iterstuff 1.0.2
Useful tools for working with iterators
License: MIT
Keywords: iterator generator development

iterstuff
=========

**Useful tools for working with iterators**

If the python2 `itertools` module is the Swiss Army Knife of functions for
iterables, `iterstuff` is the cut-down single-blade version that you can keep on
your keyring.

## Lookahead

The Lookahead class is the main feature of iterstuff. It 'wraps' an iterable
and allows:

* Detection of the end of the generator using the `atend` property
* 'Peeking' at the next item to be yielded using the `peek` property

We'll look at examples in a moment, but first here's a summary of usage:

```python
>>> # Create a generator that will yield three integers
>>> g = xrange(3)
>>> # Wrap it in a Lookahead
>>> from iterstuff import Lookahead
>>> x = Lookahead(g)
```
    
Now we can use the properties of the Lookahead to check whether we're at the
start and/or end of the generator sequence, and to look at the next element
that would be yielded:

```python
>>> x.atstart
True
>>> x.atend
False
>>> x.peek
0
```

Let's grab the first element and see how the properties change:

```python
>>> x.next()
0
>>> x.atstart
False
>>> x.atend
False
x.peek
1
```

We have two ways to iterate over a sequence wrapped in a Lookahead:

```python
>>> # The usual way
>>> x = Lookahead(xrange(3))
>>> for y in x: print y
0
1
2

>>> # By checking for the end of the sequence
>>> x = Lookahead(xrange(3))
>>> while not x.atend:
...     y = x.next()
...     print y
...     
0
1
2
```

And we can detect a completely empty Lookahead:

```python
>>> if x.atstart and x.atend:
...    # x is an empty Lookahead
```

The obvious question is: _how is this useful?_

### Repeating a `takewhile`

The [itertools.takewhile](https://docs.python.org/2/library/itertools.html#itertools.takewhile) function
can yield items from an iterable while some condition is satisfied. However,
it only yields items up until the condition is no longer satisfied, then it
stops, **after** testing the next element. Let's see what happens if we
want to use it to break a sequence of characters into letters and digits.

```python
>>> from itertools import takewhile
>>> # Build a generator that returns a sequence
>>> data = iter('abcd123ghi')
>>>
>>> # Ok, let's get the characters that are not digits
>>> print list(takewhile(lambda x: not x.isdigit(), data))
['a', 'b', 'c', 'd']
>>> 
>>> # Great, now let's get the digits
>>> print list(takewhile(lambda x: x.isdigit(), data))
['2', '3']
```

What happened to '1'? When we were processing the non-digits, the `takewhile`
function read the '1' from `data`, passed it to the `lambda` and when that
returned False, terminated. But of course, by then the '1' had already been
consumed, so when we started the second `takewhile`, the first character it
got was '2'.

We can solve this with a Lookahead. Here's a repeatable `takewhile` equivalent
(that's in the `iterstuff` module):

```python
def repeatable_takewhile(predicate, iterable):
    """
    Like itertools.takewhile, but does not consume the first
    element of the iterable that fails the predicate test.
    """
    
    # Assert that the iterable is a Lookahead. The act of wrapping
    # an iterable in a Lookahead consumes the first element, so we
    # cannot do the wrapping inside this function.
    if not isinstance(iterable, Lookahead):
        raise TypeError("The iterable parameter must be a Lookahead")
    
    # Use 'peek' to check if the next element will satisfy the
    # predicate, and yield while this is True, or until we reach
    # the end of the iterable.
    while (not iterable.atend) and predicate(iterable.peek):
        yield iterable.next()
```

Let's see how this behaves:

```python
>>> from iterstuff import repeatable_takewhile, Lookahead
>>> data = Lookahead('abcd123ghi')
>>> print list(repeatable_takewhile(lambda x: not x.isdigit(), data))
['a', 'b', 'c', 'd']
>>> print list(repeatable_takewhile(lambda x: x.isdigit(), data))
['1', '2', '3']
```

### Examine data before it's used

The *pandas* library can build a `DataFrame` from almost any sequence of
records. The `DataFrame` constructor checks the first record to determine the
data types of the columns. If we pass a generator `data` to the `DataFrame`
constructor, almost the first thing that happens is that `data` is turned into
a list, so that *pandas* can access `data[0]` to examine the data types. If
your generator yields many records, though, this is bad - it's just built a
list of those many records in memory, effectively doubling the amount of
memory used (memory to hold the list plus memory to hold the DataFrame).

A Lookahead allows code to peek ahead at the next row. So we could do the
same job as *pandas* in a different way:

```python
# Wrap the data in a Lookahead so we can peek at the first row
peekable = Lookahead(data)

# If we're at the end of the Lookahead, there's no data
if peekable.atend:
    return
    
# Grab the first row so we can look at the data types
first_row = peekable.peek

# ...process the data types...
```

### Simple `pairwise`
    
There's a beautiful recipe in the `itertools` documentation for yielding
pairs from an iterable:

```python
def pairwise(iterable):
    "s -> (s0,s1), (s1,s2), (s2, s3), ..."
    a, b = tee(iterable)
    next(b, None)
    return izip(a, b)
```

Beautiful, but a little complex. We can make a simpler version with a
Lookahead:

```python
def pairwise(iterable):
    "s -> (s0,s1), (s1,s2), (s2, s3), ..."
    it = Lookahead(iterable)
    while not it.atend:
        yield it.next(), it.peek
```

Let's try it:

```python
>>> data = iter('abcd123ghi')
>>> print list(pairwise(data))
[('a', 'b'), ('b', 'c'), ('c', 'd'), ('d', '1'), ('1', '2'), ('2', '3'), ('3', 'g'), ('g', 'h'), ('h', 'i'), ('i', None)]
```

### Chunking

Chunking is like using the repeatable `takewhile`, but for a specific use-case.

Suppose you're reading data from a database: the results of a big query over a
LEFT OUTER JOIN between several tables. Let's create a simplified (but
real-world) example.

We store data that relates to timing of web pages. We store an `event` for
each page, and for each event we store multiple `value`s. Our tables look
something like:

    Event
    ID  Created             Session URL
    01  2014-12-17 01:00:00 ab12f43 http://www.mobify.com/
    02  2014-12-17 01:00:01 ab12f43 http://www.mobify.com/jobs
    ...and so on for millions of events...
    
    Value
    Event_ID  Name              Value
    01        DOMContentLoaded     83
    01        Load                122
    02        DOMContentLoaded     64
    02        Load                345
    ...and so on for millions of values for millions of events...
    
At the end of every day, we process the records for that day, by doing a
query like:

```sql
SELECT *
FROM Event LEFT OUTER JOIN Value ON Event.ID = Value.Event_ID
ORDER BY Event.ID
```

We'll probably end up with something like a SQLAlchemy `ResultProxy` or a
Django `QuerySet` - an iterable thing that yields records (and here we're
assuming that your database will stream the results back to your Python
client so that you can process much more data than you could ever fit into
memory). Let's call that iterable thing `records`.

What we want to do is to process each event. The problem is that if we just
iterate over the `records`:

```python
for record in records:
    print record.ID, record.Created, record.Name, record.Value
```

...we'll get one record per **value** - more than one record per **event**:

    01 2014-12-17 01:00:00 DOMContentLoaded 83
    01 2014-12-17 01:00:00 Load 122
    02 2014-12-17 01:00:01 DOMContentLoaded 64
    02 2014-12-17 01:00:01 Load 345

It's be better if we could handle all the records for one event together, then
all the records for the next event, and so on.

We could use `repeatable_takewhile` to grab all the records belonging to the
same event:

```python
it = Lookahead(records)

while not it.atend:
    current_event_id = it.peek.ID
    event_records = list(
        repeatable_takewhile(
            lambda r: r.ID == current_event_id,
            it
        )
    )
    
    # Now we have just the records for the next event
    ...process...
```
        
But because this is a common use case, Lookahead has a helper function to
make this even easier. The `chunked` function takes a function
to extract a 'key' value from each element, and yields successive
iterables, each of which has records with the same key value.

```python
from iterstuff import chunked
for records_for_events in chunked(
        records,
        lambda r: r.ID
    ):
    # records_for_events is a sequence of records for
    # one event.
    ...process...
```

In fact, we can use chunking in the character class problem we showed earlier:

```python
>>> data = (x for x in 'abcd123ghi')
>>> for charset in chunked(data, lambda c: c.isdigit()):
...     print list(charset)
...     
['a', 'b', 'c', 'd']
['1', '2', '3']
['g', 'h', 'i']
```

## Batching

The `batch` method is a simplification of a common use for `itertools.islice`.

Suppose your generator yields records that you're reading from a file, or a
database. Suppose that there may be many hundreds of thousands of records, or
even millions, so you can't fit them all into memory, and you need to do them in
batches of 1000.

Here's one way to do this using `islice`:

```python
from itertools import islice
CHUNK = 1000
while True:
    # Listify the records so that we can check if
    # there were any returned.
    chunk = list(islice(records, CHUNK))
    if not chunk:
        break
    
    # Process the records in this chunk
    for record in chunk:
        process(record)
```

Or the iterstuff `batch` function will do this for you in a simpler way:

```python
from iterstuff import batch
CHUNK = 1000
for chunk in batch(records, CHUNK):
    # Chunk is an iterable of up to CHUNK records
    for record in chunk:
        process(record)
```
    
Here's an elegant `batch` solution provided by Hamish Lawson for ActiveState recipes:
[http://code.activestate.com/recipes/303279-getting-items-in-batches/](http://code.activestate.com/recipes/303279-getting-items-in-batches/))

```python
from itertools import islice, chain
def batch(iterable, size):
    sourceiter = iter(iterable)
    while True:
        batchiter = islice(sourceiter, size)
        yield chain([batchiter.next()], batchiter)
```
        
Note how this uses a call to `batchiter.next()` to cause `StopIteration` to be
raised when the source iterable is exhausted. Because this consumes an element,
`itertools.chain` needs to be used to 'push' that element back onto the head
of the chunk. Using a Lookahead allows us to peek at the next element of the
iterable and avoid the push.  Here's how `iterstuff.batch` works:

```python
def batch(iterable, size):
    # Wrap an enumeration of the iterable in a Lookahead so that it
    # yields (count, element) tuples
    it = Lookahead(enumerate(iterable))

    while not it.atend:
        # Set the end_count using the count value
        # of the next element.
        end_count = it.peek[0] + size

        # Yield a generator that will then yield up to
        # 'size' elements from 'it'.
        yield (
            element
            for counter, element in repeatable_takewhile(
                # t[0] is the count part of each element
                lambda t: t[0] < end_count,
                it
            )
        )
```

## A Conclusion

Python generators are a wonderful, powerful, flexible language feature. The
`atend` and `peek` properties of the Lookahead class enable a whole set of 
simple recipes for working with generators.

You can see examples of use in the unit tests for this package, and run them
by executing the `tests.py` file directly.