Data type optimization in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to convert the 'age' column to a smaller integer type.

Data Analysis Python

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

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

A"int8"

B"int64"

C"float64"

D"object"

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing 'int64' which uses more memory than needed.

Using 'float64' which is unnecessary for integer data.

Using int8 reduces memory usage by storing integers in 8 bits, suitable for small integer ranges like age.

2fill in blank

medium

Complete the code to convert the 'category' column to a categorical data type.

Data Analysis Python

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

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

Aastype('category')

Bastype

Cto_numeric

Dastype('object')

Attempts:

3 left

💡 Hint

Common Mistakes

Using to_numeric which converts to numbers, not categories.

Calling astype('category') as a method without arguments.

The astype method is used to convert data types. You need to pass the type as a string argument.

3fill in blank

hard

Fix the error in the code to convert the 'score' column to float32.

Data Analysis Python

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

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

A"float32"

B"float"

Cfloat32

Dfloat

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the type without quotes causing a NameError.

Using just float which defaults to 64-bit float.

The astype method requires the data type as a string, so use "float32" to specify 32-bit float.

4fill in blank

hard

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

Data Analysis Python

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 < which filters shorter words.

Using word instead of len(word) for the value.

The dictionary comprehension maps each word to its length using len(word). The condition filters words with length greater than 3 using >.

5fill in blank

hard

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

Data Analysis Python

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() for keys.

Using < instead of > in the condition.

The dictionary comprehension uses k.upper() to make keys uppercase, keeps values as v, and filters values greater than zero with >.