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Meet Series1:14 with Craig Dennis
A Series is like a Python dictionary and a NumPy array got together and had a kid.
A series is a one-dimensional array with additional labels that allow you to access specific values.
It feels an awful lot like a Python dictionary wrapped around a NumPy Array.
We are just about to start exploring how to create and interact with a Series data structure. I'm going to do that in the next step.
There are links throughout each of these Instruction Steps for places to go and learn more.
Pandas introduces a new data structure called a series. 0:00 A series is a single dimensional container object. 0:03 It's ordered and it's typed, it's also indexable. 0:06 It's like a Python dictionary and a NumPy array got together and had a kid. 0:09 It inherits the qualities of both of its parents. 0:14 You know how you can specify the data type or d type of a NumPy array? 0:17 Well, the same is true here of our cute little series. 0:21 You can constrain and ensure that you're only using the memory and 0:24 space that you need. 0:27 And you already know how powerful dictionaries are for accessing data. 0:28 You can grab a value by key as well as check for the existence of a specific key. 0:31 Well, this little series kid here can do that too. 0:36 The series calls what a dictionary would refer to as keys, labels. 0:39 You'll also hear the keys defined as the index of the series. 0:43 There are a couple of differences, labels are not required to be unique, 0:46 unlike the keys of a dictionary. 0:51 All Pandas data structures come with some extra tricks, they are missing data aware. 0:53 You'll find familiar NumPy array methods on the series, but those methods 0:57 are actually taking missing data into account during their calculations, 1:01 we'll take a look at those here shortly. 1:04 I'm sure you're ready to get your hands on this new series object, so 1:06 let's explore it a bit and then learn more about what makes them so powerful 1:10
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