enhancingperf.rst

.. currentmodule:: pandas

.. ipython:: python
   :suppress:

   import os
   import csv
   from pandas import DataFrame
   import pandas as pd

   import numpy as np
   np.random.seed(123456)
   randn = np.random.randn
   randint = np.random.randint
   np.set_printoptions(precision=4, suppress=True)

Enhancing Performance

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.

Pure python

We have a DataFrame to which we want to apply a function row-wise.

.. ipython:: python

   df = DataFrame({'a': randn(1000), 'b': randn(1000),'N': 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 by using apply (row-wise):

.. ipython:: python

   %timeit df.apply(lambda x: integrate_f(x['a'], x['b'], x['N']), axis=1)

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.

Plain cython

First we're going to need to import the cython magic function to ipython:

.. ipython:: python

   %load_ext cythonmagic

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.

.. ipython:: python

   %timeit df.apply(lambda x: integrate_f_plain(x['a'], x['b'], x['N']), axis=1)

Already this has shaved a third off, not too bad for a simple copy and paste.

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
      ...:

.. ipython:: python

   %timeit df.apply(lambda x: integrate_f_typed(x['a'], x['b'], x['N']), axis=1)

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)

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

apply_integrate_f(df['a'], df['b'], df['N'])

Use .values to get the underlying ndarray

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.

.. ipython:: python

   %timeit apply_integrate_f(df['a'].values, df['b'].values, df['N'].values)

We've gone another three times faster! 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.

More advanced techniques

There is still scope 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[Py_ssize_t] col_N):
      ...:     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(n):
      ...:         res[i] = integrate_f_typed(col_a[i], col_b[i], col_N[i])
      ...:     return res
      ...:

.. ipython:: python

   %timeit apply_integrate_f_wrap(df['a'].values, df['b'].values, df['N'].values)

This shaves another third off!

Further topics

• Loading C modules into cython.

Read more in the cython docs.

Expression Evaluation via :func:`~pandas.eval` (Experimental)

.. versionadded:: 0.13

The top-level function :func:`~pandas.eval` implements expression evaluation of :class:`~pandas.Series` and :class:`~pandas.DataFrame` objects.

Note

To benefit from using :func:`~pandas.eval` you need to install numexpr. See the :ref:`recommended dependencies section <install.recommended_dependencies>` for more details.

The point of using :func:`~pandas.eval` for expression evaluation rather than plain Python is two-fold: 1) large :class:`~pandas.DataFrame` objects are evaluated more efficiently and 2) large arithmetic and boolean expressions are evaluated all at once by the underlying engine (by default numexpr is used for evaluation).

Note

You should not use :func:`~pandas.eval` for simple expressions or for expressions involving small DataFrames. In fact, :func:`~pandas.eval` is many orders of magnitude slower for smaller expressions/objects than plain ol' Python. A good rule of thumb is to only use :func:`~pandas.eval` when you have a :class:`~pandas.core.frame.DataFrame` with more than 10,000 rows.

:func:`~pandas.eval` supports all arithmetic expressions supported by the engine in addition to some extensions available only in pandas.

Note

The larger the frame and the larger the expression the more speedup you will see from using :func:`~pandas.eval`.

Supported Syntax

These operations are supported by :func:`~pandas.eval`:

• Arithmetic operations except for the left shift (<<) and right shift (>>) operators, e.g., df + 2 * pi / s ** 4 % 42 - the_golden_ratio
• Comparison operations, e.g., 2 < df < df2
• Boolean operations, e.g., df < df2 and df3 < df4 or not df_bool
• list and tuple literals, e.g., [1, 2] or (1, 2)
• Attribute access, e.g., df.a
• Subscript expressions, e.g., df[0]
• Simple variable evaluation, e.g., pd.eval('df') (this is not very useful)

This Python syntax is not allowed:

• Expressions
  • Function calls
  • is/is not operations
  • if expressions
  • lambda expressions
  • list/set/dict comprehensions
  • Literal dict and set expressions
  • yield expressions
  • Generator expressions
  • Boolean expressions consisting of only scalar values
• Statements
  • Neither simple nor compound statements are allowed. This includes things like for, while, and if.

:func:`~pandas.eval` Examples

:func:`~pandas.eval` works wonders for expressions containing large arrays

First let's create 4 decent-sized arrays to play with:

.. ipython:: python

   import pandas as pd
   from pandas import DataFrame, Series
   from numpy.random import randn
   import numpy as np
   nrows, ncols = 20000, 100
   df1, df2, df3, df4 = [DataFrame(randn(nrows, ncols)) for _ in xrange(4)]

Now let's compare adding them together using plain ol' Python versus :func:`~pandas.eval`:

.. ipython:: python

   %timeit df1 + df2 + df3 + df4

.. ipython:: python

   %timeit pd.eval('df1 + df2 + df3 + df4')

Now let's do the same thing but with comparisons:

.. ipython:: python

   %timeit (df1 > 0) & (df2 > 0) & (df3 > 0) & (df4 > 0)

.. ipython:: python

   %timeit pd.eval('(df1 > 0) & (df2 > 0) & (df3 > 0) & (df4 > 0)')

:func:`~pandas.eval` also works with unaligned pandas objects:

.. ipython:: python

   s = Series(randn(50))
   %timeit df1 + df2 + df3 + df4 + s

.. ipython:: python

   %timeit pd.eval('df1 + df2 + df3 + df4 + s')

Note

Operations such as

1 and 2  # would parse to 1 & 2, but should evaluate to 2
3 or 4  # would parse to 3 | 4, but should evaluate to 3
~1  # this is okay, but slower when using eval

should be performed in Python. An exception will be raised if you try to perform any boolean/bitwise operations with scalar operands that are not of type bool or np.bool_. Again, you should perform these kinds of operations in plain Python.

The DataFrame.eval method (Experimental)

In addition to the top level :func:`~pandas.eval` function you can also evaluate an expression in the "context" of a DataFrame.

.. ipython:: python

   df = DataFrame(randn(5, 2), columns=['a', 'b'])
   df.eval('a + b')

Any expression that is a valid :func:`~pandas.eval` expression is also a valid DataFrame.eval expression, with the added benefit that you don't have to prefix the name of the DataFrame to the column you're interested in evaluating.

In addition, you can perform in-line assignment of columns within an expression. This can allow for formulaic evaluation. Only a signle assignement is permitted. It can be a new column name or an existing column name. It must be a string-like.

.. ipython:: python

   df = DataFrame(dict(a = range(5), b = range(5,10)))
   df.eval('c=a+b')
   df.eval('d=a+b+c')
   df.eval('a=1')
   df

Local Variables

You can refer to local variables the same way you would in vanilla Python

.. ipython:: python

   df = DataFrame(randn(5, 2), columns=['a', 'b'])
   newcol = randn(len(df))
   df.eval('b + newcol')

Note

The one exception is when you have a local (or global) with the same name as a column in the DataFrame

df = DataFrame(randn(5, 2), columns=['a', 'b'])
a = randn(len(df))
df.eval('a + b')
NameResolutionError: resolvers and locals overlap on names ['a']

To deal with these conflicts, a special syntax exists for referring variables with the same name as a column

.. ipython:: python
   :suppress:

   a = randn(len(df))

.. ipython:: python

   df.eval('@a + b')

The same is true for :meth:`~pandas.DataFrame.query`

.. ipython:: python

   df.query('@a < b')

.. ipython:: python
   :suppress:

   del a

:func:`~pandas.eval` Parsers

There are two different parsers and and two different engines you can use as the backend.

The default 'pandas' parser allows a more intuitive syntax for expressing query-like operations (comparisons, conjunctions and disjunctions). In particular, the precedence of the & and | operators is made equal to the precedence of the corresponding boolean operations and and or.

For example, the above conjunction can be written without parentheses. Alternatively, you can use the 'python' parser to enforce strict Python semantics.

.. ipython:: python

   expr = '(df1 > 0) & (df2 > 0) & (df3 > 0) & (df4 > 0)'
   x = pd.eval(expr, parser='python')
   expr_no_parens = 'df1 > 0 & df2 > 0 & df3 > 0 & df4 > 0'
   y = pd.eval(expr_no_parens, parser='pandas')
   np.all(x == y)

The same expression can be "anded" together with the word :keyword:`and` as well:

.. ipython:: python

   expr = '(df1 > 0) & (df2 > 0) & (df3 > 0) & (df4 > 0)'
   x = pd.eval(expr, parser='python')
   expr_with_ands = 'df1 > 0 and df2 > 0 and df3 > 0 and df4 > 0'
   y = pd.eval(expr_with_ands, parser='pandas')
   np.all(x == y)

The and and or operators here have the same precedence that they would in vanilla Python.

:func:`~pandas.eval` Backends

There's also the option to make :func:`~pandas.eval` operate identical to plain ol' Python.

Note

Using the 'python' engine is generally not useful, except for testing other :func:`~pandas.eval` engines against it. You will acheive no performance benefits using :func:`~pandas.eval` with engine='python'.

You can see this by using :func:`~pandas.eval` with the 'python' engine is actually a bit slower (not by much) than evaluating the same expression in Python:

.. ipython:: python

   %timeit df1 + df2 + df3 + df4

.. ipython:: python

   %timeit pd.eval('df1 + df2 + df3 + df4', engine='python')

:func:`~pandas.eval` Performance

:func:`~pandas.eval` is intended to speed up certain kinds of operations. In particular, those operations involving complex expressions with large DataFrame/Series objects should see a significant performance benefit. Here is a plot showing the running time of :func:`~pandas.eval` as function of the size of the frame involved in the computation. The two lines are two different engines.

Note

Operations with smallish objects (around 15k-20k rows) are faster using plain Python:

This plot was created using a DataFrame with 3 columns each containing floating point values generated using numpy.random.randn().