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Python Data Science Handbook
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Introducing Pandas Objects
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:
The Pandas Series Object
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
as generalized NumPy array
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 as specialized dictionary
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.
Constructing Series objects
We've already seen a few ways of constructing a Pandas Series
from scratch; all of them are some version of the following:
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.
The Pandas DataFrame Object
The next fundamental structure in Pandas is the DataFrame
. Like the Series
object discussed in the previous section, the DataFrame
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 a Series
is an analog of a one-dimensional array with flexible indices, a DataFrame
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 a DataFrame
as a sequence of aligned Series
objects. Here, by "aligned" we mean that they share the same index.
To demonstrate this, let's first construct a new Series
listing the area of each of the five states discussed in the previous section:
Now that we have this along with the population
Series from before, we can use a dictionary to construct a single two-dimensional object containing this information:
Like the Series
object, the DataFrame
has an index
attribute that gives access to the index labels:
Additionally, the DataFrame
has a columns
attribute, which is an Index
object holding the column labels:
Thus the DataFrame
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 a DataFrame
as a specialization of a dictionary. Where a dictionary maps a key to a value, a DataFrame
maps a column name to a Series
of column data. For example, asking for the 'area'
attribute returns the Series
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 first row. For a DataFrame
, data['col0']
will return the first column. Because of this, it is probably better to think about DataFrame
s 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 indexing DataFrame
s in Data Indexing and Selection.
Constructing DataFrame objects
A Pandas DataFrame
can be constructed in a variety of ways. Here we'll give several examples.
From a single Series object
A DataFrame
is a collection of Series
objects, and a single-column DataFrame
can be constructed from a single Series
:
From a list of dicts
Any list of dictionaries can be made into a DataFrame
. 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 with NaN
(i.e., "not a number") values:
From a dictionary of Series objects
As we saw before, a DataFrame
can be constructed from a dictionary of Series
objects as well:
From a two-dimensional NumPy array
Given a two-dimensional array of data, we can create a DataFrame
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 in Structured Data: NumPy's Structured Arrays. A Pandas DataFrame
operates much like a structured array, and can be created directly from one:
The Pandas Index Object
We have seen here that both the Series
and DataFrame
objects contain an explicit index that lets you reference and modify data. This Index
object is an interesting structure in itself, and it can be thought of either as an immutable array or as an ordered set (technically a multi-set, as Index
objects may contain repeated values). Those views have some interesting consequences in the operations available on Index
objects. As a simple example, let's construct an Index
from a list of integers:
Index as immutable array
The Index
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 between Index
objects and NumPy arrays is that indices are immutable–that is, they cannot be modified via the normal means:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-34-40e631c82e8a> in <module>()
----> 1 ind[1] = 0
/Users/jakevdp/anaconda/lib/python3.5/site-packages/pandas/indexes/base.py in __setitem__(self, key, value)
1243
1244 def __setitem__(self, key, value):
-> 1245 raise TypeError("Index does not support mutable operations")
1246
1247 def __getitem__(self, key):
TypeError: Index does not support mutable operations
This immutability makes it safer to share indices between multiple DataFrame
s 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. The Index
object follows many of the conventions used by Python's built-in set
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 example indA.intersection(indB)
.