How can I dynamically evaluate arithmetic expressions within Pandas DataFrames?

Dynamically Evaluating Expressions from a Formula Using Pandas

Problem:

Evaluate arithmetic expressions using pd.eval while accounting for variables, operator precedence, and dataframes' complex structures.

Answer:

1. Using pd.eval

pd.eval(
    "df1.A + (df1.B * x)",
    local_dict={"x": 5},
    target=df2,
    parser="python",
    engine="numexpr",
)

Arguments:

• expression: The formula to evaluate as a string.
• local_dict: A dictionary containing variables not defined in the global namespace.
• target: The dataframe to assign the result to.
• parser: Specifies the parser used to parse the expression (pandas or python).
• engine: Specifies the backend used to evaluate the expression (numexpr or python).

2. Using df.eval

df1.eval(
    "A + (B * @x)",
    target=df2,
    parser="python",
    engine="numexpr",
)

Arguments:

• df: The dataframe on which the expression is being evaluated.
• expression: The formula to evaluate as a string.
• target: The dataframe to assign the result to.
• parser: Specifies the parser used to parse the expression (pandas or python).
• engine: Specifies the backend used to evaluate the expression (numexpr or python).

3. Differences between pd.eval and df.eval

• pd.eval evaluates expressions on any objects, while df.eval evaluates expressions specifically on dataframes.
• df.eval requires preceding column names with the at symbol (@) to avoid confusion, while pd.eval does not.
• df.eval can handle multiline expressions with assignment, while pd.eval cannot.

Additional notes:

• Ensure the expression is enclosed in double quotes.
• x = 5 assigns the value 5 to the variable x in the global namespace.
• parser='python' is recommended when dealing with Python's operator precedence rules and complex expressions.
• target=df2 ensures the result is assigned to the specified dataframe.
• engine='numexpr' utilizes the optimized numexpr engine for improved performance.
• inplace=True can be used to modify the original dataframe in place.
• df.query can also be used for conditional expressions, returning rows that meet the specified criteria.

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