Extract Episodes into DataFrame

extract the episodes and their runtime into a dataframe

an episode has the following format

The episode is in an item tag, and the runtime in itunes:duration tags

To extract the episodes and their runtimes from the provided XML file into a dataframe, I'll first read the file and then parse the XML to find the relevant information. Let's start by loading and examining the content of the XML file.

The XML file appears to be structured as expected, with the usual declarations and namespaces for an RSS feed. I will now parse the XML content to extract the episode titles and their runtimes, and then organize this information into a dataframe.

It seems there was an issue with handling the namespaces in the XML. I'll adjust the approach to correctly parse the namespaced elements for the episode titles and durations. Let's try again.

The extraction was successful. Here are the first few rows of the dataframe containing episode titles and their runtimes in seconds:

If you need more information or further analysis of this data, feel free to let me know!

also extract the pubDate

The dataframe has been updated to include the publication date of each episode. Here are the first few rows:

If there's anything else you need, such as formatting the dates or additional data processing, please let me know!

plot the episodes and their runtime over time

Here is the plot showing the runtimes of the episodes over time. Each point on the graph represents an episode, with its publication date on the x-axis and its runtime in seconds on the y-axis.

The plot provides a visual representation of how the durations of the episodes have varied across different publication dates.

If you need any additional analysis or adjustments to the plot, please feel free to let me know!

plot a linear trend over time to show how episode lengths have on average increased

The updated plot now includes a linear trendline, illustrating how the average length of episodes has changed over time. This trendline gives a visual indication of whether the durations have generally increased, decreased, or remained relatively stable across the dataset's time span.

If there's more detailed analysis or another type of trend representation you're interested in, feel free to let me know!