Chunked reading for large files in Pandas - Mini Project: Build & Apply

Chunked reading for large files

📖 Scenario: Imagine you have a very large sales data file that is too big to load into memory all at once. You want to read it in smaller parts, called chunks, to analyze the total sales amount.

🎯 Goal: Build a program that reads a large CSV file in chunks using pandas, sums the sales amounts from each chunk, and then shows the total sales.

📋 What You'll Learn

Use pandas to read CSV files in chunks

Create a variable to hold the running total of sales

Loop over each chunk and add the sales amounts

Print the final total sales amount

💡 Why This Matters

🌍 Real World

Large datasets often cannot fit into memory all at once. Reading data in chunks helps analyze big files efficiently.

💼 Career

Data scientists and analysts use chunked reading to handle big data files without crashing their computers.

1

Create a pandas chunk reader

Import pandas as pd and create a variable called chunk_reader that reads the CSV file named 'large_sales.csv' in chunks of size 1000 using pd.read_csv with the chunksize parameter.

Pandas

# Import pandas and create chunk_reader to read 'large_sales.csv' in chunks of 1000 rows
# Your code here

Hint

Use pd.read_csv with chunksize=1000 to read the file in parts.

2

Create a total sales variable

Create a variable called total_sales and set it to 0. This will hold the sum of sales amounts as you read each chunk.

Pandas

import pandas as pd
chunk_reader = pd.read_csv('large_sales.csv', chunksize=1000)
# Create total_sales variable and set it to 0
# Your code here

Hint

Just create a variable total_sales and assign it 0.

3

Sum sales amounts from each chunk

Use a for loop with the variable chunk to iterate over chunk_reader. Inside the loop, add the sum of the 'sales_amount' column from chunk to total_sales.

Pandas

import pandas as pd
chunk_reader = pd.read_csv('large_sales.csv', chunksize=1000)
total_sales = 0
# Loop over chunk_reader and add sum of 'sales_amount' from each chunk to total_sales
# Your code here

Hint

Use for chunk in chunk_reader: and inside add chunk['sales_amount'].sum() to total_sales.

4

Print the total sales amount

Write a print statement to display the text 'Total sales amount:' followed by the value of total_sales.

Pandas

import pandas as pd
chunk_reader = pd.read_csv('large_sales.csv', chunksize=1000)
total_sales = 0
for chunk in chunk_reader:
    total_sales += chunk['sales_amount'].sum()
# Print the total sales amount
# Your code here

Hint

Use print('Total sales amount:', total_sales) to show the result.

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