Chunked reading for large files in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of chunked reading with pandas

What will be the output of this code snippet that reads a CSV file in chunks and sums a column?

Data Analysis Python

import pandas as pd

chunk_iter = pd.read_csv('data.csv', chunksize=3)
total_sum = 0
for chunk in chunk_iter:
    total_sum += chunk['value'].sum()
print(total_sum)

A The sum of the first 3 rows only

B The sum of all 'value' entries in the CSV file

C A TypeError because 'chunk' is not iterable

D A FileNotFoundError because 'data.csv' does not exist

💡 Hint

Think about what chunksize means and how the loop processes each chunk.

The code reads the CSV file in chunks of 3 rows each. For each chunk, it sums the 'value' column and adds it to total_sum. After processing all chunks, total_sum holds the sum of the entire 'value' column.

❓ data_output

intermediate

Number of chunks read from a large file

Given a CSV file with 1000 rows, what is the number of chunks read when using pd.read_csv with chunksize=200?

Data Analysis Python

import pandas as pd

chunk_iter = pd.read_csv('large.csv', chunksize=200)
count = 0
for chunk in chunk_iter:
    count += 1
print(count)

A 5

B 4

C 6

D 200

💡 Hint

Divide total rows by chunksize and consider if there is a remainder.

1000 rows divided by chunksize 200 equals 5 chunks exactly, so the loop runs 5 times.

🔧 Debug

advanced

Identify the error in chunked reading code

What error will this code raise when trying to read a CSV file in chunks and access a column?

Data Analysis Python

import pandas as pd

chunk_iter = pd.read_csv('data.csv', chunksize=4)
for chunk in chunk_iter:
    print(chunk['values'].mean())

A AttributeError because 'chunk_iter' is not iterable

B TypeError because 'chunk' is not a DataFrame

C KeyError because 'values' column does not exist

D No error, prints mean of 'values' column

💡 Hint

Check the column name carefully.

If the CSV file does not have a column named 'values' (note the plural), accessing chunk['values'] raises a KeyError.

🚀 Application

advanced

Efficiently calculate average from large CSV using chunks

Which code snippet correctly calculates the average of the 'score' column from a large CSV file using chunked reading?

A

total = 0
for chunk in pd.read_csv('scores.csv', chunksize=1000):
    total += chunk['score'].mean()
print(total)

B

df = pd.read_csv('scores.csv')
avg = df['score'].mean()
print(avg)

C

avg = 0
for chunk in pd.read_csv('scores.csv', chunksize=1000):
    avg += chunk['score'].mean()
avg = avg / 1000
print(avg)

D

total = 0
count = 0
for chunk in pd.read_csv('scores.csv', chunksize=1000):
    total += chunk['score'].sum()
    count += chunk['score'].count()
avg = total / count
print(avg)

💡 Hint

Remember to weight the means by counts when combining chunk averages.

Option D sums all scores and counts all entries, then divides total sum by total count to get the correct average. Option D sums means without weighting, which is incorrect. Option D divides by 1000 regardless of chunk count, which is wrong. Option D reads entire file at once, not chunked.

🧠 Conceptual

expert

Memory advantage of chunked reading

Why is chunked reading beneficial when working with very large CSV files?

A It reads the file in small parts, reducing memory usage and avoiding crashes

B It automatically cleans and preprocesses data during reading

C It compresses the file to save disk space

D It loads the entire file into memory at once for faster processing

💡 Hint

Think about memory limits and file size.

Chunked reading loads small parts of the file sequentially, which uses less memory and prevents crashes when files are too large to fit in memory.