.. currentmodule:: pandas
.. ipython:: python
:suppress:
import numpy as np
np.random.seed(123456)
np.set_printoptions(precision=4, suppress=True)
import pandas as pd
pd.options.display.max_rows=8
import os
import csv
.. _enhancingperf.cython:
Cython (Writing C extensions for pandas)
----------------------------------------
For many use cases writing pandas in pure python and numpy is sufficient. In some
computationally heavy applications however, it can be possible to achieve sizeable
speed-ups by offloading work to `cython `__.
This tutorial assumes you have refactored as much as possible in python, for example
trying to remove for loops and making use of numpy vectorization, it's always worth
optimising in python first.
This tutorial walks through a "typical" process of cythonizing a slow computation.
We use an `example from the cython documentation `__
but in the context of pandas. Our final cythonized solution is around 100 times
faster than the pure python.
.. _enhancingperf.pure:
Pure python
~~~~~~~~~~~
We have a DataFrame to which we want to apply a function row-wise.
.. ipython:: python
df = pd.DataFrame({'a': np.random.randn(1000),
'b': np.random.randn(1000),
'N': np.random.randint(100, 1000, (1000)),
'x': 'x'})
df
Here's the function in pure python:
.. ipython:: python
def f(x):
return x * (x - 1)
def integrate_f(a, b, N):
s = 0
dx = (b - a) / N
for i in range(N):
s += f(a + i * dx)
return s * dx
We achieve our result by using ``apply`` (row-wise):
.. code-block:: ipython
In [7]: %timeit df.apply(lambda x: integrate_f(x['a'], x['b'], x['N']), axis=1)
10 loops, best of 3: 174 ms per loop
But clearly this isn't fast enough for us. Let's take a look and see where the
time is spent during this operation (limited to the most time consuming
four calls) using the `prun ipython magic function `__:
.. ipython:: python
%prun -l 4 df.apply(lambda x: integrate_f(x['a'], x['b'], x['N']), axis=1)
By far the majority of time is spend inside either ``integrate_f`` or ``f``,
hence we'll concentrate our efforts cythonizing these two functions.
.. note::
In python 2 replacing the ``range`` with its generator counterpart (``xrange``)
would mean the ``range`` line would vanish. In python 3 ``range`` is already a generator.
.. _enhancingperf.plain:
Plain cython
~~~~~~~~~~~~
First we're going to need to import the cython magic function to ipython (for
cython versions < 0.21 you can use ``%load_ext cythonmagic``):
.. ipython:: python
:okwarning:
%load_ext Cython
Now, let's simply copy our functions over to cython as is (the suffix
is here to distinguish between function versions):
.. ipython::
In [2]: %%cython
...: def f_plain(x):
...: return x * (x - 1)
...: def integrate_f_plain(a, b, N):
...: s = 0
...: dx = (b - a) / N
...: for i in range(N):
...: s += f_plain(a + i * dx)
...: return s * dx
...:
.. note::
If you're having trouble pasting the above into your ipython, you may need
to be using bleeding edge ipython for paste to play well with cell magics.
.. code-block:: ipython
In [4]: %timeit df.apply(lambda x: integrate_f_plain(x['a'], x['b'], x['N']), axis=1)
10 loops, best of 3: 85.5 ms per loop
Already this has shaved a third off, not too bad for a simple copy and paste.
.. _enhancingperf.type:
Adding type
~~~~~~~~~~~
We get another huge improvement simply by providing type information:
.. ipython::
In [3]: %%cython
...: cdef double f_typed(double x) except? -2:
...: return x * (x - 1)
...: cpdef double integrate_f_typed(double a, double b, int N):
...: cdef int i
...: cdef double s, dx
...: s = 0
...: dx = (b - a) / N
...: for i in range(N):
...: s += f_typed(a + i * dx)
...: return s * dx
...:
.. code-block:: ipython
In [4]: %timeit df.apply(lambda x: integrate_f_typed(x['a'], x['b'], x['N']), axis=1)
10 loops, best of 3: 20.3 ms per loop
Now, we're talking! It's now over ten times faster than the original python
implementation, and we haven't *really* modified the code. Let's have another
look at what's eating up time:
.. ipython:: python
%prun -l 4 df.apply(lambda x: integrate_f_typed(x['a'], x['b'], x['N']), axis=1)
.. _enhancingperf.ndarray:
Using ndarray
~~~~~~~~~~~~~
It's calling series... a lot! It's creating a Series from each row, and get-ting from both
the index and the series (three times for each row). Function calls are expensive
in python, so maybe we could minimise these by cythonizing the apply part.
.. note::
We are now passing ndarrays into the cython function, fortunately cython plays
very nicely with numpy.
.. ipython::
In [4]: %%cython
...: cimport numpy as np
...: import numpy as np
...: cdef double f_typed(double x) except? -2:
...: return x * (x - 1)
...: cpdef double integrate_f_typed(double a, double b, int N):
...: cdef int i
...: cdef double s, dx
...: s = 0
...: dx = (b - a) / N
...: for i in range(N):
...: s += f_typed(a + i * dx)
...: return s * dx
...: cpdef np.ndarray[double] apply_integrate_f(np.ndarray col_a, np.ndarray col_b, np.ndarray col_N):
...: assert (col_a.dtype == np.float and col_b.dtype == np.float and col_N.dtype == np.int)
...: cdef Py_ssize_t i, n = len(col_N)
...: assert (len(col_a) == len(col_b) == n)
...: cdef np.ndarray[double] res = np.empty(n)
...: for i in range(len(col_a)):
...: res[i] = integrate_f_typed(col_a[i], col_b[i], col_N[i])
...: return res
...:
The implementation is simple, it creates an array of zeros and loops over
the rows, applying our ``integrate_f_typed``, and putting this in the zeros array.
.. warning::
In 0.13.0 since ``Series`` has internaly been refactored to no longer sub-class ``ndarray``
but instead subclass ``NDFrame``, you can **not pass** a ``Series`` directly as a ``ndarray`` typed parameter
to a cython function. Instead pass the actual ``ndarray`` using the ``.values`` attribute of the Series.
Prior to 0.13.0
.. code-block:: python
apply_integrate_f(df['a'], df['b'], df['N'])
Use ``.values`` to get the underlying ``ndarray``
.. code-block:: python
apply_integrate_f(df['a'].values, df['b'].values, df['N'].values)
.. note::
Loops like this would be *extremely* slow in python, but in Cython looping
over numpy arrays is *fast*.
.. code-block:: ipython
In [4]: %timeit apply_integrate_f(df['a'].values, df['b'].values, df['N'].values)
1000 loops, best of 3: 1.25 ms per loop
We've gotten another big improvement. Let's check again where the time is spent:
.. ipython:: python
%prun -l 4 apply_integrate_f(df['a'].values, df['b'].values, df['N'].values)
As one might expect, the majority of the time is now spent in ``apply_integrate_f``,
so if we wanted to make anymore efficiencies we must continue to concentrate our
efforts here.
.. _enhancingperf.boundswrap:
More advanced techniques
~~~~~~~~~~~~~~~~~~~~~~~~
There is still hope for improvement. Here's an example of using some more
advanced cython techniques:
.. ipython::
In [5]: %%cython
...: cimport cython
...: cimport numpy as np
...: import numpy as np
...: cdef double f_typed(double x) except? -2:
...: return x * (x - 1)
...: cpdef double integrate_f_typed(double a, double b, int N):
...: cdef int i
...: cdef double s, dx
...: s = 0
...: dx = (b - a) / N
...: for i in range(N):
...: s += f_typed(a + i * dx)
...: return s * dx
...: @cython.boundscheck(False)
...: @cython.wraparound(False)
...: cpdef np.ndarray[double] apply_integrate_f_wrap(np.ndarray[double] col_a, np.ndarray[double] col_b, np.ndarray[int] col_N):
...: cdef int i, n = len(col_N)
...: assert len(col_a) == len(col_b) == n
...: cdef np.ndarray[double] res = np.empty(n)
...: for i in range(n):
...: res[i] = integrate_f_typed(col_a[i], col_b[i], col_N[i])
...: return res
...:
.. code-block:: ipython
In [4]: %timeit apply_integrate_f_wrap(df['a'].values, df['b'].values, df['N'].values)
1000 loops, best of 3: 987 us per loop
Even faster, with the caveat that a bug in our cython code (an off-by-one error,
for example) might cause a segfault because memory access isn't checked.