diff() for differences in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the difference between consecutive values in the 'sales' column.

Pandas

df['diff_sales'] = df['sales'].[1]()

Drag options to blanks, or click blank then click option'

Adiff

Bsum

Cmean

Dcumsum

Attempts:

3 left

💡 Hint

Common Mistakes

Using sum() instead of diff() which adds values instead of finding differences.

Using mean() which calculates average, not differences.

The diff() function calculates the difference between consecutive rows in a column.

2fill in blank

medium

Complete the code to calculate the difference over 2 periods in the 'temperature' column.

Pandas

df['temp_diff_2'] = df['temperature'].diff([1])

Drag options to blanks, or click blank then click option'

A2

B1

C0

D-1

Attempts:

3 left

💡 Hint

Common Mistakes

Using 1 which is the default and only looks at the previous row.

Using 0 which returns NaN for all rows.

Passing 2 to diff() calculates the difference between the current row and the row 2 steps before.

3fill in blank

hard

Fix the error in the code to correctly calculate the difference of the 'price' column.

Pandas

df['price_diff'] = df.price.[1]()

Drag options to blanks, or click blank then click option'

Adifference

Bdiff

Csubtract

Ddiffs

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'difference' or 'diffs' which are not pandas methods.

Using 'subtract' which is a function but not a method on Series.

The correct method name is diff(). Other options are invalid and cause errors.

4fill in blank

hard

Fill both blanks to create a new column with the difference over 3 periods for the 'stock' column.

Pandas

df['stock_diff'] = df['stock'].diff([1]).fillna([2])

Drag options to blanks, or click blank then click option'

A3

B0

CNaN

D0.0

Attempts:

3 left

💡 Hint

Common Mistakes

Using 0 for periods which returns NaN for all rows.

Filling NaNs with 'NaN' string instead of numeric 0.0.

Use diff(3) to get difference over 3 rows. Then fill missing values with 0.0 to avoid NaNs.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps each word to its difference in length compared to the previous word, only if the length difference is positive.

Pandas

length_diff = {word: len(word) - len(prev) for word, prev in zip(words[[1]:], words[:-[2]]) if (len(word) - len(prev)) [3] 0}

Drag options to blanks, or click blank then click option'

A1

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using 0 for the slice which causes misalignment.

Using < instead of > which filters negative differences.

The comprehension compares each word with the previous one by shifting the list by 1. It keeps only positive length differences using > 0.

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