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.

Practice

1. What is the main purpose of pandas.eval() in data analysis?

easy

A. To read CSV files into pandas DataFrames

B. To create plots and visualizations from DataFrames

C. To evaluate string expressions involving pandas objects efficiently

D. To merge two DataFrames based on a key column

Solution

1. Step 1: Understand the role of eval()

   pandas.eval() is designed to evaluate string expressions involving pandas objects like DataFrames and Series.

2. Step 2: Compare with other pandas functions

   Functions like plotting, reading CSV, or merging are done by other pandas methods, not eval().

3. Final Answer:

   To evaluate string expressions involving pandas objects efficiently -> Option C

4. Quick Check:

   eval() evaluates expressions [OK]

Hint: Remember: eval() runs expressions as strings on pandas data [OK]

Common Mistakes:

• Confusing eval() with plotting functions
• Thinking eval() reads files
• Mixing eval() with DataFrame merging

2. Which of the following is the correct syntax to add a new column z as the sum of columns x and y using pandas.eval() on DataFrame df?

easy

A. df['z'] = pd.eval('x + y', local_dict={'x': df['x'], 'y': df['y']})

B. df['z'] = pd.eval('df.x + df.y')

C. df['z'] = pd.eval('df.x + df.y', local_dict=df)

D. df['z'] = pd.eval('df["x"] + df["y"]')

Solution

1. Step 1: Understand how to pass variables to eval()

   When using pandas.eval(), variables like columns must be passed via local_dict as Series objects.

2. Step 2: Check each option's correctness

   df['z'] = pd.eval('x + y', local_dict={'x': df['x'], 'y': df['y']}) correctly passes x and y as Series from df. Options A and D incorrectly reference df inside the string. df['z'] = pd.eval('df.x + df.y', local_dict=df) tries to pass df directly, which is invalid.

3. Final Answer:

   df['z'] = pd.eval('x + y', local_dict={'x': df['x'], 'y': df['y']}) -> Option A

4. Quick Check:

   Pass columns as local_dict to eval() [OK]

Hint: Use local_dict to pass DataFrame columns to eval() [OK]

Common Mistakes:

• Referencing DataFrame inside eval string directly
• Not passing columns via local_dict
• Using wrong quotes inside expression string

3. Given the DataFrame df below:

   a  b
0  2  5
1  3  6
2  4  7

What is the output of the code?

import pandas as pd
result = pd.eval('df.a * df.b', local_dict={'df': df})
print(result.tolist())

medium

A. [10, 18, 28]

B. [7, 9, 11]

C. [2, 3, 4]

D. SyntaxError

Solution

1. Step 1: Understand the expression df.a * df.b

   This multiplies column a by column b element-wise: [2*5, 3*6, 4*7] = [10, 18, 28].

2. Step 2: Confirm usage of local_dict

   The DataFrame df is passed correctly in local_dict, so the expression evaluates without error.

3. Final Answer:

   [10, 18, 28] -> Option A

4. Quick Check:

   Element-wise multiply columns = [10, 18, 28] [OK]

Hint: Multiply columns element-wise inside eval string [OK]

Common Mistakes:

• Forgetting to pass df in local_dict
• Expecting sum instead of product
• Syntax errors from missing quotes

4. What is the error in this code snippet?

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

medium

A. No error, prints [4, 6]

B. SyntaxError due to missing quotes

C. TypeError because x and y are integers

D. NameError because variables x and y are not defined in eval scope

Solution

1. Step 1: Check variable scope in eval()

   The expression uses x and y but they are not passed via local_dict or global_dict.

2. Step 2: Understand the error raised

   Because x and y are undefined in the eval environment, a NameError occurs.

3. Final Answer:

   NameError because variables x and y are not defined in eval scope -> Option D

4. Quick Check:

   Undefined variables in eval cause NameError [OK]

Hint: Always pass variables to eval via local_dict [OK]

Common Mistakes:

• Assuming eval sees DataFrame columns automatically
• Confusing SyntaxError with NameError
• Not passing variables explicitly

5. You have a DataFrame df with columns temp and humidity. You want to create a new column comfort_index defined as 0.5 * temp + 0.3 * humidity using pandas.eval(). Which code correctly does this in one line?

hard

A. df['comfort_index'] = pd.eval('0.5 * temp + 0.3 * humidity')

B. df['comfort_index'] = pd.eval('0.5 * temp + 0.3 * humidity', local_dict={'temp': df['temp'], 'humidity': df['humidity']})

C. df['comfort_index'] = pd.eval('0.5 * df.temp + 0.3 * df.humidity')

D. df['comfort_index'] = 0.5 * df['temp'] + 0.3 * df['humidity']

Solution

1. Step 1: Understand how to use eval() with DataFrame columns

   Columns must be passed as variables in local_dict for eval() to recognize them.

2. Step 2: Check each option

   df['comfort_index'] = pd.eval('0.5 * temp + 0.3 * humidity', local_dict={'temp': df['temp'], 'humidity': df['humidity']}) correctly passes temp and humidity as Series in local_dict. Options A and B fail to pass variables, causing errors. df['comfort_index'] = 0.5 * df['temp'] + 0.3 * df['humidity'] is valid pandas arithmetic but does not use eval().

3. Step 3: Confirm the requirement

   The question asks for a one-line solution using pandas.eval(), so df['comfort_index'] = pd.eval('0.5 * temp + 0.3 * humidity', local_dict={'temp': df['temp'], 'humidity': df['humidity']}) is correct.

4. Final Answer:

   df['comfort_index'] = pd.eval('0.5 * temp + 0.3 * humidity', local_dict={'temp': df['temp'], 'humidity': df['humidity']}) -> Option B

5. Quick Check:

   Pass columns via local_dict for eval() [OK]

Hint: Pass columns as local_dict to eval for new column [OK]

Common Mistakes:

• Using df.column inside eval string without local_dict
• Not passing variables causes NameError
• Confusing direct arithmetic with eval usage