Chunked reading for large files in Pandas - Step-by-Step Execution

Concept Flow - Chunked reading for large files

Start reading file

↓

Read chunk of rows

↓

Process chunk

↓

More data?

No→Stop reading

Yes↓

Read chunk of rows

The file is read in small parts called chunks. Each chunk is processed before reading the next. This repeats until the whole file is done.

Execution Sample

Pandas

import pandas as pd
chunk_size = 3
for chunk in pd.read_csv('data.csv', chunksize=chunk_size):
    print(chunk)

Reads a CSV file in chunks of 3 rows and prints each chunk.

Execution Table

Step	Action	Chunk Data (rows)	Output
1	Read first chunk	[Row 0, Row 1, Row 2]	Prints first 3 rows
2	Process first chunk	Same as above	Displayed on screen
3	Read second chunk	[Row 3, Row 4, Row 5]	Prints next 3 rows
4	Process second chunk	Same as above	Displayed on screen
5	Read third chunk	[Row 6, Row 7]	Prints last 2 rows (less than chunk size)
6	Process third chunk	Same as above	Displayed on screen
7	Check for more data	No more rows	Stop reading file

💡 No more rows to read, chunked reading ends

Variable Tracker

Variable	Start	After 1	After 2	After 3	Final
chunk	None	[Row 0, Row 1, Row 2]	[Row 3, Row 4, Row 5]	[Row 6, Row 7]	None (loop ends)

Key Moments - 3 Insights

Why does the last chunk have fewer rows than the chunk size?

Because the file's total rows may not be a multiple of the chunk size. The last chunk reads only the remaining rows, as shown in execution_table step 5.

Does chunked reading load the whole file into memory at once?

No, chunked reading loads only one chunk at a time into memory, making it efficient for large files. See execution_table steps 1, 3, and 5 where chunks are read separately.

How do we know when to stop reading chunks?

When the read chunk is empty or smaller than the chunk size and no more data is available, the loop stops. This is shown in execution_table step 7.

Visual Quiz - 3 Questions

Look at the execution_table, what rows does the second chunk contain?

A [Row 3, Row 4, Row 5]

B [Row 0, Row 1, Row 2]

C [Row 6, Row 7]

D No rows, chunk is empty

💡 Hint

Check execution_table row 3 under 'Chunk Data (rows)'

At which step does the chunked reading stop?

A Step 5

B Step 7

C Step 3

D Step 1

💡 Hint

Look at execution_table step 7 where it says 'Stop reading file'

If the chunk size was 2 instead of 3, how would the first chunk change?

A It would contain all rows at once

B It would contain 3 rows as before

C It would contain 2 rows instead of 3

D It would be empty

💡 Hint

Chunk size controls how many rows are read each time, see variable_tracker for chunk sizes

Concept Snapshot

Chunked reading reads large files in small parts called chunks.
Use pandas read_csv with chunksize to set chunk size.
Process each chunk separately to save memory.
Loop ends when no more data is left.
Last chunk may be smaller than chunk size.

Full Transcript

Chunked reading is a way to read big files in small pieces called chunks. We set a chunk size, for example 3 rows. The program reads the first 3 rows, processes them, then reads the next 3 rows, and so on. This continues until the whole file is read. The last chunk may have fewer rows if the total number of rows is not a multiple of the chunk size. This method helps avoid loading the entire file into memory at once, which is useful for very large files. The reading stops when no more rows are left to read.

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