Continuo da qui iniziando **Pandas**, copio qui.

At the very basic level, Pandas objects can be thought of as enhanced versions of NumPy structured arrays in which the rows and columns are identified with labels rather than simple integer indices. As we will see during the course of this chapter, Pandas provides a host of useful tools, methods, and functionality on top of the basic data structures, but nearly everything that follows will require an understanding of what these structures are. Thus, before we go any further, let’s introduce these three fundamental Pandas data structures: the `Series`

, `DataFrame`

, and `Index`

.

We will start our code sessions with the standard NumPy and Pandas imports:

**Gli oggetti **`Series`

A Pandas `Series`

is a one-dimensional array of indexed data. It can be created from a list or array as follows:

As we see in the output, the `Series`

wraps both a sequence of values and a sequence of indices, which we can access with the values and index attributes. The values are simply a familiar NumPy array:

The `index`

is an array-like object of type `pd.Index`

, which we’ll discuss in more detail momentarily.

Like with a NumPy array, data can be accessed by the associated `index`

via the familiar Python square-bracket notation:

As we will see, though, the Pandas `Series`

is much more general and flexible than the one-dimensional NumPy array that it emulates.

`Series`

come NumPy array generalizzato

From what we’ve seen so far, it may look like the `Series`

object is basically interchangeable with a one-dimensional NumPy array. The essential difference is the presence of the `index`

: while the Numpy Array has an implicitly defined integer index used to access the values, the Pandas `Series`

has an explicitly defined `index`

associated with the values.

This explicit `index`

definition gives the `Series`

object additional capabilities. For example, the `index`

need not be an integer, but can consist of values of any desired type. For example, if we wish, we can use strings as an index, and the item access works as expected:

We can even use non-contiguous or non-sequential indices:

`Series`

come dictionary specializzato

In this way, you can think of a Pandas `Series`

a bit like a specialization of a Python dictionary. A dictionary is a structure that maps arbitrary keys to a set of arbitrary values, and a `Series`

is a structure which maps typed keys to a set of typed values. This typing is important: just as the type-specific compiled code behind a NumPy array makes it more efficient than a Python list for certain operations, the type information of a Pandas `Series`

makes it much more efficient than Python dictionaries for certain operations.

The Series-as-dictionary analogy can be made even more clear by constructing a `Series`

object directly from a Python dictionary:

By default, a `Series`

will be created where the `index`

is drawn from the sorted keys. From here, typical dictionary-style item access can be performed:

Unlike a dictionary, though, the `Series`

also supports array-style operations such as slicing:

We’ll discuss some of the quirks of Pandas indexing and slicing in Data Indexing and Selection.

**Costruire oggetti Series**

We’ve already seen a few ways of constructing a Pandas `Series`

from scratch; all of them are some version of the following:

`pd.Series(data, index=index)`

where `index`

is an optional argument, and `data`

can be one of many entities.

For example, `data`

can be a list or NumPy array, in which case `index`

defaults to an integer sequence:

`data`

can be a scalar, which is repeated to fill the specified `index`

:

`data`

can be a dictionary, in which `index`

defaults to the sorted dictionary keys:

In each case, the `index`

can be explicitly set if a different result is preferred:

Notice that in this case, the `Series`

is populated only with the explicitly identified keys.