# Filtering

The `BedTool.filter()` method lets you pass in a function that accepts an `Interval` as its first argument and returns True for False. This allows you to perform “grep”-like operations on `BedTool` objects. For example, here’s how to get a new `BedTool` containing features from `a` that are more than 100 bp long:

    >>> a = pybedtools.example_bedtool('a.bed')
    >>> b = a.filter(lambda x: len(x) > 100)
    >>> print(b)
    chr1        150     500     feature3        0       -

The `filter()` method will pass its `*args` and `**kwargs` to the function provided. So here is a more generic case, where the function is defined once and different arguments are passed in for filtering on different lengths:

    >>> def len_filter(feature, L):
    ...     "Returns True if feature is longer than L"
    ...     return len(feature) > L

Now we can pass different lengths without defining a new function for each length of interest, like this:

    >>> a = pybedtools.example_bedtool('a.bed')

    >>> print(a.filter(len_filter, L=10))
    chr1        1       100     feature1        0       +
    chr1        100     200     feature2        0       +
    chr1        150     500     feature3        0       -
    chr1        900     950     feature4        0       +

    >>> print(a.filter(len_filter, L=99))
    chr1        100     200     feature2        0       +
    chr1        150     500     feature3        0       -

    >>> print(a.filter(len_filter, L=200))
    chr1        150     500     feature3        0       -

See Using BedTool objects as iterators/generators for more advanced and space-efficient usage of `filter()` using iterators.

Note that we could have used the built-in Python function, `filter()`, but that would have returned an iterator that we would have to construct a new `pybedtools.BedTool` out of. The `BedTool.filter()` method returns a ready-to-use `BedTool` object, which allows embedding of `BedTool.filter()` calls in a chain of commands, e.g.:

    >>> a.intersect(b).filter(lambda x: len(x) < 100).merge()

## Fast filtering functions in Cython

The `featurefuncs` module contains some ready-made functions written in Cython that will be faster than pure Python equivalents. For example, there are `greater_than()` and `less_than()` functions, which are about 70% faster. In IPython:

    >>> from pybedtools.featurefuncs import greater_than

    >>> len(a)
    310456

    >>> def L(x,width=100):
    ...     return len(x) > 100

    >>> # The %timeit command is from IPython, and won't work
    >>> # in a regular Python script:
    >>> %timeit a.filter(greater_than, 100)
    1 loops, best of 3: 1.74 s per loop

    >>> %timeit a.filter(L, 100)
    1 loops, best of 3: 2.96 s per loop