How Can I Dynamically Evaluate Expressions in Pandas?

Evaluating Expressions Dynamically with Pandas

Problem Statement

You want to perform dynamic operations on DataFrames using pd.eval, including variable substitution and complex arithmetic.

Solution

1. Using pd.eval()

# Import necessary libraries
import pandas as pd
import numpy as np

# Create sample DataFrames
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'))

# Evaluate expression using a variable
x = 5
result = pd.eval("df1.A + (df1.B * x)")

# Alternatively, assign the result to a new column
pd.eval("df2['D'] = df1.A + (df1.B * x)")

Arguments for Performance

The following arguments can be used to optimize pd.eval performance:

• engine='numexpr': Use the highly optimized numexpr engine.
• parser='pandas': Use the default pandas parser, which aligns with Pandas' operator precedence.
• global_dict and local_dict: Supply dictionaries of global and local variables for substitution. This avoids the need to define variables in the global namespace.

Assignment and in-place Modification

You can assign the result of pd.eval directly to a DataFrame using the target argument.

df3 = pd.DataFrame(columns=list('FBGH'), index=df1.index)
pd.eval("df3['B'] = df1.A + df2.A", target=df3)

# In-place modification
pd.eval("df2.B = df1.A + df2.A", target=df2, inplace=True)

2. Using df.eval()

# Evaluate expression in df1
result = df1.eval("A + B")

# Perform variable substitution
df1.eval("A > @x", local_dict={'x': 5})

Comparison with df.query()

While pd.eval is suitable for evaluating expressions, df.query() is more concise and efficient for conditional queries, as it filters the DataFrame based on a Boolean expression.

# Query df1
df1.query("A > B")

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