Counting duplicates in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to count duplicate rows in the DataFrame.

Pandas

duplicate_count = df.[1]().sum()

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

Aunique

Bdrop_duplicates

Ccount

Dduplicated

Attempts:

3 left

💡 Hint

Common Mistakes

Using drop_duplicates() which removes duplicates instead of identifying them.

Using count() which counts non-null values, not duplicates.

The duplicated() method returns a boolean Series marking duplicate rows. Counting True values gives the number of duplicates.

2fill in blank

medium

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

Pandas

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

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

Acount

Bsize

Clen

Dsum

Attempts:

3 left

💡 Hint

Common Mistakes

Using sum() which sums values but the DataFrame rows are not numeric.

Using count() which counts non-null values per column, not rows.

Filtering the DataFrame with duplicated() returns only duplicate rows. Using len() counts how many rows are duplicates.

3fill in blank

hard

Fix the error in the code to count duplicate rows correctly.

Pandas

duplicate_rows = df.[1]().sum()

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

Adrop_duplicates

Bduplicated

Cunique

Dvalue_counts

Attempts:

3 left

💡 Hint

Common Mistakes

Using drop_duplicates() which returns a DataFrame, not booleans.

Using unique() which returns unique rows, not duplicates.

The duplicated() method returns a boolean Series where duplicates are True. Summing this Series counts duplicates. Using drop_duplicates() removes duplicates and does not return booleans.

4fill in blank

hard

Fill both blanks to create a dictionary with counts of duplicate rows and unique rows.

Pandas

counts = {'duplicates': df.[1]().sum(), 'unique': len(df.[2]())}

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

Aduplicated

Bdrop_duplicates

Ccount

Dunique

Attempts:

3 left

💡 Hint

Common Mistakes

Using unique() which returns unique values of a Series, not rows.

Using count() which counts non-null values, not unique rows.

duplicated() marks duplicates as True, summing counts duplicates. drop_duplicates() removes duplicates, so its length gives unique rows count.

5fill in blank

hard

Fill all three blanks to create a dictionary with counts of duplicate rows, unique rows, and total rows.

Pandas

counts = {'duplicates': df.[1]().sum(), 'unique': len(df.[2]()), 'total': df.[3]

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

Aduplicated

Bdrop_duplicates

Cshape[0]

Dcount

Attempts:

3 left

💡 Hint

Common Mistakes

Using count which counts non-null values per column, not total rows.

Using unique() which is for Series, not DataFrame rows.

duplicated() marks duplicates; summing counts them. drop_duplicates() removes duplicates; length counts unique rows. shape[0] gives total number of rows.