Meet DataFrames1:33 with Craig Dennis
A DataFrame is basically just a two dimensional collection of Series.
Good job learning about the power of the single dimensional data structure series. 0:00 Those labels sure do help to locate as well as align exactly what it is that 0:04 we're looking for. 0:09 Good job making through that mini series of lessons. 0:10 The mini series, see the, sorry that's bad. 0:13 Now I think we're ready to move on to the two dimensional version, 0:15 the wonderful and much respected, dataframe. 0:19 I'd love for you to imagine the data range as bunch of series, 0:22 in a line next to each other, one after the other. 0:27 Notice how the labels all line up? 0:30 Let's just squish those together. 0:32 Now we should probably know what each of these are so 0:35 that we can label each of the series, like a column heading. 0:38 Actually, would you look at that? 0:41 We've got rows and columns, just like a spreadsheet. 0:44 And you know how powerful spreadsheets can be. 0:47 You've already seen the power of two-dimensional arrays and 0:49 numBi the matrix, whoa. 0:53 Well, this dataframe structure is even more powerful because the indices 0:54 are labeled. 0:58 And you can very clearly specify what it is that you're referring to. 0:59 Unlike NumPy's in the arrays, 1:03 panda's dataframe can have multiple data types all in one structure, 1:05 like we saw in that example, the number of conferences and their names. 1:08 Let's see if we can't build ourselves some of these dataframes to explore. 1:12 I bet you're dying to get your hands on them. 1:15 Quick reminder, if anything isn't clear, make sure to get a hold of the notebooks, 1:18 and explore around in your own environment. 1:22 Explore, and also ask questions of your fellow students. 1:24 Don't forget to check, and see if you can help a fellow student out with a question. 1:27 Ready, let's do this. 1:32
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