How to Dynamically Evaluate Expressions from Formulas in Pandas using pd.eval?

Dynamically evaluate an expression from a formula in Pandas

The evaluation of arithmetic expressions on one or more dataframe columns using pd.eval is a common task, especially when automating workflows. Consider the following code snippet:

`x = 5
df2['D'] = df1['A'] (df1['B'] * x)``

This code adds a new column D to df2 by performing an operation on the columns A and B from df1, and multiplying the result by a variable x. The goal is to repeat this data manipulation dynamically, leveraging pd.eval's ability to execute expressions as strings.

First, let's introduce the input DataFrames:

import pandas as pd
import numpy as np

np.random.seed(0)
df1 = pd.DataFrame(np.random.choice(10, (5, 4)), columns=list('ABCD'))
df2 = pd.DataFrame(np.random.choice(10, (5, 4)), columns=list('ABCD'))

df1

   A  B  C  D
0  5  0  3  3
1  7  9  3  5
2  2  4  7  6
3  8  8  1  6
4  7  7  8  1

df2

   A  B  C  D
0  5  9  8  9
1  4  3  0  3
2  5  0  2  3
3  8  1  3  3
4  3  7  0  1

To evaluate the expression dynamically using pd.eval, one can use the following code:

result = pd.eval('df1.A (df1.B * x)')

This line of code creates a new DataFrame called result that contains the evaluated expression. The eval function can also be used to perform conditional evaluations, such as:

pd.eval('df1.A > df2.A')

To assign the result of the expression back to df2, use the following syntax:

df2['D'] = pd.eval('df1.A (df1.B * x)', target=df2)

To pass an argument inside the expression string, use the @ symbol:

pd.eval('df1.A (df1.B * @x)', local_dict={'x': 5})

For maximum performance, consider the following arguments:

parser='python' for controlling the syntax rules and ensuring consistency with Python's operator precedence.
engine='numexpr' for faster evaluation using the optimized numexpr backend.
This should provide you with a comprehensive understanding of how to dynamically evaluate expressions from formulas in Pandas using pd.eval.

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