlist can be broken down in a two-step process: 1) selecting tracks, and 2) creating a compelling order. Or ascending multiple times? That's what the Periods control is for. Look up track metadata using acoustic fingerprints. Automatically detect the tonal key of your songs. Here are some more examples of what you can do with beaTunes: Automatically determine BPM (beats per minute) and store the result in iTunes. And what about first ascending and then descending? No problem. And once you have a clean collection, the built-in playlist generator works even better. Obviously a choice between ascending and descending values is useful. To re-order a list, so that all songs are ordered by their BPM values, just select a song in the list, right-click, and select Order Playlist by. The difference is that re-ordering is permanent, while sorting is just temporary. In it's simplest form this is like sorting by clicking on a table column header.
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