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.

Practice

1. What is the main purpose of using chunksize when reading large files in pandas?

easy

A. To read the file in smaller parts to save memory

B. To speed up reading by reading the whole file at once

C. To sort the data while reading

D. To convert data types automatically

Solution

1. Step 1: Understand what chunksize does

   The chunksize parameter tells pandas to read the file in smaller parts (chunks) instead of loading all data at once.

2. Step 2: Why use smaller parts?

   Reading in smaller parts helps save memory and allows processing large files that don't fit into memory.

3. Final Answer:

   To read the file in smaller parts to save memory -> Option A

4. Quick Check:

   Chunksize = read in parts [OK]

Hint: Chunksize means reading file piece by piece, not all at once [OK]

Common Mistakes:

• Thinking chunksize speeds up reading by reading all at once
• Confusing chunksize with sorting or data conversion
• Assuming chunksize changes file content

2. Which of the following is the correct way to read a CSV file in chunks of 1000 rows using pandas?

easy

A. pd.read_csv('file.csv', chunk=1000)

B. pd.read_csv('file.csv', chunk_size=1000)

C. pd.read_csv('file.csv', sizechunk=1000)

D. pd.read_csv('file.csv', chunksize=1000)

Solution

1. Step 1: Recall the correct parameter name

   The correct parameter to read in chunks is chunksize, not chunk, chunk_size, or sizechunk.

2. Step 2: Check the syntax

   The syntax is pd.read_csv('file.csv', chunksize=1000) to read 1000 rows at a time.

3. Final Answer:

   pd.read_csv('file.csv', chunksize=1000) -> Option D

4. Quick Check:

   Correct parameter is chunksize [OK]

Hint: Remember exact parameter name: chunksize, no underscores [OK]

Common Mistakes:

• Using chunk or chunk_size instead of chunksize
• Misspelling the parameter name
• Using sizechunk which does not exist

3. What will be the output of this code snippet?

import pandas as pd
chunks = pd.read_csv('data.csv', chunksize=2)
for chunk in chunks:
    print(chunk.shape)

Assuming 'data.csv' has 5 rows and 3 columns.

medium

A. Error because chunksize cannot be 2

B. (2, 3) (3, 3)

C. (2, 3) (2, 3) (1, 3)

D. (5, 3)

Solution

1. Step 1: Understand chunk sizes and total rows

   The file has 5 rows and 3 columns. Reading in chunks of 2 rows means the first two chunks have 2 rows each, and the last chunk has the remaining 1 row.

2. Step 2: Predict the output shapes

   Each chunk's shape is printed. So output shapes are (2, 3), (2, 3), and (1, 3).

3. Final Answer:

   (2, 3) (2, 3) (1, 3) -> Option C

4. Quick Check:

   Chunks split rows evenly except last [OK]

Hint: Divide total rows by chunksize to find chunk shapes [OK]

Common Mistakes:

• Assuming all chunks have equal rows
• Expecting one chunk with all rows
• Thinking chunksize cannot be less than total rows

4. Identify the error in this code snippet that reads a large CSV file in chunks and sums a column:

import pandas as pd
chunks = pd.read_csv('large.csv', chunksize=1000)
total = 0
for chunk in chunks:
    total += chunk['value']
print(total)

medium

A. Adding a Series to an integer causes an error

B. The variable total should be a list, not an integer

C. You cannot sum a column directly like this

D. Chunksize must be larger than 1000

Solution

1. Step 1: Analyze the addition operation

   Inside the loop, chunk['value'] is a Series (a column of numbers). Adding a Series directly to an integer causes a TypeError.

2. Step 2: Correct way to sum values

   You should sum the Series first using chunk['value'].sum() before adding to total.

3. Final Answer:

   Adding a Series to an integer causes an error -> Option A

4. Quick Check:

   Sum Series before adding to total [OK]

Hint: Sum chunk column before adding to total [OK]

Common Mistakes:

• Adding Series directly to integer total
• Not calling .sum() on chunk column
• Assuming chunksize must be bigger than 1000

5. You want to calculate the average of the 'score' column from a very large CSV file without loading it all at once. Which approach correctly uses chunked reading to do this?

hard

A. Read chunks and average each chunk separately, then average those averages

B. Read chunks, sum 'score' and count rows in each chunk, then divide total sum by total count

C. Read the whole file at once and then calculate average

D. Read chunks and keep only the last chunk's average as final result

Solution

1. Step 1: Understand how to calculate overall average from chunks

   To get the correct average, sum all 'score' values and count all rows across chunks, then divide total sum by total count.

2. Step 2: Why not average chunk averages?

   Averaging chunk averages can be wrong if chunks have different sizes. The correct method weights by counts.

3. Final Answer:

   Read chunks, sum 'score' and count rows in each chunk, then divide total sum by total count -> Option B

4. Quick Check:

   Weighted average by total sum and count [OK]

Hint: Sum and count all chunks, then divide for average [OK]

Common Mistakes:

• Averaging chunk averages without weighting
• Using only last chunk's average
• Trying to load entire file at once