pandas
python
import numpy as np np.random.seed(123456) np.set_printoptions(precision=4, suppress=True) import pandas as pd pd.options.display.max_rows=15
import os import csv
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.
We have a DataFrame to which we want to apply a function row-wise.
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:
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):
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:
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.
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
):
python
%load_ext Cython
Now, let's simply copy our functions over to cython as is (the suffix is here to distinguish between function versions):
- 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.
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.
We get another huge improvement simply by providing type information:
- 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 ...:
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:
python
%prun -l 4 df.apply(lambda x: integrate_f_typed(x['a'], x['b'], x['N']), axis=1)
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.
- 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.
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:
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.
There is still hope for improvement. Here's an example of using some more advanced cython techniques:
- 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 ...:
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.
A recent alternative to statically compiling cython code, is to use a dynamic jit-compiler, numba
.
Numba gives you the power to speed up your applications with high performance functions written directly in Python. With a few annotations, array-oriented and math-heavy Python code can be just-in-time compiled to native machine instructions, similar in performance to C, C++ and Fortran, without having to switch languages or Python interpreters.
Numba works by generating optimized machine code using the LLVM compiler infrastructure at import time, runtime, or statically (using the included pycc tool). Numba supports compilation of Python to run on either CPU or GPU hardware, and is designed to integrate with the Python scientific software stack.
Note
You will need to install numba
. This is easy with conda
, by using: conda install numba
, see installing using miniconda<install.miniconda>
.
Note
As of numba
version 0.20, pandas objects cannot be passed directly to numba-compiled functions. Instead, one must pass the numpy
array underlying the pandas
object to the numba-compiled function as demonstrated below.
Using numba
to just-in-time compile your code. We simply take the plain python code from above and annotate with the @jit
decorator.
import numba
@numba.jit
def f_plain(x):
return x * (x - 1)
@numba.jit
def integrate_f_numba(a, b, N):
s = 0
dx = (b - a) / N
for i in range(N):
s += f_plain(a + i * dx)
return s * dx
@numba.jit
def apply_integrate_f_numba(col_a, col_b, col_N):
n = len(col_N)
result = np.empty(n, dtype='float64')
assert len(col_a) == len(col_b) == n
for i in range(n):
result[i] = integrate_f_numba(col_a[i], col_b[i], col_N[i])
return result
def compute_numba(df):
result = apply_integrate_f_numba(df['a'].values, df['b'].values, df['N'].values)
return pd.Series(result, index=df.index, name='result')
Note that we directly pass numpy
arrays to the numba function. compute_numba
is just a wrapper that provides a nicer interface by passing/returning pandas objects.
In [4]: %timeit compute_numba(df)
1000 loops, best of 3: 798 us per loop
numba
can also be used to write vectorized functions that do not require the user to explicitly loop over the observations of a vector; a vectorized function will be applied to each row automatically. Consider the following toy example of doubling each observation:
import numba
def double_every_value_nonumba(x):
return x*2
@numba.vectorize
def double_every_value_withnumba(x):
return x*2
# Custom function without numba
In [5]: %timeit df['col1_doubled'] = df.a.apply(double_every_value_nonumba)
1000 loops, best of 3: 797 us per loop
# Standard implementation (faster than a custom function)
In [6]: %timeit df['col1_doubled'] = df.a*2
1000 loops, best of 3: 233 us per loop
# Custom function with numba
In [7]: %timeit df['col1_doubled'] = double_every_value_withnumba(df.a.values)
1000 loops, best of 3: 145 us per loop
Note
numba
will execute on any function, but can only accelerate certain classes of functions.
numba
is best at accelerating functions that apply numerical functions to numpy arrays. When passed a function that only uses operations it knows how to accelerate, it will execute in nopython
mode.
If numba
is passed a function that includes something it doesn't know how to work with -- a category that currently includes sets, lists, dictionaries, or string functions -- it will revert to object mode
. In object mode
, numba will execute but your code will not speed up significantly. If you would prefer that numba
throw an error if it cannot compile a function in a way that speeds up your code, pass numba the argument nopython=True
(e.g. @numba.jit(nopython=True)
). For more on troubleshooting numba
modes, see the numba troubleshooting page.
Read more in the numba docs.
0.13
The top-level function pandas.eval
implements expression evaluation of ~pandas.Series
and ~pandas.DataFrame
objects.
Note
To benefit from using ~pandas.eval
you need to install numexpr
. See the recommended dependencies section
<install.recommended_dependencies>
for more details.
The point of using ~pandas.eval
for expression evaluation rather than plain Python is two-fold: 1) large ~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 ~pandas.eval
for simple expressions or for expressions involving small DataFrames. In fact, ~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 ~pandas.eval
when you have a ~pandas.core.frame.DataFrame
with more than 10,000 rows.
~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 ~pandas.eval
.
These operations are supported by 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, including chained comparisons, e.g.,
2 < df < df2
- Boolean operations, e.g.,
df < df2 and df3 < df4 or not df_bool
list
andtuple
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) - Math functions, sin, cos, exp, log, expm1, log1p, sqrt, sinh, cosh, tanh, arcsin, arccos, arctan, arccosh, arcsinh, arctanh, abs and arctan2.
This Python syntax is not allowed:
- Expressions
- Function calls other than math functions.
is
/is not
operationsif
expressionslambda
expressionslist
/set
/dict
comprehensions- Literal
dict
andset
expressions yield
expressions- Generator expressions
- Boolean expressions consisting of only scalar values
- Statements
pandas.eval
works well with expressions containing large arrays.
First let's create a few decent-sized arrays to play with:
python
nrows, ncols = 20000, 100 df1, df2, df3, df4 = [pd.DataFrame(np.random.randn(nrows, ncols)) for _ in range(4)]
Now let's compare adding them together using plain ol' Python versus ~pandas.eval
:
python
%timeit df1 + df2 + df3 + df4
python
%timeit pd.eval('df1 + df2 + df3 + df4')
Now let's do the same thing but with comparisons:
python
%timeit (df1 > 0) & (df2 > 0) & (df3 > 0) & (df4 > 0)
python
%timeit pd.eval('(df1 > 0) & (df2 > 0) & (df3 > 0) & (df4 > 0)')
~pandas.eval
also works with unaligned pandas objects:
python
s = pd.Series(np.random.randn(50)) %timeit df1 + df2 + df3 + df4 + s
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.
0.13
In addition to the top level pandas.eval
function you can also evaluate an expression in the "context" of a ~pandas.DataFrame
.
python
- try:
del a
- except NameError:
pass
- try:
del b
- except NameError:
pass
python
df = pd.DataFrame(np.random.randn(5, 2), columns=['a', 'b']) df.eval('a + b')
Any expression that is a valid 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 ~pandas.DataFrame
to the column(s) you're interested in evaluating.
In addition, you can perform assignment of columns within an expression. This allows for formulaic evaluation. The assignment target can be a new column name or an existing column name, and it must be a valid Python identifier.
0.18.0
The inplace
keyword determines whether this assignment will performed on the original DataFrame
or return a copy with the new column.
Warning
For backwards compatability, inplace
defaults to True
if not specified. This will change in a future version of pandas - if your code depends on an inplace assignment you should update to explicitly set inplace=True
python
df = pd.DataFrame(dict(a=range(5), b=range(5, 10))) df.eval('c = a + b', inplace=True) df.eval('d = a + b + c', inplace=True) df.eval('a = 1', inplace=True) df
When inplace
is set to False
, a copy of the DataFrame
with the new or modified columns is returned and the original frame is unchanged.
python
df df.eval('e = a - c', inplace=False) df
0.18.0
As a convenience, multiple assignments can be performed by using a multi-line string.
python
df.eval(""" c = a + b d = a + b + c a = 1""", inplace=False)
The equivalent in standard Python would be
python
df = pd.DataFrame(dict(a=range(5), b=range(5, 10))) df['c'] = df.a + df.b df['d'] = df.a + df.b + df.c df['a'] = 1 df
0.18.0
The query
method gained the inplace
keyword which determines whether the query modifies the original frame.
python
df = pd.DataFrame(dict(a=range(5), b=range(5, 10))) df.query('a > 2') df.query('a > 2', inplace=True) df
Warning
Unlike with eval
, the default value for inplace
for query
is False
. This is consistent with prior versions of pandas.
In pandas version 0.14 the local variable API has changed. In pandas 0.13.x, you could refer to local variables the same way you would in standard Python. For example,
df = pd.DataFrame(np.random.randn(5, 2), columns=['a', 'b'])
newcol = np.random.randn(len(df))
df.eval('b + newcol')
UndefinedVariableError: name 'newcol' is not defined
As you can see from the exception generated, this syntax is no longer allowed. You must explicitly reference any local variable that you want to use in an expression by placing the @
character in front of the name. For example,
python
df = pd.DataFrame(np.random.randn(5, 2), columns=list('ab')) newcol = np.random.randn(len(df)) df.eval('b + @newcol') df.query('b < @newcol')
If you don't prefix the local variable with @
, pandas will raise an exception telling you the variable is undefined.
When using DataFrame.eval
and DataFrame.query
, this allows you to have a local variable and a ~pandas.DataFrame
column with the same name in an expression.
python
a = np.random.randn() df.query('@a < a') df.loc[a < df.a] # same as the previous expression
With pandas.eval
you cannot use the @
prefix at all, because it isn't defined in that context. pandas
will let you know this if you try to use @
in a top-level call to pandas.eval
. For example,
python
a, b = 1, 2 pd.eval('@a + b')
In this case, you should simply refer to the variables like you would in standard Python.
python
pd.eval('a + b')
There are two different parsers 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.
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 and
as well:
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.
There's also the option to make ~pandas.eval
operate identical to plain ol' Python.
Note
Using the 'python'
engine is generally not useful, except for testing other evaluation engines against it. You will achieve no performance benefits using ~pandas.eval
with engine='python'
and in fact may incur a performance hit.
You can see this by using pandas.eval
with the 'python'
engine. It is a bit slower (not by much) than evaluating the same expression in Python
python
%timeit df1 + df2 + df3 + df4
python
%timeit pd.eval('df1 + df2 + df3 + df4', engine='python')
~pandas.eval
is intended to speed up certain kinds of operations. In particular, those operations involving complex expressions with large ~pandas.DataFrame
/~pandas.Series
objects should see a significant performance benefit. Here is a plot showing the running time of pandas.eval
as function of the size of the frame involved in the computation. The two lines are two different engines.
This plot was created using a DataFrame
with 3 columns each containing floating point values generated using numpy.random.randn()
.
Expressions that would result in an object dtype or involve datetime operations (because of NaT
) must be evaluated in Python space. The main reason for this behavior is to maintain backwards compatibility with versions of numpy < 1.7. In those versions of numpy
a call to ndarray.astype(str)
will truncate any strings that are more than 60 characters in length. Second, we can't pass object
arrays to numexpr
thus string comparisons must be evaluated in Python space.
The upshot is that this only applies to object-dtype'd expressions. So, if you have an expression--for example
python
- df = pd.DataFrame({'strings': np.repeat(list('cba'), 3),
'nums': np.repeat(range(3), 3)})
df df.query('strings == "a" and nums == 1')
the numeric part of the comparison (nums == 1
) will be evaluated by numexpr
.
In general, DataFrame.query
/pandas.eval
will evaluate the subexpressions that can be evaluated by numexpr
and those that must be evaluated in Python space transparently to the user. This is done by inferring the result type of an expression from its arguments and operators.