Chunked reading for large files in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of chunked sum aggregation

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

Pandas

import pandas as pd
from io import StringIO

data = '''id,value
1,10
2,20
3,30
4,40
5,50
'''

chunk_iter = pd.read_csv(StringIO(data), chunksize=2)
total = 0
for chunk in chunk_iter:
    total += chunk['value'].sum()
print(total)

A SyntaxError

B 100

C 50

D 150

💡 Hint

Sum all values in the 'value' column across all chunks.

The code reads the CSV in chunks of 2 rows each. The values are 10,20,30,40,50. Summing all gives 150.

❓ data_output

intermediate

Number of chunks read

How many chunks will be read from this CSV file with 7 rows and chunksize=3?

Pandas

import pandas as pd
from io import StringIO

data = '''id,value
1,5
2,10
3,15
4,20
5,25
6,30
7,35
'''

chunk_iter = pd.read_csv(StringIO(data), chunksize=3)
count = 0
for chunk in chunk_iter:
    count += 1
print(count)

A 4

B 3

C 2

D 7

💡 Hint

Divide total rows by chunk size and round up.

7 rows with chunksize 3 means chunks: 3,3,1 rows. So total 3 chunks.

🔧 Debug

advanced

Identify error in chunk processing code

What error does this code raise when reading a CSV in chunks and trying to access a non-existent column?

Pandas

import pandas as pd
from io import StringIO

data = '''id,value
1,100
2,200
'''

chunk_iter = pd.read_csv(StringIO(data), chunksize=1)
for chunk in chunk_iter:
    print(chunk['amount'].sum())

A KeyError

B TypeError

C ValueError

D No error, prints 0

💡 Hint

Check if the column 'amount' exists in the data.

The column 'amount' does not exist in the DataFrame, so accessing it raises a KeyError.

🚀 Application

advanced

Efficiently count rows with condition using chunks

You want to count how many rows in a large CSV have 'value' greater than 50 using chunked reading. Which code snippet correctly does this?

A

count = 0
for chunk in pd.read_csv('file.csv', chunksize=1000):
    count += chunk['value'].count() > 50
print(count)

B

count = 0
for chunk in pd.read_csv('file.csv', chunksize=1000):
    count += chunk['value'].sum() > 50
print(count)

C

count = 0
for chunk in pd.read_csv('file.csv', chunksize=1000):
    count += (chunk['value'] > 50).sum()
print(count)

D

count = 0
for chunk in pd.read_csv('file.csv', chunksize=1000):
    count = (chunk['value'] > 50).sum()
print(count)

💡 Hint

Sum the boolean condition counts for each chunk.

Option C correctly sums the number of rows where 'value' > 50 across all chunks. Others either overwrite count or use wrong logic.

🧠 Conceptual

expert

Why use chunked reading for large files?

Which is the main reason to use chunked reading when processing large CSV files?

A To reduce memory usage by loading only parts of the file at a time

B To speed up reading by loading the entire file at once

C To convert CSV files into JSON format

D To automatically clean data during reading

💡 Hint

Think about memory limits when files are very large.

Chunked reading loads small parts of the file sequentially, avoiding high memory use from loading the whole file.

Practice

1. What is the main purpose of using chunksize in pd.read_csv when working with large files?

easy

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

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

C. To convert the file into a different format

D. To skip rows randomly in the file

Solution

1. Step 1: Understand the role of chunksize

   The chunksize parameter splits a large file into smaller parts (chunks) when reading.

2. Step 2: Identify the benefit of chunked reading

   Reading in chunks helps save memory by not loading the entire file at once.

3. Final Answer:

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

4. Quick Check:

   Chunked reading = smaller parts = memory saving [OK]

Hint: Chunksize reads file in parts to save memory [OK]

Common Mistakes:

• Thinking chunksize speeds up reading by reading all at once
• Confusing chunksize with file format conversion
• Assuming chunksize skips random rows

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', chunksize=1000)

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

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

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

Solution

1. Step 1: Recall the correct parameter name

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

2. Step 2: Verify the syntax

   The syntax pd.read_csv('file.csv', chunksize=1000) correctly reads the file in chunks of 1000 rows.

3. Final Answer:

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

4. Quick Check:

   Correct parameter is chunksize [OK]

Hint: Use chunksize, not chunk or size [OK]

Common Mistakes:

• Using chunk or chunk_size instead of chunksize
• Confusing chunksize with size parameter
• Missing quotes around filename

3. What will be the output type of the following code?

chunks = pd.read_csv('data.csv', chunksize=500)
print(type(chunks))

medium

A. <class 'pandas.core.frame.DataFrame'>

B. <class 'list'>

C. <class 'pandas.io.parsers.TextFileReader'>

D. <class 'generator'>

Solution

1. Step 1: Understand what pd.read_csv returns with chunksize

   When chunksize is set, pd.read_csv returns a TextFileReader object, which allows iteration over chunks.

2. Step 2: Check the type printed

   The printed type is <class 'pandas.io.parsers.TextFileReader'>, not a DataFrame or list.

3. Final Answer:

   <class 'pandas.io.parsers.TextFileReader'> -> Option C

4. Quick Check:

   Chunksize returns TextFileReader [OK]

Hint: Chunksize returns TextFileReader object [OK]

Common Mistakes:

• Assuming it returns a DataFrame directly
• Thinking it returns a list of DataFrames
• Confusing with generator type

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

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

medium

A. The file path should be absolute

B. The chunksize parameter is missing

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

D. You cannot sum a column directly; use chunk['value'].sum()

Solution

1. Step 1: Analyze the summation operation

   The code tries to add a pandas Series (chunk['value']) directly to an integer total, which causes an error.

2. Step 2: Correct the summation method

   We should sum the values in the chunk column first using chunk['value'].sum() before adding to total.

3. Final Answer:

   You cannot sum a column directly; use chunk['value'].sum() -> Option D

4. Quick Check:

   Sum each chunk column before adding [OK]

Hint: Sum chunk column with .sum() before adding [OK]

Common Mistakes:

• Adding Series directly to integer total
• Forgetting chunksize parameter
• Using list instead of integer for total

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 using chunked reading is correct?

hard

A. Use chunksize but ignore chunk processing and calculate average on last chunk only

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

C. Load entire file with pd.read_csv and then calculate average

D. Read only the first chunk and calculate average from it

Solution

1. Step 1: Understand the goal of chunked average calculation

   To calculate average without loading all data, sum the 'score' column and count rows in each chunk.

2. Step 2: Combine sums and counts from all chunks

   After processing all chunks, divide total sum by total count to get the overall average.

3. Final Answer:

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

4. Quick Check:

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

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

Common Mistakes:

• Calculating average from only one chunk
• Loading entire file defeats chunking purpose
• Ignoring counts leads to wrong average