Pandas DataFrame eval() Function

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  • Post last modified:December 6, 2024
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In pandas, the eval() function is used to evaluate string expressions using the DataFrame’s columns directly. This function is useful for performing arithmetic operations, comparisons, and other computations more concisely and efficiently.

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In this article, I will explain the pandas DataFrame eval() function and using its syntax, parameters, and usage how we can apply multiple string expressions directly on the DataFframes columns. Also explained using the inplace parameter how we can update the dataFrame directly without creating a copy.

Key Points –

  • The eval() function allows you to evaluate string expressions involving DataFrame columns, which can simplify code for complex operations.
  • It leverages the numexpr library to potentially speed up computations by optimizing the evaluation of expressions, particularly beneficial for large datasets.
  • The inplace parameter enables you to apply changes directly to the original DataFrame, reducing the need for additional variable assignments.
  • You can use local variables in your expressions by prefixing them with @, enabling dynamic computation based on external values.
  • Since eval() executes code, it should be used cautiously with trusted input to avoid potential security risks.

Syntax of Pandas DataFrame eval() Function

Following is the syntax of the pandas DataFrame eval() function.


# Syntax of the eval() function
DataFrame.eval(expr, inplace=False, **kwargs)

Parameters of the DataFrame

Following are the parameters of the DataFrame eval() function.

  • expr – A string representing the expression to evaluate. The expression should reference columns within the DataFrame by their column names.
  • inplace – A boolean (default is False). If set to True, the DataFrame is modified in place with the result of the expression. If False, a new DataFrame with the result is returned.
  • **kwargs – Additional keyword arguments to pass. These can include local variables that are referenced in the expression, or flags such as engine or parser to specify the evaluation engine and parser.

Return Value

It returns a pandas object containing the result of the evaluation.

Usage of Pandas DataFrame eval() Function

The eval() function in pandas DataFrame is used for evaluating string expressions that involve DataFrame columns.

To run some examples of the Pandas DataFrame eval() function, let’s create a Pandas DataFrame using data from a dictionary.


# Create DataFrame
import pandas as pd
studentdetails = {
       "student_name":["Ram","Sam","Scott","Ann","John"],
       "mathematics" :[80,90,85,70,95],
       "science" :[85,95,80,90,75],
       "english" :[90,85,80,70,95]
              }
index_labels=['r1','r2','r3','r4','r5']
df = pd.DataFrame(studentdetails ,index=index_labels)
print("Create DataFrame:\n", df

Yields below output.

pandas eval

Creating a New Column

To create a new column in a pandas DataFrame, you can use the eval() function or directly assign a new column based on calculations involving existing columns. Let’s create a new column based on the existing column using the eval() function.


# Create a new column using eval() function
df.eval('total_score = mathematics + science + english', inplace=True)
print("DataFrame with new column:\n", df)

Here,

  • We’ll create a new column called total_score that sums up the scores in mathematics, science, and english for each student.
  • The eval() function is used to compute the total_score by summing the mathematics, science, and english columns. Setting inplace=True directly modifies the DataFrame.
  • A new column total_score is added to the DataFrame with the total scores for each student. This example yields the below output.  
pandas eval

Updating an Existing Column

Alternatively, to update an existing column in a pandas DataFrame, you can use the eval() function or direct assignment to change the column’s values. Let’s update the specified column using the eval() function with the inplace parameter to modify the DataFrame directly.


# Update the 'mathematics' column by adding 5 bonus points
df.eval('mathematics = mathematics + 5', inplace=True)
print("DataFrame after Updating 'mathematics' Column:\n", df)

# Output:
# DataFrame after Updating 'mathematics' Column:
#    student_name  mathematics  science  english
# r1          Ram           85       85       90
# r2          Sam           95       95       85
# r3        Scott           90       80       80
# r4          Ann           75       90       70
# r5         John          100       75       95

Here,

  • The eval() function is used to add 5 points to each score in the mathematics column. Setting inplace=True updates the DataFrame directly.
  • The mathematics column is updated to reflect the new scores with the additional bonus points.

Performing a Complex Calculation

To perform a complex calculation using the eval() function in pandas, you can combine multiple operations and references to different DataFrame columns in a single expression.

Let’s perform a complex calculation to create a new column based on existing columns.


# Perform a complex calculation to create a 'average_score' column
df.eval('average_score = (mathematics * 0.4 + science * 0.3 + english * 0.3)', inplace=True)
print("DataFrame with complex calculation 'average_score' column:\n", df)

# Output:
# DataFrame with complex calculation 'average_score' column:
#    student_name  mathematics  science  english  average_score
# r1          Ram           80       85       90           84.5
# r2          Sam           90       95       85           90.0
# r3        Scott           85       80       80           82.0
# r4          Ann           70       90       70           76.0
# r5         John           95       75       95           89.0

Here,

  • The eval() function is used to calculate a weighted average score by multiplying each subject’s score by its respective weight and summing the results.
  • The new column average_score is created, which contains the weighted average of each student’s scores in mathematics, science, and English. The inplace=True parameter modifies the DataFrame directly without creating a copy.

Using Local Variables in Expressions

Similarly, you can use local variables within expressions evaluated by the pandas eval() function. To include a local variable in an expression, prefix the variable name with the @ symbol. This allows you to incorporate dynamic, external values into your DataFrame computations.


# Local variable to hold the highest score in the DataFrame
max_score = df[['mathematics', 'science', 'english']].max().max()

# Use eval() to calculate the normalized score using the local variable
df.eval('normalized_score = (mathematics + science + english) / @max_score', inplace=True)
print("DataFrame with local variable 'normalized_score' column:\n", df)

# Output:
# DataFrame with local variable 'normalized_score' column:
#    student_name  mathematics  science  english  normalized_score
# r1          Ram           80       85       90          2.684211
# r2          Sam           90       95       85          2.842105
# r3        Scott           85       80       80          2.578947
# r4          Ann           70       90       70          2.421053
# r5         John           95       75       95          2.789474

Here,

  • The local variable max_score is computed outside the eval() function and represents the maximum score across all subjects. Inside the eval() function, the @ symbol is used to refer to this local variable.
  • The normalized_score column is calculated by summing the scores in mathematics, science, and english and then dividing by max_score. This normalization scales the total scores relative to the highest individual score in the DataFrame.
  • The DataFrame is updated in place, adding the normalized_score column directly to the existing DataFrame.

FAQ on Pandas DataFrame eval() Function

What is the purpose of the eval() function in pandas?

The eval() function in pandas allows you to evaluate string expressions directly using the DataFrame’s columns. This function is useful for performing arithmetic operations, comparisons, and other computations efficiently and concisely, often reducing the need for creating intermediate DataFrame objects.

Can I use local variables in expressions with eval()?

You can use local variables within expressions in the eval() function. To include a local variable, you must prefix the variable name with the @ symbol. This feature allows you to incorporate dynamic, external values into your DataFrame computations.

What types of operations can be performed using eval()?

eval() supports a wide range of operations, including arithmetic (addition, subtraction, multiplication, division), comparisons (greater than, less than, equal to), logical operations (and, or, not), and more complex expressions involving multiple columns.

How do I modify the DataFrame in place using eval()?

To modify the DataFrame in place using eval(), set the inplace parameter to True. This will apply the expression directly to the DataFrame without creating a new copy. For example: df.eval('new_column = col1 + col2', inplace=True).

Conclusion

In conclusion, the eval() function in pandas is a powerful tool for efficiently executing operations on DataFrame columns. It offers several advantages, such as reduced memory usage and enhanced performance by eliminating the need for intermediate DataFrames and optimizing expression evaluation. Using eval(), you can manage complex calculations, update existing columns, create new ones, and integrate local variables into expressions, all while keeping your code concise and readable.

Happy Learning!!

Reference

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