WEBVTT

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Had it come to the penthouse group by a section so group operations at one of the most powerful data

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analysis tools and pandas and the functionality and flexibility is almost unlimited.

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And that's why group bias.

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Actually my favorite pandas too.

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So it's simply fun to perform group by operations.

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All right so now I want to give you a very first intuition.

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What we can do with the group by method and let's assume we fill our data frame with a male presence

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us and female presence us and what we can simply do we can calculate the mean or the average age over

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all passengers with the mean method.

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But what if we are interested in the group's specific mean nature.

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So for example the mean age of all female passengers and the mean age of or made passengers and group

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by actually provides us the easiest and most comfortable way to do this and to perform group specific

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operations so actually within one line of code we can transform our data frame into a summary data frame

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where we have had the mean age for all female passengers and the mean age for all male passengers and

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we can simply do this with the group biomass set where we have to pass the key by which we want to split

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our data frame here by the sex key so we have male and female in our sex column and then we apply the

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mean method and by doing so within one line of code we are getting here our new data frame.

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So no Barry I will explain this in more detail in the next videos and we will also practice this in

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more detail.

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So this was just a little outlook and actually this is just the tip of the iceberg of what we can do

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this group by.

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And as I said to the functionality and complexity is unlimited here.

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So we will see this in the next videos maybe we'll dive deep into this topic.

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So I hope to see you there by.