Chunked reading for large files 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

import pandas as pd
chunks = pd.read_csv('large_file.csv', chunksize=[1])
for chunk in chunks:
    print(chunk.head())

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

A-1

B10

C1000

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Using 0 or negative numbers for chunksize causes errors.

Not using chunksize reads the whole file at once.

The chunksize parameter sets how many rows to read at a time. Using 1000 reads the file in chunks of 1000 rows.

2fill in blank

medium

Complete the code to sum a column named 'sales' from each chunk.

Pandas

import pandas as pd
chunks = pd.read_csv('large_file.csv', chunksize=1000)
total_sales = 0
for chunk in chunks:
    total_sales += chunk['[1]'].sum()
print(total_sales)

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Aquantity

Bsales

Cprice

Ddate

Attempts:

3 left

💡 Hint

Common Mistakes

Using a wrong column name causes a KeyError.

Forgetting to sum each chunk's column.

We sum the values in the 'sales' column from each chunk to get the total sales.

3fill in blank

hard

Fix the error in the code to correctly read chunks and count rows.

Pandas

import pandas as pd
chunks = pd.read_csv('large_file.csv', chunksize=500)
total_rows = 0
for chunk in chunks:
    total_rows += len([1])
print(total_rows)

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Achunk

Bpd

Cchunks

Dtotal_rows

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'chunks' inside the loop causes an error because 'chunks' is the iterator.

Using 'total_rows' inside len() is incorrect.

Inside the loop, each chunk is a DataFrame. We count its rows with len(chunk).

4fill in blank

hard

Fill both blanks to create a dictionary with word lengths for words longer than 3 letters.

Pandas

words = ['apple', 'bat', 'carrot', 'dog', 'elephant']
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 '<' instead of '>' filters wrong words.

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

The dictionary comprehension maps each word to its length if the word length is greater than 3.

5fill in blank

hard

Fill all three blanks to create a filtered dictionary with uppercase keys and values greater than 0.

Pandas

data = {'a': 1, 'b': -2, 'c': 3, 'd': 0}
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()' keeps keys lowercase.

Using '<' instead of '>' filters wrong values.

This dictionary comprehension creates a new dictionary with keys in uppercase and values only if they are greater than zero.