<Data Manipulation with Pandas |Contents |Data Indexing and Selection >

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: theSeries,DataFrame, andIndex.

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

The Pandas Series Object¶

A PandasSeries 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, theSeries wraps both a sequence of values and a sequence of indices, which we can access with thevalues andindex attributes.Thevalues are simply a familiar NumPy array:

Theindex is an array-like object of typepd.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 PandasSeries is much more general and flexible than the one-dimensional NumPy array that it emulates.

Series as generalized NumPy array¶

From what we've seen so far, it may look like theSeries object is basically interchangeable with a one-dimensional NumPy array.The essential difference is the presence of the index: while the Numpy Array has animplicitly defined integer index used to access the values, the PandasSeries has anexplicitly defined index associated with the values.

This explicit index definition gives theSeries 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 as specialized dictionary¶

In this way, you can think of a PandasSeries 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 aSeries 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 PandasSeries makes it much more efficient than Python dictionaries for certain operations.

TheSeries-as-dictionary analogy can be made even more clear by constructing aSeries object directly from a Python dictionary:

By default, aSeries 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, theSeries also supports array-style operations such as slicing:

We'll discuss some of the quirks of Pandas indexing and slicing inData Indexing and Selection.

Constructing Series objects¶

We've already seen a few ways of constructing a PandasSeries from scratch; all of them are some version of the following:

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

whereindex is an optional argument, anddata can be one of many entities.

For example,data can be a list or NumPy array, in which caseindex defaults to an integer sequence:

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

data can be a dictionary, in whichindex 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, theSeries is populated only with the explicitly identified keys.

The Pandas DataFrame Object¶

The next fundamental structure in Pandas is theDataFrame.Like theSeries object discussed in the previous section, theDataFrame can be thought of either as a generalization of a NumPy array, or as a specialization of a Python dictionary.We'll now take a look at each of these perspectives.

DataFrame as a generalized NumPy array¶

If aSeries is an analog of a one-dimensional array with flexible indices, aDataFrame is an analog of a two-dimensional array with both flexible row indices and flexible column names.Just as you might think of a two-dimensional array as an ordered sequence of aligned one-dimensional columns, you can think of aDataFrame as a sequence of alignedSeries objects.Here, by "aligned" we mean that they share the same index.

To demonstrate this, let's first construct a newSeries listing the area of each of the five states discussed in the previous section:

Now that we have this along with thepopulation Series from before, we can use a dictionary to construct a single two-dimensional object containing this information:

Like theSeries object, theDataFrame has anindex attribute that gives access to the index labels:

Additionally, theDataFrame has acolumns attribute, which is anIndex object holding the column labels:

Thus theDataFrame can be thought of as a generalization of a two-dimensional NumPy array, where both the rows and columns have a generalized index for accessing the data.

DataFrame as specialized dictionary¶

Similarly, we can also think of aDataFrame as a specialization of a dictionary.Where a dictionary maps a key to a value, aDataFrame maps a column name to aSeries of column data.For example, asking for the'area' attribute returns theSeries object containing the areas we saw earlier:

Notice the potential point of confusion here: in a two-dimesnional NumPy array,data[0] will return the firstrow. For aDataFrame,data['col0'] will return the firstcolumn.Because of this, it is probably better to think aboutDataFrames as generalized dictionaries rather than generalized arrays, though both ways of looking at the situation can be useful.We'll explore more flexible means of indexingDataFrames inData Indexing and Selection.

Constructing DataFrame objects¶

A PandasDataFrame can be constructed in a variety of ways.Here we'll give several examples.

From a single Series object¶

ADataFrame is a collection ofSeries objects, and a single-columnDataFrame can be constructed from a singleSeries:

From a list of dicts¶

Any list of dictionaries can be made into aDataFrame.We'll use a simple list comprehension to create some data:

Even if some keys in the dictionary are missing, Pandas will fill them in withNaN (i.e., "not a number") values:

From a dictionary of Series objects¶

As we saw before, aDataFrame can be constructed from a dictionary ofSeries objects as well:

From a two-dimensional NumPy array¶

Given a two-dimensional array of data, we can create aDataFrame with any specified column and index names.If omitted, an integer index will be used for each:

From a NumPy structured array¶

We covered structured arrays inStructured Data: NumPy's Structured Arrays.A PandasDataFrame operates much like a structured array, and can be created directly from one:

The Pandas Index Object¶

We have seen here that both theSeries andDataFrame objects contain an explicitindex that lets you reference and modify data.ThisIndex object is an interesting structure in itself, and it can be thought of either as animmutable array or as anordered set (technically a multi-set, asIndex objects may contain repeated values).Those views have some interesting consequences in the operations available onIndex objects.As a simple example, let's construct anIndex from a list of integers:

Index as immutable array¶

TheIndex in many ways operates like an array.For example, we can use standard Python indexing notation to retrieve values or slices:

Index objects also have many of the attributes familiar from NumPy arrays:

One difference betweenIndex objects and NumPy arrays is that indices are immutable–that is, they cannot be modified via the normal means:

This immutability makes it safer to share indices between multipleDataFrames and arrays, without the potential for side effects from inadvertent index modification.

Index as ordered set¶

Pandas objects are designed to facilitate operations such as joins across datasets, which depend on many aspects of set arithmetic.TheIndex object follows many of the conventions used by Python's built-inset data structure, so that unions, intersections, differences, and other combinations can be computed in a familiar way:

These operations may also be accessed via object methods, for exampleindA.intersection(indB).

<Data Manipulation with Pandas |Contents |Data Indexing and Selection >