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])

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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]()

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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])

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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}

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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.