Chunked reading for large files in Data Analysis Python

Introduction

Sometimes files are too big to open all at once. Chunked reading helps us read big files bit by bit without using too much memory.

When you have a huge CSV file that can't fit into your computer's memory.

When you want to process data step-by-step instead of all at once.

When you want to save memory while analyzing large datasets.

When you want to read and analyze data in smaller parts for faster results.

Syntax

Data Analysis Python

import pandas as pd

for chunk in pd.read_csv('filename.csv', chunksize=1000):
    # process each chunk here
    print(chunk.head())

chunksize sets how many rows to read at a time.

Each chunk is a smaller DataFrame you can work with.

Examples

This reads the file in parts of 500 rows and prints the size of each part.

Data Analysis Python

import pandas as pd

for chunk in pd.read_csv('data.csv', chunksize=500):
    print(chunk.shape)

This counts the total number of rows by adding up rows from each chunk.

Data Analysis Python

import pandas as pd

chunks = pd.read_csv('data.csv', chunksize=1000)
total_rows = 0
for chunk in chunks:
    total_rows += len(chunk)
print(f'Total rows: {total_rows}')

Sample Program

This program creates a CSV file with 2500 numbers, reads it in chunks of 1000 rows, and sums all numbers step-by-step.

Data Analysis Python

import pandas as pd

# Create a sample CSV file with 2500 rows
sample_data = {'number': range(2500)}
df = pd.DataFrame(sample_data)
df.to_csv('sample_large.csv', index=False)

# Read the CSV file in chunks of 1000 rows
chunks = pd.read_csv('sample_large.csv', chunksize=1000)
total_sum = 0
for chunk in chunks:
    total_sum += chunk['number'].sum()

print(f'Sum of all numbers: {total_sum}')

Important Notes

Chunk size depends on your computer memory and file size.

Processing chunks separately helps avoid crashes from memory overload.

You can combine results from chunks to get final answers.

Summary

Chunked reading lets you handle big files in small parts.

Use chunksize to control how many rows to read at once.

Process each chunk and combine results for full analysis.

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