How pandas eval works in Python? Best example

How pandas eval works in Python? Best example
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When working with pandas, performance is often a key concern, especially when dealing with large datasets. One function that can significantly optimize certain operations is pandas.eval(). This function allows you to evaluate expressions efficiently using pandas’ built-in computation engine. But how does it actually work, and when should you use it? Let’s dive into the details.

Understanding pandas.eval()

The pandas.eval() function evaluates a string expression that involves pandas objects, NumPy arrays, or standard Python expressions. Unlike normal Python expressions, pandas.eval() can be significantly faster because it optimizes execution using numexpr (if available).

Basic Syntax

Here’s how you can use pandas.eval() at its simplest:

import pandas as pd

df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

df.eval("C = A + B", inplace=True)

print(df)

Output:


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

In this example, df.eval() calculates a new column C as the sum of columns A and B. The inplace=True argument ensures that the result is stored in the original DataFrame.

Why Use pandas.eval()?

There are several good reasons to use pandas.eval() over traditional methods:

  • Performance: It is optimized with numexpr, which can provide a speed boost, especially with large datasets.
  • Readability: Complex expressions inside eval() can often be easier to read than their corresponding pandas alternatives.
  • Memory Efficiency: Reduces the creation of intermediate objects, which can save memory in large computations.

Advanced Usage of pandas.eval()

While basic arithmetic expressions are useful, pandas.eval() can also handle more advanced operations.

1. Conditional Expressions

You can use conditions inside eval() to filter a DataFrame:

df_filtered = df.query("C > 6")
print(df_filtered)

Output:


   A  B  C
1  2  5  7
2  3  6  9

This selects only the rows where column C is greater than 6.

2. Using Variables Inside eval()

If you want to use external variables inside your expressions, you can pass them explicitly:

threshold = 6
df.eval("D = C * 2 + @threshold", inplace=True)
print(df)

The @ symbol allows you to reference a Python variable inside eval(), making expressions more dynamic.

3. Boolean Expressions

You can also use logical operators:

df["is_large"] = df.eval("C > 6 and A < 3")
print(df)

Output:


   A  B  C  is_large
0  1  4  5     False
1  2  5  7      True
2  3  6  9     False

This evaluates the logical expression and adds a new boolean column based on the condition.

Performance Comparison

So how does pandas.eval() compare to regular pandas operations? Let’s find out.

import timeit

setup = "import pandas as pd; import numpy as np; df = pd.DataFrame(np.random.randn(1000000, 2), columns=['A', 'B'])"

expr_pandas = "df['C'] = df['A'] + df['B']"
expr_eval = "df.eval('C = A + B', inplace=True)"

time_pandas = timeit.timeit(expr_pandas, setup=setup, number=10)
time_eval = timeit.timeit(expr_eval, setup=setup, number=10)

print(f"Pandas Operations Time: {time_pandas:.5f} seconds")
print(f"Pandas eval() Time: {time_eval:.5f} seconds")

On large datasets, pandas.eval() often executes faster because of its internal optimizations.

Limitations of pandas.eval()

Despite its performance benefits, pandas.eval() has some limitations:

  • Only supports expressions that are compatible with numexpr.
  • Cannot be used for operations requiring more complex Python logic or method chaining.
  • Expressions inside eval() must be written as strings, which can sometimes reduce readability.

Conclusion

Using pandas.eval() can be a great way to optimize pandas expressions, especially for large datasets. It speeds up computations, enhances readability, and improves memory efficiency by avoiding unnecessary intermediate objects. However, it’s important to remember its limitations and use it only where it provides a clear advantage.

``` Other interesting article: How pandas query works in Python? Best example