Categorical data type optimization in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to convert the 'color' column to a categorical type.

Data Analysis Python

df['color'] = df['color'].[1]

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

Aastype('category')

Bstr

Cto_numeric

Dcopy

Attempts:

3 left

💡 Hint

Common Mistakes

Using to_numeric on a string column causes errors.

Using str() does not change the data type in pandas.

Using copy() just duplicates data without changing type.

Using astype('category') converts the column to a categorical type, which saves memory.

2fill in blank

medium

Complete the code to check the memory usage of the DataFrame before optimization.

Data Analysis Python

print(df.memory_usage([1]=True))

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

Amemory

Bverbose

Cindex

Ddeep

Attempts:

3 left

💡 Hint

Common Mistakes

Using verbose does not affect memory usage calculation.

Using index only shows index memory, not full data.

Using memory is not a valid parameter.

The deep=True option gives a detailed memory usage including object types.

3fill in blank

hard

Fix the error in converting the 'city' column to categorical by filling the blank.

Data Analysis Python

df['city'] = df['city'].[1]('category')

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

Aastype

Bto_category

Cconvert

Dchange_type

Attempts:

3 left

💡 Hint

Common Mistakes

Using non-existent methods like convert or to_category.

Trying to use change_type which is not a pandas method.

The correct method to convert data types in pandas is astype.

4fill in blank

hard

Fill both blanks to create a dictionary of category counts for the 'fruit' column.

Data Analysis Python

counts = df['fruit'].[1]().[2]()

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Avalue_counts

Bcount

Cto_dict

Dunique

Attempts:

3 left

💡 Hint

Common Mistakes

Using count() instead of value_counts() returns total count, not per category.

Using unique() returns unique values, not counts.

value_counts() counts each category, and to_dict() converts the result to a dictionary.

5fill in blank

hard

Fill all three blanks to optimize the 'category' column and check its new memory usage.

Data Analysis Python

df['category'] = df['category'].[1]('category')
mem_after = df.memory_usage([2]=[3]).sum()

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

Aastype

Bdeep

CTrue

Dcopy

Attempts:

3 left

💡 Hint

Common Mistakes

Using copy instead of astype causes no type change.

Not using deep=True underestimates memory usage.

Convert the column using astype('category') and check memory with deep=True.