eval() for expression evaluation in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of pandas.eval() with arithmetic expression

What is the output of the following code snippet using pandas.eval()?

Pandas

import pandas as pd
result = pd.eval('3 + 4 * 2')
print(result)

A SyntaxError

B 14

C 10

D 11

💡 Hint

Remember the order of operations: multiplication before addition.

The expression 3 + 4 * 2 is evaluated with multiplication first, so 4*2=8, then 3+8=11.

❓ data_output

intermediate

Evaluating a boolean expression on a DataFrame

Given the DataFrame df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}), what is the output of pd.eval('df.A > 1 & df.B < 6')?

Pandas

import pandas as pd
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
result = pd.eval('df.A > 1 & df.B < 6')
print(result)

A [False, True, False]

B [False, True, True]

C [True, False, False]

D SyntaxError

💡 Hint

Check each row: is A > 1 and B < 6 both true?

Row 0: A=1 >1? False, B=4 <6? True → False overall.
Row 1: A=2 >1? True, B=5 <6? True → True overall.
Row 2: A=3 >1? True, B=6 <6? False → False overall.

🔧 Debug

advanced

Identify the error in pandas.eval() expression

What error does the following code raise?

import pandas as pd
df = pd.DataFrame({'x': [1, 2], 'y': [3, 4]})
pd.eval('df.x + df.z')

Pandas

import pandas as pd
df = pd.DataFrame({'x': [1, 2], 'y': [3, 4]})
pd.eval('df.x + df.z')

A KeyError: 'z'

B NameError: name 'df' is not defined

C SyntaxError

D TypeError

💡 Hint

Check if all columns used in the expression exist in the DataFrame.

The column 'z' does not exist in the DataFrame, so pandas.eval() raises a KeyError.

🚀 Application

advanced

Using pandas.eval() to filter rows efficiently

You want to filter rows in DataFrame df where column 'score' is greater than 50 and 'age' is less than 30. Which code using pandas.eval() produces the correct filtered DataFrame?

Pandas

import pandas as pd
df = pd.DataFrame({'score': [45, 60, 70], 'age': [25, 35, 28]})

A filtered = df[pd.eval('score > 50 and age < 30')]

B filtered = df[pd.eval('score > 50 & age < 30')]

C filtered = df[pd.eval('df.score > 50 & df.age < 30')]

D filtered = df[pd.eval('df.score > 50 and df.age < 30')]

💡 Hint

Use the 'df.' prefix for column names when not passing target=df to pd.eval(). Use bitwise '&' for element-wise logical operations.

Option C correctly uses the 'df.' prefix to reference columns and bitwise '&' operator for element-wise AND.
Option C lacks 'df.' prefix and target=df, raising NameError: name 'score' is not defined.
Options C and D use 'and' instead of '&', which is invalid for element-wise operations on Series.

🧠 Conceptual

expert

Understanding the scope of variables in pandas.eval()

Consider the code below. What will be the output of pd.eval('a + b')?

import pandas as pd
a = 5
b = 10
result = pd.eval('a + b')
print(result)

Pandas

import pandas as pd
a = 5
b = 10
result = pd.eval('a + b')
print(result)

A NameError: name 'a' is not defined

B 15

C TypeError

D SyntaxError

💡 Hint

pandas.eval() can access variables from the local Python environment.

pandas.eval() evaluates the expression using variables available in the local scope, so it computes 5 + 10 = 15.