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So how'd you do?

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Allow me show you how to combine
all of my Billboard chart data and

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all of my Spotify chart data.

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We have two data frames
containing Billboard data.

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billboard contains data from 2017 and
2018,

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billboard19 contains 2019 data.

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We wanna concatenate these datasets into
a new data frame called billboard_all.

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The required argument is a Python
list containing the data frames.

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We don't need any optional
arguments in this case.

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So we type billboard_all

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= pd.concat([billboard,

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underscore billboard19.]).

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Let's do the same with
the two Spotify dataframes.

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Spotify_all = pd.concat,

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([spotify, spotify19]).

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Let's do a quick confidence check.

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Billboard.shape, billboard19.shape.

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Some quick math, billboard_all
should have roughly 12,800 rows.

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Billboard_all.shape.

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And they all have 7 columns, great.

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I'll do the same for
Spotify, spotify.shape,

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spotify19.shape.

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Let's see, 20 and 7, let's call it 54,

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spotify_all should have around
26,100 rows and 5 columns.

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spotify_all.shape.

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Now we have one large data frame with
30 months of Billboard chart data and

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another large data frame with
30 months of Spotify chart data.

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In the next video, we'll create our
final dataset that is merged and

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concatenated from smaller datasets,
all using the pandas tool.

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Can't wait.