00:00 There is some prework necessary to
00:02 get to plotting, but we can now do it.
00:04 Let's start by transposing the
00:06 post by month in X and Y axis.
00:09 Then it becomes pretty easy.
00:13 You can just type data
00:17 equals a list,
00:19 bar, taking, X equals X,
00:22 Y equals Y.
00:28 You can then say plotly offline,
00:32 you have to specify the mode, iplot,
00:36 data, and you can give it a file name.
00:42 Look at that.
00:44 Even has numbers if you hover over the months.
00:48 It also posted when I was preparing to a URL
00:53 so you can access those here.
00:55 That's a nice feature of plotly that
00:57 you can make your work easily accessible.
01:01 Let's move on to the breakdown of the blog categories.
01:04 The code is very similar actually.
01:07 I'm going to use my transpose helper.
01:09 X and Y now make a bit better naming labels and values.
01:14 Again I use the go object but this time I calls with pie
01:20 and I give it labels equals labels and values equals values.
01:25 Then we call plotly again, offline mode, iplot,
01:33 put the pie object in the list and give it a file name.
01:42 Look at that.
01:44 Challenges is our big thing, you probably know by now
01:48 but we do as many articles, relatively.
01:52 We have news and some special and guests.
01:57 Thirdly, the comment tags, similar code
02:01 and to transpose list of tuples,
02:04 and here I have to take the top tags as we find before.
02:13 Tags equals go, again I do a pie chart
02:17 and I could be using other types of graph
02:21 but I find a pie chart being adequate for this type of data.
02:26 I also want to keep it simple.
02:27 So, in the coming days you can
02:29 explore the library further yourself.
02:32 Similarly, has to have labels and values defined.
02:37 Plotly, offline, iplot, and it's important
02:42 to put this in a list, filename,
02:45 just going to say tags for now.
02:51 Cool, right?
02:52 Python learning Twitter Code Challenges.
02:56 Of course similar to the categories
02:58 but here you see also, when we get to a bit
03:01 more granular level you get into Flask and Django.
03:04 It's true that we write quite a bit about those.
03:07 There's even some machine learning, etc.
