Chunked reading for large files in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to read a large CSV file in chunks using pandas.

Data Analysis Python

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'

A0

B1000

C10

D-1

Attempts:

3 left

💡 Hint

Common Mistakes

Using 0 or negative numbers for chunksize causes errors.

Not specifying chunksize reads the whole file at once.

The chunksize parameter specifies how many rows to read at a time. 1000 is a common chunk size for large files.

2fill in blank

medium

Complete the code to sum a column named 'sales' from each chunk.

Data Analysis Python

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'

Asales

Bquantity

Cdate

Dprofit

Attempts:

3 left

💡 Hint

Common Mistakes

Using a wrong column name causes KeyError.

Summing a non-numeric column causes errors.

We want to sum the 'sales' column from each chunk to get the total sales.

3fill in blank

hard

Fix the error in the code to filter rows where 'quantity' is greater than 10 in each chunk.

Data Analysis Python

import pandas as pd
chunks = pd.read_csv('large_file.csv', chunksize=500)
filtered_rows = []
for chunk in chunks:
    filtered = chunk[chunk['quantity'] [1] 10]
    filtered_rows.append(filtered)
result = pd.concat(filtered_rows)
print(result)

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

A=>

B==

C<=

D>

Attempts:

3 left

💡 Hint

Common Mistakes

Using '=>' causes syntax error.

Using '<=' or '==' filters wrong rows.

The correct operator to filter rows where quantity is greater than 10 is '>'.

4fill in blank

hard

Fill both blanks to create a dictionary with product names as keys and total sales as values from chunks.

Data Analysis Python

import pandas as pd
chunks = pd.read_csv('large_file.csv', chunksize=1000)
sales_dict = {}
for chunk in chunks:
    for product, group in chunk.groupby([1]):
        sales_dict[product] = sales_dict.get(product, 0) + group[[2]].sum()
print(sales_dict)

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

A'product_name'

B'sales'

C'quantity'

D'date'

Attempts:

3 left

💡 Hint

Common Mistakes

Grouping by wrong column causes incorrect results.

Summing wrong column leads to wrong totals.

We group by 'product_name' and sum the 'sales' column to accumulate total sales per product.

5fill in blank

hard

Fill all three blanks to read a large CSV, filter rows where 'region' is 'East', and count rows per 'category'.

Data Analysis Python

import pandas as pd
chunks = pd.read_csv('large_file.csv', chunksize=[1])
category_counts = {}
for chunk in chunks:
    filtered = chunk[chunk[[2]] == [3]]
    counts = filtered['category'].value_counts()
    for cat, count in counts.items():
        category_counts[cat] = category_counts.get(cat, 0) + count
print(category_counts)

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

A500

B'region'

C'East'

D'sales'

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong chunk size may slow processing.

Filtering on wrong column or value returns wrong data.

We read in chunks of 500 rows, filter where 'region' equals 'East', then count categories.

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