• The power of PyData stack is built upon the ability of Numpy and Pandas to push basic operations into lower-level compiled code via an intuitive higher-level syntax.
• While these abstractions are effcient and effective for many common use cases, they often rely on the creation of temporary intermediate objects, which can cause undue(=excessive) overhead in computational time and memory use.
• To address this, Pandas includes some methods that allow you to directly access C-speed operations without costly allocation of intermediate arrays: eval and query

Motivating query and eval: Compound Expressions

# In[1]
rng=np.random.default_rng(42)
x=rng.random(1000000)
y=rng.random(1000000)
%timeit x+y

# Out[1]
4.26 ms ± 915 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

• This is much faster than doing the addition via a Python loop or comprehension.
• But this abstraction can become less efficient when computing compound expressions.

# In[2]
mask=(x>0.5)&(y<0.5)

• Because Numpy evaluates each subexpression, this is roughly equivalent to the following code.

# In[3]
tmp1=(x>0.5)
tmp2=(y<0.5)
mask=tmp1&tmp2

• In 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.

# In[4]
import numexpr
mask_numexpr=numexpr.evaluate('(x>0.5)&(y<0.5)')
np.all(mask==mask_numexpr)

# Out[4]
True

• The benefit here is that NumExpr evaluates the expression in a way that avoids temporary arrays where possible, and thus can be much more efficient than Numpy, especially for long sequences of computations on large arrays.
• The Pandas eval and query tools are essentially Pandas-specific wrappers of NumExpr functionality.

pandas.eval for Efficient Operations

• The eval function in Pandas uses string expressions to efficiently compute operations on DataFrame objects.

# In[5]
nrows,ncols=100000, 100
df1,df2,df3,df4=(pd.DataFrame(rng.random((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.

# In[6]
%timeit df1+df2+df3+df4

# Out[6]
167 ms ± 25.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

• The same result can be computed via pd.eval by constructing the expression as a string.
• The eval version of this expression is about 50% faster, while giving the same result.

# In[7]
%timeit pd.eval('df1+df2+df3+df4')

# Out[7]
73.5 ms ± 14.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

---

# In[8]
np.allclose(df1+df2+df3+df4,pd.eval('df1+df2+df3+df4'))

# Out[8]
True

• pd.eval supports a wide range of operations.

# In[10]
df1,df2,df3,df4,df5=(pd.DataFrame(rng.integers(0,1000,(100,3))) 
for i in range(5))

1. Arithmetic operators

# In[11]
result1=-df1*df2/(df3+df4)-df5
result2=pd.eval('-df1*df2/(df3+df4)-df5')
np.allclose(result1,result2)

# Out[11]
True

2. Comparison operators

# In[12]
result1=(df1<df2)&(df2<=df3)&(df3!=df4)
result2=pd.eval('df1<df2<=df3!=df4')
np.allclose(result1,result2)

# Out[12]
True

3. Bitwise operators

# In[13]
result1=(df1<0.5)&(df2<0.5)|(df3<df4)
result2=pd.eval('(df1<0.5)&(df2<0.5)|(df3<df4)')
np.allclose(result1,result2)

# Out[13]
True

• Additionally, it supports the use of the literal and and or in Boolean expressions.

# In[14]
result3=pd.eval('(df1<0.5) and (df2<0.5) or (df3<df4)')
np.allclose(result1,result2)

# Out[14]
True

4. Object attributes and indices

• pd.eval supports access to object attributes via the obj.attr syntax and indexes via the obj[index] syntax

# In[15]
result1=df2.T[0]+df3.iloc[1]
result2=pd.eval('df2.T[0]+df3.iloc[1]')
np.allclose(result1,result2)

# Out[15]
True

5. Other operators

• 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 types of expressions, you can use the NumExpr library itself.

Please reference this url about the np.allclose :
About np.allclose

DataFrame.eval for Column-Wise Operations

• Just as Pandas has a top-level pd.eval function, DataFrame objects have an eval method that works in similar ways.
• The benefit of the eval method is that columns can be referred to by name.

# In[16]
df=pd.DataFrame(rng.random((1000,3)),columns=['A','B','C'])
df.head()

# Out[16]
           A	       B	       C
0	0.850888	0.966709	0.958690
1	0.820126	0.385686	0.061402
2	0.059729	0.831768	0.652259
3	0.244774	0.140322	0.041711
4	0.818205	0.753384	0.578851

• By using pd.eval, we can compute expressions with the three columns.

# In[17]
result1=(df['A']+df['B'])/(df['C']-1)
result2=pd.eval("(df.A+df.B)/(df.C-1)")
np.allclose(result1,result2)

# Out[17]
True

• We treat column names as variables within the evaluated expression, and the result is what we would wish.

Assignment in DataFrame.eval

• DataFrame.eval also allows assignment to any column.
• We can use df.eval to create a new column 'D' and assign to it a value computed from the other columns.
• If inplace is True, the value is reflected in the original. On the contrary, False (default), just returned the result.

# In[18]
df.eval('D=(A+B)/C',inplace=True)
df.head()

# Out[18]
           A	       B	       C	        D
0	0.850888	0.966709	0.958690	 1.895916
1	0.820126	0.385686	0.061402	19.638139
2	0.059729	0.831768	0.652259	 1.366782
3	0.244774	0.140322	0.041711	 9.232370
4	0.818205	0.753384	0.578851	 2.715013

• In the same way, any existing column can be modified.

# In[19]
df.eval('D=(A-B)/C',inplace=True)
df.head()

# Out[19]
           A	       B	       C	        D
0	0.850888	0.966709	0.958690	-0.120812
1	0.820126	0.385686	0.061402	 7.075399
2	0.059729	0.831768	0.652259	-1.183638
3	0.244774	0.140322	0.041711	 2.504142
4	0.818205	0.753384	0.578851	 0.111982

Local Variables in DataFrame.eval

• The DataFrame.eval method supports an additional syntax that lets it work with local Python variables.

# In[20]
column_mean=df.mean(1)
result1=df['A']+column_mean
result2=df.eval('A+@column_mean')
np.allclose(result1,result2)

# Out[20]
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.
• 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.

The DataFrame.query Method

• The DataFrame has another method based on evaluated strings, called query.

# In[21]
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)

# Out[21]
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 DataFrame.eval syntax.
• Instead, for this type of filtering operation, you can use the query method.

# In[22]
result2=df.query('A < 0.5 and B < 0.5')
np.allclose(result1,result2)

# Out[22]
True

• In addtion to being a more efficient computation, compared to the masking expression this is much easier to read and understand.
• query method also accpets the @ flag to mark local variables.

# In[23]
Cmean=df['C'].mean()
result1=df[(df.A < Cmean) & (df.B < Cmean)]
result2=df.query('A < @Cmean and B < @Cmean')
np.allclose(result1,result2)

# Out[23]
True

Performance: When to Use These Functions

• When considering whether to use eval and query, there are two considerations: computation time and memory use.
• Every compound expression involving Numpy arrays or Pandas DataFrames will result in implicit creation of temporary arrays.
  
  
• If the size of the temporary DataFrames is significant compared to your available system memory, 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 like this.

# In[24]
df.values.nbytes

# Out[24]
32000

• On the performance side, eval can be faster even when you are not maxing out your system memory.
• The difference in computation time between the traditional methods and the eval/query method is usually not significant.
• The benefit of eval/query is mainly in the saved memory, and the sometimes cleaner syntax they offer.

For more information on eval/query , you can refer these Pandas documentation :
1. Pandas.eval
2. Pandas.DataFrame.eval
3. Pandas.DataFrame.query