Removing duplicates (drop_duplicates) in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to remove duplicate rows from the DataFrame df.

Data Analysis Python

df_unique = df.[1]()

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

Asort_values

Bdropna

Cfillna

Ddrop_duplicates

Attempts:

3 left

💡 Hint

Common Mistakes

Using dropna() which removes missing values, not duplicates.

Using fillna() which fills missing values.

Using sort_values() which sorts data but does not remove duplicates.

The drop_duplicates() method removes duplicate rows from a DataFrame.

2fill in blank

medium

Complete the code to remove duplicates based only on the 'Name' column.

Data Analysis Python

df_unique = df.drop_duplicates(subset=[1])

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A['City']

B['Name']

C['Salary']

D['Age']

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing a column unrelated to duplicates.

Not using a list for the subset parameter.

The subset parameter specifies which columns to consider when identifying duplicates. Here, only 'Name' is used.

3fill in blank

hard

Fix the error in the code to remove duplicates and keep the last occurrence.

Data Analysis Python

df_unique = df.drop_duplicates(keep=[1])

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A'last'

B'none'

C'first'

D'all'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'first' which keeps the first occurrence instead.

Using invalid options like 'none' or 'all'.

The keep='last' option keeps the last occurrence of duplicates and removes earlier ones.

4fill in blank

hard

Fill both blanks to remove duplicates based on 'City' and keep only the first occurrence.

Data Analysis Python

df_unique = df.drop_duplicates(subset=[1], keep=[2])

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A['City']

B'first'

C'last'

D['Name']

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong column for subset.

Using 'last' instead of 'first' for keep.

Use subset=['City'] to check duplicates by city, and keep='first' to keep the first occurrence.

5fill in blank

hard

Fill all three blanks to create a dictionary with names as keys and ages as values, only for unique names.

Data Analysis Python

unique_ages = { [3].[1]: [3].[2] for [3] in df.drop_duplicates(subset=['Name']).itertuples() }

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

AName

BAge

Crow

Ddf

Attempts:

3 left

💡 Hint

Common Mistakes

Using the DataFrame name 'df' as loop variable.

Swapping keys and values in the dictionary.

Not using the correct loop variable.

This dictionary comprehension uses row to iterate over unique rows by 'Name', then maps row.Name to row.Age.