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.