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A Series is pandas' one-dimensional column: a sequence of values that all share one name and one type. Think of it as a single spreadsheet column. 📊

A plain Python list just holds values in order. A Series pairs every value with a label, so each item has both a position and a name you can look up by.

Pass any list to pd.Series and pandas wraps it for you. This line below builds your very first Series from three numbers.

Notice the numbers on the left of your output. That is the index: a label for every value. Without one supplied, pandas auto-numbers from 0.

You can set meaningful labels with the index argument. Now each value is named, which makes lookups read like plain English. 🏷️

Every Series carries a single dtype, like int64 or float64. Shared types are what let pandas run fast math across the whole column at once.

A Series can also carry its own name, separate from the index. That name becomes the column header once the Series joins a DataFrame.

You can pull the two parts out on their own. The .values attribute gives the raw data array; .index gives the labels that tag it.

Hand pd.Series a dictionary and the keys become the index automatically. It is the fastest way to build a labeled column in one step.

len(s) tells you how many values sit inside, and .head() shows just the first few. Both are handy first checks on any Series.

Stack several Series side by side and you get a DataFrame. So mastering the Series means you already understand half of pandas. 🧱

Let's lock in what a Series carries.

You learned a Series is a named, single-typed column where every value has an index label. It is the basic block that DataFrames are built from. Nice work! 🎉