diff() for differences in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of diff() with default parameters

What is the output of the following code snippet using pandas diff() with default settings?

Pandas

import pandas as pd
s = pd.Series([10, 15, 20, 25])
result = s.diff()
print(result)

A

0    NaN
1    10.0
2    15.0
3    20.0
dtype: float64

B

0    NaN
1    5.0
2    5.0
3    5.0
dtype: float64

C

0    10.0
1    5.0
2    5.0
3    5.0
dtype: float64

D

0    NaN
1    15.0
2    20.0
3    25.0
dtype: float64

💡 Hint

Remember diff() calculates the difference between the current and previous element by default.

The diff() function by default calculates the difference between the current value and the previous value (periods=1). The first element has no previous value, so it is NaN.

❓ data_output

intermediate

Effect of periods parameter in diff()

Given the DataFrame below, what is the output of df['A'].diff(periods=2)?

Pandas

import pandas as pd
df = pd.DataFrame({'A': [3, 8, 15, 24, 35]})
result = df['A'].diff(periods=2)
print(result)

A

0     NaN
1     NaN
2    12.0
3    16.0
4    20.0
dtype: float64

B

0     NaN
1     NaN
2    15.0
3    24.0
4    35.0
dtype: float64

C

0     NaN
1     5.0
2     7.0
3     9.0
4    11.0
dtype: float64

D

0     NaN
1     NaN
2     8.0
3    15.0
4    24.0
dtype: float64

💡 Hint

The periods parameter defines how many rows back to subtract from the current row.

diff(periods=2) subtracts the value two rows before from the current row. The first two rows have no value two rows before, so they are NaN.

🔧 Debug

advanced

Identify the error in diff() usage

What error will the following code produce and why?

Pandas

import pandas as pd
s = pd.Series([1, 2, 3])
result = s.diff(periods='two')
print(result)

A No error, outputs the difference with periods=2

B TypeError: unsupported operand type(s) for -: 'int' and 'str'

C SyntaxError: invalid syntax

D ValueError: periods must be an integer

💡 Hint

Check the type of the periods argument.

The periods parameter must be an integer. Passing a string causes a ValueError.

🚀 Application

advanced

Using diff() to find daily sales changes

You have a DataFrame with daily sales data. Which code snippet correctly adds a new column 'daily_change' showing the difference in sales from the previous day?

Pandas

import pandas as pd
df = pd.DataFrame({'date': ['2024-01-01', '2024-01-02', '2024-01-03'], 'sales': [100, 150, 130]})

A df['daily_change'] = df['sales'].diff(axis=1)

B df['daily_change'] = df['sales'].diff(periods=0)

C df['daily_change'] = df['sales'].diff()

D df['daily_change'] = df['sales'].diff(periods=-1)

💡 Hint

Default diff() compares current row with previous row.

diff() with default periods=1 calculates difference from previous row, which is what daily change means.

🧠 Conceptual

expert

Understanding diff() behavior with non-numeric data

What happens when you apply diff() on a pandas Series containing strings?

Pandas

import pandas as pd
s = pd.Series(['a', 'b', 'c'])
result = s.diff()

A Raises TypeError because subtraction is not supported for strings

B Returns the original Series unchanged

C Returns the difference in ASCII values between consecutive characters

D Returns a Series of NaN values

💡 Hint

diff() uses subtraction internally.

diff() tries to subtract consecutive values. Strings cannot be subtracted, so it raises a TypeError.

Practice

1. What does the diff() function in pandas do when applied to a DataFrame column?

easy

A. Calculates the difference between each value and the previous value in the column

B. Calculates the sum of all values in the column

C. Sorts the column values in ascending order

D. Replaces missing values with zeros

Solution

1. Step 1: Understand the purpose of diff()

   The diff() function calculates the difference between each element and the one before it in the same column.

2. Step 2: Compare options with the function's behavior

   Only Calculates the difference between each value and the previous value in the column correctly describes this behavior. Other options describe different functions.

3. Final Answer:

   Calculates the difference between each value and the previous value in the column -> Option A

4. Quick Check:

   diff() = difference between consecutive values [OK]

Hint: Remember, diff() shows step-by-step changes in data [OK]

Common Mistakes:

• Thinking diff() sums values
• Confusing diff() with sorting
• Assuming diff() fills missing data

2. Which of the following is the correct syntax to calculate the difference between rows in a pandas DataFrame column named sales?

easy

A. df.diff('sales')

B. df['sales'].diff()

C. df.diff[sales]()

D. df['sales'].difference()

Solution

1. Step 1: Identify correct method call on a DataFrame column

   To calculate differences on a column, call diff() on the Series: df['sales'].diff().

2. Step 2: Check syntax of each option

   df['sales'].diff() uses correct syntax. df.diff('sales') wrongly passes column name to diff. df.diff[sales]() uses wrong brackets. df['sales'].difference() uses a non-existent method.

3. Final Answer:

   df['sales'].diff() -> Option B

4. Quick Check:

   Correct syntax = df['column'].diff() [OK]

Hint: Use diff() directly on the column Series like df['col'].diff() [OK]

Common Mistakes:

• Passing column name as argument to diff()
• Using square brackets instead of parentheses
• Calling non-existent methods like difference()

3. Given the DataFrame:

import pandas as pd
df = pd.DataFrame({'A': [10, 15, 20, 18]})
df['diff'] = df['A'].diff()

What is the content of the diff column?

medium

A. [NaN, 10, 15, 20]

B. [10, 15, 20, 18]

C. [NaN, 5.0, 5.0, -2.0]

D. [0, 5, 5, -2]

Solution

1. Step 1: Calculate differences between consecutive values in column 'A'

   Values: 10, 15, 20, 18. Differences: NaN (first row), 15-10=5, 20-15=5, 18-20=-2.

2. Step 2: Match calculated differences with options

   [NaN, 5.0, 5.0, -2.0] matches the list [NaN, 5.0, 5.0, -2.0]. Others do not.

3. Final Answer:

   [NaN, 5.0, 5.0, -2.0] -> Option C

4. Quick Check:

   diff() = [NaN, 5, 5, -2] [OK]

Hint: First diff is always NaN; subtract previous value from current [OK]

Common Mistakes:

• Assuming first diff is zero instead of NaN
• Confusing original values with differences
• Ignoring negative differences

4. You run this code:

import pandas as pd
df = pd.DataFrame({'X': [3, 7, 2]})
df['change'] = df.diff(axis=1)

But you get an error or unexpected output. What is the problem?

medium

A. diff() cannot be used with axis=1 on a single-column DataFrame

B. You must specify the period argument in diff()

C. The DataFrame must have numeric column names for diff()

D. diff() requires the DataFrame to have at least 5 rows

Solution

1. Step 1: Understand axis parameter in diff()

   axis=1 means calculate difference across columns (left to right). With only one column, no previous column exists to subtract.

2. Step 2: Identify why error or unexpected output occurs

   Since only one column exists, diff(axis=1) returns NaN or error because no previous column to compare.

3. Final Answer:

   diff() cannot be used with axis=1 on a single-column DataFrame -> Option A

4. Quick Check:

   diff(axis=1) needs multiple columns [OK]

Hint: diff(axis=1) needs 2+ columns; single column causes NaN/error [OK]

Common Mistakes:

• Thinking period argument is mandatory
• Assuming column names must be numeric
• Believing diff() needs many rows

5. You have a DataFrame showing daily temperatures:

import pandas as pd
data = {'Day': ['Mon', 'Tue', 'Wed', 'Thu'], 'Temp': [20, 22, 21, 25]}
df = pd.DataFrame(data)

You want to add a column Temp_change showing how much temperature changed from the previous day. Which code correctly does this?

hard

A. df['Temp_change'] = df['Temp'].cumsum()

B. df['Temp_change'] = df['Temp'].diff(periods=2)

C. df['Temp_change'] = df['Temp'].shift()

D. df['Temp_change'] = df['Temp'].diff()

Solution

1. Step 1: Identify how to calculate daily temperature change

   Temperature change from previous day is difference between current and previous value, which diff() calculates by default with period=1.

2. Step 2: Evaluate each option

   df['Temp_change'] = df['Temp'].diff() uses diff() correctly. df['Temp_change'] = df['Temp'].cumsum() sums temperatures, not differences. df['Temp_change'] = df['Temp'].shift() shifts values but does not calculate difference. df['Temp_change'] = df['Temp'].diff(periods=2) calculates difference over 2 days, not 1.

3. Final Answer:

   df['Temp_change'] = df['Temp'].diff() -> Option D

4. Quick Check:

   Use diff() for stepwise changes [OK]

Hint: Use diff() without arguments for previous row difference [OK]

Common Mistakes:

• Using cumsum() instead of diff()
• Using shift() which only moves data
• Using diff(periods=2) for daily change