How to Dynamically Evaluate Expressions in Pandas Using pd.eval(), DataFrame.eval(), and DataFrame.query()?

Dynamically Evaluating an Expression from a Formula in Pandas

In pandas, there are multiple ways to dynamically evaluate an expression from a formula:

1. pd.eval()

This function evaluates arithmetic expressions using a string as input. It supports mathematical operations, logical operators, and conditional statements. You can use it as follows:

expression = "df1['A'] + (df1['B'] * x)"
pd.eval(expression)

2. DataFrame.eval()

Similar to pd.eval(), this method evaluates expressions within a DataFrame. It provides a convenient way to access columns without specifying the "df1." prefix.

df1.eval("A + (B * x)")

3. DataFrame.query()

This function evaluates a conditional expression and returns a boolean mask. You can then use the mask to filter the DataFrame.

condition = "A >= B"
df1.query(condition)

Answers to Specific Questions:

1. Optimal performance: Use pd.eval() or DataFrame.eval() with the "numexpr" backend. The "python" backend offers no performance benefits and has security risks.

2. Assigning results: You can assign the result of an expression back to a DataFrame using the "target=" argument.

   df2 = pd.DataFrame()
   pd.eval("df2['D'] = df1['A'] + (df1['B'] * x)", target=df2)

3. Passing arguments: You can pass arguments as variables within the expression using the "@" symbol.

   expression = "df1['A'] + (@x * df1['B'])"
   pd.eval(expression, local_dict={"x": 5})

Additional Considerations:

• Choose the appropriate method based on your needs and the type of expression you're evaluating.
• Use parenthe for operator precedence when necessary.
• The "resolvers=" argument can be used to provide custom functions or variables for use in the expression.
• For multiline expressions and assignment, use DataFrame.eval(), as query() only accepts a single-line condition.

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