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)

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

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)

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

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}

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

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 }

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

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.

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