#100DaysOfCode in Python Transcripts
Chapter: Days 82-84: Data visualization with Plotly
Lecture: Prep 2: useful data structures for plotting
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So that was some prework, but I have not spoke about
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yet is what are we going to plot.
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And there are three graphs I want to make.
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First, I want to make a bar chart of our
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posting activity, what months did we put more content out,
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and what months less.
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Secondly, I want a pie chart of breakdown of our categories,
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so each blog post has one category associated,
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and that will show us what we blog about most.
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Similarly, that also is true for tags,
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tags give an indication what we blog about.
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But we use more tags than categories.
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One blog post can be a ten tags,
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so it's a bit more granular.
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So it still will be another angle,
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or another inside into our data.
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Next up, there are three exercises to get the data
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into a format that I can easily make those three graphs.
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So first of all, I want to have the published entries.
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So I'm going to use Counter,
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to count all the entries by year, month.
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And here's where the helper comes in,
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because we're going to use a list comprehension,
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we've dealt with in, I believe Day 16,
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and we can say pub dates,
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and make a list comprehension.
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And, I can just say for entry in entries,
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and entries is our complete RSS feed broken down
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into nice entries by feedparser.
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And we saw an entry here, laid out.
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So I'm going to look over these entries
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and for every entry, here's the helper,
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I'm going to convert to datetime,
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entry, and I'll take the published fields
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and what's funny, I'm actually going to
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prepare to those two, using the dictionary way.
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But I should actually be able to do a dot notation
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which is much nicer.
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I put that into convert to datetime,
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and convert to datetime, it's actually not
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100 percent accurate.
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It's more like, I mean that was the initial intent,
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but let's actually call
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it date, year, month.
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Because that's actually what it's returning, right?
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So we should make our functions descriptive.
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And, yeah let's give the first five to see if
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I'm going in the right direction.
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And I am.
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And the nice thing about Counter as we've seen in day four
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in the collections module lesson,
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is that I can give it a list of items,
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and it just does a count.
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So if I want to have posts by month,
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so counter can just get this pub dates list,
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and look what happens.
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Wow. Boom.
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I mean I didn't have to keep track of,
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well we saw that in the previous lesson right,
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they can hide it in a manual loophole
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for all the items, keep in account and etc.
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But this is all done, understand the library.
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Secondly, we need to break down the categories.
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So, similar as list comprehension, we're
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going to look over the entries.
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But instead of getting your month,
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I'm going to use the other helper we defined
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and just get category.
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And I'm going to do that on the link.
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And those are not pub dates, those are categories.
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Again, counter is your best friend.
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Tags is almost the same, so I'm going to just copy it over.
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Tags, that is actually a bit more complex.
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Let me go from start, so for entry and entries,
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and here I have an exceptional case
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for a nested for list comprehension.
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For each entry, loop through the tag.
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And each tie has a term, let's lower case
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that to not have to deal with upper and lower case.
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So for each entry, because one entry has a list of tags,
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I'm looping through this list of tags,
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and I'm taking out the term.
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That's what I'm basically doing.
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I lower case that tag, so we have all the tags,
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for all the entries.
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And again, I can use a counter to get that all counted up.
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Let's give most common a limitation of 20,
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and let's print the first five.
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And obviously five then is at the top.
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Right, that was a lot of preparation but the good news,
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is that the data is now in a structure that
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we can easily make plots.
