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In Chapter 2, we looked in detail at methods and tools to access, set, and modify values in NumPy arrays.
These included indexing (e.g., arr[2, 1]), slicing (e.g., arr[:, 1:5]), masking (e.g., arr[arr > 0]), fancy indexing (e.g., arr[0, [1, 5]]), and combinations thereof (e.g., arr[:, [1, 5]]).
Here we'll look at similar means of accessing and modifying values in Pandas Series and DataFrame objects.
If you have used the NumPy patterns, the corresponding patterns in Pandas will feel very familiar, though there are a few quirks to be aware of.
We'll start with the simple case of the one-dimensional Series object, and then move on to the more complicated two-dimesnional DataFrame object.
As we saw in the previous section, a Series object acts in many ways like a one-dimensional NumPy array, and in many ways like a standard Python dictionary.
If we keep these two overlapping analogies in mind, it will help us to understand the patterns of data indexing and selection in these arrays.
In [1]:
import pandas as pd
data = pd.Series([0.25, 0.5, 0.75, 1.0],
index=['a', 'b', 'c', 'd'])
data
Out[1]:
In [2]:
data['b']
Out[2]:
We can also use dictionary-like Python expressions and methods to examine the keys/indices and values:
In [3]:
'a' in data
Out[3]:
In [4]:
data.keys()
Out[4]:
In [5]:
list(data.items())
Out[5]:
Series objects can even be modified with a dictionary-like syntax.
Just as you can extend a dictionary by assigning to a new key, you can extend a Series by assigning to a new index value:
In [6]:
data['e'] = 1.25
data
Out[6]:
This easy mutability of the objects is a convenient feature: under the hood, Pandas is making decisions about memory layout and data copying that might need to take place; the user generally does not need to worry about these issues.
A Series builds on this dictionary-like interface and provides array-style item selection via the same basic mechanisms as NumPy arrays – that is, slices, masking, and fancy indexing.
Examples of these are as follows:
In [7]:
# slicing by explicit index
data['a':'c']
Out[7]:
In [8]:
# slicing by implicit integer index
data[0:2]
Out[8]:
In [9]:
# masking
data[(data > 0.3) & (data < 0.8)]
Out[9]:
In [10]:
# fancy indexing
data[['a', 'e']]
Out[10]:
Among these, slicing may be the source of the most confusion.
Notice that when slicing with an explicit index (i.e., data['a':'c']), the final index is included in the slice, while when slicing with an implicit index (i.e., data[0:2]), the final index is excluded from the slice.
These slicing and indexing conventions can be a source of confusion.
For example, if your Series has an explicit integer index, an indexing operation such as data[1] will use the explicit indices, while a slicing operation like data[1:3] will use the implicit Python-style index.
In [11]:
data = pd.Series(['a', 'b', 'c'], index=[1, 3, 5])
data
Out[11]:
In [12]:
# explicit index when indexing
data[1]
Out[12]:
In [13]:
# implicit index when slicing
data[1:3]
Out[13]:
Because of this potential confusion in the case of integer indexes, Pandas provides some special indexer attributes that explicitly expose certain indexing schemes.
These are not functional methods, but attributes that expose a particular slicing interface to the data in the Series.
First, the loc attribute allows indexing and slicing that always references the explicit index:
In [14]:
data.loc[1]
Out[14]:
In [15]:
data.loc[1:3]
Out[15]:
The iloc attribute allows indexing and slicing that always references the implicit Python-style index:
In [16]:
data.iloc[1]
Out[16]:
In [17]:
data.iloc[1:3]
Out[17]:
A third indexing attribute, ix, is a hybrid of the two, and for Series objects is equivalent to standard []-based indexing.
The purpose of the ix indexer will become more apparent in the context of DataFrame objects, which we will discuss in a moment.
One guiding principle of Python code is that "explicit is better than implicit."
The explicit nature of loc and iloc make them very useful in maintaining clean and readable code; especially in the case of integer indexes, I recommend using these both to make code easier to read and understand, and to prevent subtle bugs due to the mixed indexing/slicing convention.
In [18]:
area = pd.Series({'California': 423967, 'Texas': 695662,
'New York': 141297, 'Florida': 170312,
'Illinois': 149995})
pop = pd.Series({'California': 38332521, 'Texas': 26448193,
'New York': 19651127, 'Florida': 19552860,
'Illinois': 12882135})
data = pd.DataFrame({'area':area, 'pop':pop})
data
Out[18]:
The individual Series that make up the columns of the DataFrame can be accessed via dictionary-style indexing of the column name:
In [19]:
data['area']
Out[19]:
Equivalently, we can use attribute-style access with column names that are strings:
In [20]:
data.area
Out[20]:
This attribute-style column access actually accesses the exact same object as the dictionary-style access:
In [21]:
data.area is data['area']
Out[21]:
Though this is a useful shorthand, keep in mind that it does not work for all cases!
For example, if the column names are not strings, or if the column names conflict with methods of the DataFrame, this attribute-style access is not possible.
For example, the DataFrame has a pop() method, so data.pop will point to this rather than the "pop" column:
In [22]:
data.pop is data['pop']
Out[22]:
In particular, you should avoid the temptation to try column assignment via attribute (i.e., use data['pop'] = z rather than data.pop = z).
Like with the Series objects discussed earlier, this dictionary-style syntax can also be used to modify the object, in this case adding a new column:
In [23]:
data['density'] = data['pop'] / data['area']
data
Out[23]:
This shows a preview of the straightforward syntax of element-by-element arithmetic between Series objects; we'll dig into this further in Operating on Data in Pandas.
In [24]:
data.values
Out[24]:
With this picture in mind, many familiar array-like observations can be done on the DataFrame itself.
For example, we can transpose the full DataFrame to swap rows and columns:
In [25]:
data.T
Out[25]:
When it comes to indexing of DataFrame objects, however, it is clear that the dictionary-style indexing of columns precludes our ability to simply treat it as a NumPy array.
In particular, passing a single index to an array accesses a row:
In [26]:
data.values[0]
Out[26]:
and passing a single "index" to a DataFrame accesses a column:
In [27]:
data['area']
Out[27]:
Thus for array-style indexing, we need another convention.
Here Pandas again uses the loc, iloc, and ix indexers mentioned earlier.
Using the iloc indexer, we can index the underlying array as if it is a simple NumPy array (using the implicit Python-style index), but the DataFrame index and column labels are maintained in the result:
In [28]:
data.iloc[:3, :2]
Out[28]:
Similarly, using the loc indexer we can index the underlying data in an array-like style but using the explicit index and column names:
In [29]:
data.loc[:'Illinois', :'pop']
Out[29]:
The ix indexer allows a hybrid of these two approaches:
In [30]:
data.ix[:3, :'pop']
Out[30]:
Keep in mind that for integer indices, the ix indexer is subject to the same potential sources of confusion as discussed for integer-indexed Series objects.
Any of the familiar NumPy-style data access patterns can be used within these indexers.
For example, in the loc indexer we can combine masking and fancy indexing as in the following:
In [31]:
data.loc[data.density > 100, ['pop', 'density']]
Out[31]:
Any of these indexing conventions may also be used to set or modify values; this is done in the standard way that you might be accustomed to from working with NumPy:
In [32]:
data.iloc[0, 2] = 90
data
Out[32]:
To build up your fluency in Pandas data manipulation, I suggest spending some time with a simple DataFrame and exploring the types of indexing, slicing, masking, and fancy indexing that are allowed by these various indexing approaches.
In [33]:
data['Florida':'Illinois']
Out[33]:
Such slices can also refer to rows by number rather than by index:
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data[1:3]
Out[34]:
Similarly, direct masking operations are also interpreted row-wise rather than column-wise:
In [35]:
data[data.density > 100]
Out[35]:
These two conventions are syntactically similar to those on a NumPy array, and while these may not precisely fit the mold of the Pandas conventions, they are nevertheless quite useful in practice.