Chunked reading for large files in Data Analysis Python - Mini Project: Build & Apply

Chunked reading for large files

📖 Scenario: Imagine you have a very large sales data file that cannot fit into your computer's memory all at once. You want to analyze this data step-by-step without crashing your program.

🎯 Goal: You will learn how to read a large CSV file in small parts called chunks, process each chunk, and combine the results to get the total sales.

📋 What You'll Learn

Create a variable with the file path to the sales data CSV

Set a chunk size to control how many rows to read at once

Use pandas to read the CSV file in chunks

Calculate the total sales from all chunks

Print the final total sales

💡 Why This Matters

🌍 Real World

Large companies often have huge data files that cannot fit into memory. Reading data in chunks helps analyze such files efficiently.

💼 Career

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

1

Set the file path for the sales data

Create a variable called file_path and set it to the string 'sales_data.csv'.

Data Analysis Python

# Create a variable called file_path and set it to 'sales_data.csv'
# Your code here

Hint

Use a simple assignment like file_path = 'sales_data.csv'.

2

Set the chunk size for reading the file

Create a variable called chunk_size and set it to the integer 1000.

Data Analysis Python

file_path = 'sales_data.csv'
# Create a variable called chunk_size and set it to 1000
# Your code here

Hint

Set chunk_size to 1000 to read 1000 rows at a time.

3

Read the CSV file in chunks and sum the sales

Import pandas as pd. Create a variable called total_sales and set it to 0. Use pd.read_csv with file_path and chunksize=chunk_size to read the file in chunks. Use a for loop with the variable chunk to iterate over the chunks. Inside the loop, add the sum of the 'sales' column of each chunk to total_sales.

Data Analysis Python

file_path = 'sales_data.csv'
chunk_size = 1000
# Import pandas as pd
# Create total_sales = 0
# Use pd.read_csv with chunksize=chunk_size
# Use for chunk in ... to iterate
# Add sum of 'sales' column in chunk to total_sales
# Your code here

Hint

Use pd.read_csv(file_path, chunksize=chunk_size) to get chunks. Sum the 'sales' column in each chunk and add to total_sales.

4

Print the total sales

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

Data Analysis Python

import pandas as pd

file_path = 'sales_data.csv'
chunk_size = 1000

total_sales = 0
for chunk in pd.read_csv(file_path, chunksize=chunk_size):
    total_sales += chunk['sales'].sum()

# Print the total sales
# Your code here

Hint

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

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