While for the other columns, 'Artwork', 'Genre IDs' and 'Feed URL', I obtained the data by querying the iTunes API. I extracted the data for the columns, 'Name', 'Episode Count', 'Episode Durations', 'iTunes URL', 'Podcast URL', and 'Description', by parsing the iTunes page of the particular podcast. How the Data is Collected for the Dataframe if a podcast is in the first row in the dataframe, which is indexed as 0 then its text file is named as 0.txt.Ġ2. The name of the text file corresponds to the location of the podcast in the dataframe, i.e. For the cases when this information wasn't available, the corresponding text file is either left empty or only includes the word "empty". Each text file contains all the titles and descriptions of all episodes as a bulk, including the general description, of a podcast, if they were available in its RSS feed (details on this later). There are eleven compressed files in this folder, named such as 01_raw_data.zip, all of which include 10,155 text files, one for each podcast. Podcast URL: The URL link of the podcast's website.ĭescription: The general description of the podcast as written on its iTunes page.Īpart from this dataframe, there is also a corpus of text that you can find under the /data folder. ITunes URL: The URL link to the podcast on iTunes.įeed URL: The URL link of the RSS feed of the podcast. Here, I limit the dataframe to contain only podcasts that have a minimum number of 20 episodes.Įpisode Durations: A list of durations of each episode of a podcast in minutes. Of course, this number is changing, but could still be useful. I also provide what these IDs stand for in a separate file, called genre_IDs.txt.Įpisode Count: The number of episodes released so far (August 2017) from a particular podcast. Genre IDs: A list of genre IDs of the genres that a podcast is categorized in. The columns of this dataframe are:Īrtwork: The link to the artwork of the podcast. The dataframe saved as df_popular_podcasts.csv includes the information of 10,155 of these popular podcasts. For each genre and subgenre, the podcasts are grouped alphabetically from A to Z but also, there is a list of "popular podcasts". On the iTunes website for podcasts there is the list of all genres and subgenres. In the following, I will explain what is in each notebook, and the details of this dataset. I also added 3 different Jupyter notebooks where you can see how exactly I collected this dataset. If you download and decompress 11 files here, you will get ~10,000 text files. The descriptions of all episodes of each podcast is in this text file, which is named after the particular podcast's position in the dataframe and can be found in the zipped files inside the /data folder. The corpus includes one text file for each podcast. The file, df_popular_podcasts.csv, is a Pandas dataframe which includes podcast name, the artwork, its genres, the number of the episodes, the duration of the episodes, three different associated URLs and the general description of the podcast. While you are taking a sound clip from video, it will let you access numerous other features you can use.Here you can find a dataset of approximately 10,000 podcasts that I collected from iTunes, plus a corpus of text which includes the full description of all episodes of these podcasts. Multiple FeaturesĪmong other things, Keevi has multiple features. ![]() Not only is Keevi a fast, easy to use online editor, but also it's flexible. Keevi supports and works perfectly on all kinds of computers and mobile phone devices, whether it's an Android or iPhone device or a MAC or any other computer system. You can strip audio from videos as short as a few minutes and as long as 5 hours or even more. ![]() You also don't have to worry about the length of the videos. You can extract audio from YouTube videos as many times as you want. There is no limit to the number of videos you can convert. Keevi allows you to convert your favorite YouTube videos to mp3 in high quality, providing you with an ultra-fast conversion speed. ![]() A lot of online YouTube video extractors require this, but not Keevi. There is no need to download software before ripping your audio file from a YouTube video. What Makes Keevi Different From The Other YouTube Audio Stripper? Easy to Use Keevi is available online, and it is free. Don't use an online YouTube video extractor that requires you to download software.Once the video is uploaded, you can extract the audio. It is an online tool that lets you access the YouTube video directly from its upload panel. Don’t download a video from YouTube first and then extract the audio file. ![]() Two Things You Shouldn’t Do When You Want To Strip Audio From YouTube
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