如何動態評估 Pandas 中的表達式？

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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