Why duplicate detection matters in Pandas - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to check for duplicate rows in the DataFrame.

Pandas

duplicates = df.[1]()

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

Aduplicated

Bdrop_duplicates

Cunique

Disnull

Attempts:

3 left

💡 Hint

Common Mistakes

Using drop_duplicates() removes duplicates instead of identifying them.

Using unique() is for unique values, not rows.

Using isnull() checks for missing values, not duplicates.

The duplicated() method returns a boolean Series showing which rows are duplicates.

2fill in blank

medium

Complete the code to count how many duplicate rows exist in the DataFrame.

Pandas

num_duplicates = df.duplicated().[1]()

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Amean

Bsum

Ccount

Dsize

Attempts:

3 left

💡 Hint

Common Mistakes

Using count() counts all rows, not just duplicates.

Using mean() gives the fraction of duplicates, not the count.

Using size returns total number of rows.

Summing the boolean Series counts how many True values (duplicates) there are.

3fill in blank

hard

Fix the error in the code to remove duplicate rows from the DataFrame.

Pandas

df_clean = df.[1](keep='first')

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Aunique

Bduplicated

Cdrop_duplicates

Ddropna

Attempts:

3 left

💡 Hint

Common Mistakes

Using duplicated() returns a boolean Series, not a DataFrame.

Using unique() is for unique values, not rows.

Using dropna() removes missing values, not duplicates.

The drop_duplicates() method removes duplicate rows, keeping the first occurrence.

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 3 characters.

Pandas

lengths = {word: [1] for word in words if len(word) [2] 3}

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Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' filters shorter words.

Using 'word' as value stores the word, not its length.

The dictionary comprehension maps each word to its length if the word length is greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase keys and values greater than zero.

Pandas

result = [1]: [2] for k, v in data.items() if v [3] 0}

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Ak.upper()

Bv

C>

Dk

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'k' instead of 'k.upper()' keeps keys lowercase.

Using '<' instead of '>' filters values less than zero.

Using 'k' as value instead of 'v' stores keys as values.

This dictionary comprehension creates keys as uppercase versions of k and includes only items where the value v is greater than zero.