Working with large datasets strategies in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to read a large CSV file in chunks using pandas.

Pandas

chunk_iter = pd.read_csv('large_data.csv', chunksize=[1])

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A1000

B10000

C100

D10

Attempts:

3 left

💡 Hint

Common Mistakes

Using too small chunksize slows down processing.

Using no chunksize reads entire file at once, causing memory issues.

Using a chunksize of 10000 allows pandas to read the file in manageable pieces, which helps with memory efficiency.

2fill in blank

medium

Complete the code to select only specific columns while reading a large CSV file.

Pandas

df = pd.read_csv('large_data.csv', usecols=[1])

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A['col1', 'col2']

B['all']

C['col1', 'col3', 'col5']

D['1', '2']

Attempts:

3 left

💡 Hint

Common Mistakes

Passing column indexes as strings instead of names.

Using 'all' which is not a valid option.

Selecting only needed columns reduces memory use and speeds up processing.

3fill in blank

hard

Fix the error in the code to convert a large DataFrame column to category type for memory saving.

Pandas

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

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Achange_type

Bconvert

Cto_type

Dastype

Attempts:

3 left

💡 Hint

Common Mistakes

Using non-existent methods like convert or to_type.

Trying to assign type directly without a method.

The astype method converts the column to the specified type, here 'category' to save memory.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths only if length is greater than 3.

Pandas

{word: [1] for word in words if [2] > 3}

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Alen(word)

Bword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as the value instead of its length.

Checking the word instead of its length in the condition.

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

5fill in blank

hard

Fill all three blanks to create a filtered dictionary from data where keys are uppercase and values are positive.

Pandas

result = [1]: [2] for [3], v in data.items() if v > 0}

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Ak.upper()

Bv

Ck

Dv.upper()

Attempts:

3 left

💡 Hint

Common Mistakes

Using value instead of key for uppercase conversion.

Including negative or zero values.

This comprehension creates a dictionary with keys converted to uppercase and values kept as is, only including positive values.