Why categorical type matters in Pandas - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

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

Pandas

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

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Aastype('category')

Bto_numeric

Cstr

Dcopy

Attempts:

3 left

💡 Hint

Common Mistakes

Using to_numeric tries to convert to numbers, which is wrong here.

Using str converts to string, not categorical.

Using astype('category') converts the column to categorical type, which saves memory and speeds up operations.

2fill in blank

medium

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

Pandas

memory_before = df.memory_[1](deep=True)

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Ausage

Bsize

Cinfo

Ddescribe

Attempts:

3 left

💡 Hint

Common Mistakes

Using size returns number of elements, not memory.

Using info prints info but does not return memory usage.

The memory_usage(deep=True) method returns the memory used by the DataFrame including object types.

3fill in blank

hard

Fix the error in the code to convert the 'size' column to categorical.

Pandas

df['size'] = df['size'].astype([1])

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

Astr

B'category'

C'categorical'

Dcategory

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the type name without quotes causes an error.

Using 'categorical' is not a valid dtype.

The argument to astype must be the string 'category' to convert to categorical type.

4fill in blank

hard

Fill both blanks to create a dictionary of counts for categories with more than 2 entries.

Pandas

counts = {cat: count for cat, count in df['fruit'].value_counts().items() if count [1] [2]

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A>

B2

C<

D==

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' or '==' will filter wrong categories.

Forgetting to compare count to a number.

This dictionary comprehension filters categories with counts greater than 2.

5fill in blank

hard

Fill all three blanks to create a new categorical column with ordered categories.

Pandas

df['rating_cat'] = pd.Categorical(df['rating'], categories=[1], ordered=[2])

unique_cats = df['rating_cat'].[3]()

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A['low', 'medium', 'high']

BTrue

Cunique

DFalse

Attempts:

3 left

💡 Hint

Common Mistakes

Setting ordered to False when order matters.

Using unique without parentheses.

We define ordered categories, set ordered=True, and get unique categories with unique().