eval() for expression evaluation in Pandas - Mini Project: Build & Apply

Using pandas eval() for Expression Evaluation

📖 Scenario: You work in a small store and keep track of sales data in a table. You want to quickly calculate new values based on existing data without writing long code.

🎯 Goal: Learn how to use pandas.eval() to evaluate expressions on a DataFrame for fast calculations.

📋 What You'll Learn

Create a pandas DataFrame with sales data

Create a variable to hold a calculation expression

Use pandas.eval() to calculate a new column based on the expression

Print the updated DataFrame

💡 Why This Matters

🌍 Real World

Stores and businesses often need to calculate totals, discounts, or taxes quickly from sales data tables.

💼 Career

Data analysts and scientists use pandas.eval() to write fast, readable code for data transformations and calculations.

1

Create the sales DataFrame

Create a pandas DataFrame called sales with these columns and values:
'product': ['apple', 'banana', 'orange'],
'price': [0.5, 0.3, 0.8],
'quantity': [10, 20, 15]

Pandas

import pandas as pd
# Create the sales DataFrame with product, price, and quantity columns
# Your code here

Need a hint?

Use pd.DataFrame() with a dictionary of lists for columns.

2

Create the expression string

Create a variable called expr and set it to the string 'price * quantity' to represent the total sales calculation.

Pandas

import pandas as pd
sales = pd.DataFrame({
    'product': ['apple', 'banana', 'orange'],
    'price': [0.5, 0.3, 0.8],
    'quantity': [10, 20, 15]
})
# Create the expression string to calculate total sales
# Your code here

Need a hint?

Set expr to the string exactly as shown.

3

Calculate total sales using pandas.eval()

Use pandas.eval() with the expression expr and the sales DataFrame to create a new column called 'total' in sales.

Pandas

import pandas as pd
sales = pd.DataFrame({
    'product': ['apple', 'banana', 'orange'],
    'price': [0.5, 0.3, 0.8],
    'quantity': [10, 20, 15]
})
expr = 'price * quantity'
# Use pandas.eval() to calculate total sales and add as 'total' column
# Your code here

Need a hint?

Use pd.eval(expr, local_dict=sales.to_dict('series')) to evaluate the expression with the DataFrame columns.

4

Print the updated DataFrame

Print the sales DataFrame to show the new 'total' column with calculated values.

Pandas

import pandas as pd
sales = pd.DataFrame({
    'product': ['apple', 'banana', 'orange'],
    'price': [0.5, 0.3, 0.8],
    'quantity': [10, 20, 15]
})
expr = 'price * quantity'
sales['total'] = pd.eval(expr, local_dict=sales.to_dict('series'))
# Print the sales DataFrame
# Your code here

Need a hint?

Use print(sales) to display the DataFrame.