Dictionary-based CSV handling in Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to open a CSV file for reading using a dictionary reader.

Python

import csv
with open('data.csv', [1]) as file:
    reader = csv.DictReader(file)
    for row in reader:
        print(row)

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

A'x'

B'r'

C'a'

D'w'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'w' or 'a' mode instead of 'r' causes errors or overwrites the file.

To read a CSV file, you open it in read mode using 'r'.

2fill in blank

medium

Complete the code to write a list of dictionaries to a CSV file using DictWriter.

Python

import csv
fieldnames = ['name', 'age']
data = [{'name': 'Alice', 'age': 30}, {'name': 'Bob', 'age': 25}]
with open('output.csv', 'w', newline='') as file:
    writer = csv.DictWriter(file, fieldnames=[1])
    writer.writeheader()
    writer.writerows(data)

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

Afieldnames

Bfile

Cwriter

Ddata

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the data list instead of the fieldnames list to DictWriter.

The fieldnames parameter tells DictWriter the order and names of columns.

3fill in blank

hard

Fix the error in the code to correctly read and print the 'age' from each row in the CSV.

Python

import csv
with open('people.csv', 'r') as file:
    reader = csv.DictReader(file)
    for row in reader:
        print(row[[1]])

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

Arow['age']

B'name'

Cage

D'age'

Attempts:

3 left

💡 Hint

Common Mistakes

Using variable names without quotes as dictionary keys.

Trying to use row['age'] inside the brackets.

To access the 'age' value in each row dictionary, use the key as a string: 'age'.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps names to ages from a list of rows.

Python

rows = [{'name': 'Anna', 'age': '22'}, {'name': 'Ben', 'age': '28'}]
name_age = { [1]: [2] for row in rows }

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

Arow['name']

Brow['age']

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping keys and values in the comprehension.

Using the same expression for both key and value.

The dictionary comprehension uses row['name'] as key and row['age'] as value.

5fill in blank

hard

Fill all three blanks to filter rows where age is greater than 25 and create a dictionary of name to age.

Python

rows = [{'name': 'Cara', 'age': 24}, {'name': 'Dave', 'age': 30}, {'name': 'Eve', 'age': 27}]
filtered = { [1]: [2] for row in rows if row[[3]] > 25 }

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

Arow['name']

Brow['age']

C'age'

D'name'

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong key in the filter condition.

Swapping keys and values in the dictionary comprehension.

The comprehension uses row['name'] as key, row['age'] as value, and filters where row['age'] > 25.

Practice

1. What is the main advantage of using csv.DictReader over csv.reader when reading CSV files?

easy

A. It writes data back to the CSV file.

B. It reads the entire file into memory at once.

C. It automatically converts all values to integers.

D. It allows accessing data by column names instead of index positions.

Solution

1. Step 1: Understand csv.reader behavior

   csv.reader reads CSV rows as lists, so you access data by index positions.

2. Step 2: Understand csv.DictReader behavior

   csv.DictReader reads rows as dictionaries, letting you access data by column names, which is clearer and safer if column order changes.

3. Final Answer:

   It allows accessing data by column names instead of index positions. -> Option D

4. Quick Check:

   DictReader uses column names for access [OK]

Hint: DictReader uses column names, not positions, for easier access [OK]

Common Mistakes:

• Thinking DictReader reads entire file at once
• Assuming DictReader converts data types automatically
• Confusing reading with writing functions

2. Which of the following is the correct way to create a csv.DictWriter object to write a CSV with columns 'name' and 'age'?

easy

A. csv.DictWriter(file, fieldnames=['name', 'age'])

B. csv.DictWriter(file, columns=['name', 'age'])

C. csv.DictWriter(file, keys=['name', 'age'])

D. csv.DictWriter(file, headers=['name', 'age'])

Solution

1. Step 1: Recall the parameter name for columns in DictWriter

   The correct parameter to specify column names is fieldnames.

2. Step 2: Check the options

   Only csv.DictWriter(file, fieldnames=['name', 'age']) uses fieldnames correctly; others use incorrect parameter names.

3. Final Answer:

   csv.DictWriter(file, fieldnames=['name', 'age']) -> Option A

4. Quick Check:

   Use fieldnames to set columns [OK]

Hint: Use 'fieldnames' to specify columns in DictWriter [OK]

Common Mistakes:

• Using 'columns' or 'keys' instead of 'fieldnames'
• Forgetting to pass a file object first
• Confusing DictReader and DictWriter parameters

3. What will be the output of this code snippet?

import csv
from io import StringIO

csv_data = "name,age\nAlice,30\nBob,25"
file = StringIO(csv_data)
reader = csv.DictReader(file)
for row in reader:
    print(row['name'], row['age'])

medium

A. Alice 30
Bob 25

B. ['Alice', '30']
['Bob', '25']

C. {'name': 'Alice', 'age': '30'}
{'name': 'Bob', 'age': '25'}

D. 30 Alice
25 Bob

Solution

1. Step 1: Understand the CSV data and DictReader

   The CSV has two rows with columns 'name' and 'age'. DictReader reads each row as a dictionary.

2. Step 2: Analyze the print statement

   It prints the values of 'name' and 'age' keys separated by space for each row.

3. Final Answer:

   Alice 30 Bob 25 -> Option A

4. Quick Check:

   Prints name and age values separated by space [OK]

Hint: DictReader rows are dicts; print keys to get values [OK]

Common Mistakes:

• Printing the whole dictionary instead of values
• Mixing order of printed values
• Confusing list output with string output

4. Identify the error in this code that writes a CSV file using csv.DictWriter:

import csv
with open('output.csv', 'w') as f:
    writer = csv.DictWriter(f, fieldnames=['name', 'age'])
    writer.writerow({'name': 'Alice', 'age': 30})
    writer.writerow({'name': 'Bob', 'age': 25})

medium

A. Dictionaries passed to writerow must have string values only.

B. Fieldnames list should be a tuple, not a list.

C. Missing call to writer.writeheader() before writing rows.

D. The file should be opened in binary mode 'wb'.

Solution

1. Step 1: Check DictWriter usage

   DictWriter requires calling writeheader() to write the header row before writing data rows.

2. Step 2: Verify other parts

   Opening file in text mode 'w' is correct in Python 3, fieldnames can be a list, and values can be int or str.

3. Final Answer:

   Missing call to writer.writeheader() before writing rows. -> Option C

4. Quick Check:

   Always call writeheader() before writerow() [OK]

Hint: Call writeheader() before writing rows with DictWriter [OK]

Common Mistakes:

• Forgetting writeheader() call
• Opening file in binary mode unnecessarily
• Thinking fieldnames must be tuple
• Assuming all values must be strings

5. You have a CSV file with columns 'id', 'name', and 'score'. You want to read it using csv.DictReader and create a dictionary mapping each 'id' to the 'score' as an integer. Which code snippet correctly does this?

hard

A. with open('data.csv') as f:
reader = csv.DictReader(f)
result = {int(row['id']): row['score'] for row in reader}

B. with open('data.csv') as f:
reader = csv.DictReader(f)
result = {row['id']: int(row['score']) for row in reader}

C. with open('data.csv') as f:
reader = csv.reader(f)
result = {row['id']: int(row['score']) for row in reader}

D. with open('data.csv') as f:
reader = csv.DictReader(f)
result = {row['score']: int(row['id']) for row in reader}

Solution

1. Step 1: Use DictReader to access columns by name

   Only csv.DictReader allows accessing 'id' and 'score' by keys.

2. Step 2: Create dictionary with 'id' as key and integer 'score' as value

   with open('data.csv') as f: reader = csv.DictReader(f) result = {row['id']: int(row['score']) for row in reader} correctly converts 'score' to int and uses 'id' as key.

3. Final Answer:

   with open('data.csv') as f: reader = csv.DictReader(f) result = {row['id']: int(row['score']) for row in reader} -> Option B

4. Quick Check:

   DictReader + dict comprehension + int conversion [OK]

Hint: Use DictReader and dict comprehension with int() conversion [OK]

Common Mistakes:

• Using csv.reader instead of DictReader
• Swapping keys and values in dictionary
• Not converting score to int
• Converting id to int instead of score