{"id":141,"date":"2017-12-29T10:34:49","date_gmt":"2017-12-29T10:34:49","guid":{"rendered":"http:\/\/fcpython.com\/?p=141"},"modified":"2020-12-18T20:10:10","modified_gmt":"2020-12-18T20:10:10","slug":"series","status":"publish","type":"post","link":"https:\/\/fcpython.com\/data-analysis\/series","title":{"rendered":"Series"},"content":{"rendered":"
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If you have just taken a look at NumPy’s arrays, then Pandas’ series will be really easy to pick up.<\/p>\n

The key difference between these two data types is that series allow us to label our axes, making our grids a lot easier to read, index and utilise.<\/p>\n

Let’s fire up NumPy and Pandas and create some series. Remember to install these modules if you haven’t already.<\/p>\n<\/div>\n<\/div>\n<\/div>\n

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In [1]:<\/div>\n
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<\/span>import<\/span> numpy<\/span> as<\/span> np<\/span>\r\nimport<\/span> pandas<\/span> as<\/span> pd<\/span>\r\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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In [2]:<\/div>\n
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<\/span>Capacity<\/span> =<\/span> pd<\/span>.<\/span>Series<\/span>(<\/span>data<\/span>=<\/span>[<\/span>60432<\/span>,<\/span>55097<\/span>,<\/span>39460<\/span>])<\/span>\r\nCapacity<\/span>\r\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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0    60432\r\n1    55097\r\n2    39460\r\ndtype: int64<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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So there we have our first series, created from a list of [100,200,300]. You’ll notice that this looks quite different from our previous lists and arrays because we have an index running alongside it.<\/p>\n
What is really cool about series, is that they allow us to change these index labels:<\/p>\n<\/div>\n<\/div>\n<\/div>\n
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In [3]:<\/div>\n
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<\/span>Capacity<\/span> =<\/span> pd<\/span>.<\/span>Series<\/span>(<\/span>data<\/span>=<\/span>[<\/span>60432<\/span>,<\/span>55097<\/span>,<\/span>39460<\/span>],<\/span>\r\n                     index<\/span>=<\/span>[<\/span>"Emirates Stadium"<\/span>,<\/span>"Etihad Stadium"<\/span>,<\/span>"Elland Road"<\/span>])<\/span>\r\nCapacity<\/span>\r\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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Emirates Stadium    60432\r\nEtihad Stadium      55097\r\nElland Road         39460\r\ndtype: int64<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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Passing an index argument changes the index labels – our data is now so much easier to read when we need to. Easier to select, too:<\/p>\n<\/div>\n<\/div>\n<\/div>\n
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In [4]:<\/div>\n
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<\/span>Capacity<\/span>[<\/span>"Elland Road"<\/span>]<\/span>\r\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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39460<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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In this example, our stadium capacities and labels were in two separate lists. We can do the same thing with a dictionary:<\/p>\n<\/div>\n<\/div>\n<\/div>\n
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In [5]:<\/div>\n
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<\/span>CapacityDict<\/span> =<\/span> {<\/span>'Ewood Park'<\/span>:<\/span>31367<\/span>,<\/span>\r\n                'Liberty Stadium'<\/span>:<\/span>20937<\/span>,<\/span>\r\n                'Portman Road'<\/span>:<\/span>30311<\/span>}<\/span>\r\n\r\nCapacity<\/span> =<\/span> pd<\/span>.<\/span>Series<\/span>(<\/span>CapacityDict<\/span>)<\/span>\r\nCapacity<\/span>\r\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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Ewood Park         31367\r\nLiberty Stadium    20937\r\nPortman Road       30311\r\ndtype: int64<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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Summary<\/h3>\n
Told you series would be easy to understand. A simple concept, but one that makes our data a bit more comfortable to use – we can now understand data by labels, not just index numbers.<\/p>\n
Pandas’ data frame builds on this further to create labelled grids. Once we understand these we can really get started with data analysis in Python.<\/p>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"
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