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03.12 Performance Eval and Query
As we've already seen in previous sections, the power of the PyData stack is built upon the ability of NumPy and Pandas to push basic operations into C via an intuitive syntax: examples are vectorized/broadcasted operations in NumPy, and grouping-type operations in Pandas.
While these abstractions are efficient and effective for many common use cases, they often rely on the creation of temporary intermediate objects, which can cause undue overhead in computational time and memory use.
As of version 0.13 (released January 2014), Pandas includes some experimental tools that allow you to directly access C-speed operations without costly allocation of intermediate arrays.
These are the
eval() and query() functions, which rely on the Numexpr package.
In this notebook we will walk through their use and give some rules-of-thumb about when you might think about using them.Motivating query() and eval(): Compound Expressions
We've seen previously that NumPy and Pandas support fast vectorized operations; for example, when adding the elements of two arrays:
import numpy as np
rng = np.random.RandomState(42)
x = rng.rand(1000000)
y = rng.rand(1000000)
%timeit x + y100 loops, best of 3: 3.39 ms per loop
As discussed in Computation on NumPy Arrays: Universal Functions, this is much faster than doing the addition via a Python loop or comprehension:
%timeit np.fromiter((xi + yi for xi, yi in zip(x, y)), dtype=x.dtype, count=len(x))1 loop, best of 3: 266 ms per loop
But this abstraction can become less efficient when computing compound expressions.
For example, consider the following expression:
mask = (x > 0.5) & (y < 0.5)Because NumPy evaluates each subexpression, this is roughly equivalent to the following:
tmp1 = (x > 0.5)
tmp2 = (y < 0.5)
mask = tmp1 & tmp2In other words, every intermediate step is explicitly allocated in memory. If the
x and y arrays are very large, this can lead to significant memory and computational overhead.
The Numexpr library gives you the ability to compute this type of compound expression element by element, without the need to allocate full intermediate arrays.
The Numexpr documentation has more details, but for the time being it is sufficient to say that the library accepts a string giving the NumPy-style expression you'd like to compute:import numexpr
mask_numexpr = numexpr.evaluate('(x > 0.5) & (y < 0.5)')
np.allclose(mask, mask_numexpr)True
The benefit here is that Numexpr evaluates the expression in a way that does not use full-sized temporary arrays, and thus can be much more efficient than NumPy, especially for large arrays.
The Pandas
eval() and query() tools that we will discuss here are conceptually similar, and depend on the Numexpr package.pandas.eval() for Efficient Operations
The
eval() function in Pandas uses string expressions to efficiently compute operations using DataFrames.
For example, consider the following DataFrames:import pandas as pd
nrows, ncols = 100000, 100
rng = np.random.RandomState(42)
df1, df2, df3, df4 = (pd.DataFrame(rng.rand(nrows, ncols))
for i in range(4))To compute the sum of all four
DataFrames using the typical Pandas approach, we can just write the sum:%timeit df1 + df2 + df3 + df410 loops, best of 3: 87.1 ms per loop
The same result can be computed via
pd.eval by constructing the expression as a string:%timeit pd.eval('df1 + df2 + df3 + df4')10 loops, best of 3: 42.2 ms per loop
The
eval() version of this expression is about 50% faster (and uses much less memory), while giving the same result:np.allclose(df1 + df2 + df3 + df4,
pd.eval('df1 + df2 + df3 + df4'))True
Operations supported by pd.eval()
As of Pandas v0.16,
pd.eval() supports a wide range of operations.
To demonstrate these, we'll use the following integer DataFrames:df1, df2, df3, df4, df5 = (pd.DataFrame(rng.randint(0, 1000, (100, 3)))
for i in range(5))Arithmetic operators
pd.eval() supports all arithmetic operators. For example:result1 = -df1 * df2 / (df3 + df4) - df5
result2 = pd.eval('-df1 * df2 / (df3 + df4) - df5')
np.allclose(result1, result2)True
Comparison operators
pd.eval() supports all comparison operators, including chained expressions:result1 = (df1 < df2) & (df2 <= df3) & (df3 != df4)
result2 = pd.eval('df1 < df2 <= df3 != df4')
np.allclose(result1, result2)True
Bitwise operators
pd.eval() supports the & and | bitwise operators:result1 = (df1 < 0.5) & (df2 < 0.5) | (df3 < df4)
result2 = pd.eval('(df1 < 0.5) & (df2 < 0.5) | (df3 < df4)')
np.allclose(result1, result2)True
In addition, it supports the use of the literal
and and or in Boolean expressions:result3 = pd.eval('(df1 < 0.5) and (df2 < 0.5) or (df3 < df4)')
np.allclose(result1, result3)True
Object attributes and indices
pd.eval() supports access to object attributes via the obj.attr syntax, and indexes via the obj[index] syntax:result1 = df2.T[0] + df3.iloc[1]
result2 = pd.eval('df2.T[0] + df3.iloc[1]')
np.allclose(result1, result2)True
Other operations
Other operations such as function calls, conditional statements, loops, and other more involved constructs are currently not implemented in
pd.eval().
If you'd like to execute these more complicated types of expressions, you can use the Numexpr library itself.DataFrame.eval() for Column-Wise Operations
Just as Pandas has a top-level
pd.eval() function, DataFrames have an eval() method that works in similar ways.
The benefit of the eval() method is that columns can be referred to by name.
We'll use this labeled array as an example:df = pd.DataFrame(rng.rand(1000, 3), columns=['A', 'B', 'C'])
df.head()Using
pd.eval() as above, we can compute expressions with the three columns like this:result1 = (df['A'] + df['B']) / (df['C'] - 1)
result2 = pd.eval("(df.A + df.B) / (df.C - 1)")
np.allclose(result1, result2)True
The
DataFrame.eval() method allows much more succinct evaluation of expressions with the columns:result3 = df.eval('(A + B) / (C - 1)')
np.allclose(result1, result3)True
Notice here that we treat column names as variables within the evaluated expression, and the result is what we would wish.
Assignment in DataFrame.eval()
In addition to the options just discussed,
DataFrame.eval() also allows assignment to any column.
Let's use the DataFrame from before, which has columns 'A', 'B', and 'C':df.head()We can use
df.eval() to create a new column 'D' and assign to it a value computed from the other columns:df.eval('D = (A + B) / C', inplace=True)
df.head()In the same way, any existing column can be modified:
df.eval('D = (A - B) / C', inplace=True)
df.head()Local variables in DataFrame.eval()
The
DataFrame.eval() method supports an additional syntax that lets it work with local Python variables.
Consider the following:column_mean = df.mean(1)
result1 = df['A'] + column_mean
result2 = df.eval('A + @column_mean')
np.allclose(result1, result2)True
The
@ character here marks a variable name rather than a column name, and lets you efficiently evaluate expressions involving the two "namespaces": the namespace of columns, and the namespace of Python objects.
Notice that this @ character is only supported by the DataFrame.eval() method, not by the pandas.eval() function, because the pandas.eval() function only has access to the one (Python) namespace.DataFrame.query() Method
The
DataFrame has another method based on evaluated strings, called the query() method.
Consider the following:result1 = df[(df.A < 0.5) & (df.B < 0.5)]
result2 = pd.eval('df[(df.A < 0.5) & (df.B < 0.5)]')
np.allclose(result1, result2)True
As with the example used in our discussion of
DataFrame.eval(), this is an expression involving columns of the DataFrame.
It cannot be expressed using the DataFrame.eval() syntax, however!
Instead, for this type of filtering operation, you can use the query() method:result2 = df.query('A < 0.5 and B < 0.5')
np.allclose(result1, result2)True
In addition to being a more efficient computation, compared to the masking expression this is much easier to read and understand.
Note that the
query() method also accepts the @ flag to mark local variables:Cmean = df['C'].mean()
result1 = df[(df.A < Cmean) & (df.B < Cmean)]
result2 = df.query('A < @Cmean and B < @Cmean')
np.allclose(result1, result2)True
Performance: When to Use These Functions
When considering whether to use these functions, there are two considerations: computation time and memory use.
Memory use is the most predictable aspect. As already mentioned, every compound expression involving NumPy arrays or Pandas
DataFrames will result in implicit creation of temporary arrays:
For example, this:x = df[(df.A < 0.5) & (df.B < 0.5)]Is roughly equivalent to this:
tmp1 = df.A < 0.5
tmp2 = df.B < 0.5
tmp3 = tmp1 & tmp2
x = df[tmp3]If the size of the temporary
DataFrames is significant compared to your available system memory (typically several gigabytes) then it's a good idea to use an eval() or query() expression.
You can check the approximate size of your array in bytes using this:df.values.nbytes