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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:
In [2]:
import numpy as np
import pandas as pd
In [3]:
data = pd.Series([0.25, 0.5, 0.75, 1.0])
data
Out[3]:
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:
In [4]:
data.values
Out[4]:
The index is an array-like object of type pd.Index, which we'll discuss in more detail momentarily.
In [5]:
data.index
Out[5]:
Like with a NumPy array, data can be accessed by the associated index via the familiar Python square-bracket notation:
In [6]:
data[1]
Out[6]:
In [7]:
data[1:3]
Out[7]:
As we will see, though, the Pandas Series is much more general and flexible than the one-dimensional NumPy array that it emulates.
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:
In [7]:
data = pd.Series([0.25, 0.5, 0.75, 1.0],
index=['a', 'b', 'c', 'd'])
data
Out[7]:
And the item access works as expected:
In [8]:
data['b']
Out[8]:
We can even use non-contiguous or non-sequential indices:
In [9]:
data = pd.Series([0.25, 0.5, 0.75, 1.0],
index=[2, 5, 3, 7])
data
Out[9]:
In [10]:
data[5]
Out[10]:
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:
In [8]:
population_dict = {'California': 38332521,
'Texas': 26448193,
'New York': 19651127,
'Florida': 19552860,
'Illinois': 12882135}
population = pd.Series(population_dict)
population
Out[8]:
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:
In [12]:
population['California']
Out[12]:
Unlike a dictionary, though, the Series also supports array-style operations such as slicing:
In [9]:
population['California':'Illinois']
Out[9]:
We'll discuss some of the quirks of Pandas indexing and slicing in Data Indexing and Selection.
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:
In [14]:
pd.Series([2, 4, 6])
Out[14]:
data can be a scalar, which is repeated to fill the specified index:
In [15]:
pd.Series(5, index=[100, 200, 300])
Out[15]:
data can be a dictionary, in which index defaults to the sorted dictionary keys:
In [16]:
pd.Series({2:'a', 1:'b', 3:'c'})
Out[16]:
In each case, the index can be explicitly set if a different result is preferred:
In [17]:
pd.Series({2:'a', 1:'b', 3:'c'}, index=[3, 2])
Out[17]:
Notice that in this case, the Series is populated only with the explicitly identified keys.
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.
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:
In [10]:
area_dict = {'California': 423967, 'Texas': 695662, 'New York': 141297,
'Florida': 170312, 'Illinois': 149995}
area = pd.Series(area_dict)
area
Out[10]:
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:
In [11]:
states = pd.DataFrame({'population': population,
'area': area})
states
Out[11]:
Like the Series object, the DataFrame has an index attribute that gives access to the index labels:
In [20]:
states.index
Out[20]:
Additionally, the DataFrame has a columns attribute, which is an Index object holding the column labels:
In [21]:
states.columns
Out[21]:
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.
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:
In [22]:
states['area']
Out[22]:
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 DataFrames 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 DataFrames in Data Indexing and Selection.
In [23]:
pd.DataFrame(population, columns=['population'])
Out[23]:
In [24]:
data = [{'a': i, 'b': 2 * i}
for i in range(3)]
pd.DataFrame(data)
Out[24]:
Even if some keys in the dictionary are missing, Pandas will fill them in with NaN (i.e., "not a number") values:
In [25]:
pd.DataFrame([{'a': 1, 'b': 2}, {'b': 3, 'c': 4}])
Out[25]:
In [26]:
pd.DataFrame({'population': population,
'area': area})
Out[26]:
In [27]:
pd.DataFrame(np.random.rand(3, 2),
columns=['foo', 'bar'],
index=['a', 'b', 'c'])
Out[27]:
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:
In [28]:
A = np.zeros(3, dtype=[('A', 'i8'), ('B', 'f8')])
A
Out[28]:
In [29]:
pd.DataFrame(A)
Out[29]:
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:
In [13]:
ind = pd.Index([2, 3, 5, 7, 11])
ind
Out[13]:
In [14]:
ind[1]
Out[14]:
In [15]:
ind[::2]
Out[15]:
Index objects also have many of the attributes familiar from NumPy arrays:
In [16]:
print(ind.size, ind.shape, ind.ndim, ind.dtype)
One difference between Index objects and NumPy arrays is that indices are immutable–that is, they cannot be modified via the normal means:
In [17]:
ind[1] = 0
This immutability makes it safer to share indices between multiple DataFrames and arrays, without the potential for side effects from inadvertent index modification.
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:
In [35]:
indA = pd.Index([1, 3, 5, 7, 9])
indB = pd.Index([2, 3, 5, 7, 11])
In [36]:
indA & indB # intersection
Out[36]:
In [37]:
indA | indB # union
Out[37]:
In [38]:
indA ^ indB # symmetric difference
Out[38]:
These operations may also be accessed via object methods, for example indA.intersection(indB).