Dictionary-based CSV handling in Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What Python module is commonly used for reading and writing CSV files using dictionaries?

The csv module provides DictReader and DictWriter classes to handle CSV files as dictionaries.

beginner

How does csv.DictReader represent each row of a CSV file?

csv.DictReader reads each row as a dictionary where keys are column headers and values are the row's data.

beginner

What method do you use to write a dictionary row to a CSV file using csv.DictWriter?

Use the writerow() method of csv.DictWriter to write a single dictionary as a CSV row.

intermediate

Why is using dictionary-based CSV handling helpful compared to normal CSV reading?

It allows you to access data by column names instead of index positions, making code easier to read and less error-prone.

beginner

What must you do before writing rows with csv.DictWriter to ensure the CSV file has headers?

Call the writeheader() method once to write the column headers before writing any rows.

Which class reads CSV rows as dictionaries with column headers as keys?

A csv.DictReader

B csv.reader

C csv.DictWriter

D csv.writer

csv.DictReader reads CSV rows as dictionaries using headers as keys.

What method writes the header row when using csv.DictWriter?

A writeheader()

B writerow()

C write()

D writeheaders()

The writeheader() method writes the CSV header row.

If you want to write a dictionary {'name': 'Anna', 'age': 30} as a CSV row, which method do you use?

A readrow()

B writerow()

C writeheader()

D write()

writerow() writes a single dictionary as a CSV row.

What is the main advantage of using dictionary-based CSV handling?

A Compresses CSV files

B Faster file reading

C Automatically sorts data

D Access data by column names, improving readability

Using dictionaries lets you access data by column names, making code clearer.

Which of these is NOT a feature of csv.DictReader?

A Reads rows as dictionaries

B Uses column headers as keys

C Writes CSV files

D Handles missing fields gracefully

csv.DictReader reads CSV files; it does not write them.

Explain how to read a CSV file using dictionary-based handling in Python.

Think about how to open a file and use DictReader to get rows as dictionaries.

Describe the steps to write a list of dictionaries to a CSV file using csv.DictWriter.

Remember to write headers before rows.

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