pandas introduces two key objects to Python, series and DataFrames, with the latter arguably being the most useful, but pandas DataFrames can be thought of as series bound together. A series is a sequence of data, like a list in basic Python or a 1D NumPy array. And, like the NumPy array, a series has a single data type, but indexing with a series is different. With NumPy there is not much control over row and column indices; but with a series, each element in the series must have a unique index, name, key, however you want to think about it. The index could consist of strings, such as cities in a nation, with the corresponding elements of the series denoting some statistical value, such as the city's population; or dates, such as trading days for a stock series.
A DataFrame can be thought of as multiple series of common length, with a common index, bound together in a single tabular object. This object resembles a NumPy 2D ndarray, but it is not the same thing. Not all...