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Pandas introduces a new data
structure called a series.

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A series is a single
dimensional container object.

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It's ordered and it's typed,
it's also indexable.

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It's like a Python dictionary and
a NumPy array got together and had a kid.

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It inherits the qualities
of both of its parents.

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You know how you can specify the data
type or d type of a NumPy array?

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Well, the same is true here
of our cute little series.

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You can constrain and ensure that
you're only using the memory and

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space that you need.

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And you already know how powerful
dictionaries are for accessing data.

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You can grab a value by key as well as
check for the existence of a specific key.

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Well, this little series
kid here can do that too.

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The series calls what a dictionary
would refer to as keys, labels.

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You'll also hear the keys defined
as the index of the series.

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There are a couple of differences,
labels are not required to be unique,

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unlike the keys of a dictionary.

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All Pandas data structures come with some
extra tricks, they are missing data aware.

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You'll find familiar NumPy array methods
on the series, but those methods

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are actually taking missing data into
account during their calculations,

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we'll take a look at those here shortly.

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I'm sure you're ready to get your
hands on this new series object, so

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let's explore it a bit and then learn
more about what makes them so powerful